Biology subjects

Bhatt, N.

Publications and source records attributed to Bhatt, N..

16 recordsLinked to original sources

bioRxiv2026

Generalist method to reconstruct metabolic networks from multi-omics data at large-scale

Metabolic network databases often suffer from inconsistent pathways with blocked reactions. Furthermore, current reconstruction methods lack scalable ways to integrate multi-omics data and cannot flexibly combine diverse modeling pipelines. We introduce SPECTRA, a unified platform that reconstructs metabolic networks across multiple biological scales. SPECTRA uses distinct optimization formulations and achieves a 56-fold speed improvement over existing algorithms for flux consistency based model reconstruction. We demonstrated SPECTRA across three applications. First, we extracted models for 1,479 cancer cell lines, capturing more cancer hallmark reactions than current methods. Second, we gap-filled 7,302 microbial reconstructions from the AGORA2 database, reducing blocked reactions from an average of 482 to 169 per model. Third, we modeled synthetic gut microbiota at the community scale with superior performance in reaction inclusion. This framework provides a scalable and flexible approach for modeling metabolic networks, and generates testable hypotheses for machine learning applications in biology.

systems biology↗

bioRxiv2026

Interpretable machine learning meets systems biology to decode genotype-phenotype maps

Resolving causal genes from quantitative trait loci (QTL) remains fundamentally limited by linkage disequilibrium. We developed an interpretable machine learning framework that captures higher-order nonlinear genotype-phenotype relationships and allows conditional evaluation of genetic variants, enabling statistical decorrelation of linked loci. Applied to Saccharomyces cerevisiae segregants across chemical stress conditions, our method achieved >75% prediction accuracy and identified known causal genes, including MKT1 (genotoxic stress) and IRA2 (osmotic stress). SHAP-based analysis recovered 56% of the validated pleiotropic genes, compared with 36% by conventional contingency testing. Integration with genome-scale metabolic models revealed pathway enrichments distinguishing high-growing strains, including carbon transport, glycolysis, and oxidative phosphorylation. Notably, gene regulatory network analysis identified a novel function for PDR8 in protein mannosylation and cell wall integrity--functions extending beyond its role in drug resistance. This framework demonstrates that interpretable machine learning, coupled with systems biology, transforms QTL associations into mechanistic biological insight.

systems biology↗

bioRxiv2025

Disease-specific tau polymorphs define unique protein interaction networks across proteinopathies

Tau protein aggregates exhibit distinct conformations across tauopathies, but their disease-specific protein interactions remain poorly understood. Here, we demonstrate that disease-specific tau conformations determine unique protein interaction landscapes across Alzheimers disease (AD), progressive supranuclear palsy (PSP), and dementia with Lewy bodies (DLB). Through comprehensive interactome profiling of misfolded tau aggregates from PBS- and sarkosyl-soluble fractions. We identified 493 high-confidence proteins with remarkable disease specificity--notably, no common interactors overlapping across all three tauopathies. Machine learning classification achieved compelling discrimination between diseases using as few as 4-6 proteins features, demonstrating robust molecular signatures underlying clinical heterogeneity. AD derived tau aggregates uniquely engaged cellular metabolism machinery, including key glycolytic enzymes and TCA cycle proteins, alongside glutamate/GABA neurotransmitter cycling components, with the astrocytic glutamate transporter SLC1A2 showing 27-fold enrichment over other tauopathies. In contrast, PSP tau displayed the most distinctive profile, with extensive protein depletion (52/57 significant proteins) and selective enrichment of proteasome components, particularly PSMB7 showing >3000-fold abundance. DLB tau is associated with neurogenesis modulators while depleting neuroinflammatory mediators. These interaction patterns were validated through proximity ligation assays and correlated with distinct post-translational modification profiles, with PSP tau exhibiting globally elevated ubiquitination, AD showing mixed modification patterns, and DLB displaying minimal ubiquitination. Critically, sarkosyl-soluble fractions revealed reduced interactome complexity across diseases, except for PSP tau which maintained robust interactions with GPCR-ERK signaling and kinetochore proteins, suggesting unique aggregation mechanisms. Our findings establish that conformationally distinct tau strains dictate disease-specific protein interaction networks, providing molecular insight into tauopathy diversity and identifying novel therapeutic targets for precision medicine approaches in neurodegeneration.

