Biology subjects

Venkatesh, S.

Publications and source records attributed to Venkatesh, S..

At least 19 recordsLinked to original sources

bioRxiv2026

C19 trityl and silyl andrographolide analogs exhibit in vitro anticancer activity in murine cancer models

Andrographolide, a labdane diterpenoid isolated from A. paniculata has been investigated for activity in a broad panel of biological contexts, including its anti-inflammatory, antiviral, and anticancer properties. Previously, A-ring analogs of andrographolide bearing silyl and trityl ethers at C19 have demonstrated improved in vitro anticancer activity in human cancer cell lines, with putative activity through the Wnt1/{beta}-catenin signaling axis. In order to investigate the activity of this series of C19 andrographolide analogs, alongside a cyclic A-ring acetonide analog, as anticancer agents in murine cancer cell models, we evaluated antiproliferative activity of these compounds in B16F10 melanoma, CT26 colorectal adenocarcinoma, Neuro2a neuroblastoma, and 4T1 mammary ductal carcinoma cell lines, and found that most compounds exhibited potent micromolar level anticancer activity. Among these, all trityl ethers exhibited nearly indistinguishable IC50 values of 4-20 M, and generally exhibited greater potency compared to C19 silyl ether analogs. This anticancer activity was further validated through apoptosis flow cytometry, wherein we concluded that our compounds induced apoptosis to a greater extent than the parent compound, andrographolide. Further, we examine the role of modulation of canonical Wnt1/{beta}-catenin signaling in a series of co-delivery assays with CHIR-99021, a selective GSK-3{beta} kinase inhibitor, and MSAB, a selective {beta}-catenin degrader. Finally, we demonstrate that a selection of silyl ether analogs of andrographolide elicit cell cycle arrest by flow cytometry in a manner that is decoupled to in vitro potency trends. Altogether, we report the first in vitro evaluation of trityl and silyl ether analogs of andrographolide within murine adherent cancer cells.

cancer biology↗

bioRxiv2026

A training protocol for human classification of Asian elephant images from trail cameras

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

ecology↗

bioRxiv2026

Microbial odours activate protective immune response in Caenorhabditis elegans via specific olfactory neurons

Caenorhabditis elegans, like other animals, relies on attractive odours for foraging and on aversive odours for avoidance of pathogens. An odour produced by pathogenic bacterium Pseudomonoas aeruginosa induces immune response as well as avoidance in C. elegans via AWB olfactory neurons. We asked whether AWB neurons provide broader immunity to a wide range of pathogens. We activated AWB neurons using three chemically distinct bacterial odours and found that activation indeed induces protective immunity. Conversely, silencing of AWB neurons early during infection enhances susceptibility to infections. Mechanistic investigation revealed that the activation of AWB using odours upregulates detoxification pathways and other protective pathways in non-neuronal tissues. Specifically, odour exposure activates UDP-glucuronosyltransferase UGT-18 in the intestine that protects worms from the phenazine toxin of Pseudomonas aeruginosa and promotes broader immunity to Gram-negative bacteria (P. aeruginosa and Salmonella enterica), Gram-positive bacteria, (Enterococcus faecalis and Staphylococcus aureus), and yeast (Cryptococcus neoformans). Altogether, our findings highlights microbial odours as non-canonical molecular patterns for activating immune responses.

neuroscience↗

bioRxiv2026

NNMT Loss Drives Cancer Progression by enhancing SAM availability for mTORC1 Signaling and Chromatin Methylation

