Bioinformatics Data Research Articles

Branched-chain amino acid catabolic defect in vascular smooth muscle cells drives thoracic aortic dissection via mTOR hyperactivation

Metabolic reprogramming of vascular smooth muscle cell (VSMC) plays a critical role in the pathogenesis of thoracic aortic dissection (TAD). Previous researches have mainly focused on dysregulation of fatty acid or glucose metabolism, while the impact of amino acids catabolic disorder in VSMCs during the development of TAD remains elusive. Here, we identified branched-chain amino acid (BCAA) catabolic defect as a metabolic hallmark of TAD. The bioinformatics analysis and data from human aorta revealed impaired BCAA catabolism in TAD individuals. This was accompanied by upregulated branched-chain α-ketoacid dehydrogenase kinase (BCKDK) expression and BCKD E1 subunit alpha (BCKDHA) phosphorylation, enhanced vascular inflammation, and hyperactivation of mTOR signaling. Further in vivo experiments demonstrated that inhibition of BCKDK with BT2 (a BCKDK allosteric inhibitor) treatment dephosphorylated BCKDHA and re-activated BCAA catabolism, attenuated VSMCs phenotypic switching, alleviated aortic remodeling, mitochondrial reactive oxygen species (ROS) damage and vascular inflammation. Additionally, the beneficial actions of BT2 were validated in a TNF-α challenged murine VSMC cell line. Meanwhile, rapamycin conferred similar beneficial effects against VSMC phenotypic switching, cellular ROS damage as well as inflammatory response. However, co-treatment with MHY1485 (a classic mTOR activator) reversed the beneficial effects of BT2 by reactivating mTOR signaling. Taken together, the in vivo and in vitro evidence showed that impairment of BCAA catabolism resulted in aortic accumulation of BCAA and further caused VSMC phenotypic switching, mitochondrial ROS damage and inflammatory response via mTOR hyperactivation. BCKDK and mTOR signaling may serve as the potential drug targets for the prevention and treatment of TAD.

A Study on the Analysis of Important Gene Networks and Pathways Involved in Progression of Endometriosis to Ovarian Endometrioma Cyst.

Endometriosis (EM) is a gynecological condition known by the manifestation of endometrium alike soft tissue external to the usual place affecting up to 10% of all womenfolk in the reproductively active stage. However, the pathological process of endometriosis is not identified fully. The study aims to investigate the genes associated with the progression of endometriosis and its pathways using bioinformatics tools and techniques. The gene expression profile of three sets was retrieved, and bioinformatics data analysis was carried out for the microarray samples using GEO, DAVID, and STICH. Differently expressed genes (DEGs) refer to genes that exhibit significant changes in their expression levels between different conditions or groups, such as between different cell types, treatments, disease states, or developmental stages. DEG was determined based on a significant cutoff resulting in 298 unique elements based on the GEO Venn diagram map. DAVID (database for annotation, visualization, and integrated discovery) helps understand the biological significance of the data by identifying overrepresented biological terms, pathways, and functional annotations among a set of genes or proteins of interest. DAVID analysis revealed positively and negatively associated genes and followed by target proteins. DAVID is helpful for getting results of molecular mechanisms and pathways associated with DEGs. The gene expression studies showed that the m-RNA expression of all the genes was upregulated in the PA1 cell line. The present study identified five genes (COMT, CYP19A1, GALT, LTA, and STAR) from 298 unique DEGs using microarray data analysis, and 5 protein targets were also identified that were linked with EM. The study concludes that this information may provide a bridging gap in understanding the progression of endometriosis.

Functional analysis of a putative HER2-associated expressed enhancer, Her2-Enhancer1, in breast cancer cells

