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2.
Am J Respir Cell Mol Biol ; 67(5): 520-527, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2138375

ABSTRACT

Coronavirus disease (COVID-19) begins with upper airway symptoms but proceeds in a significant proportion of patients to life-threatening infection of the lower respiratory tract, where an exuberant inflammatory response, edema, and adverse parenchymal remodeling impair gas exchange. Respiratory failure is caused initially by flooding of the airspaces with plasma exudate, sloughed epithelium, and inflammatory cells. For many patients with COVID-19, this acute phase has been observed to give way to a prolonged course of acute respiratory distress syndrome, and a significant proportion of patients go on to develop fibroproliferative remodeling of the lung parenchyma, which lengthens the duration of respiratory impairment and mechanical ventilation. Monocyte-derived macrophages have previously been implicated in the fibrotic phase of lung injury in multiple models. From several recent studies that used single-cell genomic techniques, a profile of the transcriptomic state of COVID-19 lung macrophages has emerged. Linkages have been made between these macrophages, which are monocyte-derived and CD163+, and profibrotic macrophages found in other contexts, including animal models of fibrosis and idiopathic pulmonary fibrosis. Here, emerging concepts of macrophage profibrotic function in COVID-19 are highlighted with a focus on gaps in knowledge to be addressed by future research.


Subject(s)
COVID-19 , Respiratory Insufficiency , Animals , Transcriptome , Macrophages, Alveolar , Lung
3.
Viruses ; 14(10)2022 10 09.
Article in English | MEDLINE | ID: covidwho-2143671

ABSTRACT

For industrial vaccine production, overwhelming the existing antiviral innate immune response dominated by type I interferons (IFN-I) in cells would be a key factor improving the effectiveness and production cost of vaccines. In this study, we report the construction of an IFN-I receptor 1 (IFNAR1)-knockout DF-1 cell line (KO-IFNAR1), which supports much more efficient replication of the duck Tembusu virus (DTMUV), Newcastle disease virus (NDV) and gammacoronavirus infectious bronchitis virus (IBV). Transcriptomic analysis of DTMUV-infected KO-IFNAR1 cells demonstrated that DTMUV mainly activated genes and signaling pathways related to cell growth and apoptosis. Among them, JUN, MYC and NFKBIA were significantly up-regulated. Furthermore, knockdown of zinc-fingered helicase 2 (HELZ2) and interferon-α-inducible protein 6 (IFI6), the two genes up-regulated in both wild type and KO-IFNAR1 cells, significantly increased the replication of DTMUV RNA. This study paves the way for further studying the mechanism underlying the DTMUV-mediated IFN-I-independent regulation of virus replication, and meanwhile provides a potential cell resource for efficient production of cell-based avian virus vaccines.


Subject(s)
Flavivirus Infections , Flavivirus , Interferon Type I , Poultry Diseases , Animals , Ducks , Chickens/genetics , Transcriptome , Flavivirus/genetics , Cell Line , Interferon Type I/genetics , Antiviral Agents , Apoptosis , RNA , Interferon-alpha/genetics , Zinc
4.
Front Immunol ; 13: 918817, 2022.
Article in English | MEDLINE | ID: covidwho-2141935

ABSTRACT

Most transcriptomic studies of SARS-CoV-2 infection have focused on differentially expressed genes, which do not necessarily reveal the genes mediating the transcriptomic changes. In contrast, exploiting curated biological network, our PathExt tool identifies central genes from the differentially active paths mediating global transcriptomic response. Here we apply PathExt to multiple cell line infection models of SARS-CoV-2 and other viruses, as well as to COVID-19 patient-derived PBMCs. The central genes mediating SARS-CoV-2 response in cell lines were uniquely enriched for ATP metabolic process, G1/S transition, leukocyte activation and migration. In contrast, PBMC response reveals dysregulated cell-cycle processes. In PBMC, the most frequently central genes are associated with COVID-19 severity. Importantly, relative to differential genes, PathExt-identified genes show greater concordance with several benchmark anti-COVID-19 target gene sets. We propose six novel anti-SARS-CoV-2 targets ADCY2, ADSL, OCRL, TIAM1, PBK, and BUB1, and potential drugs targeting these genes, such as Bemcentinib, Phthalocyanine, and Conivaptan.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/drug therapy , COVID-19/genetics , Cell Line , Humans , Leukocytes, Mononuclear , Transcriptome
5.
Genome Med ; 14(1): 134, 2022 Nov 29.
Article in English | MEDLINE | ID: covidwho-2139391

