Biology subjects

Srinivasan, S.

Publications and source records attributed to Srinivasan, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Unsupervised source separation and clustering of soundscape recordings predicts avian richness in hyperdiverse systems

1: Passive Acoustic Monitoring (PAM) has emerged as a leading approach for biodiversity research, but analysing the vast quantities of data produced by this method remains challenging. Acoustic indices are often employed to estimate species richness in soundscape recordings, but their generalisability and overall efficacy in speciose regions has increasingly been challenged. An alternative, multi-species classification models, still suffer from performance limitations, particularly in the tropics. 2: We introduce an unsupervised approach that estimates avian species richness using an integrated source separation model trained on birds (BirdMixIT), a leading bird vocalization embedding model (Perch 2.0), and hierarchical agglomerative clustering. We count the clusters of embeddings generated from separated channels to estimate the species richness in recordings at differing timescales. 3: We test the generalisability and predictive performance of our approach, "source-clustering", using a large combined dataset featuring 103 total hours of manually-annotated soundscape recordings from the Amazon rainforest in Peru and Brazil. To compare the performance of source-clustering to existing approaches, we estimated species richness in the same recordings with both traditional acoustic indices and Perch classifications at various confidence thresholds. 4: Our source-clustering method performed consistently well between our separate datasets. Source-clustering performed better for predicting avian species richness than traditional acoustic indices at all levels, and was complementary with Perch, performing similarly overall to its best settings combinations, though it was more robust for short recordings, and should be more resilient in locations with less training data. Source-clustering generally predicted species richness better than the best-performing Perch settings at the 15-second, 1-minute, and 15-minute temporal resolutions, while most settings of Perch performed better at the 1-hour resolution. 5: Source-clustering can complement or exceed the performance of presently established methods for estimating avian species richness from soundscape recordings, namely traditional acoustic indices and the out-of-box versions of leading automated identification modes. This is particularly true at narrow temporal resolutions, in systems where training data is scarce, and where species density is high enough to generate significant overlapping vocalisations, rendering it especially useful in hyperdiverse tropical ecosystems where other methods for interpreting soundscape recordings face persistent challenges.

bioinformatics↗

bioRxiv2026

Tobacco Carcinogen Induces Persistent DNA Damage and Homologous Recombination Dysregulation in Pancreatic Ductal Carcinogenesis

Cigarette smoking is a well-established risk factor for the development of pancreatic ductal adenocarcinoma (PDAC), yet the mechanisms by which tobacco carcinogens, such as Nicotine-Derived Nitrosamine Ketone (NNK), drive pancreatic carcinogenesis remain incompletely understood. We investigated the impact of NNK on pancreatic epithelium by treating human pancreatic ductal epithelial cells (HPDE6-E6E7; H6c7) with NNK for 5 or 50 days followed by genome-wide RNA sequencing, qRT-PCR, immunofluorescence, comet assay, and {gamma}H2AX ELISA. We assessed {gamma}H2AX by immunohistochemistry in human PDAC tissue microarrays and in TCGA-PAAD datasets to identify clinical correlates. Acute NNK exposure (5 days) induced DNA double-strand breaks and activated ATM-{gamma}H2AX signaling, accompanied by increased RAD51 and BRCA2 recruitment, consistent with activation of homologous recombination (HR)-mediated DNA repair. In contrast, chronic NNK exposure (50 days) resulted in persistent DNA damage signaling and transcriptional upregulation of HR-associated genes, including RAD51, BRCA1, BRCA2, FANCD2, and PLK1. Despite sustained accumulation of RAD51- and BRCA2-positive repair foci, chronic NNK exposure was associated with altered RAD51 localization, persistent DNA damage, and chromosomal instability, suggesting dysregulation of HR repair fidelity during prolonged genotoxic stress. {gamma}H2AX expression was significantly elevated in NNK-treated cells, KRAS-driven premalignant pancreatic lesions, and pancreatic tissues from smokers. Increased {gamma}H2AX expression correlated with tumor progression and poor overall survival, while elevated RAD51 expression was associated with adverse outcomes in patients with PDAC. Collectively, these findings reveal a biphasic DNA damage response to NNK characterized by early activation of ATM-{gamma}H2AX-dependent DNA repair followed by persistent DNA damage signaling, dysregulated HR repair, and genomic instability during chronic exposure. Tobacco-induced disruption of DNA repair homeostasis may represent a key mechanism driving pancreatic carcinogenesis and supports {gamma}H2AX as a clinically relevant prognostic biomarker in tobacco-associated PDAC.

