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1.
Int J Mol Sci ; 23(22)2022 Nov 11.
Article in English | MEDLINE | ID: covidwho-2143220

ABSTRACT

The assessment of molecular genetic landscape changes during NAC and the relationship between molecular signatures in residual tumors are promising approaches for identifying effective markers of outcome in breast cancer. The majority of the data in the literature present the relationship between the molecular genetic landscape and the response to NAC or are simply descriptive. The present study aimed to determine changes in expression profiles during NAC and assess the relationship between gene expression and the outcome of patients with luminal B HER2 breast cancer depending on distant hematogenous metastasis. The study included 39 patients with luminal B HER2-BC. The patients received 6-8 courses of NAC, and paired samples consisting of biopsy and surgical materials were analyzed. A full transcriptome microarray analysis was performed using the human Clariom™ S Assay platform (Affymetrix, 3450 Central Expy, Santa Clara, CA, 95051, USA). A comparison of the expression profiles of patients with breast cancer before and after NAC, depending on the status of hematogenous metastasis, was conducted. It was shown that the amount of DEGs in the tumor was reduced by more than six times after NAC. The top 10 signaling pathways were also found, the activity of which varied depending on the status of hematogenous metastasis before and after NAC. In addition, the association of DEGs with hematogenous metastasis in patients with breast cancer was evaluated: MFS was assessed depending on the expression level of 21 genes. It was shown that MFS was significantly associated with the expression level and pattern of nine genes. The expression levels of nine DEGs in the tumors of patients with breast cancer after NAC were significantly correlated with MFS when the status of hematogenous metastasis was taken into account.


Subject(s)
Breast Neoplasms , Neoadjuvant Therapy , Humans , Female , Breast Neoplasms/drug therapy , Breast Neoplasms/genetics , Breast Neoplasms/metabolism , Gene Expression Profiling , Neoplasm, Residual
2.
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
3.
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
4.
Eur J Med Res ; 27(1): 251, 2022 Nov 15.
Article in English | MEDLINE | ID: covidwho-2115714

ABSTRACT

BACKGROUND: Patients with non-alcoholic fatty liver disease (NAFLD) may be more susceptible to coronavirus disease 2019 (COVID-19) and even more likely to suffer from severe COVID-19. Whether there is a common molecular pathological basis for COVID-19 and NAFLD remains to be identified. The present study aimed to elucidate the transcriptional alterations shared by COVID-19 and NAFLD and to identify potential compounds targeting both diseases. METHODS: Differentially expressed genes (DEGs) for COVID-19 and NAFLD were extracted from the GSE147507 and GSE89632 datasets, and common DEGs were identified using the Venn diagram. Subsequently, we constructed a protein-protein interaction (PPI) network based on the common DEGs and extracted hub genes. Then, we performed gene ontology (GO) and pathway analysis of common DEGs. In addition, transcription factors (TFs) and miRNAs regulatory networks were constructed, and drug candidates were identified. RESULTS: We identified a total of 62 common DEGs for COVID-19 and NAFLD. The 10 hub genes extracted based on the PPI network were IL6, IL1B, PTGS2, JUN, FOS, ATF3, SOCS3, CSF3, NFKB2, and HBEGF. In addition, we also constructed TFs-DEGs, miRNAs-DEGs, and protein-drug interaction networks, demonstrating the complex regulatory relationships of common DEGs. CONCLUSION: We successfully extracted 10 hub genes that could be used as novel therapeutic targets for COVID-19 and NAFLD. In addition, based on common DEGs, we propose some potential drugs that may benefit patients with COVID-19 and NAFLD.


Subject(s)
COVID-19 , MicroRNAs , Non-alcoholic Fatty Liver Disease , Humans , Non-alcoholic Fatty Liver Disease/genetics , Non-alcoholic Fatty Liver Disease/metabolism , Gene Regulatory Networks , Systems Biology , Gene Expression Profiling , Computational Biology , COVID-19/genetics , MicroRNAs/genetics
5.
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
6.
Sci Rep ; 12(1): 18710, 2022 Nov 04.
Article in English | MEDLINE | ID: covidwho-2106460

