Biology subjects

Chakraborty, A. K.

Publications and source records attributed to Chakraborty, A. K..

30 records · Page 2Linked to original sources

bioRxiv2022

Organization and regulation of nuclear condensates by gene activity

Condensation by phase separation has recently emerged as a mechanism underlying many nuclear compartments essential for cellular functions. Nuclear condensates enrich nucleic acids and proteins, localize to specific genomic regions, and often promote gene expression. How diverse properties characteristic of nuclear condensates are shaped by genome organization and activity is poorly understood. Here, we develop a physics-based model to interrogate this interplay between condensation, active transcription, and genome organization. We show that spatial clustering of active genes enables precise localization and de novo nucleation of condensates. We find that strong clustering and activity drives aspherical condensate morphologies. Condensates flow towards distant gene clusters and competition between multiple clusters lead to stretched morphologies and activity-dependent repositioning. Overall, our model predicts and recapitulates morphological and dynamical features of diverse nuclear condensates and offers a unified mechanistic framework to study the interplay between non-equilibrium processes, genome structure, and multicomponent condensates in cell biology.

biophysics↗

bioRxiv2022

Antigen presentation dynamics shape the response to emergent variants like SARS-CoV-2 Omicron strain after multiple vaccinations with wild type strain

The Omicron variant of SARS-CoV-2 evades neutralization by most serum antibodies elicited by two doses of mRNA vaccines, but a third dose of the same vaccine increases anti-Omicron neutralizing antibodies. By combining computational modeling with data from vaccinated humans we reveal mechanisms underlying this observation. After the first dose, limited antigen availability in germinal centers results in a response dominated by B cells with high germline affinities for immunodominant epitopes that are significantly mutated in an Omicron-like variant. After the second dose, expansion of these memory cells and differentiation into plasma cells shape antibody responses that are thus ineffective for such variants. However, in secondary germinal centers, pre-existing higher affinity antibodies mediate enhanced antigen presentation and they can also partially mask dominant epitopes. These effects generate memory B cells that target subdominant epitopes that are less mutated in Omicron. The third dose expands these cells and boosts anti-variant neutralizing antibodies.

immunology↗

bioRxiv2022

Design of immunogens for eliciting antibody responses that may protect against SARS-CoV-2 variants

The rise of SARS-CoV-2 variants and the history of outbreaks caused by zoonotic coronaviruses point to the need for next-generation vaccines that confer protection against variant strains. Here, we combined analyses of diverse sequences and structures of coronavirus spikes with data from deep mutational scanning to design SARS-CoV-2 variant antigens containing the most significant mutations that may emerge. We trained a neural network to predict RBD expression and ACE2 binding from sequence, which allowed us to determine that these antigens are stable and bind to ACE2. Thus, they represent viable variants. We then used a computational model of affinity maturation (AM) to study the antibody response to immunization with different combinations of the designed antigens. The results suggest that immunization with a cocktail of the antigens is likely to promote evolution of higher titers of antibodies that target SARS-CoV-2 variants than immunization or infection with the wildtype virus alone. Finally, our analysis of 12 coronaviruses from different genera identified the S2 cleavage site and fusion peptide as potential pan-coronavirus vaccine targets. Author SummarySARS-CoV-2 variants have already emerged and future variants may pose greater threats to the efficacy of current vaccines. Rather than using a reactive approach to vaccine development that would lag behind the evolution of the virus, such as updating the sequence in the vaccine with a current variant, we sought to use a proactive approach that predicts some of the mutations that could arise that could evade current immune responses. Then, by including these mutations in a new vaccine antigen, we might be able to protect against those potential variants before they appear. Toward this end, we used various computational methods including sequence analysis and machine learning to design such antigens. We then used simulations of antibody development, and the results suggest that immunization with our designed antigens is likely to result in an antibody response that is better able to target SARS-CoV-2 variants than current vaccines. We also leveraged our sequence analysis to suggest that a particular site on the spike protein could serve as a useful target for a pan-coronavirus vaccine.

