Ab0163 functional annotation of differentially-expressed genes between takayasu arteritis and healthy controls using whole-blood transcriptome analysis reveals differences related to protein binding and intracellular signaling processes

Background The pathophysiology of Takayasu arteritis (TAK) is incompletely understood. Gene expression profile of peripheral blood mononuclear cells (PBMCs) in TAK has been scarcely studied and may provide new insights into its pathogenesis. Objectives To identify and functionally annotate differentially expressed genes (DEGs) in immunosuppressive-naïve TAK compared with healthy controls (HCs). Methods Fifteen immunosuppressive-naïve TAK [10 Females, mean (SD) age 34.13(13.28) years] and 10 healthy controls (HC) [8 females, mean (SD) age 31.8(4.57) years] were recruited after obtaining written informed consent. After isolating PBMCs, RNA was extracted using Trizol method. RNA quality was checked using Nanodrop TM (260/280~2, 260/230≥2). Whole gene transcriptome signature was assessed using Affymetrix Clariom TM D human microarray. Using Transcriptome analysis console (version 4.0.3.1.4), signal output from raw CEL files was normalized (group-averages with standard deviations and gene-level limma) to generate CHP files which were analysed for DEGs (up- & down-regulated), filtered on fold-change in TAK versus HC (≥2 or ≤-2) and p value < 0.05.Thereafter, functional annotation of up- and down-regulated genes was performed using Database for Annotation, Visualization, and Integrated Discovery (DAVID) to evaluate Gene Ontology (GO) clusters and processes. Results Of 135750 genes, 558 genes passed filter criteria [up-regulated 185 - Coding 33 (17.84%), micro RNA 1 (0.54%), multiple complexes 93 (50.27%), non-coding 23 (12.43%); down-regulated 373 - Coding 53 (14.21%), micro RNA 61 (16.35%), multiple complexes 35 (9.38%), non-coding 190 (50.94%)] (Volcano plot in Figure 1). After functional annotation for 111 up-regulated genes identified on DAVID, two clusters were generated. Cluster 1 (enrichment score 2.20) comprised hemoglobin complex, oxygen transport activity, heme binding, and oxygen binding pathways. Cluster 2 (enrichment score 0.70) comprised JNK cascade, protein kinase activity, and protein phosphorylation. Sixty-seven up-regulated processes were identified (Table 1). Functional annotation for 14 down-regulated genes identified on DAVID identified one cluster (enrichment score 0.76) comprising extracellular exosomes, cytoplasm, nucleoplasm, cytosol and protein binding pathways. Four down-regulated processes were identified (Table 1). Conclusion We identified an up-regulation of processes related to protein-specific domain binding, serine/threonine kinase activity, magnesium ion binding, oxygen transporter activity, and various cellular components in TAK than in HC. Down-regulated processes in TAK than in controls related to protein translation and generation of exosomes. A detailed pathway analysis of these processes may provide novel pathophysiological insights into TAK. Figure 1. Volcano plot of DEGs for TAK vs HCs Table 1. Functional annotation of top DEGs in TAK compared with HCs using DAVID Functional annotation of up-regulated genes % of total up-regulated DAVID IDs (n= 67) Functional annotation of down-regulated genes % of total down-regulated DAVID IDs (n=4) GO annotation Process GO annotation Process CC Cytosol 45.05% BP Cytoplasmic translation 14.99% CC Membrane 34.23% CC Extracellular exosome 28.67% CC Specific granule membrane 5.41% MF Structural constituent of ribosome 14.36% BP Erythrocyte development 3.60% BP Translation 14.36% CC Tertiary granule membrane 4.50% MF Protein domain specific binding 7.21% MF Magnesium ion binding 6.31% CC Hemoglobin complex 2.70% BP Oxygen transport 2.70% MF Oxygen transporter activity 2.70% CC Ficolin-1-rich granule membrane 3.60% CC Extracellular exosome 19.82% CC Cortical cytoskeleton 2.70% CC Integral component of membrane 36.94% BP Positive regulation of protein serine/threonine kinase activity 3.60% BP= Biological processes; CC=Cellular functions; MF= Molecular functions Acknowledgements Funding from Indian Council of Medical Research (ICMR) – grant number 5/4/ 1-2/2019-NCD-II and grant number No. 3/1/1(20)/2022-NCD-I. Disclosure of Interests None Declared.