ABSTRACT

The purpose of this study is to manually and semi-automatically curate a database and develop an R package that will act as a comprehensive resource to understand how biological processes are dysregulated due to interactions with environmental factors. The initial database search run on the Gene Expression Omnibus and the Molecular Signature Database retrieved a total of 90,018 articles. After title and abstract screening against pre-set criteria, a total of 237 datasets were selected and 522 gene modules were manually annotated. We then curated a database containing four environmental factors, cigarette smoking, diet, infections and toxic chemicals, along with a total of 25,789 genes that had an association with one or more of gene modules. The database and statistical analysis package was then tested with the differentially expressed genes obtained from the published literature related to type 1 diabetes, rheumatoid arthritis, small cell lung cancer, COVID-19, cobalt exposure and smoking. On testing, we uncovered statistically enriched biological processes, which revealed pathways associated with environmental factors and the genes. The curated database and enrichment tool are available as R packages at https://github.com/AhmedMehdiLab/E.PATH and https://github.com/AhmedMehdiLab/E.PAGE respectively.


Subject(s)
COVID-19 , Gene Expression Profiling , Humans , Gene Regulatory Networks , Databases, Factual , Gene Expression
7.
Int J Mol Sci ; 23(21)2022 Nov 05.
Article in English | MEDLINE | ID: covidwho-2099580

ABSTRACT

Transcriptome studies have reported the dysregulation of cell cycle-related genes and the global inhibition of host mRNA translation in COVID-19 cases. However, the key genes and cellular mechanisms that are most affected by the severe outcome of this disease remain unclear. For this work, the RNA-seq approach was used to study the differential expression in buffy coat cells of two groups of people infected with SARS-CoV-2: (a) Mild, with mild symptoms; and (b) SARS (Severe Acute Respiratory Syndrome), who were admitted to the intensive care unit with the severe COVID-19 outcome. Transcriptomic analysis revealed 1009 up-regulated and 501 down-regulated genes in the SARS group, with 10% of both being composed of long non-coding RNA. Ribosome and cell cycle pathways were enriched among down-regulated genes. The most connected proteins among the differentially expressed genes involved transport dysregulation, proteasome degradation, interferon response, cytokinesis failure, and host translation inhibition. Furthermore, interactome analysis showed Fibrillarin to be one of the key genes affected by SARS-CoV-2. This protein interacts directly with the N protein and long non-coding RNAs affecting transcription, translation, and ribosomal processes. This work reveals a group of dysregulated processes, including translation and cell cycle, as key pathways altered in severe COVID-19 outcomes.


Subject(s)
COVID-19 , RNA, Long Noncoding , Humans , COVID-19/genetics , SARS-CoV-2 , Transcriptome , Gene Expression Profiling , RNA, Long Noncoding/genetics , Cell Cycle/genetics
8.
Sci Transl Med ; 14(669): eabq4433, 2022 Nov 02.
Article in English | MEDLINE | ID: covidwho-2097911

ABSTRACT

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.


Subject(s)
COVID-19 , Influenza A Virus, H1N1 Subtype , Sepsis , Adult , Humans , Child , Gene Expression Profiling , Sepsis/genetics , Transcriptome/genetics
9.
Biochem Genet ; 60(6): 2052-2068, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2094662

ABSTRACT

Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) is an enveloped single-stranded RNA virus that can lead to respiratory symptoms and damage many organs such as heart, kidney, intestine, brain and liver. It has not been clearly documented whether myocardial injury is caused by direct infection of cardiomyocytes, lung injury, or other unknown mechanisms. The gene expression profile of GSE150392 was obtained from the Gene Expression Omnibus (GEO) database. The processing of high-throughput sequencing data and the screening of differentially expressed genes (DEGs) were implemented by R software. The R software was employed to analyze the Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The protein-protein interaction (PPI) network of the DEGs was constructed by the STRING website. The Cytoscape software was applied for the visualization of PPI network and the identification of hub genes. The statistical analysis was performed by the GraphPad Prism software to verify the hub genes. A total of 516 up-regulated genes and 191 down-regulated genes were screened out. The top 1 enrichment items of GO in biological process (BP), Cellular Component (CC), and Molecular Function (MF) were type I interferon signaling pathway, sarcomere, and receptor ligand activity, respectively. The top 10 enrichment pathways, including TNF signaling pathway, were identified by KEGG enrichment analysis. A PPI network was established, consisting of 613 nodes and 3,993 edges. The 12 hub genes were confirmed as statistically significant, which was verified by GSE151879 dataset. In conclusion, the hub genes of human iPSC-cardiomyocytes infected with SARS-CoV-2 were identified through bioinformatics analysis, which may be used as biomarkers for further research.


