Impact of Long-Term Poor and Good Glycemic Control on Metabolomics Alterations in Type 1 Diabetic People

Dutta, Tumpa; Kudva, Yogish C.; Persson, Xuan Mai T.; Schenck, Louis A.; Ford, G. C.; Singh, Ravinder J.; Carter, Rickey E.; Nair, K. Sreekumaran

doi:10.1210/jc.2015-2640

Cited by 47 publications

(51 citation statements)

References 38 publications

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“…Profiling data was acquired under both positive and negative electrospray ionization conditions over a mass range m / z of 100–1700 at a resolution of 10,000 (separate runs) in scan mode. Metabolite separation was achieved using two columns of differing polarity, a hydrophilic interaction column (HILIC, ethylene-bridged hybrid 2.1 x 150 mm, 1.7 μm; Waters) and a reversed-phase C18 column (high-strength silica 2.1 × 150 mm, 1.8 μm; Waters) with gradient described previously [51–53]. Run time was 18 min for HILIC and 27 min for C18 column using a flow rate of 400 μl/min.…”

Section: Methodsmentioning

confidence: 99%

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Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Hale

Jeraldo

Mundy

et al. 2018

Methods

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Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

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Section: Methodsmentioning

confidence: 99%

“…Metabolites detected in at least ≥80% of one of two groups were selected for differential expression analyses. Metabolite peak intensities and differential regulation of metabolites between groups were determined as described previously [51,53]. Each sample was normalized to the internal standard and log 2 transformed.…”

Section: Methodsmentioning

confidence: 99%

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Hale

Jeraldo

Mundy

et al. 2018

Methods

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“…Plasma nontargeted metabolomics profiling was performed using liquid chromatography/time-of-flight mass spectrometry (6220 ToF MS; Agilent, Inc) operated both in positive and negative electrospray ionization modes, using a scan range of m/z 100–1200 at a resolution of 10,000, as described previously (20, 29). Small-molecule metabolites were extracted from 100 μL of plasma by deproteinization with 80% methanol.…”

Section: Methodsmentioning

confidence: 99%

“…Small-molecule metabolites were extracted from 100 μL of plasma by deproteinization with 80% methanol. Before deproteinization, an internal standard solution (4 μL) of 13 C6-phenylalanine (250 ng/μL) was added to each sample and plasma quality control (QC) samples to monitor recovery and reproducibility in metabolite extraction (29). Dried samples were stored at −20°C until analysis.…”

Section: Methodsmentioning

confidence: 99%

“…Each QC sample was analyzed in duplicate, and chromatographic separation was achieved using hydrophilic interaction liquid chromatography and reverse phase (C18) liquid chromatography separately. The instrument settings were as follows: nebulizer gas temperature, 325°C; capillary voltage, 3.5 kV; capillary temperature, 300°C; fragmenter voltage, 150 V; skimmer voltage, 58 V; octapole voltage, 250 V; cycle time, 0.5 seconds; and run time, 15.0 minutes (20, 29). …”

Section: Methodsmentioning

confidence: 99%

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Combining a nontargeted and targeted metabolomics approach to identify metabolic pathways significantly altered in polycystic ovary syndrome

et al. 2017

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Objective Polycystic ovary syndrome (PCOS) is a condition of androgen excess and chronic anovulation frequently associated with insulin resistance. We combined a nontargeted and targeted metabolomics approach to identify pathways and metabolites that distinguished PCOS from metabolic syndrome (MetS). Methods Twenty obese women with PCOS were compared with 18 obese women without PCOS. Both groups met criteria for MetS but could not have diabetes mellitus or take medications that treat PCOS or affect lipids or insulin sensitivity. Insulin sensitivity was derived from the frequently sampled intravenous glucose tolerance test. A nontargeted metabolomics approach was performed on fasting plasma samples to identify differentially expressed metabolites, which were further evaluated by principal component and pathway enrichment analysis. Quantitative targeted metabolomics was then applied on candidate metabolites. Measured metabolites were tested for associations with PCOS and clinical variables by logistic and linear regression analyses. Results This multiethnic, obese sample was matched by age (PCOS, 37 ± 6; MetS, 40 ± 6 years) and body mass index (BMI) (PCOS, 34.6 ± 5.1; MetS, 33.7 ± 5.2 kg/m2). Principal component analysis of the nontargeted metabolomics data showed distinct group separation of PCOS from MetS controls. From the subset of 385 differentially expressed metabolites, 22% were identified by accurate mass, resulting in 19 canonical pathways significantly altered in PCOS, including amino acid, lipid, steroid, carbohydrate, and vitamin D metabolism. Targeted metabolomics identified many essential amino acids, including branched-chain amino acids (BCAA) that were elevated in PCOS compared with MetS. PCOS was most associated with BCAA (P = .02), essential amino acids (P = .03), the essential amino acid lysine (P = .02), and the lysine metabolite α-aminoadipic acid (P = .02) in models adjusted for surrogate variables representing technical variation in metabolites. No significant differences between groups were observed in concentrations of free fatty acids or vitamin D metabolites. Evaluation of the relationship of metabolites with clinical characteristics showed 1) negative associations of essential and BCAA with insulin sensitivity and sex hormone–binding globulin and 2) positive associations with homeostasis model of insulin resistance and free testosterone; metabolites were not associated with BMI or percent body fat. Conclusions PCOS was associated with significant metabolic alterations not attributed exclusively to androgen-related pathways, obesity, or MetS. Concentrations of essential amino acids and BCAA are increased in PCOS, which might result from or contribute to their insulin resistance.

