Biology subjects

Singh, A.

Publications and source records attributed to Singh, A..

At least 397 records · Page 22Linked to original sources

bioRxiv2023

Transcript-specific induction of stop codon readthrough using CRISPR-dCas13 system

Stop codon readthrough (SCR) is the process where translation continues beyond a stop codon on an mRNA. Here, we describe a strategy to enhance or induce SCR in a transcript-selective manner using CRISPR-dCas13 system. Using specific guide RNAs, we targeted dCas13 to the downstream region of the canonical stop codons of mammalian AGO1 and VEGFA, which are known to exhibit natural SCR. Results of readthrough assays revealed the enhancement of SCR of these mRNAs (both exogenous and endogenous) caused by dCas13. This effect was associated with ribosomal pausing, which has been reported in several SCR events. Furthermore, our results show that CRISPR-dCas13 can induce SCR across premature termination codons (PTC) in the mRNAs of green fluorescent protein and TP53. Finally, we demonstrate the utility of this strategy in the induction of readthrough across the thalassemia-causing PTC in HBB mRNA. Thus, CRISPR-dCas13 can be programmed to enhance or induce SCR in a transcript-selective and stop codon-specific manner.

molecular biology↗

bioRxiv2023

The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian

Stochastic variation in gene products ("noise") is an inescapable by-product of gene expression. Noise must be minimized to allow for the reliable execution of cellular functions. However, noise cannot be suppressed beyond an intrinsic lower limit. For constitutively expressed genes, this limit is believed to be Poissonian, meaning that the variance in mRNA numbers cannot be lower than their mean. Here, we show that several cell division genes in fission yeast have mRNA variances significantly below this limit, which cannot be explained by the classical gene expression model for low-noise genes. Our analysis reveals that multiple steps in both transcription and mRNA degradation are essential to explain this sub-Poissonian variance. The sub-Poissonian regime differs qualitatively from previously characterized noise regimes, a hallmark being that cytoplasmic noise is reduced when the mRNA export rate increases. Our study re-defines the lower limit of eukaryotic gene expression noise and identifies molecular requirements for ultra-low noise which are expected to support essential cell functions.

molecular biology↗

bioRxiv2023

Feature selection followed by a residuals-based normalization simplifies and improves single-cell gene expression analysis

Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are to reduce the systematic biases primarily introduced through technical sources and to transform the data to make it more amenable for application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.

bioinformatics↗

bioRxiv2023

Mycobacterium tuberculosis methyltransferase perturbs host epigenetic programming to promote bacterial survival

Mycobacterium tuberculosis (Mtb) has evolved several mechanisms to counter host defense arsenal for its proliferation. We show that Mtb employs multi-pronged approach to modify host epigenetic machinery for its survival upon infection. It secretes methyltransferase (MTase) Rv2067c into macrophages, trimethylating K79 of histone H3 in non-nucleosomal context. Rv2067c downregulates host MTase DOT1L, decreasing its H3K79me3 mark added nucleosomally on pro-inflammatory response genes. Consequent inhibition of caspase8 dependent apoptosis and enhancement of RIPK3 mediated necrosis results in increased pathogenesis. In parallel, Rv2067c enhances the expression of SESTRIN3, NLRC3 and TMTC1 enabling the pathogen to overcome host inflammatory and oxidative response. We provide structural basis for differential methylation of H3 by Rv2067c and DOT1L. The structure of Rv2067c and DOT1L explain how their action on H3K79 is temporally and spatially separated enabling Rv2067c to effectively intercept the host epigenetic circuit and downstream signalling.

microbiology↗

bioRxiv2023

Stochastic dynamics of ecological populations subjected to environmental perturbations

The stochastic logistic model is widely used to capture random fluctuations arising from birth-death processes in ecological populations. We use this model to study the impact of environmental perturbations that may occur naturally or as a consequence of population harvesting. In our model formulation, environmental perturbations occur randomly as per a Poisson process, and the perturbations result in each individual dying with a certain probability of death. Moment closure schemes are employed to derive expressions for the mean and variability in population numbers. Moreover, to quantify the impact of population extinction in our model we compute the probability of extinction using the Finite State Projection (FSP) numerical method. Our analysis shows that rare environmental perturbations with a high probability of death lead to overall larger random fluctuations and extinction risk as compared to frequent perturbations with a low probability of death. Finally, we formulate the problem in the context of population harvesting to find the optimal harvesting rate that maximizes the cumulative yield.

