JournalTOCs

Bioinformatics and Biology Insights
Journal Prestige (SJR): 1.141

Citation Impact (citeScore): 2
Number of Followers: 13

This is an Open Access Journal

Open Access journal
ISSN (Print) 1177-9322
Published by Sage Publications

[1174 journals]

Exploring the Structural and Functional Effects of Nonsynonymous SNPs in
the Human Serotonin Transporter Gene Through In Silico Approaches
- Authors: Md Arzo Mia, Md Nasir Uddin, Yasmin Akter, Jesmin, Lolo Wal Marzan
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  The sodium-dependent serotonin transporter SLC6A4 (solute carrier family 6 member 4) gene encodes an intrinsic membrane protein that transmits the serotonin neurotransmitter from synaptic clefts into presynaptic neurons. The product of the SLC6A4 gene is related to the regulation of mood and social behavior, sleep, appetite, memory, digestion, and sexual desire. This protein is a target for antidepressant and psychostimulant drugs, thus prolonged neurotransmitter signaling remains blocked. In this study, the functional consequences of nsSNPs in the human SLC6A4 gene were explored through computational tools: PhD-SNP, SIFT, Align GVGD, PROVEAN, PMut, nsSNP Analyzer, SNPs&GO, SNAP2, PolyPhen2, and PANTHER to identify the most deleterious and damaging nsSNPs. Then the mutant protein stabilities were assessed using I-Mutant, MUpro, and MutPred2; amino acid conservation using ConSurf, and posttranslational modification analysis using MusiteDEEP and PROSPER. Furthermore, the 3-dimensional (3D) model of the mutated proteins was predicted and validated using SPARKS-X, Verify3D, and PROCHECK. The protein–ligand binding sites were analyzed using the COACH meta-server. Results from this study predicted that T192M, G342E, R607C, W282S, R104C, P131L, P156L, and N351S were the most structurally and functionally significant nsSNPs in the human SLC6A4 gene. Arg607 and Pro156 were the predicted sites for posttranslational modifications, and Thr192 and Try282 were the ligand-binding sites in the human SLC6A4 gene. The analyzed data also suggested that R104C, P131L, P156L, T192M, G342E, and W282S mutants might affect the binding of sodium ions with this protein. Taken together, this study provided important information on structurally and functionally important nsSNPs of the human SLC6A4 gene for further experimental validation. In the future, these damaging nsSNPs of the SLC6A4 gene have the potential to be evaluated as prognostic biomarkers for SLC6A4-related disorder diagnosis and research.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-06-09T12:19:20Z
  DOI: 10.1177/11779322221104308
  Issue No: Vol. 16 (2022)
In Silico Study of Cucurbita maxima Compounds as Potential Therapeutics
Against Schistosomiasis
- Authors: Floryn Lynorah Mtemeli, Ryman Shoko, Joice Ndlovu, Grace Mugumbate
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  Schistosomiasis, a disease usually related to poverty and poor sanitation, affects more than 200 million people worldwide. Since the 1970s, the medical sector has depended on a single drug, praziquantel, for the treatment of the disease. The emerging evidence of resistance of the Schistosoma parasite to praziquantel and the drug’s inefficacy against juvenile stages of the parasite makes the need to find alternative drugs an urgent matter. In this study, we explored the inhibition potential of compounds from Cucurbita maxima using molecular docking studies on Schistosoma mansoni purine nucleoside phosphorylase (SmPNP) and Schistosoma haematobium 28-kDa glutathione S-transferase (Sh28kDaGST). Following molecular docking studies and analysis of the active sites, the primary amino acids that were observed and shown to be involved in the SmPNP-ligand interaction are CYS 33, ARG 86, HIS 88, TYR 90, ALA 118, ALA 119, PRO 200, TYR 202, GLU 203, VAL 219, MET 221, THR 244, ASN 245, PRO 257 and HIS 259. For the Sh28dKa-ligand interaction, the primary amino acids were PHE 11, ARG 16, TRP 41, LEU 53, GLU 70 and SER 71. Momordicoside I aglycone binds to SmPNP with the lowest binding affinity of -7.9 kcal/mol by pi sigma bond interactions with HIS 88. Balsaminoside B binds to Sh28kDaGST with a binding affinity of −7.6 kcal/mol by hydrogen bond interaction with TRP 41, LEU 53 and SER 71. Pharmacokinetic studies showed favourable drug-like properties for the 10 compounds that exhibited the lowest binding energies. Therefore, we propose that bioactive compounds from C. maxima be considered as potential novel drug hits in the treatment of schistosomiasis.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-05-21T06:59:56Z
