Biology subjects

Jha, A.

Publications and source records attributed to Jha, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Myelinated mechano-nociceptors encode noxious cold and underlie hypersensitivity in pulpitis

The ability to identify dangerous temperatures is critical, but the neural basis for cold sensation remains enigmatic. By combining in vivo functional imaging with tissue-specific spatiotemporal cooling, we unveil myelinated high threshold-mechanonociceptors (HTMRs) as the neural origin of tooth cold sensitivity. Critically, this neural encoding is driven by threshold noxious cold temperature, not cooling rate and remains independent of the cool receptor TRPM8 or pain transducer Nav1.8. Furthermore, while Bmpr1b-Cre captures the intradental HTMRs, it further reveals myelinated HTMRs mediate trigeminal cold encoding in soft tissues. Finally, we determine that inflammation potentiates intradental HTMRs, providing functional basis for clinical hypersensitivity in pulpitis. Collectively, our results reveal a new neural mediator of cold sensation and provide insight into painful transformation of cold sensory encoding in inflammation.

neuroscience↗

bioRxiv2026

Inferring the evolving objectives that organize animal behavior

Understanding how the brain supports flexible, goal-directed behavior is a central aim of neuroscience. Yet such behavior is highly variable and unfolds over long timescales, making it challenging to understand its organization. Advances in behavioral tracking quantify movements but do not reveal the objectives that guide them. Motivated by this, we develop inverse reinforcement learning (IRL) into a general framework for inferring these objectives from naturalistic behavior. Our approach represents an animal's objectives as internal rewards, and infers these rewards as well as the policies they induce from observed behavior. It applies across diverse behavioral settings, from freely moving behaviors to structured tasks. It can infer objectives that switch over time or gradually change with experience. In mice capturing crickets, it identified a small set of objectives that the animals switched between over the course of a trial. In mice learning a cued labyrinth, it inferred how the animals' internal rewards changed as learning progressed. Across both datasets, the models predicted held-out actions and generated extended behavior that reproduced key features of animal performance. By transforming long, variable behavioral sequences into time-resolved estimates of the animal's objectives, our approach provides a quantitative basis for studying how the brain organizes natural behavior.

neuroscience↗

bioRxiv2026

scWeave: A deep learning model that bidirectionally translates between gene expression and chromatin structure at single cell resolution

Chromatin structure and gene expression are intimately linked, yet characterizing how the two covary has proven challenging, primarily because the two modalities are rarely measured in the same cells. Recently, single-cell co-assay protocols have enabled simultaneous profiling of both modalities within the same cells, but these experiments remain costly and technically challenging. To better characterize the relationship between 3D chromatin architecture and gene expression and to enable cross-modality inference from single-modality measurements, we developed a model called scWeave that bidirectionally translates between gene expression (scRNA-seq) and 3D chromatin architecture (scHi-C) at single-cell resolution. The scWeave model employs dual autoencoders to extract separate cell-level latent representations and learns to translate between these representations using dedicated translation modules. We evaluate scWeave on six publicly available co-assay datasets spanning mouse embryonic development, mouse cortex, mouse olfactory epithelium, and human bone marrow. On held-out mouse cells, scWeave outperforms a nearest-neighbor baseline and existing methods adapted to single-cell resolution, achieving a 57% improvement in median Spearman correlation when predicting gene expression from chromatin structure and an 18.8% improvement in median HiCRep similarity in the reverse direction relative to the next-best baseline. We further show that scWeave learns cross-modally aligned latent representations at single-cell resolution, enabling cells profiled in one modality to be matched to their counterparts in the other. Finally, scWeave generalizes to entirely held-out developmental timepoints in mouse olfactory epithelium and performs well on held-out human bone marrow cells despite limited human training data. By predicting the unmeasured chromatin architecture or transcriptional state from a single measured modality, scWeave offers a route to extend the benefits of costly co-assays to the many cell types, developmental stages, and species that are currently profiled with only one modality.

genomics↗

bioRxiv2026

Industrialization restructures the domestic dog gut microbiome while preserving host specificity