Subject(s)
COVID-19 , Induced Pluripotent Stem Cells , Humans , SARS-CoV-2 , Gene Expression Profiling , Myocytes, Cardiac , COVID-19/genetics , Computational Biology , Signal Transduction/genetics
10.
Biomed Res Int ; 2022: 1806427, 2022.
Article in English | MEDLINE | ID: covidwho-2088968

ABSTRACT

COVID-19 is still prevalent in more world regions and poses a severe threat to human health due to its high pathogenicity. The incidence of COPD patients is gradually increasing, especially in patients over 45 years old. COPD patients are susceptible to COVID-19 due to the specific lung receptor ACE2 of SARS-CoV-2. We attempt to reveal the genetic basis by analyzing the expression of common DEGs of the two diseases through bioinformatics approaches and find potential therapeutic agents based on the target genes. Thus, we search the GEO database for COVID-19 and COPD transcriptomic gene expression. We also study the enrichment of signaling regulatory pathways and hub genes for potential therapeutic treatments. There are 34 common DEGs in the two datasets. The signaling pathways are mainly enriched in intercellular junctions between virus and cytokine regulation. In the PPI network of common DEGs, we extract 5 hub genes. We find that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN could be therapeutic agents for both diseases. We also analyze the regulatory network of differential genes with transcription factors and miRNAs. Therefore, we conclude that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN can be therapeutic candidates in COPD combined with COVID-19.


Subject(s)
COVID-19 , Pulmonary Disease, Chronic Obstructive , Artesunate , COVID-19/genetics , Computational Biology , Gene Expression Profiling , Gene Regulatory Networks , Humans , Middle Aged , Pulmonary Disease, Chronic Obstructive/genetics , SARS-CoV-2 , Verapamil
11.
Brief Bioinform ; 23(6)2022 Nov 19.
Article in English | MEDLINE | ID: covidwho-2087743

ABSTRACT

Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.


Subject(s)
COVID-19 , Gene Regulatory Networks , Humans , Gene Expression Profiling/methods , Transcriptome , Algorithms
12.
Genome Med ; 14(1): 18, 2022 02 21.
Article in English | MEDLINE | ID: covidwho-1688773

ABSTRACT

BACKGROUND: Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another. METHODS: This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies. RESULTS: Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69-0.97 for viral classification. Signature size varied (1-398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months-1 year and 2-11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets. CONCLUSIONS: In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature's size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.


Subject(s)
Bacterial Infections/diagnosis , Datasets as Topic/statistics & numerical data , Host-Pathogen Interactions/genetics , Transcriptome , Virus Diseases/diagnosis , Adult , Bacterial Infections/epidemiology , Bacterial Infections/genetics , Biomarkers/analysis , COVID-19/diagnosis , COVID-19/genetics , Child , Cohort Studies , Diagnosis, Differential , Gene Expression Profiling/statistics & numerical data , Genetic Association Studies/statistics & numerical data , Humans , Publications/statistics & numerical data , SARS-CoV-2/pathogenicity , Validation Studies as Topic , Virus Diseases/epidemiology , Virus Diseases/genetics
13.
Front Immunol ; 13: 988479, 2022.
Article in English | MEDLINE | ID: covidwho-2065517

ABSTRACT

Background: The coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma. Methods: Two sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury. Results: We discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein-protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor-gene interactions, DEG-microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes. Conclusion: We identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.


