Advances in Kidney Research George M. O'Brien Kidney Center at Yale 5/13/22

May 17, 2022

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00:00Hello everyone, I'd like to welcome
00:04everyone to the 2022 O'Brien Kidney
00:08Center at Yale Research Symposium.
00:11Advances in kidney research.
00:13Very grateful to shoot a Shelby for
00:16organizing this with antibodies help
00:18and thank you all for coming in
00:21attendance and and thank the speakers
00:23who are here and and speakers who will
00:26participate virtually for wonderful
00:28symposium that SHOOTED has organized.
00:30I want to say a couple of words.
00:32About what our O'Brien Center is about.
00:34Because there are services that
00:36might be useful to members of
00:38the kidney community, so let me.
00:43The NDK has several kinds of research
00:46centers of these research centers.
00:49The O'Brien kidney centers are picked up,
00:51particular subcategory and and these really
00:54provide core services to aid investigators.
00:58There's eight O'Brien
00:59centers around the country.
01:01These are not not not not have
01:02major research grants that the major
01:04mission of these is to support
01:06research by providing core services,
01:08they also have pilot grant
01:11programs including ours.
01:12The organization of our particular.
01:14Center is shown here.
01:16The we have three main cores that
01:20provide different types of services,
01:22but they kind of integrate to study kidney
01:24disease at different levels of investigation.
01:27We have an animal Physiology
01:29and phenotyping core.
01:30This led by Pat Price big that does
01:33measurements different physiological
01:34categories on small animals,
01:36mainly mice.
01:37We have a disease models and
01:39mechanisms core that generates a
01:41mouse and and cell line models of
01:44disease and then we have a really a a.
01:46Complex core,
01:47the Human Genetics and clinical
01:49research core that provides
01:51resources for genetic studies
01:53and clinical research studies,
01:54biomarker assays, kidney injury studies,
01:58and I'll say more about the
01:59services in a minute.
02:04So the animal Physiology and
02:06phenotyping core provides a range
02:08of services that are listed here.
02:11GFR measurements and small
02:13animals mice profusion fixation.
02:16Essentially, a chemistry lab for measuring
02:18relevant serum and urine electrolytes,
02:21and creatinine. Blood,
02:23gas parameters, balance studies,
02:25circadian rhythm studies.
02:26Blood pressure measurements both
02:28in anesthetized mice and in awake
02:31mice by radio telemetry.
02:33Of the disease models and mechanisms core,
02:35which is led by Steve somehow and,
02:37like Cantley,
02:37provides access to unique mouse
02:40models and cell line resources.
02:42And it includes bacterial
02:45artificial chromosome transgenesis
02:46the support for CRISPR cast.
02:49You know, I'm editing support support
02:52for kidney cell line production.
02:54Performance of kidney ischemia,
02:56reperfusion surgery,
02:57and support for development of imaging,
02:59mass cytometry,
03:00and human kidney biopsy samples.
03:03And then finally,
03:05there's the.
03:06Genetics and clinical research
03:08for that has multiple directors
03:10overseeing different components.
03:12Perry Wilson, Srikant Mani and Steve.
03:14Somehow here at Yale and Chirag Parikh
03:17overseeing the component of Johns Hopkins,
03:19and this provides services to enhance
03:21different aspects of translational
03:23studies and kidney disease,
03:24the genetics and genomic studies
03:26include DNA extraction and archiving,
03:28snip genotyping, exome sequencing,
03:31transcriptome analysis,
03:33and bioinformatics support.
03:35Clinical Research Services.
03:37Through protocol development,
03:38patient recruitment and sample processing,
03:40bio and data banking archive
03:43samples from NIH studies.
03:45Many types of biomarker assays,
03:47and importantly and most recently,
03:49extraction analysis of
03:50electronic health record data.
03:52Machine learning and biostatistical
03:54support and Perry Wilson provides
03:57a course on research methods
03:59and statistical interpretation
04:00that's available through Coursera
04:02and that can be found online.
04:07And we're very grateful
04:08to our External Board,
04:09the Chair Peter Igarashi has traveled
04:11here from Minnesota to attend today.
04:13The other members of the board of Laura
04:15Denver at Ali Garavi and Marco Cusa,
04:17and they provide a very important function in
04:20providing advice and guidance to the center,
04:22and also they provide the review
04:24of our pilot grant program.
04:25We recently had a deadline and
04:27the beginning of May and and these
04:29grants will be reviewed by by this
04:31group and we're very grateful
04:32for their participation and
04:34helping the center be successful.
04:37So with that,
04:38we'll get to the business that you're
04:40here for is to hear the wonderful
04:41talks that have been lined up by
04:43shutta and let me turn it over to
04:45Shruti to introduce our first speaker.
04:53It's great that we were able
04:55to do this online this year,
04:57and people who were able to join
04:59us physically as well as virtually
05:00for those who are joining us.
05:02Virtually. Please ask your questions
05:04by chat and I'll be visualizing
05:07it while I listen to these talks.
05:10So the first speaker will be doctor Jody
05:13Babbitt from Harvard Medical School,
05:15and she'll be talking to us
05:18about systemic iron homeostasis,
05:19translating molecular discoveries
05:21to chronic kidney disease patients.
05:24Thank you.
05:51Great thank you for that kind
05:53introduction and I thank you for
05:56the opportunity to speak with you.
05:58Very exciting to finally be
05:59back at some in person meetings
06:02or hybrid meetings at least.
06:04So I'm going to talk to you today
06:05about systemic iron homeostasis,
06:07translating molecular discoveries,
06:09sophisticated patients
06:10disclosures are shown here.
06:13So the goals of this presentation
06:15are to understand the important
06:16role of iron in health disease.
06:18To understand the central role of the
06:20upside and Fairport and access and
06:22systemic iron homeostasis regulation and
06:24iron disorders including the anemia,
06:26afrinic kidney disease.
06:27Do you understand the molecular
06:29regulation of hepcidin and how
06:30abnormalities in these pathways contribute
06:32to iron disorders and to discuss
06:35potential translational applications
06:36of these molecular discoveries?
06:39So as I'm sure I don't need to
06:41tell this audience anemia is
06:42prevalent in chronic kidney disease.
06:44This is data from CK Dopps showing
06:46that in countries across the world
06:47as patients reach stage three,
06:49CKD already about 50% of patients are anemic.
06:53This increases to 90% in stage five
06:56CKD and becoming almost universal
06:59in hemodialysis patients.
07:01Anemia and CKD is associated
07:03with numerous address outcomes,
07:04including a reduced quality of life,
07:06cardiovascular disease,
07:07hospitalizations, cognitive impairment,
07:09CKD progression, and mortality.
07:13And and this is illustrated in one such
07:16study here of of US male veterans showing
07:19that as patients become more anemic,
07:22there is an increasing risk
07:24of the end point of end stage,
07:26kidney disease or mortality.
07:27And this is still significant even after
07:30adjusting for potential confounders.
07:35Now there are two important ingredients
07:37in red cell production that are
07:39disturbed in kidney disease patients.
07:40One is the mythopoetic,
07:41the hormone made by the kidney that's
07:44important to induce the maturation of
07:46fluoridation of red cells from the precursors
07:48in the bone marrow and the 2nd is iron,
07:51which is an essential component of hemoglobin
07:54that allows it to transport oxygen.
07:56The problem with EPO is
07:58illustrated in this slide.
07:59So in patients without
08:01chronic kidney disease,
08:02as patients become more anemic,
08:04they're able to robustly induce the
08:06production of reports in which can help treat
08:09the reverse the anemia as CKD progresses,
08:11patients lose the ability for anemia to
08:15induce the production of earth portion,
08:17and this is one of the major causes
08:19of anemia and chronic kidney disease.
08:21And of course,
08:22this has led to the use of recombinant
08:24erythropoietin or other erythropoiesis.
08:27Stimulating agents which are
08:28a mainstay of anemia therapy,
08:29and these have really revolutionized
08:31anemia therapy and that they have
08:33improved quality of life and have
08:36reduced transfusion requirements.
08:37But they do not improve other adverse
08:39outcomes associated with anemia,
08:41including cardiovascular disease,
08:42hospitalization and mortality,
08:44and prospective randomized control trials.
08:49Some of the key trials are listed here.
08:53So, So what about iron?
08:55So just as anemia is prevalent in CKD,
08:58so is iron deficiency.
08:59This is again data from CK Docs showing
09:01that in countries across the world
09:03and across different stages of CKD,
09:05about 40 to 60% of patients have
09:07some form of iron deficiency.
09:13With adverse outcomes,
09:14this is a data from a historical
09:16cohort of US veterans.
09:18Almost 33,000 patients,
09:19and what you can see is that patients
09:22in the lowest quartiles of transparent
09:26saturation that is below 16.6% had an
09:29increased risk of one year mortality
09:31compared to the reference group and
09:33and as is usual in the in biology,
09:35there where there are often J shaped curves,
09:38they even found that that
09:39patients in the highest.
09:41Modi also tended to have an
09:43increased risk of talented,
09:44quite rich.
09:47So in order to try to understand a little bit
09:51more about why iron deficiency may be bad,
09:53and perhaps too much iron may be harmful,
09:55it's helpful to know a little bit
09:57more about the biology of iron.
09:58So let's discuss that briefly.
10:01So iron is a transition metal.
10:03It's it's ability to be able to readily
10:05donate and accept electrons is what allows
10:08iron to perform its biologic functions.
10:10This allows iron to perform the most
10:12well known function as a component
10:15of heme and and transporting oxygen.
10:17But it's probably less well recognized
10:18and folks outside this field.
10:20It turns out that heme itself,
10:22as well as other iron functional
10:23groups such as iron sulfur crossers,
10:25are actually key components of a
10:27number of other proteins that perform
10:29fundamental cellular processes,
10:31and everything from the TCA cycle
10:34to electron transport to DNA
10:36synthesis and many other functions.
10:38And so iron really is essential for life.
10:42And iron deficiency can not only lead to
10:45anemia but also cardiovascular strain,
10:48impaired muscle function,
10:50exercise tolerance and work performance,
10:52altered immune function and
10:54increases in children.
10:55Developmental defects,
10:57growth retardation and neurologic defects.
11:00This property of iron that allows
11:02it to perform its biological
11:03functions also means that when an
11:06excess iron can participate in the
11:08so-called Fenton mediated reaction,
11:10which leads to the production of pre
11:12oxygen radicals that can damage proteins,
11:14lipids and nucleic acids.
11:17Leading to cellular damage and dysfunction,
11:20and the most clear clinical evidence
11:21of of the the adverse consequences
11:23of iron overload come from genetic
11:25disorders of iron overload,
11:27such as hereditary hemochromatosis,
11:28where excess iron deposits and organs,
11:31such as the liver, heart,
11:32and endocrine glands,
11:33leading to organ dysfunction.
11:35There's also another category of
11:37diseases called iron loading anemias,
11:38of which Felicia is a prototypical
11:41example in these disorders.
11:42Mutations in the protein components
11:44of hemoglobin and lead to ineffective
11:46with the poises and anemia.
11:48In more severe forms of these diseases,
11:50patients are transfusion dependent
11:51and can get secondary iron overload
11:53from the transfusions.
11:54But even in less severe forms
11:56of this disease,
11:56there's actually underlying
11:57pathophysiology in this disease that
11:59leads to dietary iron hyper absorption,
12:02which also contributes to iron overload,
12:04and iron overload is a major cause of
12:07morbidity and mortality in this disorder.
12:09Now,
12:10because you know,
12:11it turns out that even outside
12:13the massive iron overload that we
12:15see in these genetic disorders,
12:17there's also evidence in the literature
12:19that excess iron levels can also be
12:21associated with more common disorders.
12:22Everything from diabetes melodus
12:24to cardiovascular disease,
12:26neurogenic disorders, acute kidney injury,
12:28and malignancy,
12:29and just as all cells in our body
12:31need iron to grow proliferate.
12:32So do infectious organisms and and.
12:34And we know when and patients
12:36with iron overload disorders,
12:37that they're more prone to infection.
12:39Certain types of infections.
12:40For example Center for the bacteria.
12:43Now, because iron is essential,
12:45but too much iron can be toxic,
12:46iron levels must be very carefully regulated,
12:49both at the cellular level and systemically,
12:52and it's systemic.
12:53Own homemade stasis that we're
12:54going to talk about today.
12:56So the way this works is we absorb
12:57iron from the diet in the duodenum,
12:59about 1 to 2 milligrams per day.
13:01Iron circulates in the bloodstream,
13:04bound to a carrier protein
13:05called transferrin.
13:06This helps to keep iron inert and also
13:08helps it be delivered to all cells in the
13:11body via uptake via transparent receptors.
13:14But much of the iron does go into the bone
13:16marrow for the production of red blood cells.
13:18When the red blood cells get old,
13:20they get taken up into the macrophages,
13:22which can then recycle that iron and
13:24release it back into circulation.
13:26That's needed,
13:27we also get have iron storage
13:29in the liver and other tissues.
13:32Now it turns out that most of the iron is
13:34provided through this recycling process,
13:36about 20 to 25 milligrams per day,
13:39so really much more than what's
13:41provided through dietary absorption.
13:43And in fact the circulating pool
13:45of iron is actually quite small.
13:46It's only about 3 milligrams,
13:48so as you can see,
13:49you actually have to turn over the
13:50circulating pool about seven or eight
13:52times a day in order to meet the daily
13:54requirement for red cell production.
13:56And finally,
13:56there's really no regulated mechanism
13:58for iron removal from the body.
14:00We lose iron through bleeding
14:02through sloughing of cells,
14:03but for the most part,
14:04the regulation of iron homeostasis
14:06occurs through the regulation of
14:08the absorption from the diet and
14:10the release from body stores.
14:12And a key mediator of this process
14:14is a hormone called have decided,
14:16and we're going to talk a little
14:17bit more about this in a minute,
14:19but I just want to bring this
14:20back to kidney disease patients,
14:22so it turns out in kidney disease patients,
14:24there are numerous disturbances.
14:26And these homeostatic mechanisms,
14:28first of all,
14:29patients tend to be a negative iron balance.
14:31Particularly hemodialysis patients
14:32have been estimated to lose between
14:35one and a half to 3 grams of iron per
14:37year due to increased bleeding tendency.
14:40Blood trapping in the dialyzer tubing,
14:42frequent phlebotomy and many of our
14:46patients don't absorb don't get very much.
14:49A dietary iron.
14:49Some of this may be due to nutritional
14:52deficits or medications that
14:54interfere with iron absorption.
14:56But patients with kidney disease
14:57also have access levels of this
14:59iron regulatory hormone hepcidin,
15:01which actually also interferes
15:03with iron absorption.
15:05Now these problems can lead to
15:07a total body deficit of iron,
15:09something that we call absolute
15:10iron deficiency.
15:11And of course it makes sense to treat
15:13this by giving iron supplementation.
15:16But it turns out that the excess
15:18helpside levels also contribute
15:20to this problem of of reticulate
15:22endothelial cell iron blockade,
15:24whereby even though the stores of iron
15:25in the body may be adequate or even high,
15:28they stores are just not able
15:30to be released into circulation,
15:32at least not efficiently
15:33enough to meet the needs for
15:34red cell production.
15:36And this can be exacerbated
15:38when patients are on ASA's,
15:39which cause a rapid burst of
15:42erythropoiesis that rapidly
15:43deplete the circulating pool
15:44faster than it can be replenished.
15:46And this is a problem that we
15:48call functional iron deficiency,
15:50where it really the circulating
15:51pool of iron is limiting for with
15:53voices and for this entity it's less
15:55clear that iron supplementation is
15:57the right therapeutic strategy,
15:59as we we might worry that giving
16:00patients more and more iron may
16:02lead to problems with iron overload,
16:04and so for this reason I think
16:05it's helpful to understand a
16:06little bit more about hepcidin,
16:08biology and and thinking about
16:10whether this may be a different way
16:12of of targeting this therapeutically.
16:14So what is subsiding?
16:15It's a 25 amino acid.
16:17Peptide hormone that's made by
16:19the liver circulates in the blood
16:21and is excreted by the kidneys.
16:23The function of hepcidin is
16:24illustrated in this slide,
16:26So what I'm showing you here is
16:27the enterocyte that absorbs iron
16:29in the in the intestine,
16:30so elemental iron is taken up
16:33across the apical surface through
16:34a transporter called DMT one.
16:36Once inside the cell,
16:37iron can be used by that cell
16:39for its old metabolic processes,
16:41or if it's not needed,
16:42it'll get stored in early in ferritin,
16:45which is the iron storage protein
16:46and all cells in our body.
16:48And if this happens,
16:49this iron is going to get lost as
16:50these cells get sloughed off every few days.
16:53In order for that iron to be
16:54accessible to the Organism at large,
16:56it needs to be exported across the
16:59basolateral surface through an iron
17:01export protein called ferroportin.
17:02The macrophages,
17:03which recycle iron from the red
17:05cells again for that iron to be
17:07released back into the circulation
17:09and also needs to go through
17:10Fairport so Fairport has really the
17:13gatekeeper that controls iron entry
17:15into the circulation both from the
17:17diet and from the body stores.
17:19So the function of hepcidin is
17:21illustrated by this nice cryoem image
17:22that was published recently in nature,
17:24where and you can see here have
17:26siding this colored in orange
17:28and Fairport and green,
17:29and what you can see is that hepcidin
17:31actually binds directly to the
17:33central cavity of Fairport and where
17:35it blocks iron export directly.
17:37Peptide binding to Fairport and also
17:39induces the internalization and
17:41degradation of Fairport and thereby
17:43furthering inhibiting iron export.
17:45So the consequence of this is that
17:47when hepcidin levels are high
17:48it binds ferroportin blocks.
17:50Iron export induces the degradation of
17:52Fairport and to further inhibit iron export,
17:55thereby leading to reduced
17:56circulating levels of iron.
17:58And when this happens chronically
17:59this can lead to iron restricted
18:01with voices and anemia as we see for
18:03example in kidney disease patients
18:04even though the stores of iron in the body.
18:07Maybe normal or high?
18:09Low hepcidin actually leads to
18:11the opposite situation.
18:12There's unregulated Fairport and
18:13expression you can't turn off the
18:16absorption of iron from the diet.
18:17Iron is continually released from the
18:19cells designed to store it safely.
18:21The macrophages and this excess iron then
18:23can deposit in other tissues where it
18:25can lead to organ damage and dysfunction,
18:27and this is really the underlying
18:29pathophysiology of of iron
18:30overload and hemochromatosis,
18:32as well as thalassemia.
18:36Now as a key regulator of
18:38systemic iron homeostasis,
18:39the production of peptide and the liver
18:41is regulated by a number of key signals.
18:43So one is iron.
18:44So if you give a patient and I an
18:45injection of iron or take an iron pill,
18:47this will induce Obsidian to
18:49inhibit fair portion to inhibit
18:51iron absorption and release.
18:53This helps to maintain the steady state.
18:55Iron deficiency suppresses have
18:57decided to increase iron availability,
18:59again, keeping you in steady state.
19:02Anything that increases the risk
19:03aquatic drive. So this would be anemia.
19:05Hypoxia, erythropoietin injections.
19:07These all suppress upside and thereby
19:10increasing iron availability to
19:12support the needed red cell production.
19:15And finally,
19:16inflammation is a stimulator
19:17of hepcidin production.
19:19This likely developed evolutionarily
19:20as a protective mechanism to sequester
19:22iron from invading pathogens that
19:24need iron to grow and proliferate.
19:26But it's this pathway that,
19:27in the setting of chronic
19:29inflammatory diseases,
19:30can contribute to iron restricted
19:32with prices and anemia.
19:33And indeed,
19:34kidney disease patients do
19:35have excess upside levels.
19:37That's been illustrated
19:38by a number of studies.
19:39This is one such study in Ashby at all,
19:41showing that hemodialysis patients
19:42have much higher levels of circulating
19:45hepcidin compared to control patients
19:47and then patients with non dialysis CKD.
19:50There's an inverse correlation between
19:52estimated GFR and upside down,
19:53and again there's probably
19:552 mechanisms for this.
19:56Hepcidin is up regulated by inflammation,
19:57so the inflammatory milieu of CKD
20:00and hemodialysis can stimulate
20:01herbicide and production, and,
20:03you know, as a small peptoid.
20:04Form an excreted by the kidneys,
20:06kidney disease can lead to
20:07reduced herbicide and clearance,
20:09which can also contribute to excess levels.
20:13So what our group has been interested
20:15in is trying to understand how is it
20:17that the liver integrates all of these
20:19different signals to appropriately
20:21regulate upside and production.
20:22And that's what I'm going to talk
20:23to you a little bit about today,
20:25and then can we think about
20:27using this therapeutically?
20:28Potentially so?
20:29First, let's talk about the iron signal,
20:32so clues for understanding how
20:34iron regulate hepcidin come
20:35from hereditary hemochromatosis.
20:37We mean we discuss this briefly.
20:39This has caused a disorder of iron overload
20:41caused by mutations in one of several genes.
20:44So mutation in HFE are the most common cause.
20:47Transparent receptor 2 causes of more
20:49rare adult onset form of this disease
20:51and mutations and have side in Excel
20:53or another gene called him a jubilant
20:55actually cause a more rare but more
20:57severe juvenile onset form of this disease.
21:00All patients who have mutations in any
21:02of these genes have the same problem and
21:05this is a deficiency of hepcidin that
21:06fails to be appropriately induced by iron,
21:09so that's illustrated in this slide
21:10so you can see that these these these,
21:13these proteins HIV
21:14transferred to human javelin.
21:16They're all expressed in the liver,
21:18and so somehow they must be involved in
21:20sensing iron levels and transducing that
21:23signal to appropriately regulate upside and
21:26and so that if any of these genes is mutated,
21:28this leads to hepcidin
21:30deficiency in iron overload.
21:32Now we got into this field because we were
21:34studying him adjuvant for a different reason,
21:36and that's because it's a family member
21:38of a family of three genes called the
21:40repulsive guidance molecule or RGM family,
21:42which in work that I did as a postdoc
21:45in Hartland's lab we showed function as
21:47Co receptors for the bull morphogenetic
21:50protein or BMP signaling pathway.
21:52So what are BMPS there?
21:53There are some family of the Tiger
21:55beta superfamily of ligands,
21:56which is a super family of over 40 members
22:00including TGF Betas and BMP's themselves.
22:02As well as malaria and
22:04inhibiting substance activists,
22:05inhibitions and growth in
22:07differentiation factors,
22:08these are disulfide and dimers that share
22:11overall structural similarities and a
22:13common paradigm of signal transduction.
22:14Where the login will bind to a
22:16complex of two type one and two
22:19Type 2 serine 39 kinase receptors.
22:21Upon formation of the complex,
22:22the Type 2 receptors flex sporulate,
22:24the Type 1 receptors which then
22:27phosphorylate interest sosmed proteins.
22:29There are two subsets of smeds Med 1/5
22:31and eight that are activated by the beam.
22:33Keys and some ads.
22:34Two and three that are activated
22:36by activists and TGF fades.
22:38These form a complex with the
22:39common mediators.
22:40Med four and these are transcription
22:42factors that that migrate to the
22:44nucleus and regulate gene transcription,
22:46leading to a diverse array of
22:49biological functions.
22:50Now, as I mentioned,
22:51there's over 40 ligands and one of
22:53the interesting questions in this
22:54field is how is it that these ligands
22:56are able to lead to this
22:57diverse array of biological functions
22:59with a very limited subset of receptors?
23:03So there's only five Type 2 receptors,
23:067 type 1 receptors,
23:08and these two subsets of SMAD proteins.
23:11And at least part of the answer
23:12to that question is that there's
23:14regulation of this pathway.
23:15A number of different levels
23:16from the extracellular surface,
23:18which we'll hear a little bit about later,
23:20so the membrane surface to intracellularly
23:22and one of the the levels of
23:25regulation is through Co receptors.
23:27And this is where our data
23:29suggests that HEMA, jubilant,
23:30and the other GM's are functioning as Co
23:33receptors for the BMP side of the pathway.
23:36And this is based on data
23:37such as the following.
23:38So if we take liver cells and culture
23:40and we transfect them with the BMP
23:43responsive luciferase reporter,
23:45when we add in exogenous BMP
23:46ligands we get an increase in
23:49luciferase activity and we see a
23:51similar effect if if we transfect
23:53in C DNA encoding human juggling.
23:55This is specific for the BMP side
23:57of the pathway because if we use
23:59another reporter that responds
24:00to TGF beta signals for example,
24:02we don't see any effect from
24:05human junelyn transfection.
24:06And we showed that this is working
24:08through the canonical BMP SMAD signaling
24:10pathway that I showed to you through.
24:12Endogenously expressed the empty lagans,
24:15the empty receptors and BMP
24:16smads because if we knock down or
24:18inhibit any of these components,
24:20this blocks the signaling.
24:21Now as I was generating this data,
24:24a paper came out linking mutations
24:27and HEMA juvelen to hemochromatosis.
24:29So we asked ourselves the obvious question,
24:31is this BMP signaling function of human
24:34javelin somehow important for its
24:36role in iron homeostasis regulation?
24:38And of course,
24:38the role that we thought about
24:39was in the regulation of hepcidin.
24:41Since this is where human Julian
24:43was proposed to act,
24:44and indeed we found that the MP's
24:46are quite potent stimulators of
24:47hepcidin production.
24:48So this is another cell culture assay
24:50using liver cells and we treat them.
24:52What different Joe Biden super
24:54family ligands and measure hepcidin
24:56by Q PCR and you can see that BMP's
24:59induced have siding by 200 to 1000
25:01folds and many BMP's can do this,
25:03including BMP, 24567 and nine,
25:06but it's really the BMP is rather
25:09than the other Super family members
25:11of the most robust inducers of
25:13hepcidin and it was subsequently
25:15shown that this is acting directly
25:17to transcriptional level through
25:19two specific SMAD binding elements
25:21in the hepcidin promoter.
25:22Now subsequently we were interested in
25:25understanding which of these ligands
25:27is important endogenously in vivo,
25:29right?
25:30If we add them exogenously,
25:31many of them can induce subsided,
25:33but which ones are really the
25:35important players in vivo?
25:36So clues for this came from 2 avenues.
25:38One was,
25:39some was from our work looking at
25:41the binding affinity of the different
25:43RGM proteins to BMP ligands.
25:44In our initial paper,
25:46we had demonstrated that our GM
25:48proteins bind directly to the
25:49ligands and and and in
25:512015 Christian Siebold.
25:52They're actually published a
25:54beautiful crystal structure showing
25:55this that the binding interaction.
25:57This is our data using
25:59surface plasmon resonance.