Industrialization has reshaped human gut microbiomes, but its effects on other human-associated mammals remain poorly understood. Domestic dogs provide an informative comparative system because they have shared human environments and food systems for millennia yet retain distinct host biology. However, most canine microbiome studies have focused on industrialized companion animals, limiting our understanding of the ecological range of the domestic dog gut microbiome. We analyzed fecal 16S rRNA gene profiles from 261 dogs sampled across Nepal, Thailand, the United Arab Emirates, and the United States, spanning forager, agrarian, pastoralist, urban, and industrialized lifestyles; 257 dogs remained after excluding recent antibiotic exposure. Lifestyle was the strongest measured correlate of canine gut microbiome composition, and this structure persisted in restricted analyses of mature, non-shelter dogs sampled from temperate climate regions. Industrialized dogs differed from non-industrialized dogs through directional genus-level turnover, restructuring of VANISH- and BloSSUM-like microbial guilds, and shifts in predicted functional potential. Non-industrialized dogs were not microbiologically uniform: pastoralist dogs carried non-industrialized microbiome profiles but diverged from a simple forager-to-industrialized continuum. Cross-species comparisons with humans sampled across matched lifestyle categories showed parallel lifestyle-associated restructuring in both hosts, but host species remained the dominant axis of variation and the genera responding to industrialization were largely host-specific. These findings expand the ecological baseline for the domestic dog gut microbiome and identify industrialization as a major axis of microbiome restructuring in a long-term human-associated mammal. More broadly, they show that shared lifestyle transitions can impose parallel ecological pressures across host species without overriding host-specific community assembly.

microbiology↗

bioRxiv2026

Lipid Droplets Direct Amyloid Assembly and β-Cell Stress through Interfacial Control of Amylin Aggregation

Islet amyloid polypeptide (IAPP, amylin) aggregation is a central pathological feature of type 2 diabetes, yet the cellular factors governing its conformational conversion remain incompletely understood. Here, we identify lipid droplets (LDs) as active modulators of amylin structure, aggregation, and {beta}-cell stress. Using artificial and native LDs, we show that amylin binds LD surfaces with high affinity and undergoes accelerated conversion into {beta}-sheet-rich conformations. LDs promote rapid nucleation while constraining fibril elongation, yielding shorter and morphologically distinct amyloid assemblies. Residue-resolved NMR mapping reveals a conserved N-terminal interaction interface, which is amplified upon removal of LD surface proteins, indicating that the LD proteome modulates peptide engagement and aggregation pathways. In {beta}-cells, lipid loading drives intracellular colocalization of amylin with LDs and reshapes transcriptional stress responses, attenuating ER stress and apoptosis while altering markers of {beta}-cell identity. Finally, systemic lipidomic profiling reveals coordinated remodeling of neutral lipid species across dysglycaemic states, linking intracellular LD dynamics with whole-body lipid metabolism. Together, our findings establish lipid droplets as dynamic scaffolds that reshape amylin aggregation pathways and associated {beta}-cell stress responses, providing a mechanistic bridge between lipid dysregulation and islet amyloidosis in diabetes. Significance StatementLipid droplets accumulate in pancreatic {beta}-cells during metabolic stress, yet their role in amylin aggregation remains unclear. Using structural, biophysical, and cellular approaches, we show that lipid droplet interfaces directly bind human amylin, reshape its aggregation pathway, and alter fibril morphology. Native lipid droplets and their associated surface proteins further modulate aggregation kinetics and peptide conformations. In {beta}-cells, lipid loading enhances amylin colocalization with lipid droplets and modifies stress and survival responses. Complementary lipidomic profiling reveals systemic remodeling of neutral lipid species across dysglycaemic states. These findings identify lipid droplets as active regulators of amyloid-associated proteostasis, linking lipid dysregulation to {beta}-cell dysfunction in type 2 diabetes.

