Biology subjects

Davuluri, R. V.

Publications and source records attributed to Davuluri, R. V..

10 recordsLinked to original sources

bioRxiv2026

Keratin 17 Defines an Epithelial-Mesenchymal Transition-Associated Migratory Cell State in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is characterized by early invasion, metastasis, and therapeutic resistance that contribute substantially to its poor clinical outcome. Keratin 17 (K17) is a recognized marker of the aggressive basal-like subtype of PDAC and an independent predictor of adverse prognosis; however, the biological basis for this association remains incompletely understood. To define K17-associated cellular programs, we integrated single-cell RNA sequencing (scRNA-seq) data from 42 patients, Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomic data from 140 tumors, MACSima multiplex spatial imaging of 63 patients, dual-plex immunohistochemistry of 60 cases, and three-dimensional spheroid migration assays. Single-cell transcriptomic analysis demonstrated significant enrichment of epithelial-mesenchymal transition (EMT)-associated gene programs among K17-positive malignant epithelial cells. Consistent with this finding, K17-positive tumors in the CPTAC cohort exhibited enrichment of the Hallmark EMT pathway at the protein level. Among six candidate markers identified through transcriptomic analyses, laminin subunit gamma-2 (LAMC2) was the only candidate associated with K17 at every validation step. Tumors positive for both K17 and LAMC2 had significantly worse overall survival than double-negative tumors (adjusted hazard ratio = 2.53). Multiplex spatial imaging localized K17-positive cells predominantly to poorly differentiated tumor regions and demonstrated frequent single-cell co-expression with LAMC2. Whole-slide dual-plex immunohistochemistry further confirmed the association between K17 and LAMC2 and showed progressive enrichment of K17/LAMC2-positive tumor cells with increasing intratumoral histologic grade. Functionally, gain- and loss-of-function studies in three-dimensional PDAC spheroids demonstrated that K17 promotes migratory outgrowth, and differences in proliferation did not account for this effect. Collectively, these findings identify K17 as a marker of a poorly differentiated, EMT-associated migratory cell state in PDAC and provide functional evidence that K17 contributes to tumor-cell migration.

cancer biology↗

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

Development of gemcitabine-modified miR-15a as a novel, multimodal treatment strategy to overcome 5-FU and oxaliplatin resistance in colorectal cancer

BackgroundResistance to 5-fluorouracil (5-FU)-based chemotherapy is a major clinical obstacle in colorectal cancer (CRC), highlighting the urgent need to overcome established resistance mechanisms. MicroRNA-based therapeutics have emerged as compelling candidates in this context, given their inherently pleiotropic mode of action; however, their clinical translation remains hindered by poor stability and suboptimal delivery. MethodsTo address these limitations, Gem-miR-15a, a unique gemcitabine-modified tumor-suppressor microRNA-15a was designed to synergistically integrate the tumor-suppressive activity of miR-15a with the chemotherapeutic potency of gemcitabine into a single molecular entity. Therapeutic efficacy of Gem-miR-15a was evaluated across a spectrum of preclinical models, including parental and drug-resistant CRC cell lines, 3D tumor spheroids, patient-derived organoids and in vivo metastatic models. Cell viability, apoptosis and cell cycle analyses were performed, along with RNA sequencing and protein validation. Statistical analyses were conducted using Students t-test or two-way ANOVA with mixed effects, and data were presented as mean {+/-} SD. ResultsGem-miR-15a exhibited potent anti-proliferative activity with IC50 values in the low nanomolar range, achieving [~]100-5000-fold greater potency relative to 5-FU and oxaliplatin. Importantly, it retained efficacy in both 5-FU- and oxaliplatin-resistant CRC models, effectively overcoming acquired chemoresistance. Mechanistically, Gem-miR-15a induced S-phase cell cycle arrest, eliminated the G2-phase cell population, and triggered apoptosis, accompanied by suppression of key oncogenic targets including WEE1, CHK1, YAP1 and BMI1. RNA-seq analysis further demonstrated modulation of pathways such as p53 signaling and reversal of resistance-associated gene expression, that were corroborated at the protein level. In vivo, Gem-miR-15a significantly reduced tumor growth at a dose [~]12-fold lower than gemcitabine, with no observable toxicity. ConclusionGem-miR-15a represents a potent, multi-targeted therapeutic strategy capable of overcoming chemoresistance in CRC. Its enhanced stability, effective delivery and robust efficacy across resistant models and a favorable safety profile highlight its strong potential for clinical translation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/720825v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@c20034org.highwire.dtl.DTLVardef@9b8478org.highwire.dtl.DTLVardef@161f1dorg.highwire.dtl.DTLVardef@54d826_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