cancer biology↗

bioRxiv2026

Tuning nanoscale lipid clustering controls influenza viral binding

Influenza virus attaches to cells by binding to sialylated glycans. Subsequent endocytosis of the virus and endosomal maturation leads to a conformational change in the hemagglutinin coat protein that ultimately results in membrane fusion and release of the viral genome into the host cytoplasm, where it is trafficked to the nucleus and commences replication. Individual hemagglutinin-glycan binding affinities are weak (Kd approximately mM), so multivalency is key to viral attachment. Receptor density or clustering has previously been shown to affect influenza binding, but the overall spatial heterogeneity of both plasma and endosomal membranes has not been fully addressed. Here, we show that nanoscale lipid clustering can increase local concentration of glycosphingolipids and greatly enhance influenza viral binding in a way that macroscopic phase separation does not, and that binding avidity is dependent on the spatiotemporal scales of this clustering. This clustering is driven by sphingolipids and cholesterol and the nanoscale lipid environment of these clusters persists to affect the viral entry process. This finding demonstrates how fine receptor patterning can drive infection and illustrates the importance of tuning spatial scale in liquid-liquid phase separation.

biophysics↗

bioRxiv2026

Constructing microbiome co-occurrence networks with confidence: A conditional, nonparametric, inference-based approach

Constructing microbial association networks is a common strategy for exploring relationships among taxa in microbiome studies. Although marginal correlation methods are easy to implement and allow formal inference, they can produce spurious edges driven by indirect associations through other taxa. Conditional graphical-modeling methods aim to recover direct associations, but many rely on Gaussian or linear assumptions and often provide limited uncertainty quantification. We propose a conditional, nonparametric approach based on the scaled expected conditional covariance (SEcov). SEcov measures population-level conditional association by residualizing each taxon with respect to the remaining taxa and scaling the resulting expected conditional covariance. The resulting estimator can incorporate flexible machine-learning methods for conditional-mean estimation and admits asymptotic normal inference, enabling p-values and confidence intervals for taxon-pair associations. We demonstrate through simulation studies that our proposed approach improves network recovery relative to other methods, and we illustrate the new method via construction of a co-occurrence network for the vaginal microbiome during pregnancy. IMPORTANCEHigh-throughput sequencing has made it possible to characterize microbial communities at large scale, and network analysis is widely used to summarize relationships among taxa. However, networks based on marginal correlations may include indirect associations, whereas many conditional graphical models rely on assumptions that may be difficult to justify for sparse, zero-inflated, compositional microbiome data. SEcov offers a practical alternative by estimating conditional associations nonparametrically and attaching inferential uncertainty to individual edges. This allows investigators to construct microbiome networks using statistically interpretable evidence for taxon-pair associations, rather than relying solely on arbitrary correlation cutoffs or regularization tuning parameters.

bioinformatics↗

bioRxiv2026

Chromatin Landscape of Cancer Cell Lines Identifies Enhancer Subtypes

Epigenetic aberrations are a hallmark of cancer; however, systematic chromatin state maps of cancer cells are unavailable. We generated and analyzed 803 histone mark profiles in 142 cancer cell lines and 114 human tumors belonging to 9 solid tumor types. Irrespective of their cell-of-origin, cancer cells segregate from normal tissues based on their enhancer patterns, suggesting enhancer deregulation is a fundamental epigenetic feature in cancer. Enhancer based clustering defined 5 distinct subgroups of cancer cells (EpiC1-5) with unique developmental trajectories, molecular features and dependencies. Importantly, we define a set of core TFs that are critical for EpiC-specific enhancer patterns and survival. Notably, EpiC4 represented a predominantly epigenetic, pan-cancer subtype that displays poor survival, activation and dependence on a FN1-CAV1-SRC-PI3K-AKT signaling network. Together, these data uncover enhancer heterogeneity in pan-cancer systems with identification of a novel enhancer-based subtype and identify potential new therapeutic targets associated with unique epigenetic features.

