Biology subjects

Mohanty, S. K.

Publications and source records attributed to Mohanty, S. K..

12 recordsLinked to original sources

bioRxiv2026

The Gαi/o GPCR LPAR2 Acts as a Non-Classical Factor for Aldosterone Induced Injury in a Cardiomyocyte Model

Aldosterone is a key regulator of systemic sodium, potassium, and fluid homeostasis. Chronic elevation of aldosterone, as observed in primary aldosteronism, chronic heart and kidney diseases, promotes cardiac hypertrophy, inflammation, fibrosis, and heart failure, which are further enhanced in aging. Although these pathological effects are primarily attributed to the mineralocorticoid receptor (MR), emerging evidence suggests roles for nonclassical G protein-coupled receptor (GPCR) signaling. We identified lysophosphatidic acid receptor 2 (LPAR2) as a novel GPCR candidate that mediates aldosterone signaling in cardiomyocytes. Using AC16 human cardiomyocyte cells and reporter gene assays, we demonstrate that aldosterone activates a Gi/o-dependent GPCR pathway. Pharmacological inhibition of LPAR2 internalization confirms its specific reaction to aldosterone. Functional studies revealed that aldosterone-induced cardiomyocyte injury requires both MR and LPAR2, where specific antagonists to both receptors reduced the ability of aldosterone to induce collagen expression and cell death markers. These findings uncover a previously unrecognized non-classical mechanism of aldosterone action in the heart and highlight LPAR2 as a promising therapeutic target for aldosterone-driven cardiovascular disease.

systems biology↗

bioRxiv2026

HPRC2: A human pangenome reference with near-complete coverage of common genetic variation

A pangenome reference overcomes the inherent limitation of any individual reference genome by integrating the variation present in a population. We present the Human Pangenome Reference Consortiums (HPRC) Release 2 (HPRC2), an openly available, second phase pangenome that is an approximately fivefold expansion in genome number over HPRC Release 1 (HPRC1) and measurable improvement in genome completeness, contiguity, and accuracy. Selecting samples with a principled algorithm prioritising common variant coverage, HPRC2 contributes 460 haplotypes that together capture over 99% of common variation observed in the All of Us Research Program v8 cohort. Combining high-coverage long and ultra-long reads with modern assemblers and polishers, we produce thousands of telomere-to-telomere (T2T) chromosomes, and relative to HPRC1 halve the number of structurally unreliable regions as well as individual base errors per haplotype. We complement the assemblies with whole genome multiple alignments and gene annotations, and derive formal pangenome coordinate systems for addressing off-reference variation, demonstrating that individual human genomes contain more than one hundred thousand variants not succinctly described with respect to existing reference genomes. We also present the first matched long-read backed pantranscriptome and panepigenome at this scale, provide continuous local-ancestry estimates spanning every genome, and outline a host of new tools and applications that leverage the pangenome resource for improved genomics analysis.

genomics↗

bioRxiv2026

Variation and selection at predicted G-quadruplexes across the human pangenome

G-quadruplexes (G4s), non-canonical DNA structures whose sequence motifs occupy approximately 1% of the human genome, are important for myriad cellular functions, including regulating transcription and replication. Yet they also contribute to genomic instability by increasing mutations and structural variation. Despite their significance, G4 motifs have not been studied in detail across multiple human genomes. Here, we conducted a comprehensive analysis of presence/absence and sequence variation, measured selection strength, and evaluated gene expression regulation potential for predicted G4s (pG4s) across population groups in the second release of the Human Pangenome Reference Consortium dataset, comprising high-quality, near-telomere-to-telomere diploid genomes from 231 individuals worldwide, along with three reference assemblies. Across the human pangenome, we identified over 353 million pG4s, including 1.15 million pG4s absent from reference assemblies but shared across other haplotypes. Our analysis revealed that pG4 sharing patterns recapitulate human population structure: African individuals displayed lower levels of pG4 sharing than non-Africans, whereas East Asian individuals exhibited higher levels of sharing. By analyzing the site frequency spectrum across various genomic annotations, we computed and compared selection coefficients (Sd) at pG4 vs. non-pG4 sites. As expected, the strongest purifying selection (Sd [≥] 10) was detected at protein-coding exons, where pG4 sites had similar or lower selection coefficients compared with those for pG4 sites. Strikingly, this pattern reversed at regulatory regions: although purifying selection was weaker overall at promoters, introns, enhancers, and replication origins (1 [≤] Sd < 10), pG4 sites at these regions experienced stronger selection than non-pG4 sites--suggesting that pG4s play functional roles outside coding sequences. Additionally, by integrating pG4 data with long-read transcriptome data profiles from this large cohort, we found that pG4s located at promoters and at (or near) exon-intron junctions may influence variation in gene expression levels and transcript isoforms, respectively, across the human pangenome individuals. Leveraging extensive population-scale data, our research illuminates the fundamental importance and functional relevance of G4s across human genomes.