Aberrant epigenetic reprogramming together with dysregulated mTOR signaling are hallmarks of cancer, where altered chromatin methylation and nutrient-sensing pathways cooperate to drive tumor progression. S-adenosylmethionine (SAM), the universal methyl donor, is essential for these processes, yet how tumors sustain elevated SAM availability to support oncogenic transmethylation reactions remains poorly defined. Here, using prostate cancer (PCa) as a model system, we identify nicotinamide N-methyltransferase (NNMT) as a critical metabolic-epigenetic regulator and tumor suppressor. Using a prostate-specific Nnmt knockout mouse model, we demonstrate that NNMT loss accelerates PCa progression, particularly in the context of Pten deletion, resulting in infiltrating carcinoma and reduced survival. Mechanistically, NNMT functions as a "SAM-sink," and its loss increases intracellular SAM abundance, thereby activating mTORC1 signaling through SAMTOR-dependent sensing and broadly enhancing chromatin methylation. In human PCa, recurrent genomic deletions of NNMT occur in up to 7% of cases, and NNMT protein expression is largely absent in primary tumors and metastases. NNMT-deficient PCa cells exhibit elevated SAM:SAH ratios, increased histone methylation, and heightened mTORC1 activity, enabling sustained tumor growth even under dietary methionine-restriction (MR). Notably, combined MR and pharmacologic mTORC1 inhibition synergistically suppresses the growth of NNMT-deficient tumors, revealing a previously unrecognized therapeutic vulnerability. Collectively, these findings establish NNMT as a key tumor suppressor that constrains SAM-driven epigenetic and signaling programs in PCa and suggest a rational, diet-based therapeutic strategy for advanced cancers with NNMT loss.

cancer biology↗

bioRxiv2026

ANCHOR : Atlas of Neurochemical Characterization of the Human Brainstem with 3D Reconstruction

The human brainstem is a complex division of the brain comprised of more than 200 nuclei and fiber tracts. The brainstem is essential for the functioning of the entire body. We introduce here the most detailed human brainstem Atlas across the human lifespan: fetus, child, adult. ANCHOR, the Atlas of Neurochemical Characterization of the Human Brainstem, is an online platform that includes more than 800 serial histological sections, stained for Nissl and seven immunochemical (IHC) markers, from the human brainstem of three ages: 25 fetal gestational weeks (GW), 9 years old, and 54 years old. This makes ANCHOR the most comprehensive human brainstem Atlas to date. In these three brainstems, we identified and manually annotated over 200 structures. We further characterized these structures with the seven IHC markers. We specifically describe the catecholaminergic groups in the human brainstem across all three age groups. In addition, we identified the protoplasmic commissural dendrites of the hypoglossal nucleus and we describe the pretectal nuclei in the Nissl-stained fetal 25 GW brainstem. ANCHOR includes an online viewer that integrates multimodal data, from magnetic resonance imaging and block face imaging to Nissl- and IHC-stained serial sections and 3D reconstruction of the entire brainstem. For the 9-year-old specimen, the online viewer allows simultaneous navigation of annotated sections with corresponding IHC, for viewing the specific region-wise cellular features accessible at https://anchor.humanbrain.in/.

neuroscience↗

bioRxiv2025

The H3K36M oncohistone inhibits NSD2 to activate a SETD2-dependent antiviral-like immune response in KRAS-driven lung cancer

Mutations in histone 3 at or near lysine 36 (H3K36) have dominantly acting oncogenic effects in multiple tumor types by limiting H3K36-directed methyltransferases. Paradoxically, we find that expression of the H3K36M oncohistone unexpectedly inhibits tumor formation in KRAS-driven lung adenocarcinoma by inducing a potent immune-mediated tumor clearance. Mechanistically, oncohistone expression derepresses endogenous retroviral element transcription, results in the accumulation of double-stranded RNA (dsRNA), and activates an innate antiviral-like immune response that eradicates tumor growth. Surprisingly, while inactivation of the H3K36 di-methyltransferase NSD2 replicated all effects of oncohistone expression, inactivation of the H3K36 tri-methyltransferase SETD2 abolished element derepression and all associated downstream anti-cancer effects that are induced by oncohistone expression. These observations restructure our understanding of the roles of H3K36 methylation, the consequences of its deregulation in cancer, and shape our expectations for therapeutic interventions targeting H3K36 methyltransferases.

cancer biology↗

bioRxiv2025

A nuclear branched-chain amino acid catabolism pathway controls histone propionylation in pancreatic cancer