ABSTRACT

BACKGROUND: COVID-19 manifests with a wide spectrum of clinical phenotypes, ranging from asymptomatic and mild to severe and critical. Severe and critical COVID-19 patients are characterized by marked changes in the myeloid compartment, especially monocytes. However, little is known about the epigenetic alterations that occur in these cells during hyperinflammatory responses in severe COVID-19 patients. METHODS: In this study, we obtained the DNA methylome and transcriptome of peripheral blood monocytes from severe COVID-19 patients. DNA samples extracted from CD14 + CD15- monocytes of 48 severe COVID-19 patients and 11 healthy controls were hybridized on MethylationEPIC BeadChip arrays. In parallel, single-cell transcriptomics of 10 severe COVID-19 patients were generated. CellPhoneDB was used to infer changes in the crosstalk between monocytes and other immune cell types. RESULTS: We observed DNA methylation changes in CpG sites associated with interferon-related genes and genes associated with antigen presentation, concordant with gene expression changes. These changes significantly overlapped with those occurring in bacterial sepsis, although specific DNA methylation alterations in genes specific to viral infection were also identified. We also found these alterations to comprise some of the DNA methylation changes occurring during myeloid differentiation and under the influence of inflammatory cytokines. A progression of DNA methylation alterations in relation to the Sequential Organ Failure Assessment (SOFA) score was found to be related to interferon-related genes and T-helper 1 cell cytokine production. CellPhoneDB analysis of the single-cell transcriptomes of other immune cell types suggested the existence of altered crosstalk between monocytes and other cell types like NK cells and regulatory T cells. CONCLUSION: Our findings show the occurrence of an epigenetic and transcriptional reprogramming of peripheral blood monocytes, which could be associated with the release of aberrant immature monocytes, increased systemic levels of pro-inflammatory cytokines, and changes in immune cell crosstalk in these patients.


Subject(s)
COVID-19 , Monocytes , Humans , Transcriptome , Cytokines , COVID-19/genetics , Interferons , Antiviral Agents , Epigenesis, Genetic
6.
Zool Res ; 43(6): 1041-1062, 2022 Nov 18.
Article in English | MEDLINE | ID: covidwho-2111387

ABSTRACT

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes diverse clinical manifestations and tissue injuries in multiple organs. However, cellular and molecular understanding of SARS-CoV-2 infection-associated pathology and immune defense features in different organs remains incomplete. Here, we profiled approximately 77 000 single-nucleus transcriptomes of the lung, liver, kidney, and cerebral cortex in rhesus macaques ( Macaca mulatta) infected with SARS-CoV-2 and healthy controls. Integrated analysis of the multi-organ dataset suggested that the liver harbored the strongest global transcriptional alterations. We observed prominent impairment in lung epithelial cells, especially in AT2 and ciliated cells, and evident signs of fibrosis in fibroblasts. These lung injury characteristics are similar to those reported in patients with coronavirus disease 2019 (COVID-19). Furthermore, we found suppressed MHC class I/II molecular activity in the lung, inflammatory response in the liver, and activation of the kynurenine pathway, which induced the development of an immunosuppressive microenvironment. Analysis of the kidney dataset highlighted tropism of tubule cells to SARS-CoV-2, and we found membranous nephropathy (an autoimmune disease) caused by podocyte dysregulation. In addition, we identified the pathological states of astrocytes and oligodendrocytes in the cerebral cortex, providing molecular insights into COVID-19-related neurological implications. Overall, our multi-organ single-nucleus transcriptomic survey of SARS-CoV-2-infected rhesus macaques broadens our understanding of disease features and antiviral immune defects caused by SARS-CoV-2 infection, which may facilitate the development of therapeutic interventions for COVID-19.