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↗

bioRxiv2025

Genomic Language Model for Predicting Enhancers and Their Allele-Specific Activity in the Human Genome

Predicting and deciphering the regulatory logic of enhancers is a challenging problem, due to the intricate sequence features and lack of consistent genetic or epigenetic signatures that can accurately discriminate enhancers from other genomic regions. Recent machine-learning based methods have spotlighted the importance of extracting nucleotide composition of enhancers but failed to learn the sequence context and perform suboptimally. Motivated by advances in genomic language models, we developed DNABERT-Enhancer, a novel enhancer prediction method, by applying DNABERT pre-trained language model on the human genome. We trained two different models, using large collection of enhancers curated from the ENCODE registry of candidate cis-Regulatory Elements. The best fine-tuned model achieved 88.05% accuracy with Matthews correlation coefficient of 76% on independent set aside data. Further, we present the analysis of the predicted enhancers for all chromosomes of the human genome by comparing with the enhancer regions reported in publicly available databases. Finally, we applied DNABERT-Enhancer along with other DNABERT based regulatory genomic region prediction models to predict candidate SNPs with allele-specific enhancer and transcription factor binding activity. The genome-wide enhancer annotations and candidate loss-of-function genetic variants predicted by DNABERT-Enhancer provide valuable resources for genome interpretation in functional and clinical genomics studies.

bioinformatics↗

bioRxiv2024

Onco-fetal protein Nogo-A restricts human and mouse glioma vascularization and growth via VEGF-Notch-hippo-metabolic signaling

Glioblastoma is one of the most deadly human cancers characterized by high degrees of vascularization, but targeting its vasculature has resulted in very limited success so far. Angiogenesis, the growth of new blood vessels, is highly dynamic during brain development, enters a mostly quiescent state in the adult homeostatic brain, and is reactivated in vascular-dependent CNS diseases including brain tumors. In consequence, a better understanding of the relevance of the onco-fetal axis - describing the reactivation of fetal signaling programs in tumors - in endothelial- and perivascular cells of the human brain tumor vasculature harbors great translational potential, yet remains poorly defined. In development, neurovascular link (NVL) molecules guide both neuronal growth cones as well as capillary endothelial tip cells. Nogo-A is an NVL molecule known to inhibit axonal growth in the developing and adult CNS and to restrict angiogenesis during brain development, but its role in the mouse and human brain tumor vasculature along the onco-fetal axis remains unknown. Here, we characterize Nogo-A as an onco-fetal protein expressed in the neurovascular unit (NVU) in human fetal brains and human gliomas in vivo that negatively regulates sprouting angiogenesis and endothelial tip cells in glioma vascularization. The Nogo-A-specific Delta 20 domain restricts angiogenic sprouting and branching and promotes vascular normalization while inhibiting glioma growth in experimental gliomas. Moreover, Nogo-A expression in tumor cells negatively correlate/s with glioma malignancy in vivo. In vitro, Nogo-A Delta 20 reduced human brain- and brain tumor endothelial cell (HBMVEC, HBTMVEC) and human umbilical vein endothelial cell (HUVEC) spreading, migration, and sprouting, in a dose-dependent manner and inhibited filopodia extension glucose metabolism. Mechanistically, RNA sequencing of Nogo-A Delta 20-treated HBMVECs and HBTMVECs revealed Nogo-A Delta 20-induced positive regulation of the angiogenesis-inhibiting Dll4-Notch-pathway and inhibition of the angiogenesis-promoting VEGF-VEGFR and Hippo-YAP-TAZ pathways, whereas metabolomics and functional metabolic assays revealed Nogo-A Delta 20-induced negative regulation of endothelial glycolysis in HBMVECs and HBTMVECs. These findings characterize Nogo-A as an onco-fetal protein in the human glial brain tumor vasculature and identify Nogo-A Delta 20 signaling as an important negative regulator of human glioma vascularization and growth. Enhancing Nogo-A signaling may be an attractive alternative or combinatorial anti-angiogenic therapy to restrict human glioma/glioblastoma vascularization and growth.

