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bioRxiv2023

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Diabetic patients are prone to diabetic kidney disease (DKD), which may cause cardiovascular damage, hypertension and obesity, and reduce quality of life. As a result, the life quality of patients was seriously reduced. However, the pathogenesis of diabetic kidney disease (DKD) has not been fully elucidated, and current treatments remain inadequate. Therefore, it is essential to explore the molecular mechanism of DKD and its complications. Next Generation Sequancing (GSE217709) dataset was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were picked out by R software. Then Gene ontology (GO) and REACTOME pathway enrichment analysis were performed by g:Profiler database, protein-protein interaction (PPI) of DEGs was constructed by Human Integrated Protein-Protein Interaction rEference (HIPPIE) database. Module analysis was carried out by Cytoscape plug-in PEWCC. Subsequently, miRNA-hub gene regulatory network and TF-hub gene regulatory network were performed by miRNet database and NetworkAnalyst database. Finally, validation of hub genes was performed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. In total, 958 DEGs, including 479 up regulated and 479 down regulated genes, were identified. The GO and pathway enrichment changes of DEGs were mainly enriched in biological regulation, multicellular organismal process, signaling by GPCR and extracellular matrix organization. Ten hub genes (HSPA8, HSP90AA1, HSPA5, SDCBP, HSP90B1, VCAM1, MYH9, FLNA, MDFI and PML) associated with DKD and its complications were identified. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of DKD and its complications. The identified hub genes may participate in the onset and development of DKD and its complications and serve as therapeutic targets.

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bioRxiv2023

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCEStudying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep-learning tool, microbial graph attention (MEGA), to refine the organisms that interact with tumors.

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bioRxiv2023

Hidden Markov Models based search in combination with structural bioinformatics pipeline leads to the identification of DAF-12 distant orthologous in Meloidogyne incognita

Root-knot nematode (RKN) Meloidogyne spp. is one of the most damaging parasites due to its wide range of hosts. Here, we report a C. elegans receptor DAF-12 ortholog gene in Meloidogyne incognita (DAF-12Minc), a promising molecular target to modify the RKN life cycle. Using a combination of Hidden Markov Models (HMM) based sequence search and phylogenetic analysis we identified three DAF-12Minc genes. Although the global sequence identity between previously reported DAF-12 genes and DAF-12Minc was acceptable, the correlation between binding site residues was low in the multiple sequence alignment (MSA). Since those residues are critical for DAF-12 interaction with its ligand, the dafachronic acids (DAs), and thus its biological role, we investigated whether even if the sequence conservation is low, the active site structure was conserved and thus able to bind DAs. For this purpose, we built accurate homology models of DAF-12Minc and used them to identify and characterize the ligand binding site (LBS) and its molecular interactions with DAs-like compounds. Finally, we cloned, expressed, and evaluated the biological role of DAF-12Minc in vitro and in vivo using a DAF-12 antagonist. These in vivo results suggest that our strategy was effective to find orthologous genes among species even when sequence similarity is low. Author summaryRoot-knot nematodes are parasitic to plants and responsible for causing a significant loss of millions of dollars every year in crops worldwide, which makes it necessary to develop effective strategies to combat them. One popular approach is to identify genes that can serve as molecular targets. Typically, such molecular targets are discovered through basic research on model organisms. However, since they can be quite different from the target organism, conventional tools may not always be efficient in extrapolating results. Dafachronic acids (DAs) are a crucial class of steroid hormones that regulate the development and physiology of nematodes. They are synthesized from cholesterol and are controlled by a nuclear hormone receptor known as DAF-12. This receptor acts as a master regulator of gene expression, playing a vital role in nematode biology, including development, reproduction, metabolism, stress response, and longevity. Therefore, DAF-12 is a promising molecular target for controlling parasitic nematodes. Although DAF-12 was initially discovered in the model organism Caenorhabditis elegans and subsequently found in some parasitic nematodes, previous attempts to identify molecular targets in the Meloidogyne genus failed to detect DAF-12 orthologs. To address this gap, we employed a combination of sequence and structure analysis to identify potential candidates for DAF-12, a known and validated molecular target, which had not yet been found in Meloidogyne incognita. Our bioinformatics predictions were experimentally validated, which may serve as a starting point for future campaigns aimed at developing parasite control strategies based on this relevant molecular target.

