Nonalcoholic fatty liver disease stratification by liver lipidomics

Abstract

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Enhancing Hepatic MBOAT7 Expression Does Not Improve Nonalcoholic Steatohepatitis in Mice

“…(D) The MBOAT7 product, 38:4 PI, measured in (21), is negatively associated with total NAFLD activity score (NAS). (E) Total PI, measured in (21), is negatively associated with total NAS. Data presented as mean ± SD, analyzed by one-way ANOVA with Tukey’s post-hoc correction for multiple comparisons, * p < 0.05, **** p < 0.0001.…”

“…We also mined publicly available lipidomic analyses for PI measured in human NAFLD/NASH compared to control livers. These included data deposited in Metabolomics Workbench ST000915 (20), as well as supplemental data from an open access publication (21). Total PI levels as well as the predominant arachidonoylated PI species (38:4), were assessed in a total of 80 NC livers, 51 HO livers, 160 NAFL livers, and 134 NASH livers.…”

Lipid network and moiety analysis for revealing enzymatic dysregulation and mechanistic alterations from lipidomics data

“…The inclusion of quantitative proteome information could further improve the performance of our enrichment algorithm presented in this paper and open up the possibility to directly identify causal proteins. This but could be of great value for the causal interpretation of lipidome changes, which would directly translate into relevance for clinical applications, due to the many associations of lipids with various disorders [27, 38, 29, 59, 63].…”

“…For a detailed description, please refer to the Methods section. Additionally, studies sometimes report individually identified (but not quantified) molecular species [63]. These can be used as input for LINEX to refine the network extension.…”

“…The field, also known as lipidomics, is becoming more relevant for clinical applications and biomarker research [66]. While MS based lipidomics is not yet used for diagnoses, potential biomarkers have been discussed [38, 67, 49] and disease stratifications based on lipidomics proposed [63, 58]. To gain more insights into disease mechanisms, it is necessary to go beyond classification and prediction by proposing functional interpretations of lipid changes and links to other omics layers.…”