Subject(s)
COVID-19 , Animals , COVID-19/genetics , COVID-19/veterinary , Macaca mulatta , SARS-CoV-2 , Transcriptome , Viral Load/veterinary
7.
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
8.
Front Immunol ; 13: 1027180, 2022.
Article in English | MEDLINE | ID: covidwho-2109770

ABSTRACT

Under the background of the severe human health and world economic burden caused by COVID-19, the attenuation of vaccine protection efficacy, and the prevalence and immune escape of emerging variants of concern (VOCs), the third dose of booster immunization has been put on the agenda. Systems biology approaches can help us gain new perspectives on the characterization of immune responses and the identification of factors underlying vaccine-induced immune efficacy. We analyzed the antibody signature and transcriptional responses of participants vaccinated with COVID-19 inactivated vaccine and protein subunit vaccine as a third booster dose. The results from the antibody indicated that the third booster dose was effective, and that heterologous vaccination with the protein subunit vaccine as a booster dose induced stronger humoral immune responses than the homologous vaccination with inactivated vaccine, and might be more effective against VOCs. In transcriptomic analysis, protein subunit vaccine induced more differentially expressed genes that were significantly associated with many important innate immune pathways. Both the homologous and heterologous boosters could increase the effectiveness against COVID-19, and compared with the inactivated vaccine, the protein subunit vaccine, mediated a stronger humoral immune response and had a more significant correlation with the innate immune function module, which provided certain data support for the third booster immunization strategy.


Subject(s)
COVID-19 , Immunity, Humoral , Humans , Transcriptome , Protein Subunits , Immunization, Secondary , COVID-19/prevention & control , Vaccines, Inactivated , Vaccines, Subunit
9.
Front Immunol ; 13: 960709, 2022.
Article in English | MEDLINE | ID: covidwho-2109764

ABSTRACT

Porcine reproductive and respiratory syndrome virus (PRRSV) is a highly contagious disease that affects the global pig industry. To understand mechanisms of susceptibility/resistance to PRRSV, this study profiled the time-serial white blood cells transcriptomic and serum metabolomic responses to PRRSV in piglets from a crossbred population of PRRSV-resistant Tongcheng pigs and PRRSV-susceptible Large White pigs. Gene set enrichment analysis (GSEA) illustrated that PRRSV infection up-regulated the expression levels of marker genes of dendritic cells, monocytes and neutrophils and inflammatory response, but down-regulated T cells, B cells and NK cells markers. CIBERSORT analysis confirmed the higher T cells proportion in resistant pigs during PRRSV infection. Resistant pigs showed a significantly higher level of T cell activation and lower expression levels of monocyte surface signatures post infection than susceptible pigs, corresponding to more severe suppression of T cell immunity and inflammatory response in susceptible pigs. Differentially expressed genes between resistant/susceptible pigs during the course of infection were significantly enriched in oxidative stress, innate immunity and humoral immunity, cell cycle, biotic stimulated cellular response, wounding response and behavior related pathways. Fourteen of these genes were distributed in 5 different QTL regions associated with PRRSV-related traits. Chemokine CXCL10 levels post PRRSV infection were differentially expressed between resistant pigs and susceptible pigs and can be a promising marker for susceptibility/resistance to PRRSV. Furthermore, the metabolomics dataset indicated differences in amino acid pathways and lipid metabolism between pre-infection/post-infection and resistant/susceptible pigs. The majority of metabolites levels were also down-regulated after PRRSV infection and were significantly positively correlated to the expression levels of marker genes in adaptive immune response. The integration of transcriptome and metabolome revealed concerted molecular events triggered by the infection, notably involving inflammatory response, adaptive immunity and G protein-coupled receptor downstream signaling. This study has increased our knowledge of the immune response differences induced by PRRSV infection and susceptibility differences at the transcriptomic and metabolomic levels, providing the basis for the PRRSV resistance mechanism and effective PRRS control.


