Biology subjects

Srivastava, A.

Publications and source records attributed to Srivastava, A..

11 recordsLinked to original sources

bioRxiv2018

Quantitative modelling explains distinct STAT1 and STAT3 activation dynamics in response to both IFNγ and IL-10 stimuli and predicts emergence of reciprocal signalling at the level of single cells

IFN{gamma} and IL10 pathways elicit functionally opposing cell fates, however, these two stimuli activate common transcription factors like STAT1 and STAT3 (S/1/3). How the two stimuli regulate the dynamics of S/1/3 activation remains less understood. Here, we experimentally measured the signaling dynamics of S/1/3 in response to IFN{gamma} and IL10 and found that STAT3, in particular, exhibits a bell-shaped response to both stimuli with maximal activation in an intermediate dose. We built a mathematical model, which quantitatively captured the S/1/3 dynamics and by model analysis, we identified the primary regulators controlling the bell-shaped STAT3 responses in both pathways. As the STATs are activated in response to both stimuli, in a scenario when cells are subjected to co-stimulation of IFN{gamma} and IL10, an interpathway competition to activate the common substrate is plausible. Additionally, a strong transcriptional induction of SOCS1 (a negative regulator of IFN{gamma} pathway) was observed upon IL10 stimulation, suggesting that IL10 pathway can potentially inhibit the IFN{gamma} pathway via SOCS1 induction. To quantitatively understand the S/1/3 responses to co-stimulation we next simulated the same which predicted STAT3 activation and SOCS1 induction dynamics would robustly remain IL10 driven. Subsequent experiments validated the model predictions. Further, the model analysis identified the primary regulators controlling the robustness of IL10-STAT3 axis in co-stimulation. Together, our data-driven model quantitatively captured the dynamics of the STATs in response to both pro (IFN{gamma}) and anti (IL10)-inflammatory stimuli and identifies distinct regulatory elements controlling STAT3 signaling in individual and co-stimulation conditions.

systems biology↗

bioRxiv2018

Bioinformatics workflows for genomic analysis of tumors from Patient Derived Xenografts (PDX): challenges and guidelines

Bioinformatics workflows for analyzing genomic data obtained from xenografted tumor (e.g., human tumors engrafted in a mouse host) must address several challenges, including separating mouse and human sequence reads and accurate identification of somatic mutations and copy number aberrations when paired normal DNA from the patient is not available. We report here data analysis workflows that address these challenges and result in reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from patient derived xenograft models. We validated our analytical approaches using simulated data and by assessing concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). The commands and parameters for the workflows are available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows.

bioinformatics↗

bioRxiv2018

The novel lncRNA lnc-NR2F1 is pro-neurogenic and mutated in human neurodevelopmental disorders

Long noncoding RNAs (lncRNAs) have been shown to act as important cell biological regulators including cell fate decisions but are often ignored in human genetics. Combining differential lncRNA expression during neuronal lineage induction with copy number variation morbidity maps of a cohort of children with autism spectrum disorder/intellectual disability versus healthy controls revealed focal genomic mutations affecting several lncRNA candidate loci. Here we find that a t(5:12) chromosomal translocation in a family manifesting neurodevelopmental symptoms disrupts specifically lnc-NR2F1. We further show that lnc-NR2F1 is an evolutionarily conserved lncRNA functionally enhances induced neuronal cell maturation and directly occupies and regulates transcription of neuronal genes including autism-associated genes. Thus, integrating human genetics and functional testing in neuronal lineage induction is a promising approach for discovering candidate lncRNAs involved in neurodevelopmental diseases.

neuroscience↗

bioRxiv2018

Localisation and hypersecretion of Nerve Growth Factor in breast Phyllodes tumours: evidence from a preliminary study

BackgroundThe pathophysiology of the breast phyllodes tumours is uncertain. Currently, wide surgical removal is the only available treatment option. The histopathological diagnosis of phyllodes tumours is often confused with that of fibroadenomas due to a striking histological resemblance; hence a distinctive biomarker for this tumour type is warranted.\n\nMaterial & MethodsFresh human breast tissue was obtained from surgically excised breast phyllodes and fibroadenoma tumours (test, 2 cases each), breast cancer (positive control, 2 cases) and normal breast tissue (negative control, 1 case). Immunohistochemistry was performed for the detection of nerve growth factor (NGF) on frozen sections of the test and control tissues fixed in 4% paraformaldehyde, using the indirect streptavidin-biotin-peroxidase complex method. Sandwich ELISA on tissue homogenates of the same test and control cases was also performed to validate the immunohistochemical findings.\n\nResultsA marked difference in NGF expression was detected in phyllodes tumours compared to fibroadenomas. The maximum NGF expression was observed in phyllodes tissue followed by cancer tissue, and the least expression in fibroadenomas (3-5 times less than in phyllodes; comparable with normal breast tissue).\n\nConclusionNGF is known for its growth inducing potential in breast cancer, but its secretion by a benign breast tumour is not known in literature. This study reports abundant NGF secretion by breast phyllodes, raising the possibility of its potential role in tumour pathogenesis and progression that can be exploited therapeutically in future. We also propose that NGF may be used as a distinct biomarker of phyllodes tumours, for differentiating them from fibroadenomas during histopathology.