26:00What we did is we compared the binding
26:02affinity of the different RGM for
26:04the different BMP ligands and they're
26:06colored differently based on their
26:07different subfamilies and what you
26:09can see is that all of the RGM's had
26:12the relatively highest affinity for BMP,
26:14two and four,
26:15none of them bound to BMP nine and
26:17what stood out about hemogoblin
26:19compared to the other GM's is it had a
26:22relatively higher affinity for the BMP.
26:24567 subfamily compared to the other GM's,
26:27so we thought if there was some
26:29function of human Julian that couldn't
26:31be compensated by the other GM's,
26:33we thought it might have something
26:34to do with the subfamily.
26:35In particular BMP 6.
26:38Another clue came from this experiment,
26:41so here we took mice and we made them
26:43iron deficient by putting them on a
26:44low iron diet or iron overloaded by
26:46putting them on a high iron diet.
26:48And you can see,
26:49as discussed earlier,
26:50the iron deficiency suppresses upside
26:52in and iron overload induces subside in.
26:55So we asked the question,
26:56are any BMP ligands?
26:58Is there expression regulated
26:59by iron and the liver?
27:01And it turns out only two ligands are
27:04one is being P6 reduced by a low iron
27:07diet induced by a high iron diet?
27:09And the other was BMP two,
27:11so these were really good candidate
27:13and dodging US regulators.
27:15But in order so we wanted to validate
27:17this using a genetic approach.
27:19But in order to do this we needed
27:20to know which were the cells
27:22that we're making the ligands,
27:23because the amps are very important
27:25during development and and can
27:27lead to developmental problems.
27:28And so we looked and it turns out
27:30in the liver it's predominantly the
27:32endothelial cells that are making both
27:35the P6 and P and P2 rather than the
27:37parenchymal cells that had ascites,
27:38which are the cells that actually make the.
27:40Side and or the resident tissue
27:43macrophages the Cooper cells.
27:45So we then went on to make a knockout
27:47mice and this is the data for BP
27:496 and you can see in the both the
27:51the the global knockout is red,
27:53the endothelial knockout is blue,
27:55macrophage is green and hepatocyte is purple.
27:58And what you can see is both a
28:00global and the endothelial BMP.
28:016 knockout mice had profound
28:03deficiency of hepcidin and this
28:05was associated with all of the
28:07features of hemochromatosis including
28:09massive iron overload and the liver,
28:11which is quantitated here on the left.
28:13Here you can see the global knockout in red.
28:15In the end of the and that kind of
28:17blue and on the right that this is
28:19a Prussian blue stain that stains
28:20the iron blue.
28:21So you can clearly visualize the
28:23massive iron overload in these mice.
28:25For me and B2,
28:26the global knockout mice are embryonic
28:27lethal, but we did look at endothelial
28:30BMP 2 knockout mice and similar to BMP 6.
28:32These mice also had profound
28:34hepcidin deficiency and all of
28:35the features of hemochromatosis,
28:37including iron overload and in fact
28:41more recent data suggests that BMP
28:42two and six are actually working
28:44together to regulate hepcidin.
28:46It turns out these ligands,
28:47their dimers, and they can either
28:49function as as homodimers where
28:51there's two of the same ligand,
28:53or heterodimers where they're
28:54two different ligands.
28:55And there's an increasing recognition
28:57in this field that there are some
28:59biological contexts where only
29:01heterodimers can function and
29:02homodimers can't compensate and
29:04our data suggests that that's
29:05how BP two and six are working.
29:08So just to summarize what
29:10I've shown you so far.
29:12Iron increases in iron will induce and
29:16athelia cells in the liver to produce BMP,
29:18two and BMP 6 ligands.
29:20These bind to the Coreceptor came a
29:22jubilant activate the BMP receptor complex,
29:25induce the phosphorylation of SMAD proteins,
29:27which is a major transcriptional
29:30regulator of hepcidin.
29:32So now let's switch gears and talk a little
29:34bit about the erythropoietic signal.
29:36Now it's been known for a long
29:37time that whatever the signal is,
29:39it must be coming from the bone marrow,
29:40because if you wipe out the bone
29:43marrow with chemotherapy or radiation,
29:45and the ability of erythropoietin
29:46and these other stimulators to
29:48suppressive side and goes away,
29:50so it's been hypothesized that
29:52there must be some secreted factor
29:54that the bone marrow is making
29:56that is suppressing hepcidin.
29:58Several years ago now,
29:59Tom Ganz's Group discovered one of
30:01these erythroid regulators of peptide,
30:03and this is called erythro Faron or Urfi,
30:06or FAM 132B,
30:07and this is some data from Toms Group.
30:10What you can see is if you phlebotomies
30:12mice or treat them with a report,
30:15and this causes a robust induction
30:17of the production of aritha faron
30:19in the bone marrow by 4 hours,
30:22which then starts to go down after 15 hours.
30:25And this is a sort of a mirror
30:26image of what happens to have.
30:28Right,
30:28and so after a referral goes up
30:30upside and gets suppressed and is
30:32a referral starts to come down.
30:33Hepcidin levels rebound.
30:34And indeed if you inject purified
30:37it with a ferret into mice,
30:38it's the prices have sign expression
30:40and moreover if you look at everything
30:43fair and knockout mice you know in
30:45wild type mice where when you give
30:47them phlebotomy or with points in,
30:49this will suppress upside and but in
30:51the earth of Fair knockout mice the
30:53suppression is significantly blunted,
30:55although there is still some suppression,
30:57suggesting that.
30:57There are other friend is not
30:59the only erythroid regulator,
31:01but certainly it is an important one
31:03and this is functionally relevant
31:04because it delays the recovery from
31:07anemia and these knockout mice.
31:09And they also showed that this plays
31:11an important role in the hepcidin
31:13deficiency in iron overload and thalassemia.
31:16So our group was interested in
31:18trying to understand how
31:19does Erythro Farren work
31:20to suppress upside in.
31:21So we went back to our cell culture
31:23model and liver cells and you can
31:25see here that similar to what
31:26happens in the mice if you treat
31:28cells with a rifle and protein,
31:30this will suppressive Sidon.
31:31And interestingly,
31:32this also led to a reduction
31:34in SMAD signaling,
31:35as evidenced by reduction in the
31:38phosphorylation of Smad 15 proteins.
31:40So this suggests that the the way
31:42Erythro Farren is working is by
31:44inhibiting the BMP SMAD pathway.
31:46Now because of whether fairness
31:48a secreted protein,
31:48we reasoned it might be interacting
31:50directly with BMP ligands or receptors
31:53to interfere with this pathway.
31:54And so we tested that using a
31:57coimmunoprecipitation approach.
31:58So here what we did is we mixed
32:00BMP ligands and a flag tag.
32:02The with warfarin together and this
32:04here is a BMP 26 heterodimeric Lagann
32:06and here is BMP 6 homodimeric ligand.
32:09We we immunoprecipitated erythroxylum
32:10with a flag antibody and you can
32:13see that the ligand comes down
32:14with it so this demonstrates that.
32:16Both are fair and is actually
32:18binding directly to the BMP ligands.
32:20In contrast,
32:21there is no interaction between over
32:24the farm and the BMP receptors.
32:27So the way we think this is
32:28working is illustrated by this
32:30immunoprecipitation experiment.
32:31So here what we do is we're mixing together
32:34the BMP ligand and the BMP receptors.
32:36So if you immuno precipitate the receptor,
32:39the ligand comes down with
32:40it as you would expect,
32:42the ligand binds to the receptor.
32:43But this is in the absence of Aritha Faron.
32:46When you add increasing
32:47Aritha Faron to this mixture,
32:49what happens is you compete for the ability
32:51of the ligand to bind to the receptor.
32:54So this suggests that the way with
32:56referring is working is like.
32:57Ligand trek,
32:57it binds the ligands and sequesters
32:59it and prevents it from interacting
33:02with the receptors,
33:03thereby inhibiting signaling.
33:06So,
33:06just to summarize,
33:07this part of the talk,
33:08here's the the canonical BMP signaling
33:10pathway that we discussed earlier,
33:12and Othello cells make BMP ligands
33:14which bind to the BMP receptor
33:17complex to induce the transcription
33:19of hepcidin in the context of Earth,
33:21reported drive like EPO injections or anemia.
33:24This acts on the kidney to induce the
33:27production of erythropoietin which
33:28acts on the bone marrow to induce
33:30the production of Arthur Farum,
33:32which then goes to the liver
33:34where spines and sequesters.
33:36BMP ligands inhibit signaling
33:38through this pathway,
33:39thereby lowering hepcidin expression.
33:43So finally,
33:43let's talk a little bit about inflammation.
33:45You know we mentioned this is
33:47probably one of the the mechanisms
33:48by which have sided is increased
33:50in chronic kidney disease.
33:51So how does inflammation regulate herbicide?
33:53And so this was illustrated by a
33:56number of different groups that
33:57inflammatory cytokines such as
33:59aisle 6 will act through the Jack
34:01stat pathway through a direct
34:03transcriptional mechanism through
34:05a stat binding element on the
34:07herbicide and promoter.
34:08Now,
34:08although this is a distinct
34:10pathway from the BMP
34:11SMAD pathway. It still turns out that
34:14this being piece Med pathway is important
34:16for the inflammatory reaction to occur,
34:19and that's illustrated in this experiment.
34:21So what we did is we made mice
34:23where we knocked out the smed
34:251/5 and eight in the parasites,
34:27and as you might expect,
34:29these mice have a profound
34:31have signed deficiency.
34:32Kind of like the BMP ligand knockout mice,
34:35and compared to the the control mice.
34:37Now if we treat these mice with
34:40lipopolysaccharide to induce inflammation.
34:42Hepcidin is actually induced both in the
34:45knockout mice and in the wild type mice
34:47because this aisle 6 pathway is intact,
34:50but because pepsin levels start out
34:51so much lower in the knockout mice,
34:54the ending helpside values are still much
34:56lower in this man knockout mice because
34:59of the reduction in basil hepcidin levels,
35:02so this suggests that one could
35:04actually think about targeting any
35:06of these molecular pathways as as as
35:08a strategy to think about lowering
35:10upside and in chronic kidney disease.
35:12Questions and a number of groups
35:14have done a number of these things
35:15and I'm just going to show you a
35:17little bit of data from our group.
35:19So the first strategy that we thought
35:21about was something was making
35:23a soluble form of human javelin.
35:25So here what we do is we replace
35:27the GPI anchor that anchors him.
35:28The jubilant to the membrane surface,
35:30and we replace it with an FC
35:33tail from immunoglobulin.
35:34The idea being kind of like erythroxylum
35:36because it binds to the ligand,
35:38but it's not associated anymore
35:40with the cell surface.
35:41It may act as a ligand track.
35:43And the beauty of this type of
35:44strategy is we were able to design
35:47this even before we knew which were
35:49the important endogenous ligands.
35:51We reasoned that whatever those ligands are,
35:52they must bind to human Julian
35:55because it's the endogenous receptor.
35:57Subsequently,
35:57we also developed a BMP 6
36:01neutralizing antibody strategy.
36:02Once we identified BMP six as
36:05an important endogenous ligand.
36:07So here's a little bit of data.
36:09This was done in collaboration with
36:11Igor thorough and Doctor Weiss.
36:13This is in a rat model of any of the
36:16information induced by injection of
36:18peptidoglycan from Group A strep.
36:20When you inject this into mice,
36:22they get a chronic relapsing
36:23arthritis with all of the features
36:24of anemia of inflammation,
36:26including high peptide levels.
36:27So what you can see here is that
36:29the rats treated with the soluble
36:31human Julian had at least a trend
36:33toward a reduction and hepcidin,
36:34although that didn't quite reach
36:36statistical significance in this study,
36:38but it was enough to stabilize
36:40Fairport and protein expression and
36:42mobilize iron out of the stores.
36:44Into the the blood and importantly,
36:47it was able to improve anemia.
36:51Similar data we're also shown what the
36:54neutralizing the MP six antibody now,
36:56interestingly, as a study was published
36:59recently where they did a first in
37:01human trial for the neutralizing BMP 6
37:03antibody as a way to lower hepcidin.
37:06Anemia of chronic kidney disease. Now,
37:08as as I'm sure most of this audience knows,
37:11in phase one trials, the purpose of
37:13the trial is to look for toxicity.
37:15A dose finding right?
37:16It's not an efficacy.
37:17It's not designed as an efficacy trial, but.
37:19At times people of course do measure
37:21some efficacy and points to get some
37:23hints if if your drug might be doing
37:25what you think it's supposed to do,
37:27and that's what they did in the study.
37:29So here these are all in kidney
37:31disease patients and they the red
37:33triangles show patients who got
37:34one dose of the neutralizing BMP 6
37:36antibody and the black circles are the
37:38control and they didn't provide any
37:40statistical analysis in the study.
37:42And again I'm sure it was underpowered
37:43to look at these efficacy endpoints,
37:45but what you can see here intriguingly
37:47is that the neutralizing the MP
37:49six antibodies seem to suppress.
37:50Side and levels.
37:53Lowered ferritin,
37:54so ferritin is the iron storage protein,
37:56so this suggests that iron is
37:58being mobilized out of the stores,
38:00increased serum iron levels,
38:01and actually at least cause a tendency
38:04to increase hemoglobin levels.
38:05So kind of intriguing hints that that
38:08there may be a clinical possibilities here,
38:12and so you know,
38:13understanding all these molecular
38:15pathways has LED not only us,
38:17but a number of different groups to
38:19think about targeting these pathways
38:20as a strategy. And people have.
38:22Targeted everything from hepcidin
38:24and Fairport and directly to BMP.
38:276 logins to him.
38:29Adjuvant coreceptor to BMP receptors,
38:31SMAD proteins and also the aisle.
38:336 stat. 3 pathway.
38:34All of these strategies are actually
38:37shown benefit in animal models and a
38:39number of them have actually advanced
38:42to various stages of of clinical trials.
38:45So finally,
38:46I'll just conclude with the saying
38:48that that I hope I've convinced
38:50you that there's been a revolution,
38:52and our molecular understanding of
38:54systemic iron homeostasis regulation
38:55and the pathogenesis of iron disorders,
38:57including anemia, chronic kidney disease,
39:00and these findings hold the promise for
39:03more targeted therapies for iron disorders,
39:05particularly for kidney disease.
39:06Patients have signed.
39:07This elevates that in these patients
39:09and contributes to iron restricted
39:11with poisonous and anemia, peptide,
39:13and fairpoint and access modulators.
39:15May have a role in treating anemia,
39:17CKD by increasing iron availability from
39:19the diet and from the patient's own body.
39:22Stores and studies are ongoing to
39:25determine efficacy and safety of these
39:28strategies in inpatient patients.
39:30I'll just end by thanking the folks in
39:32my lab and our collaborators around the
39:34world who've contributed to these studies,
39:36as well as our funding sources and
39:37thank everyone for your attention,
39:39and I'll be happy to answer any questions.
39:52Sure. That was a lovely talk
39:55couple of questions like my so.
39:58So first of all, it sounds like
40:00the iron sensor governing the
40:02production side really designed for.
40:06So this it's actually more complicated
40:09than that, so it turns out that there's
40:11two different kinds of iron signals.
40:12There's the stores signal
40:13that's kind of reflective.
40:15The total body stores,
40:16and that signal we think is coming
40:18from the endothelial cells and data.
40:20I didn't have time to show you today.
40:22We and other groups have worked out
40:24that one of the mechanisms is that
40:26iron induces oxidative stress and
40:28NRF 2 pathway and endothelial cells.
40:30And this is actually a
40:32transcriptional regulator of B6.
40:34So that's one pathway.
40:35But it turns out that circulating iron
40:38levels actually seem to be sensed.
40:40We think directly in the hepatocytes
40:43via transparent receptors,
40:44so there's two different transparent
40:46receptors in the liver transparent
40:48receptor 2 which is mutated in
40:50hemochromatosis and transparent
40:52receptor one which actually binds to
40:54the other hemochromatosis protein HFV.
40:56And when circulating iron levels go up,
40:58it's sensed by those proteins and and
41:01somehow that causes HF and transparent
41:04receptor 2 to regulate hepcidin.
41:06So it's actually a complex I.
41:08I simplified it here and and
41:09focused on one part of the pathway,
41:11but there's probably multiple signals.
41:15Have some items on the 25. Please.
41:21What's the biology?
41:23Yes, so it is cleaved at.
41:25It's cleaved by Furin and furin.
41:27Like proteins.
41:29There isn't any people.
41:31Have looked at the Pro, the pro hormone.
41:34It doesn't have a biological function
41:36as far as we know and there's no clear
41:39known function of the cleavage project.
41:41The end terminal cleavage,
41:43the other cleavage products
41:44that have sighted.
41:46That's all we know right now.
42:02Yeah, so hepcidin turns out this turned
42:05over really quickly and that's been
42:07I think one of the the downsides of
42:09trying to target upside and directly.
42:11So obviously that was one of the first
42:12things that people tried to target,
42:14but I think that's one of the limitations
42:17of targeting peptide and protein directly.
42:19I don't have the numbers on the top of
42:21my head, but it's very fast. Server.
42:25I don't know that it's fully understood.
42:27It's there's some evidence that suggests
42:29that it could be taken up in the the
42:32proximal tubule and maybe degraded there,
42:34but I don't know that people have
42:36fully worked out how that happens.
42:43Do you think that transferring and
42:45ferritin are still the things to measure?
42:47Are there other modalities that that
42:50we seem like since, like training?
42:54Yeah, I think that's a great question.
42:55I think they're definitely limitations
42:58of the the the measures we have.
43:01You know, particularly ferritin,
43:02because ferritin is also
43:03an acute phase reactant,
43:05so it's also induced by inflammation,
43:06and if you have liver disease or malignancy,
43:08these things kind of affect ferritin,
43:10so it makes it very hard if ferritin is low,
43:12it's a good indicator that iron stores
43:14are low, but if ferritin is not low,
43:15it's hard to know is it's from
43:17inflammation is it's from iron,
43:19so we do need better tests and and
43:22you know transparent saturation.
43:24You know it's helpful,
43:25but I think functional markers
43:26would be useful.
43:27I think there's some interest in in
43:30trying to adapt more widely things
43:33like reticulocyte hemoglobin or a
43:35percentage of hypochromic red cells
43:37which are more functional markers.
43:40But I think we this is 1 area where
43:43we need more work to develop better
43:46biomarkers to help us understand what
43:48the iron status of our patients are.
43:51Karen I was curious about.
44:12Yeah, I think that's a good
44:13question and you know when Aiden
44:14was first discovered there was
44:15a lot of interest in thinking.
44:17Oh, can we use hepcidin as
44:18a new biomarker to you know?
44:19Figure out if patients are,
44:22you know, truly are deficient
44:23or functionally are deficient,
44:24but the problem is that have signed is
44:26regulated by so many different things
44:27that are changing in our patients.
44:29People have shown that erythropoietin
44:31suppresses subsided in in
44:33kidney disease patients in no.
44:35It's induced by inflammation if you
44:36give iron that will actually induce
44:38subsidence of patients were an IV iron,
44:40you know,
44:40so it's there's so many factors that
44:41are influencing have signed expression.
44:43I think that's why it hasn't
44:45necessarily been useful as a biomarker.
44:47You know the group that discovered
44:49everything Farren has developed
44:50and Eliza assay to measure Earth
44:52of Farrah levels.
44:52And I know this is something that
44:54they're that people are looking into as
44:56to whether that can be a useful biomarker.
45:13So the question is, what do we know about?
45:20Specifically.
45:24Yeah, so that's a great question.
45:25I think it's not just
45:26macrophages in the spleen,
45:27I think that's classically thought about,
45:29but actually there there was a
45:31study that I was part of where
45:34they actually did certain like.
45:36Partial hepatectomy and compared it with
45:38splenectomy to look at how his iron where
45:40is most of the iron turnover happening.
45:42And it turns out that that the
45:44liver macrophages also play an
45:45important role in an iron recycling.
45:47It's not just swing.
45:49I don't think that much is
45:51known about kidney macrophages.
45:52As far as how much of a role
45:54they play in iron recycling,
45:56it's at the the spleen, and the liver
45:58are probably the predominant sites.
45:59But it's an interesting
46:00question and you know,
46:01there's there these sort of conflicting
46:03data about is iron helpful or harmful,
46:05and acute kidney injury.
46:06And I think part of it
46:08may be where the iron is.
46:09There's you know.
46:10Macrophages are designed
46:12to store iron safely,
46:13and there's some evidence that,
46:14like with preconditioning,
46:15you can induce protective
46:17antioxidant pathways.
46:18And maybe that's a good thing,
46:19but it probably depends where the iron is,
46:21and I think when you're thinking
46:22about iron levels of different organs,
46:24that's an important point.
46:25But that's not.
46:27I don't know that a lot of
46:28work has been done on that.
46:30I think that's an interesting
46:31area for future research.
46:45Not that progress.
47:04The next speaker is Doctor
47:06Peter Harris from Mayo Clinic,
47:08and he'll be discussing genetic
47:11complexity in AD PKD. Well.
47:18So I went to thank Judy and the
47:22organizers for inviting me here.
47:24I mean it's we will appreciate the
47:26work that the O'Brien Center is on
47:28the P80 Centers for that matter,
47:30do around the country providing
47:33resources for for kidney research.
47:36So I'm doing something a
47:38little dangerous here.
47:39I'm talking about a PKD at Yale and
47:41think that some people here may know
47:43more about the disease than I do,
47:45but I'll try and stick to the genetic.
47:47Aspects of the disease and and and
47:50see what our understanding of the
47:53complexity of this disease can provide
47:57in terms of understanding pathogenesis.
48:00So you know about 80 PKD.
48:04This is a a common genetic
48:07disease about one in 1000.
48:10Individuals have this disorder.
48:12It's a progressive disease that develops
48:14over the lifetime of the patients,
48:16so that 50% of patients experienced
48:19renal failure around 60 years
48:22of age and about 5% of the the
48:25population that has on dialysis or
48:28transplantation in this country.
48:30Has ADPKD and and worldwide
48:33that number is even higher.
48:36So the the major genes are peak 81 and
48:41P82P-81 is a a kind of complicated gene,
48:43has an open reading frame or a
48:46coding region of nearly 13 KB.
48:48It lies on the light green.
48:51Here shows that the area that lies
48:54within a duplicated part of the genome.
48:57So even though now with whole
48:59exome sequencing and capturing
49:01methods it's still a little tricky
49:04to screen this gene for.
49:06Pathogenic variants.
49:07Peak 82 is a more normal gene.
49:12An open reading frame of about
49:153 KB here with a 15 exons,
49:18so about 78% of patients have PKD,
49:21one as the cause of their ADP
49:24KD and about 15% PKD 2 and
49:28then we for the remaining 7%.
49:31Some of these are unresolved,
49:34there's some other loci.
49:35That I'm going to go on to talk about
49:38and some other genetic complexity
49:40that I want to mention too.
49:45So a PKD or PKD 1?
49:47PKD 2 are very likely heterogeneous
49:50as a wide range of different
49:52mutations that cause the disease.
49:55If we look at the different
49:57types of mutations that we can
50:00find causing human disease,
50:01then all of those are represented
50:03here for beginning one we can see the
50:06mutations are in all parts of the gene.
50:08There's no real hot spots,
50:09although some areas probably have
50:12an enrichment for missense changes.
50:15I think 80 PKD is a common disease
50:18because any single inactivating variant
50:21can cause polycystic kidney disease.
50:24There's no single variant accounts for
50:26more than 2% of the families worldwide,
50:30and there's over 1600 different
50:32variants that have been described.
50:38So as well as genetics,
50:40we can use the size of the kidneys to
50:43determine the severity of the disease.
50:46This is work done by Maria Rosabel at
50:49Mayo and the idea here is to divide
50:51the the size of the kidney divided by
50:54the hate at the height of the patient.
50:59So total kidney volume measured
51:01by MRI and then determining put
51:04them into different groups here.
51:06So these are obviously patients with larger
51:09kidneys and patients with smaller kidneys.
51:12And then if you look at the outcome
51:15of those measurements in terms of
51:19decline in renal function or EGFR.
51:22We can see that the larger kidneys are much
51:25more likely to proceed to more rapidly
51:28to renal failure on the on the list,
51:31smaller kidneys are less likely to.
51:35We did some analysis using both genotypic
51:39groups and a size to the kidneys,
51:42so the Mayo imaging class to look at
51:45outcomes in terms of end stage renal
51:48disease and we can see here that topic
51:5082 has been known for a long time,
51:52as the mildest form of the the disease,
51:56truncating peak.
51:56Anyone mutations have the and the average
52:00age and then stage of around 55 years,
52:03and then we divided the non truncating.
52:05Changes here,
52:06so these are mainly missense changes,
52:08but we've used bioinformatic methods
52:10to do to predict ones that are more
52:13likely to be fully penetrant and
52:15less likely to be fully penetrant.
52:17And you can see that these fit
52:19in somewhere between the the
52:21peak 81 truncating and peak 82.
52:24If we look at the imaging classes,
52:26you can see that the patients which would
52:28have the smallest kidneys don't usually
52:31proceed to end stage renal disease,
52:33whereas the ones with larger kidneys.
52:36Have an average age event stage at
52:3845 years so we can see that the the
52:40size of the kidneys is a is a fairly
52:43good predictor of when patients
52:44are going to reach end stage,
52:46although with all of these measurements
52:48as quite a a spread here in the
52:51in the population.
52:52So obviously the size of the kidney
52:55is is reflecting more than just the
52:58the germline mutation information.
53:00Probably other genetic modifiers
53:03and other phenotypic and lifestyle.
53:07And factors that are influencing
53:09the severity of the disease.
53:13We looked at how the disease progresses
53:16in terms of decline in the EGFR
53:20of both by these genotypic groups
53:23and by the imaging classes here,
53:26the kind of classical view in
53:2780P KD is kind of like this.
53:29I think, where patients have
53:31preserved renal function for a
53:33while and then it starts to decline.
53:36But we found, at least in the
53:37the most severe groups,
53:39the patients with the largest kidneys
53:41and also pick any one truncating.
53:43Mutations was a decline from a fairly
53:46early age and in a fairly linear way.
53:49For these more severe groups and
53:52only in the milder groups did
53:54we see this preservation of of
53:56function and then decline later on.
53:58And as you see here,
53:59the 1A not really a declining
54:02into to renal failure.
54:06We also looked at how the kidneys
54:10increase in size over time,
54:13depending again on these same groups and
54:16and for P-80 for the different P-81 groups.
