Biology subjects

Pathak, A.

Publications and source records attributed to Pathak, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Understanding when and how knowledge graphs add value to EHR-based clinical prediction across Parkinson's disease, multiple sclerosis, and chronic pain

Biomedical knowledge graphs (KGs) have emerged as a promising approach for incorporating biological knowledge into machine-learning models trained on electronic health record (EHR) data. However, their large scale and heterogeneous structure impose substantial computational cost, and it remains unclear whether their benefits generalize across diseases or depend on how the graph is constructed. We systematically evaluated KG integration across three diseases spanning distinct biological and clinical characteristics: multiple sclerosis, Parkinson's disease, and chronic pain. For each disease we compared five modelling strategies, spanning EHR-only features and graph diffusion over concept-level, cohort-derived, disease-anchored, and full SPOKE (Scalable Precision Medicine Open Knowledge Engine) subgraphs, asking whether graph construction affects predictive performance and computational cost, whether KG integration improves classification over EHR-only models, and what the graph does to model behavior. Cohort-derived subgraphs, comprising roughly 45% of SPOKE's nodes and one-fifth of its edges, matched or modestly exceeded the full graph's performance while substantially reducing computational cost in all three diseases. The predictive benefit of KG integration, however, was strongly disease-dependent: it improved classification in Parkinson's disease, had no measurable effect in multiple sclerosis, and reduced performance in chronic pain. These differences were not explained by the amount of biological information incorporated: the disease with the largest shift toward molecular features showed no improvement, the sole beneficiary showed only a modest shift, and the disease with no molecular shift was penalized. Mechanistically, graph diffusion redistributed each disease's existing signal across many correlated features rather than adding independent biological signal, so that any gain depended on whether it added information beyond the EHR. These findings show that cohort-derived subgraphs offer a computationally efficient alternative to full-scale knowledge graphs, and that KG integration functions less as a universal accuracy booster than as a data- and disease-dependent transformation of the patient representation.

bioinformatics↗

bioRxiv2026

Palmitoylation of p75 neurotrophin receptor is required for retrograde regressive signaling

Target-derived neurotrophins regulate neuronal survival and pruning via retrograde signaling. Unlike Trk receptors, which are transported as transmembrane proteins, the p75 neurotrophin receptor (p75NTR) undergoes regulated intramembrane proteolysis to release a soluble intracellular domain (ICD) that must somehow associate with axonal endosomes for retrograde transport. Here, we identify palmitoylation of a conserved juxta membrane cysteine (C281) as a critical requirement for this process. In sympathetic neurons, we demonstrate that p75NTR palmitoylation is dispensable for surface trafficking and proteolytic cleavage but is strictly required for ligand-induced internalization and the biogenesis of p75ICD-positive signaling endosomes. Consequently, blocking palmitoylation, either pharmacologically or via a palmitoylation-deficient C281A mutation, prevents the accumulation of p75ICD in axonal endosomes and blocks axon-initiated apoptosis. In vivo, C281A knock-in mice exhibit reduced developmental apoptosis of sympathetic neurons, similar to p75NTR deletion. These findings define palmitoylation as an essential checkpoint that couples p75NTR cleavage to endosomal segregation, thereby enabling the long-distance transmission of regressive signals.

neuroscience↗

bioRxiv2026

Mechanism-selective deep mutational scanning distinguishes ERCC2 disease phenotypes

Pathogenic ERCC2 variants cause xeroderma pigmentosum (XP), trichothiodystrophy (TTD) or both, yet variant effect scores are usually interpreted only as measures of pathogenicity rather than of which disease mechanism is disrupted. XPD, the ERCC2-encoded TFIIH subunit, functions in both nucleotide excision repair and transcription. Using yeast complementation deep mutational scanning, we measured the effects of nearly all XPD amino acid substitutions. The assay was mechanism-selective: it preferentially reported transcription-associated function, with pronounced intolerance at the p44 interface, whereas many substitutions affecting DNA binding and helicase activity retained near-wild-type fitness. Accordingly, TTD variants had much lower fitness than XP variants. Computational predictors discriminated pathogenic from benign variants similarly across phenotypes, but the DMS distinguished XP from TTD variants better than all 73 predictors tested. Phenotype-specific ACMG/AMP calibration provided evidence in both directions for TTD but mainly pathogenic evidence for XP. Thus, the selectivity of functional assays, often viewed as a limitation, can reveal disease mechanisms and support phenotype-aware variant interpretation.