genomics↗

bioRxiv2026

Uncertainty-aware benchmarking reveals ambiguous transcripts in mRNA-lncRNA classification

BackgroundLong non-coding RNAs (lncRNAs) have gained significant attention in recent years, yet distinguishing them from protein-coding transcripts remains challenging. Indeed, many lncRNAs share mRNA-like processing and existing sequence-derived signals do not fully capture the coding/non-coding boundary. Recent GENCODE annotation efforts revealed tens of thousands of novel lncRNA sequences as well as the reclassification of some lncRNAs into the protein-coding class, highlighting the need to better characterize transcript features associated with classification uncertainty and errors. ResultsWe performed uncertainty-aware benchmarking by retraining and evaluating eight transcript classifiers under a controlled protocol on a label-stable GENCODE v46-v47 subset. Beyond conventional model evaluation metrics, we quantified inter-tool agreement and entropy-based uncertainty to stratify transcripts into consensus, discordant, and consensus-error groups. To expand standard sequence and ORF-derived signals, we incorporated repeat-derived features from mature transcripts and non-B DNA motif features across gene bodies. Although aggregate performance was high, [~]45% of transcripts showed inter-tool discordance, particularly among lncRNAs. Feature analyses linked low-uncertainty predictions to strong coding-like signals, whereas high-uncertainty profiles exhibited mixed signatures. Alongside classical predictors in global importance analyses, repeat-derived features appear as main contributors. ConclusionsBy combining controlled benchmarking with transcript-level agreement and uncertainty stratification, together with extended feature profiling, we identified patterns associated with classifier disagreement and misclassification. This novel framework provides practical guidance for interpreting predictions, motivating the development of more robust coding/non-coding classifiers, while also shedding light on the sequence properties that distinguish lncRNA sequences.

bioinformatics↗

bioRxiv2025

Chemical Dice Integrator (CDI): A Scalable Framework for Multimodal Molecular Representation Learning

The machine learning landscape for molecular property prediction is fragmented, with numerous Featurizers each capturing a narrow, specialized view of chemical structure. This heterogeneity forces a suboptimal choice of representation a priori, limiting model generalizability. We introduce the Chemical Dice Integrator (CDI), a hierarchical framework that unifies six orthogonal molecular representations, physicochemical (Mordred), topological (GROVER), visual (ImageMol), biological (Signaturizer), quantum-mechanical (MOPAC), and linguistic (ChemBERTa), into a single, coherent embedding. The framework consists of CDI-Basic, a two-tiered autoencoder that fuses these modalities, and CDI-Generalised, a Mamba State-Space Model (SSM) that learns a direct, efficient map from SMILES strings to the unified embedding space. Extensive benchmarking across 23 classification (171 tasks) and 10 regression datasets demonstrates that CDI embeddings consistently achieve superior predictive performance compared to individual Featurizers and standard feature aggregation methods. The CDI-Generalised model achieves this performance with exceptional computational efficiency, outperforming deep learning Featurizers in terms of speed and resource overhead. Furthermore, we demonstrate that the CDI embedding is chemically intuitive, allowing for the sensitive distinction of nuanced structural variants, such as chiral enantiomers and kekulized SMILES forms. By bridging multimodal chemical intelligence with scalable, sequence-based inference, CDI offers a strong foundation for molecular machine learning.

bioinformatics↗

bioRxiv2025

SynGlue: AI-Driven Designer for Clinically Actionable Multi-Target Therapeutics

The rational design of protein degraders, such as proteolysis-targeting chimeras (PROTACs), requires the simultaneous optimization of multiple molecular properties, a complex challenge that limits efficient discovery. Here, we introduce SynGlue, a generative artificial intelligence (AI) framework that addresses this challenge through two core modules: data-driven, leveraging large-scale protein-ligand intelligence, and structure-guided, for physics-aware molecular design. SynGlue harness MagnetDB, a curated database of 6.37 million experimental protein-ligand interactions, and couples it with deep learning models that quantitatively predict degradation potency (DC50), maximal degradation (Dmax), and guide ternary-complex-compatible linker design. Benchmarked against 6,935 compounds, SynGlue demonstrates superior performance in relevant pharmacology prediction. To validate SynGlue, we engineered degraders for BRD4 and GSPT1. Our data-driven design for BRD4 yielded compounds with novel warhead scaffolds (<50% warhead similarity with known PROTACs), which proved to be potent degraders in vitro (DC50 = 0.19 nM) and efficacious in vivo in mouse models. Independently, our structure-guided de novo design for GSPT1 produced ultrapotent degraders (DC50 {approx} 0.0011 M) that are also effective both in vitro and in vivo, uncovering a new oncogenic dependency. By unifying data-driven and physics-aware design, SynGlue establishes a generalizable AI framework for the rapid development of clinically relevant protein degraders, with principled extension to other multi-target modalities.

