Biology subjects

Vora, A. A.

Publications and source records attributed to Vora, A. A..

4 recordsLinked to original sources

bioRxiv2025

Inference of germinal center evolutionary dynamics via simulation-based deep learning

B cells and the antibodies they produce are vital to health and survival, motivating research on the details of the mutational and evolutionary processes in the germinal centers (GCs) from which mature B cells arise. It is known that B cells with higher affinity for their cognate antigen (Ag) will, on average, tend to have more offspring. However, the exact form of this relationship between affinity and fecundity, which we call the "affinity-fitness response function", is not known. Here we use deep learning and simulation-based inference to learn this function from a unique experiment that replays a particular combination of GC conditions many times. All code is freely available at https://github.com/matsengrp/gcdyn, while datasets and inference results can be found at https://doi.org/10.5281/zenodo.15022130.

evolutionary biology↗

bioRxiv2025

Nucleotide context models outperform protein language models for predicting antibody affinity maturation

Antibodies play a crucial role in adaptive immunity. They develop as B cell receptors (BCRs): membrane-bound forms of antibodies that are expressed on the surfaces of B cells. BCRs are refined through affinity maturation, a process of somatic hypermutation (SHM) and natural selection, to improve binding to an antigen. Computational models of affinity maturation have developed from two main perspectives: molecular evolution and language modeling. The molecular evolution perspective focuses on nucleotide sequence context to describe mutation and selection; the language modeling perspective involves learning patterns from large data sets of protein sequences. In this paper, we compared models from both perspectives on their ability to predict the course of antibody affinity maturation along phylogenetic trees of BCR sequences. This included models of SHM, models of SHM combined with an estimate of selection, and protein language models. We evaluated these models for large human BCR repertoire data sets, as well as an antigen-specific mouse experiment with a pre-rearranged cognate naive antibody. We demonstrated that precise modeling of SHM, which requires the nucleotide context, provides a substantial amount of predictive power for predicting the course of affinity maturation. Notably, a simple nucleotide-based convolutional neural network modeling SHM outperformed state-of-the-art protein language models, including one trained exclusively on antibody sequences. Furthermore, incorporating estimates of selection based on a custom deep mutational scanning experiment brought only modest improvement in predictive power. To support further research, we introduce EPAM (Evaluating Predictions of Affinity Maturation), a benchmarking framework to integrate evolutionary principles with advances in language modeling, offering a road map for understanding antibody evolution and improving predictive models.

immunology↗

bioRxiv2025

Replaying germinal center evolution on a quantified affinity landscape

Darwinian evolution of immunoglobulin genes within germinal centers (GCs) underlies the progressive increase in antibody affinity following antigen exposure. Whereas the cellular mechanics of how competition between B cells produces increases in affinity are well established, the evolutionary dynamics of this process are less clear. We developed an experimental evolution model where we "replay" over one hundred monoclonal GC reactions, assigning affinities to each cell using deep mutational scanning. Our data reveal how GCs achieve predictable outcomes by means of noisy but persistent selection on an affinity landscape whose exploration is heavily constrained by somatic hypermutation biases. We infer a fitness landscape that quantitatively recapitulates the affinity maturation trajectory of our clone and find that apparent features of GC selection such as permissiveness to low-affinity lineages and rapid plateauing of affinity are likely artifacts of survivorship biases that distort our view of how B cell affinity progresses over time.

immunology↗

bioRxiv2024

Systemic deficits in lipid homeostasis promote aging-associated impairments in B cell progenitor development

Organismal aging has been associated with diverse metabolic and functional changes across tissues. Within the immune system, key features of physiological hematopoietic cell aging include increased fat deposition in the bone marrow, impaired hematopoietic stem and progenitor cell (HSPC) function, and a propensity towards myeloid differentiation. This shift in lineage bias can lead to pre-malignant bone marrow conditions such as clonal hematopoiesis of indeterminate potential (CHIP) or clonal cytopenias of undetermined significance (CCUS), frequently setting the stage for subsequent development of age-related cancers in myeloid or lymphoid lineages. At the systemic as well as sub-cellular level, human aging has also been associated with diverse lipid alterations, such as decreased phospholipid membrane fluidity that arises as a result of increased saturated fatty acid (FA) accumulation and a decay in n-3 polyunsaturated fatty acid (PUFA) species by the age of 80 years, however the extent to which impaired FA metabolism contributes to hematopoietic aging is less clear. Here, we performed comprehensive multi-omics analyses and uncovered a role for a key PUFA biosynthesis gene, ELOVL2, in mouse and human immune cell aging. Whole transcriptome RNA-sequencing studies of bone marrow from aged Elovl2 mutant (enzyme-deficient) mice compared with age-matched controls revealed global down-regulation in lymphoid cell markers and expression of genes involved specifically in B cell development. Flow cytometric analyses of immune cell markers confirmed an aging-associated loss of B cell markers that was exacerbated in the bone marrow of Elovl2 mutant mice and unveiled CD79B, a vital molecular regulator of lymphoid progenitor development from the pro-B to pre-B cell stage, as a putative surface biomarker of accelerated immune aging. Complementary lipidomic studies extended these findings to reveal select alterations in lipid species in aged and Elovl2 mutant mouse bone marrow samples, suggesting significant changes in the biophysical properties of cellular membranes. Furthermore, single cell RNA-seq analysis of human HSPCs across the spectrum of human development and aging uncovered a rare subpopulation (<7%) of CD34+ HSPCs that expresses ELOVL2 in healthy adult bone marrow. This HSPC subset, along with CD79B-expressing lymphoid-committed cells, were almost completely absent in CD34+ cells isolated from elderly (>60 years old) bone marrow samples. Together, these findings uncover new roles for lipid metabolism enzymes in the molecular regulation of cellular aging and immune cell function in mouse and human hematopoiesis. In addition, because systemic loss of ELOVL2 enzymatic activity resulted in down-regulation of B cell genes that are also associated with lymphoproliferative neoplasms, this study sheds light on an intriguing metabolic pathway that could be leveraged in future studies as a novel therapeutic modality to target blood cancers or other age-related conditions involving the B cell lineage.

cell biology↗