neuroscience↗

bioRxiv2024

Mechanism of Nucleotide-Dependent Allosteric Regulation in Escherichia coli Aspartate Transcarbamoylase

Aspartate transcarbamoylase (ATCase) from Escherichia coli catalyzes a key step in pyrimidine nucleotide biosynthesis and has long served as a model for allosteric regulation. Despite decades of study, how nucleotide binding at distant regulatory sites controls cooperativity between active sites remained unresolved. Here we show that ATCase does not simply interconvert between two conformations, as traditionally depicted, but instead samples a continuum of conformations that tune enzyme cooperativity. Using complementary cryo-electron microscopy, small-angle X-ray scattering, and crystallography under conditions that ensure full assembly of the allosteric sites, we show that ATCase behaves like a flexible balloon whose global "breathing" motions directly regulate activity: compression enforces high cooperativity, inhibiting the enzyme, whereas expansion relieves this cooperativity and activates the enzyme. We further show that all four ribonucleoside triphosphates act in symmetric pairs to tune this motion, with the pyrimidines CTP and UTP compressing the enzyme to limit further pyrimidine production, and the purines ATP and GTP expanding it to balance pyrimidine and purine pools. Together, these findings uncover a dynamic breathing mechanism for long-range allosteric communication in ATCase.

biochemistry↗

bioRxiv2024

Characterizing the effect of impeller design in plant cell fermentations using CFD modeling

Cultivation of plant cell cultures in conventional bioreactors designed for microbial cells often results in the decrease of biomass productivity as compared to that in shake flasks, presumably due to the imbalance between the mass transfer requirements and compromise with cell viability. Hit and trial methods are usually adapted to achieve high biomass productivity in the bioreactor. In this study, a rational approach has been developed to choose a suitable impeller for Viola odorata cell suspension culture using computational fluid dynamics (CFD). A two-phase CFD model was employed to characterize the non-Newtonian fluid dynamics in a stirred tank reactor using different impeller designs, a setric, Rushton and marine impeller. The simulations were performed adopting Euler-Euler approach for the two-phase flow and dispersed{kappa} - {varepsilon} turbulence model. The numerical model was validated with good agreement with experimental determination of volumetric mass transfer coefficient. The impact of impeller design was then investigated on critical process parameters like mixing, oxygen mass transfer and shear. The developed CFD model demonstrated that setric impeller is a suitable choice for V. odorata cell cultivation offering low-shear environment at equivalent velocity magnitudes at reactor bottom with higher cell-lift capabilities which is preferable in high cell-density plant cell cultivations.

bioengineering↗

bioRxiv2024

Sub-clinical glutamate receptor antagonist combinations prevent progressive demyelination.

Multiple sclerosis (MS) affects almost 3 million people globally who suffer demyelination as a series of relapses and remissions that tend towards progressive deterioration over time. The proximate cause is auto-immune attack by the adaptive immune system; therapies directed against this are effective during the relapsing-remitting phase but are less effective or ineffective during progression where other injury mechanisms may be significant. LPS- and cuprizone-induced experimental demyelination share features of progressive demyelination in MS but the underlying mechanisms are not well understood. We show here that these demyelination models can be reproduced ex vivo using short protocols, revealing that combined antagonism of two types of glutamate receptor, NMDA and AMPA, using clinically approved antagonists at sub-clinical doses, can protect against these forms of demyelination. Combined low dose therapy was subsequently shown to be effective in vivo against dietary cuprizone and experimental autoimmune-encephalomyelitis (EAE) models of demyelination and, in particular, protected the smaller myelinated axons that are the main substrate of the function loss in progressive MS.