HER-2/neu (HER2) is a member of the epidermal growth factor receptors family, encoding a protein with tyrosine kinase activity. Following the gene amplification or increased HER2 transcription, carcinogenesis has been observed in some cancers. Genetic and epigenetic changes occurring in enhancer sequences can deeply affect the expression and transcriptional regulation of downstream genes, which can cause some physiological and pathological changes, including tumor progression. A therapeutic approach that directly targets the genomic sequence alterations is of high importance, with low side effects on healthy cells. Here, we employed the CRISPR/Cas9 method to genetically knockout an expressed putative enhancer (GH17J039694; we coined it as Her2-Enhancer1) located within the HER2 gene, 17q12: 39,694,339–39,697,219 (UCSC-hg38). We then investigated the potential regulatory effect of Her2-Enhancer1 on HER2 and HER2-interacting genes. To evaluate the cis and trans effects of Her2-Enhancer1, genetic manipulation of this region was performed in HER2-positive and -negative breast cancer cells. Our bioinformatics and real-time PCR data revealed that this putative enhancer region is indeed expressed, and acts as an expressed enhancer. Further functional analysis on edited and unedited cells revealed a significant alteration in the expression of HER2 variants, as well as some other target genes of HER2. Moreover, the apoptosis rate was considerably elevated within the edited cells. As we expected, Western blot analysis confirmed a reduction in protein levels of HER2, GRB7, the gene interacting with HER2, and P-AKT in the PI3K/AKT pathway. Altogether, our findings revealed an enhancer regulatory role for Her2-Enhancer1 on HER2 and HER2-interacting genes; and that this region has a potential for targeted therapy of HER2-positive cancers.

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis.

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

The updated Structural Database of Allergenic Proteins (SDAP 2.0) provides 3D models for allergens and incorporated bioinformatics tools.

Allergenic proteins can cause IgE-mediated adverse reactions in sensitized individuals. Although the sequences of many allergenic proteins have been identified, bioinformatics data analysis with advanced computational methods and modeling is needed to identify the basis for IgE binding and cross-reactivity. We aim to present the features and use of the updated Structural Database of Allergenic Proteins 2.0 (SDAP 2.0) webserver, a unique, publicly available resource to compare allergens using specially designed computational tools and new high-quality 3-D models for most known allergens. Previously developed and novel software tools for identifying cross-reactive allergens using sequence and structure similarity are implemented in SDAP 2.0. Acomprehensive set of high-quality 3-D models of most allergens was generated with the state-of-the-art AlphaFold 2 software. Agraphics tool enables the interactive visualization of IgE epitopes on experimentally determined and modeled 3-D structures. A user can search for allergens similar to a given input sequence with the FASTA algorithm or the window-based World Health Organization/International Union of Immunological Societies (WHO/IUIS) guidelines on safety concerns of novel food products. Peptides similar to known IgE epitopes can be identified with the property distance tool and conformational epitopes by the Cross-React method. The updated database contains 1657 manually curated sequences including all allergens from the IUIS database, 334 experimentally determined X-ray or NMR structures, and 1565 3-D models. Each allergen/isoallergen is classified according to its protein family. SDAP provides access to the steadily increasing information on allergenic structures and epitopes with integrated bioinformatics tools to identify and analyze their similarities. In addition to serving the research and regulatory community, it provides clinicians with tools to identify potential coallergies in a sensitive patient and can help companies to design hypoallergenic foods and immunotherapies.

Structure and function of SPP/SPPL proteases: insights from biochemical evidence and predictive modeling.

More than 20 years ago, signal peptide peptidase (SPP) and its homologues, the signal peptide peptidase-like (SPPL) proteases have been identified based on their sequence similarity to presenilins, a related family of intramembrane aspartyl proteases. Other than those for the presenilins, no high-resolution structures for the SPP/SPPL proteases are available. Despite this limitation, over the years bioinformatical and biochemical data have accumulated, which altogether have provided a picture of the overall structure and topology of these proteases, their localization in the cell, the process of substrate recognition, their cleavage mechanism, and their function. Recently, the artificial intelligence-based structure prediction tool AlphaFold has added high-confidence models of the expected fold of SPP/SPPL proteases. In this review, we summarize known structural aspects of the SPP/SPPL family as well as their substrates. Of particular interest are the emerging substrate recognition and catalytic mechanisms that might lead to the prediction and identification of more potential substrates and deeper insight into physiological and pathophysiological roles of proteolysis.

Big Data in Bioinformatics and Computational Biology: Basic Insights.

