Second Look - Change Maker Talk by Akiko Iwasaki

Name: Second Look - Change Maker Talk by Akiko Iwasaki
Uploaded: 2021-04-07T20:45:37.803Z
Duration: 38 min 31 s

April 07, 2021

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ID: 6387
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00:05So hello everyone this morning.
00:07I have the privilege of hosting one of the
00:11three concurrent changemaker faculty talks,
00:13and after I introduce our invited speaker,
00:15I will ask that if you have any questions,
00:18submit them directly to me via the Zoom chat
00:21function when the presentation is finished,
00:24I'll read the posted questions allowed
00:26and then Doctor Wysocky will address
00:28as many questions as time will permit.
00:30So today I have the honor of
00:33introducing Doctor Akiko Iwasaki,
00:34who is a distinguished physician
00:36scientist in Yale School of Medicine.
00:38Faculty member.
00:39Who will be presenting on the
00:41topic understanding covid immunity
00:43in real time during the pandemic?
00:45So this is extremely topical.
00:47It probably can get any more topical
00:49than this Professor Masaaki has
00:51made major discoveries in innate,
00:53antiviral,
00:54and mucosal immunity that have
00:56resulted in paradigm shifts in the
00:58understanding of the immune response of
01:01pathogens as well as in vaccine design.
01:03So her research focuses on the
01:05mechanisms of immune defense against
01:07viruses at the mucosal surfaces.
01:09Which are a major site of entry for
01:12infectious agents, as we all now know,
01:15the knowledge gained in her lab can be
01:17used to design more effective vaccines
01:19or microbicides to prevent transmission
01:21of viral and bacterial pathogens.
01:23Her research group also developed a
01:25new vaccine strategy termed cryman
01:27pull that can be used to treat
01:29those with infected with the virus.
01:31Unlike many vaccines that are
01:33given preventatively,
01:34so it's under phase two
01:35clinical trials right now.
01:37This particular method for the treatment
01:39of high grade cervical lesions caused by.
01:41HPV so you have Doctor Sakis Bio
01:44in your booklets that you received.
01:46You also have her title of her talk and
01:49then a description of her talk as well,
01:51but for those of you who are in or
01:54off Doctor Osaki whose name we see
01:56quite often in the Yale News articles
01:59in the Yale School of Medicine,
02:01articles that come every week,
02:03I just like to say that despite
02:05this really impressive background
02:06on a personal level,
02:08Doctor Wysocky is extremely humble,
02:09very approachable,
02:10and just a very generous human being.
02:12So with that.
02:13And without further ado,
02:15I'd like to welcome Doctor Masaki.
02:18Thank you so much Doctor Fernando,
02:20for that very kind introduction and I'm
02:23really delighted to be here with you today.
02:26I see your faces on some of your
02:28faces and normally we would do
02:31this in person and I missed that.
02:33But hopefully next year we will
02:35be doing this in person anyway.
02:38Really welcome to Yale Medical School
02:40and congratulations on your admittance.
02:42It's really a great honor to for
02:44me to introduce my research and
02:47sort of highlight some of the.
02:49Key aspect of yell and why it makes
02:52it so great for me to be here,
02:55so just this slight background.
02:57I'm a professor of Immunobiology.
02:59I've been here at Yale for 20
03:01years and I've had my lab here and
03:04ever since I became a faculty and
03:07it's been wonderful to be here.
03:09It's a very collegial place.
03:11I like numerous collaborations with
03:13so many people across the school,
03:15not just medical school,
03:17but you know, school,
03:18Arts and Sciences and everything else.
03:21And public health as well.
03:22I'm going to highlight that today.
03:25And just wanted to kind of highlight
03:28the rigor of science here that combines
03:32basic insights and clinical medicine
03:34together that makes this place a
03:37really unique place to study and work.
03:40So I'm going to start talking and
03:44would really welcome your questions.
03:47Alright. OK, so can you see this?
03:54Yes. OK, perfect thank you.