  DOI: 10.1177/11779322221100741
  Issue No: Vol. 16 (2022)
EpiBuilder: A Tool for Assembling, Searching, and Classifying B-Cell
Epitopes
- Authors: Renato Simões Moreira, Vilmar Benetti Filho, Nathália Anderson Calomeno, Glauber Wagner, Luiz Claudio Miletti
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  Epitopes are portions of a protein that are recognized by antibodies. These small amino acid sequences represent a significant breakthrough in a branch of bioinformatics called immunoinformatics. Various software are available for linear B-cell epitope (BCE) prediction such as ABCPred, SVMTrip, EpiDope, and EpitopeVec; a well-known BCE predictor is BepiPred-2.0. However, despite the prediction, there are several essential steps, such as epitope assembly, evaluation, and searching for epitopes in other proteomes. Here, we present EpiBuilder (https://epibuilder.sourceforge.io), a user friendly software that assists in epitope assembly, classifying and searching using input results of BepiPred-2.0. EpiBuilder generates several output results from these data and supports a proteome-wide processing approach. In addition, this software provides the following features: Chou & Fasman beta-turn prediction, Emini surface accessibility prediction, Karplus and Schulz flexibility prediction, Kolaskar and Tongaonkar antigenicity, Parker hydrophilicity prediction, N-glycosylation domains, and hydropathy. These information generate a unique topology for each epitope, visually demonstrating its characteristics. The software can search the entire epitope sequence in various FASTA files, and it allows to use BLASTP to identify epitopes that eventually have sequence variations. As an EpiBuilder application, we developed a epitope dataset from the protozoan Trypanosoma brucei gambiense, the gram-positive bacterium Clostridioides difficile, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-05-11T10:57:10Z
  DOI: 10.1177/11779322221095221
  Issue No: Vol. 16 (2022)
Impact of Aligner, Normalization Method, and Sequencing Depth on TempO-seq
Accuracy
- Authors: Logan J Everett, Deepak Mav, Dhiral P Phadke, Michele R Balik-Meisner, Ruchir R Shah
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  High-throughput transcriptomics has advanced through the introduction of TempO-seq, a targeted alternative to traditional RNA-seq. TempO-seq platforms use 50 nucleotide probes, each specifically designed to target a known transcript, thus allowing for reduced sequencing depth per sample compared with RNA-seq without compromising the accuracy of results. Thus far, studies using the TempO-seq method have relied on existing tools for processing the resulting short read data. However, these tools were originally designed for other data types. While they have been used for processing of early TempO-seq data, they have not been systematically assessed for accuracy or compared to determine an optimal framework for processing and analyzing TempO-seq data. In this work, we re-analyze several publicly available TempO-seq data sets covering a range of experimental designs and use corresponding RNA-seq data sets as a gold standard to rigorously assess accuracy at multiple levels. We compare 6 aligners and 5 normalization methods across various accuracy and performance metrics. Our results demonstrate the overall robust accuracy of the TempO-seq platform, independent of data processing methods. Complex aligners and advanced normalization methods do not appear to have any general advantage over simpler methods when it comes to analyzing TempO-seq data. The reduced complexity of the sequencing space, and the fact that TempO-seq probes are all equal length, appears to reduce the need for elaborate bioinformatic or statistical methods used to address these factors in RNA-seq data.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-04-30T12:03:16Z
  DOI: 10.1177/11779322221095216
  Issue No: Vol. 16 (2022)
From Beginning to End: Expanding the SERINC3 Interactome Through an in
silico Analysis
- Authors: Mckenzie Tu, Sarah Saputo