Branched-chain amino acid (BCAA) catabolism contributes prominently to the TCA cycle in the healthy pancreas but is suppressed in pancreatic ductal adenocarcinoma (PDA). The impact of this metabolic remodeling on cancer phenotypes remains poorly understood. Here, we find that the BCAA isoleucine is a primary source of propionyl-CoA in PDA cells. Reduction of propionyl-CoA availability by either genetic perturbation or isoleucine and valine starvation decreases histone propionylation (Kpr) without impacting histone acetylation on specific lysine sites, correlating with reduced transcription of certain lipid- and immune-related genes. Mechanistically, we find that multiple enzymes of isoleucine catabolism unexpectedly localize to and carry out multi-step isoleucine oxidation within the nuclei of PDA cells. Importantly, nuclear localization of the rate-limiting branched-chain alpha ketoacid dehydrogenase (BCKDH) complex is essential for isoleucine-dependent Kpr and gene regulation. Moreover, we demonstrate that isoleucine-sensitive Kpr and its associated gene expression are driven by the MYST family of lysine acyltransferases (KATs), and that the BCKDHA subunit of the BCKDH complex interacts with KAT7 within the nuclear compartment. BCAA catabolism enzymes are apparent in the nuclei of PanIN lesions in mice and PDA tumors in patients, contrasting that in healthy pancreatic acinar and ductal cells. Collectively, these findings unveil a nuclear isoleucine catabolism pathway and highlight its role in controlling histone Kpr and tumorigenic transcriptional programs in PDA.

cancer biology↗

bioRxiv2023

Multiple TonB Homologs are Important for Carbohydrate Utilization by Bacteroides thetaiotaomicron

The human gut microbiota is able to degrade otherwise undigestible polysaccharides, largely through the activity of the Bacteroides. Uptake of polysaccharides into Bacteroides is controlled by TonB-dependent transporters (TBDT) whose transport is energized by an inner membrane complex composed of the proteins TonB, ExbB, and ExbD. Bacteroides thetaiotaomicron (B. theta) encodes 11 TonB homologs which are predicted to be able to contact TBDTs to facilitate transport. However, it is not clear which TonBs are important for polysaccharide uptake. Using strains in which each of the 11 predicted tonB genes are deleted, we show that TonB4 (BT2059) is important but not essential for proper growth on starch. In the absence of TonB4, we observed an increase in abundance of TonB6 (BT2762) in the membrane of B. theta, suggesting functional redundancy of these TonB proteins. Growth of the single deletion strains on pectin galactan, chondroitin sulfate, arabinan, and levan suggests a similar functional redundancy of the TonB proteins. A search for highly homologous proteins across other Bacteroides species and recent work in B. fragilis suggests that TonB4 is widely conserved and may play a common role in polysaccharide uptake. However, proteins similar to TonB6 are found only in B. theta and closely related species suggesting that the functional redundancy of TonB4 and TonB6 may be limited across the Bacteroides. This study extends our understanding of the protein network required for polysaccharide utilization in B. theta and highlights differences in TonB complexes across Bacteroides species. ImportanceThe human gut microbiota, including the Bacteroides, is required for the degradation of otherwise undigestible polysaccharides. The gut microbiota uses polysaccharides as an energy source and the fermentation products such as short chain fatty acids are beneficial to the human host. This use of polysaccharides is dependent on the proper pairing of a TonB protein with polysaccharide-specific TonB-dependent transporters; however, formation of these protein complexes is poorly understood. In this study, we examine the role of 11 predicted TonB homologs in polysaccharide uptake. We show that two proteins, TonB4 and TonB6, may be functionally redundant. This may allow for development of drugs targeting Bacteroides species containing only a TonB4 homolog with limited impact on species encoding the redundant TonB6.

microbiology↗

bioRxiv2023

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimers disease cases and 149 controls.

genomics↗

bioRxiv2021

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10,000 distinct trait-specific risk loci, but the underlying causal mechanisms at these loci remain incompletely characterized. We performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399,835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 TWAS signals associated with hematological traits distinct from previously discovered GWAS variants, including 10 completely novel gene-trait pairs corresponding to 9 unique genes. Among the 557 associations, 301 were available for replication in a cohort of 141,286 participants of European ancestry from the Million Veteran Program (MVP). Of these 301 associations, 199 replicated at a nominal threshold ( = 0.05) and 108 replicated at a strict Bonferroni adjusted threshold ( = 0.05/301). Using our TWAS results, we systematically assigned 4,261 out of 16,900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity to assign variants to target genes.