Subject(s)
Asthma , COVID-19 , MicroRNAs , Animals , Asthma/genetics , Biomarkers, Tumor/genetics , COVID-19/genetics , Computational Biology , Gene Expression Profiling , Gene Regulatory Networks , Interleukin-13/genetics , Interleukin-5/genetics , Mice , MicroRNAs/genetics , Systems Biology , Transcription Factors/genetics
14.
Exp Mol Med ; 54(10): 1756-1765, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2062184

ABSTRACT

Clonal hematopoiesis of indeterminate potential (CHIP), a common aging-related process that predisposes individuals to various inflammatory responses, has been reported to be associated with COVID-19 severity. However, the immunological signature and the exact gene expression program by which the presence of CHIP exerts its clinical impact on COVID-19 remain to be elucidated. In this study, we generated a single-cell transcriptome landscape of severe COVID-19 according to the presence of CHIP using peripheral blood mononuclear cells. Patients with CHIP exhibited a potent IFN-γ response in exacerbating inflammation, particularly in classical monocytes, compared to patients without CHIP. To dissect the regulatory mechanism of CHIP (+)-specific IFN-γ response gene expression in severe COVID-19, we identified DNMT3A CHIP mutation-dependent differentially methylated regions (DMRs) and annotated their putative target genes based on long-range chromatin interactions. We revealed that CHIP mutant-driven hypo-DMRs at poised cis-regulatory elements appear to facilitate the CHIP (+)-specific IFN-γ-mediated inflammatory immune response. Our results highlight that the presence of CHIP may increase the susceptibility to hyperinflammation through the reorganization of chromatin architecture, establishing a novel subgroup of severe COVID-19 patients.


Subject(s)
COVID-19 , Clonal Hematopoiesis , Humans , Transcriptome , Hematopoiesis/genetics , COVID-19/genetics , Leukocytes, Mononuclear , Mutation , Chromatin/genetics , Gene Expression Profiling
15.
J Clin Lab Anal ; 36(11): e24672, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2047648

ABSTRACT

BACKGROUND: The pandemic COVID-19 has caused a high mortality rate and poses a significant threat to the population of the entire world. Due to the novelty of this disease, the pathogenic mechanism of the disease and the host cell's response are not yet fully known, so lack of evidence prevents a definitive conclusion about treatment strategies. The current study employed a small RNA deep-sequencing approach for screening differentially expressed microRNA (miRNA) in blood and bronchoalveolar fluid (BALF) samples of acute respiratory distress syndrome (ARDS) patients. METHODS: In this study, BALF and blood samples were taken from patients with ARDS (n = 5). Control samples were those with suspected lung cancer candidates for lung biopsy (n = 3). Illumina high-throughput (HiSeq 2000) sequencing was performed to identify known and novel miRNAs differentially expressed in the blood and BALFs of ARDS patients compared with controls. RESULTS: Results showed 2234 and 8324 miRNAs were differentially expressed in blood and BALF samples, respectively. In BALF samples, miR-282, miR-15-5p, miR-4485-3p, miR-483-3p, miR-6891-5p, miR-200c, miR-4463, miR-483-5p, and miR-98-5p were upregulated and miR-15a-5p, miR-548c-5p, miR-548d-3p, miR-365a-3p, miR-3939, miR-514-b-5p, miR-513a-3p, miR-513a-5p, miR-664a-3p, and miR-766-3p were downregulated. On the contrary, in blood samples miR-15b-5p, miR-18a-3p, miR-486-3p, miR-486-5p, miR-146a-5p, miR-16-2-3p, miR-6501-5p, miR-365-3p, miR-618, and miR-623 were top upregulated miRNAs and miR-21-5p, miR-142a-3p, miR-181-a, miR-31-5p, miR-99-5p, miR-342-5p, miR-183-5p, miR-627-5p, and miR-144-3p were downregulated miRNAs. Network functional analysis for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), in ARDS patients' blood and BALF samples, showed that the target genes were more involved in activating inflammatory and apoptosis process. CONCLUSION: Based on our results, the transcriptome profile of ARDS patients would be a valuable source for understanding molecular mechanisms of host response and developing clinical guidance on anti-inflammatory medication.