genetics↗

bioRxiv2026

Loss of PTPRB function remodels VEGFR1 activation in tumors overexpressing the receptor tyrosine kinase

The VEGF Receptor-1 (VEGFR1) is a deceptive receptor tyrosine kinase (RTK). In early embryonic development, VEGFR1 negatively regulates angiogenesis by acting like a decoy receptor. Ligand binding transiently phosphorylates the receptor and induces a weak activation, even at high receptor density. Yet, in multiple cancers, overexpression of VEGFR1 plays a central role in tumor vascularization and growth. Unlike many pro-oncogenic RTKs, extensive patient data analysis revealed no somatic mutation in VEGFR1 that may spontaneously activate the tyrosine kinase. The mechanism by which VEGFR1 is activated in cancers has remained an open question for more than two decades. Here, we evaluated the multi-omics profiles of VEGFR1 and its regulators in a pan-cancer database. We observed an inverse correlation between VEGFR1 and PTPRB phosphatase expression in KIRC patients and disease outcome. We observed that patients overexpressing VEGFR1 and deficient in PTPRB expression have a lower likelihood of survival. Using super-resolution single-cell imaging, we discovered that inhibiting PTPRB spontaneously activates VEGFR1 by inducing ligand-independent dimerization, possibly by shifting the equilibrium toward the active state. PTPRB inhibition induces sustained, ligand-dependent phosphorylation of VEGFR1, which may promote tumor vascularization. We conclude that a subtle phosphatase imbalance is fundamental in determining VEGFR1s role in pathological angiogenesis in tumors.

biochemistry↗

bioRxiv2026

Comparative Analysis of Ultrafine Particulate Matter, Black Carbon, and Polystyrene Nanoplastics Identifies Mitochondrial Stress Adaptation as a Conserved Mechanism of Immunotoxicity

Several studies have been conducted on human exposure to ultrafine particulate matter (UFPM), Black carbon (BC), and polystyrene nanoplastics (PS-NPs). However, it remains unclear whether different chemical types of environmental nanoparticles induce a similar mitochondrial stress response or a unique particle-specific response. In the present study, we examined the molecular mechanisms underlying nanoparticle-induced mitochondrial stress response and immunotoxicity using human peripheral blood mononuclear cells exposed to UFPM, BC, and PS-NPs under similar experimental conditions. Oxidative stress, mitochondrial adaptation, respiratory chain integrity, mitochondrial integrated stress response, inflammatory signaling, and systems-level interactions between molecules were analyzed through the evaluation of the expression of NRF2, HIF-1, PGC-1, TFAM, OMA1, DELE1, mitochondrial ND1, Complex I-V, NF-{kappa}B, TNF-, and NLRP3 and the use of principal component analysis, hierarchical clustering, and correlation networks. All three nanoparticles caused oxidative stress and mitochondrial dysfunction with different kinetics and mechanisms. UFPM mostly induced an acute antioxidant response and mitochondrial adaptation; BC led to chronic mitochondrial dysfunction, chronic activation of the OMA1-DELE1-mediated mitochondrial ISR pathway, and inflammation; while PS-NPs induced low but chronic mitochondrial adaptation along with mitochondrial biogenesis and stress responses. Our systems-level analysis showed that oxidative stress, mitochondrial adaptation, mitochondrial ISR, and inflammation represent a highly connected molecular network regardless of the physicochemical nature of the nanoparticles, with the OMA1- DELE1 axis being a key regulatory node connecting mitochondrial stress response and inflammation. Overall, we have found that mitochondrial stress response is a common mechanism underlying the toxicity of chemically different nanoparticles and have also revealed particle-specific stress-response dynamics responsible for the degree and persistence of cellular damage. The current work presents novel insights into the molecular mechanisms of nanoparticle-induced immunotoxicity and suggests OMA1, DELE1, NRF2, PGC-1, TFAM, ND1, and Complex I-V as potential biomarkers.