genetics↗

bioRxiv2021

A Neural Model of RNA Splicing: Learning Motif Distances with Self-Attention and Toeplitz Max Pooling

Alternative RNA splicing is an important regulator of tissue development and specificity, and a relevant mechanism in cancer progression. There exists a strong motivation to disentangle the rules that govern RNA splicing, in part because this knowledge may one day yield new clinically relevant diagnostic tools and therapeutics. It is no easy to task to reverse engineer how the splicesome machinery choreographs the removal and addition of RNA elements following transcription. Here, we propose an interpretable neural network called the Toeplitz ATtention Architecture (TATA), which learns distance-dependent motif interactions through a novel Toeplitz max pool layer that captures the relative distance between interacting CNN filters. TATA is a completely transparent "clear-box" solution: every model parameter is human-interpretable. We validate TATA on simulated data, then apply it to real data to identify putative cis-regulatory elements that interact with primary RNA splice sites.

bioinformatics↗

bioRxiv2020

Learning distance-dependent motif interactions: an interpretable CNN model of genomic events

In many biological studies, prediction is used primarily to validate the model; the real quest is to understand the underlying phenomenon. Therefore, interpretable deep models for biological studies are required. Here, we propose the Hyper-parameter eXplainable Motif Pair framework (HyperXPair) to model biological motifs and their distance-dependent context through explicitly interpretable parameters. This makes HyperXPair more than a decision-support tool; it is also a hypothesis-generating tool designed to advance knowledge in the field. We demonstrate the utility of our model by learning distance-dependent motif interactions for two biological problems: transcription initiation and RNA splicing.

bioinformatics↗

bioRxiv2019

A random walk down personalized single-cell networks: predicting the response of any gene to any drug for any patient

BackgroundThe last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient. MethodsWe take 2 approaches to benchmarking a "dual-channel" random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses. ConclusionsOur manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a "dual-channel" method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.

systems biology↗

bioRxiv2019

Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome

Technological advances in next-generation sequencing (NGS) and chromatographic assays [e.g., liquid chromatography mass spectrometry (LC-MS)] have made it possible to identify thousands of microbe and metabolite species, and to measure their relative abundance. In this paper, we propose a sparse neural encoder-decoder network to predict metabolite abundances from microbe abundances. Using paired data from a cohort of inflammatory bowel disease (IBD) patients, we show that our neural encoder-decoder model outperforms linear univariate and multivariate methods in terms of accuracy, sparsity, and stability. Importantly, we show that our neural encoder-decoder model is not simply a black box designed to maximize predictive accuracy. Rather, the networks hidden layer (i.e., the latent space, comprised only of sparsely weighted microbe counts) actually captures key microbe-metabolite relationships that are themselves clinically meaningful. Although this hidden layer is learned without any knowledge of the patients diagnosis, we show that the learned latent features are structured in a way that predicts IBD and treatment status with high accuracy. By imposing a non-negative weights constraint, the network becomes a directed graph where each downstream node is interpretable as the additive combination of the upstream nodes. Here, the middle layer comprises distinct microbe-metabolite axes that relate key microbial biomarkers with metabolite biomarkers. By pre-processing the microbiome and metabolome data using compositional data analysis methods, we ensure that our proposed multi-omics workflow will generalize to any pair of -omics data. To the best of our knowledge, this work is the first application of neural encoder-decoders for the interpretable integration of multi-omics biological data.

bioinformatics↗

bioRxiv2019

GraphDTA: prediction of drug-target binding affinity using graph convolutional networks

The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug--target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug--target affinity. We show that graph neural networks not only predict drug--target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug--target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. Availability of data and materialsThe proposed models are implemented in Python. Related data, pre-trained models, and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post-hoc statistical analysis are available from https://doi.org/10.5281/zenodo.3603523. ContactThin.Nguyen@deakin.edu.au

bioinformatics↗

bioRxiv2019

DeepTRIAGE: Interpretable and Individualised BiomarkerScores using Attention Mechanism for the Classification of Breast Cancer Sub-types