immunology↗

bioRxiv2021

Multiscale affinity maturation simulations to elicit HIV broadly neutralizing antibodies

The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications. Author Summary Vaccination has saved more lives than any other medical procedure, and the impending end of the COVID-19 pandemic is also due to the rapid development of highly efficacious vaccines. But, we do not have robust ways to develop vaccines against highly mutable pathogens. For example, there is no effective vaccine against HIV, and a universal vaccine against diverse strains of influenza is also not available. The development of immunization strategies to elicit antibodies that can neutralize diverse strains of highly mutable pathogens (so-called broadly neutralizing antibodies, or bnAbs) would enable the design of universal vaccines against such pathogens, as well as other viruses that may emerge in the future. In this paper, we present an agent-based model of affinity maturation - the Darwinian process by which antibodies evolve against a pathogen - that, for the first time, enables the in silico investigation of real germline nucleotide sequences of antibodies known to evolve into potent bnAbs, evolving against real amino acid sequences of HIV-based vaccine-candidate proteins. Our results provide new insights into bnAb evolution against HIV, and can be used to qualitatively guide the future design of vaccines against highly mutable pathogens.

immunology↗

bioRxiv2021

Inferring the intrinsic mutational fitness landscape of influenza-like evolving antigens from temporally ordered sequence data

There still are no effective long-term protective vaccines against viruses that continuously evolve under immune pressure such as seasonal influenza, which has caused, and can cause, devastating epidemics in the human population. For finding such a broadly protective immunization strategy it is useful to know how easily the virus can escape via mutation from specific antibody responses. This information is encoded in the fitness landscape of the viral proteins (i.e., knowledge of the viral fitness as a function of sequence). Here we present a computational method to infer the intrinsic mutational fitness landscape of influenza-like evolving antigens from yearly sequence data. We test inference performance with computer-generated sequence data that are based on stochastic simulations mimicking basic features of immune-driven viral evolution. Although the numerically simulated model does create a phylogeny based on the allowed mutations, the inference scheme does not use this information. This provides a contrast to other methods that rely on reconstruction of phylogenetic trees. Our method just needs a sufficient number of samples over multiple years. With our method we are able to infer single-as well as pairwise mutational fitness effects from the simulated sequence time series for short antigenic proteins. Our fitness inference approach may have potential future use for design of immunization protocols by identifying intrinsically vulnerable immune target combinations on antigens that evolve under immune-driven selection. This approach may in the future be applied to influenza and other novel viruses such as SARS-CoV-2, which evolves and, like influenza, might continue to escape the natural and vaccine-mediated immune pressures.

evolutionary biology↗

bioRxiv2020

Mechanisms underlying vaccination protocols that may optimally elicit broadly neutralizing antibodies against highly mutable pathogens

Effective prophylactic vaccines usually induce the immune system to generate potent antibodies that can bind to an antigen and thus prevent it from infecting host cells. B cells produce antibodies by a Darwinian evolutionary process called affinity maturation (AM). During AM, the B cell population evolves in response to the antigen to produce antibodies that bind specifically and strongly to the antigen. Highly mutable pathogens pose a major challenge to the development of effective vaccines because antibodies that are effective against one strain of the virus may not protect against a mutant strain. Antibodies that can protect against diverse strains of a mutable pathogen have high "breadth" and are called broadly neutralizing antibodies (bnAbs). In spite of extensive studies, an effective vaccination strategy that can generate bnAbs in humans does not exist for any highly mutable pathogen. Here we study a minimal model to explore the mechanisms underlying how the selection forces imposed by antigens can be optimally chosen to guide AM to maximize the evolution of bnAbs. For logistical reasons, only a finite number of antigens can be administered in a finite number of vaccinations; that is, guiding the non-equilibrium dynamics of AM to produce bnAbs must be accomplished non-adiabatically. The time-varying Kullback-Leibler divergence (KLD) between the existing B cell population distribution and the fitness landscape imposed by antigens is a quantitative metric of the thermodynamic force acting on B cells. If this force is too small, adaptation is minimal. If the force is too large, contrary to expectations, adaptation is not faster; rather, the B cell population is extinguished for reasons that we describe. We define the conditions necessary for the force to be set optimally such that the flux of B cells from low to high breadth states is maximized. Even in this case we show why the dynamics of AM prevent perfect adaptation. If two shots of vaccination are allowed, the optimal protocol is characterized by a relatively low optimal KLD during the first shot that appropriately increases the diversity of the B cell population so that the surviving B cells have a high chance of evolving into bnAbs upon subsequently increasing the KLD during the second shot. Phylogenetic tree analysis further reveals the evolutionary pathways that lead to bnAbs. The connections between the mechanisms revealed by our analyses and recent simulation studies of bnAb evolution, the problem of generalist versus specialist evolution, and learning theory are discussed.

