Caracterización de la haptoglobina porcina mediante una aproximación Proteómica

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Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data

Background: Bioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. Results: An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. Conclusions: We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome

Biochemical and proteomic analyses of the physiological response induced by individual housing in gilts provide new potential stress markers

BACKGROUND: The objective assessment of animal stress and welfare requires proper laboratory biomarkers. In this work, we have analyzed the changes in serum composition in gilts after switching their housing, from pen to individual stalls, which is generally accepted to cause animal discomfort. RESULTS: Blood and saliva samples were collected a day before and up to four days after changing the housing system. Biochemical analyses showed adaptive changes in lipid and protein metabolism after the housing switch, whereas cortisol and muscular markers showed a large variability between animals. 2D-DIGE and iTRAQ proteomic approaches revealed variations in serum protein composition after changing housing and diet of gilts. Both techniques showed alterations in two main homeostatic mechanisms: the innate immune and redox systems. The acute phase proteins haptoglobin, apolipoprotein A-I and α1-antichymotrypsin 3, and the antioxidant enzyme peroxiredoxin 2 were found differentially expressed by 2D-DIGE. Other proteins related to the innate immune system, including lactotransferrin, protegrin 3 and galectin 1 were also identified by iTRAQ, as well as oxidative stress enzymes such as peroxiredoxin 2 and glutathione peroxidase 3. Proteomics also revealed the decrease of apolipoproteins, and the presence of intracellular proteins in serum, which may indicate physical injury to tissues. CONCLUSIONS: Housing of gilts in individual stalls and diet change increase lipid and protein catabolism, oxidative stress, activate the innate immune system and cause a certain degree of tissue damage. We propose that valuable assays for stress assessment in gilts may be based on a score composed by a combination of salivary cortisol, lipid metabolites, innate immunity and oxidative stress markers and intracellular proteins

Making sense of metabolomic data: comprehensive analysis of altered metabolic pathways in diabetes and obesity

Podeu consultar el III Workshop anual INSA-UB complet a: http://hdl.handle.net/2445/118993Sessió 1. Pòster núm.

Metabotypes of response to bariatric surgery independent of the magnitude of weight loss

Objective Bariatric surgery is considered the most efficient treatment for morbid obesity and its related diseases. However, its role as a metabolic modifier is not well understood. We aimed to determine biosignatures of response to bariatric surgery and elucidate short-term metabolic adaptations. Methods We used a LC- and FIA-ESI-MS/MS approach to quantify acylcarnitines, (lyso)phosphatidylcholines, sphingomyelins, amino acids, biogenic amines and hexoses in serum samples of subjects with morbid obesity (n = 39) before and 1, 3 and 6 months after bariatric surgery. K-means cluster analysis allowed to distinguish metabotypes of response to bariatric surgery. Results For the first time, global metabolic changes following bariatric surgery independent of the baseline health status of the subjects have been revealed. We identify two metabolic phenotypes (metabotypes) at the interval 6 months-baseline after surgery, which presented differences in the levels of compounds of urea metabolism, gluconeogenic precursors and (lyso)phospholipid particles. Clinically, metabotypes were different in terms of the degree of improvement in insulin resistance, cholesterol, low-density lipoproteins and uric acid independent of the magnitude of weight loss. Conclusions This study opens new perspectives and new hypotheses on the metabolic benefits of bariatric surgery and understanding of the biology of obesity and its associated diseases

Characterization of metabolomic profile associated with metabolic improvement after bariatric surgery in subjects with morbid obesity

