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1.
Front Immunol ; 13: 975848, 2022.
Article in English | MEDLINE | ID: covidwho-2142004

ABSTRACT

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.


Subject(s)
COVID-19 , Sepsis , Biomarkers , Computational Biology/methods , Critical Illness , Cytokines/genetics , Emetine , Gene Expression Profiling/methods , Humans , Molecular Docking Simulation , NF-kappa B/genetics , Progesterone , Receptors, Cytokine/genetics , SARS-CoV-2 , Sepsis/genetics , Sepsis/metabolism
2.
J Vis Exp ; (188)2022 Oct 21.
Article in English | MEDLINE | ID: covidwho-2110320

ABSTRACT

Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as regulators of various biological processes. Notably, emerging evidence demonstrates that host circRNAs can be differentially expressed (DE) upon infection with pathogens (e.g., influenza and coronaviruses), suggesting a role for circRNAs in regulating host innate immune responses. However, investigations on the role of circRNAs during pathogenic infections are limited by the knowledge and skills required to carry out the necessary bioinformatic analysis to identify DE circRNAs from RNA sequencing (RNA-seq) data. Bioinformatics prediction and identification of circRNAs is crucial before any verification, and functional studies using costly and time-consuming wet-lab techniques. To solve this issue, a step-by-step protocol of in silico prediction and characterization of circRNAs using RNA-seq data is provided in this manuscript. The protocol can be divided into four steps: 1) Prediction and quantification of DE circRNAs via the CIRIquant pipeline; 2) Annotation via circBase and characterization of DE circRNAs; 3) CircRNA-miRNA interaction prediction through Circr pipeline; 4) functional enrichment analysis of circRNA parental genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). This pipeline will be useful in driving future in vitro and in vivo research to further unravel the role of circRNAs in host-pathogen interactions.


Subject(s)
MicroRNAs , RNA, Circular , RNA, Circular/genetics , Sequence Analysis, RNA , MicroRNAs/genetics , Computational Biology/methods , Host-Pathogen Interactions/genetics , Gene Expression Profiling/methods
3.
Int J Mol Sci ; 23(22)2022 Nov 17.
Article in English | MEDLINE | ID: covidwho-2116050

ABSTRACT

MSClustering is an efficient software package for visualizing and analyzing complex networks in Cytoscape. Based on the distance matrix of a network that it takes as input, MSClustering automatically displays the minimum span clustering (MSC) of the network at various characteristic levels. To produce a view of the overall network structure, the app then organizes the multi-level results into an MSC tree. Here, we demonstrate the package's phylogenetic applications in studying the evolutionary relationships of complex systems, including 63 beta coronaviruses and 197 GPCRs. The validity of MSClustering for large systems has been verified by its clustering of 3481 enzymes. Through an experimental comparison, we show that MSClustering outperforms five different state-of-the-art methods in the efficiency and reliability of their clustering.


Subject(s)
Computational Biology , Software , Computational Biology/methods , Phylogeny , Reproducibility of Results , Cluster Analysis
4.
Front Immunol ; 13: 1008653, 2022.
Article in English | MEDLINE | ID: covidwho-2119881

ABSTRACT

Background: The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body's ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients. Methods: COVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the "Deseq2" package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the "limma" package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification. Results: In this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients. Conclusion: In this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.


Subject(s)
COVID-19 , HIV Infections , Humans , Transcriptome , COVID-19/genetics , Leukocytes, Mononuclear , Computational Biology/methods , SARS-CoV-2 , Gene Expression Profiling/methods , HIV Infections/drug therapy , HIV Infections/genetics
5.
Immunobiology ; 227(6): 152287, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2105123