neuroscience↗

bioRxiv2023

Epithelial-Mesenchymal Transition: An Organizing Principle of Mammalian Regeneration

The MRL mouse strain is one of the few examples of a mammal capable of healing appendage wounds by regeneration, a process that begins with the formation of a blastema, a structure containing de-differentiating mesenchymal cells. HIF-1 expression in the nascent MRL wound site blastema is one of the earliest identified events and is sufficient to initiate the complete regenerative program. However, HIF-1 regulates many cellular processes modulating the expression of hundreds of genes. A later signal event is the absence of a functional G1 checkpoint leading to G2 cell cycle arrest with increased cellular DNA but little cell division seen in the blastema. This lack of mitosis in MRL blastema cells is also a hallmark of regeneration in classical invertebrate and vertebrate regenerators such as the planaria, hydra, and newt. Here, we explore the cellular events occurring between HIF-1 upregulation and its regulation of the genes involved in G2 arrest (EVI-5, {gamma}H3, Wnt5a, and ROR2), and identify EMT (Twist, Slug) and chromatin remodeling (EZH-2 and H3K27me3) as key intermediary processes. The locus of these cellular events is highly regionalized within the blastema, occurring in the same cells as determined by double staining using immunohistochemistry and FACS analysis, and appear as EMT and chromatin remodeling followed by G2 arrest determined by kinetic expression studies.

developmental biology↗

bioRxiv2018

ExTraMapper: Exon- and Transcript-level mappings for orthologous gene pairs

Access to large-scale genomics and transcriptomics data from various tissues and cell lines allowed the discovery of wide-spread alternative splicing events and alternative promoter usage in mammalians. However, evolutionary studies primarily focus on gene-level orthology relationships, which hinders the importance of transcript-level diversity. Between human and mouse, gene-level orthology is currently present for nearly 16k protein-coding genes spanning a diverse repertoire of over 200k total transcript isoforms. Here we describe a novel method, ExTraMapper, which leverages sequence conservation between exons of a pair of organisms and identifies a fine-scale orthology mapping at the exon and then transcript level. ExTraMapper identifies more than 250k exon, as well as 30k transcript mappings between human and mouse using only sequence and gene annotation information. We demonstrate that ExTraMapper identifies a larger number of exon and transcript mappings compared to previous methods. Further, it identifies exon fusions, splits, and losses due to splice site mutations, and finds mappings between microexons that are previously missed. By reanalysis of RNA-seq data from 13 matched human and mouse tissues, we show that ExTraMapper improves the correlation of transcript-specific expression levels suggesting a more accurate mapping of human and mouse transcripts. ExTraMapper also reports better transcript-level mappings compared to Ensembl orthology for the human proto-oncogene BRAF and its mouse ortholog as well as several other example genes with important isoform-specific functions. ExTraMapper is applicable to any pair of organisms that have orthologous gene pairs and is available at https://github.com/ay-lab/ExTraMapper and http://ay-lab-tools.lji.org/extramapper

bioinformatics↗