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bioRxiv2021

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the metabolic diseases during pregnancy. The identification of the central molecular mechanisms liable for the disease pathogenesis might lead to the advancement of new therapeutic options. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non GDM samples were analyzed with limma package. Gene ontology (GO) and REACTOME enrichment analysis were performed using ToppGene. Then we constructed the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA-hub gene network and TF-hub gene regulatory network by the miRNet database and NetworkAnalyst database. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up regulated and 430 down regulated genes. Biological process analysis of GO enrichment analysis showed these DEGs were mainly enriched in reproduction, nuclear outer membrane-endoplasmic reticulum membrane network, identical protein binding, cell adhesion, supramolecular complex and signaling receptor binding. Signaling pathway enrichment analysis indicated that these DEGs played a vital in cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3, and PRKCA in the center of the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. Our results indicated that HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3, and PRKCA could be the potential novel biomarkers for GDM diagnosis, prognosis and the promising therapeutic targets. The current might be essential to understanding the molecular mechanism of GDM initiation and development.

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bioRxiv2021

A Comprehensive Phylogenetic and Bioinformatics Survey of Lectins in the Fungal kingdom

Fungal lectins are a large family of glycan-binding proteins, with no enzymatic activity. They play fundamental biological roles in the interactions of fungi with their environment and are found in many different species throughout the fungal kingdom. In particular, their contribution to defence against feeders has been emphasized and extracellular lectins may be involved in the recognition of bacteria, fungal competitors and specific host plants. Their carbohydrate specificities and quaternary structures vary widely, but evidence for an evolutionary relationship within the different classes of lectins is provided by the high degree of amino acid sequence identity shared by the different fungal lectins. The UniLectin3D database contains 194 3D structures of fungal lectins, of which 129 are characterized with their carbohydrate ligand. UniLectin3D lectin classes from all origins were used to construct 107 lectin motifs in 26 folding configurations and to screen 1,223 species deposited in the genomic portal MycoCosm of the Joint Genome Institute. The resulting 33 485 protein sequences of putative lectins are organized in MycoLec, a publicly available and searchable database. The characterization of the lectin candidates in fungal genomes is based on systematic statistics regarding potential carbohydrate ligands, protein lengths, signal peptides, relative motif positions and amino acid compositions of fungal lectins. These results shed light on the evolution of the lectin gene families.

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bioRxiv2021

Bioinformatics analysis of SARS-CoV-2 RBD mutant variants and insights into antibody and ACE2 receptor binding

Prevailing COVID-19 vaccines are based on the spike protein of earlier SARS-CoV-2 strain that emerged in Wuhan, China. The continuously evolving nature of SARS-CoV-2 resulting emergence of new variants raises the risk of immune absconds. During the last few months, several RBD (receptor-binding domain) variants have been reported to affect the vaccine efficacy considerably. Soon after reporting of a new double mutant variant (L452R & E484Q) in India, the country facing a deadlier second wave of infections which prompts researchers to suspects this variant to be accountable. To address the relevant concerns about this new variant affecting vaccine efficacy, we performed molecular simulation dynamics based structural analysis of spike protein of double mutant (L452R & E484Q) along with K417G variants and earlier reported RBD variants and found structural changes in RBD region after comparing with the wild type. Comparison of the binding affinity of the double mutant and earlier reported RBD variant for ACE2 (angiotensin 2 altered enzymes) receptor and CR3022 antibody with the wild-type strain revealed the lowest binding affinity of the double mutant for CR3022 among all other variants. These findings suggest that the newly emerged double mutant could significantly reduce the impact of the current vaccine which threatens the protective efficacy of current vaccine therapy.

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bioRxiv2021

Identification and verification of the potential effect of therapeutic miRNA-mRNA pairs on ferroptosis in small cell lung cancer by bioinformatics analysis

Owing to the high mortality rate of small cell lung cancer (SCLC), it is essential to determine a novel therapeutic approach for treating patients with SCLC. miRNA is a type of non-coding RNA that plays a role in translational control. By applying this identity, it is applicable for treating patients using miRNA and nanotechnology. Ferroptosis is a newly discovered type of programmed cell death. Accumulated evidence suggests the possibility of using ferroptosis in treating patients with SCLC. Thus, identifying potential therapeutic miRNA-mRNA pairs that have an impact on ferroptosis will be valuable. In the in silico analysis, several Gene Expression Omnibus datasets were analyzed. The results were verified using other data. Here, we report that the miR-30 family and KIF11 pair have a crucial influence on SCLC cells, which may also affect ferroptosis.