03:56So today I'm going to share with you
03:59our very recent work on studying
04:01immune response to COVID-19 and
04:03it really happened in real time.
04:06And I'll show you how that went.
04:10Oh, by the way, I'm a basic scientist.
04:13I'm a PhD scientist,
04:15but we really have this very tight
04:17collaboration with people from my eyes.
04:20As I mentioned from all parts of the
04:24University that came together to try to
04:28understand immune response in real time.
04:31So one of the key feature of
04:34COVID-19 disease is that it's
04:36very heterogeneous in its outcome,
04:39so unlike a common cold which
04:41has very limited sort of outcome,
04:44mostly mild infection, respiratory syndromes,
04:47what we're seeing with COVID-19 is
04:49a very huge variety of outcomes,
04:52from asymptomatic to severe,
04:54to lethal cobin.
04:55And some people have acute disease
04:58while others have very long
05:01term symptoms for long haulers.
05:03And today I'm going to cover three
05:06different topics around this energy
05:08and 81st is to really dig into the
05:11mechanism of covid acute covid infection
05:14that results in moderate to severe diseases.
05:192nd,
05:20I'm going to focus on *** differences
05:23in immune response to this virus,
05:25and 3rd,
05:26I'm going to talk about the some
05:29of the key possible ways of getting
05:32longer symptoms from this virus.
05:37So I'd like to start with this
05:40slide because it demonstrates the
05:43fact that every cell in our body
05:46has a way of sensing viruses,
05:49and these are receptors that detect features.
05:52The virus, known as pattern
05:54recognition receptors,
05:55and Yale is really the 1st place that
05:58found pattern recognition receptor an.
06:01It's linked to adaptive immunity,
06:03so these receptors are either expressed
06:06on the endosomal surface or on the.
06:09Plasma membrane surface and detect unique
06:11features associated with the virus,
06:13such as the RNA or DNA within the
06:16endosome and there are also these
06:19cytosolic sensors regay in MDA
06:21five that detect cytosolic RNA
06:23features that are unique to virus,
06:26such as the five prime triphosphate arning.
06:30Once these sensors detect the
06:32presence of virus through these
06:34unusual features of the virus,
06:37it they signal to induce
06:39interferon responses.
06:40Interference come in many flavors.
06:42The Alphas betas, gammas,
06:44lambdas and so on,
06:46but ultimately what they do is they
06:49bind to neighboring cells through the
06:51receptor to induce signals that will
06:54ultimately induce expression of hundreds,
06:57if not thousands of genes.
07:00That all collectively control
07:01the virus replication,
07:03and so this is how we are
07:06protected against viruses.
07:07However, the viruses are also very ingenious.
07:11They encode their own proteins to
07:13block both the innate sensing as
07:16well as signaling pathway as well as
07:19even interference singling pathway.
07:21So all the genes that are indicated in
07:24the boxes are actually the virus genes
07:28that encode proteins that interfere
07:30specifically with these pathways.
07:33So virtually all infectious viruses
07:35that you find that are successful
07:37in replicating in human cells.
07:40Have they figured out this entire
07:42process and have already countered them?
07:44So that is why we suffer from
07:47virus infections because of their
07:49ability to escape these innate
07:51detection and resistance mechanisms.
07:55Another thing is that it's not just the
07:59innate immune resistance that protects us.
08:01We also have the adaptive immunity,
08:04which is what gives us sort
08:07of long term protection,
08:08antigen specific virus specific
08:10protection throughout our lives,
08:12and that is all the basis of vaccines.
08:15And normally how an adaptive
08:17response develops is through our
08:20naive lymphocytes such as T cells
08:22indicated here that are specific to.
08:25Numerous different types of pathogens
08:27when there are stimulated with
08:30an issue in presenting cells,
08:32they are instructed to become certain
08:34types of cells that are indicated here.
08:37So you know,
08:38a T cell might become AT
08:41follicular helper cell.
08:42TH two cells that are great at
08:45countering parasite infections.
08:46TH one sells for viruses and bacteria
08:50TH 17 cells for fungi in it.