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bioRxiv2023

Flow: a web platform and open database to analyse, store, curate and share bioinformatics data at scale

Ever-increasing volumes of sequencing data offer potential for large-scale meta-analyses to address significant biological questions. However, challenges such as insufficient data processing information, data quality concerns, and issues related to accessibility and curation often present obstacles. Additionally, most experimental biologists lack the time and expertise needed to independently analyse, manage and share their own data. To overcome these hurdles, we present Flow, a web-based platform that links bioinformatic analysis and database solutions with a user-friendly interface and web API. Flow currently accommodates a range of genomics methods and further DSL2-compliant Nextflow pipelines can be added via a simple JSON schema file. Deployable on local systems or cloud services, an instance is freely accessible to academic researchers at https://flow.bio.

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bioRxiv2023

Comparative evaluation of bioinformatic tools for virus-host prediction and their application to a highly diverse community in the Cuatro Cienegas Basin, Mexico

The sheer diversity of unculturable viruses has prompted the need to describe new viruses through culture-independent techniques. The associated host is one important phenotypic feature that can be inferred from metagenomic viral contigs -- thanks to the development of various bioinformatic tools. Here we compare the performance of recently developed tools for virus-host prediction on a dataset of 1,046 virus-host pairs and then apply the best-performing tools on a metagenomic dataset derived from a highly diverse transiently hypersaline site known as Archaean Domes within the Cuatro Cienegas Basin, Coahuila, Mexico. We also introduce a virus-host prediction tool called CrisprCustomDB, which uses specific criteria to solve controversial host assignments with custom spacers databases. Host-dependent alignment-based methods showed an average precision of 83% and a sensitivity from 13.7% to 17.7%, whereas host-dependent alignment-free methods achieved an average precision of 75.7% and a sensitivity of 57.5%. RaFAH, a virus-dependent alignment-based tool, had the best performance overall (F1_score = 95.7%). However, when applied to the highly diverse metagenomic dataset, the host-dependent alignment-based (e.g., CrisprCustomDB) and alignment-free (e.g., PHP) methods showed the greatest agreement with each other, even though they are fundamentally different methods. This is because instead of depending on known hosts or viruses-with-known-host databases, they can directly relate metagenomic viral contigs and metagenome-assembled genomes from the same dataset. Such methods also showed the greatest consistency between the source environment and the predicted host taxonomy, habitat, lifestyle, or metabolism, revealing that Archaean Domes viruses likely infect halophilic Archaea as well as a variety of Bacteria which may be halophilic, halotolerant, alkaliphilic, thermophilic, oligotrophic, sulfate-reducing or marine-related. Consequently, using a combination of methods and qualitative validations relating to the source environment and the predicted host biology will increase the number of correct predictions, mainly when dealing with novel viruses.

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bioRxiv2023

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed.. Using the g:Profiler, the function and pathway enrichment analysis of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst database was used to construct the targeted microRNAs (miRNAs) and transcription factors (TFs) of the hub genes. Finally receiver operating characteristic (ROC) curve analysis was used to validates the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8 and EFHC2 were selected. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

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bioRxiv2023

MADAME: a user-friendly bioinformatic tool for data and metadata retrieval in microbiome research

Microbiome research advancements have provided countless insights. Despite the massive amount of data currently stored in public repositories, these resources remain vastly underutilized due to the intricacy of data and metadata retrieval from these databases. However, leveraging data-driven approaches is crucial for microbiome research progress by overcoming variations between studies and identifying generalizable trends. We designed the open-access and user-friendly bioinformatic tool MADAME (MetADAta MicrobiomE) to streamline the data and metadata retrieval process. MADAME addresses the challenges posed by the public repositories current limitations, allowing users to retrieve publications associated with the accession codes of interest. Additionally, MADAME allows users to visually explore retrieved results through the generation of a comprehensive report with plots and statistics. These unique features of MADAME let users maximize their time and resources, enabling them to assess metadata suitability before pursuing data download. To showcase its diverse functionalities, we recreate several scenarios to meet the diverse requirements that researchers may have.

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bioRxiv2023

Bioinformatics analysis of Uncyclotides: a potential antimicrobial peptide to protect against heart failure diseases

