Biology subjects

Agrawal, P.

Publications and source records attributed to Agrawal, P..

34 records · Page 2Linked to original sources

bioRxiv2022

Adjuvants influence the maturation of VRC01-like antibodies during immunization

Once naive B cells expressing germline VRC01-class B cell receptors become activated by germline-targeting immunogens, they enter germinal centers and undergo affinity maturation. Booster immunizations with heterologous Envs are required for the full maturation of VRC01-class antibodies. Here, we examined whether and how three adjuvants, Poly(I:C), GLA-LSQ, or Rehydragel, that activate different pathways of the innate immune system, influence the rate and type of somatic mutations accumulated by VRC01-class BCRs that become activated by the germline-targeting 426c.Mod.Core immunogen and the heterologous HxB2.WT.Core booster immunogen. We report that although the adjuvant used had no influence on the durability of plasma antibody responses after the prime, it influenced the plasma VRC01 antibody titers after the boost and the accumulation of somatic mutations on the elicited VRC01 antibodies. ONE SENTENCE SUMMARYVRC01-class BCRs with different somatic mutations are being selected depending on the adjuvant used during immunization

immunology↗

bioRxiv2021

Broad misappropriation of developmental splicing profile by cancer in multiple organs

Oncogenesis mimics key aspects of embryonic development. However, the underlying molecular determinants are not completely understood. Leveraging temporal transcriptomic data during development in multiple human organs, we demonstrate that the embryonic positive (EP) alternative splicing events, specifically active during human organogenesis, are broadly reactivated in the organ-specific tumor. EP events are associated with key oncogenic processes and their reactivation predicts proliferation rates in cancer cell lines as well as patient survival. EP exons are significantly enriched for nitrosylation and transmembrane domains coordinately regulating splicing in multiple genes involved in intracellular transport and N-linked glycosylation respectively, known critical players in cancer. We infer critical splicing factors (CSF) potentially regulating these EP events and show that CSFs exhibit copy number amplifications in cancer and are upregulated specifically in malignant cells in the tumor microenvironment. Mutational inactivation of CSFs results in decreased EP splicing, further supporting their causal role. Multiple complementary analyses point to MYC and FOXM1 as potential transcriptional regulators of CSFs in brain and liver, which can be potentially targeted using FDA approved drugs. Our study provides the first comprehensive demonstration of a splicing-mediated link between development and cancer, and suggest novel targets including splicing events, splicing factors, and transcription factors.

cancer biology↗

bioRxiv2020

Microfluidic Device Facilitates Novel In Vitro Modeling of Human Neonatal Necrotizing Enterocolitis-on-a-Chip

Necrotizing enterocolitis (NEC) is a deadly gastrointestinal disease of premature infants characterized by an exaggerated inflammatory response, dysbiosis of the gut microbiome, decreased epithelial cell proliferation, and gut barrier disruption. Here, we describe a novel in vitro model of human neonatal small intestinal epithelium (Neonatal-Intestine-on-a-Chip) that mimics key features of intestinal physiology by utilizing a combination of premature infant intestinal enteroids co-cultured with human intestinal microvascular endothelial cells within a microfluidic device. We used our Neonatal-Intestine-on-a-Chip to recapitulate NEC pathophysiology in an in vitro model system of the premature gut inoculated with infant-derived microbiota. This model, also known as NEC-on-a-Chip, emulates the prominent features of NEC, demonstrating significant upregulation of pro-inflammatory cytokines, decreased intestinal epithelial cell markers, reduced epithelial proliferation and disrupted epithelial barrier integrity. NEC-on-a-Chip provides a novel preclinical model of NEC, which may be used as a personalized medicine approach to test new therapeutics for this devastating disease.

cell biology↗

bioRxiv2020

Defining a critical enhancer near Nanog using chromatin-focused approaches identifies RNA Pol II recruitment as required for expression

