114 resultados para metabolic acidosis
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Background: Calpain-10 protein (intracellular Ca2+-dependent cysteine protease) may play a role in glucose metabolism, pancreatic β cell function, and regulation of thermogenesis. Several CAPN10 polymorphic sites have been studied for their potential use as risk markers for type 2 diabetes and the metabolic syndrome (MetS). Fatty acids are key metabolic regulators that may interact with genetic factors and influence glucose metabolism. Objective: The objective was to examine whether the genetic variability at the CAPN10 gene locus is associated with the degree of insulin resistance and plasma fatty acid concentrations in subjects with MetS. Design: The insulin sensitivity index, glucose effectiveness, insulin resistance [homeostasis model assessment of insulin resistance (HOMA-IR)], insulin secretion (disposition index, acute insulin response, and HOMA of β cell function), plasma fatty acid composition, and 5 CAPN10 single nucleotide polymorphisms (SNPs) were determined in a cross-sectional analysis of 452 subjects with MetS participating in the LIPGENE dietary intervention cohort. Results: The rs2953171 SNP interacted with plasma total saturated fatty acid (SFA) concentrations, which were significantly associated with insulin sensitivity (P < 0.031 for fasting insulin, P < 0.028 for HOMA-IR, and P < 0.012 for glucose effectiveness). The G/G genotype was associated with lower fasting insulin concentrations, lower HOMA-IR, and higher glucose effectiveness in subjects with low SFA concentrations (below the median) than in subjects with the minor A allele (G/A and A/A). In contrast, subjects with the G/G allele with the highest SFA concentrations (above the median) had higher fasting insulin and HOMA-IR values and lower glucose effectiveness than did subjects with the A allele. Conclusion: The rs2953171 polymorphism at the CAPN10 gene locus may influence insulin sensitivity by interacting with the plasma fatty acid composition in subjects with MetS. This trial was registered at clinicaltrials.gov as NCT00429195.
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Background: Adiponectin gene expression is modulated by peroxisome proliferator–activated receptor γ, which is a transcription factor activated by unsaturated fatty acids. Objective: We investigated the effect of the interaction between variants at the ADIPOQ gene locus, age, sex, body mass index (BMI), ethnicity, and the replacement of dietary saturated fatty acids (SFAs) with monounsaturated fatty acids (MUFAs) or carbohydrates on serum adiponectin concentrations. Design: The RISCK (Reading, Imperial, Surrey, Cambridge, and Kings) study is a parallel-design, randomized controlled trial. Serum adiponectin concentrations were measured after a 4-wk high-SFA (HS) diet and a 24-wk intervention with reference (HS), high-MUFA (HM), and low-fat (LF) diets. Single nucleotide polymorphisms at the ADIPOQ locus −11391 G/A (rs17300539), −10066 G/A (rs182052), −7734 A/C (rs16861209), and +276 G/T (rs1501299) were genotyped in 448 participants. Results: In white Europeans, +276 T was associated with higher serum adiponectin concentrations (n = 340; P = 0.006) and −10066 A was associated with lower serum adiponectin concentrations (n = 360; P = 0.03), after adjustment for age, BMI, and sex. After the HM diet, −10066 G/G subjects showed a 3.8% increase (95% CI: −0.1%, 7.7%) and G/A+A/A subjects a 2.6% decrease (95% CI: −5.6%, 0.4%) in serum adiponectin (P = 0.006 for difference after adjustment for the change in BMI, age, and sex). In −10066 G/G homozygotes, serum adiponectin increased with age after the HM diet and decreased after the LF diet. Conclusion: In white −10066 G/G homozygotes, an HM diet may help to increase adiponectin concentrations with advancing age. This trial was registered at clinicaltrials.gov as ISRCTN29111298.
