Biology subjects

Narayanan, M.

Publications and source records attributed to Narayanan, M..

At least 19 recordsLinked to original sources

bioRxiv2026

ProcessNets: Towards an efficient approach for ensemble analysis of biological networks

Integrative analysis of the properties of multiple networks pertaining to a biological system is a key problem in systems biology. But computing the properties of thousands of large networks poses a challenge. Most techniques that partially address this challenge, such as parallelism and optimized algorithms, treat the analysis of each network as an independent task. However, the networks arising from the same biological system are often similar to each other, and can we harness this similarity to achieve speedup? Towards this end, we propose a phylogeny-like data structure, nc-tree, to compactly represent an ensemble of related biological networks; and a versatile framework, ProcessNets, that runs incremental algorithms over the nc-tree to speedup property computations on the input networks. Our asymptotic analysis of space and time complexity, coupled with empirical evaluations of diverse ensembles of simulated and real-world GTEx gene coexpression networks, demonstrate that ProcessNets leads to significant space and time advantages when computing different network science measures on ensembles of similar networks. For instance, we achieve a compression factor of 2.1x and 8.1x in storing two representative ensembles of GTEx Muscle Skeletal networks with 1000 nodes/genes, and a time speedup of 2.86x to 3.97x over the fastest baseline for computing degree centrality in ensembles of networks with at least 14,500 nodes derived from subsampled GTEx datasets of ten tissues. These results are promising and encourage application of ProcessNets to analyze biological network ensembles derived from rapidly accumulating consortium/biobank-based datasets.

bioinformatics↗

bioRxiv2026

Systematic assessment of the biological impact of cellular deconvolution on downstream analyses of disease transcriptomes

Background Cellular deconvolution methods estimate cell type proportions from bulk RNA seq data, typically using single cell RNA seq derived signatures, enabling separation of disease associated transcriptional changes into composition driven and cell intrinsic effects. However, these approaches depend on model assumptions and the stability of cell type signatures, and it remains unclear how deconvolution related uncertainties influence downstream analyses and biological conclusions. Results We systematically evaluated the effect of cell type correction on disease relevant transcriptomic insights, using Alzheimer's disease (AD) as a model and the Mount Sinai Brain Bank cohort as a primary dataset. Applying dtangle, selected after comparison with another deconvolution approach, we estimated cell type proportions across four brain regions and assessed how correction reshaped differential gene expression and pathway enrichment. Cell type correction (CTC) markedly altered differentially expressed gene (DEG) profiles in a region dependent manner: the superior temporal gyrus lost all significant signals, while the frontal pole gained DEGs with improved cross region concordance. At the pathway level, correction shifted enrichment from synaptic loss and immune activation toward suppression of stress response and immune regulatory programs, suggesting that composition changes partly obscure cell intrinsic regulatory signals. Overlap with AD genome wide association study loci and replication in an independent cohort indicated that cell intrinsic changes are more consistently validated than composition driven changes. Notably, KCNN2 and RIMS1, not currently recognized as canonical AD biomarkers, emerged as robust transcriptional signatures, potentially reflecting both compositiondriven and cell intrinsic dysregulation and warranting further investigation. Conclusions Parallel evaluation of uncorrected and CTC analyses distinguishes composition driven from cell intrinsic transcriptional effects and highlights robust disease signatures in heterogeneous tissues such as the brain.

bioinformatics↗

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

TB-Bench: A Systematic Benchmark of Machine Learning and Deep Learning Methods for Second-Line TB Drug Resistance Prediction