08:52Rags for dampening the hyper immune
08:55responses.
08:56They are instructed because the
08:58dendritic cells have sensed the arnad
09:00the virus through its innate sensors
09:02and knows what kind of cytokinins
09:04to instruct the city sells with.
09:06But what I'm going to show you today is
09:09that this program does not hold for covid.
09:12We actually see all of these things
09:14being engaged in severe covid.
09:18So this all began last March by
09:22establishing this biorepository
09:23called Impact Yale and this was
09:26headed by Professor Albert Co.
09:28Of school public health.
09:30He and a number of us got together
09:34in March to preemptively decide
09:36to collect patient samples.
09:39Lanja Tude only overtime so that
09:42we can study their immune response
09:45in viral dynamics and so on.
09:48And this is was really amazing of
09:51Doctor Kohs part because at the
09:53time we met there was no cases in
09:55Connecticut and we were thinking,
09:58well this repository.
09:59Hopefully you know it.
10:00Not going to be that useful because
10:02we won't have that many cases,
10:04but of course we had so many
10:06cases and so many deaths,
10:07and it was just amazing insight that
10:10Doctor Goh had to do this because
10:12he's very used to other pandemics.
10:14In any case,
10:16we recruited patients as well as
10:19health care workers to collect various
10:23biospecimen from them overtime,
10:25including blood, saliva, urine,
10:28feces, and variety of other samples.
10:32And so,
10:33using this repository we've
10:35done so many analysis.
10:36So the first story I'm going to
10:38tell you is a longitudinal immune
10:41phenotyping in the covid patient,
10:43and this was led by Carolina Lucas,
10:46Patrick one, John Kline and Tiago Castro.
10:51The way in which we did this study was to
10:54recruit patients who had moderate disease,
10:58severe disease, or the health
11:00care workers who were uninfected.
11:02An we subdivided the moderate
11:04and severe patients further into
11:06six different clinical scores,
11:08depending on the oxygen supplemental
11:10oxygen is that they require during
11:13the hospital stay, one being none,
11:15and for being more than 680 per minute
11:18and five being mechanical ventilation an.
11:216 being deaf.
11:25So we collected various samples and
11:28started analyzing the cells that
11:30are found in the peripheral blood.
11:33And the first thing we notice was a quite
11:36a dramatic drop in the T cell count.
11:39So in green I'm showing
11:41you health care workers.
11:43These are the healthy controls.
11:45And then the Navy, an in pink.
11:48I'm showing you people who have moderate
11:51or severe disease respectively and
11:53you'll see that the T cell numbers are
11:56really drop even in moderate cases.
11:59An inversely,
12:00we started seeing things like
12:02inflammatory monocytes coming
12:03up and also these so called low
12:06density granulocytes which are
12:08neutrophils and eosinophils that
12:09come into the P BNC fraction and
12:12this is becoming elevated over time.
12:14And in addition we saw all kinds of
12:17cytokines and key mykines and antiviral
12:20factors such as the interferon
12:22that I introduced you earlier.
12:24They all coming up in in covid patients.
12:30So when we sum up all the analysis
12:32in the peripheral blood that we did,
12:35basically what we found was a
12:37drop in the T cells,
12:39not so much impact on Anki or B cells,
12:42but an increase in monocytes drop in
12:44dendritic cells and increase in neutrophils.
12:47And your sense feels these are
12:49the granular sites that come
12:51in the fraction as I mentioned.
12:55And so we dug into all this data
12:57and try to find out what makes
13:00some people have severe versus
13:02moderate disease and we found was
13:05one factor that was very useful in
13:07determining their disease course,
13:09which is the viral titer that we
13:11find in the nasopharynx and in
13:14the severe cases we see that the
13:16viral load stays up all the time.
13:19You know, regardless of the
13:21number of days from symptom onset.
13:23Whereas people with moderate disease,
13:25they were ultimately able to control the
13:29virus which are in this Navy color here.
13:32And so further we looked at what
13:34correlates with the viral load and
13:36found that kind of ironically,
13:38these antiviral factors that are
13:40supposed to dampen the virus load is
13:43actually being elevated by the viral load.