pharmacology and toxicology↗

bioRxiv2026

Impact of thermal variation on the transcriptome of Plasmodium falciparum infected Anopheles stephensi

Temperature is a key determinant of malaria transmission, influencing both parasite development and mosquito physiology, yet the underlying mechanisms remain poorly understood. Here, we examined how temperature and time modulate gene expression in Anopheles stephensi infected with Plasmodium falciparum. Using RNA-sequencing over 1-19 days post-blood meal and three temperature regimes (20, 24, and 28{degrees}C with diurnal fluctuations of 9{degrees}C), we characterize transcriptome responses to infection with P. falciparum at the site of infection in the midgut, and systemically, in the carcasses. Oocyst prevalence and density declined over the thermal gradient, albeit with distinct, non-linear temporal dynamics in parasite development rates. Although infection contributed minimally to global variation in gene expression relative to temperature and time, infection-associated genes in the midgut showed coordinated transcriptional responses enriched in canonical Plasmodium associated extracellular, proteolytic, immune, and metabolic functions; notably, decline in oocyst infections in the midguts over the thermal gradient was reflected in reduced expression of immune genes known to regulate P. falciparum. Network analysis demonstrated that these genes participate in a significantly interconnected protein-protein interaction network, within which a small number of high betweenness centrality proteins act as bottlenecks linking immune, metabolic, reproductive and behavioral processes. Our results suggest responses to infection may be mediated through coordinated physiological networks rather than large-scale transcriptional changes. Our findings also indicate that differences in thermal conditions may be an important factor when comparing mechanisms of vector- parasite interactions between Plasmodium species. Together, our results highlight the importance of integrating thermal context into mechanistic studies of vector-parasite interactions.

ecology↗

bioRxiv2026

Dancing to the beats of the mammalian connectome: Topological and dynamical asymmetries shape infra-slow oscillations

Flexible brain network dynamics unfold on several timescales, despite being constrained by a relatively static structural connectome. Recent cross-species evidence has described infra-slow network modulation, < 0.1 Hz, through two distinct but fundamentally aligned frameworks: transitions between states of integration and segregation identified by high and low global coherence respectively, and periodic transitions between internally and externally oriented attention indexed by the dynamic modulation of alpha power. Here we propose that these infra-slow, structured fluctuations in network engagement stem from a common underlying dynamical motif. Using a network of coupled oscillators embedded in human and macaque empirical connectomes, we show that topological features of brain organization such as modularity, hierarchy and intrinsic dynamical asymmetries naturally give rise to low-frequency collective modes that could nest high frequency states of neuronal communication. These "breathing" dynamics, analogous to beat phenomena in acoustic systems, produce infra-slow fluctuations in global synchrony. Disrupting the connectome topology abolishes such slow coherence oscillations, indicating their dependence on network topology. Furthermore, analytical results from reduced oscillator models reveal how small frequency differences between weakly coupled modules generate slow coherence oscillations, highlighting the importance of dynamical asymmetry. Finally, how neuromodulatory inputs can tune these emergent timescales is discussed, providing a mechanistic link between structural architecture and the dynamic regulation of large-scale brain function.

neuroscience↗

bioRxiv2026

Mechanistic simulation identifies predictive dose-dependent biomarkers of propofol anesthesia

Understanding how receptor-level pharmacological modulation reorganizes large-scale brain circuits remains a central challenge in neuropharmacology. We introduce a multiscale mechanistic model with explicit core-matrix thalamocortical architecture, driven solely by GABA-A modulation without parameter fitting to any anesthesia data, to examine how propofol reorganizes brainwide activity from individual receptors to systems-level circuits. The model exhibits anesthetic effects spanning individual synaptic conductances to widespread changes in spiking, field potentials, and coherence. Without training on any task-specific data, our simulation of sensory processing in a standard auditory oddball paradigm matches independent macaque datasets. The same simulation, unmodified, also reproduces changes to functional connectivity in anesthetized humans, exhibiting selective attenuation of matrix thalamocortical loops relative to core loops. Most importantly, the simulation identified a dose-dependent biomarker of propofol concentration -- elevated residual inter-stimulus cortical activity -- that was subsequently confirmed in empirical macaque data where it had previously gone unnoticed. This simulation-first discovery, arising from mechanistic circuit dynamics rather than statistical comparison of clinical populations, illustrates a generative framework for translating receptor-level modulation into circuit-scale biomarkers with potential applications across predictive neuropharmacology.