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  The serine incorporator (SERINC) family of proteins are a family of multipass transmembrane proteins associated with biosynthesis of serine-containing phospholipids and sphingolipids. Humans have 5 paralogs, SERINC1-5, which have been linked to disease including variable expression in tumor lines and possessing activity as restriction factors against HIV-1. Despite recent studies, the cellular function of SERINC proteins have yet to be fully elucidated. The goal of this study as to investigate the role of SERINC3 by expanding upon its interactome. We used a variety of bioinformatic tools to identify cellular factors that interact with SERINC3 and assessed how sequence variation might alter these interactions. Analysis of the promoter region indicates that SERINC3 is putatively regulated by transcription factors involved in tissue-specific development. Analysis of the unique 3′-untranslated region of one variant of HsSERINC3 revealed that this region serves as a conserved site of regulation by both RNA binding proteins and miRNA. In addition, SERINC3 is putatively regulated at the protein level by several posttranslational modifications. Our results show that extra-membrane portions of SERINC3 are subject to variation in the coding sequence as well as areas of relatively low conservation. Overall, our data suggest that regions of low homology as well as presence of variations in the nucleotide and protein sequences of HsSERINC3 suggest that these variations may lead to aberrant function and alternative regulatory mechanisms in homologs. The functional consequences of these sequence and structural variations need to be explored systematically to fully appreciate the role of SERINC3 in both health and disease.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-04-26T05:38:32Z
  DOI: 10.1177/11779322221092944
  Issue No: Vol. 16 (2022)
In Silico Identification and Characterization of a Hypothetical Protein
From Rhodobacter capsulatus Revealing S-Adenosylmethionine-Dependent
Methyltransferase Activity
- Authors: Spencer Mark Mondol, Depro Das, Durdana Mahin Priom, M Shaminur Rahman, M Rafiul Islam, Md Mizanur Rahaman
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  Rhodobacter capsulatus is a purple non-sulfur bacteria widely used as a model organism to study bacterial photosynthesis. It exhibits extensive metabolic activities and demonstrates other distinctive characteristics such as pleomorphism and nitrogen-fixing capability. It can act as a gene transfer agent (GTA). The commercial importance relies on producing polyester polyhydroxyalkanoate (PHA), extracellular nucleic acids, and commercially critical single-cell proteins. These diverse features make the organism an exciting and environmentally and industrially important one to study. This study was aimed to characterize, model, and annotate the function of a hypothetical protein (Accession no. CAA71016.1) of R capsulatus through computational analysis. The urf7 gene encodes the protein. The tertiary structure was predicted through MODELLER and energy minimization and refinement by YASARA Energy Minimization Server and GalaxyRefine tools. Analysis of sequence similarity, evolutionary relationship, and exploration of domain, family, and superfamily inferred that the protein has S-adenosylmethionine (SAM)-dependent methyltransferase activity. This was further verified by active site prediction by CASTp server and molecular docking analysis through Autodock Vina tool and PatchDock server of the predicted tertiary structure of the protein with its ligands (SAM and SAH). Normally, as a part of the gene product of photosynthetic gene cluster (PGC), the established roles of SAM-dependent methyltransferases are bacteriochlorophyll and carotenoid biosynthesis. But the STRING database unveiled its association with NADH-ubiquinone oxidoreductase (Complex I). The assembly and regulation of this Complex I is mediated by the gene products of the nuo operon. As a part of this operon, the urf7 gene encodes SAM-dependent methyltransferase. As a consequence of these findings, it is reasonable to propose that the hypothetical protein of interest in this study is a SAM-dependent methyltransferase associated with bacterial NADH-ubiquinone oxidoreductase assembly. Due to conservation of Complex I from prokaryotes to eukaryotes, R capsulatus can be a model organism of study to understand the common disorders which are linked to the dysfunctions of complex I.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-04-23T05:57:53Z
  DOI: 10.1177/11779322221094236
  Issue No: Vol. 16 (2022)
Modeling and Simulation of Cell Signaling Networks for Subsequent
Analytics Processes Using Big Data and Machine Learning
- Authors: Máximo Eduardo Sánchez-Gutiérrez, Pedro Pablo González-Pérez
