Ontology highlight
ABSTRACT:
PROVIDER: PRJNA962007 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR24308233_1.fastq.gz | Fastqsanger.gz | |||
| SRR24308233_2.fastq.gz | Fastqsanger.gz | |||
| SRR24308234_1.fastq.gz | Fastqsanger.gz | |||
| SRR24308234_2.fastq.gz | Fastqsanger.gz | |||
| SRR24308235_1.fastq.gz | Fastqsanger.gz |
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Project description:High-calorie diets lead hepatic steatosis and to the development of non-alcoholic fatty liver disease (NAFLD), which can evolve over many years into the inflammatory form non-alcoholic steatohepatits (NASH) posing a risk for the development of hepatocellular carcinoma (HCC). Due to the diet and the liver alteration, the axis between liver and gut is disturbed, resulting in gut microbiome alterations. Consequently, detecting these gut microbiome alterations repre-sents a promising strategy for early NASH and HCC detection. We analyzed medical parame-ters and the fecal metaproteome of 19 healthy controls, 32 NASH, and 29 HCC patients target-ing the discovery of diagnostic biomarkers. Here, NASH and HCC resulted in increased in-flammation status and shifts within the composition of the gut microbiome. Increased abun-dance of kielin/chordin, E3 ubiquitin ligase, and nucleophosmin 1 represented valuable fecal biomarkers indicating disease-related changes in the liver. Whereas a single biomarker failed to separate NASH and HCC, machine learning-based classification algorithms provided 0.86% accuracy in distinguishing between controls, NASH, and HCC. Conclusion: Fecal metaproteomics enables early detection of NASH and HCC by providing single biomarkers and ma-chine learning-based metaprotein panels.
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Project description:Mardinoglu2014 - Genome-scale metabolic model
(HMR version 2.0) - human hepatocytes (iHepatocytes2322)
This model is described in the article:
Genome-scale metabolic
modelling of hepatocytes reveals serine deficiency in patients
with non-alcoholic fatty liver disease.
Mardinoglu A, Agren R, Kampf C,
Asplund A, Uhlen M, Nielsen J.
Nat Commun 2014; 5: 3083
Abstract:
Several liver disorders result from perturbations in the
metabolism of hepatocytes, and their underlying mechanisms can
be outlined through the use of genome-scale metabolic models
(GEMs). Here we reconstruct a consensus GEM for hepatocytes,
which we call iHepatocytes2322, that extends previous models by
including an extensive description of lipid metabolism. We
build iHepatocytes2322 using Human Metabolic Reaction 2.0
database and proteomics data in Human Protein Atlas, which
experimentally validates the incorporated reactions. The
reconstruction process enables improved annotation of the
proteomics data using the network centric view of
iHepatocytes2322. We then use iHepatocytes2322 to analyse
transcriptomics data obtained from patients with non-alcoholic
fatty liver disease. We show that blood concentrations of
chondroitin and heparan sulphates are suitable for diagnosing
non-alcoholic steatohepatitis and for the staging of
non-alcoholic fatty liver disease. Furthermore, we observe
serine deficiency in patients with NASH and identify PSPH,
SHMT1 and BCAT1 as potential therapeutic targets for the
treatment of non-alcoholic steatohepatitis.
This model is hosted on
BioModels Database
and identified by:
MODEL1402200003.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2005-01-01 | MODEL1402200003 | BioModels
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