Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

A functional genomics predictive network model identifies regulators of inflammatory bowel disease: Microarray Analysis of Human Blood and Intestinal Biopsy Samples from a Phase 2b, Double-blind, Placebo-controlled Study of Ustekinumab in Crohn's Disease

ABSTRACT: Microarray Analysis of Human Whole Blood and Intestinal Biopsy Samples from a Phase 2b, Multicenter, Randomized, Double-blind, Placebo-controlled, Parallel-group Study of Ustekinumab in Crohn’s Disease 329 Crohn's biopsies from multiple regions in the intestine of 87 anti TNFa refractory patients and blood samples from 204 patients at Week 0 prior to drug treatment are included in this study

ORGANISM(S): Homo sapiens

SUBMITTER: Eric Schadt

PROVIDER: E-GEOD-100833 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

A functional genomics predictive network model identifies regulators of inflammatory bowel disease.

Peters Lauren A LA Perrigoue Jacqueline J Mortha Arthur A Iuga Alina A Song Won-Min WM Neiman Eric M EM Llewellyn Sean R SR Di Narzo Antonio A Kidd Brian A BA Telesco Shannon E SE Zhao Yongzhong Y Stojmirovic Aleksandar A Sendecki Jocelyn J Shameer Khader K Miotto Riccardo R Losic Bojan B Shah Hardik H Lee Eunjee E Wang Minghui M Faith Jeremiah J JJ Kasarskis Andrew A Brodmerkel Carrie C Curran Mark M Das Anuk A Friedman Joshua R JR Fukui Yoshinori Y Humphrey Mary Beth MB Iritani Brian M BM Sibinga Nicholas N Tarrant Teresa K TK Argmann Carmen C Hao Ke K Roussos Panos P Zhu Jun J Zhang Bin B Dobrin Radu R Mayer Lloyd F LF Schadt Eric E EE

Nature genetics 20170911 10

A major challenge in inflammatory bowel disease (IBD) is the integration of diverse IBD data sets to construct predictive models of IBD. We present a predictive model of the immune component of IBD that informs causal relationships among loci previously linked to IBD through genome-wide association studies (GWAS) using functional and regulatory annotations that relate to the cells, tissues, and pathophysiology of IBD. Our model consists of individual networks constructed using molecular data gen ...[more]

PMID: 28892060

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Project description:Background & Aims Obesity is a major risk factor for metabolic associated steatotic liver disease (MASLD) which can progress from metabolic associated steatotic liver (MASL) to metabolic associated steatohepatitis (MASH). There are currently no effective and validated screening tools to stratify obese patients with a greater risk for MASH, independent of liver fibrosis, at a population level. We aimed to characterise the highly abundant and small protein plasma proteomes of worsening MASLD and overlay the liver-secreted proteome to generate a predictive model to stratify patients with and without MASH. Methods Venous blood and liver wedge biopsies were taken from 160 patients undergoing bariatric surgery. MASLD severity was assessed histologically. Liver biopsies from a subset of 96 patients were precision-cut and cultured to assess liver-secreted proteins. Proteomic analysis was performed using liquid chromatography-tandem mass spectrometry on the plasma and the incubation medium cutoff the liver slices. Results Current non-invasive scores failed to stratify MASH in our cohort. The top200 plasma proteome exhibited mild changes in patients with MASH compared to those with No pathology, while the SPEA approach identified substantial differences in plasma proteins of patients with MASH compared to those without MASH. Liver-secreted proteins were remodelled in MASH compared to MASL and individuals with No pathology. There were no significant changes in the liver-secreted proteins and plasma proteome when comparing MASL to those with No pathology. The APASHA model, comprised of APOF, PCSK9, AFM, and S100A6, HbA1c % and AZGP1 stratified MASH in the discovery (AUROC: 0.887, p<0.0001) and validation cohorts (AUROC:0.7673, p=0.0002) and outcompeted other non-invasive scores. Conclusions These proteomic investigations provide a detailed description of liver-secreted and plasma proteome with worsening MASLD. MASH remodels plasma and liver-secreted proteins. Plasma proteomics generated the APASHA model validated in two Australian bariatric cohorts. Further investigation is warranted to interrogate the utility of the APASHA model as a non-invasive risk prediction model in additional cohorts. Lay Summary: Metabolic-associated steatohepatitis (MASH), a more advanced form of metabolic-associated steatotic liver disease (MASLD), alters the levels of many proteins secreted by the liver and in the blood and those. The APASHA model, which is based on proteins that change in the blood or are secreted by the liver in cases of MASH, could potentially be developed into a simple blood test to predict MASH in high-risk groups.

Dataset Information

A functional genomics predictive network model identifies regulators of inflammatory bowel disease: Microarray Analysis of Human Blood and Intestinal Biopsy Samples from a Phase 2b, Double-blind, Placebo-controlled Study of Ustekinumab in Crohn's Disease

Publications

A functional genomics predictive network model identifies regulators of inflammatory bowel disease.

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