ABSTRACT

BACKGROUND: Epitope selection is the key to peptide vaccines development. Bioinformatics tools can efficiently improve the screening of antigenic epitopes and help to choose the right ones. OBJECTIVE: To predict, synthesize and testify peptide epitopes at spike protein, assess the effect of mutations on epitope humoral immunity, thus provide clues for the design and development of epitope peptide vaccines against SARS-CoV-2. METHODS: Bioinformatics servers and immunological tools were used to identify the helper T lymphocyte, cytotoxic T lymphocyte, and linear B lymphocyte epitopes on the S protein of SARS-CoV-2. Physicochemical properties of candidate epitopes were analyzed using IEDB, VaxiJen, and AllerTOP online software. Three candidate epitopes were synthesized and their antigenic responses were evaluated by binding antibody detection. RESULTS: A total of 20 antigenic, non-toxic and non-allergenic candidate epitopes were identified from 1502 epitopes, including 6 helper T-cell epitopes, 13 cytotoxic T-cell epitopes, and 1 linear B cell epitope. After immunization with antigen containing candidate epitopes S206-221, S403-425, and S1157-1170 in rabbits, the binding titers of serum antibody to the corresponding peptide, S protein, receptor-binding domain protein were (415044, 2582, 209.3), (852819, 45238, 457767) and (357897, 10528, 13.79), respectively. The binding titers to Omicron S protein were 642, 12,878 and 7750, respectively, showing that N211L, DEL212 and K417N mutations cause the reduction of the antibody binding activity. CONCLUSIONS: Bioinformatic methods are effective in peptide epitopes design. Certain mutations of the Omicron would lead to the loss of antibody affinity to Omicron S protein.


Subject(s)
COVID-19 , Viral Vaccines , Animals , Humans , Rabbits , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Computational Biology/methods , Epitopes, T-Lymphocyte/genetics , COVID-19 Vaccines/genetics , Immunity, Humoral , Epitopes, B-Lymphocyte/genetics , Vaccines, Subunit , Peptides
6.
Genes (Basel) ; 13(11)2022 10 28.
Article in English | MEDLINE | ID: covidwho-2090055

ABSTRACT

Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models.


Subject(s)
COVID-19 , Pandemics , Humans , COVID-19/genetics , Cluster Analysis , Algorithms , Computational Biology/methods
7.
BMC Med Genomics ; 15(Suppl 2): 94, 2022 04 23.
Article in English | MEDLINE | ID: covidwho-2089198

ABSTRACT

BACKGROUND: MicroRNAs (miRNAs) are a class of small non-coding RNA that can downregulate their targets by selectively binding to the 3' untranslated region (3'UTR) of most messenger RNAs (mRNAs) in the human genome. MiRNAs can interact with other molecules such as viruses and act as a mediator for viral infection. In this study, we examined whether, and to what extent, the SARS-CoV-2 virus can serve as a "sponge" for human miRNAs. RESULTS: We identified multiple potential miRNA/target pairs that may be disrupted during SARS-CoV-2 infection. Using miRNA expression profiles and RNA-seq from published studies, we further identified a highly confident list of 5 miRNA/target pairs that could be disrupted by the virus's miRNA sponge effect, namely hsa-miR-374a-5p/APOL6, hsa-let-7f-1-3p/EIF4A2, hsa-miR-374a-3p/PARP11, hsa-miR-548d-3p/PSMA2 and hsa-miR-23b-3p/ZNFX1 pairs. Using single-cell RNA-sequencing based data, we identified two important miRNAs, hsa-miR-302c-5p and hsa-miR-16-5p, to be potential virus targeting miRNAs across multiple cell types from bronchoalveolar lavage fluid samples. We further validated some of our findings using miRNA and gene enrichment analyses and the results confirmed with findings from previous studies that some of these identified miRNA/target pairs are involved in ACE2 receptor network, regulating pro-inflammatory cytokines and in immune cell maturation and differentiation. CONCLUSION: Using publicly available databases and patient-related expression data, we found that acting as a "miRNA sponge" could be one explanation for SARS-CoV-2-mediated pathophysiological changes. This study provides a novel way of utilizing SARS-CoV-2 related data, with bioinformatics approaches, to help us better understand the etiology of the disease and its differential manifestation across individuals.