ecology↗

bioRxiv2023

Out-of-equilibrium gene expression fluctuations in presence of extrinsic noise

Cell-to-cell variability in protein concentrations is strongly affected by extrinsic noise, especially for highly expressed genes. Extrinsic noise can be due to fluctuations of several possible cellular factors connected to cell physiology and to the level of key enzymes in the expression process. However, how to identify the predominant sources of extrinsic noise in a biological system is still an open question. This work considers a general stochastic model of gene expression with extrinsic noise represented as colored fluctuations of the different model rates, and focuses on the out-of-equilibrium expression dynamics. Combining analytical calculations with stochastic simulations, we fully characterize how extrinsic noise shapes the protein variability during gene activation or inactivation, depending on the prevailing source of extrinsic variability, on its intensity and timescale. In particular, we show that qualitatively different noise profiles can be identified depending on which are the fluctuating parameters. This indicates an experimentally accessible way to pinpoint the dominant sources of extrinsic noise using time-coarse experiments. Author summaryGenetically identical cells living in the same environment may differ in their phenotypic traits. These differences originate from the inherent stochasticity in all cellular processes, starting from the basic process of gene expression. At this level, large part of the variability comes from cell-to-cell differences in the rates of the molecular reactions due to stochasticity in the level of key enzymes or in physiological parameters such as cell volume or growth rate. Which expression rates are predominantly affected by these so-called "extrinsic" fluctuations and how they impact the level of protein concentration are still open research questions. In this work, we tackle the protein fluctuation dynamics while approaching a steady state after gene activation or repression in presence of extrinsic noise. Our analytical results and simulations show the different consequences of alternative dominant sources of extrinsic noise, thus providing an experimentally-accessible way to distinguish them in specific systems.

systems biology↗

bioRxiv2023

Phenotypical mapping of TP53 unique missense mutations spectrum in human cancers

The p53 tumor suppressor is one of the most mutated genes responsible for tumorigenesis in most human cancers. Out of 29,891 genomic mutations reported in the TP53 Database (https://tp53.isb-cgc.org/), 1297 are identified as unique missense somatic mutants excluding frameshift, intronic, deletion, nonsense, silent, splice and other unknown mutations. we have comprehensively analyzed all these 1297 unique missense mutations and created a phenotypical map based on the distribution of mutants in each domain, the functional state of the protein, and their occurrence in different types of tissues and organs. Our mutation map shows that almost 118 unique missense mutants are reported in the transactivation domain (TADs) and proline-rich domain (PRR), 1,065 in the central DNA-binding domain (DBD), and 113 in the oligomerization (OD) and regulatory domain (RD). Based on the phenotype these 1297 mutations are subdivided into 46 super trans, 491 functional, 315 partially functional, and 415 non-functional mutants. The prevalence of all these mutations was checked in 71 different types of tissues and found the mutant R248Q is reported in 51 types of tissues followed by R175H and R273H in 46 types. The propensity calculation of mutation for each amino acid in p53, showed Proline, Arginine, and Leucine/Glutamic acid are the most frequently mutated residues in the TAD domain, DBD, and TD respectively. We have correlated the impact of these mutations in the structure and function of p53 and highlighted the TP53 unique missense mutants that can be a potential therapeutic drug target with tremendous clinical applications.

cancer biology↗

bioRxiv2023

Constrained dynamics of oligonucleotides in the phase-separated droplets

Phase-separated droplets are excellent means of compartmentalizing functional molecules and have been shown as excellent models for protocells. Although complex functions based on oligonucleotides have been studied, we still lack an understanding of how the oligonucleotide dynamics are affected by the condensed internal environment of these droplets. Particularly, we lack high-resolution experimental measurements of the dynamical parameters that control oligonucleotide diffusion inside the phase-separated droplets. In addition, there is no clarity on how these dynamical parameters differ in the charged (coacervates) vs non-charged (aqueous two-phase system, ATPS) environment of these droplets. In this study, using fluorescence correlation spectroscopy (FCS), we demonstrate the constrained dynamics of oligonucleotides inside membraneless phase-separated droplets at an unprecedented resolution. We further compare transport properties at different lengths of oligonucleotides as well as salt concentrations. We observe that among all the parameters the oligonucleotides caging (spatial restriction in the movement) inside the matrix has a considerable impact on the diffusive dynamics. Our study provides a way of unravelling, quantifying and understanding physical parameters governing the polymer transport dynamics inside the phase-separated droplets.

