Background & Aims Nonalcoholic fatty liver disease (NAFLD) is a consequence of defects in diverse metabolic pathways that involve hepatic accumulation of triglycerides. Features of these aberrations might determine whether NAFLD progresses to nonalcoholic steatohepatitis (NASH). We investigated whether the diverse defects observed in patients with NAFLD are due to different NAFLD subtypes with specific serum metabolomic profiles, and whether these can distinguish patients with NASH from patients with simple steatosis. Methods We collected liver and serum from methionine adenosyltransferase 1a knockout (MAT1A-KO) mice, which have chronically low level of hepatic S-adenosylmethionine (SAMe) and spontaneously develop steatohepatitis, as well as C57Bl/6 mice (controls); the metabolomes of all samples were determined. We also analyzed serum metabolomes of 535 patients with biopsy-proven NAFLD (353 with simple steatosis and 182 with NASH) and compared them with serum metabolomes of mice. MAT1A-KO mice were also given SAMe (30 mg/kg/day for 8 weeks); liver samples were collected and analyzed histologically for steatohepatitis. Results Livers of MAT1A-KO mice were characterized by high levels of triglycerides, diglycerides, fatty acids, ceramides, and oxidized fatty acids, as well as low levels of SAMe and downstream metabolites. There was a correlation between liver and serum metabolomes. We identified a serum metabolomic signature associated with MAT1A-KO mice that was also present in 49% of the patients; based on this signature, we identified 2 NAFLD subtypes. We identified specific panels of markers that could distinguish patients with NASH from patients with simple steatosis for each subtype of NAFLD. Administration of SAMe reduced features of steatohepatitis in MAT1A-KO mice. Conclusions In an analysis of serum metabolomes of patients with NAFLD and MAT1A-KO mice with steatohepatitis, we identified 2 major subtypes of NAFLD and markers that differentiate steatosis from NASH in each subtype. These might be used to monitor disease progression and identify therapeutic targets for patients.

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue-specific or tissue-enriched proteins were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode tissue-specific proteins. Discordance of RNA and protein enrichment revealed potential sites of synthesis and action of secreted proteins. The tissue-specific distribution of proteins also provides an indepth view of complex biological events that require the interplay of multiple tissues. Most importantly, our study demonstrated that protein tissue-enrichment information can explain phenotypes of genetic diseases, which cannot be obtained by transcript information alone. Overall, our results demonstrate how understanding protein levels can provide insights into regulation, secretome, metabolism, and human diseases.

Methionine adenosyltransferase α1 (MATα1, encoded by MAT1A) is responsible for hepatic biosynthesis of S‐adenosyl methionine, the principal methyl donor. MATα1 also act as a transcriptional cofactor by interacting and influencing the activity of several transcription factors. Mat1a knockout (KO) mice have increased levels of cytochrome P450 2E1 (CYP2E1), but the underlying mechanisms are unknown. The aims of the current study were to identify binding partners of MATα1 and elucidate how MATα1 regulates CYP2E1 expression. We identified binding partners of MATα1 by coimmunoprecipitation (co‐IP) and mass spectrometry. Interacting proteins were confirmed using co‐IP using recombinant proteins, liver lysates, and mitochondria. Alcoholic liver disease (ALD) samples were used to confirm relevance of our findings. We found that MATα1 negatively regulates CYP2E1 at mRNA and protein levels, with the latter being the dominant mechanism. MATα1 interacts with many proteins but with a predominance of mitochondrial proteins including CYP2E1. We found that MATα1 is present in the mitochondrial matrix of hepatocytes using immunogold electron microscopy. Mat1a KO hepatocytes had reduced mitochondrial membrane potential and higher mitochondrial reactive oxygen species, both of which were normalized when MAT1A was overexpressed. In addition, KO hepatocytes were sensitized to ethanol and tumor necrosis factor α–induced mitochondrial dysfunction. Interaction of MATα1 with CYP2E1 was direct, and this facilitated CYP2E1 methylation at R379, leading to its degradation through the proteasomal pathway. Mat1a KO livers have a reduced methylated/total CYP2E1 ratio. MATα1’s influence on mitochondrial function is largely mediated by its effect on CYP2E1 expression. Patients with ALD have reduced MATα1 levels and a decrease in methylated/total CYP2E1 ratio. Conclusion: Our findings highlight a critical role of MATα1 in regulating mitochondrial function by suppressing CYP2E1 expression at multiple levels.