Transcriptional enhancers have been defined by their ability to operate independent of distance and orientation in plasmid-based reporter assays of gene expression. Currently, histone marks are used heavily to identify and define enhancers but both methods do not consider the endogenous role of an enhancer in the context of native chromatin. We employed a combination of genomic editing, single cell analyses, and sequencing approaches to investigate a Nanog-associated cis-regulatory element (CRE) which has been reported by others to be either an alternative promoter or a super-enhancer (SE). We first demonstrate both distance and orientation independence in native chromatin, eliminating the issues raised with plasmid-based approaches. We also demonstrate that the dominant SE modulates Nanog globally and operates by recruiting and/or initiating RNA Polymerase II. Our studies have important implications to how transcriptional enhancers are defined and how they regulate gene expression. AUTHOR SUMMARYDifferent DNA elements help regulate the levels of gene expression. One such element are enhancers, short sequences that interact with genes to modulate levels of expression but can operate over large distances. Previously, these sequences were defined by their ability to regulate expression independent of their distance from a gene and the orientation of the sequence. However, these characteristics were found using techniques that did not recapitulate the native environment. Here, we have shown that an enhancer of one gene is indeed an enhancer by testing its distance and orientation-independence within the native environment. We also show that the mechanisms by which the enhancer is regulating expression is by controlling the levels of RNA Polymerase II at a gene. RNA Polymerase II is the protein that converts the gene sequence to a form usable by a cell, called mRNA. This is interesting because while this has been considered historically the main way enhancers operate, more recent work has focused on other, later regulatory steps involved in controlling mRNA production.

genetics↗

bioRxiv2020

AntiCP 2.0: An updated model for predicting anticancer peptides

Increasing use of therapeutic peptides for treating cancer has received considerable attention of the scientific community in the recent years. The present study describes the in silico model developed for predicting and designing anticancer peptides (ACPs). ACPs residue composition analysis revealed the preference of A, F, K, L and W. Positional preference analysis revealed that residue A, F and K are preferred at N-terminus and residue L and K are preferred at C-terminus. Motif analysis revealed the presence of motifs like LAKLA, AKLAK, FAKL, LAKL in ACPs. Prediction models were developed using various input features and implementing different machine learning classifiers on two datasets main and alternate dataset. In the case of main dataset, ETree Classifier based model developed using dipeptide composition achieved maximum MCC of 0.51 and 0.83 AUROC on the training dataset. In the case of alternate dataset, ETree Classifier based model developed using amino acid composition performed best and achieved the highest MCC of 0.80 and AUROC of 0.97 on the training dataset. Models were trained and tested using five-fold cross validation technique and their performance was also evaluated on the validation dataset. Best models were implemented in the webserver AntiCP 2.0, freely available at https://webs.iiitd.edu.in/raghava/anticp2. The webserver is compatible with multiple screens such as iPhone, iPad, laptop, and android phones. The standalone version of the software is provided in the form of GitHub package as well as in docker technology.

bioinformatics↗

bioRxiv2020

A repository of web-based bioinformatics resources developed in India

IndiaBioDb (https://webs.iiitd.edu.in/raghava/indiabiodb/) is a manually curated comprehensive repository of bioinformatics resources developed and maintained by Indian researchers. This repository maintains information about 543 freely accessible functional resources that include around 258 biological databases. Each entry provides a complete detail about a resource that includes the name of resources, web link, detail of publication, information about the corresponding author, name of institute, type of resource. A user-friendly searching module has been integrated, which allows users to search our repository on any field. In order to retrieve categorized information, we integrate the browsing facility in this repository. This database can be utilized for extracting the useful information regarding the present scenario of bioinformatics inclusive of all research labs funded by government and private bodies of India. In addition to web interface, we also developed mobile to facilitate the scientific community.