54:20It was not really a significant
54:22difference in the way that the that
54:24the the speed of the progression
54:27of the disease for for the imaging
54:31class we can see some difference
54:33here with a with a lower rate of.
54:36Of progression here for 1A and 1B
54:40compared to the 1E and 1D groups.
54:43If we look at them together on the
54:46on the same slide we can see here,
54:49but I've showed you with this kind
54:52of rapid decline in renal function in
54:55terms of EGFR for the for the larger
54:59kidneys and then preserved for the for
55:03the for the for the for the smaller kidneys.
55:06If we look at the the genotype here we
55:08can see there's not really a difference,
55:10although there is a difference in
55:12the in the start of the recordings.
55:15Even at 20 years of age and we can
55:17see that that's even clearer here
55:19in the imaging classes,
55:21so that's just telling us before 20
55:24years of age during the pediatric
55:26period that the the the rate that the
55:29kidneys grow is quite a lot different
55:32in the patients which are going to have
55:34the worst outcomes with the largest kidneys.
55:37Compared to the largest cities
55:39compared to the smaller kidneys
55:41and also for PKD 1 truncation,
55:44they also develop more
55:45quickly during that period.
55:47So it seems like the development
55:50and growth of kidneys during this
55:52pediatric period is important for
55:55determining the the outcomes of the.
55:59The patients.
56:02So this would be the bottom.
56:04Here would be our typical 80 P80
56:07pedigree where it's inherited in a
56:10dominant fashion of males and females
56:12are affected but but we don't.
56:14We quite often see this setup where
56:17apparently we have a a new mutation
56:19occurring in the in an individual here
56:22with sibs and the parents apparently
56:24unaffected and at least 10 to 20% of 80
56:28P80 families have this type of structure
56:30where we suspect that the Dinovo.
56:33Mutation has occurred.
56:36One possibility with the DENOVO
56:37mutation is that it hasn't occurred
56:40in the sperm or the egg here,
56:42but it's occurred at a later
56:44stage of four cell stage here,
56:46and the result of that is that the
56:49patient is a mosaic of of cells that
56:53have the the mutation of ones that
56:56don't have the the the mutation.
56:59Then this has an influence on
57:01how the disease progresses,
57:03presents and progresses.
57:04If we look at.
57:06That we published a paper
57:07a couple of years ago,
57:09about 20 families that with mosaicism
57:11and this is an example of one of them.
57:13Here, the mother is a mosaic.
57:16Can see this rather odd pattern
57:18of a rather small number,
57:20but at large assess within the
57:22kidney you can see the sun here
57:25has more typical presentation
57:27and now this is at 20 years of
57:30age compared to 47 years of age
57:32when we did the genetic analysis
57:34we're able to find this insertion.
57:36Deletion mutation.
57:38That's a 50% of the cells in the sun,
57:41but only at 17% of cells in the
57:44in the in the mother here,
57:46so we can see that this lower
57:50level of of mutant cells leads to
57:52a a milder progression of disease,
57:55and if we look at the data overall,
57:57we can see that here.
57:58I hope you can see the red spots.
58:00Here are the mosaics and the other is
58:03a control population of similar types
58:05of mutations and you can see that.
58:07They tend to have preserved
58:09kidney function and they have a
58:12smaller kidneys than we would see
58:15without the without the mosaicism.
58:17We're probably at least 1%
58:19of families with ADP.
58:21KD have this type of mosaicism,
58:24but maybe more than that because
58:26low level of mosaicism may not get
58:28into the blood cells that we usually
58:31screen for pathogenic variants.
58:36So as you know, by the by the name,
58:40a PKD is normally a
58:42dominantly inherited disease,
58:44but sometimes it's a biallelic or
58:47has a recessive inheritance pattern,
58:51and then you can see an example
58:53here in this consanguineous family.
58:56The only individuals that reached
58:58end stage here were ones that were
59:01homozygous for a missense change.
59:03You can see the missense change.
59:05Is it a well conceived position
59:07in Orthodox and homologs,
59:09and we can see that individuals that
59:12just had one copy of this variant
59:14tended to have very mild disease,
59:16like just a few cysts within
59:20the within the kidney.
59:22So it was a lot of controversy about this.
59:25People didn't really believe it,
59:26I guess.
59:27So we we developed a model which
59:30in mimic this RC,
59:33Leo and and this this showed that we have a.
59:41We have this inherited slowly
59:44inheritance of the the disease in the
59:49homozygous RC animals here so that the.
59:52A disease developed slowly over
59:54the the the lifetime of the the
59:57mouse up to 12 months of age.
59:59So this showing if we if we
01:00:01had two inactivating mutations,
01:00:03then the animal would die embryonically.
01:00:06And obviously if this was a
01:00:09variant of unknown significance,
01:00:11we wouldn't develop polycystic kidneys.
01:00:13So I think this is fairly good evidence
01:00:15that this is a a hypomorphic allele.
01:00:20I don't know how to get rid
01:00:21of this thing at the top.
01:00:22Any good ideas? Anyway,
01:00:26the so one other presentation of
01:00:31the disease that is unusual in ADP.
01:00:34KD is when we have very early onset
01:00:38disease where where it's represents
01:00:40very much like the recessive form
01:00:43of polycystic kidney disease.
01:00:46With this very large kidneys,
01:00:48even found in utero and this is a family
01:00:51with this very early onset disease.
01:00:54They had a a typical truncating.
01:00:56Mutation in the three individuals
01:00:58here that had a typical presentation
01:01:01of 80 PKD but in the child here,
01:01:05with the in utero onset disease,
01:01:07we found that the same hypomorphic
01:01:09variant that we found in the in the
01:01:12previous family in homozygosity was
01:01:14also inherited from the other allele.
01:01:17So we think together these are lowering
01:01:20the level of the the POLICYSTAT expression
01:01:23and account for this more severe disease.
01:01:27And again we mimic that
01:01:28situation in the mouse here,
01:01:29where they now are C model.
01:01:32These animals die on average around
01:01:3428 days of age and you can see
01:01:38by 25 days of age that the 25%
01:01:42of the the body weight is made
01:01:44up of these very cystic kidneys.
01:01:46So again supporting this kind of level
01:01:49of policy system being associated
01:01:52with the severity of the disease.
01:01:55If we look at.
01:01:58The level of polycystin expression
01:02:01the functional polycystic expression.
01:02:03We did this using urinary
01:02:06extracellular vesicles,
01:02:07which have quite a high level of
01:02:10the the the police system protein
01:02:13where we can see here in the RC
01:02:15model we have a a reduced level
01:02:18of the protein to the back.
01:02:2040% of the the normal level compared to
01:02:23what we we see in the the normal situation.
01:02:28So we think that the level of
01:02:31the functional protein,
01:02:32not the level of expression,
01:02:33but a functional and trafficked
01:02:36protein is is what's driving
01:02:39this more severe disease.
01:02:44So occasionally we see a digenic
01:02:48inheritance also in in any PKD.
01:02:51Your pay is described.
01:02:53A couple of pedigrees like this,
01:02:56and this is one from the halt
01:02:58PKD study from from Rome Peron.
01:03:02You can see that the individual here
01:03:05has an inframe deletion of Piketty one,
01:03:09but a nonsense mutation in P82.
01:03:11You can see the age at end stage here 43.
01:03:14Here so 10 to 15 years earlier than
01:03:18typical for P-81 truncating change.
01:03:20Can see that the child here at
01:03:24six months was already had cysts
01:03:26in the in the in the kidney,
01:03:29so we think that the combination of these
01:03:31variants makes the disease more severe,
01:03:34although not to the early very
01:03:36early onset we see with the second
01:03:39hypomorphic PICKITY 1 variant.
01:03:41And again,
01:03:42we're able to mimic this type of digenic
01:03:46inheritance using Steve's Piketty 2 WS,
01:03:5025 minus model and RP81 RC model.
01:03:55Both of these by themselves
01:03:57have rather mild disease,
01:03:59but if we combine them together,
01:04:00we see that we have this
01:04:02much more severe disease.
01:04:04So we think that the.
01:04:07The the functional consequence
01:04:08is of of of of a.
01:04:11The level of both of these genes
01:04:13is important to the to the severity
01:04:15of the disease that we see.
01:04:20And if we look at.
01:04:23Western blot of of these animals with
01:04:26the P-81 and Piketty 2 mutation.
01:04:28If we look at and the situation
01:04:32normally this is the the the.
01:04:36This is the mature form
01:04:38of of polycystin 1 here.
01:04:41This is the immature form of photosystem one.
01:04:44We went for use and OH is cut down
01:04:47to a smaller form and this is PNG's
01:04:50here removing all of the sugars and
01:04:52the see if we look at peak 82 now
01:04:55sells or we see that we don't see
01:04:58this mature form of of police system
01:05:01and one so police system one is not
01:05:04able to traffic and mature properly.
01:05:06D2 is is not present and if we lower
01:05:10the level of P82 to half the level,
01:05:12we see a reduced level by about
01:05:1525% of the the mature form of
01:05:18the the the polycystin one.
01:05:21If we add in this RC Elio here we
01:05:24further lower this mature level
01:05:26and the situation is a little
01:05:29bit complicated here because
01:05:31the RC allele also inhibits the
01:05:34the the normal cleavage of the.
01:05:36Phone system protein at this point here,
01:05:39so we see more of the the full length,
01:05:41which we also think is is not a
01:05:44functional form of other polar
01:05:45system in this situation.
01:05:50So we've used this interaction between
01:05:53policies and one and policies and two
01:05:56to develop and and in vivo in vitro
01:05:58assay to assay different variants to see
01:06:01if they are likely to be pathogenic.
01:06:05So here was expressing these are
01:06:09expressing full length constructs which
01:06:11are tagged full length policy system,
01:06:14one for full length policy system two
01:06:17and then if we express them together
01:06:19we can see we find this surface
01:06:21localized form of a police system.
01:06:23So if we measure this level of of
01:06:26surface policy system one it gives us
01:06:29an indication of whether the the variant
01:06:31we're looking at is able to fold and traffic.
01:06:35That to the surface of the cell and
01:06:37we're kind of using the surface of the
01:06:39cell as a surrogate here for for the
01:06:42ciliary localization of these proteins.
01:06:47If we look at variants that are either
01:06:50truncating or strongly predicted to
01:06:53be pathogenic changes, we don't see
01:06:56much as surplus police system one.
01:06:59These are some of the weaker variants and
01:07:01I said right showed you earlier and they
01:07:04kind of dividing into two groups here,
01:07:06ones that seem to be fully inactivating
01:07:09one and ones that seem to be much
01:07:12weaker and have a a significant
01:07:14level of surface polycystin 1.
01:07:17Some of these are splicing mutations,
01:07:19so this is a maybe explain it or explaining
01:07:22why these don't seem to be altered.
01:07:25If we look at the some of the potential
01:07:30hypomorphic alleles like the R3277C,
01:07:32we can see that the level of surface
01:07:35localization of that is is somewhere again
01:07:39intermediate between fully inactivating
01:07:41and and the wild type level here,
01:07:43so that at a lower level we can
01:07:45see that for some of these.
01:07:47Other hypomorphic marker variance.
01:07:55If we so this is,
01:07:56I think this data is showing that a lot
01:07:59of polycystin one and two variants,
01:08:02even if they're even if they're
01:08:05missense changes are are actually
01:08:08folding and trafficking mutations and and,
01:08:12and the reason that they're they're non
01:08:15functional is because that they don't
01:08:18fold and trafficker appropriately.
01:08:20Interestingly, if we treat the cells
01:08:22at a lower level at 30 degrees, so.
01:08:25Give more chance for these these the
01:08:28protein to fold and traffic we can
01:08:31see that for a start the level of a
01:08:36surplus polycystin one is a wild type.
01:08:39It is increased and you can see
01:08:42for a lot of other variants that
01:08:44we see within the gene,
01:08:46including some that are strongly
01:08:48predicted to be pathogenic.
01:08:50We see then some surface protein.
01:08:55From these and also the same
01:08:57for policies in two variants,
01:09:00some maintain and completely
01:09:02inactivated if we.
01:09:03If we use this lower temperature
01:09:05for the level of police system two
01:09:07is increased and the wild type,
01:09:09but also some other variants.
01:09:11So this suggests to us that a
01:09:14chaperone type treatment that enables
01:09:16the policy system one in particular
01:09:19to to fold more efficiently.
01:09:22May be a useful therapy for some
01:09:25patients with missing changes,
01:09:27and since the level of wild type
01:09:30polar system one is also increased
01:09:32and I think that there's evidence
01:09:35that wild type polar system one may
01:09:38be also important within the within
01:09:40the kidney and the ad PKD kidney that
01:09:43this may be helpful even in patients
01:09:45with a with a truncating variant.
01:09:51We can also get complex alleles in in.
01:09:54This is a situation where we have
01:09:58more more than one variant insist
01:10:00that is a that is generating the
01:10:03the pathogenic allele and we can
01:10:06see three different variants here,
01:10:08including our favorite, our 3277C,
01:10:12and we can see that was found
01:10:15in two different families here.
01:10:18Conceived from the segregation that
01:10:21they're insists rather than in trans,
01:10:23and these patients have fairly
01:10:26typical AD PKD.
01:10:28Although we know that this variant
01:10:30by itself only leads to just a few
01:10:32cents developing in the kidney.
01:10:34Again, if we look at the level
01:10:36of surface policies to 1 here,
01:10:38you can see that that's
01:10:40significantly reduced to the R3277C.
01:10:42The other two variants reduced,
01:10:44but to a lesser extent.
01:10:46But if we look at.
01:10:483 variants together or even
01:10:50two of these variants.
01:10:51Together we can see this brings it down
01:10:54to pretty much an inactivating allele,
01:10:56so that's why we feel that these patients
01:10:59have a a typical ADP KD presentation.
01:11:05So this brings us to our thought that a
01:11:09PKD is a a dosage related disorder that
01:11:14the level of functional policies to one is
01:11:16important for the severity of the disease.
01:11:19If we have one inactivating
01:11:21allele about a 50% reduction,
01:11:23we get this adult onset disease.
01:11:26If we add on a hypomorphic allele,
01:11:28we can have more severe disease.
01:11:30The hypomorphic allele by itself.
01:11:34Results in milder disease.
01:11:35Then we can have some variants
01:11:38that fit in between,
01:11:39so although they're petty,
01:11:41one variance the disease can
01:11:43be more like PKD 2.
01:11:44That doesn't mean that there's not
01:11:47a lot of other stochastic germline
01:11:50and somatic genetic variants that
01:11:52are in kidney damage that are also
01:11:55modifying the way that the the
01:11:58disease presents and progresses.
01:12:04So there's other forms of a PKD.
01:12:07This is H and F1,
01:12:09beta associated kidney disease,
01:12:11which can also result in an ADP
01:12:15KD phenotype with H and F1 beta.
01:12:19We we see that we have a wide
01:12:21range of different phenotypes,
01:12:23so as a transcription factor modulating
01:12:27the expression of many genes,
01:12:29including ones associated
01:12:31with PKD one or with.
01:12:34A arpkd, but in some cases we can
01:12:37find a presentation that really
01:12:39mimics what we see in in 80P KD
01:12:43with multiple cysts in the kidney,
01:12:46but are rather limited other phenotypes.
01:12:52So a few years ago we published a paper
01:12:56saying there was a number of paper
01:12:59number of families that were published
01:13:01in the 1990s that suggested that
01:13:04there were unlinked to P-81 and P82.
01:13:07So suggesting there may be other genes
01:13:10for for a PKD we found in four out of
01:13:14five of these families that if we look
01:13:17carefully for the pathogenic variant and
01:13:20beginning one and Piketty 2 and also.
01:13:23I revisited the linkage sometimes
01:13:26with newly collected samples,
01:13:28we could show that.
01:13:30And most of them were either Piketty,
01:13:32one or Piketty 2,
01:13:33although there was one that was unresolved.
01:13:37We said maybe prep rather prematurely
01:13:39at that stage that this reanalysis does
01:13:43not support the the existence of a.
01:13:45P-83 and and as we know,
01:13:48since that time there's been several genes
01:13:50that are mimic the the AD PKD phenotype.
01:13:56So from work from Steve's group
01:13:59and Josh Drents Group A different
01:14:02disease that AutoZone will dominant
01:14:06polycystic liver disease is due
01:14:08to either PRK or the major LOCI,
01:14:10or PRK, CSH and and sex 63.
01:14:14We're going to see the presentation here.
01:14:16A very large liver but rather limited
01:14:19cyst within the within the kidney,
01:14:23and these proteins are
01:14:25involved in trafficking.
01:14:26Or glycosylation unfolding and
01:14:29quality control of a membrane
01:14:33and and secreted proteins.
01:14:35And it seems like a polar system.
01:14:38One is particularly vulnerable
01:14:40to a defects in this pathway.
01:14:45We did some whole exome sequencing
01:14:47a number of years ago on a rather
01:14:50limited number of families.
01:14:51I I was somewhat skeptical that there
01:14:53was other genes for 80 PKD at the time,
01:14:55but the postdoc during the
01:14:58study was very persistent.
01:14:59We found a missense change in Ghana
01:15:04hub here in in one patient with a
01:15:07cyst within the kidney and also a
01:15:10cyst within the the liver in the
01:15:13two individuals within the family.
01:15:15She is a missense change.
01:15:17It was difficult to know whether
01:15:20it was significant,
01:15:21but this was a great candidate because
01:15:24it was the binding partner here
01:15:27the glucose stays out for subunit.
01:15:29Here binding with the glycosidase fetus.
01:15:33So obviously a great candidate and
01:15:35by going on and doing sequencing
01:15:37of other families,
01:15:38we were able to find other families with ADP,
01:15:41KD and AD. PLD has has Whitney.
01:15:46Let's see here in the associated with a PLD.
01:15:52If we look at ourselves that
01:15:54don't have a again and again,
01:15:57we see that the policy system
01:15:59mature form is not really a found
01:16:03and this is kind of in contrast
01:16:05what we see with some other control
01:16:08proteins where we see the the the
01:16:11level that is that is not mature.
01:16:13It is only a small part of the total amount
01:16:16of protein compared to to polar system one.
01:16:19And even in the heterozygous phenotypes.
01:16:22So the phenotype that we're
01:16:24seeing here in patients,
01:16:26we can see that the the level of the
01:16:29the mature form of the the the policy
01:16:32system one is is already decreased.
01:16:35And if we look at scanner negative cells,
01:16:38we can see that police system two
01:16:40that's normally found on the stellium.
01:16:42We didn't find localised to the cilium
01:16:45in these neural cells, so as I say,
01:16:48it seems that polar system,
01:16:49one in particular is.
01:16:53Is primed or is particularly susceptible
01:16:57to folding defects associated
01:16:59with defects in this pathway?
01:17:03So Whitney again published a couple
01:17:08of years ago or several years ago.
01:17:10Now that LG eight was a also a
01:17:15form of polycystic liver disease,
01:17:18and we think that the phenotype is
01:17:21not only a polycystic liver but
01:17:23also of polycystic kidney disease.
01:17:26This is a a family here with three
01:17:29affected individuals who can see
01:17:31that they have cysts in the kidney,
01:17:33but interestingly. Are the few.
01:17:36This in the in the liver.
01:17:39The cysts the interestingly a lot
01:17:41of the time seemed to be more in the
01:17:43left kidney than the the right kidney,
01:17:46and we're not sure completely
01:17:48sure why that is.
01:17:52The one family from our and that
01:17:56reanalysis of of P-83 families.
01:17:58Hispanic or Spanish family that
01:18:00where we didn't find Piketty,
01:18:02one of Piketty 2 variants,
01:18:04interestingly has an LG 8 variant here,
01:18:07which segregates with the disease,
01:18:10although it indicates that there's
01:18:12another affected individual in
01:18:14the family and this individual on
01:18:17ultrasound has a a couple of cysts
01:18:19within the within the kidney.
01:18:21Interestingly, there's a couple of other.
01:18:23Variance but in in ciliopathy
01:18:26genes which also so these will be
01:18:29recessive variants which are also
01:18:33segregating or partially segregating
01:18:35with the disease in the family.
01:18:37So we think that these other
01:18:40factors may be influencing the
01:18:42severity of the disease and maybe
01:18:46determining whether single a LG 8
01:18:49variants are shown to be pathogenic.
01:18:52If we look at the the families that
01:18:54we've collected with a LG eight,
01:18:56we can see that on the whole they have
01:18:59fairly preserved renal function and a
01:19:02milder than what we expect for PKD 2 and a,
01:19:07LGH LLG nine,
01:19:09which Whitney also described as
01:19:12an AD PKD gene a few years ago.
01:19:16We also found another number of
01:19:19families with this change, and again,
01:19:22the.
01:19:22The the the disease is maybe a
01:19:25little bit more severe on average
01:19:27than we see with with the LGH,
01:19:30although most of the patients
01:19:32have preserved renal function,
01:19:34but obviously some have a decline
01:19:36in in renal function here.
01:19:40We did some work with John Sayer of the UK
01:19:45Biobank and the genome England 100,000.
01:19:49Genome Project England.
01:19:51100,000 genomes project and looking
01:19:54at the UK Biobank to start with here
01:19:58we're looking at patients that have
01:20:02a rare variance with truncating
01:20:05mutations in this particular gene,
01:20:07and then we're looking at the ICD
01:20:10codes here for either policy or
01:20:12cystic kidney disease or other
01:20:14diseases of the kidney, which you,
01:20:16which also includes some cysts of the kidney.
01:20:19And also you know. Proposed.
01:20:23Acquired cystic disease.
01:20:24For instance,
01:20:26we can see that there's an enrichment
01:20:28here in the the cases compared
01:20:30to the controls for a LG 8IN.
01:20:33In both of these two populations,
01:20:36although we can see there's a greater
01:20:39enrichment here for a LG nine in the
01:20:42the cases compared to the the controls.
01:20:46If we look at the Genome Genomics,
01:20:49England project and again look at
01:20:51loss of function variants compared
01:20:53to the to the total alleles,
01:20:56we can see that these are again
01:20:58enriched in the in the the cases.
01:21:01The cystic kidney disease cases
01:21:04compared to the to the controls
01:21:07here and likewise for a LG 9.
01:21:10However,
01:21:10these variants are especially
01:21:12for a OG eight are quite common.
01:21:16Within the the population
01:21:17truncating variants,
01:21:18so I think that they don't always
01:21:21result in a in a cystic phenotype,
01:21:24and that may be that we need other
01:21:28variants to before we these.
01:21:30These diseases are are manifested,
01:21:33so we might want to consider these
01:21:35more like a maybe a a riskily old than
01:21:39a complete monogenic form of of a PKD.
01:21:44We also described a few years
01:21:47ago with Emily Kornak Gugel.
01:21:52Using whole exome sequencing again,
01:21:54a family which had a a DNA JB 11 variant.
01:21:58So this is a a coach chaperone protein
01:22:00that works with deep in the in the in the
01:22:04Plasmic reticulum and again plays a role
01:22:07in the folding and trafficking of proteins.
01:22:09This time the phenotype is of smaller
01:22:13kidneys with with multiple cysts in them,
01:22:16but in the older individuals we
01:22:18can see we see a decline in.
01:22:20In renal function this missense
01:22:23change again missense change.
01:22:25It difficult to know whether it's pathogenic,
01:22:27but it wasn't a very preserved
01:22:30conserved site within the protein.
01:22:33Again, we're able to go on and
01:22:36find additional families and then
01:22:38Emily recently has published a
01:22:40a wider range of 77 families,
01:22:4277 patients from 27 pedigrees,
01:22:45and we can see that they have a rather
01:22:48consistent phenotype where they have.
01:22:50Preserve renal function and and maybe
01:22:53just a few cysts and without kidney
01:22:55enlargement up until 50 years of age and
01:22:58then we can see we see this decline in
01:23:01renal function so that the age at end
01:23:04stage is similar to what we see in PK 82.
01:23:07In this case the kidneys stay pretty
01:23:09small and become fibrotic and so they
01:23:12look somewhat similar to what we see in.
01:23:15AutoZone will dominant tubular interstitial
01:23:17kidney disease due to you mod or or.
01:23:21Mark one variance.
01:23:24So we can see here where DNA
01:23:27JB 11 is in this.
01:23:29In this pathway involved with
01:23:32folding and trafficking of protein.
01:23:35So this is PRK, CSH and ganap.
01:23:40Here we can see also a variance in a
01:23:44LG 8 and a LG nine. As I mentioned.
01:23:48There's also a variance in PM two
01:23:51that give rise to in a recessive.
01:23:54Way and associated with particular
01:23:57a promoter. Mutations to an AR.
01:24:00PKD phenotype.
01:24:00So we're going to see that there's
01:24:03plenty of variance within this pathway
01:24:06that are associated with a PKD or a PLD
01:24:11phenotype which may be due to the to
01:24:14the susceptibility of of policy system,
01:24:16one to to variance in this pathway.
01:24:23So we also.
01:24:26A recently there's been a couple of papers
01:24:30to do with biallelic disease associated
01:24:33with DNA JB 11 from a Turkish family
01:24:38here and also from a a French family.
01:24:42We can see that the phenotype in these
01:24:46cases is is a severe disease of in utero
01:24:51presentation with the cystic kidneys.
01:24:54Here assistance also or disorganization
01:24:57within the the pancreas of
01:25:00fibrosis within the within,
01:25:03the within the liver.
01:25:06And so we can see.
01:25:10And also they looked at the
01:25:11cilia in these cases and I don't
01:25:13know if you can really see this,
01:25:15but this is the the normal size
01:25:17cilia here and they're suggesting
01:25:19in the in the patients that they
01:25:21have longer and torturous cilia.
01:25:24Obviously,
01:25:24it's a little difficult to tell if
01:25:27this is true from this analysis,
01:25:29but it does indicate that we get a
01:25:32a more syndromic type of ciliopathy
01:25:36phenotypes associated with the DNA JB 11.
01:25:41Suggesting or asking the question
01:25:43of whether it's more generally
01:25:46involved in asilia development.
01:25:51Recently we identified 1 monoallelic
01:25:55variants in FT 140 as a cause
01:25:59of an ADP KD light phenotype.
01:26:03As you know, I have T,
01:26:05as is intracellular transport process.
01:26:07That's important for moving proteins in and
01:26:11out of the cilium and generating the cilium.
01:26:15This is the I FT140 as part of the
01:26:18IFA complex, which is a thought to be
01:26:23associated particularly with the retrograde.
01:26:25IFT transport and by allelic
01:26:28variants here are associated with a
01:26:32more severe ciliopathy phenotype.
01:26:34Short rib thoratic displays Asia
01:26:37which also has a cystic kidneys
01:26:40as as part of its phenotype.