The human genome was first sequenced in 1994. It took 10years of cooperation between numerous international research organizations to reveal a preliminary human DNA sequence. Genomics labs can now sequence an entire genome in only a few days. Here, we talk about how the advent of high-performance sequencing platforms has paved the way for Big Data in biology and contributed to the development of modern bioinformatics, which in turn has helped to expand the scope of biology and allied sciences. New technologies and methodologies for the storage, management, analysis, and visualization of big data have been shown to be necessary. Not only does modern bioinformatics have to deal with the challenge of processing massive amounts of heterogeneous data, but it also has to deal with different ways of interpreting and presenting those results, as well as the use of different software programs and file formats. Solutions to these problems are tried to present in this chapter. In order to store massive amounts of data and provide a reasonable period for completing search queries, new database management systems other than relational ones will be necessary. Emerging advance programing approaches, such as machine learning, Hadoop, and MapReduce, aim to provide the capacity to easily construct one's own scripts for data processing and address the issue of the diversity of genomic and proteomic data formats in bioinformatics.

The nature and organization of satellite DNAs in Petunia hybrida, related, and ancestral genomes.

The garden petunia, Petunia hybrida (Solanaceae) is a fertile, diploid, annual hybrid species (2n=14) originating from P. axillaris and P. inflata 200 years ago. To understand the recent evolution of the P. hybrida genome, we examined tandemly repeated or satellite sequences using bioinformatic and molecular cytogenetic analysis. Raw reads from available genomic assemblies and survey sequences of P. axillaris N (PaxiN), P. inflata S6, (PinfS6), P. hybrida (PhybR27) and the here sequenced P. parodii S7 (PparS7) were used for graph and k-mer based cluster analysis of TAREAN and RepeatExplorer. Analysis of repeat specific monomer lengths and sequence heterogeneity of the major tandem repeat families with more than 0.01% genome proportion were complemented by fluorescent in situ hybridization (FISH) using consensus sequences as probes to chromosomes of all four species. Seven repeat families, PSAT1, PSAT3, PSAT4, PSAT5 PSAT6, PSAT7 and PSAT8, shared high consensus sequence similarity and organisation between the four genomes. Additionally, many degenerate copies were present. FISH in P. hybrida and in the three wild petunias confirmed the bioinformatics data and gave corresponding signals on all or some chromosomes. PSAT1 is located at the ends of all chromosomes except the 45S rDNA bearing short arms of chromosomes II and III, and we classify it as a telomere associated sequence (TAS). It is the most abundant satellite repeat with over 300,000 copies, 0.2% of the genomes. PSAT3 and the variant PSAT7 are located adjacent to the centromere or mid-arm of one to three chromosome pairs. PSAT5 has a strong signal at the end of the short arm of chromosome III in P. axillaris and P.inflata, while in P. hybrida additional interstitial sites were present. PSAT6 is located at the centromeres of chromosomes II and III. PSAT4 and PSAT8 were found with only short arrays. These results demonstrate that (i) repeat families occupy distinct niches within chromosomes, (ii) they differ in the copy number, cluster organization and homogenization events, and that (iii) the recent genome hybridization in breeding P. hybrida preserved the chromosomal position of repeats but affected the copy number of repetitive DNA.

Hypermedia-based software architecture enables Test-Driven Development.

Using agile software development practices, develop and evaluate an architecture and implementation for reliable and user-friendly self-service management of bioinformatic data stored in the cloud. Comprehensive Oncology Research Environment (CORE) Browser is a new open-source web application for cancer researchers to manage sequencing data organized in a flexible format in Amazon Simple Storage Service (S3) buckets. It has a microservices- and hypermedia-based architecture, which we integrated with Test-Driven Development (TDD), the iterative writing of computable specifications for how software should work prior to development. Relying on repeating patterns found in hypermedia-based architectures, we hypothesized that hypermedia would permit developing test "templates" that can be parameterized and executed for each microservice, maximizing code coverage while minimizing effort. After one-and-a-half years of development, the CORE Browser backend had 121 test templates and 875 custom tests that were parameterized and executed 3031 times, providing 78% code coverage. Architecting to permit test reuse through a hypermedia approach was a key success factor for our testing efforts. CORE Browser's application of hypermedia and TDD illustrates one way to integrate software engineering methods into data-intensive networked applications. Separating bioinformatic data management from analysis distinguishes this platform from others in bioinformatics and may provide stable data management while permitting analysis methods to advance more rapidly. Software engineering practices are underutilized in informatics. Similar informatics projects will more likely succeed through application of good architecture and automated testing. Our approach is broadly applicable to data management tools involving cloud data storage.

