Biology subjects

Prakash, O.

Publications and source records attributed to Prakash, O..

13 recordsLinked to original sources

bioRxiv2023

Triggering blood-lipid-profile by immunomodulatory and antimicrobial molecules for cross-evaluation of early detection of cancer

BackgroundAl-guided mapping establishes relationship between gene-signature and phenotype. Since, Immunomodulatory & antimicrobial molecules, both show anticancer as well as their relation with lipid; therefore, can be further used for cross-validation or enrichment analysis for early detection of cancer. Considering the information, a study was performed on the influence of Immunomodulatory and antimicrobial molecules on blood lipid profile at the early stage of cancer. Materials and MethodPresent study established a relationship between small molecule-vs-genotype to phenotype. Genotypic features extracted through RNAseq analysis for stomach adenocarcinoma, which was further mapped with blood lipid profile. RNA-seq expression, from a population, was collected as gene expression profile of transcriptome. Gene signature was identified through in-house processing. Blood lipid profile was as: Total Cholesterol, LDL Cholesterol, HDL Cholesterol, Triglycerides, Non-HDL-C, and TG to HDL ratio. ResultsTo evaluate the Immunomodulatory and antimicrobial molecules for anticancer activity in terms of their influence on blood lipid-profile, computational models were established for developing relationships between small molecules-vs-genotype. Immunomodulatory and antimicrobial activity showed significant impact on genotypic expression at cancerous conditions. ConclusionIt was concluded that usage of Immunomodulatory small molecules & antibiotics showed their impact on blood lipid profile. These results may support cross-checking the possibility of cancer during the early detection of cancer. Furthermore, suggested the use of immunomodulatory and antimicrobial small molecules for cross evaluation of early disease prediction of different cancer types.

bioinformatics↗

bioRxiv2023

Functional antagonistic interactions and genomic insights into the biosynthetic potential of human gut-derived microbiota

Concerning the biological interactions within the gut microbiome, the specialized small molecules encoded by commensal microbes mediate distinct functional aspects. However, the landscape of antagonistic interactions mediated by specialized strains and their small molecules broadly remains. Here, we sought to evaluate antimicrobial interactions as a defensive contributor to gain new insights into structure-related functions or to bring the therapeutic potential of derived molecules. We elucidated the antagonistic landscape within a collection of 330 human-gut-derived commensal microbial strains cultivated from healthy human subjects. We characterized potential antagonistic strains and found a strain-specific selective inhibition contrary to common antimicrobial drugs that wipe out a broad range of species usually found in environmental microbes. Using functional and genomic approaches for accessing biologically active natural product molecules, we identified significant biosynthetic gene clusters (BGCs) encoding the important compound families in representative gut strains which contribute to antagonistic activities and are important in host defense or maintaining homeostasis in the gut. The subsets of the BGCs were represented in metagenomics sequencing data from healthy individuals. The cell culture secretome of strains revealed potential biomarkers linked to hallmark pathways. Together, these microorganisms encode biosynthetic novelty and represent a source of biologically significant natural products important in developing new treatments for infectious diseases to cut the usage of broad-spectrum antibiotics and represent a way to combat antimicrobial resistance. Consortia of such strains can be utilized as an option for precise editing of the microbiomes or fine-tuning the microbiota-modulating therapies.

microbiology↗

bioRxiv2023

Gene-signatures for early detection of cancers

BackgroundGene signatures represents set of molecular modulations in disease genomes or in cells at specific conditions, and are frequently used to classify samples into different groups for better research or clinical treatment. Multiple methods and applications are available in the literature, but powerful ones that can account for early detection of cancer are still lacking. MethodIn this article, gene-signatures identified through new in-house algorithm (NCT method) by processing transcriptome data (DEGs extracted from RNA-seq dataset) from population. NCT-Method utilized for processing population dataset, from 28 different human cancer from TCGA & GTEx databases, as empirical background. NCT-score used for optimal clustering of gene-set. The identified gene clusters evaluated through survival analysis. Gene-sets with disease-vs-normal survival plot with logPvalue < 0.05 represented as reliable gene signatures. ResultsWe applied NCT algorithm to the 28 different cancers, and identified novel gene-signatures as well as inter-relation between different cancers in reference of identified signatures. ConclusionsThe algorithm uses population data, and provides validated gene signatures with reliable capacity to discriminate the cancer and normal samples with higher classification performance. The algorithm will be useful to find signature for any RNA-seq data.

bioinformatics↗

bioRxiv2020

Cancer-Associated Mutations Perturb the Structure and Interactions of the Intrinsically Disordered p53 Transactivation Domain

