PXD052784

PXD052784 is an original dataset announced via ProteomeXchange.

Title	Integrated liver-secreted and plasma proteomics approaches identify a predictive model that stratifies MASH in bariatric patients
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.
HostingRepository	PRIDE
AnnounceDate	2025-04-14
AnnouncementXML	Submission_2025-04-13_23:39:42.964.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Mark Larance
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	acetylated residue; monohydroxylated residue; iodoacetamide derivatized residue
Instrument	TripleTOF 5600

Revision	Datetime	Status	ChangeLog Entry
0	2024-06-02 20:27:18	ID requested
⏵ 1	2025-04-13 23:39:43	announced

Dataset with its publication pending

submitter keyword: MASLD, hepatokine, APASHA, MASL, liver-secreted proteome,Metabolic associated steatohepatitis, biomarker, plasma proteomics, MASH, NASH, NAFLD

Mark Larance
contact affiliation	Charles Perkins Centre, School of Medical Sciences, University of Sydney, Sydney, Australia.
contact email	mark.larance@sydney.edu.au
lab head
Mark Larance
contact affiliation	The University of Sydney
contact email	mark.larance@sydney.edu.au
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD052784
     1. Label: PRIDE project
     2. Name: Integrated liver-secreted and plasma proteomics approaches identify a predictive model that stratifies MASH in bariatric patients