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The wasting‐associated metabolite succinate disrupts myogenesis and impairs skeletal muscle regeneration

Arneson‐Wissink

Hogan

Ducharme

et al. 2020

JCSM Rapid Communications

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BackgroundMuscle wasting is a debilitating co‐morbidity affecting most advanced cancer patients. Alongside enhanced muscle catabolism, defects in muscle repair/regeneration contribute to cancer‐associated wasting. Among the factors implicated in suppression of muscle regeneration are cytokines that interfere with myogenic signal transduction pathways. Less understood is how other cancer/wasting‐associated cues, such as metabolites, contribute to muscle dysfunction. This study investigates how the metabolite succinate affects myogenesis and muscle regeneration.MethodsWe leveraged an established ectopic metabolite treatment (cell permeable dimethyl‐succinate) strategy to evaluate the ability of intracellular succinate elevation to (i) affect myoblast homeostasis (proliferation and apoptosis), (ii) disrupt protein dynamics and induce wasting‐associated atrophy, and (iii) modulate in vitro myogenesis. In vivo succinate supplementation experiments (2% succinate and 1% sucrose vehicle) were used to corroborate and extend in vitro observations. Metabolic profiling and functional metabolic studies were then performed to investigate the impact of succinate elevation on mitochondria function.ResultsWe found that in vitro succinate supplementation elevated intracellular succinate about 2‐fold and did not have an impact on proliferation or apoptosis of C2C12 myoblasts. Elevated succinate had minor effects on protein homeostasis (~25% decrease in protein synthesis assessed by O‐propargyl‐puromycin staining), and no significant effect on myotube atrophy. Succinate elevation interfered with in vitro myoblast differentiation, characterized by significant decreases in late markers of myogenesis and fewer nuclei per myosin heavy chain positive structure (assessed by immunofluorescence staining). While mice orally administered succinate did not exhibit changes in overall body composition or whole muscle weights, these mice displayed smaller muscle myofiber diameters (~6% decrease in the mean of non‐linear regression curves fit to the histograms of minimum feret diameter distribution), which was exacerbated when muscle regeneration was induced with barium chloride injury. Significant decreases in the mean of non‐linear regression curves fit to the histograms of minimum feret diameter distributions were observed 7 and 28 days post injury. Elevated numbers of myogenin positive cells (three‐fold increase) supportive of the differentiation defects observed in vitro were observed 28 days post injury. Metabolic profiling and functional metabolic assessment of myoblasts revealed that succinate elevation caused both widespread metabolic changes and significantly lowered maximal cellular respiration (~35% decrease).ConclusionsThis study broadens the repertoire of wasting‐associated factors that can directly modulate muscle progenitor cell function and strengthens the hypothesis that metabolic derangements are significant contributors to impaired muscle regeneration, an important aspect of cancer‐associated muscle wasting.

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Impact of Long-Term Poor and Good Glycemic Control on Metabolomics Alterations in Type 1 Diabetic People

Abstract: Comprehensive metabolomic profiling identified extensive metabolomic abnormalities in T1D with poor glycemic control. Chronic good glycemic control failed to normalize many of these perturbations, suggesting a potential role for these persistent abnormalities in many complications in T1D.

Cited by 47 publications

References 38 publications

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Combining a nontargeted and targeted metabolomics approach to identify metabolic pathways significantly altered in polycystic ovary syndrome

The wasting‐associated metabolite succinate disrupts myogenesis and impairs skeletal muscle regeneration

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