bioinformatics↗

bioRxiv2025

MutAIverse: An AI-Powered, Mechanism-backed Platform for Discovering Novel DNA Adducts and their precursor Genotoxins

Genotoxin exposure leads to DNA adduct formation, potentially causing mutations if unrepaired. Current DNA adductomics platforms or analytical workflows are limited by incomplete spectral libraries, reliance on experimentally validated adducts, limited cellular contexts, and inefficient computational methodologies. We introduce MutAIverse, an advanced AI-driven DNA adductomics analysis platform that overcomes these limitations by leveraging intracellular mechanistic modeling of genotoxin bioactivation to construct a comprehensive DNA adduct library and offers advanced spectral mapping. MutAIverse integrates experimentally validated and chemically valid putative DNA adducts, enabling interpretable retrograde tracking to parental genotoxins. Validation of MutAIverse against experimental MS/MS datasets demonstrated the detection of both known and novel adducts. Furthermore, application to in-house generated DNA adductomics data from tissue biopsies of smokeless tobacco-induced head and neck cancer patients revealed selective enrichment of both novel and known DNA adducts; a subset of them was validated using MS/MS analysis. Collectively, MutAIverse provides a robust, end-to-end, and interpretable platform for advanced DNA adductomics analysis.

bioinformatics↗

bioRxiv2025

Substitution spectrum and selection at G-quadruplexes in great ape telomere-to-telomere genomes

G-quadruplexes (G4s) are noncanonical DNA secondary structures formed by runs of guanines (stems) connected by other nucleotides (loops). These structures are enriched at regulatory regions such as promoters, CpG islands, untranslated regions (UTRs), enhancers, and replication origins, where they play key roles in transcription and replication. Although prior studies have demonstrated that G4s exhibit higher mutation rates than canonical DNA, little is known about the substitution patterns and selection acting specifically on G4 stems and loops. In this study, we utilized Telomere-to-Telomere (T2T) genome assemblies from human and two non-human great apes (chimpanzee and Bornean orangutan) to analyze substitution spectra and selective constraints within G4s, focusing on differences between stems and loops. We observed that fixed nucleotide substitutions leading to the gain or loss of G4 structures are more frequently located at stems, while those in G4s conserved across species are more often found at loops. On the other hand, single nucleotide polymorphisms had higher frequencies at stems than loops for all G4s, with a particularly high difference for singleton polymorphisms, suggesting higher mutation rates at stems than loops. To evaluate selection, we employed two approaches: we computed the ratio of substitution to polymorphism frequencies at stems vs. loops and performed phylogenetic modeling using PhyloFit. Both methods consistently revealed that stems of shared G4s experience stronger purifying selection than loops, particularly at promoters, enhancers, and UTRs. Our results provide novel insights into the sequence variation and selection of G4s, informing our understanding of their contributions to genome evolution and function. Significance StatementG-quadruplexes (G4s) are non-canonical DNA structures that influence transcription, genome stability, and epigenetic regulation, yet their evolutionary dynamics in primates remain poorly understood. Leveraging recent T2T genome assemblies, we conducted a sequence-level analysis of G4 evolution across three ape lineages and used two methods to infer selection in G4. Shared G4s and species-specific G4s display distinct evolutionary signatures. Using PhyloFit to estimate substitution rates and the Hudson-Kreitman-Aguade test to contrast divergence with polymorphism, we found stems under markedly stronger purifying selection than loops, especially within promoters, CpG islands, and 5'UTRs. This pattern indicates that maintaining stem integrity is functionally critical and evolutionarily conserved. Our findings reveal how selective constraints vary both within G4 motifs and across genomic landscapes, offering insights for future studies on their functional importance and structural stability.

evolutionary biology↗

bioRxiv2024

Evolutionary Dynamics of G-Quadruplexes in Human and Other Great Ape Telomere-to-Telomere Genomes