Subject(s)
COVID-19 , MicroRNAs , Respiratory Distress Syndrome , Humans , MicroRNAs/genetics , COVID-19/genetics , Sequence Analysis, RNA/methods , High-Throughput Nucleotide Sequencing/methods , Respiratory Distress Syndrome/genetics , Gene Expression Profiling
16.
Parasit Vectors ; 15(1): 297, 2022 Aug 24.
Article in English | MEDLINE | ID: covidwho-2038858

ABSTRACT

BACKGROUND: The effective transmission mode of Neospora caninum, with infection leading to reproductive failure in ruminants, is vertical transmission. The uterus is an important reproductive organ that forms the maternal-fetal interface. Neospora caninum can successfully invade and proliferate in the uterus, but the molecular mechanisms underlying epithelial-pathogen interactions remain unclear. Accumulating evidence suggests that host long noncoding RNAs (lncRNAs) play important roles in cellular molecular regulatory networks, with reports that these RNA molecules are closely related to the pathogenesis of apicomplexan parasites. However, the expression profiles of host lncRNAs during N. caninum infection has not been reported. METHODS: RNA sequencing (RNA-seq) analysis was used to investigate the expression profiles of messenger RNAs (mRNAs) and lncRNAs in caprine endometrial epithelial cells (EECs) infected with N. caninum for 24 h (TZ_24h) and 48 h (TZ_48 h), and the potential functions of differentially expressed (DE) lncRNAs were predicted by using Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of their mRNA targets. RESULTS: RNA-seq analysis identified 1280.15 M clean reads in 12 RNA samples, including six samples infected with N. caninum for 24 h (TZ1_24h-TZ3_24h) and 48 h (TZ1_48h-TZ3_48h), and six corresponding control samples (C1_24h-C3_24h and C1_48h-C3_48h). Within the categories TZ_24h-vs-C_24h, TZ_48h-vs-C_48h and TZ_48h-vs-TZ_24h, there were 934 (665 upregulated and 269 downregulated), 1238 (785 upregulated and 453 downregulated) and 489 (252 upregulated and 237 downregulated) DEmRNAs, respectively. GO enrichment and KEGG analysis revealed that these DEmRNAs were mainly involved in the regulation of host immune response (e.g. TNF signaling pathway, MAPK signaling pathway, transforming growth factor beta signaling pathway, AMPK signaling pathway, Toll-like receptor signaling pathway, NOD-like receptor signaling pathway), signaling molecules and interaction (e.g. cytokine-cytokine receptor interaction, cell adhesion molecules and ECM-receptor interaction). A total of 88 (59 upregulated and 29 downregulated), 129 (80 upregulated and 49 downregulated) and 32 (20 upregulated and 12 downregulated) DElncRNAs were found within the categories TZ_24h-vs-C_24h, TZ_48h-vs-C_48h and TZ_48h-vs-TZ_24h, respectively. Functional prediction indicated that these DElncRNAs would be involved in signal transduction (e.g. MAPK signaling pathway, PPAR signaling pathway, ErbB signaling pathway, calcium signaling pathway), neural transmission (e.g. GABAergic synapse, serotonergic synapse, cholinergic synapse), metabolism processes (e.g. glycosphingolipid biosynthesis-lacto and neolacto series, glycosaminoglycan biosynthesis-heparan sulfate/heparin) and signaling molecules and interaction (e.g. cytokine-cytokine receptor interaction, cell adhesion molecules and ECM-receptor interaction). CONCLUSIONS: This is the first investigation of global gene expression profiles of lncRNAs during N. caninum infection. The results provide valuable information for further studies of the roles of lncRNAs during N. caninum infection.


Subject(s)
Coccidiosis , Neospora , RNA, Long Noncoding , Animals , Coccidiosis/veterinary , Cytokines/genetics , Epithelial Cells/metabolism , Female , Gene Expression Profiling , Goats , Humans , Neospora/genetics , Neospora/metabolism , RNA, Long Noncoding/genetics , RNA, Long Noncoding/metabolism , RNA, Messenger/genetics , RNA, Messenger/metabolism , Receptors, Cytokine/genetics , Sequence Analysis, RNA
17.
BMC Genomics ; 23(1): 654, 2022 Sep 15.
Article in English | MEDLINE | ID: covidwho-2038658

ABSTRACT

Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a recently developed technology to assess antibody reactivity, quantifying antibody binding towards hundreds of thousands of candidate epitopes. The output from PhIP-Seq experiments are read count matrices, similar to RNA-Seq data; however some important differences do exist. In this manuscript we investigated whether the publicly available method edgeR (Robinson et al., Bioinformatics 26(1):139-140, 2010) for normalization and analysis of RNA-Seq data is also suitable for PhIP-Seq data. We find that edgeR is remarkably effective, but improvements can be made and introduce a Bayesian framework specifically tailored for data from PhIP-Seq experiments (Bayesian Enrichment Estimation in R, BEER).