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Glucokinase Regulatory Protein (GCKR) plays a central role regulating both hepatic triglyceride and glucose metabolism. Fatty acids are key metabolic regulators, which interact with genetic factors and influence glucose metabolism and other metabolic traits. Omega-3 polyunsaturated fatty acids (n-3 PUFA) have been of considerable interest, due to their potential to reduce metabolic syndrome (MetS) risk. Objective To examine whether genetic variability at the GCKR gene locus was associated with the degree of insulin resistance, plasma concentrations of C-reactive protein (CRP) and n-3 PUFA in MetS subjects. Design Homeostasis model assessment of insulin resistance (HOMA-IR), HOMA-B, plasma concentrations of C-peptide, CRP, fatty acid composition and the GCKR rs1260326-P446L polymorphism, were determined in a cross-sectional analysis of 379 subjects with MetS participating in the LIPGENE dietary cohort. Results Among subjects with n-3 PUFA levels below the population median, carriers of the common C/C genotype had higher plasma concentrations of fasting insulin (P = 0.019), C-peptide (P = 0.004), HOMA-IR (P = 0.008) and CRP (P = 0.032) as compared with subjects carrying the minor T-allele (Leu446). In contrast, homozygous C/C carriers with n-3 PUFA levels above the median showed lower plasma concentrations of fasting insulin, peptide C, HOMA-IR and CRP, as compared with individuals with the T-allele. Conclusions We have demonstrated a significant interaction between the GCKR rs1260326-P446L polymorphism and plasma n-3 PUFA levels modulating insulin resistance and inflammatory markers in MetS subjects. Further studies are needed to confirm this gene-diet interaction in the general population and whether targeted dietary recommendations can prevent MetS in genetically susceptible individuals.
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Objective To examine the impact of increasing numbers of metabolic syndrome (MetS) components on postprandial lipaemia. Methods Healthy men (n = 112) underwent a sequential meal postprandial investigation, in which blood samples were taken at regular intervals after a test breakfast (0 min) and lunch (330 min). Lipids and glucose were measured in the fasting sample, with triacylglycerol (TAG), non-esterified fatty acids and glucose analysed in the postprandial samples. Results Subjects were grouped according to the number of MetS components regardless of the combinations of components (0/1, 2, 3 and 4/5). As expected, there was a trend for an increase in body mass index, blood pressure, fasting TAG, glucose and insulin, and a decrease in fasting high-density lipoprotein cholesterol with increasing numbers of MetS components (P≤0.0004). A similar trend was observed for the summary measures of the postprandial TAG and glucose responses. For TAG, the area under the curve (AUC) and maximum concentration (maxC) were significantly greater in men with ≥ 3 than < 3 components (P < 0.001), whereas incremental AUC was greater in those with 3 than 0/1 and 2, and 4/5 compared with 2 components (P < 0.04). For glucose, maxC after the test breakfast (0-330 min) and total AUC (0-480 min) were higher in men with ≥ 3 than < 3 components (P≤0.001). Conclusions Our data analysis has revealed a linear trend between increasing numbers of MetS components and magnitude (AUC) of the postprandial TAG and glucose responses. Furthermore, the two meal challenge discriminated a worsening of postprandial lipaemic control in subjects with ≥ 3 MetS components.
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The pig is a single-stomached omnivorous mammal and is an important model of human disease and nutrition. As such, it is necessary to establish a metabolic framework from which pathology-based variation can be compared. Here, a combination of one and two-dimensional (1)H and (13)C nuclear magnetic resonance spectroscopy (NMR) and high-resolution magic angle spinning (HR-MAS) NMR was used to provide a systems overview of porcine metabolism via characterisation of the urine, serum, liver and kidney metabolomes. The metabolites observed in each of these biological compartments were found to be qualitatively comparable to the metabolic signature of the same biological matrices in humans and rodents. The data were modelled using a combination of principal components analysis and Venn diagram mapping. Urine represented the most metabolically distinct biological compartment studied, with a relatively greater number of NMR detectable metabolites present, many of which are implicated in gut-microbial co-metabolic processes. The major inter-species differences observed were in the phase II conjugation of extra-genomic metabolites; the pig was observed to conjugate p-cresol, a gut microbial metabolite of tyrosine, with glucuronide rather than sulfate as seen in man. These observations are important to note when considering the translatability of experimental data derived from porcine models.