Subject(s)
COVID-19 , MicroRNAs , SARS-CoV-2 , 3' Untranslated Regions , COVID-19/genetics , Computational Biology/methods , Humans , MicroRNAs/genetics , MicroRNAs/metabolism , RNA, Messenger/genetics , SARS-CoV-2/genetics , SARS-CoV-2/metabolism
8.
Curr Protoc ; 2(10): e534, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2059363

ABSTRACT

Genome sequencing holds the promise for great public health benefits. It is currently being used in the context of rare disease diagnosis and novel gene identification, but also has the potential to identify genetic disease risk factors in healthy individuals. Genome sequencing technologies are currently being used to identify genetic factors that may influence variability in symptom severity and immune response among patients infected by SARS-CoV-2. The GENCOV study aims to look at the relationship between genetic, serological, and biochemical factors and variability of SARS-CoV-2 symptom severity, and to evaluate the utility of returning genome screening results to study participants. Study participants select which results they wish to receive with a decision aid. Medically actionable information for diagnosis, disease risk estimation, disease prevention, and patient management are provided in a comprehensive genome report. Using a combination of bioinformatics software and custom tools, this article describes a pipeline for the analysis and reporting of genetic results to individuals with COVID-19, including HLA genotyping, large-scale continental ancestry estimation, and pharmacogenomic analysis to determine metabolizer status and drug response. In addition, this pipeline includes reporting of medically actionable conditions from comprehensive gene panels for Cardiology, Neurology, Metabolism, Hereditary Cancer, and Hereditary Kidney, and carrier screening for reproductive planning. Incorporated into the genome report are polygenic risk scores for six diseases-coronary artery disease; atrial fibrillation; type-2 diabetes; and breast, prostate, and colon cancer-as well as blood group genotyping analysis for ABO and Rh blood types and genotyping for other antigens of clinical relevance. The genome report summarizes the findings of these analyses in a way that extensively communicates clinically relevant results to patients and their physicians. © 2022 Wiley Periodicals LLC. Basic Protocol 1: HLA genotyping and disease association Basic Protocol 2: Large-scale continental ancestry estimation Basic Protocol 3: Dosage recommendations for pharmacogenomic gene variants associated with drug response Support Protocol: System setup.


Subject(s)
Blood Group Antigens , COVID-19 , COVID-19/genetics , Computational Biology/methods , Genomics , Humans , Male , SARS-CoV-2/genetics
9.
Front Immunol ; 13: 950076, 2022.
Article in English | MEDLINE | ID: covidwho-2022732

ABSTRACT

Renal injury secondary to COVID-19 is an important factor for the poor prognosis of COVID-19 patients. The pathogenesis of renal injury caused by aberrant immune inflammatory of COVID-19 remains unclear. In this study, a total of 166 samples from 4 peripheral blood transcriptomic datasets of COVID-19 patients were integrated. By using the weighted gene co-expression network (WGCNA) algorithm, we identified key genes for mild, moderate, and severe COVID-19. Subsequently, taking these genes as input genes, we performed Short Time-series Expression Miner (STEM) analysis in a time consecutive ischemia-reperfusion injury (IRI) -kidney dataset to identify genes associated with renal injury in COVID-19. The results showed that only in severe COVID-19 there exist a small group of genes associated with the progression of renal injury. Gene enrichment analysis revealed that these genes are involved in extensive immune inflammation and cell death-related pathways. A further protein-protein interaction (PPI) network analysis screened 15 PPI-hub genes: ALOX5, CD38, GSF3R, LGR, RPR1, HCK, ITGAX, LYN, MAPK3, NCF4, SELP, SPI1, WAS, TLR2 and TLR4. Single-cell sequencing analysis indicated that PPI-hub genes were mainly distributed in neutrophils, macrophages, and dendritic cells. Intercellular ligand-receptor analysis characterized the activated ligand-receptors between these immune cells and parenchyma cells in depth. And KEGG enrichment analysis revealed that viral protein interaction with cytokine and cytokine receptor, necroptosis, and Toll-like receptor signaling pathway may be potentially essential for immune cell infiltration leading to COVID-19 renal injury. Finally, we validated the expression pattern of PPI-hub genes in an independent data set by random forest. In addition, we found that the high expression of these genes was correlated with a low glomerular filtration rate. Including them as risk genes in lasso regression, we constructed a Nomogram model for predicting severe COVID-19. In conclusion, our study explores the pathogenesis of renal injury promoted by immunoinflammatory in severe COVID-19 and extends the clinical utility of its key genes.