13:45So if you look at the viral load on the X
13:49axis and the amount of cytokines that we see,
13:52such as interferon,
13:53Alpha, gamma,
13:54TNF, and trail,
13:55you see that there is a positive
13:58correlation between the amount of virus
14:00and the amount of cytokines would be fined.
14:03So unfortunately,
14:04what's happening here is that the interferons
14:07are not preventing viral replication,
14:09but rather being driven by
14:13the virus in these patients.
14:16We took this one step further and
14:18asked what happens to these cytokine
14:21levels in patients who die of this
14:23infection within the 1st 12 days
14:26from symptom onset and what we find
14:28is that these patients who died of
14:31infection have elevated levels of
14:33these antiviral cytokine interferon
14:35A2 as well as our one receptor
14:38antagonist which is a indicative that
14:40there is a one receptor signaling
14:43going on in these patients.
14:45So you know,
14:46within the 1st 12 days of symptom onset,
14:49these patients are getting elevated
14:52levels of these cytokines.
14:54And if you look all across the board
14:57for different types of cells and cytokines,
15:00what we found,
15:01as I mentioned earlier,
15:03is that all the types of immune responses,
15:06whether it be anti viral type one and
15:10type parasitic type 2 or antifungal type 3.
15:13All of these are becoming elevated
15:15in the severe patients and we see
15:18even things like Ige which one
15:20would associate with an allergic
15:23reaction is becoming more available.
15:25In these severe patients,
15:27whereas if it's maintained at the
15:30same level in the moderate patients.
15:34So when we took these,
15:36all the plasma cytokines and asked
15:38if there is any pattern associated
15:40with these plasma levels.
15:42We found that there are three
15:44clusters of patients,
15:46so each column here is a patient
15:48and the darker the color,
15:50the more presence of that site account
15:53there is in that persons plasma.
15:56And we saw that there are three clusters
15:59of patients at the first cluster,
16:01patient had the highest enrichment.
16:03In the factors that are listed here,
16:06these are sort of tissue repair
16:09growth factors such as PDF, AGF,
16:11veg F, as well I'll 7,
16:14which is a lymphocyte growth factor
16:16and 2nd cluster of patients had,
16:19in addition, key mykines that are listed
16:22here as well as mixed cytokines type 123.
16:26And the third cluster patient had
16:29the most intense levels of type 2-3
16:32cytokines as well as mixed cytokines.
16:35So what do these clusters mean?
16:39It when you follow these patients over time,
16:43what we see is that the cluster want
16:47patients were able to control the virus,
16:50the clinical score and ultimately
16:53became discharged from the hospital,
16:55whereas cluster two and three patients
16:58they have increasing clinical score
17:01overtime and had the most frequent
17:04Coagulopathy and mortality among the groups.
17:07So. And then we asked what is a
17:10biomarker for COVID-19 mortality,
17:14and what we found,
17:15was this factor cytokine known as I'll 18,
17:19which is basically it released as a
17:22result of activation of this highly toxic
17:26inflammatory complex known as inflammasomes.
17:28Followed by the Interferon
17:30Alpha and I'll 10 and so on.
17:33So these really let us to the view that
17:37severe COVID-19 in lethal kovit is really
17:40driven by these kinds of features of
17:43immune responses that are associated
17:46with quite an inflammatory response.
17:51We had all these like each patient had
17:54more than 1000 different things being
17:57analyzed parameters and so we reached
18:00out to my long term collaborator,
18:02Doctor Smith, a Christian swami,
18:05and her team to find out if there's
18:08any way of assigning the importance
18:11with respect to mortality from this
18:14disease and so of course, they said yes.
18:17Let's collaborate and after a
18:19few months of collaboration, we.
18:22Found that through the the new
18:25tool that her lab generated,
18:27which is called multiscale fate.
18:31Her group was able to find out,
18:34assign different scores of importance
18:37for mortality with for kobid.
18:40And this was really remarkable.