biophysics↗

bioRxiv2023

Variability in Forest Plant Traits along the Western Ghats of India and Their Environmental Drivers at Different Resolutions

Identifying key environmental drivers for plant functional traits is an important step to understanding and predicting ecosystem responses to a changing climate. Imaging spectroscopy offers great potential to map plant traits at fine resolution across broad regions and then assess controls on their variation across spatial resolutions. We applied permutational partial least-squares regression to map seven key foliar chemical and morphological traits using NASAs Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) for six sites spanning the Western Ghats of India. We studied the variation of trait space using principal components analysis at spatial resolutions from the plot level (4m), community level (30m and 100m) to the ecosystem level (1000m). We observed a consistent pattern of trait space across different resolutions, with one axis representing the traditional leaf economic spectrum defined by foliar nitrogen concentration and leaf mass per area (LMA) and another axis representing leaf structure and defense defined by fiber, lignin, and total phenolics. We also observed consistent directionality of environment-trait correlations across resolutions with generally higher predictive capacity of our environment-traits models at coarser resolutions. Among the seven traits, total phenolics, fiber, and lignin showed strong environmental dependencies across sites, while calcium, sugar, and nitrogen were significantly affected by site conditions. Models incorporating site as a fixed effect explained more than 50% of the trait variance at 1000m resolution. LMA showed little dependence on both environment and site conditions, implying other factors such as species composition and perhaps site history strongly affect variation in LMA. Our results show that reliable trait-trait relationships can be identified in coarse resolution imagery, but that local scale trait-trait relationships (resolutions finer than 30m) are not sensitive to broad-scale abiotic/biotc factors.

ecology↗

bioRxiv2023

A universal stress protein is essential for the survival of Mycobacterium tuberculosis

Mycobacterium tuberculosis employs several signaling pathways to regulate its cellular physiology and survival within the host. Mycobacterial genomes encode multiple adenylyl cyclases and cAMP effector proteins, underscoring the diverse ways in which these bacteria utilize cAMP. We have earlier identified universal stress proteins (USP), Rv1636 and MSMEG_3811 in M. tuberculosis and M. smegmatis respectively, as abundantly expressed, novel cAMP-binding proteins. In this study, we show that these USPs may function to regulate cAMP signaling by direct sequestration of the second messenger. In slow-growing mycobacteria, concentrations of Rv1636 were equivalent to the amounts of cAMP present in the cell, and overexpression of Rv1636 in M. smegmatis increased levels of bound cAMP. Rv1636 is secreted via the SecA2 secretion system in M. tuberculosis but is not directly responsible for the efflux of cAMP from the cell. While msmeg_3811 could be readily deleted from the genome of M. smegmatis, we find that the rv1636 gene is essential for growth of M. tuberculosis, and this functionality depends on the cAMP-binding ability of Rv1636. This is the first evidence of a sink for any second messenger in bacterial signaling that would allow mycobacterial cells to regulate the available intracellular free pool of cAMP.

microbiology↗

bioRxiv2023

Epigenetic signature of human immune aging: the GESTALT study.

Age-associated DNA methylation in blood cells convey information on health status. However, the mechanisms that drive these changes in circulating cells and their relationships to gene regulation are unknown. We identified age-associated DNA methylation sites in six purified blood borne immune cell types (naive B, naive CD4+and CD8+ T cells, granulocytes, monocytes and NK cells) collected from healthy individuals interspersed over a wide age range. Of the thousand of age-associated sites, only 350 sites were differentially methylated in the same direction in all cell types and validated in an independent longitudinal cohort. Genes close to age-associated hypomethylated sites were enriched for collagen biosynthesis and complement cascade pathways, while genes close to hypermethylated sites mapped to neuronal pathways. In-silico analyses showed that in most cell types, the age-associated hypo- and hypermethylated sites were enriched for ARNT (HIF1{beta}) and REST transcription factor motifs respectively, which are both master regulators of hypoxia response. To conclude, despite spatial heterogeneity, there is a commonality in the putative regulatory role with respect to transcription factor motifs and histone modifications at and around these sites. These features suggest that DNA methylation changes in healthy aging may be adaptive responses to fluctuations of oxygen availability.

