Bondar Galyna, Cadeiras Martin, Wisniewski Nicholas, Maque Jetrina, Chittoor Jay, Chang Eleanor, Bakir Maral, Starling Charlotte, Shahzad Khurram, Ping Peipei, Reed Elaine, Deng Mario
University of California Los Angeles, Los Angeles, CA, United States of America.
Columbia University, New York, NY, United States of America; East Carolina University, Greenville, NC, United States of America.
PLoS One. 2014 Dec 17;9(12):e115097. doi: 10.1371/journal.pone.0115097. eCollection 2014.
Heart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined.
We collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5-11), and high (≥ 12) risk categories correlating with GEP.
Results indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups.
GEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.
在美国,心力衰竭(HF)的患病率正在上升。机械循环支持(MCS)治疗是晚期心力衰竭(AdHF)患者的一种选择。围手术期,多器官功能障碍(MOD)与设备植入的影响有关,而先前存在的心力衰竭会加剧这种影响。早期识别MOD有助于更好地进行诊断、治疗和风险预测。基因表达谱分析(GEP)用于评估从患者血液样本中获得的外周血单个核细胞(PBMC)转录组的临床表型。全血(WB)样本在临床上更可行,但其与PBMC样本相比的性能尚未确定。
我们于2010年至2011年在一家机构收集了31例接受心胸外科手术的心力衰竭患者(57±15岁)和7名年龄匹配的健康对照者的血液样本。术后在单个时间点(术后第8天中位数)(25 - 75%四分位数间距为7 - 14天)采集WB和PBMC样本,并进行Illumina单色人类基因芯片HT12 v4全基因组表达阵列分析。序贯器官衰竭评估(SOFA)评分用于将MOD的严重程度分为低(≤4分)、中(5 - 11分)和高(≥12分)风险类别,这些类别与GEP相关。
结果表明,与对照组相比,MOD个体的GEP变化方向在从PBMC与WB确定时是相似的。基因本体(GO)分析的主要富集术语包括那些参与炎症反应、细胞凋亡和其他应激反应相关途径的术语。数据显示PBMC和WB之间有35个显著的GO类别和26条途径重叠。此外,使用机器学习工具进行的类别预测表明,PBMC和WB共有的重要基因子集足以作为区分SOFA组的预测指标进行训练。
WB的GEP分析有潜力成为MOD患者免疫监测的临床工具。