18:42An eye opening to me, because,
18:44you know,
18:45we've been sort of looking at all
18:48these hundreds of different parameters
18:50and what her team basically found
18:53was that the highest mortality score.
18:56So this mortality is really indicates
18:58the importance of a particular
19:01cell type for contributing for
19:03Association with mortality.
19:05And you see that these neutrophils
19:07and the osino fills that I've
19:09been telling you about are the
19:12most associated with mortality,
19:13followed by the inflammatory
19:15monocytes followed by B cells,
19:17and that's an interesting thing.
19:19We can talk about that offline
19:21someone sometime, but it's B cells.
19:23Why are they associated with mortality?
19:25Very good question,
19:26and if you look into each B cell clusters
19:30and T cell subsets and cytokines,
19:32they were able to kind of
19:34associate mortality score.
19:35For each of these things in a relative term,
19:39and that's what kind of
19:40really eye opening to me was.
19:42This assigning of mortality
19:45for these granulocytes?
19:47So to conclude,
19:48this first part of this story.
19:50Basically what we found was that
19:53persistent viral load is found in the
19:55severe covid patients and this viral
19:58load is what's driving these anti.
20:00Carol cytokines in these antiviral
20:02cytokines are associated with mortality,
20:05which is very ironic because
20:07they're supposed to help us fight
20:09off the viral infection,
20:11but I think what's going on is that
20:14this virus is figure out a way to
20:17completely block the effect of this
20:20antiviral cytokine for antiviral activity,
20:23but still remaining is the ability of
20:26these cytokines to trigger inflammation.
20:29And as I mentioned this,
20:31there seems to be this kind of a
20:35maladaptive immune response to the
20:37virus as all arms of the immune
20:40responses are being engaged.
20:43So.
20:45I'm going to move on to the
20:47second part of the story,
20:49which has to do with ***
20:51differences in covid immunity.
20:52This was led by Takahiro Takahashi
20:55and others that are listed here,
20:56and again,
20:57it's a very,
20:58very collaborative study that we did.
21:02Here what we the reason we wanted
21:05to look into the *** differences is
21:08that we knew from other infectious
21:10diseases that *** is a important
21:13biological factor that controls
21:15immunity and disease process.
21:17Anne,
21:17we knew back in March of last
21:20year that a male *** is a risk
21:24factor for severe to lethal covid,
21:26so we wanted to understand whether
21:29immune responses are generated
21:30differently. And so for this,
21:33we performed a baseline analysis
21:35looking at immune responses from
21:38the first time sampling of patients
21:41that are not in the ICU and who are
21:44not treated with immunomodulatory
21:46drugs like steroids or tocilizumab.
21:48And so in this first cohort cohort a,
21:51we had 17 men and 20 two women and
21:54we also recruited healthy health
21:56care workers and we extended
21:58the analysis to a larger cohort
22:01to to look at *** differences.
22:05First of all,
22:06when we looked in the cohort 8 patient,
22:10the male patients indicated in yellow.
22:13In female patients indicated in a purple,
22:17there was no significant difference
22:19in the level of the virus in the
22:23nasopharyngeal or the saliva between
22:25the sexes and also we didn't see
22:29a significantly different levels
22:31of antibodies against this spike.
22:33One region of the spike protein
22:37in male and female patients.
22:40We also saw a whole bunch of
22:42factors that are elevated in both
22:44sexes equally that included CCL 5,
22:46which is schema kind as well as seek seal 10.
22:50Another interferon driven chemo
22:51kind which is really one of the
22:54highest induced factors you find
22:55in the plasma of these patients,
22:58but they're equally elevated
23:00between male and female.
23:02When we looked into what might be
23:05different between female and male here,
23:07what we found were two innate
23:10cytokines violate,
23:11which is a chemotactic factor
23:13for neutrophils.
23:13And I'll 18, which is,
23:16as I mentioned,
23:17the top mortality factor for Covid was
23:20elevated in Mail than over female.