neuroscience↗

bioRxiv2026

A biophysical corticostriatal model predicts learning-dependent 7T fMRI dynamics and individual reward bias in humans

Circuit-level computational models can do more than explain existing data; they can generate novel hypotheses and capture individual differences in human populations. We demonstrate this using a biophysical corticostriatal model, transforming simulated neuronal activity from local field potential (LFP) into functional magnetic resonance imaging (fMRI) signals via the balloon model, and finally generating behavioral outcomes. We then validate the results against those obtained from human subjects. The model generates a counterintuitive yet testable prediction. Although prefrontal-striatal coherence demonstrates an increase at the neural LFP scale during learning, the same circuit-- passed through the hemodynamic transform---predicts a decrease in prefrontal-striatal BOLD correlation, driven by category-selective representations that emerge in cortex but not striatum. This prediction is confirmed in fMRI data optimized for single-subject-level detection sensitivity. The model further enables single-subject fitting of a behavioral outcome, classifying individuals into those differentially responding to positive versus negative reward bias. Established biomarkers, including activation measured by the amplitude of low-frequency fluctuations and dopaminergic effects on hemodynamic latency, are also conserved across LFP and fMRI scales. These findings reposition circuit models as generative tools for human neuroscience, capable of producing mechanistically grounded hypotheses and parsing individual variation in ways inaccessible to data-driven approaches alone.

neuroscience↗

bioRxiv2026

Corpus-wide causality: Algorithm design & application for aggregating gene-disease causal evidence

Identifying causal relationships and distinguishing them from associations is a central scientific endeavor with many applications; knowing causal links between genes and diseases, for instance, can focus drug discovery on curing diseases beyond just symptom management. Despite several studies on automatically extracting relations between entities from large biomedical literature corpora like PubMed, only a few studies extract causal relations from abstracts and even fewer summarize corpus-level evidence for causal links. Recently, Large Language Models (LLMs) have been increasingly deployed to summarize biomedical information and extract relations; however, there is a distinct lack of explicit benchmarking comparing these generalized LLM-based methods against specialized, domain-aware frameworks for corpus-wide causal inference. In this work, we develop a method to infer Corpus-Wide Causal Score (CWCS) of a gene-disease (G-D) pair by integrating two pieces of evidence: (i) network-based causal signals in a prior gene regulatory network, quantified as a CWCS-Net score using an existing multilayer network centrality algorithm; and (ii) corpus-wide literature evidence, quantified as a CWCS-TD (TD for Truth Discovery) score using a newly-developed TD algorithm. Our CWCS-TD (scoring) algorithm jointly and iteratively estimates causal scores for multiple G-D pairs while modeling the reliability of PubMed abstracts co-mentioning them; and represents an advance in the field of TD algorithms due to its incorporation of bibliometric features of publications to address the challenge of sparsity of abstracts that assert a G-D causal relation. Using OMIM as an external expert-curated reference to evaluate classifications of G-D pairs as causal or not, our CWCS method achieved a causal class F1 score of 0.600 across ten diseases, outperforming both LLMs, GPT-4o and MMed-Llama 3 (this performance trend also persists when using area under the precision-recall curve as the evaluation metric). Both LLMs exhibit high recall accompanied by comparatively low precision, resulting in lower causal class F1 scores (0.505 for GPT-4o and 0.522 for MMed-Llama 3) due to large number of false positive predictions. Taken together, these evaluations and other ablation studies show the promise of our carefully designed algorithm in collating and integrating evidence of biomedical causal relations from both network- and literature-based sources, thereby supporting its broader applicability.

