Anurupa Devi Yadavalli, PhD

I am interested in almost every question that deals with complex biological data involving B-cell development. During my Ph.D., I and my colleagues demonstrated that chromatin undergoes a dynamic switch to induce lineage-specific gene expression patterns. This study provided insights into how unique cis-regulatory networks are maintained across various B-cell stages and stood out as one of the first studies to report the dynamic nature of TADs (topologically associated domains). Later, I shifted my focus to implementing cutting-edge computational techniques and classification algorithms to identify transcriptional regulators that preserve B-cell identity.

Currently, at Schatz's lab, my research is focused on understanding mechanistic details involved in off-target Somatic Hypermutation (SHM). Antibody-mediated immunity relies on generating point mutations in rearranged immunoglobulin (Ig) loci of activated germinal center (GC) B cells. This process - called somatic hypermutation (SHM) - allows for antibody affinity maturation but also acts at certain non-Ig sites in the genome, thereby contributing to mutations and chromosomal translocations that drive B cell oncogenesis. The underlying mechanisms governing SHM targeting and mistargeting are poorly understood. In recent years, 3D genome architecture has emerged as a critical determinant in regulating gene expression and other cellular processes, and there is growing evidence for a role in SHM targeting. However, how 3D genome architecture cooperates with B cell transcription factors, epigenetic profiles, and the core transcriptional machinery to regulate SHM targeting and mistargeting remains unclear. I aim to achieve a comprehensive understanding of the multi-dimensional SHM landscape in different subsets of normal GC and oncogenic contexts by leveraging a state-of-the-art hybrid machine-learning approach that integrates various types of omics data.