References of "Zamboni, Nicola"
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See detailMultiomic profiling of the liver across diets and age in a diverse mouse population
Williams, Evan UL; Pfister, Niklas; Roy, Suheeta et al

in Cell Systems (2021)

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See detailMolecular pathways behind acquired obesity: Adipose tissue and skeletal muscle multiomics in monozygotic twin pairs discordant for BMI.
van der Kolk, Birgitta W.; Saari, Sina; Lovric, Alen et al

in Cell Reports Medicine (2021), 2(4), 100226

Tissue-specific mechanisms prompting obesity-related development complications in humans remain unclear. We apply multiomics analyses of subcutaneous adipose tissue and skeletal muscle to examine the ... [more ▼]

Tissue-specific mechanisms prompting obesity-related development complications in humans remain unclear. We apply multiomics analyses of subcutaneous adipose tissue and skeletal muscle to examine the effects of acquired obesity among 49 BMI-discordant monozygotic twin pairs. Overall, adipose tissue appears to be more affected by excess body weight than skeletal muscle. In heavier co-twins, we observe a transcriptional pattern of downregulated mitochondrial pathways in both tissues and upregulated inflammatory pathways in adipose tissue. In adipose tissue, heavier co-twins exhibit lower creatine levels; in skeletal muscle, glycolysis- and redox stress-related protein and metabolite levels remain higher. Furthermore, metabolomics analyses in both tissues reveal that several proinflammatory lipids are higher and six of the same lipid derivatives are lower in acquired obesity. Finally, in adipose tissue, but not in skeletal muscle, mitochondrial downregulation and upregulated inflammation are associated with a fatty liver, insulin resistance, and dyslipidemia, suggesting that adipose tissue dominates in acquired obesity. [less ▲]

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See detailComputational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051)
Böcker, Sebastian; Broeckling, Corey; Schymanski, Emma UL et al

in Dagstuhl Reports (2020)

Dagstuhl Seminar 20051 on Computational Metabolomics is the third edition of seminars onthis topic and focused on Cheminformatics and Machine Learning. With the advent of higherprecision instrumentation ... [more ▼]

Dagstuhl Seminar 20051 on Computational Metabolomics is the third edition of seminars onthis topic and focused on Cheminformatics and Machine Learning. With the advent of higherprecision instrumentation, application of metabolomics to a wider variety of small molecules, andever increasing amounts of raw and processed data available, developments in cheminformaticsand machine learning are sorely needed to facilitate interoperability and leverage further insightsfrom these data. Following on from Seminars 17491 and 15492, this edition convened bothexperimental and computational experts, many of whom had attended the previous sessions andbrought much-valued perspective to the week’s proceedings and discussions. Throughout theweek, participants first debated on what topics to discuss in detail, before dispersing into smaller,focused working groups for more in-depth discussions. This dynamic format was found to bemost productive and ensured active engagement amongst the participants. The abstracts inthis report reflect these working group discussions, in addition to summarising several informalevening sessions. Action points to follow-up on after the seminar were also discussed, includingfuture workshops and possibly another Dagstuhl seminar in late 2021 or 2022. [less ▲]

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See detailSystems proteomics of liver mitochondria function.
Williams, Evan UL; Wu, Yibo; Jha, Pooja et al

in Science (2016), 352(6291), 0189

Recent improvements in quantitative proteomics approaches, including Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS), permit reproducible large-scale protein measurements across ... [more ▼]

Recent improvements in quantitative proteomics approaches, including Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS), permit reproducible large-scale protein measurements across diverse cohorts. Together with genomics, transcriptomics, and other technologies, transomic data sets can be generated that permit detailed analyses across broad molecular interaction networks. Here, we examine mitochondrial links to liver metabolism through the genome, transcriptome, proteome, and metabolome of 386 individuals in the BXD mouse reference population. Several links were validated between genetic variants toward transcripts, proteins, metabolites, and phenotypes. Among these, sequence variants in Cox7a2l alter its protein's activity, which in turn leads to downstream differences in mitochondrial supercomplex formation. This data set demonstrates that the proteome can now be quantified comprehensively, serving as a key complement to transcriptomics, genomics, and metabolomics--a combination moving us forward in complex trait analysis. [less ▲]

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See detailA roadmap for interpreting (13)C metabolite labeling patterns from cells.
Buescher, Joerg M.; Antoniewicz, Maciek R.; Boros, Laszlo G. et al

in Current opinion in biotechnology (2015), 34

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. (13)C tracer analysis, although less information-rich than quantitative (13)C flux analysis that ... [more ▼]

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. (13)C tracer analysis, although less information-rich than quantitative (13)C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting (13)C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments. [less ▲]

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See detailMultilayered genetic and omics dissection of mitochondrial activity in a mouse reference population.
Wu, Yibo; Williams, Evan UL; Dubuis, Sebastien et al

in Cell (2014), 158(6), 1415-1430

The manner by which genotype and environment affect complex phenotypes is one of the fundamental questions in biology. In this study, we quantified the transcriptome--a subset of the metabolome--and ... [more ▼]

The manner by which genotype and environment affect complex phenotypes is one of the fundamental questions in biology. In this study, we quantified the transcriptome--a subset of the metabolome--and, using targeted proteomics, quantified a subset of the liver proteome from 40 strains of the BXD mouse genetic reference population on two diverse diets. We discovered dozens of transcript, protein, and metabolite QTLs, several of which linked to metabolic phenotypes. Most prominently, Dhtkd1 was identified as a primary regulator of 2-aminoadipate, explaining variance in fasted glucose and diabetes status in both mice and humans. These integrated molecular profiles also allowed further characterization of complex pathways, particularly the mitochondrial unfolded protein response (UPR(mt)). UPR(mt) shows strikingly variant responses at the transcript and protein level that are remarkably conserved among C. elegans, mice, and humans. Overall, these examples demonstrate the value of an integrated multilayered omics approach to characterize complex metabolic phenotypes. [less ▲]

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