Biology subjects

Singh, A. N.

Publications and source records attributed to Singh, A. N..

10 recordsLinked to original sources

bioRxiv2024

Chaotropic Agent-assisted Supported Lipid Bilayer Formation

Supported lipid bilayers (SLBs) are useful structures for mimicking cellular membranes, and they can be integrated with a variety of sensors. While there are a variety of methods for forming SLBs, many of these methods come with limitations in terms of the lipid compositions that can be employed and the substrates upon which the SLBs can be deposited. Here we demonstrate the use of an all-aqueous chaotropic agent exchange process that can be used to form SLBs on two different substrate materials: SiO2, which is compatible with traditional SLB formation by vesicle fusion and Al2O3, which is not compatible with vesicle fusion. When examined with quartz crystal microbalance with dissipation monitoring, the SLBs generated by chaotropic agent exchange (CASLBs) have similar frequency and dissipation shifts to SLBs formed by the vesicle fusion technique. The CASLBs block nonspecific protein adsorption on the substrate and can be used to sense protein-lipid interactions. Fluorescence microscopy was used to examine the CASLBs, and we observed long-range lateral diffusion of fluorescent probes, which confirmed the CASLBs were composed of a continuous, planar lipid bilayer. Our CASLB method provides another option for forming planar lipid bilayers on a variety of surfaces, including those that are not amenable to the widely used vesicle fusion method.

biophysics↗

bioRxiv2023

Engineering Planar Gram-Negative Outer Membrane Mimics Using Bacterial Outer Membrane Vesicles

Antibiotic resistance is a major challenge in modern medicine. The unique double membrane structure of gram-negative bacteria limits the efficacy of many existing antibiotics and adds complexity to antibiotic development by limiting transport of antibiotics to the bacterial cytosol. New methods to mimic this barrier would enable high-throughput studies for antibiotic development. In this study, we introduce an innovative approach to modify outer membrane vesicles (OMVs) from Aggregatibacter actinomycetemcomitans, to generate planar supported lipid bilayer membranes. Our method first involves the incorporation of synthetic lipids into OMVs using a rapid freeze-thaw technique to form outer membrane hybrid vesicles (OM-Hybrids). Subsequently, these OM-Hybrids can spontaneously rupture when in contact with SiO2 surfaces to form a planar outer membrane supported bilayer (OM-SB). We assessed the formation of OM-Hybrids using dynamic light scattering and a fluorescence quenching assay. To analyze the formation of OM-SBs from OM-Hybrids we used quartz crystal microbalance with dissipation monitoring (QCM-D) and fluorescence recovery after photobleaching (FRAP). Additionally, we conducted assays to detect surface-associated DNA and proteins on OM-SBs. The interaction of an antimicrobial peptide, polymyxin B, with the OM-SBs was also assessed. These findings emphasize the capability of our platform to produce planar surfaces of bacterial outer membranes, which in turn, could function as a valuable tool for streamlining the development of antibiotics.

bioengineering↗

bioRxiv2023

Identifying size-dependent toxin sorting in bacterial outer membrane vesicles

Gram-negative bacteria produce outer membrane vesicles (OMVs) that play a critical role in cell-cell communication and virulence. Despite being isolated from a single population of bacteria, OMVs can exhibit heterogeneous size and toxin content, which can be obscured by assays that measure ensemble properties. To address this issue, we utilize fluorescence imaging of individual OMVs to reveal size-dependent toxin sorting. Our results showed that the oral bacterium Aggregatibacter actinomycetemcomitans (A.a.) produces OMVs with a bimodal size distribution, where larger OMVs were much more likely to possess leukotoxin (LtxA). Among the smallest OMVs (< 100 nm diameter), the fraction that are toxin positive ranges from 0-30%, while the largest OMVs (> 200 nm diameter) are between 70-100% toxin positive. Our single OMV imaging method provides a non-invasive way to observe OMV surface heterogeneity at the nanoscale level and determine size-based heterogeneities without the need for OMV fraction separation.

