High-Throughput Identification of the Plasma Proteomic Signature of Inflammatory Bowel Disease

Abstract

Background: The molecular aetiology of inflammatory bowel disease [IBD] and its two subtypes, ulcerative colitis [UC] and Crohn's disease [CD], have been carefully investigated at genome and transcriptome levels. Recent advances in high-throughput proteome quantification has enabled comprehensive large-scale plasma proteomics studies of IBD.

Methods: The study used two cohorts: [1] The CERTIFI-cohort: 42 samples from the CERTIFI trial of anti-TNFα-refractory CD patients; [2] the PROgECT-UNITI-HCs cohort: 46 UC samples of the PROgECT study, 84 CD samples of the UNITI I and UNITI II studies, and 72 healthy controls recruited in Mount Sinai Hospital, New York, USA. The plasma proteome for these two cohorts was quantified using high-throughput platforms.

Results: For the PROgECT-UNITI-HCs cohort, we measured a total of 1310 proteins. Of these, 493 proteins showed different plasma levels in IBD patients to the plasma levels in controls at 10% false discovery rate [FDR], among which 11 proteins had a fold change greater than 2. The proteins upregulated in IBD were associated with immunity functionality, whereas the proteins downregulated in IBD were associated with nutrition and metabolism. The proteomic profiles were very similar between UC and CD. In the CERTIFI cohort, 1014 proteins were measured, and it was found that the plasma protein level had little correlation with the blood or intestine transcriptomes.

Conclusions: We report the largest proteomics study to date on IBD and controls. A large proportion of plasma proteins are altered in IBD, which provides insights into the disease aetiology and indicates a potential for biomarker discovery.

Keywords: Proteomics; differential expression analysis; inflammatory bowel disease; proteomic quantitative trait loci.

Figure 1.

Study design and workflow. [A] Data generation from the CERTIFI cohort; [B] data generation from the PROgECT-UNITI-HCs cohort; [C] differential protein analysis workflow.

Figure 2.

Comparison of CD and UC proteomic signatures. [A] Venn Diagram of the overlap of the 10% false discovery rate [FDR] signature probes lists. [B] Scatterplot of the estimated log2-fold change [FC] of CD vs HCs [horizontal axis] and UC vs HCs [vertical axis]; dotted lines mark the ±2 effect sizes thresholds. Each circle represents a somalogic probe. All tested probes are reported. [C] Scatterplot of the estimated t- test of the CD vs HCs [horizontal axis] and UC vs HCs [vertical axis]. Dotted lines mark the ±2 significance thresholds. Each circle represents somalogic probe. All tested probes are reported.

Figure 3.

Volcano plots of differential protein expression analysis. [A] CD vs UC signature. [B] IBD vs HCs signature; dotted blue line marks the empirical 10% false discovery rate [FDR] significance threshold, obtained by permuting the input dataset and repeating the analysis 1000 times. The top five significant proteins are labelled.

Figure 4.

Enrichment for genetic risk loci. Log-2 odds ratios of overlap between IBD signature (10% false discovery rate [FDR]) proteins and genes/diseases from the NHGRI-EBI catalog of genome-wide association studies. The IBD signature was further split into a list of IBD-increased and IBD-decreased proteins, respectively.