23:25And with respect to different cell
23:27types be found that these long classical
23:30inflammatory monocytes the the number 2
23:33cell type associated with mortality from
23:36Smith's analysis was elevated in Mail.
23:38And Interestingly there is a correlation
23:41between the plasma level of CCL 5 at
23:45chemotactic factor for these cells and the
23:48number of these nonclassical monocytes
23:50in the blood only in the Mail but.
23:54Not in a female.
23:57Conversely, we saw more elevated
24:00activated T cells in the female
24:03patients over male patients here,
24:06and that's holds true for CD4T cells
24:10and CD8T cells as well as this other
24:15measures of activation for city 80 cells.
24:19So we had these differences in male
24:22and female at the baseline analysis
24:24and we wanted to know what these
24:27differences mean over disease course.
24:29And so we extended this analysis to
24:32look for what happens in these patients
24:35who had different levels of these
24:38key mykines or cell types overtime.
24:41And what we found was quite remarkable.
24:44So I mentioned to you that female patients
24:47had elevated T cell activation of baseline,
24:50and if you follow these patients over time,
24:53the male patients who had very little
24:56T cell activation were the ones that
24:59went on to develop worse disease.
25:02And so again,
25:03this orange is male and purple as female.
25:06But now we're dividing them into
25:09those that develop worse disease.
25:11Which of the filled circles versus
25:13those that are have stable disease?
25:16And this held true for different
25:19activation markers?
25:20For CD8T cells,
25:21an interferon gamma secretion
25:23from CD8T cells.
25:25So it appeared to us that if you don't
25:28develop T cell activation early on you
25:31that the males who don't develop these
25:34responses go on to develop worse disease.
25:37Whereas in the female we generally see first
25:40of all active higher activation of T cells,
25:43an no difference between these groups.
25:47Conversely,
25:48in the female patients,
25:49what appeared to correlate with
25:52the worst disease outcome are the
25:55innate cytokines such as trail
25:57CCL 5 and I'll 15 listed here.
26:00And kind of remarkably,
26:02if you look over the different
26:05age group and their ability to
26:07stimulate CD8T cells or secrete
26:10interferon gamma from CD8T cells,
26:13we saw age dependent decline in
26:15the male patients but not in the
26:19female female patients indicating
26:21there's something that happens
26:23overtime in Mail during aging that
26:26is impairing the T cell activation.
26:29Where is that's not happening in the
26:32women that are that are measured here.
26:36So to conclude,
26:37the *** difference in immunity part,
26:39we found that men develop more inflammatory
26:42cytokines than women at baseline.
26:45And women develop better T cell
26:47activation than men and men who
26:50developed very little T cell activation
26:52went on to develop worse disease.
26:55Women who developed inflammatory
26:56cytokines had worse disease trajectory.
26:59Anan.
26:59Finally,
27:00women appear to age better with respect
27:03to their T cell immunity than men.
27:06So there's a lot of different
27:09factors going on,
27:10but now we have so many other
27:13studies that are highlighting
27:15*** differences in infection and
27:18immunity with covid everything,
27:20including the receptor expression,
27:22innate signaling,
27:23as well as cytokine release,
27:25and different cell types that are involved.
27:28And we were able to contribute to
27:31some of these insights in this study.
27:35And ultimately we really need to.
27:38Study *** differences and immunity to
27:41infection and vaccines going forward
27:43in order to better understand what,
27:45what.
27:46What is the best therapy or
27:49preventative mechanism for
27:50men and women?
27:53So in the last part of my talk I want to
27:57I want to highlight an unpublished study,
28:01which is a done in close collaboration
28:04with Professor Iron Ring,
28:06who is a assistant professor in the
28:09Immunobiology Department where I
28:10belong and his trainees, Eric and Lyle,
28:13as well as my students, young,
28:16Young and John all collaborated together
28:19to look for autoantibodies in covid.
28:23So COVID-19 as I mentioned,
28:26causes both acute and long term symptoms,
28:30and this long lasting symptoms
28:32involve multiple organ systems,
28:35including sort of a systemic symptoms.