Drug-resistant tuberculosis (TB), characterized by prolonged treatment regimens and suboptimal treatment outcomes, remains a major obstacle to global TB elimination. Advances in sequencing technologies have enabled the development of machine-learning (ML) approaches, including deep-learning (DL) methods, to predict drug resistance directly from genomic data. However, a significant gap remains in translating these advances into clinical practice. While current approaches reliably predict resistance to first-line drugs, they show consistently lower and more variable performance for second-line drugs compared with traditional drug-susceptibility testing. To characterize these limitations and assess practical utility, we conducted a comprehensive survey and standardized benchmarking of current approaches for predicting TB drug resistance using whole-genome sequencing (WGS) data. Using systematic selection criteria, we identified 20 traditional ML and DL models from 8 studies and evaluated drug-specific versions across 14 second-line drugs within a unified framework. To account for methodological heterogeneity, the models were evaluated using three distinct feature sets reflecting variability in input representations. We trained and evaluated the models on different subsets of the WHO dataset, comprising 50,801 samples, and assessed generalizability using an external validation dataset comprising 1,199 samples. In the internal evaluation on the held-out WHO test dataset, traditional ML models using binary features achieved higher predictive performance than DL models. For example, XGBoost achieved the highest area under the precision-recall curve (PRAUC) scores (46%-93%) for 10 of the 14 drugs. However, performance varied substantially across drugs. Notably, the superior performance of traditional ML models -- even with limited feature sets -- highlights their applicability in low-resource settings. When evaluated on the external validation dataset, the performance of traditional ML and DL models was comparable, and neither class of models demonstrated substantial improvement over catalogue-based approaches, underscoring challenges in cross-dataset generalization. Overall, this benchmarking study provides a comprehensive and systematic evaluation of current approaches, establishes a rigorous evaluation framework for future comparisons, and identifies key methodological considerations necessary to advance robust drug resistance prediction in clinical settings. To enhance reproducibility and facilitate the application of TB-Bench to additional datasets and models, we have made the source code publicly available at https://github.com/BIRDSgroup/TB-Bench.

bioinformatics↗

bioRxiv2026

Modeling and dissecting bidirectional feedback in gene-metabolite systems using the CausalFlux method

Predicting cellular behaviors, a central task in systems biology and metabolic engineering, can be enhanced through integrative modeling of processes such as gene regulation and metabolism. Information flow from gene regulation (modeled via a gene regulatory network) to metabolism (modeled via a genome-scale metabolic model) is well-studied, but the reciprocal regulation of genes by metabolites is less explored. We introduce CausalFlux, a method that models bidirectional feedback between genes and metabolites, in order to predict steady-state reaction fluxes under wild-type (WT) or perturbed (e.g., gene knockout/KO) conditions. CausalFlux does so by iteratively performing causal surgery on a Bayesian gene regulatory network and constraint-based analysis of a coupled metabolic model. CausalFlux enabled us to assess the impact of two-way feedback in several testbed models and real-world biological systems by comparing its predictions to those of TRIMER, a state-of-the-art model of gene-to-metabolite one-way feedback. Incorporating bidirectional feedback, as in CausalFlux, improved the Spearman correlation between actual and predicted fluxes in 92% of the 39 distinct simulation conditions relative to TRIMER. For predicting growth/no-growth phenotype following single-gene KOs in E. coli, CausalFlux achieved a balanced accuracy of 0.79 in identifying essential genes, and TRIMER achieved 0.71 for the same task, again highlighting the importance of modeling two-way feedback. In ablation studies that further dissect the role of specific metabolite[->]gene feedback edges in E. coli, the F1 scores of gene essentiality predictions decreased by 7.5% and 13% upon ablation of feedback edges from any metabolite to the crp gene and the 10 metabolic feedback genes with the highest influence on the KO genes, respectively. Finally, we highlight the application of CausalFlux to predict the essentiality of several hundred genes under different media conditions. Overall, our findings show that CausalFlux can crucially utilize information on feedback metabolites to predict trends in reaction fluxes and qualitative (growth/no-growth) outcomes; thereby encouraging future systems modeling efforts to carefully incorporate not only gene-to-metabolite but also metabolite-to-gene interactions. AvailabilityCode pertaining to the CausalFlux method, and its benchmarking and application is publicly available at: https://github.com/BIRDSgroup/CausalFlux. Author summaryThe myriad processes within a living cell, such as gene regulation or metabolism, are tightly interconnected. Modeling these interconnected processes can offer a deeper mechanistic understanding of cellular behaviors, as well as guide efforts that engineer the metabolic output of a cell. In this work, we develop a novel integrated model of gene regulation and metabolism that incorporates bidirectional feedback between these two processes, via the concept of metabolite-induced causal surgery on a gene regulatory network and gene-induced constraints on the fluxes of metabolic reactions. Our model, which we call CausalFlux, represents an advance over most existing models that capture just the one-way gene-to-metabolism feedback (i.e., genes coding for enzymes that control metabolic reactions). Our CausalFlux methodology opens up an unique opportunity to quantify the impact of two-way feedback in gene-metabolite systems, via comparison of CausalFluxs predictions to those of TRIMER, a published model incorporating one-way feedback alone. For predicting reaction fluxes in testbed models and essential genes in E. coli, quantitative comparison of the performance of CausalFlux vs. TRIMER showed that accounting for two-way feedback leads to more accurate and biologically meaningful predictions. CausalFlux also enabled us to quantify the effect of two-way feedback by comparing prediction performance before and after ablation of certain feedback edges from metabolites to genes. Overall, our findings highlight the importance of modeling gene regulation and metabolism as two-way interconnected systems within a living cell, and encourage future works to incorporate gene{leftrightarrow}metabolite feedback into their analyses.