neuroscience↗

bioRxiv2023

Localgini: A method for harnessing inequality in geneexpression to improve the quality of context-specific models

Genome-scale metabolic models (GEMs) are valuable tools for investigating normal and disease phenotypes of biological systems through the prediction of fluxes in biochemical reactions. However, in specific contexts such as different cell lines, tissues, or diseases, only a subset of reactions is active. To address this, several model extraction methods (MeMs) have been developed to filter the reactions in GEMs and extract context-specific models. These methods utilize gene expression data as a source of context-specific information. To construct context-specific models, MeMs require core reactions specific to the given context as input. Typically, core reactions are derived using a single threshold applied to gene expression data. Reactions associated with genes whose expression values exceed the threshold are considered as core reactions. However, it is important to note that enzyme activity is not solely determined by gene expression levels. This approach based on a single threshold may inadvertently exclude reactions that require enzymes in smaller quantities. In this study, we propose a novel thresholding algorithm called Localgini, which leverages the Gini coefficient and transcriptomics data to derive gene-specific thresholds. Localgini is implemented as a pre-processing step to obtain core reactions for MeMs. To demonstrate the effectiveness of Localgini, we construct context-specific models for NCI-60 cancer cell lines and human tissues using different MeMs. We compare the performance of Localgini with existing thresholding methods, namely LocalT2 and StanDep. The results show that the models derived using Localgini recover a greater number of housekeeping functionalities compared to the other thresholding methods. Moreover, the Localgini-derived core reactions exhibit increased self-consistency and display enhanced consensus among models built using different MeMs. By incorporating transcriptomic support, Localgini includes low-expression reactions in the core reaction list, enhancing the comprehensiveness of the resulting models. Codes used in this study, compatible with COBRA toolbox are available at https://github.com/NiravBhattLab/Localgini Author summaryGenome-scale models are becoming a desirable tool to understand the metabolism of a biological system and hence find applications in the fields of systems and synthetic biology. These models are often integrated with transcriptomics data to improve prediction accuracy. Algorithms developed to integrate transcriptomics data with genome-scale models require core reactions to be derived from omics data using a threshold. In this work, we propose a thresholding method that uses an inequality-based metric to derive thresholds. We implied the proposed method and other existing methods to datasets of cancer cell lines and human tissue. We showed that our method improves the inclusion of reactions required for basic cellular maintenance. Furthermore, we validated the built models for the reduction in variance owing to the model-extraction algorithms. Overall, the proposed method improves the quality of metabolic models by inferring inequality in the distribution of gene expression levels across samples/contexts.

systems biology↗

bioRxiv2023

Genome-scale metabolic modelling reveals functional effects of quantitative trait variant combinations

Understanding how genetic variations influence cellular function remains a major challenge in genetics. Genome-scale metabolic models (GEMs) are powerful tools used to understand the functional effects of genetic variants. While GEMs have illuminated genotype-phenotype relationships, the impact of single nucleotide polymorphisms (SNPs) in transcription factors and their interactions on metabolic fluxes remains largely unexplored. We used gene expression data from a yeast allele replacement panel to construct co-expression networks and SNP-specific GEMs. The analysis of these models helped us to understand how genetic interactions affect yeast sporulation efficiency, a quantitative trait. Our findings revealed that SNP-SNP interactions have a significant impact on the connectivity of key metabolic regulators involved in steroid biosynthesis, amino acid metabolism and histidine biosynthesis. By integrating gene expression data into GEMs and conducting genome-scale differential flux analysis, we were able to identify causal reactions within six major metabolic pathways, providing mechanistic explanations for variations in sporulation efficiency. Notably, we found that in specific SNP combinations, the pentose phosphate pathway was differentially regulated. In models where the pentose phosphate pathway was inactive, the autophagy pathway was activated, likely compensating by providing critical precursors such as nucleotides and amino acids. This compensatory mechanism may enhance sporulation efficiency by supporting processes that are dependent on the pentose phosphate pathway. Our study sheds light on how transcription factor polymorphisms interact to shape metabolic pathways in yeast and offers valuable insights into genetic variants associated with metabolic traits in genome-wide association studies.

systems biology↗

bioRxiv2023

Central and peripheral tau retention modulated by an anti-tau antibody.