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  This work explores how much the traditional approach to modeling and simulation of biological systems, specifically cell signaling networks, can be increased and improved by integrating big data, data mining, and machine learning techniques. Specifically, we first model, simulate, validate, and calibrate the behavior of the PI3K/AKT/mTOR cancer-related signaling pathway. Subsequently, once the behavior of the simulated signaling network matches the expected behavior, the capacity of the computational simulation is increased to grow data (data farming). First, we use big data techniques to extract, collect, filter, and store large volumes of data describing all the interactions among the simulated cell signaling system components over time. Afterward, we apply data mining and machine learning techniques—specifically, exploratory data analysis, feature selection techniques, and supervised neural network models—to the resulting biological dataset to obtain new inferences and knowledge about this biological system. The results showed how the traditional approach to the simulation of biological systems could be enhanced and improved by incorporating big data, data mining, and machine learning techniques, which significantly contributed to increasing the predictive power of the simulation.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-04-23T05:55:13Z
  DOI: 10.1177/11779322221091739
  Issue No: Vol. 16 (2022)
Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases
as a Case Study
- Authors: Emna Harigua-Souiai, Rafeh Oualha, Oussama Souiai, Ines Abdeljaoued-Tej, Ikram Guizani
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  Drug discovery (DD) research is a complex field with a high attrition rate. Machine learning (ML) approaches combined to chemoinformatics are of valuable input to this field. We, herein, focused on implementing multiple ML algorithms that shall learn from different molecular fingerprints (FPs) of 65 057 molecules that have been identified as active or inactive against Leishmania major promastigotes. We sought to build a classifier able to predict whether a given molecule has the potential of being anti-leishmanial or not. Using the RDkit library, we calculated 5 molecular FPs of the molecules. Then, we implemented 4 ML algorithms that we trained and tested for their ability to classify the molecules into active/inactive classes based on their chemical structure, encoded by the molecular FPs. Best performers were random forest (RF) and support vector machine (SVM), while atom-pair and topology torsion FPs were the best embedding functions. Both models were further assessed on different stratification levels of the dataset and showed stable performances. At last, we used them to predict the potential of molecules within the Food and Drug Administration (FDA)-approved drugs collection to present anti-Leishmania effects. We ranked these drugs according to their anti-Leishmanial probability and obtained in total seven anti-Leishmania agents, previously described in the literature, within the top 10 of each model. This validates the robustness of the approach, the algorithms, and FPs choices as well as the importance of the dataset size and content. We further engaged these molecules into reverse docking experiments on 3D crystal structures of seven well-studied Leishmania drug targets and could predict the molecular targets for 4 drugs. The results bring novel insights into anti-Leishmania compounds.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-04-23T05:52:34Z
  DOI: 10.1177/11779322221090349
  Issue No: Vol. 16 (2022)
RNA Editing–Associated Post-Transcriptional Gene Regulation in
Rheumatoid Arthritis
- Authors: Yashoda Ghanekar, Subhashini Sadasivam
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  Background:Rheumatoid arthritis (RA) is an autoimmune disease characterised by systemic inflammation of joints. The observed complexity of RA pathogenesis and studies that have been carried out so far indicate that RA pathogenesis is regulated at multiple levels. Given the role of RNA editing in autoimmune disease, we hypothesised that RNA editing could contribute to RA pathogenesis by regulating gene expression through post-transcriptional mechanisms.Methods:We identified RNA editing events in synovial tissues from early and established RA compared with normal subjects from an available transcriptome data set using REDItools. To investigate the potential effect of these RNA editing events on gene expression, we carried out an analysis of differential exon usage in the vicinity of the differentially edited sites using DEXSeq. We then used STRING to identify