Cell Biology↗

bioRxiv2026

Investigating Hybrid Deep Learning Architectures for Speech Envelope Reconstruction from EEG

Reconstructing speech envelopes from electroen-cephalography (EEG) signals is a challenging but valuable task for brain-computer interfaces (BCIs), with applications in assistive communication for individuals with speech impairments. While deep learning has improved reconstruction accuracy, most existing approaches are restricted to single-layer architectures such as convolutional neural networks (CNNs). This limits their ability to capture the full complexity of spatio-temporal and structural EEG patterns. In this work, we systematically extend the VLAAI framework by evaluating 26 architectures that integrate CNNs, long short-term memory networks (LSTMs), and graph convolutional networks (GCNs) in both single-layer and hybrid configurations. Experiments on the 64-channel Spar-rKULee dataset demonstrate that CNNs remain the strongest standalone models, but hybrid designs--particularly CNN-LSTM and CNN-GCN-LSTM--achieve competitive or superior performance. These results highlight the importance of combining spatial, temporal, and graph-based processing, and provide practical guidelines for hybrid architecture design. Our study offers the first large-scale comparative analysis of hybrid models for EEG-based speech envelope reconstruction, advancing robust BCI systems for non-invasive speech decoding.

neuroscience↗

bioRxiv2026

Noncoaxial Transcatheter Aortic Valve Deployment Creates Cusp-Specific Thrombogenic Microenvironments Through Altered Sinus Hemodynamics

BackgroundTranscatheter aortic valve replacement has transformed the management of aortic stenosis; however, adverse outcomes such as leaflet thrombosis and hypoattenuating leaflet thickening remain clinically significant concerns. Flow disturbances resulting from valve canting may alter local hemodynamics and promote thrombogenic conditions. We investigated how modest transcatheter heart valve canting alters cusp-specific sinus flow and washout and promotes localized thrombogenic microenvironments associated with leaflet surface thrombus formation using particle image velocimetry, a physiologic blood loop, and tissue analysis. MethodsA patient-derived aortic root model was used to evaluate the hemodynamic and thrombogenic effects of THV canting at -10{degrees} (anti-curvature), 0{degrees} (neutral), and +10{degrees} (along-curvature). High-resolution particle image velocimetry quantified sinus flow fields and washout characteristics, and complementary whole-blood loop experiments enabled histologic assessment of leaflet-associated thrombus formation. ResultsCanting redistributed systolic jet orientation and sinus recirculation in a direction-dependent manner while preserving global hemodynamic measurements. The most spatially constrained cusp showed the largest increase in stasis and the slowest washout. In the right coronary cusp, anti-curvature canting increased the fraction of sinus area with velocity magnitude <0.05 m/s to 92% versus 43% in neutral and 10% in along-curvature deployments, and prolonged neo-sinus (T90) washout to 4.7 cycles versus 2.9 and 1.8 cycles, respectively. Histology localized surface-adherent platelet/fibrin thrombus to these poorly washed regions, most prominently on the right coronary cusp leaflet in anti-curvature deployments. Left and noncoronary cusp responses shifted with tilt direction, indicating redistribution rather than uniform worsening of thrombogenic conditions. ConclusionsEven modest noncoaxial deployment is sufficient to create sinus-resolved throm-bogenic microenvironments that are not captured by global gradient or effective orifice area. Deployment configuration is therefore a modifiable determinant of post-TAVR leaflet throm-bosis risk and may contribute to HALT.

bioengineering↗

bioRxiv2026

Fluorogenic speed-optimized DNA-PAINT probes enable super-resolution imaging of whole cells

Super-resolution microscopy with DNA-PAINT enables molecular-scale, multiplexed, and quantitative imaging, but its throughput is limited by slow binding kinetics and elevated background at high probe concentrations. Recent speed-optimized and fluorogenic probes improve performance but impose strong constraints on sequence design, revealing a fundamental tradeoff between fast binding and efficient quenching. Here, we introduce a modular probe architecture that spatially decouples binding kinetics from fluorophore-quencher interactions by integrating speed-optimized sequence motifs with PEG spacers. Using DNA origami nanostructures, we demonstrate enhanced localization rates, signal-to-background ratios, and imaging efficiency compared to state-of-the-art probes. We validate our approach in cells, demonstrating its capability to image nuclear targets and enabling three-dimensional imaging of the endoplasmic reticulum using standard widefield illumination. Our work establishes a general framework for fast, multiplexed, and low-background super-resolution imaging.