Tau protein blood levels dependent on its distribution to peripheral organs and possible elimination from the body. Thus, the peripheral distribution of CSF-derived tau protein was explored, especially since there is a transition to blood-based biomarkers and the emerging idea that tau pathology may spread beyond brain. Near infrared fluorescence (NIRF) was mainly used to analyze tau (tau-NIRF) distribution after its intracisternal or intravenous injection. There was a striking uptake of blood- or CSF-derived tau-NIRF protein by the skeletal structures, liver, small intestine (duodenum), gall bladder, kidneys, urinary bladder, lymph nodes, heart, and spleen. In aging and in older APP/PS1 mice, tau uptake in regions, such as the brain, liver, and skeleton, was increased. In bone (femur) injected tau protein was associated with integrin-binding sialoprotein (IBSP), a major non-collagenous glycoprotein that is associated with mineralization. Tau-NIRF was cleared slowly from CSF via mainly across the cribriform plate, and cervical lymph nodes. In brain, some of the CSF injected tau protein was associated with NeuN-positive and PDGFRy-positive cells, which may explain its retention. The presence of tau in the bladders suggested excretion routes of tau. CSF anti-tau antibody increased CSF tau clearance, while blood anti-tau antibody decreased tau accumulation in the femur but not in liver, kidney, and spleen. Thus, the data show a body-wide distribution and retention of CSF-derived tau protein, which increased with aging and in older APP/PS1 mice. Further work is needed to elucidate the relevance of tau accumulation in each organ to tauopathy.

physiology↗

bioRxiv2022

Distinct neurotoxic TDP-43 fibril polymorphs can be generated by heterotypic interactions with α-synuclein

Amyloid aggregates of specific proteins form important pathological hallmarks in many neurodegenerative diseases, defining neuronal degeneration and disease onset. Recently, increasing numbers of patients show co-morbidities and overlaps between multiple neurodegenerative diseases, presenting distinct phenotypes. Such overlaps are often accompanied by co-localizations of more than one amyloid protein, prompting the question of whether direct interactions between different amyloid proteins could generate heterotypic amyloids. To answer this question, we investigated the effect of -synuclein (S) on TDP-43 aggregation inspired by their co-existence in pathologies such as Lewy body dementia and limbic predominant age-related TDP-43 encephalopathy. We previously showed that S and prion-like C-terminal domain (PrLD) of TDP-43 synergistically interact with one another to generate toxic heterotypic aggregates in vitro. Here, we extend these studies to investigate whether S induces structurally and functionally distinct polymorphs of PrLD aggregates. Using S -PrLD heterotypic aggregates generated in two different stoichiometric proportions, we show that S can effect PrLD fibril forms. The fibril samples have distinctive residue-level structural signatures in NMR spectra, dye-binding capability, proteinase K (PK) stability, and SDS-sensitive thermal stability. By gold nanoparticle labeling and TEM, we show the presence of both S and PrLD proteins within the same fibrils, and thus the existence of hetertypic hybrid fibrils. We also observe that S and PrLD co-localize in the cytosol of SH-SY5Y neuroblastoma cells, and show that the heterotypic PrLD fibrils selectively induce synaptic dysfunction in primary cortical neurons. These findings establish the existence of heterotypic amyloid polymorphs and provide a molecular basis for the observed overlap between synucleinopathies and TDP-43 proteinopathies.

biophysics↗

bioRxiv2022

Modeling Reactive Species Metabolism in Colorectal Cancer for Identifying Metabolic Targets and Devising Therapeutics