systems biology↗

bioRxiv2026

NLCD: A method to discover nonlinear causal relations among genes

Distinguishing correlation from causation is a fundamental challenge in many scientific fields, including biology, especially when interventions like randomized controlled trials are infeasible and only observational data are available. Methods based on statistical tests of conditional independence within the Mendelian Randomization framework can detect causality between two observed variables that are each associated with a third instrumental variable. However, these methods for detecting causal relationships between traits (e.g., two gene expression or clinical traits associated with a genetic variant, all observed in the same population) often assume a linear relationship, thereby hindering the discovery of causal gene networks from genomics data.We have developed NLCD, a method for NonLinear Causal Discovery from genomics data based on nonlinear regression modeling and conditional feature importance scoring. NLCD uses these techniques to extend the statistical tests in an existing linear causal discovery method called the Causal Inference Test (CIT). We benchmarked NLCD against current state-of-the-art methods: CIT, Findr, and MRPC. On simulated datasets, NLCD performs comparably to most methods in detecting linear relations (Average AUPRC (Area Under the Precision-Recall Curve) of NLCD=0.94, CIT=0.94, Findr=0.94, and MRPC=0.99), and outperforms them in detecting nonlinear (sine and sawtooth type) relations between two genes (Average AUPRC of NLCD=0.76, CIT=0.60, Findr=0.56, and MRPC=0.73). When tested on a nonlinear subset of a yeast genomic dataset to recover known causal relations involving transcription factors, NLCD and CIT performed comparable to each other and slightly better than Findr and MRPC (Average AUPRC of NLCD=0.82, CIT=0.81, Findr=0.71, and MRPC=0.54). On application to a human genomic dataset, NLCD revealed active causal gene pairs (IRF1 [->] PSME1 and HLA-C [->] HLA-T) in the muscle tissue, and clarified the promises and challenges in discovering causal gene networks in tissues under in vivo human settings. AvailabilityThe code implementing our method is available at: https://github.com/BIRDSgroup/NLCD.

bioinformatics↗

bioRxiv2025

Uncertainty-Aware Gene Rankings Reveal Key Players in Coexpression Networks

MotivationKey genes of a biological system are often prioritized by computing network science measures on a coexpression network inferred from transcriptomic data. But human population heterogeneity and modest sample sizes introduce uncertainty in the inferred coexpression network. Earlier studies have estimated this uncertainty using bootstrap resampling or similar approaches, but fewer have investigated how it propagates to downstream network analyses and affects gene prioritization. Methods and ResultsWe present a systematic workflow to propagate network uncertainty to downstream measures such as degree/PageRank centrality, with the goal of producing robust gene scorings/rankings. We specifically propose uncertainty-aware scorings, BooNS and BPNS, which utilize the spread of a centrality measure across bootstrapped coexpression networks to prioritize stable central genes. Across several (semi-)simulated and real-world (GTEx) datasets, BooNS and BPNS recover reference or tissue-specific genes significantly better than other traditional centrality rankings. This performance gap highlights the long-overdue adoption of uncertainty-aware gene ranking for stable biological inference. Availability and ImplementationCode and supplemental data are available at https://github.com/BIRDSgroup/Bootstrap-based_Node_Scorings_BNS, and https://tinyurl.com/BNS-suppl-data respectively.

