eLifeMolecular BiologyPeer-reviewed journal article
Global molecular landscape of early MASLD progression in obesity
Qing ZhaoiD, William De NardoiD, Ruoyu WangiD, Yi ZhongiD, Umur KelesiD, Gabrielé SakalauskaiteiD, Li Na ZhaoiD, Huiyi TayiD, Sonia C. YouhannaiD, Mengchao YaniD, Ye Xie, Youngrae KimiD, Sungdong LeeiD, Rachel Liyu LimiD, Guoshou TeoiD, Pradeep NarayanaswamyiD, Paul Robert BurtoniD, Volker Martin LauschkeiD, Hyungwon ChoiiD, Matthew J. WattiD, Philipp KaldisiD
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is often asymptomatic early on but can progress to irreversible conditions like cirrhosis. Due to limited access to human liver biopsies, systematic and integrative molecular resources remain scarce. In this study, we performed transcriptomic analyses on liver and metabolomic analyses on liver and plasma samples from morbidly obese individuals without liver pathology or at early-stage MASLD. While, the plasma metabolomic profile did not fully mirror liver histological features, dual-omics integration of liver samples revealed significantly remodeled lipid and amino acid metabolism pathways. Integrative network analysis uncoupled metabolic remodeling and gene expression as independent features of hepatic steatosis and fibrosis progression, respectively. Notably, GTPases and their regulators emerged as a novel class of genes linked to early liver fibrosis. This study offers a detailed molecular landscape of early MASLD in obesity and highlights potential targets of obesity-linked liver fibrosis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/632747v3_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1188eborg.highwire.dtl.DTLVardef@1340070org.highwire.dtl.DTLVardef@108d560org.highwire.dtl.DTLVardef@f0adda_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: bulk RNA-seq
- assay: metabolomics
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 3
- FWCI
- 9.44
- Field percentile
- top 4%
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