28:38Neuro, psychiatric symptoms, cardiovascular,
28:41pulmonary, gastrointestinal.
28:42Essentially,
28:42there were over 110 different symptoms
28:46that have been reported for a long,
28:50long covid.
28:52We wondered why so many organs are
28:55involved after a respiratory infection
28:57in one of the hypothesis that we had.
29:01Was that perhaps there is an autoimmune
29:04diseases that develop in these patients.
29:08So Doctor Rings Laboratory has
29:10developed this really cutting
29:12edge technology called reap rapid
29:15extracellular antigen profiling
29:17in which he Islam developed this
29:21comprehensive use display library
29:23expressing human EXO proteome,
29:25which is a collection of proteins
29:29that are either extracellular or sick,
29:32created and they have over 3000
29:36different exept rhodium.
29:38That are expressed on the surface
29:40of the East,
29:41and we can simply use this library to
29:44pull down the the yeast that's bound by
29:48antibodies that are present in the patient,
29:51and then we can decode what is being
29:54bound by using deep sequencing.
29:56Because each of these are
29:59marked with genetic barcode.
30:01And so using this technology we were
30:04able to profile extracellular antigen
30:07specific antibodies in the COVID patients.
30:12And the results were remarkable because
30:15we found over 100 different auto antigens
30:19that are detected by antibodies found
30:22in covid patients that we studied.
30:26And here each column again is a patient.
30:29And the brighter the yellow,
30:32the more autoantibody they have
30:34against the particular antigen,
30:36which is indicated on the each row.
30:40And so this top.
30:43Rectangle here on the left
30:45really demonstrates this.
30:47Quite a striking presence of
30:49autoantibody against interference.
30:51The ones that I've been telling you
30:53is supposed to be anti viral and
30:57essentially all of these patients had
30:59interferon specific antibody across all
31:02these interferon Alpha types as well as.
31:06So the left columns here are
31:09more severe patients.
31:10The middle one is the moderate,
31:13mild asymptomatic, an negative control.
31:16And you see that these yellow ones
31:19are really found in the severe and
31:21maybe a few in the moderate group.
31:24And there are also.
31:25We also found antibodies to cell
31:27surface molecules expressed
31:28by all kinds of immune cells,
31:30including NK cells, myeloid cells,
31:32B cells, T cells and so on.
31:36As well as key mocha against chemo,
31:39Kines and coagulations factors and
31:42inflammatory factors.
31:43So what does this mean?
31:45We think there is a functional
31:47relevance to having these autoantibodies.
31:50For instance,
31:51the auto antibodies to the
31:54antiviral interference appear to
31:56render the patients in unable to
31:59control the viral load over time,
32:01so in pink I'm showing you patients
32:05too hard auto anybody to interferon?
32:08And there are nasopharyngeal and
32:10saliva viral load over time compared
32:13to *** age and disease matched
32:16control who had no such antibody
32:19and those black or grey boxes.
32:21These patients are able to control
32:24the viral load over time zero,
32:26indicating that these not only do they
32:29have this neutralizing interferon antibody,
32:32but it's functionally blocking
32:34the ability of interferon to
32:36control the viral load over time.
32:41Another example is that the auto
32:43anybody against the cell surface
32:45molecules found on different
32:47lymphocytes such as B cells shown
32:50here have a functional consequence.
32:53So the patients with auto anybody to
32:56be cells had very low circulating
32:58diesel compared to the control.
33:01Which are these green dots here and
33:04as a consequence there they are in
33:08unable to Mount a antiviral antibody.
33:11Indicated again in pink here,
33:13compared to the green control here.
33:16So these are any bodies are
33:18not only blocking the ability
33:21of different cytokines to act,
33:23but also depleting key immune subsets
33:26such as B cells and other cell types
33:29that are there to control the viral
33:32replication and recover from this infection.
33:36In addition to these immune factors,
33:38we found a whole bunch of auto antibodies
33:41against tissue specific antigens that
33:43are expressed by different organs,
33:46the brain, the vasculature,
33:47connective tissue,
33:48hepatic cardiac, and so on.
