Transcriptomic analysis in patients with SARS-CoV-2 virus infection. Identification of the association of risk/ protective profiles against the disease

Background/Objectives: SARS-CoV2 causes the COVID-19 disease, capable of producing a severe acute respiratory syndrome. Several clinical variables and genetic variants have been related to a worse prognosis. The aim of this study is to measure if difference in the gene expression are associated with COVID-19 severity. Method(s): We performed RNA-seq Transcriptome in RNA extracted from lymphoblastoid cell line in 20 patients who require hospitalization (10 from the intensive care unit) in a GeneStudio S5 Plus Sequencer (Ion Torrent Technology). FASTQ files were obtained and trimmed using BBtools, BBduk for cutting, filtering and masking the data, and Dedupe for the elimination of duplicates. Mapping and counting matrix was done in bash using the Salmon program. Differential expression analysis and subsequent functional enrichment was performed using Rstudio (DESeq2, ClusterProfiler, GO and KEGG). Result(s): We observed that 2042 differentially expressed genes (1996 overexpressed, LFC>0 and 406 underexpressed, LFC<0) were obtained between patients who require hospitalization versus those in the intensive care unit. We found some genes previously SARS-CoV-2 associated (PGLYRP1, HDAC9 and FUT4). Furthermore, genes involved in the activity of the immune system and in inflammatory processes showed significant differences between cohorts (ABCF1 (LFC = -25.14, padj = 1.05e-13), ABHD16A (LFC = 25.00, padj = 1.05e-13) and IER3 (LFC = -24.45, padj = 2.43e-13)). Conclusion(s): We described differential expression in genes of the immune system and inflammatory processes that might be have a role in the risk of develop severe symptoms of COVID-19, including admission in the intensive care unit. This results should be validated by additional functional studies.

Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles.

COVID-19 was declared a pandemic in March 2020, and since then, it has not stopped spreading like wildfire in almost every corner of the world, despite the many efforts made to stem its spread. SARS-CoV-2 has one of the biggest genomes among RNA viruses and presents unique characteristics that differentiate it from other coronaviruses, making it even more challenging to find a cure or vaccine that is efficient enough. This work aims, using RNA sequencing (RNA-Seq) data, to evaluate whether the expression of specific human genes in the host can vary in different grades of disease severity and to determine the molecular origins of the differences in response to SARS-CoV-2 infection in different patients. In addition to quantifying gene expression, data coming from RNA-Seq allow for the discovery of new transcripts, the identification of alternative splicing events, the detection of allele-specific expression, and the detection of post-transcriptional alterations. For this reason, we performed differential expression analysis on different expression profiles of COVID-19 patients, using RNA-Seq data coming from NCBI public repository, and we obtained the lists of all differentially expressed genes (DEGs) emerging from 7 experimental conditions. We performed a Gene Set Enrichment Analysis (GSEA) on these genes to find possible correlations between DEGs and known disease phenotypes. We mainly focused on DEGs coming out from the analysis of the contrasts involving severe conditions to infer any possible relation between a worsening of the clinical picture and an over-representation of specific genes. Based on the obtained results, this study indicates a small group of genes that result up-regulated in the severe form of the disease. EXOSC5, MESD, REXO2, and TRMT2A genes are not differentially expressed or not present in the other conditions, being for that reason, good biomarkers candidates for the severe form of COVID-19 disease. The use of specific over-expressed genes, whether up-regulated or down-regulated, which have an individual role in each different condition of COVID-19 as a biomarker, can assist in early diagnosis.

In silico identification of potential miRNAs -mRNA inflammatory networks implicated in the pathogenesis of COVID-19

COVID-19 has been found to affect the expression profile of several mRNAs and miRNAs, leading to dysregulation of a number of signaling pathways, particularly those related to inflammatory responses. In the current study, a systematic biology procedure was used for the analysis of high-throughput expression data from blood specimens of COVID-19 and healthy individuals. Differentially expressed miRNAs in blood specimens of COVID-19 vs. healthy specimens were then identified to construct and analyze miRNA-mRNA networks and predict key miRNAs and genes in inflammatory pathways. Our results showed that 171 miRNAs were expressed as outliers in box plot and located in the critical areas according to our statistical analysis. Among them, 8 miRNAs, namely miR-1275, miR-4429, miR-4489, miR-6721-5p, miR-5010-5p, miR-7110-5p, miR-6804-5p and miR-6881-3p were found to affect expression of key genes in NF-KB, JAK/STAT and MAPK signaling pathways implicated in COVID-19 pathogenesis. In addition, our results predicted that 25 genes involved in above-mentioned inflammatory pathways were targeted not only by these 8 miRNAs but also by other obtained miRNAs (163 miRNAs). The results of the current in silico study represent candidate targets for further studies in COVID-19.Copyright © 2023 Elsevier B.V.