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bioRxiv2021

Identification of biomarkers, pathways and potential therapeutic targets for heart failure using bioinformatics analysis

Heart failure (HF) is a complex cardiovascular diseases associated with high mortality. To discover key molecular changes in HF, we analyzed next-generation sequencing (NGS) data of HF. In this investigation, differentially expressed genes (DEGs) were analyzed using limma in R package from GSE161472 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network, miRNA-hub gene regulatory network and TF-hub gene regulatory network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), REACTOME pathway, STRING, HiPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, we performed receiver operating characteristic curve (ROC) analysis of hub genes. A total of 930 DEGs 9464 up regulated genes and 466 down regulated genes) were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections and the citric acid (TCA) cycle and respiratory electron transport. Subsequently, the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and 10 hub genes in these network were focused on by centrality analysis and module analysis. Furthermore, data showed that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B were good diagnostic values. In summary, this study suggests that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B may act as the key genes in HF.

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bioRxiv2021

EXPLORING HUB GENES IN LUNG CANCER USING INTEGRATED BIOINFORMATICS ANALYSIS

Malaysian has suffered from lung cancer, accounting for around 10% of all cancers reported in 2021. Prioritization of early diagnosis and natural treatments of lung cancer patients is crucial to improve their survival rate.The aim of this study is to identify the miRNA-mRNA and potential hub gene corresponding to lung carcinoma and natural drugs candidates to inhibit progression of lung cancer via insilico approaches.GSE176348,GSE85841 and GSE164750 gene expression profiles were retrieved from NCBI and GEO2R analysis was carried out accordingly to principal standard p<0.05 and logFC>1 to identify up-regulated and down-regulated genes from the datasets. Protein-protein interaction analysis of up-regulated and down-regulated genes was performed in Cytoscape software and top 10 hub genes were observed by cytohubba tools plugin in Cytoscape.Next, Biological Process (BP), cellular component (CC), molecular function (MF) of top 10 up regulated and down regulated hub genes were observed via DAVID server. The survival curves of top 10 up and down regulated hub genes were constructed by the Kaplan-Meier plotter and the expression level of hub genes in lung cancer tissues and normal tissues analyzed via GEPIA2 online database. IMPPAT: Indian Medicinal Plants, Phytochemistry And Therapeutics database was utilized for selection of potential natural drug candidates for lung cancer and ADME test was carried out on the selected drug compounds.To identify the most promising drug candidates,the hub genes were docked with respective natural drugs candidates via Swiss Dock online tool.Finally, The Encyclopedia of RNA Interactomes (ENCORI) database was used to study the MIRNA-MRNA network of targeted hub genes.In summary,this study is helpful in discovery of miRNAs-mRNA and analysis of natural drug candidates to inhibit lung carcinoma.

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bioRxiv2021

Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

Myocardial infarction (MI) is the leading cardiovascular diseases in worldwide, yet relatively little is known about the genes and signaling pathways involved in MI progression. The present investigation aimed to elucidate potential crucial candidate genes and pathways in MI. expression profiling by high throughput sequencing dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 20 MI samples and 12 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. These DEGs were subsequently investigated by Gene Ontology (GO) and pathway enrichment analysis, a protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed. Hub genes were validated by receiver operating characteristic curve (ROC) analysis. In total, 958 DEGs were identified, of which 480 were up regulated and 478 were down regulated. GO and pathway enrichment analysis results revealed that the DEGs were mainly enriched in, immune system, neuronal system, response to stimulus, and multicellular organismal process. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed by using Cytoscape software, and CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 were identified as the hub genes. Our results highlight the important roles of the genes including CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 in MI pathogenesis or therapeutic management.