01:26:43We were able to identify rather large
01:26:47#38 families and 59 affected individuals
01:26:50that had variants in in FT 140.
01:26:53We know from work from Greg buzzer
01:26:56that if this protein or this gene is
01:26:59is knocked out in the kidney then we
01:27:02get a polycystic kidney phenotype.
01:27:07We can see again that we have a fairly
01:27:10distinctive phenotype in these patients,
01:27:12but this time it's just a small number
01:27:15of rather large cities that we see.
01:27:19We can see that this can be
01:27:21seen segregating in families,
01:27:22so maybe behaving a little bit more like
01:27:25a a monogenic disease, although again,
01:27:28it may not be a completely penetrant.
01:27:35Interestingly, I have two
01:27:37140 lies right next to PKD.
01:27:40One just a half a mega base away from P-81.
01:27:43So some variants in PKD one like this
01:27:47atypical splicing change here are also
01:27:50found to set Co segregate within families.
01:27:54With the with the IT 140 there and
01:27:57so it could be that in in some cases
01:28:01they're also modifying the phenotype.
01:28:04I think sometimes get families
01:28:06have been linked to pick anyone,
01:28:09and missense variants have been
01:28:12assigned as a pathogenic variant,
01:28:14but I think it's worth reconsidering
01:28:17them now and and maybe some of
01:28:20these are really I FT 140 families.
01:28:26If we look at the.
01:28:28Between the type and these families
01:28:30and see that they also have
01:28:32rather preserved renal function.
01:28:34Only one of the patients that we
01:28:37looked at had end stage renal disease,
01:28:40and they'd already had a a kidney
01:28:42removed at an early age because they had
01:28:46a Wilms tumor and we can see that the
01:28:48size of the kidneys is quite large here.
01:28:50Because of these few but very large cysts,
01:28:55so many of these will be given the kind of.
01:28:59A typical presentation defined
01:29:01by the by the imaging class.
01:29:07If again, we look at the
01:29:11UK Biobank for IT140.
01:29:14We can see that there's an
01:29:17enrichment of pathogenic variance
01:29:18in IFTTT 140 that's significant,
01:29:20so pick any one pick any two
01:29:23obviously are significantly found,
01:29:25and this is looking at the
01:29:27cystic kidney phenotype.
01:29:28There was about 1400 patients
01:29:31within this population that were
01:29:34defined as having a cystic kidneys,
01:29:37and I have two 140 was found to be
01:29:39there at the third most common variant
01:29:41associated with that being done,
01:29:43and the only one the other
01:29:44one that was significant.
01:29:45You can see I have G9.
01:29:47There is one other that was seen but
01:29:51but not at a a significant level.
01:29:54And then again if we look at.
01:29:58High impact variance,
01:30:00so variants that are likely to be
01:30:03pathogenic in activating variants
01:30:05compared to synonymous or intronic variants.
01:30:09We can see there's a Richmond here
01:30:11in the cystic kidneys different two
01:30:15different defined cystic kidney phenotype.
01:30:17Interestingly,
01:30:18also in the CKD stage.
01:30:214-5 although we didn't see renal
01:30:22failure in most of the patients,
01:30:24so we looked at but not other forms of.
01:30:29Kidney disease,
01:30:30again suggesting that there's a
01:30:33significant enrichment between
01:30:34pathogenic variants in this gene
01:30:37and PKD and the PKD phenotype.
01:30:42So I just want to follow up finish
01:30:45up by just talking about a couple
01:30:48of other dominant or mono allelic
01:30:51diseases that are associated
01:30:53with the NADPKD like phenotype
01:30:56so or facial digital one.
01:30:59This is an X linked dominant
01:31:02disease so we see this phenotype
01:31:04in in females where we just have
01:31:07a single pathogenic allele.
01:31:10They also have a variety of facial.
01:31:13World Digital and and central
01:31:16nervous system phenotype associated
01:31:18as well as the cystic kidneys,
01:31:22but it's sometimes we can see
01:31:25cystic kidney phenotype and and
01:31:27only mild other phenotypes and
01:31:29they can be mistaken for a DPKD.
01:31:35Recently we've been looking at
01:31:40specific variants in NEKADE.
01:31:42This is a a kinase that is thought
01:31:46to be associated with the cilia.
01:31:49This is following up with an
01:31:51abstract that was published last
01:31:53year or presented last year.
01:31:55The SN and in these cases
01:31:59the disease is associated so.
01:32:04Bylica Neck 8 variants are again
01:32:07associated with a a ciliopathy
01:32:10type phenotype and where some.
01:32:13Animal models are nice models JCK
01:32:17that are associated with neck height
01:32:20variance or in a recessive way.
01:32:23But here we have a just a single allele.
01:32:27This is associated with very severe disease.
01:32:30You can see end stage renal disease at
01:32:32one year of age here in the in the mother,
01:32:35but other she had the three transplants
01:32:38and was able to give rise to a child
01:32:41that also had this very severe.
01:32:43Disease here you can see the
01:32:45light and large cystic kidneys.
01:32:47OK in large cystic kidneys here,
01:32:51although interestingly made
01:32:52up of rather larger cysts,
01:32:54and we typically see in the in the RPD.
01:32:58Interestingly,
01:32:58the mother had end stage of 48
01:33:01years of age of the consider the
01:33:03kidneys look fairly similar,
01:33:05and she was only a mosaic for the
01:33:07for the variant here you can see the
01:33:10the the the level of the the mutant.
01:33:13There was very low,
01:33:15so that's why she had much milder disease.
01:33:19And this is second family.
01:33:21Here you can see that the these very
01:33:25large cystic kidneys here where
01:33:27birds started at seven years of
01:33:29age from from this parent patient.
01:33:32So the difference between the variance
01:33:34here and in neck 8 compared to the
01:33:37recessive diseases of these variants
01:33:39seem to be in the in the kinase domain,
01:33:42so they're having a much more
01:33:45severe form of the the disease.
01:33:50And just finally we can see also
01:33:54synergy synergistic interactions
01:33:57between the P-81 and the pH one.
01:34:00So the gene associated with AARP KD
01:34:03that Steve is described and we've
01:34:07confirmed this is looking at a a
01:34:10rat model of ARP KD that it has this
01:34:13slowly progressive disease PCK wrap.
01:34:16We made a a heterozygous P-81.
01:34:20Knockout a Leo which by itself
01:34:23has very few cysts developing.
01:34:26But then if we can see the the
01:34:29them together we have this much
01:34:32more severe synergistic phenotype,
01:34:35indicating that that the ARP,
01:34:38KD and AD PKD proteins.
01:34:40Although we don't think they
01:34:42interact and form a complex,
01:34:45have some type of related role
01:34:48in preventing system development.
01:34:50Within the kidney.
01:34:52Interesting,
01:34:52we did some RNA seek and in the in the
01:34:58three populations here the the the.
01:35:00The Geo terms that were all
01:35:03associated with cilia development.
01:35:05We also saw a longer cilia here
01:35:09in these diegetic animals than we
01:35:12saw in the the normal individual.
01:35:14So whether this is directly associated
01:35:17with these these mutations or is a
01:35:20response to lack of cilia signaling,
01:35:24I think is a is a question.
01:35:27So we can see as well as the
01:35:30variance associated with the folding
01:35:34of police system one,
01:35:35giving rise to an 80 P80 phenotype.
01:35:37We can see variance as well as
01:35:40the Piketty one and Piketty 2 and
01:35:43and five persistent on the cilium.
01:35:45We can see these other variants
01:35:48associated with different parts
01:35:51of which are associated with
01:35:53ciliopathies and are recessive way or.
01:35:59The X linked OFT one here,
01:36:02resulting in in something
01:36:05like an ad PKD phenotype.
01:36:07So I think the question is here,
01:36:09are these working and you can see that they
01:36:12seem to be in different complexes here,
01:36:14but may all all all be associated with.
01:36:19Determining the level of power
01:36:20system one policy system 2 maybe
01:36:23fibre system on the cilia.
01:36:24So is this a mechanism of disease or
01:36:28is there other cilia related cisgenic
01:36:32pathways as Steve's work has suggested
01:36:36that might be in important in the in
01:36:40the in the formation of this disease.
01:36:43So I just to summarize P-81 and Piketty
01:36:482 are the the the common 80 PKD genes.
01:36:52P-81 is more severe than P82.
01:36:54Truncating and more severe
01:36:57than non truncating.
01:36:59There we see genetic complexity
01:37:02of biallelic disease,
01:37:04complex alleles,
01:37:05mosaicism and digenic disease,
01:37:08a dosage model we feel fits the
01:37:11the data we see in terms of
01:37:14the genetic mechanism in in 80,
01:37:16PKD monogenic,
01:37:17pathogenic variance in ER proteins
01:37:20that involved in these different
01:37:23processes for dealing with a membrane
01:37:27and truncated and secreted proteins.
01:37:30Are associated with an ADP,
01:37:32KD or 80 PLD phenotype and maybe associated
01:37:36with Polar system one maturation.
01:37:39Now we're seeing increasing number of
01:37:42variants associated with cilia structure.
01:37:45Also associated with a ADP.
01:37:48KD like phenotype and and is
01:37:51this to do with the level of
01:37:53ciliary power system one.
01:37:57So you just want to thank the the
01:37:59people in my lab over the last decade
01:38:01that have been involved with the
01:38:03the works that we've been doing and
01:38:06also John Sayer and Eric Hollinger
01:38:09in Newcastle for the collaborations
01:38:12on the the work associated with the
01:38:15UK Biobank and the 100,000 genome project.
01:38:18Thank you.
01:38:40Showing that.
01:38:47Right? Those are one of the beach.
01:38:56Yeah.
01:39:05Yeah, I don't know.
01:39:07I guess in in our hands we also
01:39:10find that to be a trafficking
01:39:13mutation and we find less of that or
01:39:16that it doesn't traffic properly.
01:39:18So yeah, I I'm I'm not sure about that.
01:39:23I think it depends a little bit on the
01:39:25system that you're that you're using,
01:39:27but certainly.
01:39:28We've also found that to be a trafficking
01:39:32defect and and so I don't know exactly
01:39:36how to to reconcile that data.
01:39:42Pardon.
01:39:44Right, right exactly yeah, yeah.
01:39:46So overexpressed tag protein.
01:39:50Pressing policy system one and police
01:39:52system two and we're looking at salary.
01:39:55You know, surface localization here,
01:39:57not ciliary localization.
01:39:58And there could be a different layer.
01:40:01Reviews the surface as a kind
01:40:03of surrogate for the cilia,
01:40:05but you know it may not
01:40:07be a complete one to one.
01:40:12Similarity there.
01:40:17So the energy is staggering.
01:40:28We're able to develop there.
01:40:34Interesting.
01:40:51Yeah, I think it's a time where
01:40:53we can start thinking about more
01:40:56tailored therapies for for a PKD.
01:40:58As I mentioned, chaperones might be
01:41:01useful for for some missense changes.
01:41:04We've been looking at nonsense.
01:41:06We through as a as a possibility where you
01:41:09know a quarter of patients would pick anyone,
01:41:13have nonsense mutations and the and the
01:41:16read through of functional protein.
01:41:19Maybe helpful, you know.
01:41:20And this sort of dosage model is
01:41:23is making us think about that.
01:41:25Even if we could increase the level by 10%,
01:41:28that may be a significant effect.
01:41:32I think Steve's work where
01:41:34you know you can re express.
01:41:36Polar systems and and rescue the phenotype
01:41:39is also very exciting in that area,
01:41:42suggesting that these types of are,
01:41:46you know,
01:41:47increasing the level of police system
01:41:49and obviously you know going in
01:41:51with crisper and trying to repair
01:41:53the the variant or maybe using a
01:41:56transgenic approach and and re
01:41:58expressing the protein or Michael.
01:42:02Things might be work just to re
01:42:05express the the part of the sea tail.
01:42:08I think all of these are exciting
01:42:12possible therapies and some of
01:42:15them you know would we would.
01:42:17We would want to know the genotype
01:42:19of the of the patient and some
01:42:21may be more generally applicable.
01:42:23But I think so.
01:42:25I mean,
01:42:25I think that you know targeting
01:42:27the downstream pathways.
01:42:28Although we have a therapy right
01:42:30now that works to an extent.
01:42:31I think it's not a cure for for any big D,
01:42:35and I think looking more approximately
01:42:37in the pathway and trying to correct
01:42:39the basic defect is something
01:42:41that the the field should really
01:42:43be concentrating on right now.
01:43:01I know that.
01:43:31Yeah, I don't know if those two things
01:43:34are are associated with each other.
01:43:36I mean it. The association
01:43:38may be to do with folding and
01:43:40and trafficking of proteins.
01:43:43I think in a more more general way
01:43:46so but I don't know of any other
01:43:49more direct association there.
01:43:53You're very nice.
01:43:57For quite some time.
01:44:01ER
01:44:08predominant.
01:44:12Step back from our colleague.
01:44:16I would imagine that many.
01:44:20More success.
01:44:24Thank you. Perceptor
01:44:27complex finance channels.
01:44:31That's. Has anyone looked at
01:44:34patients with either the polycystic
01:44:37liver or published kidney protein
01:44:39folding associated with patients
01:44:41to keep their phenotypic analysis?
01:44:44For example, T cell.
01:44:48Cardiac myocyte organization,
01:44:49etcetera etcetera to try and get a
01:44:53sense of this phenotype extension would
01:44:56broadly to other proteins that might.
01:44:59Yeah, I mean, I think you know
01:45:00we're certainly aware of that,
01:45:02and I think it is a little bit
01:45:04naive to say that these patients
01:45:06just have PKD or or PLMD.
01:45:08And you know, we've tried to look
01:45:10for other associated phenotypes
01:45:12in the relatively small number
01:45:14of patients that we've seen.
01:45:16And although there's some clues,
01:45:18sometimes nothing really.
01:45:21Something that we can be certain of,
01:45:23and I think that's to do with the
01:45:25number of small number of patients.
01:45:26I think the you know things like
01:45:28the UK Biobank and the you know 100
01:45:31genomes projects are really the places
01:45:33to look for these where there are
01:45:36especially for a LG eight you know
01:45:39larger number of of patients and ask
01:45:41questions about whether there's other
01:45:43phenotypes associated with that.
01:45:45I know that John and Eric have
01:45:47tried to do that a little bit.
01:45:49I don't think maybe there's a definitive.
01:45:52Word on on that yet,
01:45:54but I think that that is the
01:45:55way to to look at these and and.
01:45:57But I certainly agree with you.
01:45:59I think that you know just to say
01:46:01that these are a PKD or PLD disease.
01:46:04When there's a lightly a lot of other
01:46:07proteins associated is probably
01:46:09a bit naive and under estimate.
01:46:11And obviously you know these patients
01:46:13may be at risk for for other diseases
01:46:16that that we should be telling them about.
01:46:19If we could better understand what
01:46:21what might be associated with that.
01:46:26Doctor Harris great talk
01:46:31OK.
01:46:34We'll reconvene in like 5 minutes for
01:46:36the next speaker, which will be virtual.
01:46:41All right, I think we'll reconvene our
01:46:43next speaker is Doctor Sylvia Rosas,
01:46:46who's going to be from Harbor
01:46:49Medical School and discussing non
01:46:52steroidal mineralocorticoid receptor.
01:46:55Antagonists and individuals
01:46:56with CKD and Type 2 diabetes.
01:47:04Great, thank you very much.
01:47:06I hope everybody can hear me and see me.
01:47:08If not, please let me know.
01:47:11And so it is a great honor to be
01:47:14speaking today in this symposium,
01:47:17and I want to thank the organizers
01:47:19for inviting me and I hope
01:47:21that at the end of this talk,
01:47:23you'll agree with me that it's
01:47:24a great time to be doing.
01:47:26Being in a prologistix,
01:47:27and it's a great time to be
01:47:30in the Type 2 diabetes area.
01:47:32So these are my disclosures I guess
01:47:35for this talk the most important
01:47:37disclosure is that I participated
01:47:39in the Figuran Fidelio trials,
01:47:41and I'm a fidelity investigator too,
01:47:45so I think those are the most
01:47:48important for this presentation.
01:47:49Initially,
01:47:50I'll do a quick overview of the standard
01:47:53treatment of diabetic kidney disease,
01:47:56but I'm really going to focus on some
01:47:59work that we've done using the combined
01:48:01data set of the figure and Fidelio.
01:48:04Trials and at the end I'm going to
01:48:08be presenting a case of a patient
01:48:11that we have recruited for the kidney
01:48:14Precision Medicine project in Boston.
01:48:16And so most of my time is actually
01:48:19spent doing the Apollo and the
01:48:21kidney Precision Medicine project,
01:48:23but those two projects are mostly in
01:48:25data gathering phase at this point,
01:48:27and so that would be for a
01:48:29future presentation.
01:48:30So this is the basically the standard
01:48:32treatment of diabetic kidney disease.
01:48:35It's blood pressure control trying to
01:48:38lower albuminuria diet interventions as
01:48:40smoking weight and treating complications.
01:48:42And there had really not been
01:48:45any new therapy since 2001.
01:48:48On these two landmark papers were
01:48:50published side-by-side in the
01:48:52New England Journal of Medicine,
01:48:54and that those were the treatment
01:48:57using angiotensin receptor blockers.
01:48:591 irbesartan 1 Losartan in patients
01:49:02with type 2 diabetes and that was
01:49:04the the last time that we really had
01:49:06a positive trial in Type 2 diabetes.
01:49:08Many medications followed,
01:49:10all of which turned out to be negative.
01:49:14And so we had two decades of disappointment.
01:49:17And then I'm not going to go
01:49:19over the positive SGLT 2 trials.
01:49:22But since then,
01:49:23we've got multiple SGLT 2 trials
01:49:26that have been positive.
01:49:28And so our algorithm of treatment
01:49:31has shifted.
01:49:32It's still lifestyle is the
01:49:34cornerstone of treatment.
01:49:35But now you can see that the 2022 guide.
01:49:38These are the Cadigal guidelines for the
01:49:41treatment of diabetic kidney disease have.
01:49:43SGLT 2 inhibitors and Ras blockade
01:49:45as first line of therapy for
01:49:48treatment for patients with diabetes
01:49:50and chronic kidney disease and
01:49:52then in the top of the pyramid.
01:49:54Let's say they have goal directed
01:49:57therapy for individuals that
01:49:59perhaps have residual albuminuria.
01:50:01Or are looking for better glycemic control?
01:50:08Using a GLP one inhibitor.
01:50:12Receptor agonist, so why do we care
01:50:14about residual function or albuminuria?
01:50:16And that's because we all
01:50:18know that the lower your GFR,
01:50:20whether you have diabetes or not,
01:50:22you're more likely to have cardiovascular
01:50:25morbidity and all cost mortality.
01:50:28But it's also very important to note
01:50:31that Albuminuria has a similar pattern,
01:50:34so this year,
01:50:35this last guidelines for the ADA,
01:50:38the standards of care which I'm happy to say.
01:50:41Also nephrology got upgraded.
01:50:43Now we have our own chapter.
01:50:45We're not mixed up with ophthalmology
01:50:47and neuropathy of microvascular disease,
01:50:50but it's been shown they have
01:50:52as a therapeutic target that if
01:50:54you have severe albuminuria,
01:50:55we should try at least to
01:50:57lower your albuminuria by 30%.
01:50:59That's a based on the trials that have
01:51:03been positive with SGLT 2 inhibitors.
01:51:06But even though in the data CKD study
01:51:09and the Credence study which are the
01:51:13two SGLT 2 inhibitor studies that were
01:51:16the primary goal was kidney disease
01:51:19and that's why I'm presenting those here,
01:51:22some of the other studies were looking
01:51:24really at a cardiovascular outcomes and
01:51:27this the kidney disease was secondary.
01:51:29It was really like a finding.
01:51:32It was a surprise.
01:51:34I don't know that you know it
01:51:35wasn't thought that.
01:51:36You would have such a big
01:51:38impact in kidney disease,
01:51:39and so both credence and apathetic KD
01:51:42still have a residual kidney function.
01:51:45Remember both credence and deposit
01:51:46KD to enter the study.
01:51:48You had to be on Ras blockade,
01:51:51whether it be an ACE inhibitor
01:51:53or a or an ARB,
01:51:55and therefore we cannot say SGLT 2
01:51:58inhibitors are better than race or
01:52:01that somehow SGLT 2 inhibitor will
01:52:03replace rats because the studies were done.
01:52:06On a base of brass inhibition,
01:52:09so there is still a significant
01:52:12residual risk,
01:52:13despite again the positive findings
01:52:15of these two studies and and so
01:52:18there is an opportunity obviously
01:52:21to improve kidney outcomes if we
01:52:25have other therapeutic medications.
01:52:28And so we know that diabetic
01:52:30kidney disease and chronic kidney
01:52:33disease in general is associated
01:52:35with increased inflammation.
01:52:37This is data observational data from the
01:52:39quick study using these three markers.
01:52:42Fibrinogen TNF alpha and serum albumin
01:52:44and basically you can see and I'm
01:52:46going to focus on the third model that
01:52:49the higher your inflammatory markers
01:52:51the more likely you are to progress
01:52:53in the future and you can see that
01:52:56in in all the markers and again Sir.
01:52:58Albumin has the opposite direction
01:53:00because the lower serum albumin
01:53:02is associated with inflammation.
01:53:04This is data,
01:53:06a quick basically recruited
01:53:07patients with CKD.
01:53:08This is the Framingham offspring
01:53:11cohort which has community members
01:53:13also looking at the same thing.
01:53:15The relationship between GFR and
01:53:17inflammation and I'm going to
01:53:19focus just on TNF receptor 2 which
01:53:22has been highly associated with
01:53:24diabetic kidney disease progression
01:53:25and you can see the same thing
01:53:29that the individuals that have the
01:53:31highest level it had the highest.
01:53:34You were also in the highest quartile.
01:53:37First statin see.
01:53:39So basically confirming what we've
01:53:42said before in CKD.
01:53:43Also in the general population.
01:53:45So we know that a individuals
01:53:49with diabetic kidney disease have
01:53:51upregulation of the mineralocorticoid
01:53:54receptor and in when you have a normal.
01:53:58That function, it works in the epithelial
01:54:02cell causes electrolyte and water.
01:54:04Changes, but it overactivation.
01:54:07It creates fibrosis,
01:54:09increased oxidative stress and inflammation.
01:54:12And this is another picture.
01:54:14Sort of depicting this not only in
01:54:16the kidney but also vascular damage.
01:54:18It causes vascular remodeling,
01:54:20endothelial dysfunction,
01:54:21also myocardial injury,
01:54:23and fibrosis hypertrophy, etcetera.
01:54:26So this is an animal study
01:54:28that is used for this.
01:54:31Looking at inflammation and fibrosis in Iraq.
01:54:34Model of aldosterone and
01:54:37hypertension and you can see
01:54:39here sort of the typical feature,
01:54:42some vascular and glomerular damage.
01:54:44Leukocyte infiltration,
01:54:46protein cast etcetera.
01:54:48But also very important to note
01:54:49that in the renal color text when
01:54:51they looked at messenger RNA,
01:54:53the levels of the proinflammatory
01:54:55genes in these animals was
01:54:57higher in those individuals that
01:54:59had the highest aldosterone.
01:55:04And so when you have this, not when you
01:55:07have knockout animals, then it went.
01:55:09This is and feed them at Western diet.
01:55:12That's what WD means. And these
01:55:15animals have endothelial specific Mr.
01:55:18Knockout when they looked at their
01:55:20kidneys they could see even though
01:55:22they're in the Western diet.
01:55:24If you don't, this is Western
01:55:26diet without the knockout.
01:55:27This is Western diet with the knockout
01:55:30and you can see that if you have the.
01:55:33The Western diet with the knockout you
01:55:37have the same findings of fibrosis as
01:55:40if you were not on the Western diet.
01:55:45And and here's this fibrosis
01:55:47in the interstitium.
01:55:48Here's periarterial fibrosis.
01:55:51And this is now human data,
01:55:53so this is data from 2005 but
01:55:56still relevant showing these
01:55:58were 95 patients that had kidney
01:56:01biopsies and aldosterone measured.
01:56:03And if you can see that in the X axis
01:56:06they creatinine clearance in the Y axis,
01:56:09the serum aldosterone level and
01:56:12you can see that the lower your
01:56:15GFR your aldosterone was higher and
01:56:18also in looking at the biopsies.
01:56:21In the percent scarring in the X
01:56:24axis and aldosterone and the Y axis,
01:56:26you can see that the more higher your
01:56:30fibrosis the higher your aldosterone.
01:56:34So in summary,
01:56:36there again a multiple animal studies,
01:56:39but I'm going to focus on this.
01:56:40There's significant evidence
01:56:42that it creating Mr.
01:56:44Knockouts basically spares the organs
01:56:47either the heart or the kidney,
01:56:50or the blood vessels from fibrosis,
01:56:53inflammation, etcetera.
01:56:54And we know that in the setting of diabetes,
01:56:57disease, kidney disease,
01:56:59heart failure, cardiovascular disease,
01:57:01all of these diseases and.
01:57:04To be quite honest,
01:57:05in the patients that I see,
01:57:06most of them have all of them.
01:57:08They they're severe overactivation
01:57:10of the MMR system.
01:57:12So in summary it is chronic
01:57:14kidney disease associated with
01:57:15inflammation and fibrosis.
01:57:17The more advanced chronic kidney
01:57:19disease is associated with increased
01:57:21inflammation and fibrosis and the
01:57:23mineral mineral corticoid receptor
01:57:25is involved in the regulation
01:57:27of inflammation and fibrosis,
01:57:28and this overactivation is what causes
01:57:31kidney and cardiovascular damage.
01:57:33And here's where this molecule.
01:57:35Comes along initially it was
01:57:37called Bay 9488 sixty two.
01:57:39I'm assuming the Bay comes from Bayer,
01:57:41and the later was renamed as Finerenone
01:57:44and this is the chemical structure
01:57:46and the difference between this
01:57:49and our previous mineralocorticoid
01:57:51receptor antagonist is that FINERENONE
01:57:54has a significant higher affinity
01:57:57to the mineralocorticoid receptor,
01:57:59and the other difference is that it
01:58:02it's not renally excreted, and those.
01:58:05Qualities and made made it at the time.
01:58:08Think that they would have less
01:58:11of the known complications of the
01:58:13other MRA which are gynecomastia.
01:58:16Hypogastrium mostly,
01:58:17and so these are the initial
01:58:20trials using the different doses,
01:58:23the 1.125 and and 20 but just basically
01:58:28showing you that there's really a
01:58:31some change as soon as you give it on a.