bioinformatics↗

bioRxiv2019

GPSRdocker: A Docker-based Resource for Genomics, Proteomics and Systems biology

BackgroundIn past number of web-based resources has been developed in the field of Bioinformatics. These resources are heavily used by scientific community to provide solution for challenges faced by experimental researchers particularly in the field of biomedical sciences. There are number of challenges in utilizing full potential of these services that includes internet speed, limits on computing power, and security of data. In order to enhance utilities of these web-based assets, we developed a docker-based container that integrates large number resources available in literature.\n\nResultsThis paper describes GPSRdocker a docker-based container developed for providing wide-range of computational tools in the field of bioinformatics particularly in genomics, proteomics and system biology. Majority of tools integrated in GPSRdocker are based on web services developed at Raghavas group in last two decades. Broadly, these tools can be categorized in three categories; i) general scripts, ii) supporting software and iii) major standalone software. In order to facilitate students or developers working in the field of bioinformatics, we developed general scripts in Perl and Python. These general-purpose scripts serve as building block for any bioinformatics tools like computing features/descriptors of a protein. Supporting software packages includes SCIKIT, WEKA, SVMlight, and PSI-BLAST; these software packages allow one to develop/implement bioinformatics software. Major Standalone software is core of this container which allows predicting function/class of biomolecules. These tools can be classified broadly in following categories; protein annotation, epitope-based vaccines, prediction of interaction and drug discovery.\n\nConclusionA docker-based container has been developed which can be easily run on any operating system as well as it can be directly ported on cloud. Scripts can be run to build pipelines for addressing problems at system level like prediction of vaccine candidate for a pathogen. GPSRdocker including manual is available free for academic use from https://webs.iiitd.edu.in/gpsrdocker.

bioinformatics↗

bioRxiv2019

The neuropeptide Drosulfakinin regulates social isolation-induced aggression in Drosophila

Social isolation strongly modulates behavior across the animal kingdom. We utilized the fruit fly Drosophila melanogaster to study social isolation-driven changes in animal behavior and gene expression in the brain. RNA-seq identified several head-expressed genes strongly responding to social isolation or enrichment. Of particular interest, social isolation downregulated expression of the gene encoding the neuropeptide Drosulfakinin (Dsk), the homologue of vertebrate cholecystokinin (CCK), which is critical for many mammalian social behaviors. Dsk knockdown significantly increased social isolation-induced aggression. Genetic activation or silencing of Dsk neurons each similarly increased isolation-driven aggression. Our results suggest a U-shaped dependence of social isolation-induced aggressive behavior on Dsk signaling, similar to the actions of many neuromodulators in other contexts.\n\nData availabilityThe raw sequence data from RNA-seq experiments has been deposited into the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) with accession number: PRJNA481582. Supplementary files and figures accompany this article.

neuroscience↗

bioRxiv2019

SAMbinder: A web server for predicting SAM binding residues of a protein from its amino acid sequence

MotivationS-adenosyl-L-methionine (SAM) is one of the important cofactor present in the biological system and play a key role in many diseases. There is a need to develop a method for predicting SAM binding sites in a protein for designing drugs against SAM associated disease. Best of our knowledge, there is no method that can predict the binding site of SAM in a given protein sequence.\n\nResultThis manuscript describes a method SAMbinder, developed for predicting SAM binding sites in a protein from its primary sequence. All models were trained, tested and evaluated on 145 SAM binding protein chains where no two chains have more than 40% sequence similarity. Firstly, models were developed using different machine learning techniques on a balanced dataset contain 2188 SAM interacting and an equal number of non-interacting residues. Our Random Forest based model developed using binary profile feature got maximum MCC 0.42 with AUROC 0.79 on the validation dataset. The performance of our models improved significantly from MCC 0.42 to 0.61, when evolutionary information in the form of PSSM profile is used as a feature. We also developed models on realistic dataset contains 2188 SAM interacting and 40029 non-interacting residues and got maximum MCC 0.61 with AUROC of 0.89. In order to evaluate the performance of our models, we used internal as well as external cross-validation technique.\n\nAvailability and implementationhttps://webs.iiitd.edu.in/raghava/sambinder/.

bioinformatics↗

bioRxiv2019

Computing wide range of protein/peptide features from their sequence and structure