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bioRxiv2021

Adding context to the pneumococcal core genesA bioinformatic analysis of the intergenic pangenome of Streptococcus pneumoniae

Whole genome sequencing offers great opportunities for linking genotypes to phenotypes aiding in our understanding of human disease and bacterial pathogenicity. However, these analyses often overlook non-coding intergenic regions (IGRs). By disregarding the IGRs, crucial information is lost, as genes have little biological function without expression. In this study, we present the first complete pangenome of the important human pathogen Streptococcus pneumoniae (pneumococcus), spanning both the genes and IGRs. We show that the pneumococcus species retains a small core genome of IGRs that are present across all isolates. Gene expression is highly dependent on these core IGRs, and often several copies of these core IGRs are found across each genome. Core genes and core IGRs show a clear linkage as 81% of core genes are associated with core IGRs. Additionally, we identify a single IGR within the core genome that is always occupied by one of two highly distinct sequences, scattered across the phylogenetic tree. Their distribution indicates that this IGR is transferred between isolates through horizontal regulatory transfer independent of the flanking genes and that each type likely serves different regulatory roles depending on their genetic context.

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bioRxiv2021

Identification of hub genes and pathways in nonalcoholic steatohepatitis by integrated bioinformatics analysis

BackgroundCurrently, non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases in the world. Forecasting the short-term, up to 2025, NASH due to fibrosis is one of the leading causes of liver transplantation. Cohort studies revealed that non-alcoholic steatohepatitis (NASH) has a higher risk of fibrosis progression among NAFLD patients. Identifying differentially expressed genes helps to determine NASH pathogenic pathways, make more accurate diagnoses, and prescribe appropriate treatment. Methods and ResultsIn this study, we found 11 NASH datasets by searching in the Gene Expression Omnibus (GEO) database. Subsequently, NASH datasets with low-quality control scores were excluded. Four datasets were analyzed with packages of R/Bioconductor. Then, all integrated genes were Imported into Cytoscape to illustrate the protein-protein interactions network. All hubs and nodes degree has been calculated to determine the hub genes with critical roles in networks.Possible correlations between expression profiles of mutual DEGs were identified employing Principal Component Analysis (PCA). Primary analyzed data were filtered based on gene expression (logFC > 1, logFC < -1) and adj-P-value (<0.05). Ultimately, among 379 DEGs, we selected the top 10 genes (MYC, JUN, EGR1, FOS, CCL2, IL1B, CXCL8, PTGS2, IL6, SERPINE1) as candidates among up and down regulated genes, and critical pathways such as IL-6, IL-17, TGF {beta}, and TNF were identified. ConclusionThe present study suggests an important DEGs, biological processes, and critical pathways involved in the pathogenesis of NASH disease. Further investigations are needed to clarify the exact mechanisms underlying the development and progression of NASH disease.

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bioRxiv2021

Identification of antineoplastic agents for oral squamous cell carcinoma: an integrated bioinformatics approach using differential gene expression and network biology

Oral squamous cell carcinoma (OSCC) is the most common malignant epithelial neoplasm and anatomical subtype of head and neck squamous cell carcinoma (HNSCC) with an average 5-year survival rate of less than 50%. To improve the survival rate of OSCC, the discovery of novel anti-cancer drugs is urgently needed. In the present study, we performed metanalysis of 5 gene expression datasets (GSE23558, GSE25099, GSE30784, GSE37991 and TCGA-OSCC) that resulted in 1851 statistically significant DEGs in OSCC. The DEGs were involved in key biological pathways that drive the progression of OSCC. A comprehensive protein-protein interaction (PPI) network was constructed from the DEGs and the top protein clusters (modules) were extracted in Cytoscape. The DEGs from the top modules were searched for antineoplastic agents using L1000CDS2 server. The search resulted in a total of 37 perturbing agents from which 12 well-characterized antineoplastic agents were selected. The selected 12 antineoplastic agents namely Teniposide, Palbociclib, Etoposide, Fedratinib, Tivozanib, Afatinib, Vemurafenib, Mitoxantrone, Idamycin, Canertinib, Dovitinib and Selumetinib. These drugs showed interactions with the over expressed hub genes that regulate cellular proliferation and growth in OSCC progression. These identified antineoplastic agents are candidates for their potential role in treating OSCC.