bioinformatics↗

bioRxiv2025

Dissecting the effect of single- and co- infection of TB and COVID-19 pathogens on the sputum microbiome

Tuberculosis (TB) and COVID-19 are both respiratory diseases and understanding their interaction is important for effective co-infection management. Although some studies have investigated TB and COVID-19 co-infection in terms of immune responses, microbial dysbiosis in such cases remains unexplored. In this study, we understand the interface between TB and COVID-19 by systematically inspecting the microbial composition of sputum samples collected from four groups of individuals: TB only, COVID-19 only, and both TB and COVID-19 (TBCOVID) infected patients, and uninfected group (Controls). Besides metagenomic analysis of the microbiome of these sputum samples, we also performed whole genome sequencing analysis of a subset of TB-positive samples. Different bioinformatic analyses ensured data quality and revealed significant differences in the microbial composition between Control vs. disease groups. To understand the effect of COVID-19 on TB, we compared TBCOVID vs. TB samples and observed (i) higher read counts of TB-causing bacteria in the TBCOVID group, and (ii) differential abundance of several taxa such as Capnocytophaga gingivalis, Escherichia coli, Prevotella melaninogenica and Veillonella parvula. Functional profiling with PICRUSt2 revealed significantly elevated pathways in the TBCOVID group relative to TB group, some of which are related to metabolism of pulmonary surfactant lipids (e.g., Cytidine diphosphate diacylglycerol (CDP-DAG) biosynthesis pathway with fold change of 7.46). Further clustering of these significantly elevated pathways revealed a sub-cluster of individuals with adverse treatment outcomes. Two individuals in this sub-cluster showed prevalence of respiratory pathogens like Stenotrophomonas maltophilia - knowing such information can help personalize the antibiotic treatment to match the pathogen profile of the individual. Overall, our study reveals the effect of COVID-19 in the airway microbiome of TB patients, and encourages the use of co-microbial/co-pathogen profiling to personalize TB treatment. Author summaryThe community of microbes in an individuals airway tract can play a complex role in respiratory diseases like TB and COVID-19. Although changes in microbial composition in TB and COVID-19 patients have been studied separately, we present a first-of-its-kind investigation of the airway tract microbiome of individuals simultaneously infected with TB and COVID-19 pathogens. Our results highlight that co-infection with COVID-19 in TB patients alters the abundance of certain bacterial species and their related pathways. For instance, Capnocytophaga gingivalis is abundant in co-infected patients, but not present in the TB-only patient group. This species and other differentially abundant species that we identified in the co-morbid condition, if replicated in independent cohorts, can help explain how COVID-19 could exacerbate the severity of lung infection in TB patients. Our study also stimulates future longitudinal studies using expanded datasets to understand the role of concomitant pathogens, and assess whether adjusting the antibiotic regimen accordingly can improve TB treatment outcomes.

genomics↗

bioRxiv2025

Gene Symphony: Two-Layered Musification of Gene Expression Clusters with Functional Backgrounds

Musification, the process of converting data into music, has found applications in scientific research as a novel medium for data representation and communication. However, in biology, its application has largely been limited to DNA and protein sequences or structural data. Existing approaches that attempt to musify gene expression data often do so at the level of the entire gene set or its low-dimensional representations, lacking finer biological context. In our work, we bridge this gap by representing time-series gene expression data through clusters of genes that exhibit similar expression patterns across time points. We then embed contextual biological information onto these clusters to create layered sonifications: a foreground layer capturing expression dynamics across time points through pitch variations, and a background layer reflecting biologically meaningful context, such as Gene Ontology-derived semantic similarity, conveyed through smoothed audio modulated by intensity and echo effects. This musification approach offers an alternative way to analyze traditional visualizations like gene expression heatmaps and dendrograms, providing auditory insight into both temporal and functional trends. Preliminary user survey results indicate that 72.5% of participants were able to correctly interpret gene expression patterns from the foreground melody, and 88.9% perceived differences in background layers, highlighting the effectiveness of the layered musical representation in conveying biologically relevant information. This two-layered design is broadly applicable and can be extended to other forms of musification, allowing different types of information to be conveyed simultaneously through distinct musical components.