bioinformatics↗

bioRxiv2026

Polysynaptic signal propagation in networked neural masses

The routing of information across the brain's structural network is central to its wide range of functional capabilities. However, the mechanisms underlying information routing in complex brain networks, particularly between regions that do not share a direct anatomical connection, remain poorly understood. Neural mass models (NMMs), a computational modelling framework capable of capturing complex neural dynamics across scales, can potentially be used to study the dynamical and network bases of these vital polysynaptic routing processes. In this study, we investigate polysynaptic signalling in three widely used NMMs, obeying Ornstein-Uhlenbeck, Stuart-Landau, and Jansen-Rit dynamics, by tracking the propagation of a discrete, focal, high-amplitude perturbation across the underlying network. We find that polysynaptic propagation emerges in all tested NMMs when configured within dynamical regimes that effectively enhance the persistence of perturbations. We also find distinct parameter domains that maximise signal propagation to directly connected regions and to those separated from the source by at least two hops. Finally, we benchmark in silico stimulus propagation in the brain network against an empirical dataset of direct electrical stimulation trials, to explore the relative capabilities of the NMMs in capturing signal propagation to connected versus unconnected regions. This analysis highlights the significance of dynamical repertoire in capturing stimulus propagation outcomes. Overall, this study provides insights into how dynamical and network features shape signal propagation over complex brain networks.

neuroscience↗

bioRxiv2026

Interference Between Motor Memories Arises From Implicit Recalibration

Interference between consecutively acquired motor memories is a defining feature of sensorimotor adaptation, yet its mechanistic origin remains unresolved. Because adaptation is supported by separable explicit strategies and implicit recalibration, it offers a means to identify the learning process that gives rise to interference. Across four visuomotor adaptation experiments, we examined the conditions under which the acquisition of a new, competing motor memory influences the expression of a previously acquired memory. We selectively biased new learning towards either explicit or implicit processes, and quantified its impact on the recall of the original memory 24-hours later. Under standard adaptation conditions, participants exhibited classic interference, such that re-learning was indistinguishable from naive performance. However, when new learning was driven primarily by explicit strategies induced through delayed endpoint feedback, interference was markedly attenuated and the original memory was preserved. In contrast, when the competing memory was implicitly forged under error-clamp conditions, robust interference emerged. Furthermore, disrupting posterior parietal cortex (PPC) with cathodal hd-tDCS prior to implicit learning attenuated interference, indicating that intact PPC processing is required for incorporating new learning into an existing sensorimotor representation. Taken together, these findings suggest that interference reflects the integration of new learning into a shared representational substrate via implicit recalibration, a process that limits the coexistence of competing motor memories.

neuroscience↗

bioRxiv2026

Pharmacologic DPP-4 inhibition promotes CD8⁺ T cell metabolic fitness to enhance anti-tumor activity

Metabolic dysfunction is a hallmark of CD8+ T cell exhaustion in the tumor microenvironment. Thus, there is growing interest in developing strategies that enhance anti-tumor functions of CD8+ T cells via metabolic reprogramming. Here, we identify dipeptidyl peptidase 4 (DPP-4) as a previously unknown regulator of CD8+ T cell function and metabolism. We discovered that DPP-4 is upregulated in exhausted CD8+ T cells. Pharmacological inhibition of DPP-4 with the FDA-approved anti-diabetic drug sitagliptin transcriptionally and metabolically reprogrammed CD8+ T cells, increasing spare mitochondrial respiratory capacity, proliferation, cytotoxic mediator production, and antigen-specific cancer cell killing capability. The functional effects of sitagliptin were dependent on upregulation of glutamate decarboxylase 1 (GAD1), an enzyme that feeds glutamate into the tricarboxylic acid (TCA) cycle, highlighting a new role for GAD1 in CD8+ T cell respiration and proliferation. We found that systemic inhibition of DPP-4 in preclinical mouse glioblastoma (GBM) models prolongs survival in a CD8+ T cell-dependent manner, and retrospective clinical cohort analysis revealed better outcomes in GBM patients using DPP-4 inhibitors. Importantly, preconditioning of Chimeric Antigen Receptor (CAR) T-cells with DPP-4 inhibition enhanced their cytotoxicity, persistence, and therapeutic efficacy in pediatric GBM. Together, our findings provide mechanistic and biological rationale for repurposing readily accessible DPP-4 inhibitors to enhance anti-tumor CD8+ T cell responses.