putative interactions between differentially edited genes identified from REDItools analysis. We also investigated the possible effects of these RNA editing events on miRNA-target mRNA interactions as predicted by miRanda.Results:Our analysis revealed that there is extensive RNA editing in RA, with 304 and 273 differentially edited events in early RA and established RA, respectively. Of these, 25 sites were within 11 genes in early RA, and 34 sites were within 7 genes in established RA. DEXSeq analysis revealed that RNA editing correlated with differential exon usage in 4 differentially edited genes that have previously also been associated with RA in some measure: ATM, ZEB1, ANXA4, and TIMP3. DEXSeq analysis also revealed enrichment of some non-functional isoforms of these genes, perhaps at the expense of their full-length counterparts. Network analysis using STRING showed that several edited genes were part of the p53 protein-protein interaction network. We also identified several putative miRNA binding sites in the differentially edited genes that were lost upon editing.Conclusions:Our results suggested that the expression of genes involved in DNA repair and cell cycle, including ATM and ZEB1 which are well-known functional regulators of the DNA damage response pathway, could be regulated by RNA editing in RA synovia. This may contribute to an impaired DNA damage response in synovial tissues.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-04-19T09:08:11Z
  DOI: 10.1177/11779322221088725
  Issue No: Vol. 16 (2022)
Sea Cucumber Compounds Targeting NF-κB in Cancer Treatment
- Authors: Teresa Liliana Wargasetia, Hana Ratnawati, Nashi Widodo
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  Cancer is a major health problem worldwide and the leading cause of death in many countries. It remains challenging to find anticancer treatments that work efficiently for varying types of cancer cells. Several studies revealed that nuclear factor kappa B (NF-κB) is a family of dimeric transcription factors that induce tumor promotion, progression, and therapeutic resistance, providing evidence that NF-kB may be a promising target for cancer drugs. Some research has found that sea cucumber biocompounds have anticancer properties, but further research is essential to confirm anticancer targets. This manuscript discusses the mechanisms of anticancer targeting the NF-κB signaling pathway induced by sea cucumber-derived compounds. Additional database analysis showed the protein targeted by the compounds involved in several pathways related to the NF-κB network. Moreover, SwissADME predicted druglikeliness properties of the active compounds of sea cucumber. The discussion is expected to provide new insight into the promising potential of these marine natural products for the treatment of many different types of cancers.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-04-18T06:06:17Z
  DOI: 10.1177/11779322221091740
  Issue No: Vol. 16 (2022)
Integrative Analysis for Identification of Therapeutic Targets and
Prognostic Signatures in Non-Small Cell Lung Cancer
- Authors: Özgür Cem Erkin, Betül Cömertpay, Esra Göv
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein–protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-04-07T05:17:12Z
  DOI: 10.1177/11779322221088796
  Issue No: Vol. 16 (2022)
Modeling the Cluster Size Distribution of Vascular Endothelial Growth
Factor (VEGF) Receptors
- Authors: Emine Güven, Michael J Wester, Jeremy S Edwards, Ádám M Halász
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  We previously developed a method of defining receptor clusters in the membrane based on mutual distance and applied it to a set of transmission microscopy images of vascular endothelial growth factor receptors. An optimal length parameter was identified, resulting in cluster identification and a procedure that assigned a geometric shape to each cluster. We showed that the observed particle distribution results were consistent with the random placement of receptors within the clusters and, to a lesser extent, the random placement of the clusters on the cell membrane. Here, we develop and validate a stochastic model of clustering, based on a hypothesis of preexisting domains that have a high affinity for receptors. The proximate objective is to clarify the mechanism behind cluster formation and to estimate the effect on signaling. Receptor-enriched domains may significantly impact signaling pathways that rely on ligand-induced dimerization of receptors. We define a simple statistical model, based on the preexisting domain hypothesis, to predict the probability distribution of cluster sizes. The process yielded sets of parameter values that can readily be used in dynamical calculations as the estimates of the quantitative characteristics of the clustering domains.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-03-25T11:39:37Z
