The Functional Roles of MDSCs in Severe COVID-19 Pathogenesis.

Severe COVID-19 is a major cause of morbidity and mortality worldwide, especially among those with co-morbidities, the elderly, and the immunocompromised. However, the molecular determinants critical for severe COVID-19 progression remain to be fully elucidated. Meta-analyses of transcriptomic RNAseq and single-cell sequencing datasets comparing severe and mild COVID-19 patients have demonstrated that the early expansion of myeloid-derived suppressor cells (MDSCs) could be a key feature of severe COVID-19 progression. Besides serving as potential early prognostic biomarkers for severe COVID-19 progression, several studies have also indicated the functional roles of MDSCs in severe COVID-19 pathogenesis and possibly even long COVID. Given the potential links between MDSCs and severe COVID-19, we examine the existing literature summarizing the characteristics of MDSCs, provide evidence of MDSCs in facilitating severe COVID-19 pathogenesis, and discuss the potential therapeutic avenues that can be explored to reduce the risk and burden of severe COVID-19. We also provide a web app where users can visualize the temporal changes in specific genes or MDSC-related gene sets during severe COVID-19 progression and disease resolution, based on our previous study.

STAGEs: A web-based tool that integrates data visualization and pathway enrichment analysis for gene expression studies.

Gene expression profiling has helped tremendously in the understanding of biological processes and diseases. However, interpreting processed data to gain insights into biological mechanisms remain challenging, especially to the non-bioinformaticians, as many of these data visualization and pathway analysis tools require extensive data formatting. To circumvent these challenges, we developed STAGEs (Static and Temporal Analysis of Gene Expression studies) that provides an interactive visualisation of omics analysis outputs. Users can directly upload data created from Excel spreadsheets and use STAGEs to render volcano plots, differentially expressed genes stacked bar charts, pathway enrichment analysis by Enrichr and Gene Set Enrichment Analysis (GSEA) against established pathway databases or customized gene sets, clustergrams and correlation matrices. Moreover, STAGEs takes care of Excel gene to date misconversions, ensuring that every gene is considered for pathway analysis. Output data tables and graphs can be exported, and users can easily customize individual graphs using widgets such as sliders, drop-down menus, text boxes and radio buttons. Collectively, STAGEs is an integrative platform for data analysis, data visualisation and pathway analysis, and is freely available at https://kuanrongchan-stages-stages-vpgh46.streamlitapp.com/ . In addition, developers can customise or modify the web tool locally based on our existing codes, which is publicly available at https://github.com/kuanrongchan/STAGES .

Mass Spectrometry-based Lipidomics, Lipid Bioenergetics, and Web Tool for Lipid Profiling and Quantification in Human Cells.

Lipids can play diverse roles in metabolism, signaling, transport across membranes, regulating body temperature, and inflammation. Some viruses have evolved to exploit lipids in human cells to promote viral entry, fusion, replication, assembly, and energy production through fatty acid beta-oxidation. Hence, studying the virus-lipid interactions provides an opportunity to understand the biological processes involved in the viral life cycle, which can facilitate the development of antivirals. Due to the diversity and complexity of lipids, the assessment of lipid utilization in infected host cells can be challenging. However, the development of mass spectrometry, bioenergetics profiling, and bioinformatics has significantly advanced our knowledge on the study of lipidomics. Herein, we describe the detailed methods for lipid extraction, mass spectrometry, and assessment of fatty acid oxidation on cellular bioenergetics, as well as the bioinformatics approaches for detailed lipid analysis and utilization in host cells. These methods were employed for the investigation of lipid alterations in TMEM41B- and VMP1-deficient cells, where we previously found global dysregulations of the lipidome in these cells. Furthermore, we developed a web app to plot clustermaps or heatmaps for mass spectrometry data that is open source and can be hosted locally or at https://kuanrongchan-lipid-metabolite-analysis-app-k4im47.streamlit.app/. This protocol provides an efficient step-by-step methodology to assess lipid composition and usage in host cells.

Early peripheral blood MCEMP1 and HLA-DRA expression predicts COVID-19 prognosis.

