Biology subjects

Surana, P.

Publications and source records attributed to Surana, P..

7 recordsLinked to original sources

bioRxiv2026

PGViS: Personal Genome Variant interpretation Score for lung cancer genomes

Inherited lung cancer risk arises from both protein-coding and non-coding germline variants, but the functional non-coding component is largely uncharacterized. Genome-wide association studies and polygenic risk scores identify tag variants, not causal ones. Neither resolves which regulatory element is perturbed. DNA foundation models such as DNABERT decode non-coding variant effects directly from sequence, without a large GWAS cohort. What is missing is a patient-level framework linking these predictions to population-level variant prevalence. We present PGViS (Personal Genome Variant interpretation Score), a statistical framework that quantifies individual non-coding germline regulatory risk in non-small cell lung cancer (NSCLC). PGViS integrates three variant-level signals: DNABERT-predicted disruption at transcription factor binding and splice sites, the cancer v/s reference alternate allele frequency shift, and a regulatory interaction term derived from cancer-to-reference allele frequency ratios. Each signal is weighted by cohort prevalence which are aggregated into a single ancestry-matched, reference-normalized score per patient. We applied PGViS to germline whole-genome sequencing from 1,102 TCGA and CPTAC patients, using the 1000 Genomes Project (n = 2,504 individuals) as the normal population reference. PGViS separated adenocarcinoma (AD) and squamous cell carcinoma from controls in European ancestry and East Asian AD. Genes at contributing loci were enriched for PI3K-Akt, Wnt, DNA damage response, and epithelial-mesenchymal transition programs. Smoking-stratified analysis concentrated this signal on canonical NSCLC driver pathways. PGViS is modular: it accommodates cohorts with broader ancestral representation and can adapt to other solid tumors, offering a cost-effective route to personal-genome risk assessment from germline variants alone.

bioinformatics↗

bioRxiv2026

Solid Tumors Pan Cancer Transcriptome Tissue/Cancer specific expression groups at the Isoform-Level

Most of the human genome is transcribed into diverse isoforms whose tissue specificity is profoundly disrupted in cancer, yet isoform-level dysregulation remains poorly characterized across solid tumors. Here, we introduce STPCaT (Solid Tumors Pan-Cancer Transcriptome), an isoform-centric analysis extending TransTEx to systematically classify transcript expression across TCGA solid tumors and GTEx normal tissues. STPCaT reveals a striking collapse of normal tissue-specific programs in cancer, accompanied by the emergence of two dominant expression groups: cancer-high (CanHigh) and normal-high (NorHigh) isoforms. We uncover a large repertoire of previously unannotated Cancer-Testis Antigens (CTAs), majority of which are absent from existing CTA databases, with broad relevance across multiple cancers, including gliomas. In pan-gliomas, consensus clustering and random-forest feature selection identify compact, highly discriminative isoform signatures that robustly stratify low-grade and glioblastomas with up to 97-98% accuracy using as few as five transcripts. These signatures recapitulate canonical glioma biology and highlight pathways linked to migration, development, and vesicle trafficking. Independent validation in the GLASS consortium cohort demonstrates cohort-specific trends that partially recapitulate primary findings, reflecting known biological heterogeneity across patient populations. Together, STPCaT provides a scalable, isoform-resolved resource for tumor stratification, CTAs discovery, and precision oncology applications across solid tumors. TeaserSTPCaT uncovers an isoform-level collapse of tissue specificity across solid tumors, revealing a hidden landscape of diagnostic biomarkers and unannotated cancer-testis antigens.

bioinformatics↗

bioRxiv2026

HViLM: A Foundation Model for Viral Genomics Enables Multi-Task Prediction of Pathogenicity, Transmissibility, and Host Tropism

MotivationThe emergence of novel viral pathogens poses critical threats to global health, yet current computational approaches for viral risk assessment are predominantly virus-specific and require extensive retraining for each new threat. Computational methods for rapid characterization of emerging viruses across multiple epidemiologically relevant dimensions--pathogenicity, host tropism, and transmissibility--are urgently needed to inform public health responses and guide experimental prioritization. ResultsWe present HViLM (Human Virome Language Model), the first foundation model for panviral genomic analysis through continued pre-training of DNABERT-2 on 5 million non-redundant viral sequences (MMseqs2-clustered from 25 million chunks at 80% identity) spanning 9,000 species across 45+ viral families from the VIRION database. We introduce the Human Virome Understanding Evaluation (HVUE) benchmark comprising seven curated datasets across three prediction tasks: pathogenicity classification, host tropism prediction, and transmissibility assessment. Through parameter-efficient fine-tuning with LoRA, HViLM achieves state-of-the-art performance with average accuracies of 95.32% for pathogenicity, 96.25% for host tropism, and 97.36% for transmissibility assessment. The model demonstrates robust cross-family generalization, substantially outperforming sequence-similarity baselines and general genomic foundation models. Attention-based interpretability analysis reveals that HViLM captures biologically meaningful pathogenicity determinants through molecular mimicry of host regulatory elements, including convergent evolution of eight independent sequences targeting Interferon Regulatory Factor 1 (Irf1) for immune evasion. AvailabilityThe HVUE benchmark datasets, training scripts, and complete implementation are publicly available at https://github.com/duttaprat/HViLM. Pre-trained HViLM-base model weights and fine-tuned task-specific variants are available on Hugging Face at https://huggingface.co/duttaprat/HViLM-base. Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2025