01:58:35GFR very minimal,
01:58:37and it doesn't really vary by dose
01:58:40and the potassium level stayed
01:58:44relatively stable over time,
01:58:46and the systolic blood pressure.
01:58:48It really. It's not the best
01:58:51systolic blood pressure medication,
01:58:53so it it lowers it a little bit,
01:58:55but not anything very impressive.
01:58:58Is so these are data from credence
01:59:01and I just want to mention
01:59:03the year this is 2015 to 2018.
01:59:06So SGLT 2 inhibitor was available but
01:59:09not as commonly used as it is nowadays.
01:59:13So in the fidelity of study which was
01:59:16the one that had the kidney outcome,
01:59:19the figural study was the sister
01:59:21study that had the cardiovascular
01:59:23outcome as the main outcome.
01:59:26You and then they switch
01:59:27for secondary outcomes,
01:59:28but basically 13 almost 14,000 individuals
01:59:33were enrolled and 5700 were randomized.
01:59:36The randomization was to either
01:59:3910 or funeral or 20 of finerenone
01:59:42based on your GFR and then you know
01:59:45you had your visits like this.
01:59:47And these are the final studies.
01:59:49This was published about two years ago
01:59:51and you can see that it was a positive study,
01:59:55meaning that there was a decrease
01:59:57in the primary composite outcomes.
01:59:58And in this study the primary composite
02:00:02outcome was a decrease in GFR of 40%.
02:00:07Going on dialysis or death from a renal cost.
02:00:11And here you can see the primary
02:00:14kidney failure was end stage,
02:00:16kidney disease, or GFR less than 15.
02:00:19This is the 40% decrease death of renal cost.
02:00:23And so the this is the same picture
02:00:25just depicted in a different way.
02:00:27But showing us the sort of the breakdown.
02:00:30But you can see that all of them favored
02:00:34finerenone use and this the secondary
02:00:38outcomes were cardiovascular and they key.
02:00:41Secondary outcome was positive again.
02:00:43Some of them because of.
02:00:46Numbers it did not reach
02:00:49individuals is significant.
02:00:51We can see that from early on the
02:00:54decrease in Albuminuria was still quite a
02:00:59substantial and leveled throughout the study.
02:01:03And same as SGLT 2 inhibitors,
02:01:05there's always that early decline
02:01:08in GFR and by in using finerenone
02:01:11in by month 24 or two years later,
02:01:15that's when you start really seeing
02:01:19the picture separate.
02:01:21So there's this early decline,
02:01:25and then this sort of long slope
02:01:27that is shows a difference.
02:01:30And then very importantly,
02:01:31this is what we're talking about.
02:01:33This potassium in individuals
02:01:36using finerenone did have higher
02:01:40potassium than the placebo arm,
02:01:42but it did not reach any significant
02:01:45statistical significant difference.
02:01:47But if you look at the supplement,
02:01:50you can see that they do discuss
02:01:53that five point you know 21% of
02:01:56people on the finerenone group
02:01:58had potassium greater than five.
02:02:00And five compared to placebo,
02:02:02which was almost 10% but that severe high
02:02:05potassium greater than six was still seen.
02:02:09You know,
02:02:09an almost two to three times higher in
02:02:11the Finerenone Group 4.5 versus 1.4.
02:02:14So it's still important to monitor the
02:02:18potassium after starting treatment.
02:02:22So what we did is it.
02:02:26It was a pre specified goal.
02:02:28We merge both the fidelity.
02:02:30And the Figaro trials,
02:02:32because they had exactly the same visit,
02:02:36and they have different entry criteria,
02:02:39but. We combined the studies and
02:02:43now it becomes the largest study
02:02:46in patients with chronic kidney
02:02:49disease because now there's 13,026
02:02:51patients that were randomized and
02:02:54a medium follow up of three years
02:02:57and you can see in this picture sort
02:03:01of what areas of CKD it covers.
02:03:04I don't know if you see it,
02:03:05it's a little bit sort of a blue
02:03:08darker color here, but basically
02:03:09all the individuals with severe.
02:03:11CKD, and here with moderate seeking
02:03:15and with moderate albuminuria,
02:03:17sorry and then these are the
02:03:19key inclusion criteria and key
02:03:21exclusion criteria of the study.
02:03:23Basically like I said,
02:03:24individuals had to be on the maximum
02:03:27tolerated those that's a little bit
02:03:29also different from the SGLT 2 trials
02:03:31where you just had to be on an ace or an ARB.
02:03:34You did not have to be on
02:03:37the maximum tolerated dose.
02:03:38And OK.
02:03:40So in summary,
02:03:42I already discussed that funeral known it
02:03:45was novel selective and non steroidal MRA.
02:03:48That fidelity is approved.
02:03:50Specified pool analysis of both
02:03:53studies and when they combine
02:03:55the studies and you'll see this.
02:03:57This was also already published and
02:04:01they combined outcomes was 14% risk
02:04:04reduction in the cardiovascular composite
02:04:07Antone and 23% in the kidney composite.
02:04:10Two point,
02:04:11but contrary to the previous publications,
02:04:14they had used 40% in the new publication,
02:04:17merging the two data sets you can look
02:04:21at it a you know a less common outcome,
02:04:25which was 57%.
02:04:27So I initially we obviously wanted
02:04:30to know if there was any improvement
02:04:33in those patients that were both on
02:04:36finer and SGLT 2 treatment and so
02:04:39it combining the two data sets that
02:04:42allows us to do that and there is
02:04:45some preclinical data showing and
02:04:47here you can see it that these were.
02:04:52And but these were animals on
02:04:55empagliflozin finerenone and then a
02:04:57group that were in combination and
02:05:00you can see and these are hearts.
02:05:02They looked at cardiac fibrosis,
02:05:05but you can see this is the scoring
02:05:08for fibrosis.
02:05:09But you can see that there was
02:05:12some preliminary data that in
02:05:14the combination of both perhaps
02:05:16would have decreased fibrosis.
02:05:19And there was also great a survival
02:05:21benefit in the in the animals that
02:05:23had both empire and finerenone,
02:05:25and that is this this the survival a graph.
02:05:29OK the green is the combination
02:05:32therapy and black is placebo.
02:05:34So finerenone and empower about the
02:05:36same and then combination was better.
02:05:39Placebo was worse.
02:05:42And so this is the trial
02:05:45from all those patients.
02:05:48Only 877 participants, where on an
02:05:52SGLT 2 inhibitor during the trial and
02:05:56and so that is important to note.
02:05:59So I remember. I told you that
02:06:02the study finished in 2018.
02:06:04So at that time is some of the SGLT
02:06:072 trials had not come out yet and so.
02:06:11That's probably why not
02:06:12more people were on it.
02:06:14But there are other important
02:06:16difference are that people that were
02:06:18on SGLT 2 inhibitors were also more
02:06:20likely to be on statins and they
02:06:22were also more likely to be on GLP.
02:06:25One receptor agonist so GLP
02:06:27one receptor agonist required
02:06:30injections and therefore it poses
02:06:33the theoretical option that perhaps
02:06:36this is these patients with SLE
02:06:39on SGLT 2 inhibitors were perhaps.
02:06:42Either more difficult to control
02:06:45individuals or individuals that
02:06:47were with physicians that were,
02:06:50let's say, more aggressive or more.
02:06:55Willing to.
02:06:58Give this medications that perhaps
02:07:00we're not mainstream at the time,
02:07:03so anyway, so the the GFR was a
02:07:05little bit higher in those individuals
02:07:07that were on SGLT 2 inhibitors
02:07:09compared to the others that you ACR.
02:07:12I don't really think that there's
02:07:14a big difference here. And.
02:07:18And but we we found out again,
02:07:21just highlighting that only six point 7% of
02:07:24the individuals it were an SGLT 2 inhibitor.
02:07:27So the power of this is not the best,
02:07:32but we can see that there was significant
02:07:36reduction in a in those individuals.
02:07:39Those that were on SGLT 2 or not SGLT 2
02:07:43they had the same similar reductions.
02:07:47If you were not an SGLT.
02:07:49To inhibitors with 32%.
02:07:50If you were on SGLT 2 inhibitor of 37%.
02:07:56This data was presented at ADA last year.
02:08:02And then we looked at cardiovascular
02:08:05benefit and you can see that
02:08:08the cardiovascular benefit.
02:08:09Again, the hazard ratio had
02:08:12wider confidence intervals,
02:08:14but you can see that it was positive whether
02:08:17you wear an SGLT 2 inhibitor or not.
02:08:20And there was no interaction about.
02:08:23You know whether you were an
02:08:24SGLT 2 inhibitor or not,
02:08:26and then more specifically,
02:08:27as as you know, our patients are
02:08:29more likely to develop heart failure.
02:08:31So we looked at that.
02:08:34Individually and you can see
02:08:37similarly that there wasn't a benefit.
02:08:40The interaction did not reach
02:08:44significance either,
02:08:45so there was no difference.
02:08:46We never known work the same whether
02:08:48you were on an SGLT 2 inhibitor or not.
02:08:51They we were unable to find that
02:08:53it was superior to be on both.
02:08:57And again, this is what
02:08:59I had mentioned before.
02:09:00The 57% composite outcome versus
02:09:04the 40% kidney composite outcome.
02:09:07And again there are more
02:09:10people in this lower group,
02:09:13so I'm going to focus on that.
02:09:14But basically if you were
02:09:16not an SGLT 2 inhibitor,
02:09:17you did well and and really you cannot
02:09:20really tell that this one and this
02:09:22one are different from each other.
02:09:27And so more importantly, is it safe?
02:09:29Because that's also important.
02:09:31And looking at any adverse events,
02:09:35those that were on SGLT 2 inhibitor
02:09:37versus not an SGLT 2 inhibitor had
02:09:40about similar number of adverse events
02:09:43and significantly those that were
02:09:45an SGLT 2 inhibitor and finerenone
02:09:48perhaps had even a lower levels of
02:09:52hyperkalemia than those that were not.
02:09:55So you can compare the 14 versus.
02:09:57And and the placebo was seven versus
02:10:002.7 so so that's quite important,
02:10:04because we obviously do not want
02:10:06to provide a medication that will
02:10:08have a more serious adverse events.
02:10:11So in summary,
02:10:12the patients treated with an
02:10:14SGLT 2 inhibitor at baseline had
02:10:16higher mean E GFR lower.
02:10:19You're now going into creatinine ratio and
02:10:21how you're use of statins and GLP one,
02:10:23but there was a consistent kidney
02:10:26and cardiovascular benefit
02:10:27from finerenone versus placebo.
02:10:29Whether you were on SGLT 2 inhibitors or not,
02:10:32and it looked like the safety outcomes
02:10:35were consistent irrespective of whether
02:10:37you used SGLT 2 inhibitors or not.
02:10:40And so we need obviously.
02:10:42Now the more people use SGLT 2 inhibitors,
02:10:46but it would be a fantastic to do a
02:10:49study where we can do maybe a factor
02:10:52design or combine them with or without,
02:10:55et cetera to really see if there
02:10:57is a combined benefit.
02:11:01Next I was going to discuss
02:11:03as many of you know,
02:11:04I'm very interested in health
02:11:06disparities, and surprisingly,
02:11:08this will be the largest or this
02:11:11is the largest clinical trial
02:11:14that has Hispanic patients,
02:11:16so I thought it would be very
02:11:19important to look and see if there
02:11:22was any difference in outcomes between
02:11:25patients that were Hispanic or not,
02:11:28and so this is that trial.
02:11:30This was presented this year as an abstract
02:11:34in the NKS Spring clinical meeting in Boston.
02:11:37And as you know,
02:11:38Hispanics are more likely to have diabetes,
02:11:41and they're more likely to
02:11:42have chronic kidney disease.
02:11:44This is data from the CDC,
02:11:45the latest data, which is available,
02:11:48and you can see that they're more
02:11:51likely to have undiagnosed diabetes.
02:11:54So while it's in white,
02:11:56non Hispanic whites is about 2.7% and
02:11:59Hispanics is about four point 4%,
02:12:03so that's quite a difference.
02:12:05And Hispanics.
02:12:06As well as,
02:12:08African Americans are more
02:12:09likely to progress.
02:12:11This is Melissa data showing
02:12:12that in a short period of time,
02:12:15which was five years Black and
02:12:18Hispanics were more likely to
02:12:20progress to it's severe category
02:12:23of of chronic kidney disease.
02:12:27And you can see that over time,
02:12:29the incidence of diabetes related
02:12:31in stage renal disease has
02:12:34declined a little bit.
02:12:35If you look over the 20 years a little
02:12:38bit on in blacks a little bit in
02:12:41Hispanics significantly as we have
02:12:43what we know in Native Americans.
02:12:45But still,
02:12:46that rate is much higher than other groups.
02:12:50So like I told you,
02:12:53this is the largest clinical trial
02:12:55that involves Hispanics and it.
02:12:57Basically it's because it was
02:12:58an international trial.
02:13:00So a third of these 2100 were in the
02:13:04US and the others are from Mexico,
02:13:08Colombia, Brazil,
02:13:10where the study was also done.
02:13:13They're a little bit in Asia.
02:13:15I think there were five participants
02:13:17in Asia and like 30 or so in
02:13:19Europe that identified that.
02:13:21That's Hispanic,
02:13:21but you can see and this has been
02:13:25reported in by others and including
02:13:28ourselves that A1C control is worse
02:13:31in Hispanics and also very important,
02:13:34that they're less likely to be
02:13:38on medications to improve their
02:13:41diabetic control.
02:13:42Their glycemic control,
02:13:43so they were less likely to be on GLP.
02:13:46One receptor agonist,
02:13:48so 3.6 compared to 8, and they were less.
02:13:51Likely to be an SGLT 2 inhibitors,
02:13:545% five point,
02:13:551% versus 7% the rest of it is
02:13:59about what one would expect.
02:14:01The GFR is very similar.
02:14:02The year noblemen to creatinine
02:14:04ratio was very similar, etcetera.
02:14:09And so this is the very similar as
02:14:13what I showed you before the the top
02:14:16is cardiovascular composite outcome.
02:14:18The bottom is the kidney outcome.
02:14:21The blue section here,
02:14:22and is the Venera known.
02:14:25This is placebo and anything on this side
02:14:28favors funeral or anything less than one,
02:14:31and you can see that for
02:14:33the cardiovascular outcomes,
02:14:34and again similar,
02:14:35the Hispanic group is the smallest group.
02:14:38So obviously it's going to have the.
02:14:39Confident interval,
02:14:40but we can safely say that definitely
02:14:43for the cardiovascular outcomes.
02:14:45The outcomes are very similar
02:14:46and there was no interaction and
02:14:49for the kidney composite outcome
02:14:50I have to tell you that I saw
02:14:53the 69 this 6.5 versus 6.6.
02:14:56I was like oh I don't know,
02:14:57but it's statistically they're not,
02:15:00is significantly different and you can
02:15:02see that because the numbers are smaller.
02:15:04That's confidence intervals are wider,
02:15:06and again,
02:15:07the test for interaction was also negative.
02:15:12And it's very important to note and,
02:15:14and this is known that Hispanics again
02:15:17lose kidney function at a much higher
02:15:20rate than non Hispanic patients.
02:15:22So the while the difference is
02:15:24very similar between both groups,
02:15:27the Hispanics lost about 1.2 a year.
02:15:31Here this is one you can see that
02:15:33in reality they they were losing 4.5
02:15:36a year compared to the the plus.
02:15:38This is the finerenone group.
02:15:40This is the placebo group, finerenone.
02:15:41Placebo and you can see that there
02:15:45was a you know significant difference,
02:15:46but that 4.5 is very similar to
02:15:50what we have a.
02:15:52Published before this is data from
02:15:53the Jocelyn and you can see remember
02:15:55for this study you had to have severe
02:15:58albuminuria to be part of the study,
02:16:00so that's why if you look at it,
02:16:02we have published that they almost
02:16:04lose 5 amounts a year every year.
02:16:07If you have severe albuminuria,
02:16:08so that's very consistent with
02:16:11our finding too.
02:16:12And looking at adverse events
02:16:14which are quite important,
02:16:16if you can see that the Hispanic
02:16:19group had less adverse events and
02:16:21less outcomes for hyperkalemia.
02:16:26So in conclusion, the efficacy and
02:16:27safety of reneuron known observed in
02:16:29the overall population of Fidelity,
02:16:31did not have any difference between Hispanic
02:16:35and non Hispanic patients and the data
02:16:38support the use in Hispanic patients.
02:16:40I wanted to show now a case from the
02:16:44kidney Precision Medicine project and
02:16:47I I hope that you find it interesting.
02:16:52I am as I said before,
02:16:55I spend a significant amount of
02:16:57time within these two projects that
02:16:59which are collaborative projects
02:17:01in the kidney Precision Medicine
02:17:03project and the Apollo study.
02:17:05But again,
02:17:06we are in the data gathering phase and.
02:17:10I don't, I don't.
02:17:12I'm unable to represent more precise data,
02:17:14but this is the key PMP study.
02:17:16I know you're all familiar with
02:17:18it because Doctor Wilson is an
02:17:20investigator on the study and GAIL
02:17:22has been a Ki site at the jostling
02:17:25where a chronic kidney disease site.
02:17:27And obviously because we see a
02:17:28lot of patients with diabetes,
02:17:30we mostly recruit patients with
02:17:32diabetes and chronic kidney disease.
02:17:34And these are the sites and we recruit
02:17:38patients with diabetic kidney disease.
02:17:41Whether it be type one or type 2,
02:17:44chronic kidney disease or a proteinuria.
02:17:47And of the patients that we
02:17:49have recruited at the Joslin,
02:17:51which we have 33 patients so far
02:17:53and the study you know every
02:17:56time there's a COVID pandemic,
02:17:58we have to a COVID wave we have
02:18:00to stop recruitment etcetera.
02:18:02But we have obviously found
02:18:04mostly in diabetic kidney disease,
02:18:06but we've had four cases of the 23.
02:18:1221 are patients with diabetes and
02:18:14chronic kidney disease and four cases
02:18:17have been non diabetic kidney disease.
02:18:20So we have an average 19% of our
02:18:23patients that we think have diabetic
02:18:25kidney disease really don't have
02:18:27diabetic kidney disease and they
02:18:29have something else and so I think
02:18:32this this study for at least for
02:18:34me has highlighted that it perhaps
02:18:36biopsies should be more normal than
02:18:39the exception which I think was.
02:18:42They really are thought before in this group.
02:18:46The reason why the denominator
02:18:48was on 21 is because there's two
02:18:51patients that are resistors,
02:18:53so these are truly the
02:18:55most altruistic patients.
02:18:56These are patients that have type one
02:18:58diabetes and do not have kidney disease.
02:19:00They have normal GFR.
02:19:02They have no albuminuria,
02:19:04and they still volunteer to have a kidney
02:19:08biopsy so they can help us determine.
02:19:11You know what causes kidney
02:19:13disease in patients with diabetes?
02:19:16So this is a one of our cases,
02:19:19and as you can see here, this is.
02:19:23Just what we would normally call diabetic
02:19:27kidney disease or Miss Stangel expansion.
02:19:30But the tubules are OK.
02:19:31Perhaps some in beginnings of
02:19:35increased tubular basement membrane
02:19:38and we have the benefit at jostling
02:19:42that we're we're also doing it and
02:19:45ancillary study looking at retina.
02:19:48So this patient had her eyes
02:19:50had studies in her eyes,
02:19:52and you can see all these dots are
02:19:56really burns right from laser treatment.
02:20:01Because she had diabetic retinopathy and you
02:20:03can see here she has a little hemorrhage too.
02:20:06That was her left.
02:20:07That previous one was her right.
02:20:09This is her left eye and
02:20:11when we looked at her biopsy,
02:20:14this is confocal analysis and you
02:20:17can see her glomeruli in in green.
02:20:22Right here.
02:20:23And this is highlighted the medolla.
02:20:30And here on the left, what showed up?
02:20:33We have some kidneys that are
02:20:36coming from nephrectomy for cancer,
02:20:39or for other reasons,
02:20:41and so this we we call it normal.
02:20:44So this is supposed to be the
02:20:46normal side of of that kidney,
02:20:48and we it's used sort of to compare
02:20:51our biopsies to sort of the normal.
02:20:54And using this CD 31 staining which
02:20:57is staining for endothelium cells,
02:21:00you can see that in the normal,
02:21:02then the glomeruli not quite small and very.
02:21:08Him.
02:21:11Like fixed in here,
02:21:12you can see that our little glomeruli,
02:21:15kind of hairy, and I'll show you bigger
02:21:18picture and more fuzzy let's say.
02:21:20I don't know if that's the
02:21:21scientific term for it, but anyway,
02:21:22and you can see, here's the glomerella.
02:21:25Here's the vascular poll.
02:21:27And here are our glomeruli from our patient,
02:21:30and you can see that the Vascular
02:21:32poll is thickened compared to here,
02:21:34and that it looks like there's
02:21:36a communication.
02:21:37You can see it a little bit here,
02:21:38better with the CD 31 that I'll have
02:21:41all these hairs of blood vessels
02:21:43coming and talking to each other here.
02:21:46And and that for us was a surprise.
02:21:50Remember I I just talked told you that
02:21:52we need as a group to discuss the case,
02:21:56cases and sometimes we're lucky that
02:21:59the tissue interrogation side have
02:22:01processed the the the tissue and
02:22:03are able to add to our discussions.
02:22:06And that's how we were discussing
02:22:09this case because of against her
02:22:11participation in the retina study.
02:22:13And here are some more images of these,
02:22:16you know.
02:22:16Cherry like structures that turned
02:22:19out to be endothelium coming out
02:22:22of the glomerulus.
02:22:23And.
02:22:24So here you can see again a
02:22:26depiction of the Bowmans capsule,
02:22:29and here again the the blood vessels
02:22:33that are outside the Bowman capsule.
02:22:36And to me that was new and surprising,
02:22:41but so we went back to the biopsy.
02:22:43And so we started looking.
02:22:45And then once once you see the
02:22:47other one then you can easily
02:22:50recognize it here too.
02:22:51So these are endothelial cells
02:22:53that we're seeing in the
02:22:56other staining,
02:22:57and you can see that here too.
02:23:00Again, here's the Vascular poll thickness
02:23:03and again more endothelial cells here.
02:23:08And also here in this sort of
02:23:11Broken Arrow you can see that even
02:23:14in the endothelial cells there's
02:23:16this increase based on membrane.
02:23:19And again this is all endothelium
02:23:21and all the glomeruli had this
02:23:24sort of structure in this biopsy.
02:23:27And so of course we thought this was new
02:23:29and then we found out that it's not so.
02:23:32It turns out that this investigator
02:23:351985 published in the Archives of
02:23:39Histology in Japan that he looked at
02:23:433000 glomeruli using a what I assume
02:23:46at the time was novel technology
02:23:49which was electron microscopy and
02:23:51he created this vascular cast and
02:23:55then he painstakingly counted.
02:23:57In the 3000 glomeruli,
02:23:58how many blood vessels came in
02:24:01and out of the blood vessels?
02:24:03I show you a picture.
02:24:05This is one of this picture.
02:24:06So this is the single a ferrant arterial,
02:24:09and then this is again he only found
02:24:12two in this nephrectomy that he had.
02:24:15He found two glomeruli,
02:24:17one with five blood, eferin blood vessels,
02:24:20and one with three.
02:24:21Both pictures are here,
02:24:23but I only presented the one
02:24:25with the 5E ferrant arterials.
02:24:27And here he had to cut E5 and E4
02:24:30are cut because otherwise you
02:24:32couldn't see the other ones, he said.
02:24:35And.
02:24:36And then what we weren't sure is,
02:24:40had this been described in
02:24:42diabetic kidney disease and
02:24:44then we found this one again,
02:24:46another investigator again in the
02:24:5080s that also said that one to 5% of
02:24:56the glomeruli in this diabetes and
02:25:00kidney that he saw in 12 individuals
02:25:04and have this capillary profiles.
02:25:08We called it outside the glomerella
02:25:12tough and to be honest we had four
02:25:15or five pathologists in in our group
02:25:19and it it seemed like this didn't
02:25:22really move outside this journal,
02:25:25which now forget what it's called.
02:25:27Journal of diabetic complications because
02:25:29it was also news and to the group.
02:25:32So now we're going back and looking
02:25:35at all our other diabetic biopsies.
02:25:38To see if we have the same finding.
02:25:41Because right now we actually don't
02:25:43know if this is important or not,
02:25:45or is this significant in any way clinically?
02:25:50Because other than that we have not it.
02:25:53We're looking still,
02:25:54and through the literature,
02:25:55but we haven't found anything that has
02:25:57looked at what disease hairy glomeruli
02:25:59really mean clinically for the patient,
02:26:02so I thought I would give
02:26:05this sort of little.
02:26:08Shout out to KPMP and thank you
02:26:11for your support of the project.
02:26:14This is I want to thank all of
02:26:17the individuals that are involved
02:26:19in the fidelity trials.
02:26:20And and this is the conclusion I let
02:26:25you read that I also wanted to thank
02:26:29my collaborators and all my other projects,
02:26:32my staff and my colleagues,
02:26:36and the funders obviously of
02:26:39the projects for their support,
02:26:41and I'm happy to stop sharing.
02:26:45If you have any questions.
02:26:58Thanks for a great talk,
02:26:59Sylvia. Any questions Jeff.
02:27:04Just a question about Panera.
02:27:06There were no type 1 diabetics in.
02:27:09Infidelity or Figaro,
02:27:10and that's not the the non
02:27:12diabetic pilot starting is there.
02:27:14Is there any concern or reason why it
02:27:17would be beneficial in that population?
02:27:20OK so I I have to repeat the question
02:27:23so the question is that there
02:27:25were no type one patients in the
02:27:29Fidelio trials or Figaro trials,
02:27:31and if there is any concern of using
02:27:34finerenone in type one patients,
02:27:36is that the question?
02:27:39Right, so you're correct.
02:27:41So right now these medications
02:27:43are approved for patients with or.
02:27:46The studies were done in patients
02:27:49with type 2 diabetes and a I.
02:27:52I don't see why you couldn't
02:27:54use them in type one,
02:27:56and actually now that you said that
02:27:58I'm going to read out the label
02:27:59because I don't know if the label says
02:28:01chronic kidney disease with diabetes,
02:28:03or does it specifically say type two?
02:28:05I would have to guess that
02:28:07it specifically says Type 2.
02:28:09But once the medication is FDA approved,
02:28:12it can be used.
02:28:13I guess off label,
02:28:14but I I don't see why it wouldn't work.