Functional analysis of tumor-derived immunoglobulin lambda and its interacting proteins in cervical cancer

BackgroundImmunoglobulin lambda (Igλ) has been reported to be expressed in many normal and tumor tissues and cells. However, the function and clinical significance of tumor-derived Igλ remain unclear.MethodsThe differential expressions of Immunoglobulin Lambda Constants (IGLCs) in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) were examined with The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Human Protein Atlas (HPA) databases. The effects of IGLCs on patient clinical phenotypes and prognosis were explored via bioinformatics analyses based on the TCGA databases. We used the bioinformatics analyses based on the TCGA and GTEx databases to elucidate the correlations among IGLC expressions, immunomodulator expressions, tumor stemness, and infiltration scores of tumor infiltrating immune cells. Co-immunoprecipitation (Co-IP) and silver staining combined with liquid chromatography-tandem mass spectrometry (LC–MS/MS) were used to obtain potential tumor-derived Igλ-interacting proteins. Functional annotation of candidate proteins identified by LC–MS/MS was performed in Database for Annotation, Visualization and Integrated Discovery (DAVID). The bioinformatics analyses of 7 IGLCs in CESC and normal cervical tissues were performed based on TCGA, GTEx, and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) databases. Protein–protein interaction (PPI) network was analyzed based on tumor-derived Igλ-interacting proteins in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Immunohistochemistry (IHC) was used to validate the expressions of IGLCs in CESC.ResultsWe found that the expressions of the majority of IGLCs (IGLC1, IGLC2, IGLC3, IGLC4, IGLC5, IGLC6, and IGLC7) were upregulated in CESC tissues, compared with those in normal cervical tissues. The expressions of IGLC5 and IGLC7 had significant difference in different pathologic metastasis (M), one of tumor, node, and metastasis (TNM) staging system, categories of CESC. Except for disease-free interval (DFI), 4 IGLC (IGLC1, IGLC2, IGLC3, and IGLC7) expression levels were positively associated with patient overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) respectively in CESC tissues. 5 IGLC (IGLC1, IGLC2, IGLC3, IGLC6, and IGLC7) expressions were positively correlated with the expressions of a majority of immunomodulators respectively in CESC tissues. Tumor stemness was negatively correlated with the expressions of 4 IGLCs (IGLC1, IGLC2, IGLC3, and IGLC7) respectively in CESC tissues. Except for IGLC4, IGLC5, and IGLC7, 4 IGLC (IGLC1, IGLC2, IGLC3, and IGLC6) expressions were positively correlated with infiltration scores of 6 tumor-infiltrating immune cells (B cell, T cell CD4, T cell CD8, neutrophil, macrophage, and DC). After analyses of the above bioinformatics data of tumor-derived Igλ, Co-IP and LC–MS/MS were used to confirm that 4 proteins (RPL7, RPS3, H1-5, and H1-6) might interact with tumor-derived Igλ in cervical cancer cells. Functional analyses of these candidate proteins showed that they interacted with many proteins and were involved in various cellular biological processes. Finally, IHC was used to further confirm the above bioinformatics results, it was indicated that the expression level of Igλ in cervical adenocarcinoma and cervical squamous cell carcinoma was higher than that in normal cervical tissue.ConclusionThis study comprehensively investigated the functions of tumor-derived Igλ and its interacting proteins based on bioinformatics analysis and the potential value of Igλ as a prognostic and therapeutic marker for CESC, providing new direction and evidence for CESC therapy.

Computational Technology Based Biology Project For Remote Learning During Covid-19 Crisis

Abstract: In the many months following the C o r o n a v i r u s f l a r e - u p , a l a r g e n u m b e r o f Biotechnology Engineering students throughout the world are compelled to switch over from conventional lab based learning to computational technology based learning. This uncommon interchange enormously affected the method of learning project courses. Specifically, biological science projects which require the students and staff to work in the actual lab was affected. B. Tech. Biotechnology students and a staff f r o m o u r i n s t i t u t i o n u t i l i z e d p r e s e n t d a y computational bioinformatics tool, data sets and programming to study an issue identified as betalactam resistance in a bacterium. The students utilized accessible present day computational tool, data sets and virtual products to complete the project work. This technique for doing the final year B. Tech. project has brought about the consummation of the course effectively. This procedure of utilization of freelyavailable computational tool, information databases and programming software will benefit the students worldwide to complete project based engineering courses effectively. Keywords: in silico, project, workstation, COVID- 19, bioinformatics