immunology↗

bioRxiv2025

The critical roaming hypothesis: arousal-driven transitions across critical lines reproduce human functional connectivity dynamics

Ongoing brain activity displays rich temporal variability associated with efficient cognition, with functional connectivity (FC) continually reconfiguring over time. The resulting functional connectivity dynamics (FCD) specifically show complex, fat-tailed statistics that alternate between persistent epochs and faster reconfiguration transients. While nonlinear whole-brain models tuned nearby a critical point have reproduced some aspects of FCD, they fall short of capturing its full temporal complexity. We propose that slow fluctuations in arousal offer a biologically plausible mechanism for exploring critical regimes in large-scale brain dynamics and thus enrich FCD. Using a connectome-based model of coupled cortical populations, we identified phase boundaries where system dynamics transition between regimes of faster or slower FCD. We then phenomenologically incorporated arousal changes, modeling them as stochastic fluctuations in key parameters such as cortical excitability, input gain, and noise amplitude. This non-autonomous formulation enables the system to roam dynamically across regime boundaries, flexibly tuning its distance from critical transition lines and producing intermittent transitions that mirror the stochastic evolution observed in empirical FCD. Fitting these models to human resting-state fMRI and performing model comparison, we find that arousal-driven models more accurately reproduce the distinctive quantitative features of FCD with the greatest improvements coming from the previously poorly accounted fat-tailed portions of the distributions. Together, these results suggest that arousal fluctuations -likely mediated by changes in neuromodulatory tone - shape the brains attractor landscape over time, expanding the repertoire of accessible functional network states and providing a mechanistic basis for the complexity of spontaneous functional dynamics.

neuroscience↗

bioRxiv2025

Airborne Nanoplastics Perturb Mitochondrial Complex I via the ND6 Axis: Polymer-Specific Mitoepigenetic Remodeling Integrating Experimental, In Silico, and Machine Learning Analyses

Airborne nanoplastics constitute an emerging class of environmental contaminants, but their mitoepigenetic effects on human immune cells have not been systematically investigated. Ex vivo human lymphocytes were used to investigate integrated mitochondrial, epigenetic, and inflammatory responses induced by polystyrene (PS), polypropylene (PP), and polyvinyl chloride (PVC) nanoplastics. Fluorescence microscopy at multiple exposure time points and flow cytometry confirmed efficient cellular internalization and progressive intracellular accumulation of nanoplastics. Exposure elicited coordinated transcriptional remodeling of genes regulating mitochondrial dynamics (DRP1, MFN1), mitochondrial DNA encoded oxidative phosphorylation components (MT-ATP6, MT-COX1, MT-ND6), DNA repair (OGG1, APE1), DNA methylation machinery (DNMT1, DNMT3a, DNMT3b), and mitochondrial-associated miRNAs (miR-21, miR-34a, miR-155). Functional analyses revealed polymer and time-dependent disruption of mitochondrial membrane potential and respiratory chain activities, with Complex I identified as the primary site of vulnerability. Correlation analysis showed strong positive associations among DRP1, OMA1, DELE1, and ND6 (r > 0.9, R{superscript 2} > 0.8, p < 0.001), reflecting coordinated mitochondrial stress and epigenetic signaling, while negative correlations between DRP1 and MFN1 (r = -0.54, R{superscript 2} = 0.29, p < 0.01) and between APE and ND6 (r {approx} -0.42, R{superscript 2} {approx} 0.18, p < 0.05) highlight antagonistic regulation and impaired mitochondrial network stability linked to Complex I dysfunction. In silico docking of oxidized nanoplastic oligomers identified high-affinity interactions at the Complex I Fe-S cluster and cofactor-binding sites, suggesting direct interference with electron transfer. A random forest-based model accurately predicted MT-ND6 expression from Complex I activity (R{superscript 2} > 0.85), establishing a data-driven Complex I-ND6 axis. Collectively, these findings demonstrate that airborne nanoplastics induce integrated mitoepigenetic and immunometabolic dysregulation, underpinned by coordinated and antagonistic regulatory interactions in lymphocytes.