Differentially expressed genes in Diffuse Alveolar Damage (DAD) patterns of COVID-19

Severe COVID-19 induces DAD, a condition with temporal-spatial heterogeneity. We determined the differentially expressed genes (DEGs) in the histological patterns of DAD. Twelve fatal COVID-19 cases were classified in acute DAD (n=5) and intermediate/advanced (IA) DAD (n=7). Autopsy lung RNA was extracted from COVID-19 and 4 control cases. RNA sequencing was performed on the Illumina NovaSeq 6000. Enrichment analysis was performed with clusterProfiler using Genome-wide annotation for Human R package. GO terms and KEGG pathways were considered enriched if adjusted p<=0.05. Principal component analysis showed that IA-DAD samples were grouped, while acute DAD samples were scattered. The differential expression analysis between these two groups and the control cases revealed: 261 DEGs in the acute DAD (143 Up- and 53 Down-regulated), 244 DEGs in the IA- DAD tissues (67 Up- and 116 Down-regulated), and 61 DEGs were shared between them (45 Up- and 16 Downregulated). Patients with acute DAD had up-regulated genes related to oxidative phosphorylation, blood coagulation, megakaryocytes differentiation/regulation, and platelet degranulation/activation. Patients with IA-DAD had DEGs related to immunoglobulins and extracellular matrix. The shared up-regulated DEGs between both patterns are involved in innate and adaptive immune responses. We selected 3 DEGs in each DAD pattern for validation by realtime PCR. There were no differences in acute DAD DEGs, but DEGs overexpressed in intermediate DAD (COL3A1, IGLV3-19, IGHV1-58) were significantly higher. Genes related to thrombotic events occur at the acute stage of DAD, whereas immunoglobulin production and remodeling occur at later stages of DAD.

PathExNET: A tool for extracting pathway expression networks from gene expression statistics.

A fundamental issue related to the understanding of the molecular mechanisms, is the way in which common pathways act across different biological experiments related to complex diseases. Using network-based approaches, this work aims to provide a numeric characterization of pathways across different biological experiments, in the prospect to create unique footprints that may characterise a specific disease under study at a pathway network level. In this line we propose PathExNET, a web service that allows the creation of pathway-to-pathway expression networks that hold the over- and under expression information obtained from differential gene expression analyses. The unique numeric characterization of pathway expression status related to a specific biological experiment (or disease), as well as the creation of diverse combination of pathway networks generated by PathExNET, is expected to provide a concrete contribution towards the individualization of disease, and further lead to a more precise personalised medicine and management of treatment. PathExNET is available at: https://bioinformatics.cing.ac.cy/PathExNET and at https://pathexnet.cing-big.hpcf.cyi.ac.cy/.

Single Nucleus RNA-Sequencing in Diagnostic Biopsy Cores of COVID-AKI Patients Reveals Robust Modelling, Injury, and Profibrotic Markers

Background: Renal (acute kidney injury, AKI) involvement in COVID-19 patients is associated with high mortality and morbidity. Critically ill COVID-19 patients are at twice the risk of in hospital mortality compared to nonCOVID AKI patients. The cell types that succumb to direct or indirect damage and the associated abnormal biological responses are unclear. New generation single cell technologies have the potential to provide insights into physiological states and molecular mechanisms in COVID-AKI. One of the key limitations is that biopsies are not routinely performed and the risks of procuring an additional research core is indeterminate making it difficult to get direct insights into the landscape of COVID-AKI disease in the kidney at genome wide and cellular scale. Method(s): We developed an innovative method that used remnant kidney biopsy tissue from OCT-embedded frozen diagnostic pathology biopsy core and generated single nucleus transcriptome (snRNAseq) of COVID-AKI from as little as 1 mm3 of tissue. Comparative analysis of snRNAseq of 4 COVID-AKI and 4 control cortical biopsies was done in conjunction with urine transcriptomics to find overlapping genes in these two datasets representing COVID-AKI-enriched genes and the corresponding cell types in the kidney. Result(s): snRNAseq of COVID-AKI remnant or control frozen kidney biopsies (15659 and 15604 nuclei passing QC, respectively) identified all major and minor cell types. Differential expression analysis of COVID-AKI biopsies showed pathways enriched in viral response, kidney regeneration, WNT signaling, cancer, kidney development and cytokines in several nephron epithelial cells including kidney injury markers and fibrosis indicating robust remodelling in various cell types. Ten genes were also detected in urine cells of COVID-AKI patients as potential biomarkers. Two of these genes, LRP1B and PDE3A, have been recently implicated in driving fibrosis in COVID-AKI model systems. Conclusion(s): snRNAseq is feasible on leftover kidney biopsy tissue using minimum amount of sample and enabled identification of altered kidney cell types and states with several novel genes associated with tissue injury, remodelling and fibrosis.

Interaction-based transcriptome analysis via differential network inference.

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.

Microarray-Based Transcriptome Analysis of Peripheral Blood Mononuclear Cells in Lung Cancer Patients

: Lung cancer is the most commonly occurring cancer in men worldwide. To search for new biological markers of this pathology, the transcriptome of the blood mononuclear cells from patients and healthy donors (residents of Kemerovo oblast, Russia) was studied using SurePrint G3 Human Gene Expression microarray technology. A total of 288 differentially expressed genes were identified, including 108 up-regulated genes and 180 down-regulated genes. Functional enrichment analysis using the WebGestalt resource and different databases (Gene Ontology, KEGG, and Reactome) indicated changes in the expression profiles of genes involved in the processes of immune response, protein synthesis, cell cycle control, and apoptosis. Analysis of protein–protein interactions using the STRING algorithm made it possible to identify functional clusters of gene products with different expression levels.