Proteoforms containing post-translational modifications (PTMs) represent a degree of functional diversity only harnessed through analytically precise simultaneous quantification of multiple PTMs. Here we present a method to accurately differentiate an unmodified peptide from its PTM-containing counterpart through data-independent acquisition-mass spectrometry, leveraging small precursor mass windows to physically separate modified peptidoforms from each other during MS2 acquisition. We utilize a lysine and arginine PTM-enriched peptide assay library and site localization algorithm to simultaneously localize and quantify seven PTMs including mono-, di-, and trimethylation, acetylation, and succinylation in addition to total protein quantification in a single MS run without the need to enrich experimental samples. To evaluate biological relevance, this method was applied to liver lysate from differentially methylated nonalcoholic steatohepatitis (NASH) mouse models. We report that altered methylation and acetylation together with total protein changes drive the novel hypothesis of a regulatory function of PTMs in protein synthesis and mRNA stability in NASH.

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Recent surges in mass spectrometry-based proteomics studies demand a concurrent rise in speedy and optimized data processing tools and pipelines. Although several stand-alone bioinformatics tools exist that provide protein−protein interaction (PPI) data, we developed Protein Interaction Network Extractor (PINE) as a fully automated, user-friendly, graphical user interface application for visualization and exploration of global proteome and post-translational modification (PTM) based networks. PINE also supports overlaying differential expression, statistical significance thresholds, and PTM sites on functionally enriched visualization networks to gain insights into proteome-wide regulatory mechanisms and PTM-mediated networks. To illustrate the relevance of the tool, we explore the total proteome and its PTM-associated relationships in two different nonalcoholic steatohepatitis (NASH) mouse models to demonstrate different context-specific case studies. The strength of this tool relies in its ability to (1) perform accurate protein identifier mapping to resolve ambiguity, (2) retrieve interaction data from multiple publicly available PPI databases, and (3) assimilate these complex networks into functionally enriched pathways, ontology categories, and terms. Ultimately, PINE can be used as an extremely powerful tool for novel hypothesis generation to understand underlying disease mechanisms.

Background and Aims: Methionine adenosyltransferase 1A (MAT1A) is responsible for S-adenosylmethionine (SAMe) biosynthesis in the liver. Mice lacking Mat1a have hepatic SAMe depletion and develop NASH and HCC spontaneously. Several kinases are activated in Mat1a knockout (KO) mice livers. However, characterizing the phospho-proteome and determining whether they contribute to liver pathology remain open for study. Our study aimed to provide this knowledge. Approach and Results:We performed phospho-proteomics in Mat1a KO mice livers with and without SAMe treatment to identify SAMe-dependent changes that may contribute to liver pathology. Our studies used Mat1a KO mice at different ages treated with and without SAMe, cell lines, in vitro translation and kinase assays, and human liver specimens. We found that the most striking change was hyperphosphorylation and increased content of Real-time RT-PCRTotal RNA was subjected to real-time RT-PCR using a published protocol. [22] See the Supporting Information Supplemental Methods for details. Western blottingTotal protein from cells or tissues was subjected to western blotting [21] with antibodies (Table S1). Images were quantified by densitometry using the ImageJ densitometry program (

Background: Remote patient monitoring can shift important data collection opportunities to low-cost settings. Here, we evaluate whether the quality of blood-samples taken by patients at home differs from samples taken from the same patients by clinical staff. We examine the effects of socio-demographic and patient reported outcomes (PRO) survey data on remote blood sampling compliance and quality.Methods: Samples were collected both in-clinic by study-staff and remotely by subjects at home. During cataloguing the samples were graded for quality. We used chi-squared tests and logistic regressions to examine differences in quality and compliance between samples taken in-clinic versus samples taken by subjects at-home.Results: 64.6% of in-clinic samples and 69.7% of samples collected remotely at home received a Good (compared to Not Good) quality grade (chi2=4.91; p=0.03). Regression analysis found remote samples had roughly 1.5 times higher odds of being Good quality compared to samples taken in-clinic (p<.001; 95% CI 1.18-2.03). Increased anxiety reduced odds of contributing a Good sample (p=.04; 95% CI .95-1.0). Response rates were significantly higher for in-clinic sampling (95.8% vs 89.8%; p<.001). Conclusion:Blood-samples taken by patients at home using a microsampling device yielded higher quality samples than those taken in-clinic.