molecular biology↗

bioRxiv2025

Sensorineural regulation of skull healing implicates FGF1 signaling in non-healing bone

Bone injuries demonstrate rapid peripheral nerve ingrowth followed by nerve pruning as healing ensues. However, in non-healing bone injuries, peripheral innervation remains elevated, the implications of which remain unknown. Therefore, we investigated the neuroskeletal microenvironment in subcritical-and critical-sized calvarial defects using quantitative 3D fluorescence imaging. We identified elevated densities of peripheral nerves and Osterix positive (Osx+) osteoprogenitors in critical-sized defects, while osteogenic differentiation markers were severely diminished. Moreover, Osx+ osteoprogenitors in critical-sized defects exhibited enhanced proximity to peripheral nerves, which in turn was associated with increased osteoprogenitor cell proliferation. Using retrograde tracing in conjunction with single cell RNA-sequencing of sensory nerves from the innervating trigeminal ganglia, genes encoding for nerve-secreted proliferative and anti-differentiation factors were identified. Specifically, FGF-1/FGFR-1 signaling was identified as a significant neuroskeletal interaction with critical-sized defects demonstrating higher FGF-1 expression in fluorescence imaging. Presence of FGF-1 in neurons innervating the calvarial was confirmed, and neural conditioned media depleted for FGF1 showed enhanced induction of osteogenesis when placed on parietal bone cells. Collectively, we identify a sensorineural-skeletal signaling interaction elevated in critical-sized defects that can be leveraged as a potential therapeutic target for enhancing bone healing.

bioengineering↗

bioRxiv2025

GRAYU: Graph-based Database integrating Ayurvedic formulations, medicinal plants, phytochemicals and diseases

The translation of Indias extensive traditional knowledge on indigenous medicinal plants into modern therapeutic solutions is contingent upon a systematic framework. While traditional Indian medicine offers a rich source of therapeutic leads, this knowledge is often not structured for modern computational analysis, creating a barrier to systematic drug discovery. To this end, we present GRAYU, a curated and comprehensive online database that integrates data across multiple categories, connecting 1039 traditional formulations to 12,949 indigenous plants, 129,542 phytochemicals, and 13,480 indicated diseases. It also provides insights into 1,382,362 plant-phytochemical, 116,824 plant-disease, 2,405 plant-formulation, and 4,087 formulation-disease associations. Case studies on phytochemical analogs, sustainable plant substitution, and disease-network pharmacology showcased the potential of integrative graphs to decode shared molecular signatures and therapeutic networks across traditional Ayurvedic formulations. GRAYU represents a user-friendly resource for researchers to investigate complex bio-associations and formulate novel therapeutic hypotheses, with insights from traditional Indian medicine. GRAYU is available at https://caps.ncbs.res.in/GRAYU/.

bioinformatics↗

bioRxiv2025

Human Mendelian disease and in vivo mutagenesis screening define the molecular architecture of the U8 snoRNA

Over 50 mutations in the vertebrate specific box C/D small nucleolar RNA U8 have been reported to cause leukoencephalopathy with calcifications and cysts (LCC), a progressive cerebral microangiopathy presenting at any age between early infancy and late adulthood. Notably, the majority of these disease-associated mutations are annotated as variants of uncertain significance due to an inability to predict their effect on U8 function. Here, using a zebrafish bioassay, we performed in vivo mutagenesis screening to explore the molecular pathology of LCC. We demonstrate that LCC-associated mutations cluster in function and molecular behaviour according to distinct structural domains within U8, differentially impairing U8 processing, activity and stability. Further, we show that when U8 function is hypomorphic, exogenous human U8 and the endogenous zebrafish U8.3 paralogue increase in stability through a newly discovered post-transcriptional mechanism. This latter response is compromised in patients with LCC due to the presence of a common polymorphism of an N6-methyladenosine modified nucleotide, which we predict to impact disease penetrance.

neuroscience↗