Carbon ion radiotherapy combined with immunotherapy: synergistic anti-tumor efficacy and preliminary investigation of ferroptosis

Carbon ion radiotherapy (CIRT) may yield satisfactory clinical outcomes for patients who are resistant to radiotherapy. However, the therapeutic impact of carbon ions is still limited in certain recurring or refractory tumors. Therefore, we aimed to evaluate the synergistic anti-tumor effects of immune checkpoint inhibitors (ICIs) in combination with CIRT. We then explored the involvement of ferroptosis in a preliminary investigation. A tumor-bearing mouse model was established, and mice were inoculated subcutaneously with B16-OVA cells into the flanks of both hind legs. Mice were assigned to four groups to receive CIRT, ICIs, or combined treatment. Thereafter, we conducted transcriptome sequencing (RNA-seq), bioinformatics analysis, and various immune-related experiments on the available tumor tissues to investigate differences in the synergistic anticancer effects and potential mechanisms across the groups. The combination therapies significantly improved the survival of mice and inhibited tumor growth, both at local and distant sites. Based on bioinformatics and RNA-seq data, immune-related pathways and genes, immune cell infiltration, and the production of cytokines and chemokines were the most enhanced in the combined treatment group compared to other groups. Finally, we identified a potential role for ferroptosis in the development of local anti-tumor synergy during CIRT combination treatment. In conclusion, this study showed that CIRT and ICIs can enhance the anti-tumor immune effects. We also proposed that ferroptosis may induce anti-tumor effects in CIRT combination therapy, offering a unique perspective on its ability to enhance immunotherapy responses.

The five pillars of computational reproducibility: bioinformatics and beyond.

Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.

Comprehensive analysis of ICD-related lncRNAs in predicting risk stratification, clinical prognosis and immune response for breast cancer.

Breast cancer (BRCA) represents a significant threat with high mortality rates due to relapse, metastasis, and chemotherapy resistance. As a regulated cell death process characterized by the induction of immunogenic signals, immunogenic cell death (ICD) has been identified as an effective anti-tumorigenesis approach. However, the comprehensive study and its clinical value of ICD-related lncRNAs in BRCA is still missing. The transcriptome matrix and corresponding clinical information of BRCA patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was performed to identify ICD-related lncRNAs (ICDRLs). To determine the prognostic value of the identified ICDRLs, univariate Cox regression analysis, LASSO algorithm, and multivariate Cox regression analysis were employed to construct a risk model. The prognostic risk model was subsequently evaluated using univariate and multivariate Cox regression analysis, as well as Nomogram analysis. In vitro experiments were also conducted to validate the bioinformatics findings using quantitative real-time PCR (qRT-PCR). We established a prognostic risk signature consisting of five ICDRLs. The prognostic value of this model was subsequently confirmed in guiding BRCA prognostic stratification. Furthermore, we explored the correlation of the risk score with various clinical characteristics and chemotherapy response. qRT-PCR result confirmed the abnormal expression of ICDRLs, which was consistent with the bioinformatics data. Our findings provide evidence of the critical role of ICDRLs in BRCA and offer a novel perspective for exploring precise treatment options for BRCA patients.

A network pharmacology and molecular docking approach to reveal the mechanism of Chaihu Anxin Capsule in depression.