bioengineering↗

bioRxiv2026

Stiefel Manifold Dynamical Systems for Tracking Representational Drift

Understanding neural dynamics is crucial for uncovering how the brain processes information and controls behavior. Linear dynamical systems (LDS) are widely used for modeling neural data due to their simplicity and effectiveness in capturing latent dynamics. However, LDS assumes a stable mapping from the latent states to neural activity, limiting its ability to capture representational drift--gradual changes in the brains representation of the external world. To address this, we introduce the Stiefel Manifold Dynamical System (SMDS), a new class of model designed to account for drift in neural representations across trials. In SMDS, emission matrices are constrained to be orthonormal and evolve smoothly over trials on the Stiefel manifold--the space of all orthonormal matrices--while the dynamics parameters are shared. This formulation allows SMDS to leverage data across trials while accounting for non-stationarity, thus capturing the underlying neural dynamics more accurately compared to an LDS. We apply SMDS to both simulated datasets and neural recordings across species. Our results consistently show that SMDS outperforms LDS in terms of log-likelihood and requires fewer latent dimensions to capture the same activity. Moreover, SMDS provides a powerful framework for quantifying and interpreting representational drift. It reveals a gradual drift over the course of minutes in the neural recordings and uncovers varying drift rates across dimensions, with slower drift in behaviorally and neurally significant dimensions.

neuroscience↗

bioRxiv2025

Puget predicts gene expression across cell types using sequence and 3D chromatin organization data

Gene expression is governed by both linear DNA sequence and three-dimensional (3D) chromatin architecture. Most gene expression prediction models rely on sequence alone, thereby failing to capture structural context and to generalize to unseen cell types. We present Puget, a deep learning model that predicts cell type-specific gene expression from sequence and Hi-C data, which captures 3D chromatin organization. Puget pairs pretrained sequence and Hi-C encoders with a lightweight transformer decoder. Using paired Hi-C/RNA-seq from 36 human and 4 mouse biosamples, we evaluate Pugets ability to generalize to held-out genes, held-out biosamples, and from human to mouse. Relative to a sequence-only baseline, Puget improves cross-biosample Pearson correlation by up to 25% on highly variable genes in training biosamples and, unlike the sequence-only model, generalizes to held-out biosamples and across species. In addition, in silico perturbation experiments show that Puget can prioritize experimentally validated enhancer-gene pairs. Together, these results highlight a generalizable approach for modeling gene expression from sequence and 3D chromatin organization.

bioinformatics↗

bioRxiv2025

Evo2HiC: a multimodal foundation model for integrative analysis of genome sequence and architecture

Understanding how genomic sequences shape three-dimensional (3D) genome architecture is funda-mental to interpreting diverse biological processes. Although previous studies have shown that sequence information can predict 3D genome architecture, they fall short in capturing cell type-specific structures because they are trained solely on sequence inputs. The widely available Hi-C data, which contain rich structural information across biosamples, can provide complementary features to sequence data for study-ing cell type-specific architectures. Recently, DNA foundation models have demonstrated encouraging performance in capturing long-range genomic dependencies, holding promise for modeling chromatin interactions. However, the extremely high computational cost of running these models limits their applicability to Hi-C analysis, which requires genome-wide sequence embeddings. Here, we present Evo2HiC, a multimodal foundation model that jointly models genomic sequences and structures to study cell type-specific chromatin structure. The key idea of Evo2HiC is to distill a large-scale DNA foundation model, Evo 2 (7B), into a compact encoder, while guiding the distillation with Hi-C data to preserve genomic features critical for 3D genome analysis. The model supports two types of encoders, one that operates directly on DNA sequences, and a second that additionally takes as input corresponding Hi-C data. Using the DNA-only encoder and predicting Hi-C contact matrices, Evo2HiC improved Spearman correlation by 10.9% over Orca. Moreover, by jointly embedding Hi-C and sequence information Evo2HiC achieved the best overall Pearson correlation when predicting five representative epigenomic assays. Interpretation analysis of Evo2HiC revealed its ability to identify cell type-specific sequence motifs that explain changes in epigenomic signals. Finally, we demonstrated the cross-species generalizability of Evo2HiC on 177 species from the DNA Zoo dataset for Hi-C resolution enhancement. In summary, Evo2HiC is a foundation model that integrates genome sequences and 3D chromatin structure information, substantially reduces computational cost while maintaining state-of-the-art accuracy on predicting various epigenomic signals and genome architecture, enables the identification of cell type-specific motifs, and demonstrates robust generalizability across species.

bioinformatics↗

bioRxiv2025

Exploring the Restoration of Memory Post-NeuronalRegeneration from Induced ALS-FTD in Dugesia tigrina

Amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) is a neurodegenerative disease characterized by progressive neuronal loss and motor decline. Recovery remains beyond therapeutic reach, and even complete neurogenesis does not ensure preservation of memory capacity (defined as the strength of synaptic connections). Freshwater planarians, capable of whole-body regeneration and associative learning, provide a tractable model to study the relationship between neuronal damage and behavior. Their "single-circuit" centralized nervous systems allow evaluation of oxidative stress, a key driver of ALS-FTD pathology, which was induced via hydrogen peroxide. In this study, 180 planaria underwent three phases. Phase 1 established baseline memory using a 28-day operant conditioning protocol with a light-based training device. Success was determined by the proportion of planaria in concentric circles defined as zones (A-C) with a respective gradient descent of light concentration. Phase 2 determined hydrogen peroxide (H2O2) concentrations sufficient to induce oxidative stress that triggered regenerative signaling and neuronal damage without lethality. Phase 3 applied the optimal concentration, allowed regeneration, and retested memory. The optimal concentration of hydrogen peroxide was 5 ppm, triggering blastema formation and neuronal damage while preserving viability. Zone occupancy analyses utilizing prior found concentration revealed measurable post-exposure shifts: Zone A increased initially but trended lower post-exposure, Zone B increased, and Zone C decreased significantly. These results indicate that while memory capability persists after neurogenesis, the quality of memory decreases. In accordance with this study, current treatments for ALS-FTD that focus solely on neurogenesis will not fully restore cognitive function.

neuroscience↗

bioRxiv2025

High-throughput discovery of a dearomative cycloaddition enables dual photochemical-photophysical perturbative probing of protein function

Proteins exist in multiple conformations or states, often associated with different functional capacities. The ability to selectively identify and study these states is crucial for understanding the dynamic nature of proteins and their roles in cellular processes, yet can be currently limited by the artefacts of existing molecular labels and the lack of strategies to exploit them in diverse ways. Whilst i) methods for the photochemical perturbation of proteins and ii) photophysical protein probes and sensors are both known powerful tools, simultaneous methods for site- and context-specific photochemical modification of a protein side chain with concomitant modulation of photophysical properties would allow interrogation of protein function via diverse modes (potentially reporting simultaneously on structure and dynamics of ground- and reactive-state properties). Due to their mechanistic complexity, rational approaches for the design of suitable photochemical strategies can be difficult and non-intuitive. Here, we describe a high-throughput experimentation (HTE) strategy designed to allow the photochemical modification of a genetically-encoded, minimally disruptive ( zero-size), photophysically-active - yet typically unreactive and biologically-inert - unnatural amino acid (uAA) residue in proteins. The discovery of a novel dearomative, formal [4+3] photocycloaddition of imidazopyrimidine reagents which proceeds via photochemically generated C* intermediates with polyaromatic substrates enabled chemoselective application to the normally unreactive Trp-mimetic residue, 2-naphthylalanine (Npa). Npas site-selective installation allows dual photochemical and photophysical dearomative perturbation benignly and efficiently in proteins. Replacement of Trp in the archetypal apoptotic biosensor protein Annexin A5 (AnxV) enabled dual photochemical-photophysical determination of switched, conformationally-determined reactive states induced by Ca2+-binding, without any apparent functional perturbation of its in vitro or cellular recognition of phospholipids, indicating an ordered annexin mechanism. This ready and benign unification of photochemistry and photophysics suggests a general approach for using high-throughput light-mediated experimentation in artefact-free chemical biology to capture different functional states of proteins and to interrogate those states using combined photochemical-photophysical methods. HighlightsO_LIDevelopment of a general, high-throughput experimentation (HTE) approach enabled identification of an unprecedented photochemical reaction with potential for editing amino acid side chains within proteins. C_LIO_LIGenetic incorporation of 2-naphthylalanine, a minimally-disruptive ( zero-size) Trp mimic, enabled photochemical side chain editing with concomitant modulation of its distinctive photophysical properties in proteins. C_LIO_LIA naphthalene-tagged variant of the apoptotic biomarker protein AnxV enabled dual photochemical-photophysical exploration of its dynamic conformational states induced by Ca2+-binding whilst fully maintaining its functional capacity for in vitro or cellular recognition of phospholipids. C_LI

biochemistry↗

bioRxiv2025

CardioWAS Pathway-Based RNA-Seq and Genome Integration Platform for CVD Omics-Wide Associations