Reactive species (RS) are known to play significant roles in cancer development as well as in treating or managing cancer. On the other hand, genome scale metabolic models are being used to understand cell metabolism in disease contexts including cancer, and also in planning strategies to handle diseases. Despite their crucial roles in cancers, the reactive species have not been adequately modeled in the genome scale metabolic models (GSMMs) when probing disease models for their metabolism or detection of drug targets. In this work, we have developed a module of reactive species reactions, which is scalable - it can be integrated with any human metabolic model as it is, or with any metabolic model with fine-tuning. When integrated with a cancer (colorectal cancer in this case) metabolic model, the RS module highlighted the deregulation occurring in important CRC pathways such as fatty acid metabolism, cholesterol metabolism, arachidonic acid and eicosanoid metabolism. We show that the RS module helps in better deciphering crucial metabolic targets for devising better therapeutics such as FDFT1, FADS2 and GUK1 by taking into account the effects mediated by reactive species during colorectal cancer progression. The results from this reactive species integrated CRC metabolic model reinforces ferroptosis as a potential target for colorectal cancer therapy.

systems biology↗

bioRxiv2022

Connexin 43-mediated Neurovascular Interactions Regulate Neurogenesis in the Adult Brain Subventricular Zone

The subventricular zone (SVZ) is the largest neural stem cell (NSC) niche in the adult brain; herein, the blood-brain barrier is leaky, allowing direct interactions between NSCs and endothelial cells (ECs). Mechanisms by which direct NSC-EC interactions in the SVZ control NSC behavior are unclear. We found Cx43 to be highly expressed by both NSCs and ECs in the SVZ, and its deletion in either cell type leads to increased NSC proliferation and neuroblast generation, suggesting that Cx43-mediated NSC-EC interactions maintain NSC quiescence. This is further supported by in vitro studies showing co-culture with ECs decreases NSC proliferation and increases their expression of genes associated with quiescence in a Cx43-dependent manner. Cx43 mediates these effects in a channel-independent manner involving its cytoplasmic tail and ERK activation. Such insights further advance our understanding of NSC regulation in vivo and may inform NSC maintenance ex vivo for stem cell therapies for neurodegenerative disorders.

neuroscience↗

bioRxiv2019

Improving diagnosis and monitoring of treatment response in pulmonary tuberculosis using the molecular bacterial load assay (MBLA)

ObjectivesBetter outcomes in tuberculosis require new diagnostic and treatment monitoring tools. In this paper we evaluated the utility of a marker of M. tuberculosis viable count, the Molecular Bacterial Load assay (MBLA) for diagnosis and treatment monitoring of tuberculosis in a high burden setting. MethodsPatients with smear positive pulmonary tuberculosis from two sites in Tanzania and one each in Malawi and Mozambique. Sputum samples were taken weekly for the first 12 weeks of treatment and evaluated by MBLA and mycobacterial growth indicator tube method (MGIT). ResultsThe results of high and low positive control samples confirmed inter site reproducibility. Over the 12 weeks of treatment there was a steady decline in the viable bacterial load as measured by the MBLA that corresponds to rise in time to a positive result (TTP) in the Mycobacterial Growth Indicator Tube. Both MBLA and MGIT provided similar time to test negativity. Importantly, as treatment progressed samples in MGIT were increasingly likely to be contaminated, which compromised the acquisition of results but did not affect MBLA samples. ConclusionsMBLA produces a reproducible measure of Mtb viable count comparable to that of MGIT that is not compromised by contamination in a real-world setting. As a molecular test, the results can be available in as little as four hours and could allow health care professionals to identify rapidly patients who are failing therapy.

microbiology↗

bioRxiv2018

Thermodynamically Consistent Estimation of Gibbs Free Energy from Data: Data Reconciliation Approach