biophysics↗

bioRxiv2022

NEURONS IN THE POSTRHINAL CORTEX ENCODE NONSPATIAL CONTEXT DURING VISUAL BICONDITIONAL DISCRIMINATION

Spatial context, or the physical surroundings that form the background of an experience, is an essential component of episodic memory. The rodent postrhinal cortex and its primate homolog, the parahippocampal cortex, are thought to preferentially process visuospatial information to represent the spatial features of contexts and scenes. In this study, we addressed open questions about postrhinal function and about how context modulates behavior and cognition. The first question was whether the postrhinal cortex also represents nonspatial contexts. The second question was how representations of context might interact with other cues in the environment. We recorded postrhinal neurons as rats performed a visual nonspatial biconditional discrimination task in which the pattern on the floor determined which object in a pair was correct. Critically, this task design allowed dissociation of location from non-spatial context. We found that postrhinal ensembles and neurons signaled changes in non-spatial context and coded for conjunctions of non-spatial context and objects. Importantly, postrhinal neurons coded for conjunctions of context and objects more often than they coded for conjunctions of location and object. The pattern of findings suggests that postrhinal representations of context may behave like occasion setters by modulating the meaning of other cues in the environment.

neuroscience↗

bioRxiv2021

Population structure and genetic diversity of chickpea germplasms

In various leguminous crops, chickpea is the fourth most important legume contributing 3.1% to the total legume production. Grains of chickpea are rich source of proteins, minerals and vitamins which makes them suitable for both food and feed. For any crop to be improved, the knowledge of genetic diversity of wild and elite cultivar is very important. Therefore among various available marker systems, molecular markers are more reliable and accurate, therefore are very commonly used for genetic diversity analysis, phylogenetic studies and cultivar identification. Due to several advantages of Inter Simple Sequence Repeat (ISSR) markers in present study we analyzed the genetic diversity, structure, cross-species transferability and allelic richness in 50 chickpea collection using 23 ISSR markers. The observed parameters such as allele number varied from 3 to 16, and PIC varied from 0.15 to 0.4988 respectively. Further, range of allele size varied from 150 to 1600 bp which shows the significance of ISSR markers for chickpea germplasms characterization. On the basis of ISSR marker genotypic data dendrogram were constructed which divides these 50 chickpea in group I and II showing the reliability of ISSR markers. Among 50 chickpea, the accession P 74-1 is in group I and rest are in group II. Further we made mini-core collection of 15 diverse chickpea and subgrouped them. Dendrogram, PCA, Dissimilarity matrix and Bayesian model based genetic clustering of 50 chickpea germplasms revealed that P 74-1,P 1883, P 1260 very diverse chickpea accession. Characterization of these diverse chickpea would help in maintenance breeding, conservation and in future could be used to develop climate resilient elite cultivar of chickpea. Utilization of these novel ISSRs markers in diversity analysis and population structure characterization of 50 chickpea germplasm suggests their wider efficacy in superior scale for molecular breeding studies in chickpea.

ecology↗

bioRxiv2021

BaySiCle: A Bayesian Inference joint kNN method for imputation of single-cell RNA-sequencing data making use of local effect

There is a marked technical variability and a high amount of missing observations in the single-cell data that we obtain from experiments. Apart from that clearly each of the batch of experiments have a batch effect on every cell in the batch. This batch effect can be taken into advantage for dealing with imputation, given that all the cells in a given batch belong to the same tissue. Here we introduce BaySiCle, a novel Bayesian inference based method combined with k-nearest neighbors algorithm for the imputation of missing data in scRNA-seq counts. The priors are found out based on expression value across cells for all the single cells of the same batch. We demonstrate using sample scRNA-seq datasets and simulated expression data that BaySiCle allows robust imputation of missing values generating realistic transcript distributions that match single molecule fluorescence in situ hybridization measurements. By using priors as obtained by the dataset structures in the not just the experimental set-up batch, but also the same group of cells, BaySiCle improves accuracy of imputation to be that much closer to its similar alternatives. Availability and implementationThe Python Jupyter notebook BaySiCle is published on GitHub GitHub - abinarain/BaySiCel: Single Cell Data Imputation using Bayesian statistics and kNN

bioinformatics↗

bioRxiv2021

GraphBreak: Network Community detection and annotation in biomedical regulatory informatics context

Graph network science is becoming increasingly popular, notably in big-data perspective where understanding individual entities for individual functional roles is complex and time consuming. It is likely when a set of genes are regulated by a set of genetic variants, the genes set is recruited for a common or related functional purpose. Grouping and extracting communities from network of associations becomes critical to understand system complexity, thus prioritizing genes for disease and functional associations. Workload is reduced when studying entities one at a time. For this, we present GraphBreak, a suite of tools for community detection application, such as for gene co-expression, protein interaction, regulation network, etc.Although developed for use case of eQTLs regulatory genomic network community study- results shown with our analysis with sample eQTL data-Graphbreak can be deployed for other studies if input data has been fed in requisite format, including but not limited to gene co-expression networks, protein-protein interaction network, signaling pathway and metabolic network. GraphBreak showed critical use case value in its downstream analysis for disease association of communities detected. If all independent steps of community detection and analysis are a step-by-step sub-part of the algorithm, GraphBreak can be considered a new algorithm for community based functional characterization. Combination of various algorithmic implementation modules into a single script for this purpose illustrates GraphBreaks novelty. Compared to other similar tools, with GraphBreak we can better detect communities with overrepresentation of its member genes for statistical association with diseases, therefore target genes which can be prioritized for drug-positioning or drug-repositioning as the case be.