cancer biology↗

bioRxiv2026

ViralEpiBase: a manually curated repository of epitranscriptomic modification sites across viral RNA genomes and virus-encoded transcripts

Post-transcriptional chemical modifications of RNA, collectively termed the epitranscriptome, have emerged as critical regulatory layers governing viral replication, pathogenicity, and host- virus interactions. Despite the rapid accumulation of experimental data on viral RNA modifications, no dedicated, freely accessible resource existed for systematically cataloguing these sites across diverse viral species. Here we present ViralEpiBase, a manually curated database of epitranscriptomic modification sites identified in viral RNA genomes and virus-encoded transcripts at single-nucleotide resolution. ViralEpiBase currently integrates seven chemically distinct RNA modification types: N6-methyladenosine (m6A), N1-methyladenosine (m1A), pseudouridine ({Psi}), 5-methylcytosine (m5C), 2'-O-methylation (2'OMe), inosine and N4-acetylcytidine (ac4C); across 12 viral species encompassing both DNA and RNA viruses of clinical and biological significance. Each entry is linked to its primary literature source or deposited dataset and is retrievable by modification type, genomic coordinates, or viral taxonomy. The database is freely accessible through an intuitive web interface and is updated continuously as new experimental evidence becomes available. ViralEpiBase thus provides the first unified platform dedicated exclusively to viral epitranscriptomics and is designed to facilitate mechanistic investigation of RNA modification functions in viral biology.

bioinformatics↗

bioRxiv2026

Multi-timescale learning signals in Drosophila dopaminergic neurons

Learning often requires inference over hidden task structure, including features that predict outcomes. In mammals, prediction-error signaling by midbrain dopamine neurons is considered central to learning, but how these neurons reflect the progression and stability of learning remains unclear. Using Drosophila to monitor calcium activity at trial-by-trial resolution during aversive conditioning, we found that PPM3 dopaminergic neurons exhibit hallmark prediction-error responses in which activity shifted from the unconditioned stimulus (US) to the conditioned stimulus (CS), was suppressed when an expected US was omitted, and increased when the US exceeded expectations. Strikingly, the same neurons also exhibited slower state-like dynamics across trials, including tonic activity transitions that emerged with learning and tracked the acquisition of learned behavior, and perturbation-related dynamics that were briefly disrupted when expectations were violated. Increasing task demands by inserting a temporal gap between the CS and US (trace conditioning) delayed both response types to later trials, accompanied by corresponding delays in behavior acquisition. In addition, dopaminergic activity developed an anticipatory response that tracked the expected timing of the US during the gap. These findings reveal that a single dopaminergic neuron type integrates moment-to-moment prediction errors, expected outcome timing, and a longer-timescale signal reflecting learning stabilization, establishing Drosophila as a bona fide model for dissecting the neural mechanisms that shape learning under changing task demands.

neuroscience↗

bioRxiv2026

The unconventional kinesin Kif26a is required for the guidance of major axon tracts in the developing mouse brain

Kif26a and Kif26b encode a family of unconventional kinesins with emerging roles in neurodevelopment, and mutations in both genes have been implicated in a spectrum of neurodevelopmental disorders. The precise mechanisms by which the Kif26 family orchestrates mammalian brain development, however, remain unclear. In this study, we show that Kif26a and Kif26b are expressed in distinct regions of the developing mouse brain. Using a new allelic series in mice, we demonstrate that Kif26a is required for the guidance of multiple forebrain axon tracts. This requirement is direct and cell autonomous, as cell proliferation, survival, and cortical layering are unaffected in the Kif26a mutants. These guidance defects closely resemble those reported for the Fzd3-Celsr3-Dystroglycan pathway, suggesting that Kif26a may participate within this conserved signaling axis to steer growing axons.

neuroscience↗

bioRxiv2026

The Chromosome-Scale Genome of Phyllanthus niruri Reveals Candidate Genes and a Putative Biosynthetic Framework for Phyllanthin Formation.