Subject(s)
COVID-19 , Computational Biology , Biomarkers, Tumor/genetics , COVID-19/genetics , Computational Biology/methods , Humans , Kidney/pathology , Ligands
10.
Front Immunol ; 13: 918692, 2022.
Article in English | MEDLINE | ID: covidwho-2022707

ABSTRACT

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has created an urgent global situation. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability and disease comorbidities. The pandemic continues to spread worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against COVID-19. However, the precise role of SARS-CoV-2 in the pathophysiology of the nasopharyngeal tract (NT) is still unfathomable. This study utilized machine learning approaches to analyze 22 RNA-seq data from COVID-19 patients (n = 8), recovered individuals (n = 7), and healthy individuals (n = 7) to find disease-related differentially expressed genes (DEGs). We compared dysregulated DEGs to detect critical pathways and gene ontology (GO) connected to COVID-19 comorbidities. We found 1960 and 153 DEG signatures in COVID-19 patients and recovered individuals compared to healthy controls. In COVID-19 patients, the DEG-miRNA, and DEG-transcription factors (TFs) interactions network analysis revealed that E2F1, MAX, EGR1, YY1, and SRF were the highly expressed TFs, whereas hsa-miR-19b, hsa-miR-495, hsa-miR-340, hsa-miR-101, and hsa-miR-19a were the overexpressed miRNAs. Three chemical agents (Valproic Acid, Alfatoxin B1, and Cyclosporine) were abundant in COVID-19 patients and recovered individuals. Mental retardation, mental deficit, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs. Finally, the detected DEGs mediated by TFs and miRNA expression indicated that SARS-CoV-2 infection might contribute to various comorbidities. Our results provide the common DEGs between COVID-19 patients and recovered humans, which suggests some crucial insights into the complex interplay between COVID-19 progression and the recovery stage, and offer some suggestions on therapeutic target identification in COVID-19 caused by the SARS-CoV-2.


Subject(s)
COVID-19 , MicroRNAs , Biomarkers , COVID-19/drug therapy , COVID-19/genetics , Computational Biology/methods , Gene Expression Profiling , Humans , Machine Learning , MicroRNAs/genetics , MicroRNAs/metabolism , Pandemics , SARS-CoV-2
11.
Sci Rep ; 12(1): 14372, 2022 08 23.
Article in English | MEDLINE | ID: covidwho-2016820

ABSTRACT

Improvements in cost and speed of next generation sequencing (NGS) have provided a new pathway for delivering disease diagnosis, molecular typing, and detection of antimicrobial resistance (AMR). Numerous published methods and protocols exist, but a lack of harmonisation has hampered meaningful comparisons between results produced by different methods/protocols vital for global genomic diagnostics and surveillance. As an exemplar, this study evaluated the sensitivity and specificity of five well-established in-silico AMR detection software where the genotype results produced from running a panel of 436 Escherichia coli were compared to their AMR phenotypes, with the latter used as gold-standard. The pipelines exploited previously known genotype-phenotype associations. No significant differences in software performance were observed. As a consequence, efforts to harmonise AMR predictions from sequence data should focus on: (1) establishing universal minimum to assess performance thresholds (e.g. a control isolate panel, minimum sensitivity/specificity thresholds); (2) standardising AMR gene identifiers in reference databases and gene nomenclature; (3) producing consistent genotype/phenotype correlations. The study also revealed limitations of in-silico technology on detecting resistance to certain antimicrobials due to lack of specific fine-tuning options in bioinformatics tool or a lack of representation of resistance mechanisms in reference databases. Lastly, we noted user friendliness of tools was also an important consideration. Therefore, our recommendations are timely for widespread standardisation of bioinformatics for genomic diagnostics and surveillance globally.