MotivationBreast cancer is a collection of multiple tissue pathologies, each with a distinct molecular signature that correlates with patient prognosis and response to therapy. Accurately differentiating between breast cancer sub-types is an important part of clinical decision-making. Already, this problem has been addressed using machine learning methods that separate tissue samples into distinct groups. However, there remains unexplained heterogeneity within the established sub-types that cannot be resolved by the commonly used classification algorithms. In this paper, we propose a novel deep learning architecture, called DeepTRIAGE (Deep learning for the TRactable Individualised Analysis of Gene Expression), which not only classifies cancer sub-types with comparable accuracy, but simultaneously assigns each patient their own set of interpretable and individualised biomarker scores. These personalised scores describe how important each feature is in the classification of each patient, and can be analysed post-hoc to generate new hypotheses about intra-class heterogeneity. ResultsWe apply the DeepTRIAGE framework to classify the gene expression signatures of luminal A and luminal B breast cancer sub-types, and illustrate its use for genes and gene set (i.e., GO and KEGG) features. Using DeepTRIAGE, we find that the GINS1 gene and the kinetochore organisation GO term are the most important features for luminal sub-type classification. Through classification, DeepTRIAGE simultaneously reveals heterogeneity within the luminal A biomarker scores that significantly associate with tumour stage, placing all luminal samples along a continuum of severity. Availability and implementationThe proposed model is implemented in Python using Py-Torch framework. The analysis is done in Python and R. All Methods and models are freely available from https://github.com/adham/BiomarkerAttend.

biophysics↗

bioRxiv2019

Personalized Annotation-based Networks (PAN) for the Prediction of Breast Cancer Relapse

The classification of clinical samples based on gene expression data is an important part of precision medicine. However, it has proved difficult to accurately predict survival outcomes and treatment responses for cancer patients. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graph-based methods to improve classifier performance. Existing approaches to personalized gene networks all have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced. Here, we propose a novel method, called Personalized Annotation-based Networks (PAN), that avoids this limitation by using curated annotation databases to transform gene expression data into a graph. These databases organize genes into overlapping gene sets, called annotations, that we use to build a network where nodes represent functional terms and edges represent the similarity between them. Unlike competing methods, PANs are calculated for each sample independent of the population, making it a more efficient way to obtain single-sample networks. Using three breast cancer datasets as a case study (METABRIC and a super-set of GEO studies), we show that PAN classifiers not only predict cancer relapse better than gene features alone, but also outperform PPI and population-level graph-based classifiers. This work demonstrates the practical advantages of graph-based classification for high-dimensional genomic data, while offering a new approach to making sample-specific networks. Supplementary informationThe codes and data are available at https://github.com/thinng/PAN. ContactThuc.Le@unisa.edu.au

bioinformatics↗

bioRxiv2018

A cross-cancer metastasis signature in the microRNA-mRNA axis of paired tissue samples

In the progression of cancer, cells acquire genetic mutations that cause uncontrolled growth. Over time, the primary tumour may undergo additional mutations that allow for the cancerous cells to spread throughout the body as metastases. Since metastatic development typically results in markedly worse patient outcomes, research into the identity and function of metastasisassociated biomarkers could eventually translate into clinical diagnostics or novel therapeutics. Although the general processes underpinning metastatic progression are understood, no consistent nor clear cross-cancer biomarker profile has yet emerged. However, the literature suggests that some microRNAs (miRNAs) may play an important role in the metastatic progression of several cancer types. Using a subset of The Cancer Genome Atlas (TCGA) data, we performed an integrated analysis of mRNA and miRNA expression with paired metastatic and primary tumour samples to interrogate how the miRNA-mRNA regulatory axis influences metastatic progression. From this, we successfully built mRNAand miRNA-specific classifiers that can discriminate pairs of metastatic and primary samples across 11 cancer types. In addition, we identified a number of miRNAs whose metastasis-associated dysregulation could predict mRNA metastasis-associated dysregulation. Among the most predictive miRNAs, we found several previously implicated in cancer progression, including miR-301b, miR-1296, and miR-423. Taken together, our results suggest that cross-cancer metastatic samples have unique biomarker signatures when compared with paired primary tumours, and that these miRNA biomarkers can be used to predict both metastatic status and mRNA expression.

cancer biology↗