  DOI: 10.1177/11779322221085078
  Issue No: Vol. 16 (2022)
Molnupiravir Does Not Induce Mutagenesis in Host Lung Cells during
SARS-CoV-2 Treatment
- Authors: John Maringa Githaka
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  As SARS-CoV-2 continues to evolve and spread with the emergence of new variants, interest in small molecules with broad-spectrum antiviral activity has grown. One such molecule, Molnupiravir (MOV; other names: MK-4482, EIDD-2801), a ribonucleoside analogue, has emerged as an effective SARS-CoV-2 treatment by inducing catastrophic viral mutagenesis during replication. However, there are growing concerns as MOV’s potential to induce host DNA mutagenesis remains an open question. Analysis of RNA-seq data from SARS-CoV-2–infected MOV-treated golden hamster lung biopsies confirmed MOV’s efficiency in stopping SARS-CoV-2 replication. Importantly, MOV treatment did not increase mutations in the host lung cells. This finding calls for additional mutation calls on host biopsies from more proliferative tissues to fully explore MOV’s hypothesized mutagenic risk.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-03-23T09:52:12Z
  DOI: 10.1177/11779322221085077
  Issue No: Vol. 16 (2022)
Decoding Human Genome Regulatory Features That Influence HIV-1 Proviral
Expression and Fate Through an Integrated Genomics Approach
- Authors: Holly Ruess, Jeon Lee, Carlos Guzman, Venkat S Malladi, Iván D’Orso
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  Fundamental principles of HIV-1 integration into the human genome have been revealed in the past 2 decades. However, the impact of the integration site on proviral transcription and expression remains poorly understood. Solving this problem requires the analysis of multiple genomic datasets for thousands of proviral integration sites. Here, we generated and combined large-scale datasets, including epigenetics, transcriptome, and 3-dimensional genome architecture to interrogate the chromatin states, transcription activity, and nuclear sub-compartments around HIV-1 integrations in Jurkat CD4+ T cells to decipher human genome regulatory features shaping the transcription of proviral classes based on their position and orientation in the genome. Through a Hidden Markov Model and ranked informative values prior to a machine learning logistic regression model, we defined nuclear sub-compartments and chromatin states contributing to genomic architecture, transcriptional activity, and nucleosome density of regions neighboring the integration site, as additive features influencing HIV-1 expression. Our integrated genomics approach also allows for a robust experimental design, in which HIV-1 can be genetically introduced into precise genomic locations with known regulatory features to assess the relationship of integration positions to viral transcription and fate.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-02-26T12:17:25Z
  DOI: 10.1177/11779322211072333
  Issue No: Vol. 16 (2022)
Functional Roles and Targets of COVID-19 in Blood Cells Determined Using
Bioinformatics Analyses
- Authors: Eun Jung Sohn
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  Background:Coronavirus disease 2019 (COVID-19), caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global epidemic with a high mortality rate. In this study, our goal was to identify the function and associated targets of SARS-CoV-2 from circulating monocytes in the blood and peripheral blood mononuclear cell (PBMC) dataset of patients with COVID-19.Methods:The Gene Expression Omnibus database (GSE164805 and GSE180594) was used to identify differentially expressed genes (DEGs). Gene ontology function analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed using the DAVID database.Results:Gene ontology analysis of DEG revealed that GSE164805 and GSE180594 were involved in the regulation of cell migration, upregulation of cell proliferation, and in the activation of the mitogen-activated protein kinase signaling pathway. Kyoto Encyclopedia of Genes and Genomes analysis of GSE164805 revealed that the DEGs were enriched in peroxisome, melanogenesis, and actin regulation. Peroxisome genes were highly expressed in patients with mild and severe COVID-19. Bioinformatics analysis to compare GSE180594 and public data for the single-cell atlas of the peripheral immune response in patients with COVID-19 showed that interferon-associated genes were highly increased in acute COVID-19 PBMC and in CD14+ and CD16+ monocytes from patients with COVID-19.Conclusions:We comprehensively analyzed the blood cell gene expression profile data of patients with COVID-19 using bioinformatics methods to preliminary understand the functions and associated targets of DEGs in the blood cells of these patients. Thus, our data provide targets for potential therapies against COVID-19.