Subject(s)
Anti-Bacterial Agents , Escherichia coli Infections , Anti-Bacterial Agents/pharmacology , Computational Biology/methods , Drug Resistance, Bacterial/genetics , Escherichia coli , Escherichia coli Infections/diagnosis , Escherichia coli Infections/epidemiology , Escherichia coli Infections/genetics , High-Throughput Nucleotide Sequencing , Humans , Microbial Sensitivity Tests
12.
Math Biosci Eng ; 19(11): 11172-11194, 2022 08 04.
Article in English | MEDLINE | ID: covidwho-1997893

ABSTRACT

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also known as COVID-19, is currently prevalent worldwide and poses a significant threat to human health. Individuals with cancer may have an elevated risk for SARS-CoV-2 infections and adverse outcomes. Therefore, it is necessary to explore the internal relationship between these two diseases. In this study, transcriptome analyses were performed to detect mutual pathways and molecular biomarkers in three types of common cancers of the breast, liver, colon, and COVID-19. Such analyses could offer a valuable understanding of the association between COVID-19 and cancer patients. In an analysis of RNA sequencing datasets for three types of cancers and COVID-19, we identified a sum of 38 common differentially expressed genes (DEGs). A variety of combinational statistical approaches and bioinformatics techniques were utilized to generate the protein-protein interaction (PPI) network. Subsequently, hub genes and critical modules were found using this network. In addition, a functional analysis was conducted using ontologies keywords, and pathway analysis was also performed. Some common associations between cancer and the risk and prognosis of COVID-19 were discovered. The datasets also revealed transcriptional factors-gene interplay, protein-drug interaction, and a DEGs-miRNAs coregulatory network with common DEGs. The potential medications discovered in this investigation could be useful in treating cancer and COVID-19.


Subject(s)
COVID-19 , MicroRNAs , Neoplasms , COVID-19/epidemiology , Computational Biology/methods , Humans , Neoplasms/epidemiology , Neoplasms/genetics , SARS-CoV-2
13.
Genes (Basel) ; 13(8)2022 07 26.
Article in English | MEDLINE | ID: covidwho-1957260

ABSTRACT

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a newly emerging virus well known as the major cause of the worldwide pandemic due to Coronavirus Disease 2019 (COVID-19). Major breakthroughs in the Next Generation Sequencing (NGS) field were elucidated following the first release of a full-length SARS-CoV-2 genome on the 10 January 2020, with the hope of turning the table against the worsening pandemic situation. Previous studies in respiratory virus characterization require mapping of raw sequences to the human genome in the downstream bioinformatics pipeline as part of metagenomic principles. Illumina, as the major player in the NGS arena, took action by releasing guidelines for improved enrichment kits called the Respiratory Virus Oligo Panel (RVOP) based on a hybridization capture method capable of capturing targeted respiratory viruses, including SARS-CoV-2; therefore, allowing a direct map of raw sequences data to SARS-CoV-2 genome in downstream bioinformatics pipeline. Consequently, two bioinformatics pipelines emerged with no previous studies benchmarking the pipelines. This study focuses on gaining insight and understanding of target enrichment workflow by Illumina through the utilization of different bioinformatics pipelines named as 'Fast Pipeline' and 'Normal Pipeline' to SARS-CoV-2 strains isolated from Yogyakarta and Central Java, Indonesia. Overall, both pipelines work well in the characterization of SARS-CoV-2 samples, including in the identification of major studied nucleotide substitutions and amino acid mutations. A higher number of reads mapped to the SARS-CoV-2 genome in Fast Pipeline and merely were discovered as a contributing factor in a higher number of coverage depth and identified variations (SNPs, insertion, and deletion). Fast Pipeline ultimately works well in a situation where time is a critical factor. On the other hand, Normal Pipeline would require a longer time as it mapped reads to the human genome. Certain limitations were identified in terms of pipeline algorithm, whereas it is highly recommended in future studies to design a pipeline in an integrated framework, for instance, by using NextFlow, a workflow framework to combine all scripts into one fully integrated pipeline.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/virology , Computational Biology/methods , Genome, Viral , High-Throughput Nucleotide Sequencing/methods , Humans , SARS-CoV-2/genetics
14.
Mol Biol Rep ; 49(6): 5325-5340, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1955989