molecular biology↗

bioRxiv2023

Limits on Inferring Gene Regulatory Networks Subjected to Different Noise Mechanisms

One of the most difficult and pressing problems in computational cell biology is the inference of gene regulatory network structure from transcriptomic data. Benchmarking network inference methods on model organism datasets has yielded mixed results, in which the methods sometimes perform reasonably well and other times fail to outperform random guessing. In this paper, we analyze the feasibility of network inference under different noise conditions using stochastic simulations. We show that gene regulatory interactions with extrinsic noise appear to be more amenable to inference than those with only intrinsic noise, especially when the extrinsic noise causes the system to switch between distinct expression states. Furthermore, we analyze the problem of false positives between genes that have no direct interaction but share a common upstream regulator, and explore a strategy for distinguishing between these false positives and true interactions based on noise profiles of mRNA expression levels. Lastly, we derive mathematical formulas for the mRNA noise levels and correlation using moment analysis techniques, and show how these levels change as the mean mRNA expression level changes.

systems biology↗

bioRxiv2023

Class switching is differentially regulated in RBC alloimmunization and vaccination

BackgroundStudies of human patients have shown that most anti-RBC alloantibodies are IgG1 or IgG3 subclasses, though it is unclear why transfused RBCs preferentially drive these subclasses over others. Though mouse models allow for the mechanistic exploration of class-switching, previous studies of RBC alloimmunization in mice have focused more on the total IgG response than the relative distribution, abundance, or mechanism of IgG subclass generation. Given this major gap, we compared the IgG subclass distribution generated in response to transfused RBCs relative to protein in alum vaccination, and determined the role of STAT6 in their generation. Study Design and MethodsWT mice were either immunized with Alum/HEL-OVA or transfused with HOD RBCs and levels of anti-HEL IgG subtypes were measured using end-point dilution ELISAs. To study the role of STAT6 in IgG class-switching, we first generated and validated novel STAT6 KO mice using CRISPR/cas9 gene editing. STAT6 KO mice were then transfused with HOD RBCs or immunized with Alum/HEL-OVA, and IgG subclasses were quantified by ELISA. ResultsWhen compared to antibody responses to Alum/HEL-OVA, transfusion of HOD RBCs induced lower levels of IgG1, IgG2b and IgG2c but similar levels of IgG3. Class switching to most IgG subtypes remained largely unaffected in STAT6 deficient mice in response to HOD RBC transfusion, with the one exception being IgG2b. In contrast, STAT6 deficient mice showed altered levels of all IgG subtypes following Alum vaccination. DiscussionOur results show that anti-RBC class-switching occurs via alternate mechanisms when compared to the well-studied immunogen alum vaccination.

immunology↗

bioRxiv2022

Determinants of Viscoelasticity and Flow Activation Energy in Biomolecular Condensates

The form and function of biomolecular condensates, which are phase-separated intracellular granules of proteins and RNAs, are regulated by their material and dynamical properties. Emerging reports suggest that biomolecular condensates are viscoelastic network fluids, and the primary sequence and structure of the constituent biopolymers govern their bulk fluid phase properties. Here, we employ a multi-parametric approach to dissect the molecular determinants of condensate viscoelasticity by studying a series of condensates formed by engineered multivalent arginine-rich polypeptides and single-stranded DNA. By measuring the terminal relaxation time of the condensate network through optical tweezer-based microrheology and the activation energy of viscous flow through temperature-controlled video particle tracking, we show that condensate viscoelasticity is controlled by two distinct factors - sequence-encoded inter-chain interactions of associative polymers and entropic factors emerging from their intrinsic polymer properties such as the chain length. The biomolecular diffusion in the dense phase shows a strong dependence on the flow activation energy, indicating that the intra-condensate transport properties are primarily reaction-dominant. These results provide a glimpse of the multifaceted control of viscoelasticity and transport properties within biomolecular condensates. Flow activation energy measurement of single and multicomponent condensates by thermo-rheology provides a direct route to quantify inter-chain interactions in the dense phase and dissect the roles of chain entropy and valence in dictating the viscoelastic behavior of biomolecular condensates.