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bioRxiv2021

CRAFT: a bioinformatics software for custom prediction of circular RNA functions

Circular RNAs (circRNAs), transcripts generated by backsplicing, are particularly stable and pleiotropic molecules, whose dysregulation drives human diseases and cancer by modulating gene expression and signaling pathways. CircRNAs can regulate cellular processes by different mechanisms, including interaction with microRNAs (miRNAs) and RNA-binding proteins (RBP), and encoding specific peptides. The prediction of circRNA functions is instrumental to interpret their impact in diseases, and to prioritize circRNAs for functional investigation. Currently, circRNA functional predictions are provided by web databases that do not allow custom analyses, while self-standing circRNA prediction tools are mostly limited to predict only one type of function, mainly focusing on the miRNA sponge activity of circRNAs. To solve these issues, we developed CRAFT (CircRNA Function prediction Tool), a freely available computational pipeline that predicts circRNA sequence and molecular interactions with miRNAs and RBP, along with their coding potential. Analysis of a set of circRNAs with known functions has been used to appraise CRAFT predictions and to optimize its setting. CRAFT provides a comprehensive graphical visualization of the results, links to several knowledge databases, and extensive functional enrichment analysis. Moreover, it originally combines the predictions for different circRNAs. CRAFT is a useful tool to help the user explore the potential regulatory networks involving the circRNAs of interest and generate hypotheses about the cooperation of circRNAs into the modulation of biological processes. Key pointsO_LICRAFT is a self standing tool for comprehensive circRNA function prediction. C_LIO_LICRAFT functions include circRNA sequence reconstruction, microRNA and RNA-binding protein response elements and coding potential prediction. C_LIO_LIPredictions for multiple circRNAs are connected to infer possible cooperation networks and illustrate the potential impact of circRNAs on biological and disease processes. C_LI

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bioRxiv2021

CaveCrawler: An interactive analysis suite for cavefish bioinformatics

The growing use of genomics data in diverse animal models provides the basis for identifying genomic and transcriptional differences across species and contexts. Databases containing genomic and functional data have played critical roles in the development of numerous genetic models but are lacking for most emerging models of evolution. There is a rapidly expanding use of genomic, transcriptional, and functional genetic approaches to study diverse traits of the Mexican tetra, Astyanax mexicanus. This species exists as two morphs, eyed surface populations and at least 30 blind cave populations, providing a system to study convergent evolution. We have generated a web-based analysis suite that integrates datasets from different studies to identify how gene transcription and genetic markers of selection differ between populations and across experimental contexts. Results can be processed with other analysis platforms including Gene Ontology (GO) to enable biological inference from cross-study patterns and identify future avenues of research. Furthermore, the framework that we have built A. mexicanus can readily applied to other emerging model systems.

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bioRxiv2022

Bioinformatics Analysis of the Potentially Functional circRNA-miRNA-mRNA Network in Breast Cancer

ObjectiveBreast cancer (BC) is a heterogeneous type of cancer that occurs as a result of distinct molecular alterations in breast tissue. Although there are many new developments in treatment and targeted therapy for BC in recent years, this cancer type is still the most common one among women with high morbidity and mortality. Therefore, new research is still needed for biomarker detection. MethodsGSE101124 and GSE182471 datasets were obtained from Gene Expression Omnibus (GEO) database to evaluate differentially expressed circular RNAs (circRNAs). The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases were used to identify the significantly dysregulated microRNAs (miRNAs) and genes considering the Prediction Analysis of Microarray (PAM50) classification. The circRNA-miRNA-gene relationship was investigated using the Cancer Specific CircRNA (v2.0) (CSCD), miRDB, miRWalk and miRTarBase databases. The circRNA-miRNA-mRNA regulatory network was constructed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) annotation. The protein-protein interaction network was constructed by the STRING 2021 database and visualized by the Cytoscape tool (v3.9.0). Then, raw miRNA data and genes were filtered using some selection criteria according to a specific expression level in PAM50 subgroups. A bottleneck method was utilized to obtain highly interacted hub genes using cytoHubba Cytoscape plugin. The overall survival (OS) and disease-free survival (DFS) analysis were performed for these hub genes, which are detected within the miRNA and circRNA axis in our study. ResultsWe identified three circRNAs, three miRNAs, and eighteen candidate target genes that may play an important role in BC. In addition, it has been determined that these molecules can be useful in the classification of BC, especially in determining the basal-like breast cancer (BLBC) subtype. ConclusionsWe conclude that hsa_circ_0000515/ miR-486-5p/ SDC1 axis may be an important biomarker candidate in distinguishing patients in the BLBC group, especially according to the PAM50 classification of BC.