bioinformatics↗

bioRxiv2024

MultiPopPred: A Trans-Ethnic Disease Risk Prediction Method, and its Application to the South Asian Population

Genome-wide association studies (GWAS) have guided significant contributions towards identifying disease associated Single Nucleotide Polymorphisms (SNPs) in Caucasian populations, albeit with limited focus on other understudied low-resource non-Caucasian populations. There have been active efforts over the years to understand and exploit the population specific versus shared aspects of the genotype-phenotype relation across different populations or ethnicities to bridge this gap. However, the efficacy of transfer learning models that are simpler than existing approaches and utilize individual-level data remains an open question. We propose MultiPopPred, a novel and simple trans-ethnic polygenic risk score (PRS) estimation method that taps into the shared genetic risk across populations and transfers information learned from multiple well-studied auxiliary populations to a less-studied target population. The default version of MultiPopPred (MPP-PRS+) harnesses individual-level data using a specially designed Nesterov-smoothed penalized shrinkage model and an L-BFGS optimization routine. Extensive comparative analyses performed on simulated genotype-phenotype data, assuming an infinitesimal/omnigenic model, reveal that MPP-PRS+ improves PRS prediction in the South Asian population by 38% on average across all simulation settings when compared to state-of-the-art trans-ethnic PRS estimation methods including SBayesRC-Multi and PROSPER. This improvement is enhanced in settings with low target sample sizes and in semi-simulated settings. These predictive advantages are further echoed in real-world evaluations against 16 UK Biobank quantitative and binary traits. For example, compared to SBayesRC-Multi, MPP-PRS+ achieves comparable performance (within 5% of SOTA performance) on 3 traits, superior performance on 7 other traits, and lags behind on 4 (lipid-related) traits. Neither method could meaningfully predict the remaining 2 traits. MPP-PRS+ maintains a similarly competitive, albeit distinct, performance trend compared to PROSPER. These performance trends are promising and encourage application of MultiPopPred for reliable PRS estimation in low-resource populations with individual-level data for complex omnigenic traits.

bioinformatics↗

bioRxiv2024

Beyond associations: A benchmark Causal Relation Extraction Dataset (CRED) of disease-causing genes, its comparative evaluation, interpretation and application

Information on causal relationships is essential to many sciences, including biomedical science, and beneficial (e.g., causative rather than merely associative gene-disease relations can lead to better treatments). Despite much work on Relation Extraction (RE), automatically extracting causal relations from large text corpora remains less explored. Few existing studies on CRE (Causal RE) are limited to extracting causality within a sentence or for a particular disease, mainly due to the lack of a diverse benchmark dataset. Here, we carefully curate a new CRE Dataset (CRED) of 3639 (causal and non-causal) gene-disease pairs, spanning 204 diseases and 500 genes, within or across sentences of 267 published abstracts. CRED is assembled in two phases to reduce class imbalance, and its inter-annotator agreement is 89%. To assess CREDs utility in classifying causal vs. non-causal pairs, we compared multiple classifiers and found SVM (Support Vector Machine) trained on embeddings from a deep learning transformer model called BioBERT to perform the best (F1 score 0.70). CRED outperformed a state-of-the-art RE dataset in terms of classifier performance and model interpretability, i.e., whether the model focuses importance/attention on words with causal connotations in abstracts. Moving from benchmark to real-world settings, application of our CRED-trained BioBERT+SVM model on all PubMed abstracts on Parkinsons disease (PD) revealed both well- and less-studied PD-causing genes. For instance, genes predicted to be causal for PD in at least 50 abstracts by our model were already linked to PD in books; and lends confidence to further explore the other genes predicted to be causal in fewer abstracts. Our systematically curated and evaluated CRED, and its associated classification model and gene-disease causality scores, thus offer concrete resources for advancing future research in CRE from biomedical literature.

bioinformatics↗

bioRxiv2024

Unveiling Genomic Complexity: A Framework for Genome Graph Structural Analysis and Optimised Variant Calling Workflows