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The gut microbiota enhances the host's metabolic capacity for processing nutrients and drugs and modulate the activities of multiple pathways in a variety of organ systems. We have probed the systemic metabolic adaptation to gut colonization for 20 days following exposure of axenic mice (n = 35) to a typical environmental microbial background using high-resolution (1)H nuclear magnetic resonance (NMR) spectroscopy to analyze urine, plasma, liver, kidney, and colon (5 time points) metabolic profiles. Acquisition of the gut microbiota was associated with rapid increase in body weight (4%) over the first 5 days of colonization with parallel changes in multiple pathways in all compartments analyzed. The colonization process stimulated glycogenesis in the liver prior to triggering increases in hepatic triglyceride synthesis. These changes were associated with modifications of hepatic Cyp8b1 expression and the subsequent alteration of bile acid metabolites, including taurocholate and tauromuricholate, which are essential regulators of lipid absorption. Expression and activity of major drug-metabolizing enzymes (Cyp3a11 and Cyp2c29) were also significantly stimulated. Remarkably, statistical modeling of the interactions between hepatic metabolic profiles and microbial composition analyzed by 16S rRNA gene pyrosequencing revealed strong associations of the Coriobacteriaceae family with both the hepatic triglyceride, glucose, and glycogen levels and the metabolism of xenobiotics. These data demonstrate the importance of microbial activity in metabolic phenotype development, indicating that microbiota manipulation is a useful tool for beneficially modulating xenobiotic metabolism and pharmacokinetics in personalized health care. IMPORTANCE: Gut bacteria have been associated with various essential biological functions in humans such as energy harvest and regulation of blood pressure. Furthermore, gut microbial colonization occurs after birth in parallel with other critical processes such as immune and cognitive development. Thus, it is essential to understand the bidirectional interaction between the host metabolism and its symbionts. Here, we describe the first evidence of an in vivo association between a family of bacteria and hepatic lipid metabolism. These results provide new insights into the fundamental mechanisms that regulate host-gut microbiota interactions and are thus of wide interest to microbiological, nutrition, metabolic, systems biology, and pharmaceutical research communities. This work will also contribute to developing novel strategies in the alteration of host-gut microbiota relationships which can in turn beneficially modulate the host metabolism.
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To characterize the impact of gut microbiota on host metabolism, we investigated the multicompartmental metabolic profiles of a conventional mouse strain (C3H/HeJ) (n=5) and its germ-free (GF) equivalent (n=5). We confirm that the microbiome strongly impacts on the metabolism of bile acids through the enterohepatic cycle and gut metabolism (higher levels of phosphocholine and glycine in GF liver and marked higher levels of bile acids in three gut compartments). Furthermore we demonstrate that (1) well-defined metabolic differences exist in all examined compartments between the metabotypes of GF and conventional mice: bacterial co-metabolic products such as hippurate (urine) and 5-aminovalerate (colon epithelium) were found at reduced concentrations, whereas raffinose was only detected in GF colonic profiles. (2) The microbiome also influences kidney homeostasis with elevated levels of key cell volume regulators (betaine, choline, myo-inositol and so on) observed in GF kidneys. (3) Gut microbiota modulate metabotype expression at both local (gut) and global (biofluids, kidney, liver) system levels and hence influence the responses to a variety of dietary modulation and drug exposures relevant to personalized health-care investigations.