MotivationThermodynamic analysis of biological reaction networks requires the availability of accurate and consistent values of Gibbs free energies of reaction and formation. These Gibbs energies can be measured directly via the careful design of experiments or can be computed from the curated Gibbs free energy databases. However, the computed Gibbs free energies of reactions and formations do not satisfy the thermodynamic constraints due to the compounding effect of measurement errors in the experimental data. The propagation of these errors can lead to a false prediction of pathway feasibility and uncertainty in the estimation of thermodynamic parameters.\n\nResultsThis work proposes a data reconciliation framework for thermodynamically consistent estimation of Gibbs free energies of reaction, formation and group contributions from experimental data. In this framework, we formulate constrained optimization problems that reduce measurement errors and their effects on the estimation of Gibbs energies such that the thermodynamic constraints are satisfied. When a subset of Gibbs free energies of formations is unavailable, it is shown that the accuracy of their resulting estimates is better than that of existing empirical prediction methods. Moreover, we also show that the estimation of group contributions can be improved using this approach. Further, we provide guidelines based on this approach for performing systematic experiments to estimate unknown Gibbs formation energies.\n\nAvailabilityThe MATLAB code for the executing the proposed algorithm is available for free on the GitHub repository: https://github.com/samansalike/DR-thermo\n\nContactniravbhatt@iitm.ac.in

systems biology↗

bioRxiv2018

Genetic determinants of risk and survival in pulmonary arterial hypertension

BackgroundPulmonary arterial hypertension (PAH) is a rare disorder leading to premature death. Rare genetic variants contribute to disease etiology but the contribution of common genetic variation to disease risk and outcome remains poorly characterized.\n\nMethodsWe performed two separate genome-wide association studies of PAH using data across 11,744 European-ancestry individuals (including 2,085 patients), one with genotypes from 5,895 whole genome sequences and another with genotyping array data from 5,849 further samples. Cross-validation of loci reaching genome-wide significance was sought by meta-analysis. We functionally annotated associated variants and tested associations with duration of survival.\n\nFindingsA locus at HLA-DPA1/DPB1 within the class II major histocompatibility (MHC) region and a second near SOX17 were significantly associated with PAH. The SOX17 locus contained two independent signals associated with PAH. Functional and epigenomic data indicate that the risk variants near SOX17 alter gene regulation via an enhancer active in endothelial cells. PAH risk variants determined haplotype-specific enhancer activity and CRISPR-inhibition of the enhancer reduced SOX17 expression. Analysis of median survival showed that PAH patients with two copies of the HLA-DPA1/DPB1 risk variant had a two-fold difference (>16 years versus 8 years), compared to patients homozygous for the alternative allele.\n\nInterpretationWe have found that common genetic variation at loci in HLA-DPA1/DPB1 and an enhancer near SOX17 are associated with PAH. Impairment of Sox17 function may be more common in PAH than suggested by rare mutations in SOX17. Allelic variation at HLA-DPB1 stratifies PAH patients for survival following diagnosis, with implications for future therapeutic trial design.\n\nFundingUK NIHR, BHF, UK MRC, Dinosaur Trust, NIH/NHLBI, ERS, EMBO, Wellcome Trust, EU, AHA, ACClinPharm, Netherlands CVRI, Dutch Heart Foundation, Dutch Federation of UMC, Netherlands OHRD and RNAS, German DFG, German BMBF, APH Paris, Inserm, Universite Paris-Sud, and French ANR.

genetics↗

bioRxiv2017

Sensor location optimization and joint angle prediction in hand postures

1. Sensor selection problem 1. Sensor selection problem Grasping of objects (Mason et al. 2001; Santello et al. 2002) or hand shaping during grasping is of interest in robotics, neuroscience, and biomechanics. In animation industry and biomechanics, tracking body and hand movements are essential. For tracking body movements and hand movements, a sizable number of markers are required. As number of marker increase complexity and cost also increase. Researchers have tried various approaches for selecting a set of minimum no. of markers for accurate reproduction of movements. Data-driven approaches have been proposed for classification of various grasps. Some studies have focussed on prediction of joint angles (Kang et al. 2012; Hoyet et al. 2012; Wheatland et al. 2013). Our objective is to predict joint angles from a reduced set of sensors. For preliminary sorting purpose, we used PCA based sensor ranking which is disc ...

bioengineering↗