cancer biology↗

bioRxiv2018

Alevin: An integrated method for dscRNA-seq quantification

We introduce alevin, a fast end-to-end pipeline to process droplet-based single cell RNA sequencing data, which performs cell barcode detection, read mapping, unique molecular identifier deduplication, gene count estimation, and cell barcode whitelisting. Alevins approach to UMI deduplication accounts for both gene-unique reads and reads that multimap between genes. This addresses the inherent bias in existing tools which discard gene-ambiguous reads, and improves the accuracy of gene abundance estimates.

bioinformatics↗

bioRxiv2018

Bayesian Estimation of 3D Chromosomal Structure from Single Cell Hi-C Data

The problem of 3D chromosome structure inference from Hi-C datasets is important and challenging. While bulk Hi-C datasets contain contact information derived from millions of cells, and can capture major structural features shared by the majority of cells in the sample, they do not provide information about local variability between cells. Single cell Hi-C can overcome this problem, but contact matrices are generally very sparse, making structural inference more problematic. We have developed a Bayesian multiscale approach, named SIMBA3D, to infer 3D structures of chromosomes from single cell Hi-C while including the bulk Hi-C data and some regularization terms as a prior. We study the landscape of solutions for each single-cell Hi-C dataset as a function of prior strength and demonstrate clustering of solutions using data from the same cell.

bioinformatics↗

bioRxiv2017

Structural basis of transcription inhibition by fidaxomicin (lipiarmycin A3)

Fidaxomicin is an antibacterial drug in clinical use in treatment of Clostridium difficile diarrhea1-2. The active pharmaceutical ingredient of fidaxomicin, lipiarmycin A3 (Lpm)1-4, is a macrocyclic antibiotic with bactericidal activity against Gram-positive bacteria and efflux-deficient strains of Gram-negative bacteria1-2, 5. Lpm functions by inhibiting bacterial RNA polymerase (RNAP)6-8. Lpm exhibits no cross-resistance with the classic RNAP inhibitor rifampin (Rif)7, 9 and inhibits transcription initiation at an earlier step than Rif8-11, suggesting that the binding site and mechanism of Lpm differ from those of Rif. Efforts spanning a decade to obtain a crystal structure of RNAP in complex with Lpm have been unsuccessful. Here, we report a cryo-EM12-13 structure of Mycobacterium tuberculosis RNAP holoenzyme in complex with Lpm at 3.5 [A] resolution. The structure shows that Lpm binds at the base of the RNAP \"clamp,\" interacting with the RNAP switch region and the RNAP RNA exit channel. The binding site on RNAP for Lpm does not overlap the binding sites for other RNAP inhibitors, accounting for the absence of cross-resistance of Lpm with other RNAP inhibitors. The structure exhibits an open conformation of the RNAP clamp, with the RNAP clamp swung outward by ~17{degrees} relative to its position in catalytically competent RNAP-promoter transcription initiation complexes, suggesting that Lpm traps an open-clamp conformational state. Single-molecule fluorescence resonance energy transfer14 experiments confirm that Lpm traps an open-clamp conformational state and define effects of Lpm on clamp opening and closing dynamics. We propose that Lpm inhibits transcription initiation by trapping an open-clamp conformational state, thereby preventing simultaneous engagement of transcription initiation factor {sigma} regions 2 and 4 with promoter -10 and -35 elements. The results provide information essential to understanding the mode of action of Lpm, account for structure-activity relationships of known Lpm analogs, and suggest modifications to Lpm that could yield new, improved Lpm analogs.

molecular biology↗

bioRxiv2017

Using Topological Rearrangements to Characterize De-mixed Liquid Phases in Bio-membranes