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The time-course of metabolic events following response to a model hepatotoxin ethionine (800 mg/kg) was investigated over a 7 day period in rats using high-resolution (1)H NMR spectroscopic analysis of urine and multivariate statistics. Complementary information was obtained by multivariate analysis of (1)H MAS NMR spectra of intact liver and by conventional histopathology and clinical chemistry of blood plasma. (1)H MAS NMR spectra of liver showed toxin-induced lipidosis 24 h postdose consistent with the steatosis observed by histopathology, while hypertaurinuria was suggestive of liver injury. Early biochemical changes in urine included elevation of guanidinoacetate, suggesting impaired methylation reactions. Urinary increases in 5-oxoproline and glycine suggested disruption of the gamma-glutamyl cycle. Signs of ATP depletion together with impairment of the energy metabolism were given from the decreased levels in tricarboxylic acid cycle intermediates, the appearance of ketone bodies in urine, the depletion of hepatic glucose and glycogen, and also hypoglycemia. The observed increase in nicotinuric acid in urine could be an indication of an increase in NAD catabolism, a possible consequence of ATP depletion. Effects on the gut microbiota were suggested by the observed urinary reductions in the microbial metabolites 3-/4-hydroxyphenyl propionic acid, dimethylamine, and tryptamine. At later stages of toxicity, there was evidence of kidney damage, as indicated by the tubular damage observed by histopathology, supported by increased urinary excretion of lactic acid, amino acids, and glucose. These studies have given new insights into mechanisms of ethionine-induced toxicity and show the value of multisystem level data integration in the understanding of experimental models of toxicity or disease.
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The diversification of life involved enormous increases in size and complexity. The evolutionary transitions from prokaryotes to unicellular eukaryotes to metazoans were accompanied by major innovations inmetabolicdesign.Hereweshowthat thescalingsofmetabolic rate, population growth rate, and production efficiency with body size have changed across the evolutionary transitions.Metabolic rate scales with body mass superlinearly in prokaryotes, linearly in protists, and sublinearly inmetazoans, so Kleiber’s 3/4 power scaling law does not apply universally across organisms. The scaling ofmaximum population growth rate shifts from positive in prokaryotes to negative in protists and metazoans, and the efficiency of production declines across these groups.Major changes inmetabolic processes duringtheearlyevolutionof life overcameexistingconstraints, exploited new opportunities, and imposed new constraints. The 3.5 billion year history of life on earth was characterized by
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A partial differential equation model is developed to understand the effect that nutrient and acidosis have on the distribution of proliferating and quiescent cells and dead cell material (necrotic and apopotic) within a multicellular tumour spheroid. The rates of cell quiescence and necrosis depend upon the local nutrient and acid concentrations and quiescent cells are assumed to consume less nutrient and produce less acid than proliferating cells. Analysis of the differences in nutrient consumption and acid production by quiescent and proliferating cells shows low nutrient levels do not necessarily lead to increased acid concentration via anaerobic metabolism. Rather, it is the balance between proliferating and quiescent cells within the tumour which is important; decreased nutrient levels lead to more quiescent cells, which produce less acid than proliferating cells. We examine this effect via a sensitivity analysis which also includes a quantification of the effect that nutrient and acid concentrations have on the rates of cell quiescence and necrosis.
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It is well known that gut bacteria contribute significantly to the host homeostasis, providing a range of benefits such as immune protection and vitamin synthesis. They also supply the host with a considerable amount of nutrients, making this ecosystem an essential metabolic organ. In the context of increasing evidence of the link between the gut flora and the metabolic syndrome, understanding the metabolic interaction between the host and its gut microbiota is becoming an important challenge of modern biology.1-4 Colonization (also referred to as normalization process) designates the establishment of micro-organisms in a former germ-free animal. While it is a natural process occurring at birth, it is also used in adult germ-free animals to control the gut floral ecosystem and further determine its impact on the host metabolism. A common procedure to control the colonization process is to use the gavage method with a single or a mixture of micro-organisms. This method results in a very quick colonization and presents the disadvantage of being extremely stressful5. It is therefore useful to minimize the stress and to obtain a slower colonization process to observe gradually the impact of bacterial establishment on the host metabolism. In this manuscript, we describe a procedure to assess the modification of hepatic metabolism during a gradual colonization process using a non-destructive metabolic profiling technique. We propose to monitor gut microbial colonization by assessing the gut microbial metabolic activity reflected by the urinary excretion of microbial co-metabolites by 1H NMR-based metabolic profiling. This allows an appreciation of the stability of gut microbial activity beyond the stable establishment of the gut microbial ecosystem usually assessed by monitoring fecal bacteria by DGGE (denaturing gradient gel electrophoresis).6 The colonization takes place in a conventional open environment and is initiated by a dirty litter soiled by conventional animals, which will serve as controls. Rodents being coprophagous animals, this ensures a homogenous colonization as previously described.7 Hepatic metabolic profiling is measured directly from an intact liver biopsy using 1H High Resolution Magic Angle Spinning NMR spectroscopy. This semi-quantitative technique offers a quick way to assess, without damaging the cell structure, the major metabolites such as triglycerides, glucose and glycogen in order to further estimate the complex interaction between the colonization process and the hepatic metabolism7-10. This method can also be applied to any tissue biopsy11,12.