The integration of transcriptomic and genomic data is essential for dissecting the molecular basis of complex cardiovascular diseases (CVD). Existing resources primarily focus on differential gene expression at the individual gene level, with limited support for pathway-level interpretation or genomic integration. We introduce CARDIOWAS (Cardiovascular Omics-Wide Association Analysis Platform), the first Shiny-based, cardiovascular-specific platform that performs pathway-centric expression profiling in conjunction with Genome-Wide Association Study (GWAS) integration. CARDIOWAS systematically aggregates and harmonizes RNA-seq data from 18 NCBI GEO studies, comprising 419 heart failure (HF) and 208 control samples, and applies robust batch correction followed by DESeq2-based differential expression analysis. Gene-level statistics are translated into pathway effect sizes using curated KEGG and Reactome annotations, enabling systematic evaluation of disease-relevant biological modules. CARDIOWAS extends beyond transcriptomic profiling by incorporating GWAS-identified loci, mapping variants to proximal genes and pathways, and leveraging machine learning models to highlight transcriptome-variant-pathway associations. Both germline and somatic variant contributions can be interrogated, providing a framework to link non-coding variation with altered pathway activity. Application of the platform identified 12 HF-specific and 19 CAD-enriched pathways, implicating inflammatory, metabolic, and signaling processes central to disease biology. Implemented as a user-friendly Shiny interface, CARDIOWAS allows researchers to upload transcriptomic data, visualize perturbed pathways, evaluate variant burden, and prioritize druggable targets. By uniting large-scale RNA-seq data with GWAS evidence in a pathway-centric framework, CARDIOWAS represents a novel, reproducible, and accessible tool for precision medicine in CVD and offers a resource for omics-driven hypothesis generation and translational research.

bioinformatics↗

bioRxiv2025

Aspartate transaminases are required for blood development

Red blood cells (RBCs) have a limited lifespan of approximately 120 days. This necessitates continuous RBC production, resulting in [~]200 billion new RBCs made per day to maintain oxygen delivery. Despite this enormous biosynthetic demand, the metabolic pathways supporting erythropoiesis are poorly understood. We profiled metabolites across four independent models of elevated erythropoiesis and a consistent increase in aspartate levels emerged when compared to controls. This suggested a potential role for aspartate metabolism in RBC production. To test this, we deleted the aspartate aminotransferases Got1 or Got2 globally or in an erythroid-specific manner. Loss of either enzyme resulted in anemia, with Got2 deficiency producing a more severe phenotype. Individual loss of either Got or dual Got deletion led to an erythroid defect, where early progenitors accumulated. In human and mouse models of erythropoiesis, GOT1 and GOT2 loss had opposing impacts on aspartate despite exhibiting similar anemic phenotypes, suggesting an aspartate-independent function for these enzymes. GOT1 and GOT2 are also components of the malate-aspartate shuttle (MAS), which regulates NAD(H) homeostasis. However, conditional deletion of another MAS enzyme, Mdh1, did not cause anemia, and alleviating NADH reductive stress in GOT2-deficient cells with cytoplasmic bacterial NADH oxidase (LbNOX) failed to restore erythropoiesis. Instead, transcriptomic and epigenetic analyses revealed dysregulation of chromatin histone modifications in GOT-deficient erythroid cells, implicating epigenetic dysfunction as a driver of defective erythropoiesis. Collectively, these findings identify a previously unrecognized role for GOT1 and GOT2 as a central link between metabolism and epigenetic regulation during erythroid development. These insights may inform the development of new therapeutic strategies for anemia.