Lateral heterogeneities in biomembranes play a crucial role in various physiological functions of the cell. Such heterogeneities lead to demixing of lipid constituents and formation of distinct liquid domains in the membrane. We study lateral heterogeneities in terms of topological rearrangements of lipids to identify the liquid-liquid phase coexistence in model membranes. By quantifying the degree of non-affine deformation associated with individual lipid, we are able to characterize the liquid ordered (Lo) and liquid disordered (Ld) phases in model lipid bilayers, without any prior knowledge about the chemical identity of the lipids. We explore the usage of this method on all atom and coarse-grained lipid bilayer trajectories. This method is helpful in defining the instantaneous Lo-Ld domain boundaries in complex multi-component bilayer systems.\n\nThe characterization is also used to highlight the effect of line-active molecules on the phase boundaries and domain mixing. Overall, we propose a framework to explore the molecular origin of spatial and dynamical heterogeneity in biomembrane systems, which can be exploited not only in computer simulations but also in experiments.

biophysics↗

bioRxiv2017

HOME: A histogram based machine learning approach for effective identification of differentially methylated regions

BackgroundThe development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate.\n\nResultsWe present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organisms dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME.\n\nConclusionHOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.

bioinformatics↗

bioRxiv2017

Immature HIV-1 Lattice Assembly Dynamics are Regulated by Scaffolding from Nucleic Acid and the Plasma Membrane

The packaging and budding of Gag polyprotein and viral ribonucleic acid (RNA) is a critical step in the human immunodeficiency virus-1 (HIV-1) lifecycle. High-resolution structures of the Gag polyprotein have revealed that the capsid (CA) and spacer peptide 1 (SP1) domains contain important interfaces for Gag self-assembly. However, the molecular details of the multimerization process, especially in the presence of RNA and the cell membrane, have remained unclear. In this work, we investigate the mechanisms that work in concert between the polyproteins, RNA, and membrane to promote immature lattice growth. We develop a coarse-grained (CG) computational model that is derived from sub-nanometer resolution structural data. Our simulations recapitulate contiguous and hexameric lattice assembly driven only by weak anisotropic attractions at the helical CA-SP1 junction. Importantly, analysis from CG and single-particle tracking photoactivated localization (spt-PALM) trajectories indicates that viral RNA and the membrane are critical constituents that actively promote Gag multimerization through scaffolding, while over-expression of short competitor RNA can suppress assembly. We also find that the CA amino-terminal domain imparts intrinsic curvature to the Gag lattice. As a consequence, immature lattice growth appears to be coupled to the dynamics of spontaneous membrane deformation. Our findings elucidate a simple network of interactions that regulate the early stages of HIV-1 assembly and budding.\n\nSIGNIFICANCE STATEMENTIn order for human immunodeficiency virus to proliferate, viral proteins and genomic dimers are assembled at host cell membranes and released as immature virions. Disrupting this key intermediate step in viral replication is a potential target for treatment. However, a detailed molecular view of this process remains lacking. Here, we elucidate a network of constitutive interactions that regulate viral assembly dynamics through a combined computational and experimental approach. Specifically, our analysis reveals the active roles of nucleic acid and the membrane as scaffolds that promote the multimerization of Gag polyprotein which proceeds through multi-step and self-correcting nucleation. Our findings also illustrate the functional importance of the N-terminal, C-terminal, and spacer peptide 1 protein domains.

biophysics↗

bioRxiv2016

Homozygous c.259G>A variant in ISCA1 is associated with a new multiple mitochondrial dysfunctions syndrome

The iron-sulfur (Fe-S) cluster (ISC) biogenesis pathway is indispensable for many fundamental biological processes and pathogenic variations in genes encoding several components of the Fe-S biogenesis machinery, such as NFU1, BOLA3, IBA57 and ISCA2 are already implicated in causing four types of multiple mitochondrial dysfunctions syndromes (MMDS) among other human diseases. MMDS are clinically characterized by neurodevelopmental delay, neurological deterioration, lactic acidosis, extensive white matter abnormalities and early death. We report on two unrelated families, with two affected children each with neurodevelopmental delay, regression of developmental milestones, seizures, extensive white matter abnormalities, cortical migrational abnormalities, lactic acidosis and early demise. Exome sequencing of two affected individuals, one from each family, revealed a homozygous c.259G>A variant in ISCA1 and Mendelian segregation was confirmed in both families. ISCA1 is a specialized factor known to mediate maturation of distinct Fe-S cluster (ISC) proteins. In silico functional analyses and structural modeling of the protein predict the identified ISCA1 variant to be detrimental to protein stability and function. Notably the phenotype observed in all affected subjects with the ISCA1 pathogenic variant is similar to that previously described in all 4 types of MMDS. The ISCA1 variant lies in the only shared region of homozygosity between the two families suggesting the possibility of a founder effect. To the best of our knowledge this is the first instance where ISCA1 deficiency has been shown to be associated with a human disease, a new type of multiple mitochondrial dysfunctions syndrome.

genetics↗