Identification of Biological Processes Associated with Pain in Individuals with Sickle Cell Disease

Abstract

Severe acute and chronic pain is the hallmark of sickle cell disease (SCD). Pain heterogeneity between patients suggests variable biological processes contribute, however, these processes are poorly understood. This knowledge gap is a barrier to developing targeted pain therapeutics. Thus, we sought to identify biological processes associated with pain in individuals with SCD. We conducted plasma-based transcriptional analyses of paired plasma samples from SCD patients during baseline health and acute pain and healthy Black controls. We identified transcripts differentially expressed between SCD patients in both states and controls, retaining those with highest gene variance according to median absolute deviation filter. Transcripts were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA) which clusters genes by function and determines correlation with phenotypic traits. Traits of interest were PedsQL SCD ModuleTM pain domains (Pain and Hurt, Pain Impact) and acute visits for pain in prior 3 years (Pain History). We retained WGCNA modules when eigengene exhibited a correlation of ≥0.3 with any phenotypic trait and genes within those modules that exhibited a correlation of ≥0.3 with ≥2 phenotypic traits. Data were subjected to ontological analyses using Database for Annotation, Visualization and Integrated Discovery (DAVID). We analyzed 27 SCD paired samples (baseline health, acute pain) and 45 controls. We identified 3028 differentially expressed transcripts used for WGCNA analyses that delivered 11 modules. There were 1177 genes in 9 modules that matched our filtering criteria and were subjected to analyses with DAVID. Figure 1 displays results including correlation coefficients and key gene ontology biological processes. Our work defined biological processes associated with pain in SCD patients. Our findings suggest inflammatory response and defense response to virus might be key processes that contribute to SCD pain biology. Further understanding of the effect of these processes on pain phenotype could be leveraged for developing targeted pain therapeutics.