MotivationGenome graphs represent genetic diversity by highlighting polymorphic regions, but current methods lack the ability to characterize and compare their complex structures effectively. ResultsOur study introduces GViNC: a framework for Genome graph Visualisation, Navigation, and Comparison. GViNC maps genomic coordinates onto genome graph nodes, facilitating subgraph partitioning by regions, which aids in navigating and comparing genetic data. Applied to multiple genome graphs from the 1,000 Genomes Project, we observed that genomic complexity varies by ancestry and chromosomes, with rare variants increasing variability significantly. GViNC identified key regions like HLA and DEFB loci, revealing population-specific heterogeneity linked to essential biological functions. Its versatility and scalability support extensive research on genetic diversity across different cohorts or species. Availability and ImplementationGViNC, automated with Snakemake, is available at https://github.com/IBSE-IITM/GViNC. Contact(K.R.) kraman@iitm.ac.in, (M.N) nmanik@cse.iitm.ac.in, (H.S.) sinha@iitm.ac.in Supplementary informationA supplementary document with tables and figures accompanies this manuscript.

genomics↗

bioRxiv2024

The intestinal microbiota contributes to the development of immune-mediated cardiovascular inflammation and vasculitis in mice.

Alterations in the intestinal microbiota contribute to the pathogenesis of various cardiovascular disorders, but how they affect the development of Kawasaki disease (KD), an acute pediatric vasculitis, remains unclear. We report that depleting the gut microbiota reduces the development of cardiovascular inflammation in a murine model mimicking KD vasculitis. The development of cardiovascular lesions was associated with alterations in the intestinal microbiota composition and, notably, a decreased abundance of Akkermansia muciniphila and Faecalibacterium prausnitzii. Oral supplementation with either of these live or pasteurized individual bacteria, or with short-chain fatty acids (SCFAs) produced by them, attenuated cardiovascular inflammation. Treatment with Amuc_1100, the TLR-2 signaling outer membrane protein from A. muciniphila, also decreased the severity of vascular inflammation. This study reveals an underappreciated gut microbiota-cardiovascular inflammation axis in KD vasculitis pathogenesis and identifies specific intestinal commensals that regulate vasculitis in mice by producing metabolites or via extracellular proteins acting on gut barrier function. IN BRIEFIt remains unclear whether changes in the intestinal microbiota composition are involved in the development of cardiovascular lesions associated with Kawasaki disease (KD), an immune-mediated vasculitis. Jena et al. observe alterations in the intestinal microbiota composition of mice developing vasculitis, characterized by reduced A. muciniphila and F. prausnitzii. Oral supplementation with either of these bacteria, live or pasteurized, or with bacteria-produced short-chain fatty acids (SCFAs) or Amuc_1100, the TLR-2 signaling outer membrane protein of A. muciniphila, was sufficient to alleviate the development of cardiovascular lesions in mice by promoting intestinal barrier function. HIGHLIGHTSO_LIAbsence or depletion of the microbiota decreases the severity of vasculitis in a murine model mimicking KD vasculitis. C_LIO_LISupplementation of B. wadsworthia and B. fragilis promotes murine KD vasculitis. C_LIO_LIDecreased abundances of F. prausnitzii and A. muciniphila are associated with the development of cardiovascular lesions in mice. C_LIO_LISupplementation with either live or pasteurized A. muciniphila and F. prausnitzii, or the TLR-2 signaling Amuc_1100, reduces the severity of vasculitis by promoting gut barrier function. C_LI

immunology↗

bioRxiv2024

Demixer: A probabilistic generative model to delineate different strains of a microbial species in a mixed infection sample