MotivationIn last three decades, a wide range of protein descriptors/features have been discovered to annotate a protein with high precision. A wide range of features have been integrated in numerous software packages (e.g., PROFEAT, PyBioMed, iFeature, protr, Rcpi, propy) to predict function of a protein. These features are not suitable to predict function of a protein at residue level such as prediction of ligand binding residues, DNA interacting residues, post translational modification etc.\n\nResultsIn order to facilitate scientific community, we have developed a software package that computes more than 50,000 features, important for predicting function of a protein and its residues. It has five major modules for computing; composition-based features, binary profiles, evolutionary information, structure-based features and patterns. The composition-based module allows user to compute; i) simple compositions like amino acid, dipeptide, tripeptide; ii) Properties based compositions; iii) Repeats and distribution of amino acids; iv) Shannon entropy to measure the low complexity regions; iv) Miscellaneous compositions like pseudo amino acid, autocorrelation, conjoint triad, quasi-sequence order. Binary profile of amino acid sequences provides complete information including order of residues or type of residues; specifically, suitable to predict function of a protein at residue level. Pfeature allows one to compute evolutionary information-based features in form of PSSM profile generated using PSIBLAST. Structure based module allows computing structure-based features, specifically suitable to annotate chemically modified peptides/proteins. Pfeature also allows generating overlapping patterns and feature from whole protein or its parts (e.g., N-terminal, C-terminal). In summary, Pfeature comprises of almost all features used till now, for predicting function of a protein/peptide including its residues.\n\nAvailabilityIt is available in form of a web server, named as Pfeature (https://webs.iiitd.edu.in/raghava/pfeature/), as well as python library and standalone package (https://github.com/raghavagps/Pfeature) suitable for Windows, Ubuntu, Fedora, MacOS and Centos based operating system.

bioinformatics↗

bioRxiv2018

Social isolation-induced epigenetic and transcriptional changes in Drosophila dopaminergic neurons

Epigenetic mechanisms play fundamental roles in brain function and behavior and stressors such as social isolation can alter animal behavior via epigenetic mechanisms. However, due to cellular heterogeneity, identifying cell-type-specific epigenetic changes in the brain is challenging. Here we report first use of a modified INTACT method in behavioral epigenetics of Drosophila: a method we call mini-INTACT. Using ChIP-seq on mini-INTACT purified dopaminergic nuclei, we identified epigenetic signatures in socially-isolated and socially-enriched Drosophila males. Social experience altered the epigenetic landscape in clusters of genes involved in transcription and neural function. Some of these alterations were predicted by expression changes of four transcription factors and the prevalence of their binding sites in several clusters. These transcription factors were previously identified as activity-regulated genes and their knockdown in dopaminergic neurons reduced the effects of social experience on sleep. Our work enables the use of Drosophila as a model for cell-type-specific behavioral epigenetics.

neuroscience↗

bioRxiv2017

Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants

BackgroundEvidences in literature strongly advocate the potential of immunomodulatory peptides for use as vaccine adjuvants. All the mechanisms of vaccine adjuvants ensuing immunostimulatory effects directly or indirectly stimulate Antigen Presenting Cells (APCs). While numerous methods have been developed in the past for predicting B-cell and T-cell epitopes; no method is available for predicting the peptides that can modulate the APCs.\n\nMethodsWe named the peptides that can activate APCs as A-cell epitopes and developed methods for their prediction in this study. A dataset of experimentally validated A-cell epitopes was collected and compiled from various resources. To predict A-cell epitopes, we developed Support Vector Machine-based machine learning models using different sequence-based features.\n\nResultsA hybrid model developed on a combination of sequence-based features (dipeptide composition and motif occurrence), achieved the highest accuracy of 96.91% with Matthews Correlation Coefficient (MCC) value of 0.94 on the training dataset. We also evaluated the hybrid models on an independent dataset and achieved a comparable accuracy of 94.93% with MCC 0.90.\n\nConclusionThe models developed in this study were implemented in a web-based platform VaxinPAD to predict and design immunomodulatory peptides or A-cell epitopes. This web server available at http://webs.iiitd.edu.in/raghava/vaxinpad/ and http://crdd.osdd.net/raghava/vaxinpad/ will facilitate researchers in designing peptide-based vaccine adjuvants.

bioinformatics↗

bioRxiv2017

HumCFS: A database of fragile sites in human chromosomes

Genomic instability is the hallmark of cancer and several other pathologies, such as mental retardation; preferentially occur at specific loci in genome known as chromosomal fragile sites. HumCFS (http://webs.iiitd.edu.in/raghava/humcfs/) is a manually curated database provides comprehensive information on 118 experimentally characterized fragile sites present in human chromosomes. HumCFS comprises of 19068 entries with wide range of information such as nucleotide sequence of fragile sites, their length, coordinates on the chromosome, cytoband, their inducers and possibility of fragile site occurrence i.e. either rare or common etc. Each fragile region gene is further annotated to disease database DisGenNET, to understand its disease association. Protein coding genes are identified by annotating each fragile site to UCSC genome browser (GRCh38/hg38). To know the extent of miRNA lying in fragile site region, miRNA from miRBase has been mapped. Comprehensively, HumCFS encompasses mapping of 5010 genes with 19068 transcripts, 1104 miRNA and 3737 disease-associated genes on fragile sites. In order to facilitate users, we integrate standard web-based tools for easy data retrieval and analysis.