BACKGROUND: Mass vaccination has dramatically reduced the incidence of severe COVID-19, with most cases now presenting as self-limiting upper respiratory tract infections. However, those with co-morbidities, the elderly and immunocompromised, as well as the unvaccinated, remain disproportionately vulnerable to severe COVID-19 and its sequelae. Furthermore, as the effectiveness of vaccination wanes with time, immune escape SARS-CoV-2 variants could emerge to cause severe COVID-19. Reliable prognostic biomarkers for severe disease could be used as early indicator of re-emergence of severe COVID-19 as well as for triaging of patients for antiviral therapy. METHODS: We performed a systematic review and re-analysis of 7 publicly available datasets, analysing a total of 140 severe and 181 mild COVID-19 patients, to determine the most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients. In addition, we included an independent cohort where blood transcriptomics of COVID-19 patients were prospectively and longitudinally monitored previously, to track the time in which these gene expression changes occur before nadir of respiratory function. Single cell RNA-sequencing of peripheral blood mononuclear cells from publicly available datasets was then used to determine the immune cell subsets involved. FINDINGS: The most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients were MCEMP1, HLA-DRA and ETS1 across the 7 transcriptomics datasets. Moreover, we found significantly heightened MCEMP1 and reduced HLA-DRA expression as early as four days before the nadir of respiratory function, and the differential expression of MCEMP1 and HLA-DRA occurred predominantly in CD14+ cells. The online platform which we developed is publicly available at https://kuanrongchan-covid19-severity-app-t7l38g.streamlitapp.com/, for users to query gene expression differences between severe and mild COVID-19 patients in these datasets. INTERPRETATION: Elevated MCEMP1 and reduced HLA-DRA gene expression in CD14+ cells during the early phase of disease are prognostic of severe COVID-19. FUNDING: K.R.C is funded by the National Medical Research Council (NMRC) of Singapore under the Open Fund Individual Research Grant (MOH-000610). E.E.O. is funded by the NMRC Senior Clinician-Scientist Award (MOH-000135-00). J.G.H.L. is funded by the NMRC under the Clinician-Scientist Award (NMRC/CSAINV/013/2016-01). S.K. is funded by the NMRC under the Transition Award. This study was sponsored in part by a generous gift from The Hour Glass.

RNase2 is a possible trigger of acute-on-chronic inflammation leading to mRNA vaccine-associated cardiac complication.

BACKGROUND: Post-mRNA vaccination-associated cardiac complication is a rare but life-threatening adverse event. Its risk has been well balanced by the benefit of vaccination-induced protection against severe COVID-19. As the rate of severe COVID-19 has consequently declined, future booster vaccination to sustain immunity, especially against infection with new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, may encounter benefit-risk ratios that are less favorable than at the start of the COVID-19 vaccination campaign. Understanding the pathogenesis of rare but severe vaccine-associated adverse events to minimize its risk is thus urgent. METHODS: Here, we report a serendipitous finding of a case of cardiac complication following a third shot of COVID-19 mRNA vaccine. As this case was enrolled in a cohort study, pre-vaccination and pre-symptomatic blood samples were available for genomic and multiplex cytokine analyses. FINDINGS: These analyses revealed the presence of subclinical chronic inflammation, with an elevated expression of RNASE2 at pre-booster baseline as a possible trigger of an acute-on-chronic inflammation that resulted in the cardiac complication. RNASE2 encodes for the ribonuclease RNase2, which cleaves RNA at the 3' side of uridine, which may thus remove the only Toll-like receptor (TLR)-avoidance safety feature of current mRNA vaccines. CONCLUSIONS: These pre-booster and pre-symptomatic gene and cytokine expression data provide unique insights into the possible pathogenesis of vaccine-associated cardiac complication and suggest the incorporation of additional nucleoside modification for an added safety margin. FUNDING: This work was funded by the NMRC Open Fund-Large Collaborative Grant on Integrated Innovations on Infectious Diseases (OFLCG19May-0034).

Gene Updater: a web tool that autocorrects and updates for Excel misidentified gene names.

Opening and processing gene expression data files in Excel runs into the inadvertent risk of converting gene names to dates. As pathway analysis tools rely on gene symbols to query against pathway databases, the genes that are converted to dates will not be recognized, potentially causing voids in pathway analysis. Molecular pathways related to cell division, exocytosis, cilium assembly, protein ubiquitination and nitric oxide biosynthesis were found to be most affected by Excel auto-conversion. A plausible solution is hence to update these genes and dates to the newly approved gene names as recommended by the HUGO Gene Nomenclature Committee (HGNC), which are resilient to Excel auto-conversion. Herein, we developed a web tool with Streamlit that can convert old gene names and dates back into the new gene names recommended by HGNC. The web app is named Gene Updater, which is open source and can be either hosted locally or at https://share.streamlit.io/kuanrongchan/date-to-gene-converter/main/date_gene_tool.py . Additionally, as Mar-01 and Mar-02 can each be potentially mapped to 2 different gene names, users can assign the date terms to the appropriate gene names within the Gene Updater web tool. This user-friendly web tool ensures that the accuracy and integrity of gene expression data is preserved by minimizing errors in labelling gene names due to Excel auto-conversions.