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-02-23T09:31:32Z
  DOI: 10.1177/11779322221080266
  Issue No: Vol. 16 (2022)
Retrieving Good-Quality Salmonella Genomes From the GenBank Database Using
a Python Tool, SalmoDEST
- Authors: Emeline Cherchame, Guy Ilango, Sabrina Cadel-Six
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  With the advent of next-generation whole-genome sequencing (WGS), the need for good-quality and well-characterised Salmonella genomes has increased over the past years. Good-quality complete genomes are often required for assembly reference mapping or phylogenetic single nucleotide polymorphism (SNP) analysis. Complete genomes or contigs from specific sources or serovars are also searched for clustering analysis or source attribution studies. Therefore, new bioinformatics tools are needed for the extraction of good-quality and well-characterised genomes from public databases. Here, we developed SalmoDEST, an open-source Python tool capable of extracting Salmonella genomes with a coverage higher than 50x and genome length over 4Mb from the GenBank database in the form of complete genomes or contigs, with verification of the serovar to which they belong and identification of the corresponding multi locus sequence type (MLST) profile. To validate the ability to SalmoDEST to screen for and retrieve genomes of good quality, we compared our results for S. Typhi complete genome with those available in the literature and extracted Salmonella genomes from bovine sources strains isolated worldwide. Finally, we provide in this study a list of 239 complete genomes for 123 serovars of Salmonella of high quality. SalmoDEST is a handy and easy-to-use open-source tool to extract complete genomes or contigs that can be routinely used in public health, food safety and research laboratories. SalmoDEST (SALMOnella Download gEnome Serotype sT) is available at https://github.com/I-Guy/SalmoDEST.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-02-23T09:29:27Z
  DOI: 10.1177/11779322221080264
  Issue No: Vol. 16 (2022)
Bioinformatics Approach to Identify Significant Biomarkers, Drug Targets
Shared Between Parkinson’s Disease and Bipolar Disorder: A Pilot Study
- Authors: Md. Bipul Hossain, Md. Kobirul Islam, Apurba Adhikary, Abidur Rahaman, Md. Zahidul Islam
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  Parkinson’s disease (PD) is a neurodegenerative disorder responsible for shaking, rigidity, and trouble in walking and patients’ coordination ability and physical stability deteriorate day by day. Bipolar disorder (BD) is a psychiatric disorder which is the reason behind extreme shiftiness in mood, and frequent mood inversion may reach too high called mania. People with BD have a greater chance of developing PD during the follow-up period. A lot of work has been done to understand the key factors for developing these 2 diseases. But the molecular functionalities that trigger the development of PD in people with BD are not clear yet. In our study, we are intended to identify the molecular biomarkers and pathways shared between BD and PD. We have investigated the RNA-Seq gene expression data sets of PD and BD. A total of 45 common unique genes (32 up-regulated and 13 down-regulated) abnormally expressed in both PD and BD were identified by applying statistical methods on the GEO data sets. Gene ontology (GO) and BioCarta, KEGG, and Reactome pathways analysis of these 45 common dysregulated genes identified numerous altered molecular pathways such as mineral absorption, Epstein-Barr virus infection, HTLV-I infection, antigen processing, and presentation. Analysis of protein-protein interactions revealed 9 significant hub-proteins, namely RPL21, RPL34, CKS2, B2M, TNFRSF10A, DTX2, HLA-B, ATP2A3, and TAPBP. Significant transcription factors (IRF8, SPI1, RUNX1, and FOXA1) and posttranscriptional regulator microRNAs (hsa-miR-491-3p and hsa-miR-1246) are also found by analyzing gene-transcription factors and gene-miRNAs interactions, respectively. Protein-drug interaction analysis revealed hub-protein B2M’s interaction with molecular drug candidates like N-formylmethionine, 3-indolebutyric acid, and doxycycline. Finally, a link between pathological processes of PD and BD is identified at transcriptional level. This study may help us to predict the development of PD among the people suffering from BD and gives some clue to understand significant pathological mechanisms.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-02-23T09:27:08Z