ABSTRACT

Saffron is a unique plant in many aspects, and its cellular processes are regulated at multiple levels. The genetic makeup in the form of eight chromosome triplets (2n = 3x = 24) with a haploid genetic content (genome size) of 3.45 Gbp is decoded into different types of RNA by transcription. The RNA then translates into peptides and functional proteins, sometimes involving post-translational modifications too. The interactions of the genome, transcriptome, proteome and other regulatory molecules ultimately result in the complex set of primary and secondary metabolites of saffron metabolome. These complex interactions manifest in the form of a set of traits 'phenome' peculiar to saffron. The phenome responds to the environmental changes occurring in and around saffron and modify its response in respect of growth, development, disease response, stigma quality, apocarotenoid biosynthesis, and other processes. Understanding these complex relations between different yet interconnected biological activities is quite challenging in saffron where classical genetics has a very limited role owing to its sterility, and the absence of a whole-genome sequence. Omics-based technologies are immensely helpful in overcoming these limitations and developing a better understanding of saffron biology. In addition to creating a comprehensive picture of the molecular mechanisms involved in apocarotenoid synthesis, stigma biogenesis, corm activity, and flower development, omics-technologies will ultimately lead to the engineering of saffron plants with improved phenome.


Subject(s)
Crocus , Computational Biology/methods , Crocus/metabolism , RNA/metabolism , Transcriptome/genetics
15.
PLoS One ; 17(6): e0254736, 2022.
Article in English | MEDLINE | ID: covidwho-1933199

ABSTRACT

In bioinformatics, alignment is an essential technique for finding similarities between biological sequences. Usually, the alignment is performed with the Smith-Waterman (SW) algorithm, a well-known sequence alignment technique of high-level precision based on dynamic programming. However, given the massive data volume in biological databases and their continuous exponential increase, high-speed data processing is necessary. Therefore, this work proposes a parallel hardware design for the SW algorithm with a systolic array structure to accelerate the forward and backtracking steps. For this purpose, the architecture calculates and stores the paths in the forward stage for pre-organizing the alignment, which reduces the complexity of the backtracking stage. The backtracking starts from the maximum score position in the matrix and generates the optimal SW sequence alignment path. The architecture was validated on Field-Programmable Gate Array (FPGA), and synthesis analyses have shown that the proposed design reaches up to 79.5 Giga Cell Updates per Second (GCPUS).


Subject(s)
Algorithms , Computational Biology , Computational Biology/methods , Databases, Factual , Equipment Design , Sequence Alignment , Software
16.
Viruses ; 14(7)2022 Jun 23.
Article in English | MEDLINE | ID: covidwho-1911647

ABSTRACT

Coronavirus disease 2019 (COVID-19) is caused by the SARS-CoV-2 virus, responsible for an atypical pneumonia that can progress to acute lung injury. MicroRNAs are small non-coding RNAs that control specific genes and pathways. This study evaluated the association between circulating miRNAs and lung injury associated with COVID-19. Methods: We evaluated lung injury by computed tomography at hospital admission and discharge and the serum expression of 754 miRNAs using the TaqMan OpenArray after hospital discharge in 27 patients with COVID-19. In addition, miR-150-3p was validated by qRT-PCR on serum samples collected at admission and after hospital discharge. Results: OpenArray analysis revealed that seven miRNAs were differentially expressed between groups of patients without radiological lung improvement compared to those with lung improvement at hospital discharge, with three miRNAs being upregulated (miR-548c-3p, miR-212-3p, and miR-548a-3p) and four downregulated (miR-191-5p, miR-151a-3p, miR-92a-3p, and miR-150-3p). Bioinformatics analysis revealed that five of these miRNAs had binding sites in the SARS-CoV-2 genome. Validation of miR-150-3p by qRT-PCR confirmed the OpenArray results. Conclusions: The present study shows the potential association between the serum expression of seven miRNAs and lung injury in patients with COVID-19. Furthermore, increased expression of miR-150 was associated with pulmonary improvement at hospital discharge.