immunology↗

bioRxiv2020

Sedimentation rate and organic matter dynamics shape microbiomes across a continental margin

Marine sedimentation rate and bottom-water O2 concentration control the remineralization/sequestration of organic carbon across continental margins; but whether/how they shape microbiome architecture (the ultimate effector of all biogeochemical phenomena), across shelf/slope sediments, is unknown. Here we reveal distinct microbiome structures and functions, amidst comparable pore-fluid chemistries, along ~3 m sediment-horizons underlying the seasonal (shallow coastal) and perennial (deep sea) oxygen minimum zones (OMZs) of the Arabian Sea, situated across the western-Indian margin (water-depths: 31 m and, 530 and 580 m, respectively). Along the perennial- and seasonal-OMZ sediment-cores microbial communities were predominated by Gammaproteobacteria/Alphaproteobacteria and Euryarchaeota/Firmicutes respectively. As a perennial-OMZ signature, a cryptic methane production-consumption cycle was found to operate near the sediment-surface; overall diversity, as well as the relative abundances of simple-fatty-acids-requiring anaerobes (methanogens, anaerobic methane-oxidizers, sulfate-reducers and acetogens), peaked in the topmost sediment-layer and then declined via synchronized fluctuations until the sulfate-methane transition zone was reached. The entire microbiome profile was reverse in the seasonal-OMZ sediment-horizon. We discerned that in the perennial-OMZ sediments organic carbon deposited was higher in concentration, and marine components-rich, so it potentially degraded readily to simple fatty acids; lower sedimentation rate afforded higher O2 exposure time for organic matter degradation despite perennial hypoxia in the bottom-water; thus, the resultant abundance of reduced metabolites sustained multiple inter-competing microbial processes in the upper sediment-layers. Remarkably, the whole geomicrobial scenario was opposite in the sediments of the seasonal/shallow-water OMZ. Our findings create a microbiological baseline for understanding carbon-sulfur cycling across distinct marine depositional settings and water-colum n oxygenation regimes.

microbiology↗

bioRxiv2020

Predicting the Immunogenicity of T cell epitopes: From HIV to SARS-CoV-2

We describe a physics-based learning model for predicting the immunogenicity of Cytotoxic T Lymphocyte (CTL) epitopes derived from diverse pathogens, given a Human Leukocyte Antigen (HLA) genotype. The model was trained and tested on experimental data on the relative immunodominance of CTL epitopes in Human Immunodeficiency Virus infection. The method is more accurate than publicly available models. Our model predicts that only a fraction of SARS-CoV-2 epitopes that have been predicted to bind to HLA molecules is immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but the immunogenic epitopes in the SARS-CoV-2 spike protein alone are unlikely to do so. Our model predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to those contained in low-pathogenicity coronaviruses circulating in the population. Thus, we suggest that some level of CTL immunity against COVID-19 may be present in some individuals prior to SARS-CoV-2 infection.

