Biology subjects

Mahajan, T.

Publications and source records attributed to Mahajan, T..

7 recordsLinked to original sources

bioRxiv2025

Single-cell heterogeneity in interferon induction potential is heritable and governed by variation in cell state

Type I and III interferons (IFNs) are among the first lines of defense against viral infection, yet they are generally only produced by a tiny fraction of infected cells. Here, we show that cellular heterogeneity in IFN induction potential upon treatment with immunostimulatory RNA is not due to variability in sensing of stimuli but instead is shaped by heterogeneity in tonic cell signaling state. Using complementary single-cell approaches, we found that baseline variation in the c-Jun N-terminal kinase (JNK) and activator protein (AP)-1 transcription factor families correlated with IFNL1 expression predisposition. We further show that drug-based inhibition of JNK signaling virtually eliminates the innate antiviral response to immunostimulatory RNA. Finally, we show that single cell heterogeneity in IFN induction potential is heritable and stably maintained over numerous generations. Together, our study emphasizes the influence of intrinsic variability in cell state on innate immune regulation and IFN induction heterogeneity.

systems biology↗

bioRxiv2025

Intrinsic OASL expression licenses interferon induction during influenza A virus infection

Effective control of viral infection requires rapid induction of the innate immune response, especially the type I and type III interferon (IFN) systems. Despite the critical role of IFN induction in host defense, numerous studies have established that most cells fail to produce IFNs in response to viral stimuli. The specific factors that govern cellular heterogeneity in IFN induction potential during infection are not understood. To identify specific host factors that license some cells but not others to mount an IFN response to viral infection, we developed an approach for analyzing temporal scRNA-seq data of influenza A virus (IAV)-infected cells. This approach identified the expression of several interferon stimulated genes (ISGs) within pre-infection cells as correlates of IFN induction potential of those cells, post-infection. Validation experiments confirmed that intrinsic expression of the ISG OASL is essential for robust IFNL induction during IAV infection. Altogether, our findings reveal an important role for IFN-independent, intrinsic expression of ISGs in promoting IFN induction and provide new insights into the mechanisms that regulate cell-to-cell heterogeneity in innate immune activation.

immunology↗

bioRxiv2024

noSpliceVelo infers gene expression dynamics without separating unspliced and spliced transcripts

Modern single-cell transcriptomics has revolutionized biological research, but because of its destructive nature, it provides only static snapshots. Computational approaches that infer RNA velocity from the ratio of unspliced to spliced mRNA levels can be used to predict how gene expression changes over time. However, information about unspliced and spliced transcripts is not always available and may change on a timescale too short to accurately infer transitions between cellular states. Here we present noSpliceVelo, a novel technique for reconstructing RNA velocity without relying on unspliced and spliced transcripts. Instead, it exploits the temporal relationship between the variance and mean of bursty gene expression using a well-established biophysical model. When evaluated on datasets describing mouse pancreatic endocrinogenesis, mouse and human erythroid maturation, and neuronal stimulation in mouse embryonic cortex, noSpliceVelo performed comparably or better than scVelo, a splicing-based approach. In addition, noSpliceVelo inferred key biophysical parameters of gene regulation, specifically burst size and frequency, potentially distinguishing between transcriptional and epigenetic regulation.

bioinformatics↗

bioRxiv2024

Coarse-Grained Model of Serial Dilution Dynamics in Synthetic Human Gut Microbiome

Many microbial communities in nature are complex, with hundreds of coexisting strains and the resources they consume. We currently lack the ability to assemble and manipulate such communities in a predictable manner in the lab. Here, we take a first step in this direction by introducing and studying a simplified consumer resource model of such complex communities in serial dilution experiments. The main assumption of our model is that during the growth phase of the cycle, strains share resources and produce metabolic byproducts in proportion to their average abundances and strain-specific consumption/production fluxes. We fit the model to describe serial dilution experiments in hCom2, a defined synthetic human gut microbiome with a steady-state diversity of 63 species growing on a rich media, using consumption and production fluxes inferred from metabolomics experiments. The model predicts serial dilution dynamics reasonably well, with a correlation coefficient between predicted and observed strain abundances as high as 0.8. We applied our model to: (i) calculate steady-state abundances of leave-one-out communities and use these results to infer the interaction network between strains; (ii) explore direct and indirect interactions between strains and resources by increasing concentrations of individual resources and monitoring changes in strain abundances; (iii) construct a resource supplementation protocol to maximally equalize steady-state strain abundances.

ecology↗

bioRxiv2022

Relative stability of mRNA and protein severely limits inference of gene networks from single-cell mRNA measurements

Inference of gene regulatory networks from single-cell expression data, such as single-cell RNA sequencing, is a popular problem in computational biology. Despite diverse methods spanning information theory, machine learning, and statistics, it is unsolved. This shortcoming can be attributed to measurement errors, lack of perturbation data, or difficulty in causal inference. Yet, it is not known if kinetic properties of gene expression also cause an issue. We show how the relative stability of mRNA and protein hampers inference. Available inference methods perform benchmarking on synthetic data lacking protein species, which is biologically incorrect. We use a simple model of gene expression, incorporating both mRNA and protein, to show that a more stable protein than mRNA can cause loss in correlation between the mRNA of a transcription factor and its target gene. This can also happen when mRNA and protein are on the same timescale. The relative difference in timescales affects true interactions more strongly than false positives, which may not be suppressed. Besides correlation, we find that information-theoretic nonlinear measures are also prone to this problem. Finally, we demonstrate these principles in real single-cell RNA sequencing data for over 1700 yeast genes.

systems biology↗

bioRxiv2021

Topological Constraints on Noise Propagation in Gene Regulatory Networks

Gene expression, the production of protein from DNA and mRNA in the biological cell, is inherently stochastic. Cells with identical DNA exhibit fluctuations or noise in gene expression. This noise propagates over gene regulatory networks (GRNs), which encode gene-gene interactions. The propagated extrinsic noise interacts and combines with intrinsic noise to affect biological decisions. Consequently, it is essential to understand how GRN topology affects total noise. Recently, uncertainty principles were established for noise propagation over GRN. In particular, in ring GRNs, exactly one node can have noise reduction below the intrinsic limit. We establish necessary and sufficient conditions for noise reduction in ring GRN. Specifically, for two- and three-node rings, an odd number of negative regulations is necessary for noise reduction. Further, sufficiency is ensured if sensitivities to input for feedforward and feedback regulations are bounded from below and above, respectively. These constraints are valid even if the ring GRN are regulated by an upstream gene. Finally, we use graph theory to decompose noise propagation in a general directed network over its strongly connected components. The combination of graph theory and stochastic processes may be a general framework for studying noise propagation.

systems biology↗

bioRxiv2020

Internetwork connectivity of molecular networks across species of life

BackgroundMolecular interactions have been studied as independent complex networks in systems biology. However, molecular networks dont exist independently of each other. In a network of networks approach (called multiplex), we uncover the design principles for the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. ResultsWe find that TRN and PPI networks are non-randomly coupled in the TRN-PPI multiplex across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene interactions in TRN and protein-protein interactions in PPI also non-randomly overlap in the multiplex. These design principles are conserved across the five eukaryotic species. We show that the robustness of the TRN-PPI multiplex is dependent on these design principles. Further, functionally important genes and proteins, such as essential, disease-related and those involved in host-pathogen PPI networks, are preferentially situated in essential parts of the human multiplex with highly overlapping interactions. ConclusionWe unveil the multiplex architecture of TRN and PPI networks across different species. Multiplex architecture may thus define a general framework for studying molecular networks across the different species of life. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

systems biology↗