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bioRxiv2022

UbE3-APA: A Bioinformatic Strategy to Elucidate Ubiquitin E3 Ligase Activities in Quantitative Proteomics Study

MotivationUbiquitination is widely involved in protein homeostasis and cell signaling. Ubiquitin E3 ligases are critical regulators of ubiquitination that recognize and recruit specific ubiquitination targets for the final rate-limiting step of ubiquitin transfer reactions. Understanding the ubiquitin E3 ligase ac-tivities will provide knowledge in the upstream regulator of the ubiquitination pathway and reveal po-tential mechanisms in biological processes and disease progression. Recent advances in mass spec-trometry-based proteomics have enabled deep profiling of ubiquitylome in a quantitative manner. Yet, functional analysis of ubiquitylome dynamics and pathway activity remains challenging. ResultsHere, we developed a UbE3-APA, a computational algorithm and stand-alone python-based software for Ub E3 ligase Activity Profiling Analysis. Combining an integrated annotation database with statistical analysis, UbE3-APA identifies significantly activated or suppressed E3 ligases based on quantitative ubiquitylome proteomics datasets. Benchmarking the software with published quantitative ubiquitylome analysis confirms the genetic manipulation of SPOP enzyme activity through overexpres-sion and mutation. Application of the algorithm in the re-analysis of a large cohort of ubiquitination proteomics study revealed the activation of PARKIN and the co-activation of other E3 ligases in mito-chondria depolarization-induced mitophagy process. We further demonstrated the application of the algorithm in the DIA-based quantitative ubiquitylome analysis. AvailabilitySource code and binaries are freely available for download at URL: https://github.com/Chenlab-UMN/Ub-E3-ligase-Activity-Profiling-Analysis, implemented in python and supported on Linux and MS Windows Contactyuechen@umn.edu Supplementary informationSupplementary data are available.

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bioRxiv2022

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: A bioinformatics analytic workflow

IntroductionType 2 diabetes (T2D) has a complex etiology which is not fully elucidated. Identification of gene perturbations and hub genes of T2D may assist in personalizing care. ObjectivesWe aimed to identify highly perturbed genes and hub genes associated with T2D in different tissues of adult humans via an extensive workflow. MethodsWorkflow comprised five sequential steps: systematic review of NCBI GEO database; identification and classification of differentially expressed genes (DEG); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; downstream analyses. Three meta-analytic strategies: random effects model (REM); vote counting approach (VC); p-value combining approach (CA), were applied. Nodes having above average betweenness, closeness, and degree in the network were defined as hub genes. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19 related genes, and Genotype-Tissue Expression profiles. ResultsAnalysis of 27 eligible microarrays identified 6284 DEG (4592 down-regulated and 1692 up-regulated) within four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Meta-analysis of DEG identified 49, 27, and 8 highly perturbed genes via REM, VC, and CA, respectively, producing a compiled set of 79 highly perturbed (41 down-regulated and 38 up-regulated) genes. The 28 hub genes comprised 13 up-regulated, 9 down-regulated, and 6 predicted genes. Downstream analyses identified enrichments of: shared genes with other diabetes phenotypes; insulin synthesis and action related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways, COVID-19 related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. ConclusionsWe identified highly perturbed genes and hub genes of T2D and revealed their associations with other diabetes phenotypes and COVID-19 as well as pathophysiological manifestations such as those related to insulin, immunity, and apoptosis. Broader utility of the proposed pipeline is envisaged.

