Table_4_Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV.xlsx

BackgroundThe 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.
MethodsCOVID-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.
ResultsIn 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.
ConclusionIn 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.

研究背景：重症新型冠状病毒肺炎（coronavirus disease 2019, COVID-19）是由严重急性呼吸综合征冠状病毒2（severe acute respiratory syndrome coronavirus 2, SARS-CoV-2）引发的传染性疾病，已造成现代史上最为严重的大流行。人类免疫缺陷病毒（human immunodeficiency virus, HIV）会破坏免疫系统细胞，削弱机体抵御日常感染与疾病的能力。此外，HIV感染者的COVID-19死亡风险翻倍，且新冠相关转归更差。但现有研究对COVID-19与HIV之间交互串扰的分子机制仍缺乏足够认知。本研究旨在阐明COVID-19与HIV之间的血液转录组串扰机制，并筛选可用于治疗合并HIV感染的COVID-19患者的潜在药物。

研究方法：从基因表达综合数据库（Gene Expression Omnibus, GEO）下载COVID-19相关数据集GSE171110与GSE152418，分别包含54份全血样本与33份外周血单个核细胞样本；同时从GEO数据库获取HIV相关数据集GSE37250，内含537份全血样本。随后，使用"Deseq2"包对COVID-19数据集（GSE171110与GSE152418）进行差异表达基因（differentially expressed genes, DEGs）筛选，使用"limma"包对HIV数据集GSE37250进行差异表达基因筛选。将两组差异表达基因取交集，获得共同差异表达基因，随后开展如下分析：京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路富集分析、基因本体（Gene Ontology, GO）功能富集分析、蛋白质-蛋白质相互作用（protein-protein interaction, PPI）分析、转录因子（transcription factor, TF）候选物筛选、微小RNA（microRNAs, miRNAs）候选物筛选以及药物候选物筛选。

研究结果：本研究共从合并的COVID-19数据集（GSE171110与GSE152418）中筛选得到3213个差异表达基因，从GSE37250数据集中筛选得到1718个差异表达基因。将两组差异表达基因取交集后，共获得394个共同差异表达基因。GO与KEGG富集分析结果显示，共同差异表达基因主要富集于染色体相关及细胞周期相关信号通路。基于共同差异表达基因，通过度算法筛选得到排名前十的核心基因（hub genes）：CCNA2、CCNB1、CDC20、TOP2A、AURKB、PLK1、BUB1B、KIF11、DLGAP5、RRM2。此外，按P值排名筛选得到前十种潜在药物候选物：LUCANTHONE、Dasatinib、etoposide、Enterolactone、troglitazone、testosterone、estradiol、calcitriol、resveratrol、tetradioxin，上述药物或可用于治疗合并HIV感染的COVID-19患者。

研究结论：本研究明确了可用于预测合并HIV感染的COVID-19患者不良预后的潜在分子靶点、信号通路、小分子化合物及潜在生物标志物，可为合并HIV感染的COVID-19患者的精准诊断与治疗提供理论依据。