Gene expression profiling of corona virus microarray datasets to identify crucial targets in COVID-19 patients

Ramesh, Priyanka; Shanthi, V.; Ramanathan, K.

doi:10.1016/j.genrep.2020.100980

Cited by 13 publications

(15 citation statements)

References 34 publications

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“…The rationale for the differential expression method employed in this analysis was based on the adoption of the same or similar methods described throughout biomedical literature during the time of work performed, through to the time of publication. Specifically, DESeq2 has been commonly used to compute differential expression in disease conditions compared to some reference condition, including ulcerative colitis (Lee et al 2020), aortic aneurism (Zalewski et al 2020a), lung adenocarcinoma (Xiong et al 2020), COVID-19 (Ramesh et al 2021), and more (Applebaum et al 2020; Nali et al 2020; Zalewski et al 2020b). Common criteria used to call differential expression includes P < 0.05 (after adjustment for multiple testing) and a log2FC magnitude of at least 1.0 (Andrade et al 2020; Colpo et al 2020), although the exact parametric space used across studies is variable with respect to P , fold change, and FDR levels (Applebaum et al 2020; Grabski et al 2020; Zalewski et al 2020b).…”

Section: Discussionmentioning

confidence: 99%

Differential expression of glycosyltransferases identified through comprehensive pan-cancer analysis

Vora

Cauley

et al. 2021

Preprint

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Despite accumulating evidence supporting a role for glycosylation in cancer progression and prognosis, the complexity of the human glycome and glycoproteome poses many challenges to understanding glycosylation-related events in cancer. In this study, a multifaceted genomics approach was applied to analyze the impact of differential expression of glycosyltransferases (GTs) in 16 cancers. An enzyme list was compiled and curated from numerous resources to create a consensus set of GTs. Resulting enzymes were analyzed for differential expression in cancer, and findings were integrated with experimental evidence from other analyses, including: similarity of healthy expression patterns across orthologous genes, miRNA expression, automatically-mined literature, curation of known cancer biomarkers, N-glycosylation impact, and survival analysis. The resulting list of GTs comprises 222 human enzymes based on annotations from five databases, 84 of which were differentially expressed in more than five cancers, and 14 of which were observed with the same direction of expression change across all implicated cancers. 25 high-value GT candidates were identified by cross-referencing multimodal analysis results, including PYGM, FUT6 and additional fucosyltransferases, several UDP-glucuronosyltransferases, and others, and are suggested for prioritization in future cancer biomarker studies. Relevant findings are available through OncoMX at https://data.oncomx.org, and the overarching pipeline can be used as a framework for similarly analysis across diverse evidence types in cancer. This work is expected to improve the understanding of glycosylation in cancer by transparently defining the space of glycosyltransferase enzymes and harmonizing variable experimental data to enable improved generation of data-driven cancer biomarker hypotheses.

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Section: Discussionmentioning

confidence: 99%

Differential expression of glycosyltransferases identified through comprehensive pan-cancer analysis

Vora

Cauley

et al. 2021

Preprint

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“…Our scenarios are mostly focused on integrative studies from already published COVID-19-related works; we summarize their workflows in Figure 2, while a discussion of each of them is provided below. Specifically, the first five retrace the previously mentioned integrative studies described in [34,36,1,26,35]. Despite using different analytical strategies, these studies target similar results for: extraction of relevant genes, drug repurposing, and therapeutic targets identification.…”

Section: Integrative Studies: Possible Scenariosmentioning

confidence: 94%

“…[Table 1 near here] A number of very recent papers [34,1,26,35,36] provide a set of relevant GEO Series with microarray data of interest in this chapter. In Table 1, we report a list of such datasets, plus additional ones; these are all produced with microarray technology, which refer to human gene expression analysis on tissues or cell lines infected by the most interesting viruses for our purpose (i.e., SARS-CoV-2, SARS-CoV, MERS-CoV, and Influenza A).…”