02:28:18I mean it doesn't alter any
02:28:21glycemic pathways.
02:28:22Kind of like SGLT 2 that was done
02:28:24in in type twos and the the reason
02:28:27we don't use them in type ones is
02:28:29because they're already prone to DKA,
02:28:32and we don't want to give them another
02:28:36medication that it will do harm, right?
02:28:39That will.
02:28:40Increase the risk of a serious complication,
02:28:42so, but it's a good question.
02:28:45I would say right now they're
02:28:46approved for Type 2,
02:28:47but I don't see why it couldn't be used.
02:28:55Any other questions?
02:28:59It. So Sylvia.
02:29:05More. OK.
02:29:11So the group of Mount Sinai has decided.
02:29:21To distinguish this city from Janet.
02:29:28I.
02:29:31Doctor Shiva can you repeat that question
02:29:34because I I heard something about my
02:29:37Sinai and distinguishing ethnicity,
02:29:39but I can't hear it, sorry.
02:29:43Genetic ancestry versus ethnicity.
02:29:45So I think at Mount Sinai they
02:29:49had stated that they might be
02:29:51able to tell the difference,
02:29:52and are you able to tell the
02:29:54difference in your study?
02:29:57And so, in in Hispanics, no.
02:30:01We you know, as a Hispanic myself,
02:30:05I can tell you that before
02:30:07I came to this country,
02:30:08I had never heard that we were different.
02:30:11So I think it's like something
02:30:13that when you cross the US border,
02:30:16you're no longer Colombian like I'm
02:30:19from Colombia background you're
02:30:20now Hispanic, but in Colombia.
02:30:24We know that we're a mix of different people.
02:30:31So I don't know that we don't
02:30:34have that distinguished there.
02:30:38So I I we only know where the
02:30:41the country of origin is.
02:30:44And like I said,
02:30:45most of the patients were from Mexico,
02:30:47Colombia, Brazil and the United States.
02:30:49That's that we do know,
02:30:50and we did look by country.
02:30:53If there were any differences
02:30:55in results that we do do,
02:30:57and in fact we were particularly
02:30:59interested because they have
02:31:01less adverse events and we were
02:31:03wondering if is it because reporting
02:31:06was different by country and we
02:31:08looked particularly by that.
02:31:10And that was the same everywhere.
02:31:13And. But that's it there.
02:31:18That's all the information I have about that.
02:31:23Alright, there's no other questions.
02:31:26Thank you very much.
02:31:27Doctor Roses for our wonderful talk.
02:31:41I docked emberg alright,
02:31:42so I'll introduce the next speaker.
02:31:45Are you able to share your? Let me just.
02:32:02OK, are you seeing my slides?
02:32:04That looks great.
02:32:06So like to introduce saw Doctor
02:32:08Denberg from the Children's Hospital
02:32:11of Philadelphia Permanent School
02:32:13Medicine at University of Pennsylvania,
02:32:16and she'll discuss the epidemiology
02:32:18of kidney stone disease from
02:32:20origins to complications.
02:32:23Thank you for the opportunity to speak today.
02:32:30These are my disclosures,
02:32:32not relevant to what I'm
02:32:34going to talk about today.
02:32:36Except the NIH funding.
02:32:40So by way of an outline I'm going to 1st
02:32:43talk about some of the changing epidemiology.
02:32:46In terms of incidence of kidney stone
02:32:49disease and and that the impact of that
02:32:52when we talk about origins of kidney stone
02:32:56disease focusing on emerging evidence
02:32:58for the role of the gut kidney access,
02:33:01and then I want to touch on complications,
02:33:04particularly in terms of kidney bone
02:33:07vascular access and in making this talk,
02:33:09I've tried to highlight key themes that
02:33:12have been central to my research program,
02:33:14so the work that I'm going to
02:33:16talk about integrates.
02:33:17Epidemiology and patient oriented research,
02:33:20including analysis of large electronic
02:33:22health record and claims data,
02:33:24as well as observational and
02:33:26translational patient oriented studies,
02:33:28and we've really tried to approach
02:33:30questions and evidence gaps
02:33:32from a life course perspective.
02:33:37So we know that kidney stones result
02:33:39from a disorder of minimum metabolism,
02:33:41the risk of which is determined
02:33:43by the interaction of genetics,
02:33:45diet, and environmental exposures.
02:33:48The prevalence of kidney stones has
02:33:50increased 70% over the last three decades.
02:33:53And kidney stones are increasingly common
02:33:55with an estimated prevalence of 1 and
02:33:5711 individuals in the United States.
02:33:58So comparable to the prevalence of diabetes.
02:34:02Total annual charges exceed $10
02:34:04billion and that does not account
02:34:06for medication or indirect costs.
02:34:12In terms of stone composition,
02:34:14calcium oxalate stones remain the
02:34:16most common type of stone overall.
02:34:19Although hydroxy apatite stones were
02:34:21the second most common for each 555
02:34:24in this analysis and in this study,
02:34:27women submitted more stones than
02:34:29men between the ages of 10 and
02:34:3119 and 20 to 29 years of age.
02:34:37This leads to one of the main points
02:34:39I wanted to talk about today,
02:34:40which is the rising incidence of
02:34:42kidney stones, particularly in
02:34:44younger individuals and among women.
02:34:46So several studies have shown
02:34:49this disproportionate rise,
02:34:50and this is data from our population
02:34:52based study of temporal trends
02:34:54and kidney stone incidents.
02:34:55From 1997 to 2012 in South Carolina,
02:34:59you could see the cumulative risk of
02:35:01kidney stones doubled during childhood
02:35:03and the greatest increase in incidence
02:35:05was found among 15 to 19 year olds.
02:35:06With the 26% increased risk for five years.
02:35:11In multivariable analysis,
02:35:13it incidents increased 15%
02:35:15per five years among females,
02:35:17but remains stable for males,
02:35:18resulting in a 45% increase in
02:35:20the lifetime risk of kidney stones
02:35:23for women over the study period.
02:35:25And importantly,
02:35:25this shift in kidney stones to a
02:35:27younger age of onset has caused
02:35:29increasing hospitalization surgeries
02:35:30and healthcare expenditures.
02:35:36Also reflecting the more severe phenotype
02:35:38of a disease that starts in childhood,
02:35:41the probability of a symptomatic
02:35:43recurrence within three years of the
02:35:46index stone and childhood is about 50%.
02:35:49Also, reflecting the rapid shift in the
02:35:51epidemiology of kidney stone disease,
02:35:53our knowledge base of how to reduce the risk
02:35:54of this recurrence remains quite limited.
02:36:01So one of the key questions I'm going to
02:36:03spend some time talking about is the why,
02:36:05why are we starting to form stones
02:36:07kidney stones earlier in life?
02:36:13So this brings me to the gut kidney access
02:36:16and kidney stone disease discovering
02:36:18the causes of the epidemiologic
02:36:21shift in kidney stone disease could
02:36:23reveal new therapeutic targets,
02:36:25and the rapidity of the change in
02:36:27epidemiology suggests that the driving
02:36:29forces are external exposures such
02:36:31as dietary factors or medications,
02:36:34namely antibiotics.
02:36:35When we're thinking about disruption
02:36:37of the gut microbiome,
02:36:38so many of these exposures
02:36:40could impact the gut.
02:36:41Many access,
02:36:42which is that complex interplay
02:36:44between the intestinal and urinary
02:36:45tracts in human health and disease.
02:36:51So the first thing I want to
02:36:53show is from a study we did,
02:36:55it was designed to examine the association
02:36:57between oral antibiotic exposure
02:36:59and incident kidney stone disease.
02:37:01We sought to assess the strength
02:37:03and temporality of the association
02:37:04by antibiotic class and to test the
02:37:06hypothesis that earlier life exposure
02:37:08to oral antibiotics would be associated
02:37:10with the greater risk of kidney stones.
02:37:16We conducted this study in the health
02:37:19Improvement Network database and this was a
02:37:22population based nested case control study.
02:37:24The Health improvement network within
02:37:27database is representative of the
02:37:29United Kingdom population by age, sex,
02:37:32medical conditions and mortality rates,
02:37:34and contains patient level electronic
02:37:36health record data for more than
02:37:3913 individuals among over 600
02:37:42practices in the United Kingdom.
02:37:44This database has been used to characterize
02:37:46outcomes and kidney stone disease.
02:37:47I'll show you some data later as well
02:37:50as the association between antibiotic
02:37:52exposure and inflammatory bowel disease.
02:37:55In this study,
02:37:56we looked at nearly 26,000 individuals who
02:37:58had instant kidney stone disease and to be
02:38:01considered a patient with incident stones,
02:38:03an individual had to be registered
02:38:05with their general practice for
02:38:07at least six months at the time of
02:38:08the initial qualified diagnosis.
02:38:10Code for kidney stones,
02:38:11and this has been a validated
02:38:13approach for ascertainment of
02:38:15incident diagnosis in this database.
02:38:17To increase precision,
02:38:19we included 10 controls for
02:38:21each case matched on age,
02:38:23sex,
02:38:24and general practice to each case
02:38:26that their index date of their
02:38:28first of their kidney stone.
02:38:34So this is the main output of this paper.
02:38:38It's a busy table,
02:38:40so each column represents a series of models.
02:38:44And what we found is that exposure
02:38:46to five classes of oral antibiotics,
02:38:48sulfa cephalosporins,
02:38:49fluoroquinolones nitrofurantoin,
02:38:50methenamine and broad spectrum penicillins
02:38:53were associated with increased odds of
02:38:56instant kidney stone disease within
02:38:58a 3 to 12 month exposure window,
02:38:59which was our primary exposure
02:39:01window for this analysis.
02:39:03And all of these models were adjusted
02:39:05for for numerous comorbid conditions.
02:39:07Urinary tract infection within the same
02:39:10exposure window and exposure to other
02:39:12potential confounding medications as
02:39:13well as rate of healthcare and counters.
02:39:16The models in column A made no
02:39:19adjustment for other antibiotic use.
02:39:23The models in column B adjusted for
02:39:25any other antibiotic use within the
02:39:27three to 12 month exposure window.
02:39:29And it model C.
02:39:31Each model was adjusted for every
02:39:33other antibiotic as 11 separate
02:39:36indicator variables in the model
02:39:38and Model C had the best fit.
02:39:41So the take home was that these 5
02:39:43broad spectrum antibiotic classes
02:39:44were independently associated
02:39:45with a 1.3 to 2.3 fold increase.
02:39:48The odds of kidney stones at a bumper
02:39:50only adjusted significance threshold.
02:39:56An exploratory analysis,
02:39:57we also looked for effect modification
02:40:00by age and you can see here that we
02:40:03found interactions with age for for
02:40:05all 5 classes and abilities that
02:40:07were significantly associated with
02:40:09kidney stones on the prior slide.
02:40:12So the odds of incident kidney
02:40:14stones were greater for earlier
02:40:17life exposures to antibiotics.
02:40:19An exponential increase in the odds
02:40:21of kidney stones was estimated for
02:40:22patients less than 20 years of age,
02:40:24exposed to self a drugs and broad
02:40:26spectrum penicillins and a more
02:40:28linear relationship was seen across
02:40:29the age range for cephalosporins,
02:40:31fluoroquinolones and nitrofurantoin.
02:40:38We also examined the magnitude of
02:40:40the association based on proximity
02:40:43of exposure to Orlando Vedics.
02:40:45This figure highlights that the odds
02:40:47were greatest for exposure to the
02:40:49five antibiotic classes of interest
02:40:51within three to six months of the
02:40:53index date of the kidney stone.
02:40:54The magnitude of the increased ads
02:40:56was lower with more distant exposure,
02:40:58but remains statistically significant
02:40:59from three to five years from exposure
02:41:02for all classes except Buzz Spectrum,
02:41:04penicillins, and again,
02:41:05all of these conditional logistic
02:41:07regression models were adjusted
02:41:09for prevalent comorbid conditions.
02:41:11UTI within the exposure window and
02:41:14exposure to other potential confounding
02:41:16medications within the exposure window.
02:41:21So to summarize this study,
02:41:23we found that exposure to broad spectrum
02:41:25oral antibiotics were associated increased
02:41:27odds of developing kidney stones,
02:41:29and that there appear to be greater risk
02:41:31for more recent and early life exposures.
02:41:33This data provides additional rationale,
02:41:36rationale to limit inappropriate antibiotic
02:41:38prescribing and may help explain some of
02:41:41the rising incidents of kidney stones,
02:41:43particularly among children.
02:41:47To highlight that this slide
02:41:48shows antibiotic prescribing and
02:41:50this is actually data that's
02:41:51a decade old at this point.
02:41:53But antibiotic prescribing
02:41:55for 1000 persons by state.
02:41:57And so, in 2011 there were over
02:42:00260 million courses of antibiotics
02:42:02prescribed with the highest rates of
02:42:04prescription for children younger
02:42:05than 10 years of age and women.
02:42:11Corroborating our findings,
02:42:12this study of over 5000 women
02:42:15in the nurses health studies one
02:42:17and two showed that the use of
02:42:19antibiotics for more than two months
02:42:21in early adulthood and middle age
02:42:23was associated with a higher risk
02:42:25of kidney stones later in life.
02:42:26These are the adjusted hazard
02:42:28ratios from cause specific heads or
02:42:30regression that was adjusted for age,
02:42:32body mass index, comorbid conditions,
02:42:34thiazide use, and dietary factors,
02:42:36and importantly, this study.
02:42:39Actually was able to do medical
02:42:42record review to to confirm the stone
02:42:45composition and 80% of the subset of
02:42:47stones that were confirmed by this
02:42:49review were composed of calcium oxalate.
02:42:54So what's mediating this association?
02:42:57Well, we know that we are 10% human,
02:43:00and we cohabit with 100 trillion
02:43:03microbes that make up our microbiome,
02:43:06particularly inhabiting our mouth,
02:43:08skin, and intestine.
02:43:10And we know that this community
02:43:12is really essential for health.
02:43:19So this is a figure from an article
02:43:22written by Doctor Blazer just highlighting
02:43:26the loss of biodiversity over time.
02:43:29And highlighting in the United States
02:43:32how this has happened simultaneously
02:43:34with early introduction of
02:43:36sanitation and early antibiotic use.
02:43:43There have been several studies that
02:43:45have begun to look at the microbial
02:43:49diversity of the intestinal microbiome
02:43:51in patients with kidney stones,
02:43:53so this is data from adult.
02:43:56Individuals who formed calcium
02:43:58based stones and had recurrent
02:44:00stones compared to controls.
02:44:02And you can see lower alpha
02:44:06diversity in the stone formers
02:44:08compared to healthy controls.
02:44:12In addition to demonstrating reduced
02:44:15diversity of the fecal microbiome,
02:44:18so some farmers also had lower
02:44:20representation of bacterial taxa
02:44:22predicted to be involved in Oxley
02:44:24degradation and reduced expression of
02:44:26genes belonging to oxalate degradation
02:44:29pathways and which shown here,
02:44:30is that the relative abundance of
02:44:32five tax that was also correlated with
02:44:3424 hour urinary oxalate excretion.
02:44:40This is data from a study that
02:44:42systematically reviewed six articles
02:44:45that was contained 170 adults with
02:44:48kidney stones showing lower abundance
02:44:50of several bacterial taxa as well as
02:44:53greater abundance of Bacteroides species.
02:44:58More recently, this meta analysis looked
02:45:01at 6 meta Genome Wide association studies.
02:45:05That evaluated the microbiome of the stool,
02:45:08and in some cases urine,
02:45:09as well as kidney stones themselves and
02:45:13the take home was that prevotella in the
02:45:15gut and lack the bacillus and urinary
02:45:18tract was associated with healthy.
02:45:20Individuals, while Enterobacteriaceae,
02:45:21were associated with kidney stone disease,
02:45:24both in the urine and in
02:45:26kidney stones themselves,
02:45:26and the predominant factors that
02:45:28were associated with microbiome
02:45:30composition were kidney stone status,
02:45:32stone composition, age,
02:45:33and study location.
02:45:37So what about children?
02:45:41So I'm going to share some of our work
02:45:43looking at the microbiome of children with
02:45:45kidney stones compared to healthy peers.
02:45:51So this was a case control
02:45:52study that we conducted at the
02:45:54Children's Hospital of Philadelphia,
02:45:56and we enrolled cases who were
02:45:58between 4 and 18 years of age
02:46:00with calcium kidney stones.
02:46:01So they had to have had either
02:46:03a spontaneously passed or
02:46:05surgically removed stone that was
02:46:06comprised of at least 80% calcium.
02:46:10And 44 agent sex matched controls.
02:46:14Participants did 324 hour dietary
02:46:16recalls and provided a stool sample
02:46:18for shotgun metagenomic sequencing
02:46:20and untargeted metabolomics profiling.
02:46:26Just a brief word on the hold that
02:46:29hold genome shotgun sequencing as
02:46:32compared to 16 Sr RNA sequencing,
02:46:34which was used in the majority of the
02:46:37microbiome work and kidney stones,
02:46:38which sequence is only a single
02:46:40region of the bacterial genome
02:46:42called genome shotgun sequencing.
02:46:45Sequences several random fragments of
02:46:47the genome and the major advantages
02:46:49that tax that can be more accurately
02:46:52identified at the species level.
02:46:57So this is data from that study.
02:47:00This shows a heat map of bacterial
02:47:02taxa and children with kidney stones
02:47:04and their matched healthy controls.
02:47:06Each column represents 1 fecal sample,
02:47:08and each row represents 1 bacterial taxon.
02:47:12The tax were included if the abundance
02:47:14in any sample exceeded point 1%,
02:47:16with the exception of axle bacteria
02:47:18and axial vector for migenes,
02:47:20which were included despite their
02:47:22lower abundance due to their
02:47:24presumed role in oxalate degradation.
02:47:26The overall taxonomic profile of the
02:47:28gut microbiome among participants with
02:47:30kidney stones and controls was similar
02:47:32to that observed in previous studies,
02:47:34where the Bacteroidetes and Clostridia
02:47:36species accounted for most of
02:47:38the bacterial population and you
02:47:40can see the very abundant species
02:47:42are the ones in orange and red.
02:47:49So to compare the taxonomic
02:47:51composition of all participants,
02:47:53we tested 91 bacterial techs of
02:47:55that had that greater than point 1%
02:47:57abundance and at least one stool sample.
02:48:00And we found that 31 bacterial
02:48:02techs are different in abundance
02:48:04between participants with kidney
02:48:05stones and controls at a predefined
02:48:08FDR adjusted threshold of less than
02:48:10.05 of the tax that identified,
02:48:12all were less abundant among participants
02:48:15who formed kidney stones than controls.
02:48:18And these included seven tasks
02:48:19that the produced the short chain.
02:48:21Fatty acid butyrate including
02:48:22several rose buria,
02:48:23and Clostridium species and those
02:48:25are highlighted with the red squares
02:48:29as well as lower abundance of three
02:48:31oxalate degrading bacterial taxa
02:48:33attacks that Enterococcus vocalist,
02:48:34Enterococcus PCM and Bifidobacterium
02:48:37Animalis the the lavender boxes.
02:48:41Correspondingly,
02:48:41the figure on the right shows
02:48:43that the gene abundance,
02:48:45beautiful Cohen dehydrogenase,
02:48:46the key bacterial enzyme in the
02:48:48butyrate production pathway,
02:48:49was also lower among the
02:48:51stone formers than controls.
02:48:56So looking at the fecal sorry
02:49:00fecal metabolome, overall,
02:49:01the profile of fecal metabolites
02:49:03was similar between participants
02:49:05with kidney stones and controls.
02:49:06But we carried out a linear discriminant
02:49:08analysis to determine if a subset
02:49:10of the metabolites could be used
02:49:11to distinguish the two groups and
02:49:13found that a linear discriminant
02:49:15separated those with kidney stones
02:49:16from controls with 77% accuracy.
02:49:18There were 18 metabolites that were
02:49:21significantly different between
02:49:22participants with kidney stones
02:49:23and controls at a pre specified.
02:49:25Nominal P value of less than .01 ten,
02:49:27being higher among cases.
02:49:30Sorry to think it 10 being higher,
02:49:32my cases and eight metabolites being
02:49:34lower one cases than controls.
02:49:36And importantly,
02:49:37these differences in fecal metabolites
02:49:40associated with bacterial abundance.
02:49:42So you can see in the right hand
02:49:44side of the figure that we computed
02:49:46a correlation matrix between
02:49:47metabolites and species abundance,
02:49:49which revealed strong correlations for
02:49:50many of the texts on metabolite pairs.
02:49:58So, following this untargeted analysis,
02:50:00we focused on oxalate degrading bacteria,
02:50:03and we constructed an abundance
02:50:05correlation network on the left panel,
02:50:08a proxy degrading bacteria
02:50:10detected in this study,
02:50:12and we found that it consisted of
02:50:14positive correlations among the isolated
02:50:16degrade oxalate degrading tax set and
02:50:18lacked any strong negative correlations.
02:50:21The network really consisted of two modules,
02:50:231 populated by relatively high
02:50:25abundance oxalate degrading taxa.
02:50:27And this is panel B and the other
02:50:29by low abundance lactobacilli.
02:50:35So importantly, what's shown here is the
02:50:37alpha diversity of the gut microbiome.
02:50:40So in the left hand figure you can
02:50:41see that we corroborated findings in
02:50:43adults showing lower diversity and
02:50:45participants with kidney stones compared
02:50:47to controls assessed both by the
02:50:49richness and Shannon's diversity index.
02:50:53And strikingly, the alpha diversity
02:50:55exhibited an age dependent association
02:50:57in those with kidney stones,
02:50:58but not in control.
02:50:59So as you can see in the right hand
02:51:01figure bacterial diversity first decreased
02:51:03and then increased with age among
02:51:05those who were kidney stone formers,
02:51:07with the lowest diversity found among
02:51:09individuals who first formed kidney
02:51:11stones between 9:00 and 14 years of age.
02:51:13In contrast,
02:51:14the alpha diversity of the microbiome
02:51:16of participants who are controls
02:51:18with similar across the spectrum
02:51:19and there were no significant
02:51:21associations found with the age.
02:51:26So to summarize, this study
02:51:27showed the loss of gut bacteria,
02:51:29particularly those that produce
02:51:30butyrate and degrade oxalate,
02:51:32were associated with perturbations
02:51:34of the metabolome that may
02:51:35be upstream determinants of
02:51:37early onset calcium oxalate,
02:51:39kidney stone disease.
02:51:42So I just want to shift to highlight
02:51:45some current work we're doing
02:51:47to follow up on these signals.
02:51:49So our active investigations are
02:51:51really seeking to further characterize
02:51:53the microbiome and determine how the
02:51:56composition of the gut microbiome
02:51:57affects urinary mineral excretion.
02:52:00The goal is that ultimately we can
02:52:02identify how we can restore the
02:52:04gut microbiome or its function,
02:52:05and our hypothesis is that understanding
02:52:07the gut kidney access will introduce
02:52:09a new paradigm for primary and
02:52:11secondary kidney stone prevention.
02:52:16So the active study that we were conducting,
02:52:19called the pursuing optimal
02:52:21organisms and people with stones.
02:52:23Consists of a patient oriented study
02:52:25as well as a A data analysis study
02:52:28that I'll show you in a moment.
02:52:30This is really an expansion of
02:52:33our earlier case control study.
02:52:35We're enrolling 300 children and adults,
02:52:38150 cases and 150 controls,
02:52:41and this study is using
02:52:44comprehensive nutritional profiling,
02:52:45high throughput microbiome,
02:52:46and metabolomic data analysis,
02:52:48as well as large database analytics
02:52:50really to try and define how diet and
02:52:52antibiotics perturb the gut microbiome
02:52:54and how the resulting changes in
02:52:57downstream metabolites and chemistries
02:52:58and intestinal and urinary tracts
02:53:00contribute to kidney stone disease.
02:53:02So, in this patient oriented study,
02:53:04individuals.
02:53:04Undergoing nutritional profiling
02:53:07with 24 hour diet recalls
02:53:09shotgun metagenomic sequencing
02:53:10of stool targeted and untargeted
02:53:12metabolomics of stool and urine.
02:53:1424 hour urine chemistries and
02:53:16then we will perform compositional
02:53:18mediation analysis to discover
02:53:19how the gut microbiome and its
02:53:21downstream metabolites mediates
02:53:22the direct and indirect effects
02:53:24of diet on kidney stones.
02:53:28Complementary to this approach,
02:53:31we are partnering with health
02:53:33core and leveraging the healthcare
02:53:35integrated research database,
02:53:36which is a longitudinally integrated
02:53:38medical and pharmacy claims database.
02:53:40Drawn from healthcare encounters of members
02:53:43enrolled in several commercial health plans.
02:53:45This is a extension of our work in
02:53:48the United Kingdom thin database.
02:53:50You can see we have a much larger
02:53:52sample size of over 600,000 individuals
02:53:54who were four to 65 years of age
02:53:57at the time of their first stone.
02:53:59We've cut off at 65 because of the
02:54:03introduction of Medicare at 65.
02:54:05So the.
02:54:08With the data in this database,
02:54:10then becomes less complete and
02:54:13then we have matched 5 to one
02:54:17for controls using incidence,
02:54:19density, sampling, match and age,
02:54:22sex and geographic region.
02:54:23So over 3,000,000 controls.
02:54:26So we'll be doing a nested
02:54:28case control study,
02:54:29determine the dose response
02:54:30relationship between antibiotic
02:54:31exposure and kidney stones,
02:54:33and to really try and identify subgroups
02:54:35at greatest risk for development of
02:54:37kidney stones after antibiotic exposure,
02:54:38and delve more into the age specific
02:54:41patterns in kidney stone risk.
02:54:45We will then also use a subset
02:54:48of of this population,
02:54:49so focusing on the kidney stone population,
02:54:54linking with LabCorp to link
02:54:56to their Litho link analysis.
02:54:58So we estimate this is going
02:54:59to be over 200,000 individuals.
02:55:01We've linked using privacy preserving
02:55:04record linkage with their lethal
02:55:06link data and the goal of this second
02:55:09analysis is to identify how oral
02:55:11antibiotics alter urine chemistries
02:55:13among individuals with kidney stones.
02:55:18Just want to highlight several
02:55:19areas of innovation in this work,
02:55:21which is again the use of shotgun
02:55:24metagenomic sequencing of the gut
02:55:26microbiome and contrast to most prior
02:55:28studies using 16 S RNA sequencing.
02:55:31Leveraging untargeted, untargeted,
02:55:32metabolomic profiling of stool and urine.
02:55:36Examining kidney stone disease.