Subject(s)
Bacteriophages , Antibodies , Bacteriophages/genetics , Bayes Theorem , Epitopes , Gene Expression Profiling/methods , Immunoprecipitation , Sequence Analysis, RNA/methods
18.
Med Sci Monit ; 28: e937532, 2022 Aug 29.
Article in English | MEDLINE | ID: covidwho-2025555

ABSTRACT

BACKGROUND We sought to further our understanding of the biological characteristics underlying severe COVID-19. MATERIAL AND METHODS RNA sequencing (RNA-Seq) analysis was used to evaluate peripheral blood mononuclear cells from 4 patients with severe COVID-19 and 4 healthy controls. We performed gene expression analyses to detect differentially expressed genes (DEGs). Enrichment analyses were performed to identify their molecular processes and signaling pathways, and the protein-protein interaction network was constructed to extract the core gene cluster. The investigation of protein-chemical interactions and regulatory signatures for specific regulatory checkpoints and powerful chemical agents was then conducted for these essential genes. Finally, we used single-cell RNA-Seq analysis from an online platform to show how these genes were expressed differently, depending on the kind of cell. RESULTS A total of 268 DEGs were found. The biological process of protein ubiquitination was later discovered to be highly influenced by the core gene cluster (ITCH, TRIM21, RNF130, FBXO11, UBE2J1, and ASB16) at the transcriptome level. Six transcription factors, FNIC, FOXA1, YY1, GATA2, MET2A, and FOXC1, as well as miRNAs hsa-miR-1-3p and hsa-miR-27a-3p were identified. We found a potent chemical agent, copper sulfate, may regulate protein ubiquitination genes cooperatively, and the genes regulating protein ubiquitination could be expressed highly on the macrophages. CONCLUSIONS Taken together, we suggest that protein ubiquitination is a crucial functional process in patients with severe COVID-19. This study will give a deeper insight into biological characteristics and progression of COVID-19 and facilitate development of novel therapeutics, leading to significant advancements in the COVID-19 pandemic.


Subject(s)
COVID-19 , F-Box Proteins , MicroRNAs , Gene Expression Profiling , Gene Regulatory Networks , Humans , Leukocytes, Mononuclear , Pandemics , Protein-Arginine N-Methyltransferases , Transcriptome , Ubiquitination
19.
Front Immunol ; 13: 930866, 2022.
Article in English | MEDLINE | ID: covidwho-2022713

ABSTRACT

Background: Although several key molecules have been identified to modulate SARS-CoV-2 invasion of human host cells, the molecules correlated with outcomes in COVID-19 caused by SARS-CoV-2 infection remain insufficiently explored. Methods: This study analyzed three RNA-Seq gene expression profiling datasets for COVID-19 and identified differentially expressed genes (DEGs) between COVID-19 patients and normal people, commonly in the three datasets. Furthermore, this study explored the correlation between the expression of these genes and clinical features in COVID-19 patients. Results: This analysis identified 13 genes significantly upregulated in COVID-19 patients' leukocyte and SARS-CoV-2-infected nasopharyngeal tissue compared to normal tissue. These genes included OAS1, OAS2, OAS3, OASL, HERC6, SERPING1, IFI6, IFI44, IFI44L, CMPK2, RSAD2, EPSTI1, and CXCL10, all of which are involved in antiviral immune regulation. We found that these genes' downregulation was associated with worse clinical outcomes in COVID-19 patients, such as intensive care unit (ICU) admission, mechanical ventilatory support (MVS) requirement, elevated D-dimer levels, and increased viral loads. Furthermore, this analysis identified two COVID-19 clusters based on the expression profiles of the 13 genes, termed COV-C1 and COV-C2. Compared with COV-C1, COV-C2 more highly expressed the 13 genes, had stronger antiviral immune responses, were younger, and displayed more favorable clinical outcomes. Conclusions: A strong antiviral immune response is essential in reducing severity of COVID-19.


Subject(s)
COVID-19 , Transcriptome , Antiviral Agents , COVID-19/genetics , Gene Expression Profiling , Humans , SARS-CoV-2
20.
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
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