Phyllanthus niruri (Phyllanthaceae) is a medicinally important herb known for producing phyllanthin, a bioactive dibenzylbutane lignan with reported hepatoprotective and antioxidant properties. However, the biosynthetic basis of phyllanthin production remains unresolved, largely due to the absence of a reference genome for the species. We report a Chromosome-Scale Assembly of P. niruri generated by integrating PacBio HiFi long reads and Illumina short reads, followed by reference-guided scaffolding against Phyllanthus cochinchinensis. The assembly has an L50 of 7 and 97.6% BUSCO completeness. Annotation predicted 19,254 protein-coding genes (91.1% functionally annotated), with phenylpropanoid biosynthesis emerging as the most enriched specialized-metabolism pathway in the genome. Using pathway-guided genome mining, structural similarity analysis, and comparative metabolic reconstruction, we propose a putative biosynthetic pathway for phyllanthin originating from the phenylpropanoid-lignan branch through secoisolariciresinol-like intermediates, followed by terminal O-methylation reactions. A total of 305 unique candidate genes associated with the proposed pathway were identified, including expanded families of dirigent proteins, peroxidases, secoisolariciresinol dehydrogenases, and O-methyltransferases. Comparative transcriptomic analyses across related Phyllanthus species further supported the proposed pathway through coordinated expression of lignan-associated genes and tissue-specific enrichment of O-methyltransferases. This work provides the first reference genome for P. niruri and a prioritized candidate gene set for functional characterization of phyllanthin biosynthesis.

genomics↗

bioRxiv2026

Synthetic Biomolecular Condensates as Tunable Microtubule Assembly Hubs

Phase separation of proteins and nucleic acids (NAs) into nano-to-microscale condensates can regulate biochemical processes, including assembly and organization of cytoskeletal networks such as actin and microtubules. This study examines the functional role of condensate material properties in microtubule assembly. Learning from the sequence grammar of naturally occurring intrinsically disordered regions in microtubule-associated proteins, two-component peptide-NA condensates with programmable material properties were designed. These synthetic condensates catalyze tubulin polymerization into microtubule filaments with tunable outcomes. Tubulin preferentially partitions to the condensate interface and nucleates microtubule assembly. Enhanced tubulin self-assembly produces long filaments that exhibit branching and bundling. Using a minimal stochastic chemo-mechanical model, we show that sequence-encoded condensate viscoelasticity is a tunable element that controls filament morphologies and identifies interfacial rheology as the key regulator of filament growth. Fluorescence recovery after photobleaching experiments support this model, revealing a direct correlation between interfacial tubulin mobility and condensate-directed microtubule assembly. Distinct regimes emerge due to competition between bulk adsorption and lateral diffusion of tubulin at the condensate interface, which determines whether filament tips grow or stall. Since dynamic microtubule assembly and restructuring are essential for various cellular functions, this work highlights a critical role of condensate interfacial rheology in cytoskeletal organization.

biophysics↗

bioRxiv2026

The Common Fund Data Ecosystem (CFDE)

The NIH Common Fund Data Ecosystem (CFDE) integrates data resources from 18 NIH Common Fund programs for discovery and integrative analysis. These programs generate valuable but heterogeneous datasets that can be difficult to discover, access, and reuse. CFDE aims to provide a collaborative, community-built infrastructure that links and enriches Common Fund programs. We describe the evolution, structure, and core technologies of CFDE, including practical approaches that support submission, integration, visualization, and public release of multimodal data. Training programs and workforce initiatives lower barriers to adoption. CFDE has devised solutions to critical issues facing cross-program initiatives, including data scale and heterogeneity, dataset integration, and long-term sustainability. We demonstrate the utility of linking Common Fund resources through integrative tools and cross-dataset queries to yield insights that would otherwise be infeasible. Collectively, CFDE shows that a standards-driven, federated approach enhances and unifies cross-disciplinary resources, fostering collaboration and data-driven discovery.

scientific communication and education↗

bioRxiv2026

3D chromatin compartment of round spermatids encodes the spatiotemporal program of histone-to-protamine exchange in spermiogenesis