Heart failure (also known as HF) is a clinical disorder triggered by functional and structural defects in cardiac muscles causing blood ejection or damage of ventricular filling. Even though the progression in the field of medicine, HF control, which generally presents as a syndrome, has been a task to healthcare sources. This is reproduced by the comparatively higher ration of readmissions with enhanced disease and death linked with HF. Congestive heart failure is also linked with expressional changes of human uncoupling protein proteins. So, we need to tackle the heart failure problem with the help of bioinformatics analysis. In this study, we retrieved three antimicrobial peptides (Cathelicidins, CRAMP and Uncyclotides) and four human uncoupling proteins (UCP 1, UCP 2, UCP 3 & UCP 4) from DRAMP and NCBI web server. Physiochemical properties and domains analysis of UCPs and antimicrobial peptides was performed by using Protparam, DRAMP and InterPro databases correspondingly. To predict the UCPs structure, homology modelling was performed by SWISS-MODEL server and structural assessment was done by Ramachandran plots. We detected the best antimicrobial peptide (Uncyclotides) against heart failure diseases after HPEPDOCK analysis. We also confirmed stability of UCP 1-Uncyclotides complex, and shown the slight distortion of the deformability plot supported by highest eigenvalues after Molecular Dynamics analysis. Therefore, this in silico approach identifies potential antimicrobial peptides "Uncyclotides" (acts as inhibitors) of heart failure diseases, which have to be confirmed further after experimental and clinical trials.

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bioRxiv2023

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Numerous proteins have been identified that are associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia, through exhaustive bioinformatic and next generation sequencing (NGS) data analyses using GSE20966 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction (PPI) network, module, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify the hub genes, miRNA and TFs. Potential hub genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably up regulated and 477 genes were distinctly down regulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, GPCR ligand binding, regulation of cellular process and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A and BTK were considered the potential hub genes. miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed successfully. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation.

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bioRxiv2023

Identification of Hub Genes and Functional Annotation of Glaucoma Using Bioinformatic Analysis

Glaucoma is a common eye condition that damages the optic nerve, leading to vision loss and potential blindness if not treated. It affects around 80 million people worldwide and is a major cause of irreversible blindness. This study aims to identify key genes and their functions in glaucoma using bioinformatic analysis. The research follows a five-step approach, including data retrieval, processing, identification of differentially expressed genes (DEGs), protein interaction analysis, functional enrichment analysis, and identification of hub genes. Three GSE datasets (GSE 4316, GSE 53985, and GSE 7144) were selected, resulting in a total of 632 upregulated and 625 downregulated genes. Analysis using the DAVID server revealed significant biological processes related to gene regulation, transcription, and cell adhesion. The protein networks for upregulated and downregulated genes consisted of 581 nodes and 1713 edges, and 575 nodes and 2428 edges, respectively. The Cytohubba plugin identified 10 significant genes for each group. The upregulated genes included PTPRC, INS, SRC, CXCR, FN1, TNF, ACTB, AKT1, ALB, and PECAM1, while the downregulated genes included VEGFA, FN1, TP53, ACTB, AKT1, CCND1, CTNNB1, MYC, CD44, and EGFR. These findings enhance our understanding of glaucomas molecular biology and provide valuable insights for prognosis prediction and the development of treatments and medications.

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bioRxiv2023

From Viral Infections to Alzheimer's Disease: Unveiling the Mechanistic Links Through Systems Bioinformatics

BackgroundEmerging evidence suggests that certain microorganisms, including viral infections, may contribute to the onset and/or progression of Alzheimers Disease (AD), a neurodegenerative condition characterized by memory impairment and cognitive decline. However, the precise extent of their involvement and the underlying mechanisms through which specific viruses increase AD susceptibility risk remain elusive. MethodsWe used an integrative systems bioinformatics approach to identity viral-mediated pathogenic mechanisms by which specific viral species, namely Herpes Simplex Virus 1 (HSV-1), Human Cytomegalovirus (HCMV), Epstein-Barr Virus (EBV), Kaposi Sarcoma-associated Herpesvirus (KSHV), Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Influenza A virus (IAV) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), could facilitate the pathogenesis of AD via virus-host protein-protein interactions (PPIs). We also sought to uncover potential synergistic pathogenic effects resulting from the reactivation of specific herpesviruses (HSV-1, HCMV and EBV) during acute SARS-CoV-2 infection, potentially increasing AD susceptibility. ResultsOur findings show that Herpesviridae Family members (HSV-1, EBV, KSHV, HCMV) impact AD-related processes like amyloid-beta formation, neuronal death, and autophagy. Hepatitis viruses (HBV, HCV) influence processes crucial for cellular homeostasis and dysfunction. Importantly, hepatitis viruses affect microglia activation via virus-host PPIs. Reactivation of HCMV during SARS-CoV-2 infection could potentially foster a lethal interplay of neurodegeneration, via synergistic pathogenic effects on AD-related processes like response to unfolded protein, regulation of autophagy, response to oxidative stress and amyloid-beta formation. ConclusionsCollectively, these findings underscore the complex link between viral infections and AD development. Perturbations in AD-related processes by viruses can arise from both shared and distinct mechanisms among viral species in different categories, potentially influencing variations in AD susceptibility.

