Biology subjects

Kesarwani, V.

Publications and source records attributed to Kesarwani, V..

6 recordsLinked to original sources

bioRxiv2025

Phased epigenetic fragility and stability accelerate the genetic evolution of B-cells

Non-genetic heterogeneity can provide population robustness when responding to threats or making developmental decisions, but when the biological process rests on specific individuals (tissue-resident macrophages or genetic evolution), non-genetic heterogeneity degrades performance. Vaccine responses depend on the Darwinian evolution of B-cells to generate high-affinity, genetically-encoded antibodies, yet B-cell decision-making is non-genetically variable. In fact, B-cell epigenetic states are fragile during selection but stable during the proliferation burst. Here we report that the iterative dynamics of epigenetic fragility and stability of B-cell fate decisions do not impair but accelerate affinity maturation, by modulating the generation and removal of high-affinity outliers on a fitness landscape that is non-monotonic defined not only by survival/proliferation but plasma cell differentiation. These insights reconcile classical B-cell clonal selection theory with experimentally observed dynamics of epigenetic variability. The resulting model correctly predicts emergent vaccine response properties in alternate mouse strains and may contribute to personalized vaccination strategies. HIGHLIGHTSO_LIB-cell epigenetic states are fragile during antigen selection but stable within proliferative bursts C_LIO_LIEpigenetic state fragility allows high affinity cells to escape plasma cell differentiation C_LIO_LIEpigenetic state stability within proliferative bursts maximizes progeny of fittest cells C_LIO_LIPhased epigenetic dynamics of B-cells predicts emergent vaccine response properties C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/661408v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1c2af9forg.highwire.dtl.DTLVardef@980003org.highwire.dtl.DTLVardef@128d8c0org.highwire.dtl.DTLVardef@ae48b8_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Deep-Learning on condition specific expression profiles reveal critical cytosines in gene regulation

Compared to other nucleotides, the cytosines stand as the most expressive one for gene regulation in plants due to its status as methylation-based epigenetic switch. Methylation of some of these cytosines may have higher impact on downstream genes, making them critical ones. To this date not much has been done to decipher the criticality of such cytosines. This is first such pioneering study in decoding the critical Cs where a large volume of bisulfite and RNA-seq data, including 232 WGBS and 260 corresponding RNA-seq datasets from A. thaliana and rice, have been utilized. Using a deep learning system, strong relationship was established between methylation states of cytosines in contextual manner with respect to the downstream genes expression levels. Using the same system, all the 2kb upstream regions for Arabidopsis and rice genes have been annotated for critical cytosines. Experimental validation demonstrated specific methylation changes at critical cytosines under heat stress, affecting gene expression. The universal method developed may be applied to annotate other plant genomes for critical cytosine identification. GC% similarity, rather than homology, explains gene regulatory behavior under methylation control. The resource, CritiCal-C portal, is publicly accessible, paving the way for revolutionary strategies in gene regulation and expression with minimal interventions.

bioinformatics↗

bioRxiv2025

DMRU: Generative Deep-Learning to unravel condition specific cytosine methylation in plants

Methylation at cytosines in plants influence spatio-temporal gene expression by regulating chromatin structure and accessibility. Some algorithms have been developed to identify DNA methylation but none of them are capable to tell the condition specific DNA methylation, making them hardly of any use. Here, we report a first of its kind an explainable Deep Encoders-Decoders generative system, DMRU, which learns the relationship between transcritpome status and DNA methylation states at any given time. It was also found that GC similarity is more relevant to the specificity of DNA methylation patterns than homology, concurring with reports of direct involvement of GC content in providing regulatory switches for DNA accessibility. Leveraging on which DMRU could perform with same level of accuracy in cross-species universal manner. In a comprehensive testing and benchmarking study across a huge volume of experimental data covering 85 different conditions, and multiple plant species, it has consistently achieved >90% accuracy. With this all, DMRU brings a completely new chapter in methylated cytosine discovery, giving a strong alternative to costly bisulfite sequencing experiments. DMRU may prove critical turning point in plant regulatory research and its acceleration.

bioinformatics↗

bioRxiv2024

PTF-Vac: Ab-initio discovery of plant transcription factors binding sites using deep co-learning encoders-decoders

