Unraveling potential gene biomarkers for dengue infection through RNA sequencing

This pilot study, aimed to profile the host transcriptome as a potential strategy for identifying specific biomarkers for dengue prediction and detection. High-throughput RNA-sequencing (RNA-seq) was employed to generate host transcriptome profiles in 16 dengue patients and 10 healthy controls. Differentially expressed genes (DEGs) were identified in patients with severe dengue and those with dengue with warning signs compared to healthy individuals. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to elucidate the functions of upregulated and downregulated genes. Compared to healthy controls, 6466 genes were significantly differentially expressed (P < 0.05) in the dengue with warning signs group and 3082 genes in the severe dengue group, with over half being upregulated. The major KEGG pathways implicated included transport and catabolism (14.4%-16.3%), signal transduction (6.6%-7.3%), global and overview maps (6.7%-7.1%), viral diseases (4.6%-4.8%), and the immune system (4.4%-4.6%). Several genes exhibited consistent and significant upregulation across all dengue patients, regardless of severity: Interferon alpha inducible protein 27 (IFI27), Potassium Channel Tetramerisation Domain Containing 14 (KCTD14), Syndecan 1 (SDC1), DCC netrin 1 receptor (DCC), Ubiquitin C-terminal hydrolase L1 (UCHL1), Marginal zone B and B1 cell specific protein (MZB1), Nestin (NES), C-C motif chemokine ligand 2 (CCL2), TNF receptor superfamily member 17 (TNFSF17), and TNF receptor superfamily member 13B (TNFRSF13B). Further analysis revealed potential biomarkers for severe dengue prediction, including TNF superfamily member 15 (TNFSF15), Plasminogen Activator Inhibitor-2 (SERPINB2), motif chemokine ligand 7 (CCL7), aconitate decarboxylase 1 (ACOD1), Metallothionein 1G (MT1G) and Myosin Light Chain Kinase (MYLK2), which were expressed 3.5 times, 2.9 times, 2.3 times, 2.1 times, 1.7 times, and 1.4 times greater, respectively, than dengue patients exhibiting warning signs. The identification of these host biomarkers through RNA-sequencing holds promising implications and potential to augment existing dengue detection algorithms, contributing significantly to improved diagnostic and prognostic capabilities. Seven milliliters (7ml) of human whole blood were collected in sodium heparin vacutainer.Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples using Ficoll-Paque density gradient centrifugation. Total RNA was extracted using the innuPREP RNA mini kit 2.0 (Analytical Jenna). Enrichment of mRNA was performed using Dynabeads mrRNA Purification Kit (Invitrogen) and library preparation was carried out using MGIEasy RNA Library Prep Set (MGI). After library assessment, sequencing was performed on the DNBSEQ-G400 platform (MGI).

本预实验旨在解析宿主转录组，以此作为登革热预测与检测特异性生物标志物的潜在筛选策略。研究采用高通量RNA测序（RNA-seq）技术，对16名登革热患者及10名健康对照的宿主转录组图谱进行构建。分别对比健康个体，在出现预警症状的登革热患者与重症登革热患者中鉴定差异表达基因（DEGs）。通过基因本体（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析，阐明上调及下调基因的功能机制。相较于健康对照，出现预警症状的登革热患者组中共鉴定出6466个显著差异表达基因（P < 0.05），重症登革热患者组则为3082个，其中超过半数基因呈现上调表达。主要富集的KEGG通路包括转运与分解代谢（14.4%~16.3%）、信号转导（6.6%~7.3%）、全局及概述图谱（6.7%~7.1%）、病毒性疾病（4.6%~4.8%）以及免疫系统（4.4%~4.6%）。所有登革热患者（无论病情严重程度）均存在多组持续显著上调的基因，包括干扰素α诱导蛋白27（IFI27）、钾通道四聚化结构域包含蛋白14（KCTD14）、多配体蛋白聚糖1（SDC1）、DCC神经纤毛蛋白1受体（DCC）、泛素C末端水解酶L1（UCHL1）、边缘区B细胞及B1细胞特异性蛋白（MZB1）、巢蛋白（NES）、C-C基序趋化因子配体2（CCL2）、肿瘤坏死因子受体超家族成员17（TNFSF17）以及肿瘤坏死因子受体超家族成员13B（TNFRSF13B）。进一步分析筛选出可用于重症登革热预测的潜在生物标志物，包括肿瘤坏死因子超家族成员15（TNFSF15）、纤溶酶原激活物抑制剂-2（SERPINB2）、趋化因子配体7（CCL7）、顺乌头酸脱羧酶1（ACOD1）、金属硫蛋白1G（MT1G）以及肌球蛋白轻链激酶（MYLK2），其表达水平较出现预警症状的登革热患者分别升高3.5倍、2.9倍、2.3倍、2.1倍、1.7倍与1.4倍。本研究通过RNA测序筛选宿主生物标志物，有望优化现有登革热检测算法，显著提升诊断与预后能力。研究采集7 mL人全血于肝素钠真空采血管中，采用Ficoll-Paque密度梯度离心法从全血样本中分离外周血单个核细胞（PBMCs）。使用innuPREP RNA mini kit 2.0（Analytical Jenna）提取总RNA，通过Dynabeads mRNA Purification Kit（Invitrogen）富集mRNA，并采用MGIEasy RNA Library Prep Set（MGI）完成文库构建。文库质检合格后，在DNBSEQ-G400平台（MGI）上进行测序。