Hyperinflammatory ARDS Is Characterized by Interferon-High, Neutrophil-Predominant Inflammation in the Lower Respiratory Tract

Background: Latent class analyses in patients with acute respiratory distress syndrome (ARDS) have identified “hyper-inflammatory” and “hypo-inflammatory” phenotypes with divergent clinical outcomes and treatment responses. ARDS phenotypes are defined using plasma biomarkers and clinical variables. It is currently unknown if these phenotypes have distinct pulmonary biology and if pre-clinical models of disease replicate the biology of either phenotype. Methods: 45 subjects with ARDS (Berlin Definition) and 5 mechanically ventilated controls were selected from cohorts of mechanically ventilated patients at UCSF and ZSFG. Patients with COVID-19 were excluded from this analysis. A 3-variable classifier model (plasma IL-8, protein C, and bicarbonate;Sinha 2020) was used to assign ARDS phenotypes. Tracheal aspirate (TA) RNA was analyzed using established bulk and single-cell sequencing pipelines (Langelier 2018, Sarma 2021). Differentially expressed (DE) genes were analyzed using Ingenuity Pathway Analysis (IPA). Microbial community composition was analyzed with vegan. Fgsea was used to test for enrichment of gene sets from experimental ARDS models in genes that were differentially expressed between each phenotype and mechanically ventilated controls. Results: Bulk RNA sequencing (RNAseq) was available from 29 subjects with hypoinflammatory ARDS and 10 subjects with hyperinflammatory ARDS. 2,777 genes were differentially expressed between ARDS phenotypes. IPA identified several candidate upstream regulators of gene expression in hyperinflammatory ARDS including IL6, TNF, IL17C, and interferons (Figure 1A). 2,953 genes were differentially expressed between hyperinflammatory ARDS and 5 ventilated controls;in contrast, only 243 genes were differentially expressed between hypoinflammatory ARDS and controls, suggesting gene expression in the hypoinflammatory phenotype was more heterogeneous. Gene sets from experimental models of acute lung injury were enriched in hyperinflammatory ARDS but not in hypoinflammatory ARDS (Figure 1B). Single cell RNA sequencing (scRNAseq) was available from 6 additional subjects with ARDS, of whom 3 had hyperinflammatory ARDS. 14,843 cells passed quality control filters. Hyperinflammatory ARDS subjects had a markedly higher burden of neutrophils (Figure 1C), including a cluster of stressed neutrophils expressing heat shock protein RNA that was not present in hypoinflammatory ARDS. Expression of a Th1 signature was higher in T cells from hyperinflammatory ARDS. Differential expression analysis in macrophages identified increased expression of genes associated with mortality in a previous study of ARDS patients (Morell 2019). Conclusions: The respiratory tract biology of ARDS phenotypes is distinct. Hyperinflammatory ARDS is characterized by neutrophilic inflammation with distinct immune cell polarization. Transcriptomic profiling identifies candidate preclinical disease models that replicate gene expression observed in hyperinflammatory ARDS.

A multi-tissue study of immune gene expression profiling highlights the key role of the nasal epithelium in COVID-19 severity.

Coronavirus Disease-19 (COVID-19) symptoms range from mild to severe illness; the cause for this differential response to infection remains unknown. Unravelling the immune mechanisms acting at different levels of the colonization process might be key to understand these differences. We carried out a multi-tissue (nasal, buccal and blood; n = 156) gene expression analysis of immune-related genes from patients affected by different COVID-19 severities, and healthy controls through the nCounter technology. Mild and asymptomatic cases showed a powerful innate antiviral response in nasal epithelium, characterized by activation of interferon (IFN) pathway and downstream cascades, successfully controlling the infection at local level. In contrast, weak macrophage/monocyte driven innate antiviral response and lack of IFN signalling activity were present in severe cases. Consequently, oral mucosa from severe patients showed signals of viral activity, cell arresting and viral dissemination to the lower respiratory tract, which ultimately could explain the exacerbated innate immune response and impaired adaptative immune responses observed at systemic level. Results from saliva transcriptome suggest that the buccal cavity might play a key role in SARS-CoV-2 infection and dissemination in patients with worse prognosis. Co-expression network analysis adds further support to these findings, by detecting modules specifically correlated with severity involved in the abovementioned biological routes; this analysis also provides new candidate genes that might be tested as biomarkers in future studies. We also found tissue specific severity-related signatures mainly represented by genes involved in the innate immune system and cytokine/chemokine signalling. Local immune response could be key to determine the course of the systemic response and thus COVID-19 severity. Our findings provide a framework to investigate severity host gene biomarkers and pathways that might be relevant to diagnosis, prognosis, and therapy.