bioinformatics↗

bioRxiv2021

DMWAS: Deep Machine learning omics Wide Association Study & Feature set optimization by clustering & univariate association for Biomarkers discovery as tested on GTEx pilot dataset for death due to heart-attack

Univariate and multivariate methods for association of the genomic variations with the end-or-endo phenotype have been widely used for genome wide association studies. In addition to encoding the SNPs, we advocate usage of clustering as a novel method to encode the structural variations, SVs, in genomes, such as the deletions and insertions polymorphism (DIPs), Copy Number Variations (CNVs), translocation, inversion, etc., that can be used as an independent feature variable value for downstream computation by artificial intelligence methods to predict the endo-or-end phenotype. We introduce a clustering based encoding scheme for structural variations and omics based analysis. We conducted a complete all genomic variants association with the phenotype using deep learning and other machine learning techniques, though other methods such as genetic algorithm can also be applied. Applying this encoding of SVs and one-hot encoding of SNPs on GTEx V7 pilot DNA variation dataset, we were able to get high accuracy using various methods of DMWAS, and particularly found logistic regression to work the best for death due to heart-attack (MHHRTATT) phenotype. The genomic variants acting as feature sets were then arranged in descending order of power of impact on the disease or trait phenotype, which we call optimization and that also uses top univariate association into account. Variant Id P1_M_061510_3_402_P at chromosome 3 & position 192063195 was found to be most highly associated to MHHRTATT. We present here the top ten optimized genomic variant feature set for the MHHRTATT phenotypic cause of death.

bioinformatics↗

bioRxiv2020

SPRTN protease and SUMOylation coordinate DNA-protein crosslink repair to prevent genome instability

DNA-protein crosslinks (DPCs) are a specific type of DNA lesions where proteins are covalently attached to DNA. Unrepaired DPCs lead to genomic instability, cancer, neurodegeneration and accelerated ageing. DPC proteolysis was recently discovered as a specialised pathway for DPC repair. The DNA-dependent SPRTN protease and 26S proteasome emerged as as two independent proteolytic systems for DPC repair. DPCs are also repaired by homologous recombination (HR), a canonical DNA repair pathway. While studying the role of ubiquitin and SUMO in DPC repair, we identified mutually exclusive signalling mechanisms associated with DPC repair pathway choice. DPC modification by SUMO-1 favours SPRTN proteolysis as the preferred pathway for DPC repair. DPC SUMOylation counteracts DPC ubiquitination, which promotes DNA breaks and the switch to HR. We propose that modification of DPCs by SUMO-1 promotes SPRTN proteolysis, which is essential for DPC removal to prevent DNA replication defects, chromosomal recombination and genomic instability.

cell biology↗

bioRxiv2018

SPRTN Protease and Checkpoint Kinase 1 Cross-Activation Loop Safeguards DNA Replication

The SPRTN metalloprotease is essential for DNA-protein crosslink (DPC) repair and DNA replication in vertebrate cells. Cells deficient in SPRTN protease activity exhibit severe DPC-induced replication stress and genome instability, manifesting as premature ageing and liver cancer in humans and mice. Strikingly, SPRTN-deficient cells also show a severe G2/M checkpoint defect and fail to activate a robust checkpoint kinase 1 (CHK1) signalling cascade normally triggered in response to replication stress. Here, we show that SPRTN activates the CHK1 signalling cascade during physiological (steady-state) DNA replication by proteolysis-dependent eviction of CHK1 from chromatin. The N-terminal CHK1 fragments cleaved by SPRTN protease still possess kinase activity and phosphorylate SPRTN. This CHK1-dependent phosphorylation stimulates SPRTN recruitment to chromatin to further promote CHK1 eviction from chromatin, DPC removal and unperturbed DNA replication. Our data suggest that a SPRTN-CHK1 cross-activation loop is essential for steady-state DNA replication and protection from DNA replication stress, a major source of genome instability in diseases that cause premature ageing and cancer. In addition, we disclose a mechanism of CHK1 activation during physiological DNA replication, when long stretches of single stranded (ss) DNA that are induced by genotoxic stress and activate canonical and robust CHK1 signalling are not present.

cell biology↗