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Sucralose, one kind of "sugar-free" artificial sweeteners, is widely used as food and drinks additives. It is generally considered that sucralose is safe because majority of ingested sucralose is not metabolized and absorbed by human body. However, increasing evidence shows the negative effects of artificial sweeteners in perturbations of gut microbiota which plays an important role in a variety of processes related to host health such as immune system development. Specifically, sucralose uptake can alter the homeostasis of mouse gut microbiota, resulting in the significant changes of gut bacterial genera diversity, metabolic patterns, and fecal metabolite profiles as well as inducing host liver inflammation. Therefore, there is a need to study liver proteome changes which may be potentially affected by sucralose-induced dysbiosis. In this study, isobaric labeling-based quantitative proteomics was performed to reveal the liver functional proteome changes in male C57BL/6J mice with sucralose administration in drinking water for six-month period. The labeled tryptic peptides were off-line fractionated before LC-MS/MS analysis to improve proteome coverage detected. SIGNIFICANCE: We demonstrated the first quantitative proteomics for mice liver proteome to evaluate the effect of sucralose consumption. In total, >5700 protein groups were identified from 18 mouse liver tissues (9 from control group; 9 from sucralose-treated group), and 4327 protein groups were quantified in all samples without any missing values. Among them, 113 protein groups were identified with statistical significance (q value <0.05) as differentially expressed proteins. Bioinformatics analysis revealed the systematic dysregulations of protein metabolism after sucralose treatment. Importantly, our findings proposed that enhanced inflammation may be triggered by ribosomal inactivation in sucralose treated mice liver.
Pubmed ID: 30660768
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Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis.
View all literature mentionsDatabase of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsA quantitative proteomics software package for analyzing large-scale mass-spectrometric data sets. It is a set of algorithms that include peak detection and scoring of peptides, mass calibration, database searches for protein identification, protein quantification, and provides summary statistics.
View all literature mentionsSoftware that supports biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiplehypothesis testing.
View all literature mentionsSystem that classifies genes by their functions, using published scientific experimental evidence and evolutionary relationships to predict function even in absence of direct experimental evidence. Orthologs view is curated orthology relationships between genes for human, mouse, rat, fish, worm, and fly.
View all literature mentionsSystem that classifies genes by their functions, using published scientific experimental evidence and evolutionary relationships to predict function even in absence of direct experimental evidence. Orthologs view is curated orthology relationships between genes for human, mouse, rat, fish, worm, and fly.
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