biophysics↗

bioRxiv2022

Exact distribution of the vesicle burst size in synaptic transmission

During electro-chemical signal transmission through synapses, triggered by an action potential (AP), a stochastic number of synaptic vesicles (SV), called the quantal content, release neurotransmitters in the synaptic cleft. It is widely accepted that the quantal content probability distribution is a binomial based on the number of ready-release SVs in the pre-synaptic terminal. But the latter number itself fluctuates due to its stochastic replenishment, hence the actual distribution of quantal content is unknown. We show that exact distribution of quantal content can be derived for general stochastic AP inputs in the steady-state. For fixed interval AP train, we prove that the distribution is a binomial, and corroborate our predictions by comparison with electrophysiological recordings from MNTB-LSO synapses of juvenile mice. For a Poisson train, we show that the distribution is non-binomial. Moreover, we find exact moments of the quantal content in the Poisson and other general cases, which may be used to obtain the model parameters from experiments.

biophysics↗

bioRxiv2022

Reversibility of resistance in a fluctuation test experiment modifies the tail of the Luria-Delbrück distribution

We consider a fluctuation test experiment in which cell colonies are grown from a single cell until they reach a given population size, and then they are exposed to treatment. While they grow, the cells may, with a low probability, acquire resistance to treatment and pass it on to their offspring. Unlike the classical Luria-Delbruck fluctuation test and motivated by recent work on drug-resistance acquisition in cancer/microbial cells, we allow for the resistant cell state to switch back to a drug-sensitive state. This modification does not affect the central part of the (Luria-Delbruck) distribution of the number of resistant survivors: the previously developed approximation by the Landau probability density function applies. However, the right tail of the modified distribution deviates from the power law decay of the Landau distribution. We demonstrate that the correction factor is equal to the Landau cumulative distribution function.

systems biology↗

bioRxiv2022

Protein Language Model for Prediction of Subcellular Localization of Protein Sequences from Gram-negative bacteria (ProtLM.SCL)

The prediction of bacterial protein Sub-Cellular Localization (SCL) is critical for antigen identification and reverse vaccinology, especially when determining protein localization in the lab is time consuming, expensive and not possible for all species. While PSORTb is one of the most widely used tool for predicting SCL, it has several limitations, including the tendency to label a large number of proteins as Unknown. To address these shortcomings, we present a protein language model capable of predicting the subcellular localization of a given protein (ProtLM.SCL) from gram-negative bacteria. By performing 10-fold cross validation on the PSORTb public data set, we demonstrate that ProtLM.SCL is more accurate and precise than PSORTb. When compared to empirically validated published data, our models also outperformed PSORTb, particularly when categorizing difficult occurrences.

bioinformatics↗

bioRxiv2022

The Radiogenomic and Spatiogenomic Landscapes of Glioblastoma, and Their Relationship to Oncogenic Drivers

Glioblastoma (GBM) is well-known for its molecular and spatial heterogeneity, which poses a challenge for precision therapies and clinical trial stratification. Here, in a comprehensive radiogenomics study of 358 GBMs, we investigated the associations between the imaging and spatial characteristics of the tumors with their cancer gene mutation status, as well as with the cross-sectionally inferred likely order of mutational events. We show that cross-validated machine learning analysis of multi-parametric MRI scans results in distinctive in vivo imaging signatures of several mutations, which are relatively more distinctive in homogeneous tumors which harbor only one of these mutations. These imaging signatures offer mechanistic insights into how various mutations influence the phenotype of the tumor and its surrounding infiltrated brain tissue via neovascularization and vascular leakage, increased cell density, invasion and migration, and other characteristics captured by respective imaging features. Furthermore, we found that spatial location and tumor distribution vary, depending on the GBMs molecular characteristics. Finally, distinct imaging and spatial characteristics were associated with cross-sectionally estimated evolutionary trajectories of the tumors. Collectively, our study establishes a panel of in vivo and clinically accessible imaging-AI biomarkers of GBM that reflect their molecular composition and oncogenic drivers.

bioengineering↗