biochemistry↗

bioRxiv2025

Purine nucleotides are competitive inhibitors of apo-GOT1

The malate-aspartate shuttle (MAS) plays a key role in cellular metabolism by transferring electrons from cytosolic NADH into the mitochondrial matrix, thereby supporting oxidative phosphorylation, in addition to the citric acid cycle and amino acid metabolism. Here, we sought to identify allosteric regulatory metabolites of the MAS enzymes cytosolic glutamic-oxaloacetic transaminase 1 (GOT1) and mitochondrial GOT2. Using the Atavistik Metabolite Proprietary Screening platform, we identified several structurally similar metabolite hits-- most notably deoxyadenosine monophosphate (dAMP) and deoxyguanosine monophosphate (dGMP)--as candidate interactors with GOT1. Follow-up thermal shift assays revealed that dAMP and dGMP destabilize GOT1 in the absence of its cofactor, pyridoxal 5-phosphate (PLP), but have no destabilizing effect when PLP is present. Crystallographic analysis confirmed that dAMP and dGMP bind in the PLP pocket of GOT1, suggesting competitive binding. Together, these results indicate that nucleotide metabolites can interact with GOT1, offering potential insights into MAS regulation and therapeutic intervention strategies.

molecular biology↗

bioRxiv2025

What's next for Indian Ornithology? 101 key research questions

India has a rich history of ornithology, and bird research in this field has expanded considerably in recent decades, spurred by a growing number of birdwatchers and ornithologists. Despite this progress, critical gaps remain. This paper highlights key areas where further research is needed and identifies pressing questions that will shape Indian ornithology in the coming years. Drawing from diverse inputs, we present a curated list of 101 research questions spanning across the various disciplines in ornithology - Natural History, Physiology and Disease Ecology, Behaviour, Population and Community Ecology, Habitat Ecology, Macroecology and Biogeography, Population Genetics and Evolution, Applied/Economic Ornithology and Conservation. The list was compiled through a multi-stage process, starting with a public open call for questions, followed by review and curation by a smaller panel of subject specialists. Each question was independently scored by a panel of experts based on three criteria: generality, novelty, and relevance. To account for variation in scoring styles, scores were normalised using Z-scores. The top 101 research questions were then selected based on these standardised scores. Each question is accompanied by an annotation that describes the significance of the question, and highlights opportunities to address it. While our list of research questions highlights significant research priorities, it is not intended to be exhaustive. Rather, it reflects the perspectives of those involved in its curation, who deemed these questions particularly relevant and impactful towards advancing Indian ornithology. We expect these questions to spark new project ideas among students, researchers, and citizen scientists, while guiding funders, managers, and policymakers toward priority research areas.

ecology↗

bioRxiv2025

Integrative multi-omic analysis reveals oral microbiome-metabolome signatures of obesity

Obesity is a major global health challenge and a leading risk factor for cardiometabolic disorders. The global surge in obesity, driven by industrialization and the widespread consumption of low-fiber, ultra-processed food, highlights an urgent need for deeper biological insights. While the gut microbiome has been studied in the context of obesity, the contribution of the oral microbiome--the second largest microbial ecosystem in the human body-remains largely underexplored. Here, we report findings from a deeply-phenotyped prospective-cohort of 628 Emirati adults, leveraging amplicon sequencing of mouthwash samples and multi-omics profiling and functional and metabolic activity analysis of 97 obese individuals and 95 matched controls, making this the most comprehensive multi-omics analysis of the oral microbiome. We identified significant differences in oral microbial diversity, composition, functional pathways, and metabolic profiles between obese and non-obese groups. Integrated multi-omics analysis of the 192 matched metagenomes and metabolome samples uncovered significant metabolic reprogramming and altered energy regulation in obesity. Specifically, the oral microbiome of obese participants were enriched for the proinflammatory Streptococcus parasanguinis and Actinomyces oris, and the lactate-producing Oribacterium sinus. Many microbial pathways involved in dietary carbohydrate metabolism, histidine degradation, as well as the production of obesogenic biomolecules were also enriched in obese participants; however, B-vitamin and heme production pathways were depleted. Consequently, metabolites resulting from these pathways such as lactate, histidine derivatives, choline, uridine, and uracil were elevated in obesity. These consistent microbiome-metabolite shifts were strongly associated with prominent obesity-associated cardiometabolic markers, including serum triglycerides and alkaline phosphatases, establishing a robust link between oral microbiome and obesity. These findings provide the most comprehensive insights into how disrupted microbial-metabolic cross-talk in the oral cavity may contribute to obesity and related cardiometabolic disease risk, underscoring the potential of targeting of oral microbiome-host interactions as a novel avenue for obesity prevention and intervention.

microbiology↗