Transcriptomic Responses of Peripheral Blood Leukocytes to Cardiac Surgery after Acute Inflammation, and Three Months Recovery

Traumatic perioperative conditions may trigger early systemic responses, activate leukocytes and reprogram the immune system. We hypothesize that leukocyte activation may not revert to pre-surgical states, and that protracted activation may emerge with increased risks of comorbidities. We tested this concept by examining the transcriptomes of monocytes and T cells in a representative observational cohort of patients (n=13) admitted for elective cardiac surgery. Transcriptomes in T cells and monocytes were compared from before surgery (t0), and monocytes were analyzed longitudinally after acute (t24hr), and convalescent (t3m) time points. Monocytes and T cells expressed distinct transcriptomes, reflected by statistically significant differential expression of 558 T cell related genes. Monocytes expressed genes related to protein degradation and presented atypical activation of surface markers and cytoplasmic functions over time. Additionally, monocytes exhibited limited transcriptomic heterogeneity prior to surgery, and long-term patterns of gene expression associated with atherosclerosis showed three temporally distinct signatures. These data establish that post-cardiac surgery transcriptomes of monocytes differ even at three months compared to baselines, which may reflect latent (‘smoldering’) inflammation and persistent progression of tissue degenerative processes that should inform clinical care. Whole blood was collected before the onset of surgery (t0), at 24 hours (t24hr), and during a final follow-up occurring at three months (t3m). Monocytes were separated from other peripheral blood cell types using a Ficoll-Hypaque density gradient. Monocytes were further purified by removal of B cells, NK cells and T cells using the negative separation technique. RNA was isolated from MO samples using commercial kits and protocols (Qiagen, Germantown, MD). The integrity of each RNA sample was validated using the Agilent 2100 Bioanalyser System (Agilent Technologies, Santa Clara, CA). The Next-Generation Sequencing Core at the University of Pennsylvania assisted with generating barcoded libraries. They also provided RNA-seq data using an Illumina NovaSeq 6000. Data outputs were stored as raw FASTQ files and returned to the investigative team for downstream analysis.