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bioRxiv2023

Conserved Cardiovascular Network: Bioinformatics Insights into Genes and Pathways for Establishing Caenorhabditis elegans as an Animal Model for Cardiovascular Diseases.

Cardiovascular disease (CVD) is a collective term for disorders of the heart and blood vessels. The molecular events and biochemical pathways associated with CVD are difficult to study in clinical settings on patients and in vitro conditions. Animal models play a pivotal and indispensable role in cardiovascular disease (CVD) research. Caenorhabditis elegans, a nematode species, has emerged as a prominent experimental organism widely utilised in various biomedical research fields. However, the specific number of CVD-related genes and pathways within the C. elegans genome remains undisclosed to date, limiting its in-depth utilisation for investigations. In the present study, we conducted a comprehensive analysis of genes and pathways related to CVD within the genomes of humans and C. elegans through a systematic bioinformatic approach. A total of 1113 genes in C. elegans orthologous to the most significant CVD-related genes in humans were identified, and the GO terms and pathways were compared to study the pathways that are conserved between the two species. In order to infer the functions of CVD-related orthologous genes in C. elegans, a PPI network was constructed. Orthologous gene PPI network analysis results reveal the hubs and important KRs: pmk-1, daf-21, gpb-1, crh-1, enpl-1, eef-1G, acdh-8, hif-1, pmk-2, andaha-1 in C. elegans. Modules were identified for determining the role of the orthologous genes at various levels in the created network. We also identified 9 commonly enriched pathways between humans and C. elegans linked with CVDs that include autophagy (animal), the ErbB signalling pathway, the FoxO signalling pathway, the MAPK signalling pathway, ABC transporters, the biosynthesis of unsaturated fatty acids, fatty acid metabolism, glutathione metabolism, and metabolic pathways. This study provides the first systematic genomic approach to explore the CVD-associated genes and pathways that are present in C. elegans, supporting the use of C. elegans as a prominent animal model organism for cardiovascular diseases.

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bioRxiv2024

Machine Learning-Based Identification of B4GALNT1 as a Key Player in Hepatocellular Carcinoma: A Comprehensive Bioinformatics and Structural Analysis

Liver hepatocellular carcinoma (LIHC) is one of the most frequent types of malignant cancer in the globe. The identification of new biomarkers for the LIHC is critical. We used TCGA-LIHC gene expression datasets for this study. Several feature selection methods were used to find the top gene signatures that distinguish LIHC cancer from normal samples. Eleven machine learning algorithms were used on these selected characteristics, and model performance evaluation revealed that Naive Bayes Classifiers (AUC = 0.965) performs the best for a selection of 55 protein coding genes. Among 55 protein coding genes we found B4GALNT1 (Beta-1,4-N-acetyl-galactosaminyltransferase 1) which is differentially regulated in LIHC. With several evidence B4GALNT1 plays crucial role in tumorigenesis in many cancers, therefore we conducted systematic bioinformatics approach with mutational and structural analysis of B4GALNT1 in LIHC. Moreover, survival analysis, immune cell infiltration, most significant associated methylated CpG probe and access the accuracy of B4GALNT1 conducted to find the potential role of B4GALNT1. The results suggested that B4GALNT1 was significantly expressed in most cancers including LIHC. Finally, 16 missense mutations identified through cBioportal, Cosmic Database, and Human Variant Database, among which 6 mutations (P64Q, S131F, A311S, R340Q, D478H, and P507Q) found to be deleterious when analysed by in-silico prediction algorithms such as SIFT, PolyPhen2, I Mutent2 and CADD in LIHC. Molecular Dynamics simulation analysis was performed to understand the atomic details of the structure and functional changes. Results from this study suggest the impact of these missense variants on the structure of the B4GALNT1 protein and its pathogenic relevance. Our study demonstrated that B4GALNT1 may be evaluated as a novel target for liver cancer therapy because it has been found to be overexpressed in Liver and correlates with a poor prognosis.

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bioRxiv2024

SomaScan Bioinformatics: Normalization, Quality Control, and Assessment of Pre-Analytical Variation

SomaScan is an aptamer-based proteomics assay designed for the simultaneous measurement of thousands of human proteins with a broad range of endogenous concentrations. In its most current version released on November 1, 2023, the 11K SomaScan assay v5.0 is capable of measuring 10,776 human proteins covering major biological processes and disease areas, including cardiology, inflammation, neurology, and oncology. Here, I review bioinformatic approaches to perform normalization, quality control, and variability assessments.