Section: Data Acquisitionmentioning

confidence: 99%

“…In the following, we report the most common cases of data integration and knowledge integration observed in several COVID-19-related studies. These levels were first identified in the five integrative studies [34,36,1,26,35] on which this chapter is focused, as deemed particularly explanatory of our scope of interest; relevant details are provided in Table 2. Knowledge integration levels include: K1: Meta-analysis of different viruses, extracting differentially expressed genes (sets intersections and differences, possibly based on the up/down regulation).…”

Section: Integration Levelsmentioning

confidence: 99%

“…In addition, we propose two novel scenarios, as study options for data analysts: soon, both of them will become completely feasible thanks to the increasing number of data that is being progressively collected, as a consequence of the global COVID-Fig. 2 Seven scenarios of integrative studies: five extracted from literature [36,1,26,35,34] and two proposed as prompts for future studies. Note that the acronym NA stands for Network Analysis.…”

Section: Integrative Studies: Possible Scenariosmentioning

confidence: 99%

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Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19–Related Studies

Bernasconi

Cascianelli

2021

Methods in Molecular Biology

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The COVID-19 pandemic has hit heavily many aspects of our lives. At this time, genomic research is concerned with exploiting available datasets and knowledge to fuel discovery on this novel disease. Studies that can precisely characterize the gene expression profiles of human hosts infected by SARS-CoV-2 are of significant relevance. However, not many such experiments have yet been produced to date, nor made publicly available online. Thus, it is of paramount importance that data analysts explore all possibilities to integrate information coming from similar viruses and related diseases; interestingly, microarray gene profile experiments become extremely valuable for this purpose. This chapter reviews the aspects that should be considered when integrating transcriptomics data, considering mainly samples infected by different viruses and combining together various data types and also the extracted knowledge. It describes a series of scenarios from studies performed in literature and it suggests possible other directions of noteworthy integration.

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An experimental in silico study on COVID‐19: Response of neutrophil‐related genes to antibiotics

Mousavi

Lotfi

Salmanizadeh

et al. 2022

Health Science Reports

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Background and Aims All components of the immune system are involved in alleviating severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection. Further research is required to provide detailed insights into COVID‐19‐related immune compartments and pathways. In addition, a significant percentage of hospitalized COVID‐19 patients suspect bacterial infections and antimicrobial resistance occurs following antibiotics treatment. The aim of this study was to evaluate the possible effects of antibiotics on the response of neutrophil‐related genes in SARS‐CoV‐2 patients by an experimental in silico study. Methods The two data sets GSE1739 and GSE21802 including 10 SARS positive patients and 35 influenza A (H1N1) patients were analyzed, respectively. Differentially expressed genes (DEGs) between these two data sets were determined by GEO2R analysis and the Venn diagram online tool. After determining the hub genes involved in immune responses, the expression of these genes in 30 COVID‐19 patients and 30 healthy individuals was analyzed by real‐time polymerase chain reaction (PCR). All patients received antibiotics, including levofloxacin, colistin, meropenem, and ceftazidime. Results GEO2R analysis detected 240 and 120 DEGs in GSE21802 and GSE1739, respectively. Twenty DEGs were considered as enriched hub genes involved in immune processes such as neutrophil degranulation, neutrophil activation, and antimicrobial humoral response. The central nodes were attributed to the genes of neutrophil elastase (ELANE), arginase 1 (ARG‐1), lipocalin 2 (LCN2), and defensin 4 (DEFA4). Compared to the healthy subjects, the expression of LCN2 and DEFA4 were significantly reduced in COVID‐19 patients. However, no significant differences were observed in the ELANE and AGR‐1 levels between COVID‐19 subjects and the control group. Conclusions Activation and degranulation of neutrophils were observed mainly in SARS, and H1N1 infection processes and antibiotics administration could affect neutrophil activity during viral infection. It can be suggested that antibiotics can decrease inflammation by restoring the expression of neutrophil‐related genes in COVID‐19 patients.

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Gene expression profiling of corona virus microarray datasets to identify crucial targets in COVID-19 patients

Cited by 13 publications

References 34 publications

Differential expression of glycosyltransferases identified through comprehensive pan-cancer analysis

Differential expression of glycosyltransferases identified through comprehensive pan-cancer analysis

Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19–Related Studies

An experimental in silico study on COVID‐19: Response of neutrophil‐related genes to antibiotics

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