Coordinate Transcriptomic and Metabolomic Effects of the Insulin Sensitizer Rosiglitazone on Fundamental Metabolic Pathways in Liver, Soleus Muscle, and Adipose Tissue in Diabetic db/db Mice

Bouter, Sabrina Le; Rodriguez, Marianne; Guigal-Stéphan, Nolwen; Courtade-Gaïani, Sophie; Xuereb, Laura; Montrion, Catherine de; Croixmarie, Vincent; Umbdenstock, Thierry; Boursier-Neyret, Claire; Lonchampt, Michel; Brun, Marion; Dacquet, Catherine; Ktorza, Alain; Lockhart, Brian-Paul; Galizzi, Jean‐Pierre

doi:10.1155/2010/679184

Cited by 7 publications

(6 citation statements)

References 39 publications

(41 reference statements)

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“…143 Integration of transcriptomics and metabolomics is another form of multi-tier 'omics. [144][145][146] This transcriptomics-based method uses a "guilt-by-association" approach-an assumption that relies on coexpression of genes, known and unknown, that operate in the same…”

Section: Integration Of Metabolomics With Other 'Omicsmentioning

confidence: 99%

Challenges and emergent solutions for LC‐MS/MS based untargeted metabolomics in diseases

Liang

Lee

2018

Mass Spectrometry Reviews

269

169

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In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

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Section: Integration Of Metabolomics With Other 'Omicsmentioning

confidence: 99%

Challenges and emergent solutions for LC‐MS/MS based untargeted metabolomics in diseases

Liang

Lee

2018

Mass Spectrometry Reviews

269

169

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“…To test this hypothesis, gene lists from PFAA-treated mice were compared to gene expression profiles derived from three experiments. In the first comparison, the PPARγ agonist rosiglitazone was administered to Lepr (db/db) mice and evaluated for liver gene expression (Le Bouter et al, 2010). The profile from the rosiglitazone-treated mice exhibited significant similarity (-log(p-value) ≥ 4) to profiles from PFAA-treated wild-type mice (Fig.…”

Section: Pfaas Induce Fatty Acid β-Oxidation Genes In Pparα-null Micementioning

confidence: 99%

“…A. Similarity between PFAA-regulated genes and those regulated by the PPARγ agonist rosiglitazone. The gene lists derived from PFAA-treated mice were compared to the profiles from livers of db/db mice treated with rosiglitazone (Le Bouter et al, 2010).…”

Section: Abbreviationsmentioning

confidence: 99%

PPARα-independent transcriptional targets of perfluoroalkyl acids revealed by transcript profiling

et al. 2017

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Perfluoroalkyl acids (PFAAs) are ubiquitous and persistent environmental contaminants. Compounds such as perfluoroocanoic acid (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoic acid (PFNA), and perfluorohexane sulfonate (PFHxS) are readily found in the tissues of humans and wildlife. While PFOA and PFOS have been the subject of numerous studies since they were first described over a decade ago, less is known about the biological activity of PFHxS and PFNA. Most PFAAs are activators of peroxisome proliferator-activated receptor α (PPARα), although the biological effects of these compounds are likely mediated by other factors in addition to PPARα. To evaluate the effects of PFHxS and PFNA, male wild-type and Pparα-null mice were dosed by oral gavage with PFHxS (3 or 10mg/kg/day), PFNA (1 or 3mg/kg/day), or vehicle for 7days, and liver gene expression was evaluated by full-genome microarrays. Gene expression patterns were then compared to historical in-house data for PFOA and PFOS in addition to the experimental hypolipidemic agent, WY-14,643. While WY-14,643 altered most genes in a PPARα-dependent manner, approximately 11-24% of regulated genes in PFAA-treated mice were independent of PPARα. The possibility that PFAAs regulate gene expression through other molecular pathways was evaluated. Using data available through a microarray database, PFAA gene expression profiles were found to exhibit significant similarity to profiles from mouse tissues exposed to agonists of the constitutive activated receptor (CAR), estrogen receptor α (ERα), and PPARγ. Human PPARγ and ERα were activated by all four PFAAs in trans-activation assays from the ToxCast screening program. Predictive gene expression biomarkers showed that PFAAs activate CAR in both genotypes and cause feminization of the liver transcriptome through suppression of signal transducer and activator of transcription 5B (STAT5B). These results indicate that, in addition to activating PPARα as a primary target, PFAAs also have the potential to activate CAR, PPARγ, and ERα as well as suppress STAT5B.

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“…I + ) included many organic acids, glucogenic amino acids, bile acids, purine, pyrimidine, phosphatydylcholine, ethanolamine, carnitine, and creatinine ( Supplementary Table 7 ). Several of these metabolites are involved in hepatic metabolism and lipid metabolic processes in adipose tissue ( 46 , 47 ). The potential impact of these altered pathways in T1D after insulin treatment requires future investigation.…”

Section: Discussionmentioning

confidence: 99%

Concordance of Changes in Metabolic Pathways Based on Plasma Metabolomics and Skeletal Muscle Transcriptomics in Type 1 Diabetes

et al. 2012

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Insulin regulates many cellular processes, but the full impact of insulin deficiency on cellular functions remains to be defined. Applying a mass spectrometry–based nontargeted metabolomics approach, we report here alterations of 330 plasma metabolites representing 33 metabolic pathways during an 8-h insulin deprivation in type 1 diabetic individuals. These pathways included those known to be affected by insulin such as glucose, amino acid and lipid metabolism, Krebs cycle, and immune responses and those hitherto unknown to be altered including prostaglandin, arachidonic acid, leukotrienes, neurotransmitters, nucleotides, and anti-inflammatory responses. A significant concordance of metabolome and skeletal muscle transcriptome–based pathways supports an assumption that plasma metabolites are chemical fingerprints of cellular events. Although insulin treatment normalized plasma glucose and many other metabolites, there were 71 metabolites and 24 pathways that differed between nondiabetes and insulin-treated type 1 diabetes. Confirmation of many known pathways altered by insulin using a single blood test offers confidence in the current approach. Future research needs to be focused on newly discovered pathways affected by insulin deficiency and systemic insulin treatment to determine whether they contribute to the high morbidity and mortality in T1D despite insulin treatment.

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Coordinate Transcriptomic and Metabolomic Effects of the Insulin Sensitizer Rosiglitazone on Fundamental Metabolic Pathways in Liver, Soleus Muscle, and Adipose Tissue in Diabetic db/db Mice

Cited by 7 publications

References 39 publications

Challenges and emergent solutions for LC‐MS/MS based untargeted metabolomics in diseases

Challenges and emergent solutions for LC‐MS/MS based untargeted metabolomics in diseases

PPARα-independent transcriptional targets of perfluoroalkyl acids revealed by transcript profiling

Concordance of Changes in Metabolic Pathways Based on Plasma Metabolomics and Skeletal Muscle Transcriptomics in Type 1 Diabetes

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