TSProm: Deciphering the Genomic Context of Tissue Specificity

Characterizing tissue-specific (TSp) gene expression is crucial for understanding development and disease; however, traditional expression-based methods often overlook the latent "regulatory grammar" embedded in the non-coding DNA, particularly in distal promoter regions. Here, we introduce TSProm, a framework that specializes a DNA foundation model (DNABERT2) to decipher the long-range regulatory logic of TSp promoters at the gene isoform level. The contributions of our work are two-fold. First, we propose a novel comparative design that trains two distinct models, A: for general promoter biology and B: for TSp regulation. These models enable the precise isolation of sequence motifs around the transcription start site that uniquely define tissue identity. Second, we introduce a comprehensive explainable AI (xAI) module that integrates attention-based discovery with model-agnostic SHAP analysis to provide robust, cross-validated interpretations of learned features. Applying this framework to human brain, liver, and testis promoters, we identified and validated clinically relevant transcription factors (TFs) in the brain, including SP1, MYC, and HES6, and confirmed their known roles in diseases such as gliomas and neuroblastomas. Our analysis further revealed that C2H2 Zinc Finger proteins are a dominant feature of the global landscape of TSp gene regulation. TSProm provides a novel and interpretable framework for identifying TSp gene regulatory elements, offering powerful computational tools for the study of tissue-specific gene regulation in normal and disease conditions.

bioinformatics↗

bioRxiv2023

Transcriptome-based host epistasis and pathogen co-expression in barley-powdery mildew interactions

Mildew locus a (Mla) from the Triticeae grain crop barley (Hordeum vulgare L.) encodes a multi-allelic series of nucleotide-binding leucine-rich repeat (NLR) immune receptors. These variable NLRs recognize complementary secreted effectors from the powdery mildew fungus, Blumeria hordei (Bh), to block disease progression. We used a dynamic time-course transcriptome of barley infected with Bh to infer gene effects and epistatic relationships governed by the Mla6 NLR, two other host loci critical to the interaction, Blufensin1 (Bln1) and Required for Mla6 resistance3 (Rar3), and genes that interact with them. Bln1 is an R-gene independent regulator of immunity and the resistant bln1 mutant exhibits enhanced basal defense to compatible Bh. Rar3 is required for MLA6-mediated generation of H2O2 and the hypersensitive reaction; the rar3 mutant contains an in-frame Lys-Leu deletion in the SGT1-specific domain that compromises immunity by a subset of Mla alleles. Interactions of Mla6 and Bln1 resulted in symmetric, suppression and masked epistasis on the Bh-induced barley transcriptome. Likewise, dominant or equal effects were caused by Mla6 and Sgt1. Of a total of 468 barley NLRs, 366 were expressed in our dataset and 115 of those were grouped under different gene effect models, which localized to several chromosome hotspots. The corresponding Bh infection transcriptome was classified into nine co-expressed modules, linking differential expression with pathogen development. Expression of most of 517 Bh effectors exhibited dependence on disease phenotype and was associated with appressorial or haustorial structures, suggesting that disease is regulated by a host-pathogen intercommunication network that diversifies the response.

genetics↗

bioRxiv2022

A chromosome-level genome resource for studying virulence mechanisms and evolution of the coffee rust pathogen Hemileia vastatrix

Recurrent epidemics of coffee leaf rust, caused by the fungal pathogen Hemileia vastatrix, have constrained the sustainable production of Arabica coffee for over 150 years. The ability of H. vastatrix to overcome resistance in coffee cultivars and evolve new races is inexplicable for a -pathogen that supposedly only utilizes clonal reproduction. Understanding the evolutionary complexity between H. vastatrix and its only known host, including determining how the pathogen evolves virulence so rapidly is crucial for disease management. Achieving such goals relies on the availability of a comprehensive and high-quality genome reference assembly. To date, two reference genomes have been assembled and published for H. vastatrix that, while useful, remain fragmented and do not represent chromosomal scaffolds. Here, we present a complete scaffolded pseudochromosome-level genome resource for H. vastatrix strain 178a (Hv178a). Our initial assembly revealed an unusually high degree of gene duplication (over 50% BUSCO basidiomycota_odb10 genes). Upon inspection, this was predominantly due to a single scaffold that itself showed 91.9% BUSCO Completeness. Taxonomic analysis of predicted BUSCO genes placed this scaffold in Exobasidiomycetes and suggests it is a distinct genome, which we have named Hv178a associated fungal genome (Hv178a AFG). The high depth of coverage and close association with Hv178a raises the prospect of symbiosis, although we cannot completely rule out contamination at this time. The main Ca. 546 Mbp Hv178a genome was primarily (97.7%) localised to 11 pseudochromosomes (51.5 Mb N50), building the foundation for future advanced studies of genome structure and organization.

genomics↗

bioRxiv2018

Study of the action of lactic acid bacteria on acrylamide in food products

The aim of this study is to extract Lactic acid bacteria (LAB) and test its effect on starchy food products. We extracted LAB as it is GRAS (Generally Recognised As Safe) and is abundantly present in probiotics like curd. After extraction of LAB colonies and were tested for its effect on foods rich in acrylamide content. Acrylamide formation is studied by the maillard reaction. Acrylamide is found in food products which have been subjected to very high temperatures. The application of LAB to remove many mutagens, by using the binding mechanism was assessed for acrylamide. When this acrylamide degrades it produces toxic gases like ammonia, hydrogen and carbon monoxide. Asparagine is an important precursor to acrylamide formation. So in foods containing free asparagine group with reducing sugars will form acrylamide. We used liquid chromatography to determine the presence of acrylamide in the food product. Acrylamide content in the body is responsible for various mutations at the genomic level thereby, leading to cancers like colon, breast. It also leads to neural damages and is directly linked to Alzhimers disease. This study concludes the beneficial effects of consuming probiotics and suggests a way to reduce the acrylamide formation in food products.

biochemistry↗