33:50So this sort of raises that originally
33:53a sort of hypothesis I made that
33:56perhaps some of these long term
33:58symptoms that we see in the patients
34:01are being driven by autoantibodies
34:03and the specificity dictates the
34:05kind of tissue involvement that
34:07we see in these patients.
34:11So in this part, I conclude that
34:14there are diverse autobio any
34:16bodies found in covid patients.
34:19In fact, the extent of the
34:21autoantibody is comprable,
34:23if not greater than the lupus patients,
34:26which is pretty remarkable.
34:27We also know that somebody anybody's
34:30or pre-existing while authors
34:32develop during the course of disease.
34:35And we show that auto anybody
34:36to interfere and impair viral
34:38clearance and auto anybody to
34:40leukocytes to please cell subsets
34:41such as bsal which assures you.
34:43But we also have the same data for T cells,
34:46monocytes, NK cells.
34:48An auto anybody suggest you
34:50specific conditions may contribute
34:52to organ specific dysfunction.
34:55So I want to end by kind of highlighting
34:58this amazing collaborative team
35:00we met in March 2nd of 2020 as
35:04one of the first impact meeting
35:06and I already highlighted Doctor
35:08Co from the public health.
35:10But I also this amazing other colleagues
35:13that I want to highlight which is Doctor
35:17Nathan Grubaugh also at the public
35:19health and he is a molecular virology Wiz.
35:23He's been sort of sequencing viral genomes.
35:26Across the you know,
35:27hospital,
35:28the community and everything and
35:30essentially on a real time basis letting
35:33us know what variants are out there
35:35in the Community at every single day.
35:38And it's been so wonderful to collaborate
35:41with his group to try to figure out
35:44how the variance are interfering with
35:47the immune response to this virus.
35:49Doctor Saad Omer is a director of
35:52Yelm Institute for Global Health.
35:55Has been also instrumental,
35:57he's collaborated on every single
36:00papers on this patient analysis that
36:02we've done because his team really
36:05brings this insight of the sort of
36:08statistical power to the studies.
36:10And Doctor Ellen Foxman from
36:13love medicine and in a new bio.
36:16It's been an amazing sort of
36:18communicator as well as investigator
36:20throughout this pandemic.
36:22She's really been out there
36:24just as much as I am, you know,
36:28communicating science to the public,
36:30which is incredibly important in this.
36:33In this pandemic and Doctor Marie Laundry,
36:36she's really well respected.
36:38Biologist who really stepped
36:39up from the very beginning.
36:42To help coordinate not only the sort
36:44of the clinical sample analysis,
36:46but also the research side of things.
36:49So this was a historic moment for me.
36:52You know,
36:53that started this whole thing.
36:55And I also want to highlight these
36:58four individuals who are who formed
37:01a group called Spike support in
37:03the back story is that you know
37:06I teach the medical student every
37:08year immunology course and at the
37:10end of it they were so thrilled
37:13by the immunology in the vaccine
37:15efficacy and so on that they formed
37:18this sort of data analysis group,
37:21where they are really every day
37:23looking at vaccine efficacy data.
37:25Variance an antibody levels and so
37:27on to be able to communicate to the
37:30public the importance of these findings,
37:33what it means.
37:34And they've done seminars.
37:35They've done all kinds of engagement,
37:38and I'm fortunate to be able
37:40to collaborate with this
37:42team. Ann just highlights like this.
37:44Sort of organic nature of how my
37:46teaching has led to, you know,
37:49this group forming an. And now we
37:52are collaborating on a daily basis.
37:54So many things happen here.
37:57And finally, I want to thank all the
38:00people that were involved from my lab,
38:02the impact team and many others.
38:04I didn't have a chance to
38:06acknowledge and thank which are
38:08listed in the authorship list here,
38:10so I'm delighted to take any questions
38:13you have and thank you for listening.
38:17Thank
38:17you so much Doctor Wysocky,
38:18that was a terrific presentation,
38:20so I'm going to stop the
38:22recording and open it up for Q&A.