02:55:38Really across the lifespan,
02:55:40so including children and adults so
02:55:42that we can understand each specific
02:55:44perturbations of the gut kidney access
02:55:46and calcium kidney stone disease.
02:55:48And this novel linkage of 24 year
02:55:50old chemistries with pharmaceutical
02:55:52claims data which will allow us to.
02:55:56Really evaluate how antibiotic
02:55:58exposure impacts your own chemistries.
02:56:04So now I want to shift from origins to
02:56:07complications and talk about a different
02:56:09access and kidney stone disease,
02:56:10and that's the bone vascular access.
02:56:14More than the episodic occurrence
02:56:16of debilitating stone events,
02:56:18kidney stone disease is increasingly
02:56:20recognized as a chronic systemic
02:56:22disorder of mineral homeostasis with
02:56:24considerable morbidity including increased
02:56:25risk for chronic kidney disease,
02:56:27bone fracture, and cardiovascular disease.
02:56:33So we and others have shown increased risk
02:56:37of impaired bone health in individuals.
02:56:40Kidney stone with an increased risk of
02:56:43fracture, increased risk of hypertension,
02:56:46increased risk of coronary disease as
02:56:48well as kidney function decline and
02:56:50progression to end stage kidney disease
02:56:53and what's striking from this body of work
02:56:56is the signal of a increased magnitude.
02:56:59Of risk observed in younger
02:57:02individuals and among women.
02:57:03So what does that mean for the risk
02:57:06of individuals who start forming
02:57:07stones in childhood?
02:57:09Because this is largely adult data.
02:57:14This picture highlights full and
02:57:16mineral accrual and childhood.
02:57:18It's important to recognize that peak
02:57:19bone mass is a lifelong determinant
02:57:22of osteoporosis and 90% of that peak
02:57:24bone mass is established by age 18.
02:57:26Although we do continue to
02:57:29cortical density up until age 30.
02:57:31And about 1/4 of the adult skeletal
02:57:34mass is actually laid down in
02:57:35the two year period of around
02:57:37the time of peak linear growth.
02:57:39In the growing skeleton,
02:57:41positive calcium balance is
02:57:42favored in order to achieve
02:57:44calcium retention and build bone.
02:57:50Again, what I want to highlight here is
02:57:52fracture epidemiology in healthy children,
02:57:54so fractures are not rare events in
02:57:56childhood and this is Seminole work
02:57:58done nearly two two decades ago now,
02:58:00but has been reproduced in several
02:58:03studies since showing distinct age and sex
02:58:06specific patterns and fracture incidents.
02:58:08With a peak age of about 14 in males
02:58:10and 11 years in females and this
02:58:13peak in 14 year old males are about
02:58:15280 fractures per 10,000 person.
02:58:17Years is only surpassed at age 85
02:58:19and women and never again in men.
02:58:24So wanna spend just the most of the
02:58:26rest of the time talking about impaired
02:58:29bone health in kidney stone disease and
02:58:32thinking about mineral and bone disorder?
02:58:35MD in the context of kidney stone disease.
02:58:39So again, as a chronic systemic disorder of
02:58:41mental homeostasis that's disproportionately
02:58:43increasing among adolescents,
02:58:44we really need to think about how
02:58:46this may impact long term bone
02:58:49health in this population.
02:58:50There have been many decks of
02:58:53studies showing produced aerial bomb,
02:58:55mineral density and children and adults,
02:58:57and actually now more than four
02:58:59population based cohort studies
02:59:00demonstrating increased fracture
02:59:02incidence with the hazard ratio ranging
02:59:04from 1.08 to 1.2 and older adults.
02:59:09What I want to show you here is data
02:59:11from work that we did again in the
02:59:14health Improvement Network database.
02:59:16Where we looked at over 50,000
02:59:19individuals with kidney stones and
02:59:21compared them in a retrospective cohort
02:59:24study to over 500,000 individuals
02:59:26who did not have kidney stones.
02:59:30And what's shown here is the age
02:59:32and sex specific fracture incidence
02:59:35by decile of age in participants
02:59:37with and without kidney stones.
02:59:42This figure highlights it a little bit more.
02:59:44The magnitude of this association.
02:59:47So in emails there was an
02:59:50overall hazard ratio of 1.13,
02:59:52but I want to call your attention
02:59:54to the 10 to 19 year old age window
02:59:56in males where the hazard ratio
02:59:59was actually 1.51 and females,
03:00:02there was a statistically
03:00:04significant interaction with age,
03:00:05so we can't report an overall hazard ratio.
03:00:07But you can see that the magnitude
03:00:09of the hazard ratio was greatest
03:00:10in the 4th decade of Life,
03:00:12so 30 to 39 year old women and then
03:00:15decreased to a hazard ratio of 1.21 and 8.
03:00:18Decade of life.
03:00:19It's important to note, however,
03:00:21that with the with the rate of
03:00:23osteoporotic fractures in the
03:00:258th decade of life and women,
03:00:26this hazard ratio of 1.21 represents
03:00:29a significant public health burden.
03:00:34So what is behind the increased
03:00:36fracture risk and reduce both mineral
03:00:39density in patients with kidney stones?
03:00:42And I think part of the issue with the
03:00:45existing literature is that there was
03:00:47a focus primarily on hypercalciuria,
03:00:51so this summarizes the literature on bone
03:00:55density in children with kidney stones,
03:00:58and you can see that overwhelmingly
03:01:00these studies looked at children
03:01:02with idiopathic hypercalciuria.
03:01:03With or without kidney stones,
03:01:05rather than looking at kidney
03:01:07kidney stones from a more agnostic
03:01:10perspective of underlying risk.
03:01:13The studies were cross sectional
03:01:15and again largely limited to aerial
03:01:17bone mineral density of the lumbar
03:01:19spine and Dexter does not allow to
03:01:22distinguish compartment density and
03:01:24particularly the lumbar spine prevents
03:01:26insights into cortical structure.
03:01:28Most of these studies did not
03:01:30include healthy controls,
03:01:31but you can see that the range of
03:01:34osteopenia was 22 to 54% across
03:01:36these studies and there were more
03:01:39pronounced deficits in stone
03:01:40formers than in children who had
03:01:43isolated idiopathic hypercalciuria.
03:01:45This table summarizes a review of
03:01:47preclinical and clinical data that really
03:01:49just drives home the point that it's
03:01:51probably not all about hypercalciuria,
03:01:54and that there's multiple potential factors,
03:01:56both in terms of urinary mineral excretion,
03:01:59dietary intake,
03:02:00and also vitamin D related.
03:02:02Minimum metabolism that play a
03:02:04role in the impaired bone quality
03:02:05in kidney stone disease.
03:02:11Finally, just I wanted to discuss
03:02:14cardiovascular complications of kidney
03:02:16stone disease and highlight what is
03:02:19currently a lack of data in children.
03:02:22So independent of other cardiovascular
03:02:24risk factors, several studies in
03:02:27adults have demonstrated kidney stones
03:02:29to be associated with hypertension.
03:02:31Arterial stiffness in the order
03:02:34calcification coronary disease,
03:02:35including myocardial infarction,
03:02:36stroke, and subclinical caretta
03:02:38that their sclerosis and young.
03:02:40Deals with kidney stones.
03:02:41And again, this excess myocardial infarction,
03:02:44which seemed to be more pronounced
03:02:46in younger adults.
03:02:47To my knowledge,
03:02:48there's still only one pediatric study
03:02:50that was done by Kirsten Kusumi looking
03:02:52at 15 adolescence with kidney stones.
03:02:55That showed that they had
03:02:56higher carotid intimal medial
03:02:57thickness compared to age, sex,
03:02:59and body mass index matched controls.
03:03:02So understanding the bone and
03:03:04vascular morbidity associated
03:03:06kidney stones is really important,
03:03:08particularly in our in our
03:03:10patient population that develops
03:03:11kidney stones early in life.
03:03:17So this brings me to another active study
03:03:19that we are close to completing enrollment.
03:03:22So target this is a prospective cohort study,
03:03:25just funded by our pediatric Center of
03:03:28Excellence and nephrology and target
03:03:29enrollment is 100 children and young
03:03:31adults between the ages of five and
03:03:3321 years of age with kidney stones.
03:03:36Primary outcomes are bone measures
03:03:38assessed by high resolution peripheral,
03:03:40quantitative computed tomography
03:03:42or HRCT as well as dexa.
03:03:47And participants are having these measures
03:03:49done at baseline and 12 to 24 months.
03:03:52We extended to 24 months because of COVID.
03:03:56They are also having 24 year,
03:03:5824 hour urine profiling and completing 324
03:04:01hour diet recalls at baseline and follow up.
03:04:05We were enrolling healthy controls
03:04:08and then because of COVID,
03:04:10I'm fortunate to be partnering
03:04:11with my mentor, Mary Leonard,
03:04:13who's been rolled over 200
03:04:15healthy participants at Stanford.
03:04:18Using the same second generation
03:04:20HRPC technology,
03:04:21so we are going to be comparing our
03:04:25population to that reference cohort
03:04:27because there are no normative values for
03:04:30HRPT and children as compared to Texas,
03:04:32where there are normative values
03:04:34and we can calculate Z scores.
03:04:39So just a word on HPCT.
03:04:43If this is a low radiation technology
03:04:45that provides measures of trabecular
03:04:47microarchitecture and cortical
03:04:49volumetric bone mineral density,
03:04:50porosity and structure,
03:04:52and therefore can really provide insight
03:04:54into the impact of disease effects on
03:04:56discrete components of bone strength.
03:04:58Additionally, HPCT through microfinance
03:05:01element analysis can actually
03:05:03provide indices of bone strength,
03:05:06and that one of those indices failure
03:05:08loads correlates well with xevil bio.
03:05:09Mechanical compression testing
03:05:11has provided good fracture
03:05:13discrimination in children and adults.
03:05:16Additionally,
03:05:16there's the potential to pick
03:05:18up vascular calcification,
03:05:19although we don't know if that's
03:05:21going to be possible in our
03:05:23pediatric and young adult population.
03:05:30So to summarize,
03:05:31kidney stone disease is highly prevalent,
03:05:34and on the rise and the rising
03:05:36incidence is disproportionately
03:05:38affecting adolescents and women.
03:05:40There's growing evidence for display.
03:05:42Osis is a potential mediator
03:05:44for this changing incidents.
03:05:46An increasing recognition of associated
03:05:48morbidity and long-term renal and
03:05:50extrarenal complications of kidney stones.
03:05:53And again, this is a particular concern
03:05:55with earlier onset disease and what
03:05:57this means over the life course.
03:06:00So our current work is really focused on
03:06:03delineating mechanistic underpinnings.
03:06:05About the about the origins and
03:06:07complications of kidney stones so
03:06:09that we can improve both primary
03:06:11and secondary prevention of kidney
03:06:13stone disease and its complications.
03:06:15I just want to thank all of the members
03:06:18of the research teams that worked
03:06:20on the the settings that are that
03:06:23I presented that are ongoing and my
03:06:25partner Greg Tatian in urology Tub
03:06:28who's really been my Co Pi in all of
03:06:31this work and our funding sources.
03:06:33Thank you very much.
03:06:48Thanks for a wonderful talk.
03:06:50Any questions for doctor Denver?
03:06:55Ever.
03:07:16Are you able to hear that or?
03:07:18I did not. I'm so sorry to repeat it.
03:07:21I think she's on.
03:07:23Julie Goodwin sees babies later
03:07:26six months old with the kidney
03:07:28stones and is it due to potentially
03:07:31from formula or antibiotic use?
03:07:33Or what's the potential ideology?
03:07:36That's I think that's a great question.
03:07:38I think the work that we're doing right now,
03:07:41you know, we we are not enrolling
03:07:43anybody that young, but it's.
03:07:44It's a good question whether we
03:07:46should consider it a new direction
03:07:47and because you know, I think.
03:07:51Particularly for some of the kidney
03:07:53stones we see early in life.
03:07:54You know, particularly in the NICU.
03:07:57So I think it's a great question
03:07:58and a great future direction,
03:08:00and we are really hoping to
03:08:02delve into the dietary influences
03:08:05in the ongoing poop study.
03:08:08But we don't have anybody that young.
03:08:11Alright.
03:08:26More more.
03:08:30However.
03:08:45So can you modulate the aux light content
03:08:49by by the diet or increasing your calcium
03:08:54to change also the the microbiome?
03:08:57Yeah, I think we don't know.
03:08:59We don't know yet, but I think that
03:09:01is the idea that you know can we?
03:09:03What can we learn that can help both with
03:09:07primary but even secondary prevention for
03:09:10recurrent stone farmers by manipulating?
03:09:13Might be manipulating diet
03:09:16to change the microbiome.
03:09:18I think that is where, where,
03:09:19where this work is hopefully heading.
03:09:22Is this also like important in like uric
03:09:25acid stones or other forms of stones?
03:09:28Or is it just for calcium oxalate?
03:09:31That's a good question. I mean I'm.
03:09:32I mean it, it could be.
03:09:34I mean the our work and the the body of
03:09:37literature that I'm familiar with is really
03:09:41focused on calcium based kidney stones.
03:09:44And you know,
03:09:45there's been a lot of attention to oxalate
03:09:48and the effect of the microbiome on Oxley,
03:09:52but I think that's why we really wanted to
03:09:55take a more agnostic approach to looking
03:09:58at the urine chemistries more broadly,
03:10:00the interaction.
03:10:01Between external exposures and microbiome
03:10:04and urine chemistries more broadly,
03:10:06but we we are not looking,
03:10:07we we have specifically excluded
03:10:09your against the uric acid stones.
03:10:11From our analysis and they're
03:10:13very rare in childhood.
03:10:18Any other questions?
03:10:22Thank you Doctor Dembrow for a great talk.
03:10:29So we'll reconvene after lunch at 1:00 PM,
03:10:34thanks. All right, I think we can
03:10:37get started with our last speaker,
03:10:39doctor Opeyemi Olabisi from.
03:10:43Duke Health who's going to talk to us about
03:10:46translational insights from patients.
03:10:48Stem cell derived model of April
03:10:511 nephropathy. Thank you Amy.
03:10:55Thank you very much,
03:10:57Doctor Ishibe and thank you to the
03:10:59organizer and Doctor Shebib for inviting me.
03:11:02I'm quite honored to this.
03:11:06Symposium I wish I could be there in person,
03:11:09but different scheduling
03:11:10issues that prevents me,
03:11:12so I'm quite excited to be here.
03:11:14The morning session was quite enjoyable
03:11:16listening to all the speakers,
03:11:18including Doctor, Babbitt and Doctor Roses,
03:11:21and all, so it's been very exciting day,
03:11:24and I know this is the last talk,
03:11:26so I hope you actually will enjoy it also.
03:11:32So none of the conflict is really
03:11:35relevant to the talk I'm be given today,
03:11:38and I usually do not forget.
03:11:40I want to kind of give credit to
03:11:43people who actually did all the work
03:11:46and Doctor George Lee is a postdoc in
03:11:50the lab to the left, Sarah Nystrom.
03:11:53Is nephrology rising star fellow in the lab.
03:11:58Is the first author of the paper
03:12:00that they'll be presenting later.
03:12:01Deraya minor is my clinical
03:12:05research coordinator.
03:12:07With the subnet data,
03:12:09I suppose duck Daniel Silers is the research
03:12:12tech in the lab and carries soldano.
03:12:15Is my lab manager without them
03:12:17all this stuff I'll be talking
03:12:20about today would not happen,
03:12:22so they get the front row seat.
03:12:24So my goal today is to.
03:12:28Go through this overview.
03:12:30Some of them are familiar but
03:12:33also couch this in two stories.
03:12:35One of them just recently published
03:12:37and the other one is still emerging.
03:12:40To highlight this,
03:12:41the use of patient derived IPS
03:12:44model as a way of studying disease.
03:12:47I will go over the high burden of
03:12:50kidney disease among African Americans.
03:12:52Factors that contribute especially
03:12:54biological factors that will be
03:12:57discussing today the role of experimental.
03:12:59Models in understanding the role of
03:13:02equal 1 and also I will be ending by
03:13:05transitioning to once we actually
03:13:08understand the the mechanism and and whatnot.
03:13:11How do we translate this to the community?
03:13:13How do we overcome barriers that have
03:13:16prevented translations in the past,
03:13:18and I'll be introducing a care
03:13:21and justice and NIH funded program
03:13:23that we are doing.
03:13:25Many of you may be familiar with this book.
03:13:28It it's called the warmth of
03:13:30other Suns by Isabel Wilkerson.
03:13:32I really,
03:13:33really enjoyed the book on many level.
03:13:35It chronicled the great Migration,
03:13:37the migration of about 6 million
03:13:41African Americans between 1915 to
03:13:431970 from the South to the north
03:13:47and also to the West.
03:13:50The book is the author interview
03:13:53like maybe 1000 individuals.
03:13:56Pretty much is very nice book.
03:13:59But in the subtext,
03:14:00or let me say she followed 3 characters
03:14:03as a way of telling this story,
03:14:05throw you real human characters,
03:14:07African Americans.
03:14:07This is the movement of African Americans
03:14:10from the South to the north and West.
03:14:12Three of them, one of them,
03:14:13actually ended up going West.
03:14:15He became the the personal physician
03:14:18to to the musician Ray Charles,
03:14:21of the three characters.
03:14:22She followed.
03:14:23Two of them died with kidney failure.
03:14:26So,
03:14:26and this is where I'm going with this.
03:14:28Even when you look historically into
03:14:31the African American history here you
03:14:33see that the the higher burden of
03:14:36kidney failure is not something new, it's a.
03:14:38It's a kidney failure that's been.
03:14:40And it's not a new problem in this community.
03:14:43And as you many of you know,
03:14:46in the audience that African American
03:14:48constitute 13% of US population,
03:14:51but 35% of patients on dialysis.
03:14:54And we know that the burden
03:14:56of kidney disease.
03:14:57The incidence of end stage
03:14:59kidney disease among blacks is
03:15:01at four times higher than among white.
03:15:03The risk is higher in other groups as well.
03:15:06You see the Spanish and the Native
03:15:08American Hispanics and the Asian
03:15:10relative to to European Americans.
03:15:12But focusing on African American this in this
03:15:16conversation the risk is full fold higher,
03:15:19and this risk actually has consequences.
03:15:23Here I listed some of the the
03:15:25the the reason why this problem.
03:15:27OK, kidney disease is very care.
03:15:30Failure is very deadly.
03:15:31I'm talking to many nephrologists
03:15:33this is no news.
03:15:34I mentioned it's an equal opportunity
03:15:36offender and it cost a lot of money
03:15:39and one of the fact that I found that
03:15:41just in my mind study is when you look
03:15:44at the epidemiology of kidney failure,
03:15:46black men's life,
03:15:47lost to kidney failure is similar
03:15:49to life lost to colon cancer and
03:15:52for black women it's similar to
03:15:53life loss to breast cancer.
03:15:55So a lot is quite deadly.
03:15:57And what causes this is,
03:15:58you know many factors.
03:16:00I think I believe the last two
03:16:03two year plus as I lighted the
03:16:06contribution of non biological
03:16:08factor including structural racism,
03:16:10socioeconomic factors and environment.
03:16:12But then there's also biology which is
03:16:15indisputable and this is where it will
03:16:17won't fall in that I'll be discussing today.
03:16:20Many of you know that in 2010 Doctor
03:16:23Pollack my my mentor in Boston.
03:16:27By Chinese group and many others
03:16:30collaborating together identify that
03:16:32polymorphisms in the equal 1 gene,
03:16:35illustrated as a cartoon here account
03:16:38for a high burden of kidney disease
03:16:41among people of recent African
03:16:43ancestry and the story that unfolded
03:16:45was that the reference, if well,
03:16:47when the wild type is the G0,
03:16:49shown here,
03:16:50G1 and G2 evolve around 4 to
03:16:536000 years ago in West Africa.
03:16:57Where G1 I resulted from serine
03:17:00to glycine substitution,
03:17:01isoleucine to methionine substitution.
03:17:03This mutation almost always occurs together
03:17:07and then G2 is 2 amino acid deletion.
03:17:09In this Sr a domain and it appears
03:17:12that the evolutionary benefits
03:17:14of having G1 or G2 is protection
03:17:17from the African trypanosome
03:17:19parasite in Ethereum zygote state.
03:17:22But when you have two copies in homozygous
03:17:25or compound that they're almost.
03:17:28IG1G1 or G1G2 or G2G2.
03:17:31It increases the risk of kidney disease,
03:17:34So what nature gave with one hand
03:17:36he took away with the other and we
03:17:39know that 400 years ago with the
03:17:41with the transatlantic slave trade,
03:17:43this gene came to the Americas,
03:17:45not just the United States but also to
03:17:48South America and to the Caribbean.
03:17:50Why is this genome present in European?
03:17:53Because the out of Africa migration
03:17:55to Europe are called long,
03:17:56long before 2200 thousand.
03:17:58Musical before April 1 emerge
03:18:01around 4 to 6000.
03:18:02Years before so we now know that among
03:18:07African Americans anywhere between
03:18:0910 to 15% of blacks have to risk
03:18:13halilovic poelma that's shown here.
03:18:15So I'll be referring to this
03:18:16as high as the genotype,
03:18:18but there have been cases where
03:18:20impatient with kidney disease,
03:18:22when we biopsy them and we genotype them,
03:18:25there are some self identified white
03:18:27individual, especially some self.
03:18:29Identify why this panics that also
03:18:32carry this high risk genotype.
03:18:33What does that mean?
03:18:35It means that in the lineage of that person,
03:18:38that person have recent African ancestry,
03:18:40so it's not totally accurate.
03:18:43In fact, it may be misleading
03:18:45to think of high risk of well
03:18:47one genotype as a race issue.
03:18:49In fact, it's an ancestry issue.
03:18:51There was a question before.
03:18:52How do you distinguish
03:18:53between ancestry and race?
03:18:55The best way to think of this risk
03:18:57allele is it reflect recent African.
03:18:59Necessary now is it just a
03:19:03risk factor or is it causal?
03:19:05Driver of disease work
03:19:06from Catalan zuster club.
03:19:08I think she she was the first to make
03:19:11a mouse model that actually depicted
03:19:13this where she made a mouse model that
03:19:16X Ray said that Jezero G1 or G2 equal
03:19:191 you recall that G0 is a reference,
03:19:22the wildtype G1 and G2 at
03:19:24the risk alleles now miles.
03:19:26I'm pretty much all of all
03:19:28of our experimental animal.
03:19:29There's like a poor one normally,
03:19:32so mouse doesn't have a Powell one.
03:19:34So to study it you have to do
03:19:37transition generated transgenic mice.
03:19:39She demonstrated that these mice
03:19:40actually have kidney disease,
03:19:41as indicated by proteinuria,
03:19:44elevated BUN creatinine,
03:19:46and when you look at their
03:19:47kidney they also has.
03:19:48They also have sclerosis that
03:19:51phenocopies what we see in human.
03:19:54My mentor in Boston doctor Pollack I
03:19:56wasn't involved in in this work but
03:19:59I know it took a lot of hard work.
03:20:01Diligence are demonstrated.
03:20:02Essentially the same thing.
03:20:05The difference between these
03:20:06two mouse model is that this is
03:20:08a podocyte specific expression,
03:20:10whereas this is a back transgenic
03:20:13mice that if well,
03:20:15one expression is induced under
03:20:17the human promoter here.
03:20:19So this may probably mimic more of
03:20:22the Physiology that we see in humans,
03:20:25but the picture is the same
03:20:27wherever there is the G1 and G2
03:20:30you have high proteinuria.
03:20:31And kidney disease.
03:20:32So these studies establishes that
03:20:34April 1 the risk are really well,
03:20:36one.
03:20:37They are not just risk factors
03:20:39but drivers of disease,
03:20:41working cell culture in my lab and
03:20:43other groups then show further that the
03:20:46degree of toxicity that you see with
03:20:49this equal 1 depends on the expression level.
03:20:52So it's not just a variant dependent
03:20:55toxicity but also a dose effect.
03:20:58So the more important one G1
03:21:00and G2 you express,
03:21:01the more toxicity you see.
03:21:03The mechanism by which a poor one
03:21:05causes injury is still under debate,
03:21:07but a lot of the work that we've
03:21:10done showing that one of the
03:21:12proximal factor or effect is a loss
03:21:14of intracellular potassium if one
03:21:16itself might generalizes to the
03:21:19membrane to the plasma membrane
03:21:20and causes efflux of potassium.
03:21:23Here I tried to summarize many
03:21:26studies from many groups about
03:21:28the effect of equipment,
03:21:30kind of wrapping down the introduction.
03:21:33What does what is the effect of equal 1
03:21:35on the spectrum of diseases that we know?
03:21:38So these are diseases that have
03:21:40been associated with equal 1
03:21:42FSGS all the way down to lupus.
03:21:43So if you imagine that at the train
03:21:46station there are two groups of people.
03:21:48Groups that have normal variant
03:21:50did 0G0 and it group that carry
03:21:53higher risk of poelman. Genotype
03:21:58G1G1G2G2G1G2 those individuals at
03:22:00the train station are the ones that
03:22:02are more likely to get on the train.
03:22:04For example, if I see 10 black patient that
03:22:08have idiopathic FSGS in my in my clinic,
03:22:117 out of of the 10 would have Iris
03:22:14Campbell and genotype that is quite
03:22:17outstanding, right? Remember,
03:22:18in the general for black population,
03:22:20only 10 to 15% of the black
03:22:23population carried this.
03:22:24There is genotype but among FSGS group.
03:22:2970% I do, and if you look down
03:22:31the list hypertension now,
03:22:33we found that COVID I will talk
03:22:35more about this in many cases
03:22:38in the largest group study,
03:22:3990% of people that develop COVID
03:22:42associated nephropathy karidis.
03:22:44So if well one genotype,
03:22:47hiris genotype increases the likelihood
03:22:51of developing this spectrum of disease.
03:22:54The second effect is that once
03:22:56the patient is on this train.
03:22:59Well, one is also an accelerant,
03:23:01it it it accelerate the progression
03:23:04to to dialysis.
03:23:05So if you ought to ESRD more specifically
03:23:08so this is a non equal 1 CKD train,
03:23:11this is the April 1 CKD train work
03:23:14from Ask Group and others shows
03:23:16that people that have a poor one
03:23:18in addition or in context of their
03:23:20CKD that their disease progressed
03:23:22to dialysis about 10 years on
03:23:25average earlier than regular CKD.
03:23:27And there's no known intervention.