Intrinsically disordered proteins (IDPs) are key components of regulatory networks that control crucial aspects of cell decision making. The intrinsically disordered transactivation domain (TAD) of tumor suppressor p53 mediates its interactions with multiple regulatory pathways to control the p53 homeostasis during the cellular response to genotoxic stress. Many cancer-associated mutations have been discovered in p53-TAD, but their structural and functional consequences are poorly understood. Here, by combining atomistic simulations, NMR spectroscopy, and binding assays, we demonstrate that cancer-associated mutations can significantly perturb the balance of p53 interactions with key activation and degradation regulators. Importantly, mutations do not all directly disrupt the known interaction interfaces. Instead, some mutations likely modulate the disordered state of p53-TAD, which affects the interactions. Our work suggests that the disordered conformational ensemble of p53-TAD can serve as a central conduit in regulating the response to various cellular stimuli at the protein-protein interaction level. Understanding how the disordered state of IDPs may be modulated by regulatory signals and/or disease associated perturbations will be essential in the studies on the role of IDPs in biology and diseases.

molecular biology↗

bioRxiv2020

Unique mutational changes in SARS-CoV2 genome of different state of India

COVID-19 is a global pandemic causing more than 8 million deaths till mid-August, 2020. In India, more than 3 million confirmed cases have been reported although with relatively low death rate of 1.8%. In this study, we sequenced 47 genomes of SARS-CoV-2 from the patients of 13 districts of Uttar Pradesh (UP), the largest state of India using third-generation sequencing technique. The phylogenetic clustering revealed that no UP sample was aligned with the previously defined USA clade, where the mortality was high. We identified 56 distinct SNP variations in the genomes of UP resulting in a unique mutation rate of 1.19% per sequence, which is greater than the value 0.88% obtained for the rest of India. The relatively less death rate in UP indicates that the mutation in the virus is deleterious. Further investigation is required with larger sample size to determine the degree of virulence vis-a-vis SNP variation.

bioinformatics↗

bioRxiv2020

Invasion performance-similarity found among multiple cell systems

Understanding of inter-system behavior develops biologically relevant intuition for drug repositioning as well as other biological research. But combining all the possible genes interactions into a system, and furthermore comparisons of multiple systems are a challenge on time ground with feasible experiments. In present study, 64 cell lines from 11 different organs were compared for their invasion performance. RNA expressions of 23 genes were used to create systems artificial neural network (ANN) models. ANN models were prepared for all 64 cell lines and observed for their invasion performance through network mapping. The resulted cell line clusters bear feasible capacity to perform experiments for biologically relevant research motivations as drug repositioning and selective targeting etc.; and can be used for analysis of invasion related aspects.

systems biology↗

bioRxiv2020

Protocol for clustering of non-unified protein sequences through memory-map guided deep learning

Protocol established and validated for clustering of non-unified protein sequences through memorymap guided deep learning. Data evaluated belongs to the disease causing proteins/genes from human hormonal system. Possibilities for future experiments validation was found for genes as: ACTHR, AGMX1, ATK, BPK, DPDE3, ERBA2, FSHB, GH1, GHSR, GNAS1, GSP, HANF, LCGR, LGR2, LGR3, LHRHR, NR1A2, PKR1, PRKAR1, RNF216, SBP2, SECISBP2, THR1, THRB, TPIT, TRIAD3, TSE1, UBCE7IP1, XAP2, and ZIN. Protocol is recommended for implementation with small to large dataset (protein/ DNA/ RNA sequences of unified or non-unified length) with unclassified data flags.

bioinformatics↗

bioRxiv2020

Neuroligin affects metastatic performance of CD66+ cells

Present study deals with theoretical analysis of multi-tissue RNA expressions through system model based network mapping under the constraints of in-vitro experiments. Here, effect of Neuroligin was evaluated on the performance of metastatic behavior of CD66+ cells of human female reproductive cell lines. Total 10 genes and 14 cell lines were considered in the study. Clustering of cell lines was done on the basis of network mapping under the same homeostatic constraints. Neuroligin was found to affect metastatic performance of CD66+ cells. Two CD66+ cell lines hTERT-HME1 and SK-BR-3 were found to most effective, in similar manner as brain cell lines as U-138MG & U-87MG.

cancer biology↗

bioRxiv2020

Intra-molecular Electro-potential Circuit & ElectroNegatrode: Hypothesis, Algorithm & Implementation for universal indicative rule towards activity of biomolecules

No universal indicative rule has been defined till now for activity & selectivity of biomolecules. This lacks very early perception about any molecule (specifically small molecule) in the biological system, beyond doing any experiment. The present study is all about hypothesizing, algorithm development and implementation of theoretical processing of 3D structure of biomolecules in the search of ground for developing perception about biomolecules individually or in combination of other biomolecules. The developed algorithm has been evaluated with small molecules and was found to be implementable as universal indicative rule towards activity & selectivity of biomolecules.

bioinformatics↗

bioRxiv2020

Algorithm for theoretical mapping of bio-strings for co-expression: bridging genotype to phenotype