Sperm formation requires a radical chromatin reorganization, where nucleosomes are replaced by transition proteins (TNPs) and subsequently by protamines (PRM1 and PRM2). Although essential for fertility, the regulatory logic governing this exchange is unknown, but its presumed to be stochastic and unregulated. Using endogenously tagged PRM mouse models and stage-resolved, genome-wide profiling, we revise the order of histone-to-protamine exchange and show that the chromatin remodeling process is highly programmed. Imaging and biochemical experiments reveal a direct histone-to-PRM1 exchange, while TNPs appear after PRM1 but precede PRM2 incorporation. This temporal uncoupling of PRM1 and PRM2 incorporation coincides with dynamic, region-specific chromatin remodeling that is not governed by histone acetylation but is instructed by the three-dimensional nuclear architecture of round spermatids. Therefore, by integrating ATAC-seq, CUT&Tag, and Hi-C, we define a compartment-encoded "blueprint" that prescribes the assembly of the mature sperm epigenome and establishes a complex molecular hierarchy for the histone-to-protamine exchange.

genomics↗

bioRxiv2026

A consensus genome sequence for the social amoeba Dictyostelium giganteum

The life cycle of the Dictyostelid amoebae is unusual in that it alternates between a free-living solitary phase and an aggregative social phase. We used six previously collected Dictyostelium giganteum strains from distinct ecological niches in the Mudumalai Nature Reserve, India. From them, we generated short Illumina reads and assembled a consensus genome, comprising nuclear and mitochondrial genomes, representative of all six strains. The nuclear assembly has an AT content of 75.76%, accounting for 38.52 Mb, and resolves into five chromosome-scale scaffolds that are consistent with the published karyotype. Its N50 is 3.01 Mb and L50 is 5. The BUSCO analysis shows 3.9% fragmentation and 92.1% completeness. Genome assembly and completeness are also validated using [~]5,500 genes from a publicly available transcriptome dataset (PRJNA48443) derived from the post-aggregation stage. 13,251 predicted proteins are encoded by the genome, including ABC transporters, polyketide synthases, Ras/Rho GTPases, and expanded families of protein kinases. Comparative analysis demonstrates extensive conservation of syntenic blocks related to the dictyostelids D. discoideum and D. firmibasis as well as lineage-specific rearrangements. About 18% of the nuclear genome is made up of repetitive DNA, mostly in the form of simple repeats. Major transposable element classes, including piggyBac-like fragments, were found by homology searches. Long poly-asparagine/glutamine tracts are less common than in D. discoideum, but low-complexity sequences are common due to strong AT-driven codon bias. Comparative proteome-level orthology analysis across Dictyostelium species and Entamoeba identified a conserved Amoebozoan core together with a substantial Dictyostelium-specific gene repertoire. Domain-level comparisons further revealed widespread conservation of intracellular signalling and cytoskeletal modules shared with animals, whereas canonical metazoan extracellular adhesion domains were absent, highlighting the deep evolutionary roots of regulatory complexity underlying aggregative multicellularity.

genomics↗

bioRxiv2026

Structural determinants of broadly neutralizing human antibodies binding to morphological dengue virus variants.

Dengue virus (DENV) is a global health threat, with four antigenically distinct serotypes impeding vaccine and therapeutic development. We describe here, structural features of patient-derived, heavy chain-driven E-dimer-recognizing (HEDR) antibodies that achieve potent, pan-serotype activity by binding a conserved quaternary epitope on DENVs envelope (E) glycoprotein dimer. Single-particle cryo-EM structures of three HEDR broadly neutralizing antibodies (bnAbs) complexed to DENV delineate epitope engagement via E-glycoproteins fusion loop region and conserved glycans. We also identify capsule-shaped DENV, with helical cryo-EM reconstructions of Fab-bound tubular DENV from different strains revealing that the E-glycoprotein arrangement is determined by virus strain, not bnAb identity. Our findings elucidate key binding features of HEDR bnAbs and signify the need for intervention strategies capable of neutralizing diverse DENV morphologies.