Discovery of transcription factors (TFs) binding sites (TFBS) and their motifs in plants pose significant challenges due to high cross-species variability. The interaction between TFs and their binding sites is highly specific and context dependent. Most of the existing TFBS finding tools are not accurate enough to discover these binding sites in plants. They fail to capture the cross-species variability, interdependence between TF structure and its TFBS, and context specificity of binding. Since they are coupled to predefined TF specific model/matrix, they are highly vulnerable towards the volume and quality of data provided to build the motifs. All these software make a presumption that the user input would be specific to any particular TF which renders them of very limited use for practical applications like genomic annotations of newly sequenced species. Here, we report an explainable Deep Encoders-Decoders generative system, PTF-V[a]c, founded on a universal model of deep co-learning on variability in binding sites and TF structure, PTFSpot, making it completely free from the bottlenecks mentioned above. It has successfully decoupled the process of TFBS discovery from the prior step of motif finding and requirement of TF specific motif models. Due to the universal model for TF:DNA interactions as its guide, it can discover the binding motifs in total independence from data volume, species and TF specific models. In a comprehensive benchmarking study across a huge volume of experimental data, it has outperformed most advanced motif finding deep learning (DL) algorithms. With this all, PTF-V[a]c brings a completely new chapter in ab-initio TFBS discovery through generative AI. Short SummaryThe discovery of transcription factor binding sites (TFBS) in plants is challenging due to high variability across species and context-specific interactions. Traditional tools rely on predefined models and often fail in their cross-species applications. PTF-V[a]c has implemented generative a universal deep-learning model that decouples TFBS discovery from predefined motifs, enabling accurate, species-independent TFBS/motif identification while outperforming existing methods by huge leads.

bioinformatics↗

bioRxiv2023

Deep Co-learning on transcription factors and their binding sites attains impeccable universality in plants

Unlike animals, variability in transcription factors (TF) and their binding regions (TFBR) across the plants species is a major problem which most of the existing TFBR finding software fail to tackle, rendering them hardly of any use. This limitation has resulted into underdevelopment of plant regulatory research and rampant use of Arabidopsis like model species, generating misleading results. Here we report a revolutionary transformers based deep-learning approach, PTFSpot, which learns from TF structures and their binding regions co-variability to bring a universal TF-DNA interaction model to detect TFBR with complete freedom from TF and species specific models limitations. During a series of extensive benchmarking studies over multiple experimentally validated data, it not only outperformed the existing software by >30% lead, but also delivered consistently >90% accuracy even for those species and TF families which were never encountered during model building process. PTFSpot makes it possible now to accurately annotate TFBRs across any plant genome even in the total lack of any TF information, completely free from the bottlenecks of species and TF specific models.

bioinformatics↗

bioRxiv2020

SARS-CoV-2 transcriptome analysis and molecular cataloguing of immunodominant epitopes for multi-epitope based vaccine design

SARS-CoV-2 is a single-stranded RNA virus that has caused more than 0.29 million deaths worldwide as of May 2020, and influence of COVID-19 pandemic is increasing continuously in the absence of approved vaccine and drug. Moreover, very limited information is available about SARS-CoV-2 expressed regions and immune responses. In this paper an effort has been made, to facilitate vaccine development by proposing multiple epitopes as potential vaccine candidates by utilising SARS-CoV-2 transcriptome data. Here, publicly available RNA-seq data of SARS-CoV-2 infection in NHBE and A549 human cell lines were used to construct SARS-CoV-2 transcriptome to understand disease pathogenesis and immune responses. In the first step, epitope prediction, MHC class I and II gene identification for epitopes, population coverage, antigenicity, immunogenicity, conservation and crossreactivity analysis with host antigens were performed by using SARS-CoV-2 transcriptome, and in the second step, structural compatibility of identified T-and B-cell epitopes were evaluated with MHC molecules and B-cell receptors through molecular docking studies. Quantification of MHC gene expression was also performed that indicated high variation in allele types and expression level of MHC genes with respect to cell lines. In A549 cell line, HLA-A*30:01:01:01 and HLA-B*44:03:01:01 were highly expressed, whereas 92 variants of HLA-A*24 genes such as HLA-A*24:02:01:01, HLA-A*24:286, HLA-A*24:479Q, HLA-A*24:02:134 and HLA-A*24:02:116 were highly expressed in NHBE cell lines. Prevalence of HLA-A*24 alleles was suggested as risk factors for H1N1 infection, and associated with type-1 diabetes. HLA-C*03:03, linked with male infertility factors was also highly expressed in SARS-CoV-2 infected NHBE cell lines. Finally, three potential T-cell and five B-cell epitopes were selected for molecular docking studies with twenty-two MHC molecules and two B-cell receptors respectively. The results of in silico analysis indicated that proposed epitopes have high potential to recognize immune response of SARS-CoV-2 infection. This study will facilitate in vitro and in vivo vaccine related research studies.

bioinformatics↗