Subject(s)
COVID-19 , Lung Injury , MicroRNAs , COVID-19/genetics , Computational Biology/methods , Humans , MicroRNAs/metabolism , SARS-CoV-2
17.
PLoS One ; 17(6): e0269386, 2022.
Article in English | MEDLINE | ID: covidwho-1910661

ABSTRACT

BACKGROUND: There is growing evidence of a strong relationship between COVID-19 and myocarditis. However, there are few bioinformatics-based analyses of critical genes and the mechanisms related to COVID-19 Myocarditis. This study aimed to identify critical genes related to COVID-19 Myocarditis by bioinformatic methods, explore the biological mechanisms and gene regulatory networks, and probe related drugs. METHODS: The gene expression data of GSE150392 and GSE167028 were obtained from the Gene Expression Omnibus (GEO), including cardiomyocytes derived from human induced pluripotent stem cells infected with SARS-CoV-2 in vitro and GSE150392 from patients with myocarditis infected with SARS-CoV-2 and the GSE167028 gene expression dataset. Differentially expressed genes (DEGs) (adjusted P-Value <0.01 and |Log2 Fold Change| ≥2) in GSE150392 were assessed by NetworkAnalyst 3.0. Meanwhile, significant modular genes in GSE167028 were identified by weighted gene correlation network analysis (WGCNA) and overlapped with DEGs to obtain common genes. Functional enrichment analyses were performed by using the "clusterProfiler" package in the R software, and protein-protein interaction (PPI) networks were constructed on the STRING website (https://cn.string-db.org/). Critical genes were identified by the CytoHubba plugin of Cytoscape by 5 algorithms. Transcription factor-gene (TF-gene) and Transcription factor-microRibonucleic acid (TF-miRNA) coregulatory networks construction were performed by NetworkAnalyst 3.0 and displayed in Cytoscape. Finally, Drug Signatures Database (DSigDB) was used to probe drugs associated with COVID-19 Myocarditis. RESULTS: Totally 850 DEGs (including 449 up-regulated and 401 down-regulated genes) and 159 significant genes in turquoise modules were identified from GSE150392 and GSE167028, respectively. Functional enrichment analysis indicated that common genes were mainly enriched in biological processes such as cell cycle and ubiquitin-protein hydrolysis. 6 genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) were identified as critical genes. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Etoposide, Methotrexate, Troglitazone, etc) were considered as target drugs for COVID-19 Myocarditis. CONCLUSIONS: Through bioinformatics method analysis, this study provides a new perspective to explore the pathogenesis, gene regulatory networks and provide drug compounds as a reference for COVID-19 Myocarditis. It is worth highlighting that critical genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) may be potential biomarkers and treatment targets of COVID-19 Myocarditis for future study.