Integrated cytokine and metabolic analysis of pathological responses to parasite exposure in rodents
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Parasitic infections cause a myriad of responses in their mammalian hosts, on immune as well as on metabolic level. A multiplex panel of cytokines and metabolites derived from four parasite-rodent models, namely, Plasmodium berghei-mouse, Trypanosoma brucei brucei-mouse, Schistosoma mansoni-mouse, and Fasciola hepatica-rat were statistically coanalyzed. 1H NMR spectroscopy and multivariate statistical analysis were used to characterize the urine and plasma metabolite profiles in infected and noninfected animals. Each parasite generated a unique metabolic signature in the host. Plasma cytokine concentrations were obtained using the ‘Meso Scale Discovery’ multi cytokine assay platform. Multivariate data integration methods were subsequently used to elucidate the component of the metabolic signature which is associated with inflammation and to determine specific metabolic correlates with parasite-induced changes in plasma cytokine levels. For example, the relative levels of acetyl glycoproteins extracted from the plasma metabolite profile in the P. berghei-infected mice were statistically correlated with IFN-γ, whereas the same cytokine was anticorrelated with glucose levels. Both the metabolic and the cytokine data showed a similar spatial distribution in principal component analysis scores plots constructed for the combined murine data, with samples from all infected animals clustering according to the parasite species and whereby the protozoan infections (P. berghei and T. b. brucei) grouped separately from the helminth infection (S. mansoni). For S. mansoni, the main infection-responsive cytokines were IL-4 and IL-5, which covaried with lactate, choline, and D-3-hydroxybutyrate. This study demonstrates that the inherently differential immune response to single and multicellular parasites not only manifests in the cytokine expression, but also consequently imprints on the metabolic signature, and calls for in-depth analysis to further explore direct links between immune features and biochemical pathways.
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Several insulin receptor substrate-2 (IRS-2) polymorphisms have been studied in relation to insulin resistance and type 2 diabetes. To examine whether the genetic variability at the IRS-2 gene locus was associated with the degree of insulin resistance and plasma fatty acid levels in metabolic syndrome (MetS) subjects. Methods and results: Insulin sensitivity, insulin secretion, glucose effectiveness, plasma fatty acid composition and three IRS-2 tag-single nucleotide polymorphisms (SNPs) were determined in 452 MetS subjects. Among subjects with the lowest level of monounsaturated (MUFA) (below the median), the rs2289046 A/A genotype was associated with lower glucose effectiveness (p<0.038), higher fasting insulin concentrations (p<0.028) and higher HOMA IR (p<0.038) as compared to subjects carrying the minor G-allele (A/G and G/G). In contrast, among subjects with the highest level of MUFA (above the median), the A/A genotype was associated with lower fasting insulin concentrations and HOMA-IR, whereas individuals carrying the G allele and with the highest level of ω-3 polyunsaturated fatty acids (above the median) showed lower fasting insulin (p<0.01) and HOMA-IR (p<0.02) as compared with A/A subjects. Conclusion: The rs2289046 polymorphism at the IRS2 gene locus may influence insulin sensitivity by interacting with certain plasma fatty acids in MetS subjects.