03:23:28So far,
03:23:29so it's in this context that Kovid came
03:23:33and Kovid became essentially a perfect storm,
03:23:37because once COVID happened we
03:23:39it's quickly becoming apparent
03:23:41that some individuals who are
03:23:44developing collapsing glomerulopathy,
03:23:46but they I put this as a triangle here
03:23:49over 3 overlapping circle if you will.
03:23:51High risk equal 1 genotype covian
03:23:54infection and situations that cause people
03:23:57to develop COVID in the first place.
03:23:59The evidence that came from earlier
03:24:03studies from across the country
03:24:05shows that people that have COVID
03:24:08if you look at hospitalization,
03:24:10people inpatient hospitalization
03:24:12with complication with a Ki.
03:24:15Let me put it differently.
03:24:17I could clean jury,
03:24:19complicate COVID infection
03:24:21in about 46% of cases,
03:24:23meaning that almost half of patient but
03:24:26developed acute kidney injury from COVID.
03:24:29And if you look at patient in the ICU.
03:24:33Pretty much about three quarter
03:24:35of them developing care,
03:24:36and this actually has a
03:24:39significant effect on mortality.
03:24:40This is in this some of these early
03:24:42studies showing up to 50% mortality among
03:24:45patients with a Ki with kidney disease.
03:24:49Then they begin to biopsy this patient in
03:24:52the first aggregate study that was reported.
03:24:54This is an aggregate of 159
03:24:57patients from across the country.
03:24:59One of the four.
03:25:01Striking thing is the pathology.
03:25:03Shows that collapsing glomerulopathy
03:25:06is actually the most common
03:25:09histopathology in this API.
03:25:10In the first thirty.
03:25:12Then I kind of lab.
03:25:15In cancer is also did a study following
03:25:17up on this and to actually put the
03:25:20specific number on it anywhere between
03:25:2325 to 35% to 6% of histopathology
03:25:27from AK from COVID were covan.
03:25:31Now if you look before COVID,
03:25:33how many was a fraction of
03:25:35pathology that shows COVID?
03:25:37It's like 1.8 so this is a
03:25:40robust increase in in collapsing
03:25:43glomerulopathy from from COVID.
03:25:45And here is the here's the punch.
03:25:48If you look at this chain that
03:25:51had collapsing glomerulopathy
03:25:52from COVID and genotype them.
03:25:5691.7% of them carry high risk genotype
03:25:59compared to 10 to 15% of general population,
03:26:03so this almost completes almost unity,
03:26:06showing that having high risk Capital One
03:26:09and developing collapsing glomerulopathy
03:26:11from COVID almost one and the same.
03:26:14In this setting these are images from
03:26:16our recently published study showing
03:26:18collapsing glomerulopathy where you
03:26:20have the collapse of the glomerular.
03:26:23Though you have injury of the usually.
03:26:25Petrophile and hypoplasia of the put aside,
03:26:29and ultimately resulting in podocyte
03:26:31loss and loss of filtration.
03:26:34The question came forward.
03:26:36What is the mechanism by which
03:26:39COVID causes a poor one?
03:26:41Associate their collapsing glomerulopathy
03:26:43so there were two theories.
03:26:45One theory was that when I
03:26:47prothesis was that there's a direct
03:26:50viral infection of kidney cells.
03:26:53The evidence that supports
03:26:54this in part was that.
03:26:56And biopsy in autopsy study.
03:26:58There were reports initially that
03:27:00they were testicle that looked
03:27:03like viral vesical but in almost
03:27:06all biopsy from living patients,
03:27:09they're really not being compelling
03:27:11evidence that is direct viral infection of
03:27:15podocyte or or or kidney cell parenchyma,
03:27:19so that that sort of weakens
03:27:21the argument somewhat.
03:27:23Then there's the second equal thesis
03:27:25that perhaps the cytokine storm
03:27:27provoked by COVID triggered by COVID.
03:27:31Actually,
03:27:31homes on the kidney and causes glomerular.
03:27:34Collapse either by April one through
03:27:36April one or that means this hypothesis
03:27:39is actually what we explored,
03:27:42and the paper that came from it is
03:27:44actually currently impressed in JCI insight,
03:27:47so I encourage you to take a look and
03:27:49it was led by the nephrology fellow
03:27:52that Doctor Serene stream in the lab.
03:27:54So what we did essentially was to
03:27:57collaborate with our network pathologists.
03:27:59Baby Thomas Astrid wine at Harvard
03:28:02and David Thomas at Netherhall
03:28:05and what we did was OK.
03:28:08When you have patience that was
03:28:10sent to you or biopsy that was
03:28:12sent to you for collapsing FSGS,
03:28:15let's genotype them so we have 9 cases.
03:28:18What we found that of the nine cases
03:28:207 actually have high risk of where
03:28:22one genotype. So that's 77 point.
03:28:24You know 8%.
03:28:26And even in this small sample set.
03:28:29But interestingly.
03:28:29Of course,
03:28:30all of them were African Americans
03:28:33except one person that is self described
03:28:35as a white Hispanic and she has G1G1.
03:28:38Also,
03:28:38this betrays the point that I'm
03:28:41saying before that the suspicion
03:28:42should be there even for non
03:28:45survey notified black individuals.
03:28:49All of them have proteinuria
03:28:51and progressive kidney disease,
03:28:53and then we did immunity.
03:28:54Immunohistochemistry for equal
03:28:551 to ask the question is equal
03:28:581 expression actually elevated
03:28:59in the kidney of this patient.
03:29:01So this is a biopsy reference.
03:29:04No, no kidney disease in this patient.
03:29:06There's no equal.
03:29:071 This was a patient with 0G0 who
03:29:10had COVID but no kidney disease.
03:29:12Again,
03:29:13there's no equivalent expression,
03:29:15but when we look at the kidney of
03:29:17patients that had collapsed in FSGS.
03:29:19This is a case of case six.
03:29:22We saw robust expression in the portal site,
03:29:24but also in the glomerular endothelial cells,
03:29:28and this is uniform from across all
03:29:30the patients that we we examined.
03:29:33And this is just another view
03:29:35showing that we also saw staining
03:29:37in the period epithelial cells.
03:29:39But by and large it's podocyte and
03:29:42endothelial cells that had this expression.
03:29:44So what drives this expression of a poelman?
03:29:48Sarah looked at all the.
03:29:50Reported cytokines and chemokines that
03:29:53were reported to be elevated by COVID.
03:29:57And so we took each of them and
03:29:59sort of did a reductionist to see
03:30:01which one actually have an impact on
03:30:03increasingly poor one expression.
03:30:05As you can see here.
03:30:07I also I want better interferon alpha,
03:30:10beta gamma disturbing previously known
03:30:12to induce a poor one drive a poor
03:30:15one expression here for interferon
03:30:17gamma about 250 fold however.
03:30:19The interesting thing is when you
03:30:21have all the cytokines and you
03:30:24remove the interference,
03:30:25the known trigger you still
03:30:27have about 100 fold induction,
03:30:29meaning that even noninterference cytokines,
03:30:32especially in combination,
03:30:33have a robust effect on April 1
03:30:35expression and the second fact is
03:30:37that when you combine all these
03:30:39sycophants together that were released
03:30:41in context of COVID about more than
03:30:436000 fold equal 1 expression was sent.
03:30:46This is an in glomerular endothelial cell.
03:30:48We saw the same pattern in primary.
03:30:51Photo site and then when we look at
03:30:54signaling pathway that potentially
03:30:56mediate this,
03:30:57the state 1/2 and three mediate
03:30:59the effect of the of the eight
03:31:02key cytokines that we see,
03:31:05and we use various city name which
03:31:07is an inhibitor of the Jack one
03:31:09Jack two which are necessary to to
03:31:12activate these three stacks and
03:31:14you can see both in the glomerular
03:31:16endothelial cells and podocyte.
03:31:18It totally abrogate the expression
03:31:19of equal 1 so suggesting that.
03:31:22This site outlines converges through the
03:31:24same Jack stat pathway to induce a power.
03:31:28One expression summarized here.
03:31:30So again, cytokine storm from COVID.
03:31:34Interacting with their ligand
03:31:35in photo site and in the philia
03:31:37cell and driving it well when the
03:31:39expression through a common pathway.
03:31:41Here,
03:31:41showing that that one and that
03:31:43Barry sitting it blocks it.
03:31:44So to drive this home further.
03:31:46So what if we get I PST we we make I
03:31:51PSC's from individual that has G1G2,
03:31:54and then we differentiated those
03:31:56using the standard protocol.
03:31:57This was from Melissa Little
03:31:59Protocol to make micro kidney micro
03:32:02organoid that had what they produce.
03:32:04Like a lot of endothelial cells,
03:32:06but a lot of producers that Chevrolet cells.
03:32:09And then what we did was to treat
03:32:11them or not with interference.
03:32:13So in the absence of interference,
03:32:15there's really not much equipment
03:32:17expression with interference.
03:32:18You see robust equal 1 induction,
03:32:21especially in the part that are
03:32:24positive for podocalyxin.
03:32:26Again,
03:32:26the part that is consistent
03:32:28with podocyte when we applied by
03:32:30reciting name the blocks,
03:32:32the expression when we
03:32:33applied all the cytokines.
03:32:35That I should before it gained
03:32:37robust induction and the effect of
03:32:39all those cytokines were similarly
03:32:40blocked by Barry
03:32:41sitting in consistent.
03:32:43That even in this patient derived
03:32:45kidney organoid that these cytokines
03:32:48work through the Jack stat pathway
03:32:51functionally do do this have
03:32:53functional effect in terms of toxicity.
03:32:56We confirm the equivalent expression
03:32:58here at 96 hours and we look at
03:33:01viability so viability wise,
03:33:02as you had interferon or all the cytokines.
03:33:05You see a reduction in the viability
03:33:08when you add baricitinib it rescued it.
03:33:11We look at ATP as just another
03:33:13measure of cell that is viability.
03:33:15So this data suggests that the
03:33:18expression of equal 1 at least
03:33:20downstream of the Jack stat pathway
03:33:23might be a mediator of what we see with
03:33:26collapsing FSGS as shown in this model.
03:33:28So I went over that kind of quickly
03:33:31because the the manuscript is
03:33:33now submitted and and and it's
03:33:35online and JCI insight.
03:33:36Encourage us to look at it.
03:33:39How do we translate this
03:33:41now by reciting name?
03:33:42Actually I believe yesterday or two
03:33:44days ago received a full FDA approval
03:33:47for treatment of COVID infection
03:33:49that requires that oxygenation in the
03:33:52hospital it's not approved for COVAN,
03:33:54which is a complication,
03:33:57but this study suggests a couple of
03:33:59things that the use of various citizens,
03:34:02at least for treatment of COVID
03:34:04should be explored as people
03:34:06were using this now is is now FDA
03:34:09approved for COVID in itself.
03:34:10The role of equal 1 genotyping
03:34:13I think is underscored here,
03:34:15especially in the context of people that
03:34:17have a poor one related kidney disease.
03:34:19I put a question mark on the trial.
03:34:21This would be the study the use
03:34:24of various citizen for treating
03:34:26disease need to be tested properly.
03:34:28There was suggestion initially that
03:34:31perhaps exogenous interference could
03:34:33be used as part of treatment for
03:34:37COVID because interferon deficiency.
03:34:39Seems to increase the risk of.
03:34:42Contracting kovid and having severe COVID.
03:34:45Our study here suggests that maybe for
03:34:48patient that at risk I have high risk of,
03:34:50well, one genotype given them,
03:34:52interferon could actually be heading
03:34:55flame or hiding gasoline to the flame,
03:34:58and probably should be avoided.
03:35:01Our transition quickly to the second story,
03:35:04but this one I not much time.
03:35:07Not everyone at the train station
03:35:09developed kidney disease.
03:35:10Not everyone get on the train.
03:35:12That's going to dialysis.
03:35:13So one of the question of interest in
03:35:16my lab is what differentiates people
03:35:18that have the high risk genotype.
03:35:21And people who have the high risk
03:35:22genotype and develop kidney disease.
03:35:24We think there's a three hit necessary.
03:35:26First hit mainly high risk equals
03:35:29one genotype. Second hidden.
03:35:30In this case we use interferon
03:35:32but other cytokines as our COVID
03:35:34studies have shown.
03:35:36But even when these two hits are present,
03:35:39we know from HIV that not all those
03:35:41people develop kidney disease.
03:35:43Only 20% of them do.
03:35:46Then there must be some intrinsic
03:35:47factor that is unique to the
03:35:50patients that develop disease.
03:35:51That could be teased out,
03:35:53so we took a I PSC's approach
03:35:57to to circulate.
03:35:59We identify patient with
03:36:00FSGS that have virus deep.
03:36:02Well one genotype.
03:36:03We identify healthy controls.
03:36:05We make IPS from them.
03:36:07We differentiate these two photo sites
03:36:09and then we perform transcriptomic
03:36:11analysis to sort of try and glean
03:36:14out what could be the effect of this
03:36:16modifier and how can we understand it.
03:36:18What we did in a nutshell here.
03:36:21Was to compare high risk
03:36:23cases and high risk control.
03:36:25These were limited number of of controls
03:36:27and we did a transcriptomic analysis
03:36:29to see what are the genes that are
03:36:32differentially more upregulated in
03:36:33the high risk cases than control.
03:36:36And many of those genes are downstream
03:36:39again of the Jack stat pathway,
03:36:41suggesting that the Jack stat pathway
03:36:44is more potentiated in the podocyte of
03:36:47people who are at risk of developing.
03:36:50If well, one related.
03:36:52FSGS we went further to actually validate
03:36:54this to see when we applied interferon.
03:36:57What is the equal 1 expression level
03:37:01in podocyte from cases than control
03:37:03we saw more equal 1 expression.
03:37:05We saw more upregulation of the
03:37:08Jack stat pathway and all this can
03:37:11be blocked actually by various.
03:37:13We confirm this by Western blot
03:37:15and downstream of April 1.
03:37:17Given the work in my lab,
03:37:19we know that April 1 mediate
03:37:21potassium efflux,
03:37:22so we did use a potassium tracer,
03:37:24which is rubidium to see what
03:37:26happened in a case when we induce
03:37:28the puelo one with interferon to the
03:37:30rubidium with loaded into the cell,
03:37:32you see significant reduction in rubidium,
03:37:35and there are some rescue with Barry
03:37:38sitting them against showing that
03:37:39even in these I PSC photosite model.
03:37:42It's recapitulating what we see.
03:37:45We did a knockout of April,
03:37:46one in one of the line,
03:37:48and the potassium efflux was abrogated.
03:37:51So this led us to to to to to
03:37:53write a proposal to say,
03:37:55well maybe we should try baricitinib
03:37:58as a potential therapy for it well,
03:38:00but having the drug and the treatment
03:38:03is one thing helping people who
03:38:06actually need those drug and
03:38:08treatment to accept it to use it.
03:38:10It's another entirely.
03:38:11There's a lack of.
03:38:13Awareness about kidney disease
03:38:15in the black community.
03:38:17This lack of specific people,
03:38:18one treatment which is part
03:38:19of what we're working on.
03:38:21And if you look at clinical trials as well,
03:38:23one of kidney disease in general,
03:38:26black represent less than 5%.
03:38:28So to really translate some of this work,
03:38:31we really need to address this issue,
03:38:33and that's what led us to the the study
03:38:37that we we is now funded by Nida by NIMH D.
03:38:42We call it the care.
03:38:43And justice study.
03:38:44The study has three aims.
03:38:46The aim first aim is community
03:38:48engagement and and registry and
03:38:50screening for kidney disease.
03:38:52So here we screen people for kidney disease.
03:38:55We screen them we we genotype
03:38:57them and people who are eligible
03:38:59who have clinically significant
03:39:02proteinuria and kidney disease will
03:39:04be eligible to participate in justice,
03:39:06trial and justice.
03:39:07Essentially stand for Janus
03:39:09kinase that inhibition
03:39:10to reduce equal 1 associated kidney disease.
03:39:13So we kind of pursuing care to do justice.
03:39:16The third part is to actually
03:39:18do justice in a dish.
03:39:19I will not talk much about this here,
03:39:21and we've sort of with a
03:39:22lot of work and effort.
03:39:24I encourage you to check out
03:39:26this study website as well. It's
03:39:30www.kidneycareandjustice.com.
03:39:31The goal here is to engage the
03:39:33African American community.
03:39:35We are starting small here in North Carolina,
03:39:37but we're getting a lot
03:39:39of interest from outside,
03:39:40but we have to find resources
03:39:42to actually expand this further.
03:39:43To provide free screening for kidney disease.
03:39:47To provide free equal 1 genotyping research
03:39:49based and for people who are eligible,
03:39:52especially people with FSGS or
03:39:55hypertensive nephrosclerosis,
03:39:56they will be eligible to enroll
03:39:58in a six month clinical trial,
03:40:01baricitinib of which primary end
03:40:03point is reduction of proteinuria.
03:40:05So because in the interest of
03:40:07time I'll I'll pause here,
03:40:09I know I've gone through this a little
03:40:11quickly to to engage question and to.
03:40:13Leave room for some back and forth again.
03:40:17I want to thank people in my lab.
03:40:20Who basically have done all
03:40:22the work my collaborator,
03:40:24especially on the COVID covan project
03:40:26at Doctor Thomas Neuropathologist.
03:40:28Doctor Wayne Nephro,
03:40:30pathologist at Harvard,
03:40:32and our funding agencies,
03:40:34so I'll pause here to to take questions.
03:40:38Thank you.
03:40:49That was great.
03:40:52Doctor garashi so thank
03:40:55you for that great talk.
03:41:04These
03:41:14are these are organoids purified podocytes.
03:41:19Or are they just standard organoids?
03:41:23So for the COVID project these
03:41:27are derived micro organoids,
03:41:30so they were differentiated,
03:41:32so these were not isolated
03:41:34from patients kidneys.
03:41:36What we did was essentially to
03:41:39take the blood from patients.
03:41:41We isolate peripheral blood monocytes
03:41:44and then we transduce peripheral
03:41:46blood monocyte with the four Yamanaka
03:41:49factors to reprogram them to become
03:41:52inducible pluripotent stem cells.
03:41:54One is it become pluripotent stem cells,
03:41:56then we differentiate them
03:41:58using established protocol.
03:41:59A lot of folks,
03:42:00both at Harvard and and and Melissa
03:42:03Little at the entry and Melissa Little
03:42:06Lab did the pioneering work here.
03:42:08So the protocol is actually now standard,
03:42:11where you can coax this IPSC to become
03:42:14organoid in the organoid you have
03:42:16podocyte you have tubular cell and
03:42:19so on and so the first part of the
03:42:22study that I showed you with the code.
03:42:23And the COVID was dependent on the organized.
03:42:27The second part,
03:42:28the GEOPATHIC FSGS.
03:42:30Those IPS were directly differentiated
03:42:32to photosite using Samira Moser protocol.
03:42:36So in both cases these were not
03:42:39from the kidney,
03:42:40but these were already programmed.
03:42:42I hope that answers a question.
03:42:51How do you deal with like
03:42:54neuronal contamination?
03:42:55So that's a good question.
03:42:57So when we actually take this
03:42:59kidney organized and you look
03:43:01at the single cell analysis,
03:43:03you have about 20 sub population.
03:43:07Think because the role or the
03:43:09goal of using these I PSC's is
03:43:11not to transplant into patients.
03:43:13The goal is for us to actually use a
03:43:16subset of them, which means the podocyte,
03:43:19the endothelial cells.
03:43:20Yes, there is neuronal contamination there,
03:43:23but it's across from all the patients,
03:43:25so it's not a perfect tool,
03:43:27but it's actually, in my view,
03:43:29actually outperform some of the
03:43:31immortalized procycling we've been using,
03:43:34so it's because of the fact that it it
03:43:38retains the genetic endowment of the patient.
03:43:42It allows us.
03:43:43To be able to capture that in a way that we
03:43:47cannot capture it with immortalized line.
03:43:50So we actually didn't have to do
03:43:52with the neuronal contamination,
03:43:54because that, really,
03:43:55I don't think that helped with the
03:43:57question we are trying to answer.
03:44:01So.
03:44:03Don't.
03:44:07Really enjoyed it so. You are HIV.
03:44:14The patient sample you showed that
03:44:16the April one is expressed both
03:44:18in the prototypes of the cells.
03:44:20Is that something you see
03:44:22in all of the models where.
03:44:29Genesis The thing is related
03:44:31to the support sites versus.
03:44:35Mechanism. Yeah. Did you hear
03:44:38that or I heard the first part?
03:44:41So the employment expression is in
03:44:43the photo site and endothelial cell.
03:44:45How is that? Is that a universal thing
03:44:48for April 1 mediated kidney disease?
03:44:50Is that correct?
03:44:53Yes, OK, so we've looked in COVID and
03:44:58I will say that in the COVID tissue
03:45:01of the nine cases we looked at,
03:45:03the answer is the same robust expression
03:45:06in the podocyte robust expression in the
03:45:09CD 31 positive and the failure cells.
03:45:12We are in the process of actually doing
03:45:15the same thing with idiopathic FSGS.
03:45:17To see. Whether it's also in
03:45:20those two compartment as well,
03:45:22I can tell it's robustly in the podocyte,
03:45:24but we want to do it.
03:45:25Careful work to see whether it's
03:45:27also in in bethelehem cells,
03:45:29but in the COVID for all the cases
03:45:31we've looked at, the answer is yes.
03:45:34For the endothelial cells and,
03:45:36and I think that question from Doctor,
03:45:38Babbitt, and that actually
03:45:40under score is second question,
03:45:42the the understanding of collapsing
03:45:44FSGS has been very produced,
03:45:46centric, and rightly so.
03:45:48But I'm not sure that endothelial
03:45:52cells are entirely innocent.
03:45:55So hopefully for that work
03:45:56will try to to tease that out,
03:45:58but I'm not sure whether endothelial
03:46:00cells is involving all equal
03:46:021 mediated kidney disease,
03:46:04but it appears to be involved
03:46:06in COVID in COVID.
03:46:10So one question I have is how how
03:46:13does increase in April 1 cause
03:46:17the toxicity like mechanistically?
03:46:19So that's a great question.
03:46:20So in our hands we've done a lot
03:46:25of overexpression in HK cell.
03:46:28One of the first thing we saw was
03:46:31that the most proximal phenotype
03:46:34was potassium efflux itself.
03:46:36I believe that's the gateway upstream.
03:46:42Mediator of toxicity.
03:46:43There's a lot of controversy here,
03:46:45so there's been about 7 different
03:46:49mechanism proposed as to how,
03:46:51if everybody agree that it's toxic,
03:46:53but there are at least seven
03:46:56different mechanism proposed.
03:46:57My view is that the potassium efflux,
03:47:00and in this case also sodium influx,
03:47:02is actually central to that we
03:47:04are working on trying to to see if
03:47:06that if this models will help us
03:47:08to clarify that in the knockout
03:47:10model I show when we knock.
03:47:12Without the fuel one,
03:47:13the potassium influx stopped,
03:47:15so it's telling us that if
03:47:16everyone is self doesn't mediation,
03:47:18but they're still contentious at this point.
03:47:22So I I think we have to wait.
03:47:23There's really not been compelling
03:47:25evidence one way or the other that one
03:47:27mechanism is superior to the to the other,
03:47:29but it's unresolved question.
03:47:35I hope that answers your question.
03:47:36Doctor Shiva that's great.
03:47:45Any other questions?
03:47:58Have have you compared like interferon alpha,
03:48:01gamma and beta in viral
03:48:04induced kidney disease? Yeah.
03:48:07Yes, so when in that in that graph
03:48:11that I showed when you look at the
03:48:14effect of the three interference,
03:48:16it's actually very convincing that
03:48:18interferon gamma has the strongest effect,
03:48:22so you see alpha, beta,
03:48:25and gamma at least looking both in glomerular
03:48:29endothelial cells as well as in podocyte.
03:48:33Gamma has the strongest effect in induction,
03:48:37followed by beta, followed by Alfred,
03:48:40and I should say that this actually
03:48:43mimics or confirms what David Friedman
03:48:47shown several years ago when he used
03:48:51immortalized podocytes that interferon
03:48:53gamma for whatever reason has a
03:48:57strongest effect.
03:48:58In in driving the April 1 expression
03:49:01and I also see a question about is
03:49:04there on the on the on the chat.
03:49:06Is there a well established
03:49:09protocol to organize culture
03:49:12directly from kidney tissues?
03:49:14Just like the way used in
03:49:17organic culture and cancer?
03:49:20Not that I know of.
03:49:21The organoid kidney organoid
03:49:24protocols have been more developed
03:49:27to go from I PSC's to the organoid.
03:49:30I'm not aware of a lot of robust protocol
03:49:34for from going from kidney proper.
03:49:39To the online.
03:49:53They identified with.
03:49:57Don't have yet have to do.
03:50:07In in your justice trial is
03:50:09there screening for asymptomatic
03:50:12people who may happen to have?
03:50:15These these risk allele high risk alleles.
03:50:19So the the the justice trial is
03:50:22only focusing on people who have
03:50:24disease who have kidney disease.
03:50:26But we have a preceding study,
03:50:28we call it darab.
03:50:30Duke April 1 research by repository
03:50:32where we actually recruit.
03:50:34Both people with disease as well
03:50:36as healthy volunteers and among
03:50:39the edible healthy volunteers.
03:50:41We identify people without kidney
03:50:43disease with no proteinuria
03:50:45and who are age 50 and older.
03:50:47What we are doing.
03:50:48The goal of that is to actually
03:50:50answer the question you are asking.
03:50:52If we identify people who are
03:50:54healthy carriers and the preliminary
03:50:57data from earlier studies,
03:51:00what type I showed in the.
03:51:04In the heat map,
03:51:05we hope to identify enough number of
03:51:08healthy carriers and compare their
03:51:11transcriptomic and genomic information
03:51:13to identify potential modifiers.
03:51:16So that's a great question
03:51:17and we are working on it.
03:51:23Any other questions?
03:51:27Alright, thank you very much.
03:51:28Doctor would be here for a
03:51:31wonderful talk. I got it.
03:51:43Well, just wanted to thank all the
03:51:45speakers for really a wonderful
03:51:47set of talks today and especially
03:51:49thank shuda for organizing and
03:51:51and knowing about who to invite.
03:51:52If you've been left to me we would
03:51:54have had five talks on proximal tubule
03:51:56acidification and I don't think
03:51:58anybody would been interested in that.
03:52:00So thank you. Shoulda thanked and
03:52:02prodotti for helping organize everything.
03:52:04Kyle for the Technical Support
03:52:06and thank the audience for their
03:52:08attention and excellent questions,
03:52:10and see everybody next year, hopefully.
03:52:13Bye bye.