Subject(s)
COVID-19 , Induced Pluripotent Stem Cells , MicroRNAs , Myocarditis , COVID-19/genetics , Computational Biology/methods , Gene Expression Profiling/methods , Gene Expression Regulation, Neoplastic , Gene Regulatory Networks , Humans , Induced Pluripotent Stem Cells/metabolism , MicroRNAs/genetics , Myocarditis/genetics , Protein Interaction Maps/genetics , SARS-CoV-2/genetics , Transcription Factors/metabolism
18.
Front Immunol ; 13: 882651, 2022.
Article in English | MEDLINE | ID: covidwho-1903017

ABSTRACT

Purpose: The purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB). Methods: Clinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model. Additionally, 10 intervertebral disc samples were collected for protein identification. Bioinformatics was used to analyze differentially expressed proteins (DEPs), including immune cells analysis, Gene Ontology (GO) and KEGG pathway enrichment analysis, and protein-protein interaction networks (PPI). Results: The nomogram predicted risk of STB ranging from 0.01 to 0.994. The C-index and AUC in the training set were 0.872 and 0.862, respectively. The results in the external validation set were consistent with the training set. Immune cells scores indicated that B cells naive in STB tissues were significantly lower than non-TB spinal tissues. Hub proteins were calculated by Degree, Closeness, and MCC methods. The main KEGG pathway included Coronavirus disease-COVID-19. There were 9 key proteins in the intersection of COVID-19-related proteins and hub proteins. There was a negative correlation between B cells naive and RPL19. COVID-19-related proteins were associated with immune genes. Conclusion: Lymphocytes were predictive factors for the diagnosis of STB. Immune cells showed low expression in STB. Nine COVID-19-related proteins were involved in STB mechanisms. These nine key proteins may suppress the immune mechanism of STB by regulating the expression of immune genes.


Subject(s)
COVID-19 , Tuberculosis, Spinal , Computational Biology/methods , Gene Ontology , Humans , Protein Interaction Maps/genetics
19.
Viruses ; 14(6)2022 05 26.
Article in English | MEDLINE | ID: covidwho-1869814

ABSTRACT

In silico methods for immune epitope prediction have become essential for vaccine and therapeutic design, but manual intra-species comparison of putative epitopes remains challenging and subject to human error. Created initially for analyzing SARS-CoV-2 variants of concern, comparative analysis of variant epitope sequences (CAVES) is a novel tool designed to carry out rapid comparative analyses of epitopes amongst closely related pathogens, substantially reducing the required time and user workload. CAVES applies a two-level analysis approach. The Level-one (L1) analysis compares two epitope prediction files, and the Level-two (L2) analysis incorporates search results from the IEDB database of experimentally confirmed epitopes. Both L1 and L2 analyses sort epitopes into categories of exact matches, partial matches, or novel epitopes based on the degree to which they match with peptides from the compared file. Furthermore, CAVES uses positional sequence data to improve its accuracy and speed, taking only a fraction of the time required by manual analyses and minimizing human error. CAVES is widely applicable for evolutionary analyses and antigenic comparisons of any closely related pathogen species. CAVES is open-source software that runs through a graphical user interface on Windows operating systems, making it widely accessible regardless of coding expertise. The CAVES source code and test dataset presented here are publicly available on the CAVES GitHub page.


Subject(s)
COVID-19 , SARS-CoV-2 , Computational Biology/methods , Epitopes/genetics , Epitopes, T-Lymphocyte , Humans , SARS-CoV-2/genetics , Software
20.
J Cell Mol Med ; 26(13): 3772-3782, 2022 07.
Article in English | MEDLINE | ID: covidwho-1868667

ABSTRACT

Amid the COVID-19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug-virus association entries from literature by text mining and built a human drug-virus association database. To the best of our knowledge, it is the largest publicly available drug-virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug-virus association network, the drug-drug chemical structure similarity network, and the virus-virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug-virus association is unassociated). A comparison on the curated drug-virus database shows that WRMF performs better than a few state-of-the-art methods. In addition, we selected the other two different public datasets (i.e. Cdataset and HMDD V2.0) to assess WRMF's performance. The case study also demonstrated the accuracy and reliability of WRMF to infer potential drugs for the novel virus. In summary, we offer a useful tool including a novel drug-virus association database and a powerful method WRMF to repurpose potential drugs for new viruses.


Subject(s)
COVID-19 , Viruses , Algorithms , COVID-19/drug therapy , Computational Biology/methods , Drug Repositioning , Humans , Reproducibility of Results
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