Subject(s)
Porcine Reproductive and Respiratory Syndrome , Porcine respiratory and reproductive syndrome virus , Animals , Swine , Porcine respiratory and reproductive syndrome virus/genetics , Porcine Reproductive and Respiratory Syndrome/genetics , Transcriptome , Immunity, Humoral , Adaptive Immunity/genetics
10.
Genome Biol ; 23(1): 236, 2022 Nov 08.
Article in English | MEDLINE | ID: covidwho-2108879

ABSTRACT

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , Waste Water , RNA, Viral/genetics , Transcriptome
11.
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
12.
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
13.
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
14.
OMICS ; 26(11): 608-621, 2022 11.
Article in English | MEDLINE | ID: covidwho-2087719

ABSTRACT

COVID-19 is a systemic disease affecting tissues and organs, including and beyond the lung. Apart from the current pandemic context, we also have vastly inadequate knowledge of consequences of repeated exposures to SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus causing COVID-19, in multiple organ systems and the whole organism scales when the disease evolves from a pandemic to an endemic state. This calls for a systems biology and systems medicine approach and unpacking the effects of COVID-19 in lung as well as other tissues. We report here original findings from transcriptomics analyses and differentially expressed genes (DEGs) in lung samples from 60 patients and 27 healthy controls, and in whole blood samples from 255 patients and 103 healthy individuals. A total of 11 datasets with RNA-seq transcriptomic data were obtained from the Gene Expression Omnibus and the European Nucleotide Archive. The identified DEGs were used to construct protein interaction and functional networks and to identify related pathways and miRNAs. We found 35 DEGs common between lung and the whole blood, and importantly, 2 novel genes, namely CYP1B1 and TNFAIP6, which have not been previously implicated with COVID-19. We also identified four novel miRNA potential regulators, hsa-mir-192-5p, hsa-mir-221-3p, hsa-mir-4756-3p, and hsa-mir-10a-5p, implicated in lung or other diseases induced by coronaviruses. In summary, these findings offer new molecular leads and insights to unpack COVID-19 systems biology in a whole organism context and might inform future antiviral drug, diagnostics, and vaccine discovery efforts.


Subject(s)
COVID-19 , MicroRNAs , Humans , Transcriptome/genetics , COVID-19/genetics , SARS-CoV-2/genetics , Systems Biology , MicroRNAs/metabolism , Lung/metabolism , Computational Biology
15.
Nat Commun ; 13(1): 6336, 2022 Oct 25.
Article in English | MEDLINE | ID: covidwho-2087204

ABSTRACT

Genes with moderate to low expression heritability may explain a large proportion of complex trait etiology, but such genes cannot be sufficiently captured in conventional transcriptome-wide association studies (TWASs), partly due to the relatively small available reference datasets for developing expression genetic prediction models to capture the moderate to low genetically regulated components of gene expression. Here, we introduce a method, the Summary-level Unified Method for Modeling Integrated Transcriptome (SUMMIT), to improve the expression prediction model accuracy and the power of TWAS by using a large expression quantitative trait loci (eQTL) summary-level dataset. We apply SUMMIT to the eQTL summary-level data provided by the eQTLGen consortium. Through simulation studies and analyses of genome-wide association study summary statistics for 24 complex traits, we show that SUMMIT improves the accuracy of expression prediction in blood, successfully builds expression prediction models for genes with low expression heritability, and achieves higher statistical power than several benchmark methods. Finally, we conduct a case study of COVID-19 severity with SUMMIT and identify 11 likely causal genes associated with COVID-19 severity.


