Dataset Information

Discrimination of etiologically different cholestasis by modeling proteomics datasets

ABSTRACT: Cholestasis is characterized by disrupted bile flow from the liver to the small intestine. Although etiologically different cholestasis display similar symptoms, diverse factors can contribute to the progression of the disease and determines the appropriate therapeutic option. Therefore, stratifying cholestatic patients is essential for the development of tailor-made treatment strategies. Here, we have analyzed the liver proteome from cholestatic patients of different etiology. 7,161 proteins were identified and quantified, of which 263 were differentially expressed between control and cholestasis groups. These differential proteins point to deregulated cellular processes that explain part of the molecular framework of cholestasis progression. However, the clustering of different cholestasis types was limited. Therefore, a machine learning pipeline was designed to identify a panel of 20 differential proteins that segregate different cholestasis groups with high accuracy and sensitivity. In summary, proteomics combined with machine learning algorithms provides valuable insights into the molecular mechanisms of cholestasis progression and a panel of proteins to discriminate across different types of cholestasis. This strategy may prove useful to develop precision medicine approaches for patient care.

INSTRUMENT(S): Orbitrap Exploris 240

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Liver

DISEASE(S): Cholestasis

SUBMITTER: Laura Guerrero

LAB HEAD: Fernando Corrales

PROVIDER: PXD047823 | Pride | 2024-05-22

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	20210927_TMT1_Fr.mzML	Mzml
	20210927_TMT1_Fr.mzid.gz	Mzid
	20210927_TMT1_Fr10_colestasis.raw	Raw
	20210927_TMT1_Fr1_colestasis.raw	Raw
	20210927_TMT1_Fr2_colestasis.raw	Raw

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Publications

Discrimination of Etiologically Different Cholestasis by Modeling Proteomics Datasets.

Guerrero Laura L Vindel-Alfageme Jorge J Hierro Loreto L Stark Luiz L Vicent David D Sorzano Carlos Óscar S CÓS Corrales Fernando J FJ

International journal of molecular sciences 20240326 7

Cholestasis is characterized by disrupted bile flow from the liver to the small intestine. Although etiologically different cholestasis displays similar symptoms, diverse factors can contribute to the progression of the disease and determine the appropriate therapeutic option. Therefore, stratifying cholestatic patients is essential for the development of tailor-made treatment strategies. Here, we have analyzed the liver proteome from cholestatic patients of different etiology. In total, 7161 pr ...[more]

PMID: 38612495

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Project description:Background Hypertrophic cardiomyopathy (HCM) is defined clinically by pathological left ventricular hypertrophy (LVH). We have previously developed a plasma proteomics biomarker panel that correlates with clinical markers of disease severity and sudden cardiac death (SCD) risk in adult patients with HCM. The aim of this study was to validate the adult biomarkers and perform new discovery proteomics in childhood-onset HCM. Methods Fifty-nine protein biomarkers were identified from an exploratory plasma proteomics screen in children with HCM and augmented into our existing multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay. The association of these biomarkers with clinical phenotypes and outcomes was prospectively tested in plasma collected from 148 children with HCM and 50 healthy controls. Machine learning techniques were used to develop novel paediatric plasma proteomic biomarker panels.Results Four previously validated adult HCM markers (Aldolase Fructose-Bisphosphate A, Complement C3a, Talin-1 and Thrombospondin 1) and three new markers (Glycogen Phosphorylase B, Lipoprotein A and Profilin 1) were elevated in paediatric HCM. Using supervised machine learning applied to training (n=137) and validation cohorts (n=61), this 7-biomarker panel differentiated HCM from healthy controls with an area under the curve of 1.0 in the training dataset (sensitivity 100% [95% confidence interval: 95–100]; specificity 100% [96–100]) and 0.82 in the validation dataset (sensitivity 75% [59–86]; specificity 88% [75–94]). Reduced circulating levels of 4 other peptides (Apolipoprotein L1-, Complement 5b-, Immunoglobulin Heavy Constant Epsilon- and Serum Amyloid A4-peptide) found in children with high SCD risk provided complete separation from the low and intermediate risk groups, and predicted mortality and adverse cardiovascular outcomes (hazard ratio 1.53 [1.2 –2.0], p = 0.001). ConclusionIn children, a 7-biomarker proteomics panel can distinguish HCM from controls with high sensitivity and specificity, and a second 4-biomarker panel identifies those at high risk of adverse outcomes, including sudden cardiac death.