High-Density Blood Transcriptomics Reveals Precision Immune Signatures of SARS-CoV-2 Infection in Hospitalized Individuals

Abstract

The immune response to COVID-19 infection is variable. How COVID-19 influences clinical outcomes in hospitalized patients needs to be understood through readily obtainable biological materials, such as blood. We hypothesized that a high-density analysis of host (and pathogen) blood RNA in hospitalized patients with SARS-CoV-2 would provide mechanistic insights into the heterogeneity of response amongst COVID-19 patients when combined with advanced multidimensional bioinformatics for RNA. We enrolled 36 hospitalized COVID-19 patients (11 died) and 15 controls, collecting 74 blood PAXgene RNA tubes at multiple timepoints, one early and in 23 patients after treatment with various therapies. Total RNAseq was performed at high-density, with >160 million paired-end, 150 base pair reads per sample, representing the most sequenced bases per sample for any publicly deposited blood PAXgene tube study. There are 770 genes significantly altered in the blood of COVID-19 patients associated with antiviral defense, mitotic cell cycle, type I interferon signaling, and severe viral infections. Immune genes activated include those associated with neutrophil mechanisms, secretory granules, and neutrophil extracellular traps (NETs), along with decreased gene expression in lymphocytes and clonal expansion of the acquired immune response. Therapies such as convalescent serum and dexamethasone reduced many of the blood expression signatures of COVID-19. Severely ill or deceased patients are marked by various secondary infections, unique gene patterns, dysregulated innate response, and peripheral organ damage not otherwise found in the cohort. High-density transcriptomic data offers shared gene expression signatures, providing unique insights into the immune system and individualized signatures of patients that could be used to understand the patient’s clinical condition. Whole blood transcriptomics provides patient-level insights for immune activation, immune repertoire, and secondary infections that can further guide precision treatment.

Role of Senescence and Aging in SARS-CoV-2 Infection and COVID-19 Disease

“…Transcriptome wide analyses of blood from severe COVID-19 patients reveals a series of activated immune system genes including those associated with neutrophils, secretory granules, neutrophil extracellular traps (NETS) along with clonal expansion of adaptive immune response and diminished lymphocyte gene expression [ 188 ]. The following sections provide an overview of the exuberant innate and poor adaptive immune responses and subsequent cytokine storm and tissue damage sequalae observed in severe cases of COVID-19.…”

CCR5 and Biological Complexity: The Need for Data Integration and Educational Materials to Address Genetic/Biological Reductionism at the Interface of Ethical, Legal, and Social Implications

“…The phenotypes seen in the Geno2MP data suggested a broader analysis of CCR5 in disease pathologies. With the robust expression of CCR5 in blood and our history in studying the blood of immune challenged individuals, we selected to process CCR5 biology for blood-based RNAseq of 7,280 samples from 62 different BioProjects and three of our studies where we have patient-to-transcriptome insights ( 74 – 76 ) ( Figure 7 ). All these samples were blood collected into RNA PAXgene tubes to standardize sample collection.…”

“…Therefore, we focused analysis on three cohorts our team collected blood PAXgene tube RNAseq in hospitalized patients with MODS, RSV, or COVID-19 in an age range from weeks of life to elderly. The adult hospitalized COVID-19 cohort shows highest CCR5 expression in the male Hispanic control patient 21 age 50-59 ( Figure 7C ), who had the highest interferon response, was noted to have a unique transcriptome, was the furthest outlier of the control samples, had markers of multiple organ damage, and generally seemed to be a highly divergent sample from the cohort ( 74 ). The second highest was a 50-59 yo European ancestry male who had a lethal case of COVID-19 marked by a SAPSII score of 61, suggesting multiple organ complications, had a robust interferon response, elevated cytokine expression profile, the highest cell markers of peripheral monocytes, and a weak clonal expansion of the immune repertoire.…”

“…Open Targets Genetics ( 73 ) was used for the understanding of GWAS and pheWAS associations. Samples from our previous RNAseq work and details of methods used can be found in the three publications for MODS, RSV, or COVID-19 ( 74 – 76 ). All PAXgene tube blood RNAseq datasets within the NCBI SRA were downloaded with the SRA toolkit ( ) and processed for abundance using Salmon_0.14.1 ( 77 ) and the Gencode38 transcriptome ( 78 ).…”

“…All PAXgene tube blood RNAseq datasets within the NCBI SRA were downloaded with the SRA toolkit ( ) and processed for abundance using Salmon_0.14.1 ( 77 ) and the Gencode38 transcriptome ( 78 ). Microglia datasets were extracted from BioProjects PRJNA649597, PRJNA662330, PRJNA665286, PRJNA667596, PRJNA688478, PRJNA689841, PRJNA387182, PRJNA483247 and the blood RNAseq datasets from BioProjects PRJEB14743, PRJEB20731, PRJEB23048, PRJEB27958, PRJEB27965, PRJEB33892, PRJEB36928, PRJEB41073, PRJEB44660, PRJNA201433, PRJNA230906, PRJNA232593, PRJNA251404, PRJNA277352, PRJNA305001, PRJNA315611, PRJNA327986, PRJNA329148, PRJNA352062, PRJNA354367, PRJNA357628, PRJNA358580, PRJNA369684, PRJNA378794, PRJNA380820, PRJNA384259, PRJNA390289, PRJNA397222, PRJNA398240, PRJNA401870, PRJNA427575, PRJNA437114, PRJNA454694, PRJNA476781, PRJNA493832, PRJNA494155, PRJNA504827, PRJNA511891, PRJNA526259, PRJNA526839, PRJNA533086, PRJNA552286, PRJNA562305, PRJNA588242, PRJNA591657, PRJNA600846, PRJNA601661, PRJNA607120, PRJNA630674, PRJNA632871, PRJNA634938, PRJNA638653, PRJNA639278, PRJNA647880, PRJNA664368, PRJNA679264, PRJNA679331, PRJNA680771, PRJNA683803, PRJNA686397, PRJNA702558, PRJNA728070 in addition to our groups studies on MODS, RSV, and COVID-19 ( 74 – 76 ). All Gencode38 mapped reads for these samples can be found at .…”