MotivationMulti-drug resistant or hetero-resistant Tuberculosis (TB) hinders the successful treatment of TB. Hetero-resistant TB occurs when multiple strains of the TB-causing bacterium with varying degrees of drug susceptibility are present in an individual. Existing studies predicting the proportion and identity of strains in a mixed infection sample rely on a reference database of known strains. A main challenge then is to identify de novo strains not present in the reference database, while quantifying the proportion of known strains. ResultsWe present Demixer, a probabilistic generative model that uses a combination of reference-based and reference-free techniques to delineate mixed infection strains in whole genome sequencing (WGS) data. Demixer extends a topic model widely used in text mining to represent known mutations and discover novel ones. Parallelization and other heuristics enabled Demixer to process large datasets like CRyPTIC (Comprehensive Resistance Prediction for Tuberculosis: an International Consortium). In both synthetic and experimental benchmark datasets, our proposed method precisely detected the identity (e.g., 91.67% accuracy on the experimental in vitro dataset) as well as the proportions of the mixed strains. In real-world applications, Demixer revealed novel high confidence mixed infections (101 out of 1,963 Malawi samples analyzed), and new insights into the global frequency of mixed infection (2% at the most stringent threshold in the CRyPTIC dataset) and its significant association to drug resistance. Our approach is generalizable and hence applicable to any bacterial and viral WGS data. AvailabilityAll code relevant to Demixer is available at https://github.com/BIRDSgroup/Demixer. Contactnmanik@cse.iitm.ac.in Supplementary informationThe Supplemental Data/Result Files related to Demixer are available at this link: https://drive.google.com/drive/folders/13WFACrn2EpeVTO7533-YwlAGjgF4UH3k?usp=drive_link.

bioinformatics↗

bioRxiv2023

Systems analysis of multiple diabetes-helminth cohorts reveals markers of disease-disease interaction

Understanding how the molecules in our body respond to the co-occurrence of two diseases in an individual (comorbidity) could lead to mechanistic insights into novel treatments for comorbid conditions. Studies have shown for instance that responses of our immune system to comorbid conditions could be more complex than the union of immune responses to each disease occurring separately, but a data-driven quantification of this complexity is lacking. In this study, we present a systematic methodology to quantify the interaction effect of two diseases on marker variables of interest (using a chronic inflammatory disease diabetes and parasitic infection helminth as illustrative disease pairs to identify cytokines or other immune markers that respond distinctively under a comorbid condition). To perform this systematic comorbidity analysis, we (i) collected and preprocessed data measurements from multiple single- and double-disease cohorts, (ii) extended differential expression analysis of such data to identify disease-disease interaction (DDI) markers (such as cytokines that respond antagonistically or synergistically to the double-disease condition relative to single-disease states), and (iii) interpreted the resulting DDI markers in the context of prior cytokine/immune-cell knowledgebases. We applied this three-step DDI methodology to multiple cohorts of helminth and diabetes (specifically, helminth-infected and helminth-treated individuals in diabetic and non-diabetic conditions, and non-disease control individuals), and identified cytokines such as IFN-{gamma}, TNF-, and IL-2 to be DDI markers acting at the interface of both diseases in data collected prior to helminth treatment. Validating our expectations, for these cytokines and other T helper Th-2 cytokines like IL-13 and IL-4, their DDI statuses were lost after treatment for helminth infection. For instance, the relative contribution of the DDI term in explaining the individual-to-individual variation of IFN-{gamma} and TNF- cytokines were 67.68% and 48.88% respectively before anthelmintics treatment and dropped to 6.09% and 14.56% respectively after treatment. Furthermore, signaling pathways like IL-10 and IL-4/IL-13 were found to be significantly enriched for genes targeted by certain DDI markers, thereby suggesting mechanistic hypotheses on how these DDI markers influence both diseases. Our results quantified the extent of helminth-diabetes DDI exhibited by various tested cytokine markers, and thereby delineated their role in the pathogenesis of both diseases. These results are promising and encourage the application of our DDI methodology (https://github.com/BIRDSgroup/DDI) to dissect the interaction between any two diseases, provided multi-cohort measurements of markers are available.

systems biology↗

bioRxiv2023

Robust discovery of causal gene networks via measurement error estimation and correction