As one of the most frequently diagnosed mental disorders, depression is expected to become the most common disease worldwide by 2030. Previous studies have shown that Chaihu Anxin Capsule has powerful antidepressant effects. However, its mechanisms are not fully understood. The aim of our research is to reveal the mechanisms of Chaihu Anxin Capsule in treating depression. Information about the ingredients of the herb was gathered using the TCMSP. Genes associated with antidepressants were gathered from the GeneCards database. An "herbal-ingredient-target" network was constructed and analyzed using Cytoscape software. The PPI network of the antidepressant targets of Chaihu Anxin Capsule was constructed using the STRING database. KEGG pathway and GO enrichment were used to analyze the antidepressant targets. Molecular docking technology was used to confirm the capacity of the primary active ingredients of Chaihu Anxin Capsule to bind to central targets using AutoDock Vina and PyMOL software. Network analysis showed that five targets might be therapeutic targets of Chaihu Anxin Capsule in depression, namely, JUN, IL6, AKT1, TP53, and STAT3. The gene enrichment analysis implied that Chaihu Anxin Capsule benefits patients with depression by modulating pathways related to lipids and atherosclerosis and the AGE-RAGE signaling pathway in diabetic complications. Molecular docking analyses revealed that JUN, IL6, AKT1, TP53, and STAT3 had good affinities for quercetin, beta-sitosterol and kaempferol. According to the bioinformatics data, the antidepressant effects of Chaihu Anxin Capsule may be primarily linked to cholesterol and atherosclerosis as well as the AGE-RAGE signaling pathway in diabetic complications. These results emphasize that the expected therapeutic targets may be possible indicators for antidepressant activity.

Mapping and identification of QTLs for seed fatty acids in soybean (Glycine max L.)

Soybean is one of the world's most important sources of vegetable oil. The oil content and fatty acid ratio have attracted significant attention due to their impact on the shelf-life of soybean oil products and the consumers' health. In this study, the high-density genetic map derived from Guizao 1 and Brazil 13 was utilised to analyse the quantitative trait loci of palmitic acid (PA), stearic acid (SA), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA) and oil content. A total of 54 stable QTLs were detected in genetic map linkage analysis, which shared six bin intervals. Among them, the bin interval on chromosome 13 (bin106-bin118 and bin123-bin125) was detected as stable QTLs in multiple environments, linked to OA, LA, and LNA. Eight differentially expressed genes (DEGs) within these QTLs interval were determined as candidate genes according to the joint investigation of parental resequencing, bioinformatics and RNA sequencing data. All these results will be conducive to breeding soybean with the ideal fatty acid ratio for food, and provide the genetic information basis for mining genes related to fatty acid and oil content traits in soybean.

Molecular Basis of Host-Virus Interactions to Explain Relative Transmission and Severity Caused by Omicron and Delta variants of SARS-CoV-2

In India and other affected countries, Omicron variant of SARS-CoV-2 has shown faster transmission but less clinical severities when compared to Delta strain. Present study was aimed to investigate how molecular changes in the spike proteins of Omicron variant has increased its transmission but reduced the disease severity. We report molecular interactions of Spike proteins of Delta and Omicron variants with ACE-2 receptor to explain how change in chemical and physical nature of mutated amino acids of Omicron variant has affected the internalization competence of virus into host cell. The Research Collaboratory Structural Bioinformatics (RCSB) and Protein Data Bank (PDB) were used to construct ACE2-Spike Protein interaction. The binding affinity of both omicron and delta variant spike proteins with human ACE2 receptor was observed. Spike protein of Omicron variants has revealed total number of 93 dissimilarities of amino acids from Delta strain,15 of which are in its Receptor Binding Domain (RBD). Our study showed that RBD of Delta variant contained only one hydrophobic amino acid whereas there were 6 hydrophobic amino acids in the RBD of Omicron variant. We report that increased number of Hydrophobic Amino Acids in RBD of Omicron variant affects its binding with ACE2 receptor to enter into the cell. The failure of internalization of virus has increased concentration of extracellular virions at nasopharyngeal region leading to faster expulsion of infective droplets during coughing or sneezing to increase transmission but has reduced the severity of infection. The reported observations could prove to be of public health and therapeutic significance.

Bioinformatics Research and qRT-PCR Verify Hub Genes and a Transcription Factor-MicroRNA Feedback Network in Intervertebral Disc Degeneration.