Subject(s)
COVID-19 , Transcriptome , Humans , Genome-Wide Association Study/methods , COVID-19/genetics , Quantitative Trait Loci/genetics , Multifactorial Inheritance , Polymorphism, Single Nucleotide , Genetic Predisposition to Disease/genetics
16.
J Microbiol ; 60(11): 1106-1112, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2075669

ABSTRACT

Due to the evolutionary arms race between hosts and viruses, viruses must adapt to host translation systems to rapidly synthesize viral proteins. Highly expressed genes in hosts have a codon bias related to tRNA abundance, the primary RNA translation rate determinant. We calculated the relative synonymous codon usage (RSCU) of three hepatitis viruses (HAV, HBV, and HCV), SARS-CoV-2, 30 human tissues, and hepatocellular carcinoma (HCC). After comparing RSCU between viruses and human tissues, we calculated the codon adaptation index (CAI) of viral and human genes. HBV and HCV showed the highest correlations with HCC and the normal liver, while SARS-CoV-2 had the strongest association with lungs. In addition, based on HCC RSCU, the CAI of HBV and HCV genes was the highest. HBV and HCV preferentially adapt to the tRNA pool in HCC, facilitating viral RNA translation. After an initial trigger, rapid HBV/HCV translation and replication may change normal liver cells into HCC cells. Our findings reveal a novel perspective on virus-mediated oncogenesis.


Subject(s)
COVID-19 , Carcinoma, Hepatocellular , Hepatitis B , Hepatitis C , Liver Neoplasms , Humans , Liver Neoplasms/complications , Liver Neoplasms/genetics , Hepatitis B virus/genetics , Carcinoma, Hepatocellular/complications , Carcinoma, Hepatocellular/genetics , Hepatitis B/complications , Hepatitis B/genetics , Transcriptome , SARS-CoV-2 , Codon , Carcinogenesis , RNA, Transfer , Hepatitis C/genetics
17.
Viruses ; 14(10)2022 10 16.
Article in English | MEDLINE | ID: covidwho-2071842

ABSTRACT

Over the last three years, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related health crisis has claimed over six million lives and caused USD 12 trillion losses to the global economy. SARS-CoV-2 continuously mutates and evolves with a high basic reproduction number (R0), resulting in a variety of clinical manifestations ranging from asymptomatic infection to acute respiratory distress syndrome (ARDS) and even death. To gain a better understanding of coronavirus disease 2019 (COVID-19), it is critical to investigate the components that cause various clinical manifestations. Single-cell sequencing has substantial advantages in terms of identifying differentially expressed genes among individual cells, which can provide a better understanding of the various physiological and pathological processes. This article reviewed the use of single-cell transcriptomics in COVID-19 research, examined the immune response disparities generated by SARS-CoV-2, and offered insights regarding how to improve COVID-19 diagnosis and treatment plans.


Subject(s)
COVID-19 , Humans , SARS-CoV-2/genetics , COVID-19 Testing , Transcriptome , Basic Reproduction Number
18.
Viruses ; 14(10)2022 10 01.
Article in English | MEDLINE | ID: covidwho-2066554

ABSTRACT

Infection with SARS-CoV-2 results in Coronavirus disease 2019 (COVID-19) is known to cause mild to acute respiratory infection and sometimes progress towards respiratory failure and death. The mechanisms driving the progression of the disease and accumulation of high viral load in the lungs without initial symptoms remain elusive. In this study, we evaluated the upper respiratory tract host transcriptional response in COVID-19 patients with mild to severe symptoms and compared it with the control COVID-19 negative group using RNA-sequencing (RNA-Seq). Our results reveal an upregulated early type I interferon response in severe COVID-19 patients as compared to mild or negative COVID-19 patients. Moreover, severely symptomatic patients have pronounced induction of interferon stimulated genes (ISGs), particularly the oligoadenylate synthetase (OAS) family of genes. Our results are in concurrence with other studies depicting the early induction of IFN-I response in severe COVID-19 patients, providing novel insights about the ISGs involved.


Subject(s)
COVID-19 , Interferon Type I , Humans , SARS-CoV-2 , Transcriptome , Host-Pathogen Interactions , Antiviral Agents , Interferon Type I/genetics , Lung , Ligases , RNA
19.
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
20.
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
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