biophysics↗

bioRxiv2026

Cerebellar circuits anticipate dopamine rewards

Learning motivated behaviors requires both anticipating rewards and reinforcing actions that yield them. Although cerebellar activity encodes natural rewards like water and food, it also coordinates physical movements like eating and drinking. To disentangle reward from consummatory movements, we trained mice to push for delayed dopamine rewards delivered directly into the brain. Here we show that cerebellar input streams use both predictive and instructive codes for dopamine reward. Two-photon imaging revealed that many cerebellar granule cells (GrCs) predictively encoded dopamine rewards with sustained activity that "stretched" to match 1- or 2-s delay intervals before terminating upon reward receipt. By contrast, most cerebellar climbing fibers (CFs) spiked just after dopamine delivery. In mice also trained with water rewards, encoding strength for dopamine matched or exceeded that for water. Both cell types contributed causally: chronic GrC inhibition disrupted self- stimulation learning, and CF self-stimulation "rewards" drove moderate operant learning in naive animals. Thus, cerebellar encoding of dopamine reward helps drive motivated behavior, suggesting deeper cerebellar integration in brain reward prediction networks.

neuroscience↗

bioRxiv2026

Distinct CA1 inputs support shifts in neural dimensionality and memory resolution

Event-based memories may be encoded with high (e.g., episodic) or low (e.g., gist) mnemonic resolution. While the CA1 region of the hippocampus encodes events at both scales, it is unclear how such dual coding emerges. Consistent with theoretical predictions1,2, here we show that trisynaptic (from CA3) and monosynaptic (from the medial entorhinal cortex) projections to CA1 encode high- and low-resolution event-based memories, respectively. Recruitment of the trisynaptic pathway during contextual fear conditioning promotes feedforward inhibition of CA1 via activation of parvalbumin-positive (PV+) interneurons. This allows sparse encoding of event-based memories in high-dimensional neural states that support high-resolution contextual fear memories, where mice exhibit conditioned fear only in the training context and not similar contexts. Inhibiting this input during training reduces activation of CA1 PV+ interneurons and feedforward inhibition, shifting neural dynamics to low-dimensional states that support low-resolution contextual fear memories where mice freeze in the training context and similar contexts. These experiments identify a circuit-based mechanism that causally links the dimensionality of CA1 neural dynamics to mnemonic resolution.

neuroscience↗

bioRxiv2026

circDiscoverer: A resource for curated and predicted circRNA-Protein interactions with experimental support tools

Circular RNAs (circRNAs) regulate various cellular processes by interacting with miRNAs, proteins, and by encoding peptides. Compared to circRNA-miRNA interactions, circRNA-protein interactions (CPIs) and circRNA modifications have been relatively less explored. Here, we developed a comprehensive user-friendly web resource, CircDiscoverer, to explore CPIs and modification landscapes across multiple species, including Homo sapiens, Mus musculus, Drosophila melanogaster, and Arabidopsis thaliana. CircDiscoverer integrates manually curated literature-supported CPIs with computationally predicted interactions. Furthermore, it offers comprehensive insights into protein-binding profiles and detailed information on interacting circRNAs, including qRT-PCR primer sequences, guide RNAs for experimental validation and potential RNA modifications, thereby providing an integrated platform for further research. CircDiscoverer is available at https://aclab.iiserb.ac.in.

bioinformatics↗

bioRxiv2025

Projection-specific Routing of Odor Information in the Olfactory Cortex

Sensory processing in the mammalian cortex relies on extensive feedforward and feedback connections, yet how information is routed along these pathways remains poorly understood. Here, we examined the functional properties of feedback and feedforward neurons in the mouse olfactory (piriform) cortex. We selectively labeled neurons projecting to the olfactory bulb (OB, feedback) or medial prefrontal cortex (mPFC, feedforward) and recorded their activity during passive odor exposure and learning of an odor discrimination task. We found that odor identity and reward associations were encoded by OB-projecting ensembles early during odor exposure, whereas mPFC-projecting neurons encoded this information later, aligned with behavioral responses. Moreover, mPFC-projecting neurons maintained a stable representation of valence across days, while OB-projecting neurons exhibited pronounced plasticity. Together, these findings reveal that odor information is selectively routed through feedforward and feedback pathways and suggest that the functional properties of piriform neurons mirror the computational demands of their downstream targets.

neuroscience↗