Identification of possibility of co-expression or co-performance directly from set of bio-strings as protein sequences represents an important problem of bridging between genotype to phenotype. This algorithm presents above bridging. Algorithm was implemented with proteins known from human hormone signaling system. Co-expression of proteins was cross-validated through human gene COXPRESdb v7 database. Possibility of protein-protein interaction (PPI) was also cross checked through STRING database. Results were found to be effectively fascinating. Considering the indications from results, this algorithm can be adopted for theoretical identification of co-expression or co-performance of bio-strings.

bioinformatics↗

bioRxiv2020

Algorithm for Extraction of Sub-Structure from Co-Crystallized PDB Ligand for Selective Targeting

An algorithm has been introduced for extraction of sub-structure from co-crystallized ligand of complex PDB file. Algorithm utilized location of atoms in 3D domain of complex of ligand & protein. It processed relative positioning of atoms for demarcation of influential part of ligand, which can provide potency to that ligand for binding with specific binding pocket of protein. Algorithm was validated with ligands co-crystallized with enzymes of different classes. Extracted sub-structures were validated (via evidence from database & literature) for selectivity towards one or more targets of same family.

bioinformatics↗

bioRxiv2018

Functional and Comparative Genomics of Niche-Specific Adapted Actinomycetes Kocuria rhizophila Strain D2 Isolated from Healthy Human Gut

Incidences of infection and occurrence of Kocuria rhizophila in human gut are prominent but certainly no reports on the species ability to withstand human gastrointestinal dynamics. Kocuria rhizophila strain D2 isolated from healthy human gut was comprehensively characterized. The functional analysis revealed the ability to produce various gastric enzymes and sensitive to major clinical antibiotics. It also exhibited tolerance to acidic pH and bile salts. Strain D2 displayed bile-salt hydrolytic (BSH) activity, strong cell surface traits such as hydrophobicity, auto-aggregation capacity and adherence to human HT-29 cell line. Prominently, it showed no hemolytic activity and was susceptible to the human serum. Exploration of the genome led to the discovery of the genes for the above said properties and has ability to produce various essential amino acids and vitamins. Further, comparative genomics have identified core, accessory and unique genetic features. The core genome has given insights into the phylogeny while the accessory and unique genes has led to the identification of niche specific genes. Bacteriophage, virulence factors and biofilm formation genes were absent with this species. Housing CRISPR and antibiotic resistance gene was strain specific. The integrated approach of functional, genomic and comparative analysis denotes the niche specific adaption to gut dynamics of strain D2. Moreover the study has comprehensively characterized genome sequence of each strain to know the genetic difference and intern recognize the effects of on phenotype and functionality complexity. The evolutionary relationship among strains along and adaptation strategies has been included in this study.\n\nSignificanceReports of Kocuria rhizophila isolation from various sources have been reported but the few disease outbreaks in humans and fishes have been prominent, but no supportive evidence about the survival ability of Kocuria spp. within human GIT. Here, we report the gut adaption potential of K. rhizophila strain D2 by functional and genomic analysis. Further; comparative genomics reveals this adaption to be strain specific (Gluten degradation). Genetic difference, evolutionary relationship and adaptation strategies have been including in this study.

genomics↗

bioRxiv2018

Comparative Genome Analysis Reveals Important Genetic Factors Associated with Probiotic Property in Enterococcus faecium strains

Enterococcus faecium though commensals in human gut, few strains provide beneficial effect to humans as probiotics, few are responsible for nosocomial infection and few as non-pathogens. Comparative genomics of E. faecium will help to reveal the genomic differences responsible for the said properties. In this study, we compared E. faecium strain 17OM39 with a marketed probiotic, non-pathogenic non-probiotic (NPNP) and pathogenic strains. The core genome analysis revealed, 17OM39 was closely related with marketed probiotic strain T110. Strain 17OM39 was found to be devoid of known vancomycin, tetracycline resistance genes and functional virulence genes. Moreover, 17OM39 is \"less open\" due to absence of frequently found transposable elements. Genes imparting beneficial functional properties were observed to be present in marketed probiotic T110 and 17OM39 strains. Additional, genes associated with colonization within gastrointestinal tract were detected across all the strains. Beyond shared genetic features; this study particularly identified genes that are responsible to impart probiotic, non-pathogenic and pathogenic features to the strains of E. faecium. The study also provides insights into the acquired and intrinsic drug resistance genes, which will be helpful for better understanding of the physiology of antibiotic resistance in E. faecium strains. In addition, we could identify genes contributing to the intrinsic ability of 17OM39 E. faecium isolate to be a potential probiotic.\n\nThe study has comprehensively characterized genome sequence of each strain to find the genetic variation and understand effects of these on functionality, phenotypic complexity. Further the evolutionary relationship of species along with adaptation strategies have been including in this study.

bioinformatics↗