G-quadruplexes (G4s) are non-canonical DNA structures that can form at approximately 1% of the human genome. G4s contribute to point mutations and structural variation and thus facilitate genomic instability. They play important roles in regulating replication, transcription, and telomere maintenance, and some of them evolve under purifying selection. Nevertheless, the evolutionary dynamics of G4s has remained underexplored. Here we conducted a comprehensive analysis of predicted G4s (pG4s) in the recently released, telomere-to-telomere (T2T) genomes of human and other great apes--bonobo, chimpanzee, gorilla, Bornean orangutan, and Sumatran orangutan. We annotated tens of thousands of new pG4s in T2T compared to previous ape genome assemblies, including 41,236 in the human genome. Analyzing species alignments, we found approximately one-third of pG4s shared by all apes studied and identified thousands of species- and genus-specific pG4s. pG4s accumulated and diverged at rates consistent with divergence times between the studied species. We observed a significant enrichment and hypomethylation of pG4 shared across species at regulatory regions, including promoters, 5 and 3UTRs, and origins of replication, strongly suggesting their formation and functional role in these regions. pG4s shared among great apes displayed lower methylation levels compared to species-specific pG4s, suggesting evolutionary conservation of functional roles of the former. Many species-specific pG4s were located in the repetitive and satellite regions deciphered in the T2T genomes. Our findings illuminate the evolutionary dynamics of G4s, their role in gene regulation, and their potential contribution to species-specific adaptations in great apes, emphasizing the utility of high-resolution T2T genomes in uncovering previously elusive genomic features.

genomics↗

bioRxiv2024

A Dual intervention of Triiodothyronine and Baicalein bi-directionally upregulates Klotho with attenuation of chronic kidney disease and its complications in aged BALB/c mice

Chronic kidney disease (CKD) is a global health challenge marked by progressive renal decline and increased mortality. The interplay between CKD and hypothyroidism, particularly nonthyroidal low-triiodothyronine (T3) syndrome, exacerbates disease progression, driven by HPT axis dysfunction and reduced Klotho levels due to Wnt-{beta}-catenin pathway activation. This study explored Klotho as a link between CKD and hypothyroidism using an adenine-induced CKD aged mouse model. Exogenous T3 and baicalein (BAI), targeting the Wnt pathway, were used to upregulate Klotho expression. Combined T3 and BAI treatment significantly increased Klotho levels, surpassing individual effects, and suppressed key signaling molecules (TGF, NF{kappa}B, GSK3), mitigating renal fibrosis and CKD complications, including cardiovascular disorders and dyslipidemia. This bidirectional approach, enhancing Klotho via T3 and sustained Wnt pathway inhibition, offers a novel and effective strategy for CKD management, particularly in elderly patients with hypothyroidism.

biochemistry↗

bioRxiv2023

Artificial Intelligence uncovers Evolutionarily Conserved Intracellular Allosteric Modulators of GPCR-Gα interface.

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for unbiased cavity identification, cavity-specific ligand design, synthesis, and validation across GPCR topology. Here, we developed Gcoupler, an AI-driven generalized computational toolkit that leverages an integrative approach combining de novo ligand design, statistical methods, Graph Neural Networks, and Bioactivity-based ligand prioritization for rationally predicting high-affinity ligands. Using Gcoupler, we interrogated intracellular metabolites that target and regulate the GPCR-G interface (Ste2p-Gpa1p), affecting pheromone-induced programmed cell death in yeast. Our computational analysis, complemented by experimental validations, including genetic screening, multi-omics, site-directed mutagenesis, biochemical assays, and physiological readouts, identified endogenous hydrophobic metabolites, notably sterols, as direct intracellular allosteric modulators of Ste2p. Molecular simulations coupled with biochemical signaling assessment in site-directed Ste2p mutants further confirmed that metabolites binding to GPCR-G obstruct downstream signaling, possibly via a cohesive effect. Finally, by utilizing isoproterenol-induced, GPCR-mediated human and neonatal rat cardiac hypertrophy models, we observed that elevated metabolite levels attenuate hypertrophic response, reinforcing the evolutionary relevance of this mechanism.

bioinformatics↗

bioRxiv2021

Artificial Intelligence uncovers carcinogenic human metabolites

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats due to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage response pathways, some genomic lesions trigger cells for malignant transformation. Accurate prediction of carcinogens is an ever-challenging task due to the limited information about bona fide (non)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity as well as their potential to induce proliferation, oxidative stress, genomic instability, alterations in the epigenome, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable since it reveals the contribution of the aforementioned biochemical properties in imparting carcinogenicity. Metabokiller outperforms existing best-practice methods for carcinogenicity prediction. We used Metabokiller to unravel cells endogenous metabolic threats by screening a large pool of human metabolites and predicted a subset of these metabolites that could potentially trigger malignancy in normal cells. To cross-validate Metabokiller predictions, we performed a range of functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites namely 4-Nitrocatechol and 3,4-Dihydroxyphenylacetic acid and observed high synergy between Metabokiller predictions and experimental validations.

bioinformatics↗