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bioRxiv2024

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Identification of accurate biomarkers is still particularly urgent for improving the poor survival of chronic obstructive pulmonary disease (COPD) patients. In this investigation, we aimed to identity the potential biomarkers in COPD via bioinformatics and next generation sequencing (NGS) data analysis. In this investigation, the differentially expressed genes (DEGs) in COPD were identified using NGS dataset (GSE239897) from Gene Expression Omnibus (GEO) database. Subsequently, gene ontology (GO) and pathway enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of COPD. Protein-protein interaction (PPI), modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to determine the hub genes, miRNAs and TFs. The receiver operating characteristic (ROC) analysis was performed to determine the diagnostic value of hub genes. A total of 956 overlapping DEGs (478 up regulated and 478 down regulated genes) were identified in the NGS dataset. DEGs were mainly associated with GO functional terms and pathways in cellular response to stimulus. response to stimulus, immune system and neutrophil degranulation. There were 10 hub genes (MYC, LMNA, VCAM1, MAPK6, DDX3X, SHMT2, PHGDH, S100A9, FKBP5 and RPS6KA2) identified by PPI, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis. In conclusion, the DEGs, relative GO terms, pathways and hub genes identified in the present investigation might aid in understanding of the molecular mechanisms underlying COPD progression and provide potential molecular targets and biomarkers for COPD.

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bioRxiv2024

Consensus Clustering for Robust Bioinformatics Analysis

Clustering plays an important role in a multitude of bioinformatics applications, including protein function prediction, population genetics, and gene expression analysis. The results of most clustering algorithms are sensitive to variations of the input data, the clustering algorithm and its parameters, and individual datasets. Consensus clustering (CC) is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under the above sources of variation. As part of CC, stability scores can provide an idea of the degree of reliability of the resulting clustering. This review structures the CC approaches in the literature into three principal types, introduces and illustrates the concept of stability scores, and illustrates the use of CC in applications to simulated and real-world gene expression datasets. Open-source R implementations for each of these CC algorithms are available in the GitHub repository: https://github.com/behnam-yousefi/ConsensusClustering

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bioRxiv2024

How do we respond to the next SARS CoV epidemic/pandemic? A bioinformatics approach with the promise of preventing or reducing the severity of future SARS CoV related pandemics

In this work, we develop a Bayesian weighted scheme to generate evolutionary lineages of a particular viral protein sequence of interest and through a process of clustering and choosing representative lineages from the different clusters according to an evolutionary fitness objective function, we demonstrate it is possible to have anticipated the emergence of the SARS-CoV 2 (2019) strain from the SARS-CoV 1(2004) strain and having shown this retrospectively, we discuss the possibility of applying this approach along with continuous genomic surveillance of SARS-CoVs to prevent or reduce severity of future SARS-CoV related pandemics by being prepared with broad neutralization strategies for anticipated future lineages of SARS-CoVs identified through bioinformatics approaches such as that reported in this work.

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bioRxiv2024

Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology

BackgroundCoronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has caused a global pandemic. Gastric cancer (GC) poses a great threat to peoples health, which is a high- risk factor for COVID-19. Previous studies have found some associations between GC and COVID-19, whereas the underlying molecular mechanisms are not well understood. MethodsWe used a bioinformatics and systems biology approach to investigate the relationship between GC and COVID-19. The gene expression profiles of COVID-19 (GSE196822) and GC (GSE179252) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the shared differentially expressed genes (DEGs) for GC and COVID-19, functional annotation, protein-protein interaction (PPI) network, hub genes, transcriptional regulatory networks and candidate drugs were analyzed. ResultsA total of 209 shared DEGs were identified to explore the linkages between COVID-19 and GC. Functional analyses showed that Immune-related pathway collectively participated in the development and progression of COVID-19 and GC. In addition, there are selected 10 hub genes including CDK1, KIF20A, TPX2, UBE2C, HJURP, CENPA, PLK1, MKI67, IFI6, and IFIT2. The transcription factor/gene and miRNA/gene interaction networks identified 38 transcription factors (TFs) and 234 miRNAs. More importantly, we identified ten potential therapeutic agents, including ciclopirox, resveratrol, etoposide, methotrexate, trifluridine, enterolactone, troglitazone, calcitriol, dasatinib and deferoxamine, some of which have been reported to improve and treat GC and COVID-19. This study also provides insight into the diseases most associated with mutual DEGs, which may provide new ideas for research on the treatment of COVID-19. ConclusionsThis research has the possibility to be contributed to effective therapeutic in COVID-19 and GC.

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