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bioRxiv2022

Bioinformatic analysis of B and T cell epitopes from SARS-CoV-2 Spike, Membrane and Nucleocapsid proteins as a strategy to assess possible cross-reactivity between emerging variants, including Omicron, and other human coronaviruses

The COVID-19 pandemic caused by SARS-CoV-2 produced a global health emergency since December 2019, that up to the end of January 2022 had caused the death of more than 5.6 million people worldwide. Despite emergence of new variants of concern, vaccination remains one of the most important tools to control the pandemic. All approved vaccines and most of the vaccine candidates use the spike protein of the virus as a target antigen to induce protective immune responses. Several variants of the virus present key mutations in this protein which render the virus, at different rates, to evade the neutralizing antibody response. Although experimental evidence suggests that cross-reactive responses between coronaviruses are present in the population, it is unknown which potential antigens shared between different coronaviruses could be responsible for these responses. This study provides predictions of new potential B and T cell epitopes within SARS-CoV-2 Spike (S), Membrane (M) and Nucleocapsid (N) proteins together with a review of the reported B epitopes of these proteins. We also analyse amino acid changes present in the epitopes of variants of concern (VOC) and variants being monitored (VBM), and how these might affect the immune response, as these changes may alter the peptides immunogenicity index and the antigen presentation by related HLA alleles. Finally, given these observations, we performed an identity analysis between the repertoire of potential epitopes of SARS-CoV-2 and other human coronaviruses to identify which are conserved among them. The results shown here together with the published experimental evidence, allow us to support the hypothesis that antibody and T cell cross-reactive responses to common coronaviruses epitopes, could contribute to broaden the protective response to SARS-CoV-2 and its variants. This evidence could help not only to understand cross-reactive responses among coronaviruses but also contribute to elucidate their role in immunity to SARS-CoV-2 induced by infection and/or vaccination. Finally, these findings could promote targeted analysis of antigen-specific immune responses and might orient and drive the rational development of new SARS-CoV-2 vaccines including candidates that ideally provide "universal" protection against other coronaviruses relevant to human health.

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bioRxiv2022

An assessment of bioinformatics tools for the detection of human endogenous retroviral insertions in short-read genome sequencing data

There is a growing interest in the study of human endogenous retroviruses (HERVs) given the substantial body of evidence that implicates them in many human diseases. Although their genomic characterization presents numerous technical challenges, next-generation sequencing (NGS) has shown potential to detect HERV insertions and their polymorphisms in humans, and a number of computational tools to detect them in short-read NGS data exist. In order to design optimal analysis pipelines, an independent evaluation of the currently available tools is required. We evaluated the performance of a set of such tools using a variety of experimental designs and types of NGS datasets. These included 50 human short read whole-genome sequencing samples, matching long and short read NGS data, and simulated short-read NGS data. Our results highlight the performance variability of the tools across the datasets and suggest that different tools might be suitable for different study designs. Using multiple tools and a consensus approach is advisable if computationally feasible and wet-lab validation via PCR is advisable where biological samples are available.

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bioRxiv2022

Bioinformatics and next generation sequencing data analysis to identify key genes and pathways influencing in Parkinson's disease

Parkinsons disease (PD) is the most commonly diagnosed neurodegenerative disorder Identification of novel prognostic and pathogenesis biomarkers plays a pivotal role in the management of the PD. Next generation sequencing (NGS) dataset from the GEO (Gene Expression Omnibus) database were used to identify differentially expressed genes (DEGs) in PD. Gene Ontology (GO) and REACTOME pathway enrichmental analyses were performed to elucidate the functional roles of the DEGs. Protein-protein interaction (PPI), modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were established. The receiver operating characteristic curve (ROC) analysis was used to explore the diagnostic values of hub genes in PD. In total, 957 DEGs were identified, of which 478 were up regulated genes and 479 were down regulated genes. GO and pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in nervous system development, cell junction, transporter activity and neuronal system, whereas down regulated genes were mainly enriched in response to stimulus, cell periphery, identical protein binding and immune system. The top hub genes in the constructed PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were OTUB1, PPP2R1A, AP2M1, PIN1, USP11, CDK2, IQGAP1, NEDD4, VIM and CDK1. Furthermore, ROC analysis showed that hub genes were having good diagnostic values. We identified a series of essential genes along with the pathways that were most closely related with PD initiation and progression. Our results provide a more detailed molecular mechanism for the advancement of PD, shedding light on the potential biomarkers and therapeutic targets.

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