Discovering causal relations among genes from observational data is a fundamental problem in systems biology, especially in humans where direct gene interventions or perturbations are unethical/infeasible. Furthermore, causality is emerging as an integral factor for building interpretable and generalizable machine-learning models of complex phenotypes. Existing methods can discover causal relations from observed gene expression and matched genetic data using the well-established framework of Mendelian Randomization. But, the prevalence of expression measurement errors can mislead most existing methods into making wrong causal discoveries, especially among genes transcribed at low to moderate levels and using data with large sample size (say thousands as in modern genomic or GWAS studies). In this study, we propose a new framework for causal discovery that is robust against measurement noise by extending an established statistical approach CIT (Causal Inference Test). We specifically developed a two-stage approach called RCD (Robust Causal Discovery), wherein we first estimate measurement error from gene expression data and then incorporate it to get consistent parameter estimates that could be used with appropriately extended statistical tests of correlation or mediation done in the original CIT. By quantifying and accounting for noise in the data, our RCD method is able to significantly outperform the baseline method in recovering ground-truth causal relations among simulated noisy genes and transcription factor to target gene relations among noisy yeast genes using data on 1012 yeast segregants. Encouraged by these results, we applied our RCD to a human setting where perturbations are infeasible and identified several causal relations, including ones involving transcriptional regulators in the skeletal muscle tissue. Data and Code AvailabilityThe code that implements our two-stage RCD framework is available here: https://github.com/BIRDSgroup/RCD; code for reproducing the figures/tables in this manuscript is also provided in this link.

bioinformatics↗

bioRxiv2022

Alzheimer's disease rewires gene coexpression networks coupling different brain regions

Connectome studies have revealed how neurodegenerative diseases like Alzheimers disease (AD) disrupt functional and structural connectivity among brain regions, but the molecular basis of such disruptions is less studied, with most genomic studies focusing on within-brain-region molecular analyses. We performed an inter-brain-region differential correlation (DC) analysis of postmortem human brain RNA-seq data available for four brain regions - frontal pole, superior temporal gyrus, parahippocampal gyrus, and inferior frontal gyrus - from Mount Sinai Brain Bank for hundreds of AD vs. control samples. For any two brain regions, our DC analysis identifies all pairs of genes across these regions whose coexpression/correlation strength in the AD group differs significantly from that in the Control group, after adjusting for cell type compositional effects to better capture cell-intrinsic changes. Such DC gene pairs provided information complementary to known differentially expressed genes in AD, and highlighted extensive rewiring of the network of cross-region coexpression-based couplings among genes. The most vulnerable region in AD, parahippocampal gyrus, showed the most rewiring in its coupling with other brain regions. Decomposing the DC network into bipartite (region-region) gene modules revealed enrichment for synaptic signaling and ion transport pathways in several modules, revealing the dominance of five genes (BSN, CACNA1B, GRIN1, IQSEC2, and SYNGAP1). AD cerebrospinal fluid biomarkers (AD-CSF), neurotransmitters, secretory proteins, ligand and receptors were found to be part of the DC network, suggesting how pathways comprising such signaling molecules could mediate region-region communication. A module enriched for AD GWAS (Genome-wide Association Studies) signals is also enriched for NF-{kappa}{beta} signaling pathway, a key mediator of brain inflammation in AD. Beyond modules, we also identified individual genes that act as hubs of AD dysregulation across regions, such as ZKSCAN1 (Zinc Finger with KRAB And SCAN Domains) - this gene is known to be linked to AD in GWAS studies but via unknown mechanisms, and the specific DC interactions of ZKSCAN1 found in this study can be used to dissect these mechanisms. Thus, our inter-region DC framework provides a valuable new perspective to comprehend AD aetiology.

systems biology↗

bioRxiv2022

MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication

With the evolution of multicellularity, communication among cells in different organs/tissues became pivotal to life. Molecular basis of such communication has long been studied, but genome-wide screens for biomolecules/genes mediating tissue-tissue signaling are lacking. To systematically identify inter-tissue mediators, we present a novel computational approach MultiCens (Multilayer/Multi-tissue network Centrality measures). Unlike single-layer network methods, MultiCens can distinguish within- vs. across-layer connectivity to quantify the "influence" of any gene in a tissue on a query set of genes of interest in another tissue. MultiCens enjoys theoretical guarantees on convergence and decomposability, and excels on synthetic benchmarks. On human multi-tissue datasets, MultiCens predicts known and novel genes linked to hormones. MultiCens further reveals shifts in gene network architecture among four brain regions in Alzheimers disease. MultiCens-prioritized hypotheses from these two diverse applications, and potential future ones like "Multi-tissue-expanded Gene Ontology" analysis, can enable whole-body yet molecular-level investigations in humans.

bioinformatics↗