The present study explores the potentials of bioinformatics analysis to identify hub genes linked to intervertebral disc degeneration (IDD) and explored the potential molecular mechanism of transcription factor-microRNA regulatory network. Furthermore, the hub genes were identified through quantitative reverse transcriptase PCR (qRT-PCR). GEO database expression profile datasets for candidate genes (GSE124272) were downloaded. Genes that were differentially expressed (DEGs) were detected utilizing limma technique in the R programming language. Search Tool for the Retrieval of Interacting Genes/Proteins and NetworkAnalyst software identified hub genes. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis as well as Gene Ontology annotation of the DEGs were performed using Metascape. Using Bioinformatics data from the TRRUST, StarBase, and TransmiR databases, a TF-miRNA-hub genes network was constructed. qRT-PCR was utilized to confirm the result. As compared to healthy persons, 521 DEGs, comprising 203 down-regulated and 318 up-regulated genes, as well as 7 core genes, were found in people with IDD. Analysis revealed that all seven essential genes were under-expressed. qRT-PCR further confirmed the low expression of these seven important genes. Based on the TRRUST database, 16 TFs that could target five junction genes were then predicted. According to the StarBase database, four miRNAs were linked to crucial genes, while the TransmiR database predicted regulatory connections between four miRNAs and five TFs. The expression of the TP53-(hsa-miR-183-5p)-CCNB1 TF-miRNA-mRNA interaction network was discovered to be correlated with IDD. Throughout this investigation, a network of TF-miRNA-mRNA connections was built for investigation of the probable molecular mechanisms responsible for IDD. The identification of hub genes associated with IDD may reveal promising IDD treatment strategies.

METTL14 Suppresses Tumor Stemness and Metastasis of Colon Cancer Cells by Modulating m6A-Modified SCD1.

Colon cancer (CC) is a malignant disease of the digestive tract, and its rising prevalence poses a grave threat to people's health. N6-methyladenosine (m6A) modification is essential for various crucial life processes through modulating gene expression. Methyltransferase-like 14 (METTL14), the m6A methylation transferase core protein, and its aberrant expression is intimately correlated to tumor development. This study was conducted to probe the impacts and specific mechanisms of METTL14 on the biological process of CC. Bioinformatics data disclosed that METTL14 was significantly attenuated in CC. Functional assays were executed to ascertain how METTL14 affected CC tumorigenicity, and METTL14 overexpression caused a notable decline in viability, migration, invasion, and stemness phenotype of CC cells. Then, in-depth mechanistic studies displayed that stearoyl-CoA desaturase 1 (SCD1) was a downstream target gene of METTL14-mediated m6A modification. METTL14 overexpression substantially augmented the m6A modification of SCD1 mRNA and diminished the SCD1 mRNA level. In addition, we revealed that YTHDF2 was the m6A reader to recognize METTL14 m6A-modified SCD1 mRNA and abolish its stability. Finally, we also validated that METTL14 might impede the tumorigenic process of CC through SCD1 mediated Wnt/β-catenin signaling. Taken together, this study presented that METTL14 performed as a potential therapeutic target in CC with important implications for the prognosis amelioration of CC patients.

MiR-28-3p regulates high glucose-induced endothelial dysfunction by targeting CXXC5

IntroductionIntroduction: Data from the CEO database shows microRNA (miR) -28-3p levels are elevated in diabetic patients. Nevertheless, the role of miR-28-3p in peripheral artery disease with diabetes have not been investigated.Material and methodsThe level of miR-28-3p, CXXC5, CXXC-type zinc finger protein 5, and CXXC5’s downstream molecules as well as endothelial function were investigated. Dual luciferase analyses were used to confirm the binding site of miR-28-3p and CXXC5.ResultsResults: Under high-glucose condition, miR-28-3p expression is increased whereas CXXC5 expression is downregulated. Overexpression of miR-28-3p increased cell apoptosis and inhibited cell proliferation, migration, and vessel formation, whilst inhibiting its expression had the opposite effect. The overexpression and inhibition of miR-28-3p could also influence both the mRNA and protein levels of CXXC5 and its known downstream molecules. Analysis of bioinformatics data revealed a potential binding site for miR-28-3p and CXXC5. Dual luciferase analyses demonstrated that miR-28-3p suppressed CXXC5 expression by targeting the 3'-untranslated region (3'-UTR) of CXXC5. Following that, we overexpressed both miR-28-3p and CXXC5. The level of CXXC5 and its known downstream signaling molecules decreased with miR-28-3p overexpression alone. As anticipated, co-overexpression of miR-28-3p and CXXC5 partially reversed the effect of miR-28-3p mimics.ConclusionsConclusion: These findings indicated that miR-28-3p regulated high glucose-induced endothelial dysfunction by targeting CXXC5.