immunology↗

bioRxiv2020

Optimizing immunization protocols to elicit broadly neutralizing antibodies

Natural infections and vaccination with a pathogen typically stimulates the production of potent antibodies specific for the pathogen through a Darwinian evolutionary process known as affinity maturation. Such antibodies provide protection against reinfection by the same strain of a pathogen. A highly mutable virus, like HIV or influenza, evades recognition by these strain-specific antibodies via the emergence of new mutant strains. A vaccine that elicits antibodies that can bind to many diverse strains of the virus - known as broadly neutralizing antibodies (bnAbs) - could protect against highly mutable pathogens. Despite much work, the mechanisms by which bnAbs emerge remain uncertain. Using a computational model of affinity maturation, we studied a wide variety of vaccination strategies. Our results suggest that an effective strategy to maximize bnAb evolution is through a sequential immunization protocol, wherein each new immunization optimally increases the pressure on the immune system to target conserved antigenic sites, thus conferring breadth. We describe the mechanisms underlying why sequentially driving the immune system increasingly further from equilibrium, in an optimal fashion, is effective. The optimal protocol allows many evolving B cells to become bnAbs via diverse evolutionary paths. Significance StatementThe global health burden could be substantially alleviated by the creation of universal vaccines against highly mutable pathogens like HIV and influenza. Broadly-neutralizing antibodies (bnAbs) are encouraging targets for such vaccines, because they can bind to diverse strains of highly mutable pathogens. BnAbs typically develop only rarely upon natural infection, after the immune system has been exposed to many mutated versions of a pathogen. Thus, sequentially administering multiple different pathogen-like proteins (antigens) is a promising strategy to elicit bnAbs through vaccination. However, it remains unclear how best to design and administer these antigens. We explore this matter using physics-based simulations, and provide new mechanistic insights into antibody evolution that could guide the creation of universal vaccines against highly mutable pathogens.

biophysics↗

bioRxiv2019

Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease

Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

evolutionary biology↗

bioRxiv2018

Enhancer features that drive formation of transcriptional condensates

Enhancers, DNA elements that regulate gene expression, contain transcription factor (TF) binding sites. TFs bind short sequence motifs that are present throughout the genome at much higher frequency than active enhancers, and so the features that define active enhancers are not well understood. We show that DNA elements with TF binding site valency, density, and binding affinity above sharply defined thresholds can recruit TFs and coactivators in condensates by the cooperative process of phase separation. We demonstrate that weak cooperative interactions between IDRs of TFs and coactivators in combination with specific TF-DNA interactions are required for forming such transcriptional condensates. IDR-IDR interactions are relatively non-specific with the same molecular interactions shared by many TFs and coactivators, and phase separation is a universal cooperative mechanism. Therefore, whether a genomic locus is an enhancer that can assemble a transcriptional condensate is determined predominantly by its cognate TFs binding site valency and density.

molecular biology↗

bioRxiv2018

Roles of Vitamin Metabolizing Genes in Multidrug-Resistant Plasmids of Superbugs

Superbug crisis has rocked this world with million deaths due to failure of potent antibiotics. Thousands mdr genes with hundreds of mutant isomers are generated. Small integrons and R-plasmids have combined with F-plasmids creating a space for >10-20 of mdr genes that inactivate antibiotics in different mechanisms. Mdr genes are created to save bacteria from antibiotics because gut microbiota synthesize >20 vitamins and complex bio-molecules needed for >30000 biochemical reactions of human metabolosome. In other words, mdr gene creation is protected from both sides, intestinal luminal cells and gut bacteria in a tight symbiotic signalling system. We have proposed, to avert the crisis all vitamin metabolizing genes will be acquired in MDR- plasmids if we continue oral antibiotics therapy. Therefore, we have checked the plasmid databases and have detected thiamine, riboflavin, folate, cobalamine and biotin metabolizing enzymes in MDR plasmids. Thus vit genes may mobilise recently into MDR-plasmids and are likely essential for gut microbiota protection. Analysis found that cob and thi genes are abundant and likely very essential than other vit genes.

bioinformatics↗