  DOI: 10.1177/11779322221079232
  Issue No: Vol. 16 (2022)
Functional Prediction of Biological Profile During Eutrophication in
Marine Environment
- Authors: Yousra Sbaoui, Badreddine Nouadi, Abdelkarim Ezaouine, Mohamed Rida Salam, Mariame Elmessal, Faiza Bennis, Fatima Chegdani
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  In the marine environment, coastal nutrient pollution and algal blooms are increasing in many coral reefs and surface waters around the world, leading to higher concentrations of dissolved organic carbon (DOC), nitrogen (N), phosphate (P), and sulfur (S) compounds. The adaptation of the marine microbiota to this stress involves evolutionary processes through mutations that can provide selective phenotypes. The aim of this in silico analysis is to elucidate the potential candidate hub proteins, biological processes, and key metabolic pathways involved in the pathogenicity of bacterioplankton during excess of nutrients. The analysis was carried out on the model organism Escherichia coli K-12, by adopting an analysis pipeline consisting of a set of packages from the Cystoscape platform. The results obtained show that the metabolism of carbon and sugars generally are the 2 driving mechanisms for the expression of virulence factors.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-01-05T11:58:38Z
  DOI: 10.1177/11779322211063993
  Issue No: Vol. 16 (2022)
The Vulnerability of the Developing Brain: Analysis of Highly Expressed
Genes in Infant C57BL/6 Mouse Hippocampus in Relation to Phenotypic
Annotation Derived From Mutational Studies
- Authors: Angelica Lindlöf
  Abstract: Bioinformatics and Biology Insights, Volume 16, Issue , January-December 2022.
  The hippocampus has been shown to have a major role in learning and memory, but also to participate in the regulation of emotions. However, its specific role(s) in memory is still unclear. Hippocampal damage or dysfunction mainly results in memory issues, especially in the declarative memory but, in animal studies, has also shown to lead to hyperactivity and difficulty in inhibiting responses previously taught. The brain structure is affected in neuropathological disorders, such as Alzheimer’s, epilepsy, and schizophrenia, and also by depression and stress. The hippocampus structure is far from mature at birth and undergoes substantial development throughout infant and juvenile life. The aim of this study was to survey genes highly expressed throughout the postnatal period in mouse hippocampus and which have also been linked to an abnormal phenotype through mutational studies to achieve a greater understanding about hippocampal functions during postnatal development. Publicly available gene expression data from C57BL/6 mouse hippocampus was analyzed; from a total of 5 time points (at postnatal day 1, 10, 15, 21, and 30), 547 genes highly expressed in all of these time points were selected for analysis. Highly expressed genes are considered to be of potential biological importance and appear to be multifunctional, and hence any dysfunction in such a gene will most likely have a large impact on the development of abilities during the postnatal and juvenile period. Phenotypic annotation data downloaded from Mouse Genomic Informatics database were analyzed for these genes, and the results showed that many of them are important for proper embryo development and infant survival, proper growth, and increase in body size, as well as for voluntary movement functions, motor coordination, and balance. The results also indicated an association with seizures that have primarily been characterized by uncontrolled motor activity and the development of proper grooming abilities. The complete list of genes and their phenotypic annotation data have been compiled in a file for easy access.
  Citation: Bioinformatics and Biology Insights
  PubDate: 2022-01-05T11:52:51Z
  DOI: 10.1177/11779322211062722
  Issue No: Vol. 16 (2022)