bioinformatics↗

bioRxiv2017

Evaluation of protein-ligand docking methods on peptide-ligand complexes for docking small ligands to peptides

In the past, many benchmarking studies have been performed on protein-protein and protein-ligand docking however there is no study on peptide-ligand docking. In this study, we evaluated the performance of seven widely used docking methods (AutoDock, AutoDock Vina, DOCK 6, PLANTS, rDock, GEMDOCK and GOLD) on a dataset of 57 peptide-ligand complexes. Though these methods have been developed for docking ligands to proteins but we evaluate their ability to dock ligands to peptides. First, we compared TOP docking pose of these methods with original complex and achieved average RMSD from 4.74[A] for AutoDock to 12.63[A] for GEMDOCK. Next we evaluated BEST docking pose of these methods and achieved average RMSD from 3.82[A] for AutoDock to 10.83[A] for rDock. It has been observed that ranking of docking poses by these methods is not suitable for peptide-ligand docking as performance of their TOP pose is much inferior to their BEST pose. AutoDock clearly shows better performance compared to the other six docking methods based on their TOP docking poses. On the other hand, difference in performance of different docking methods (AutoDock, AutoDock Vina, PLANTS and DOCK 6) was marginal when evaluation was based on their BEST docking pose. Similar trend has been observed when performance is measured in terms of success rate at different cut-off values. In order to facilitate scientific community a web server PLDbench has been developed (http://webs.iiitd.edu.in/raghava/pldbench/).

bioinformatics↗

bioRxiv2017

Safety evaluation of soy leghemoglobin protein preparation derived from Pichia pastoris, intended for use as a flavor catalyst in plant-based meat

The leghemoglobin protein (LegH) from soy (Glycine max) expressed in Pichia pastoris (LegH Prep) imparts a meat-like flavor profile onto plant-based food products. The safety of LegH Prep was evaluated through a series of in vitro and in vivo tests. The genotoxic potential of LegH Prep was assessed using the bacterial reverse mutation assay (Ames test) and the in vitro chromosome aberration test. LegH Prep was non-mutagenic and non-clastogenic in each test, respectively. Systemic and female reproductive toxicity were assessed in two separate 28-day dietary studies in Sprague Dawley rats. There were no mortalities associated with the administration of LegH Prep. There were no clinical observations, body weight, ophthalmological, clinical pathology, or histopathological changes attributable to LegH Prep administration. Female reproductive parameters were comparable between rats treated with LegH Prep and concurrent control rats. These studies establish an NOAEL of 750 mg/kg/day LegH, which is over 100 times greater than the 90th percentile estimated daily intake (EDI). Collectively, this work demonstrates that LegH Prep is safe for its intended use in ground beef analogue products at concentrations up to 0.8% LegH.\n\nAbbreviations

pharmacology and toxicology↗

bioRxiv2017

Prediction of residue-residue contacts in CASP12 targets from its predicted tertiary structures

One of the challenges in the field of structural proteomics is to predict residue-residue contacts in a protein. It is an integral part of CASP competitions due to its importance in the field of structural biology. This manuscript describes RRCPred 2.0 a method participated in CASP12 and predicted residue-residue contact in targets with high precision. In this approach, firstly 150 predicted protein structures were obtained from CASP12 Stage 2 tarball and ranked using clustering-based quality assessment software. Secondly, residue-residue contacts were assigned in top 10 protein structures based on distance between residues. Finally, residue-residue contacts were predicted in target protein based on consensus/average in top 10 predicted structures. This simple approach performs better than most of CASP12 methods in the categories of TBM and TBM/FM. It ranked 1st in following categories; i) TBM domain on list size L/5, ii) TBM/FM domain on list size L/5 and iii) TBM/FM domain on Top 10. These observations indicate that predicted tertiary structure of a protein can be used for predicting residue-residue contacts in protein with high accuracy.

bioinformatics↗