The exact impact of bariatric surgery in metabolically 'healthy' (MH) or 'unhealthy' (MU) phenotypes for the study of the metabolic improvement is still unknown. We applied an untargeted LC-ESI-TripleTOF-MS-driven metabolomics approach in serum samples from 39 patients with morbid obesity (MH and MU) 1, 3, and 6 months after bariatric surgery. Multiple factor analysis, along with correlation and enrichment analyses, was carried out to distinguish those metabolites associated with metabolic improvement. Hydroxypropionic acids, medium-/long-chain hydroxy fatty acids, and bile acid glucuronides were the most discriminative biomarkers of response between MH and MU phenotypes. Hydroxypropionic (hydroxyphenyllactic-related) acids, amino acids, and glycerolipids were the most significant clusters of metabolites altered after bariatric surgery in MU ( p < 0.001). After surgery, MU and MH changed toward a common metabolic state 3 months after surgery. We observed a negative correlation with changes in waist circumference and cholesterol levels with metabolites of lipid metabolism. Glycemic variables were correlated with hexoses, which, in turn, correlated with gluconic acid and amino acid metabolism. Finally, we noted that hydroxyphenyllactic acid was associated with amino acid and lipid metabolism. Microbial metabolism of amino acid and BA glucuronidation pathways may be the key points of metabolic rearrangement after surgery

Untargeted profiling of concordant/discordant phenotypes of high insulin resistance and obesity to predict the risk of developing diabetes

This study explores the metabolic profiles of concordant/discordant phenotypes of high insulin resistance (IR) and obesity. Through untargeted metabolomics (LC-ESI-QTOF-MS), we analyzed the fasting serum of subjects with high IR and/or obesity ( n = 64). An partial least-squares discriminant analysis with orthogonal signal correction followed by univariate statistics and enrichment analysis allowed exploration of these metabolic profiles. A multivariate regression method (LASSO) was used for variable selection and a predictive biomarker model to identify subjects with high IR regardless of obesity was built. Adrenic acid and a dyglyceride (DG) were shared by high IR and obesity. Uric and margaric acids, 14 DGs, ketocholesterol, and hydroxycorticosterone were unique to high IR, while arachidonic, hydroxyeicosatetraenoic (HETE), palmitoleic, triHETE, and glycocholic acids, HETE lactone, leukotriene B4, and two glutamyl-peptides to obesity. DGs and adrenic acid differed in concordant/discordant phenotypes, thereby revealing protective mechanisms against high IR also in obesity. A biomarker model formed by DGs, uric and adrenic acids presented a high predictive power to identify subjects with high IR [AUC 80.1% (68.9-91.4)]. These findings could become relevant for diabetes risk detection and unveil new potential targets in therapeutic treatments of IR, diabetes, and obesity. An independent validated cohort is needed to confirm these results

Biomarkers of Morbid Obesity and Prediabetes by Metabolomic Profiling of Human Discordant Phenotypes

Metabolomic studies aimed to dissect the connection between the development of type 2 diabetes and obesity are still scarce. In the present study, fasting serum from sixty-four adult individuals classified into four sex-matched groups by their BMI [non-obese versus morbid obese] and the increased risk of developing diabetes [prediabetic insulin resistant state versus non-prediabetic non-insulin resistant] was analyzed by LC- and FIA-ESI-MS/MS-driven metabolomic approaches. Altered levels of [lyso]glycerophospholipids was the most specific metabolic trait associated to morbid obesity, particularly lysophosphatidylcholines acylated with margaric, oleic and linoleic acids [lysoPC C17:0: R=-0.56, p=0.0003; lysoPC C18:1: R=-0.61, p=0.0001; lysoPC C18:2 R=-0.64, p<0.0001]. Several amino acids were biomarkers of risk of diabetes onset associated to obesity. For instance, glutamate significantly associated with fasting insulin [R=0.5, p=0.0019] and HOMA-IR [R=0.46, p=0.0072], while glycine showed negative associations [fasting insulin: R=-0.51, p=0.0017; HOMA-IR: R=-0.49, p=0.0033], and the branched chain amino acid valine associated to prediabetes and insulin resistance in a BMI-independent manner [fasting insulin: R=0.37, p=0.0479; HOMA-IR: R=0.37, p=0.0468]. Minority sphingolipids including specific [dihydro]ceramides and sphingomyelins also associated with the prediabetic insulin resistant state, hence deserving attention as potential targets for early diagnosis or therapeutic intervention

Proteomics and the search for welfare and stress biomarkers in animal production in the one-health context

Stress and welfare are important factors in animal production in the context of growing production optimization and scrutiny by the general public. In a context in which animal and human health are intertwined aspects of the one-health concept it is of utmost importance to define the markers of stress and welfare. These are important tools for producers, retailers, regulatory agents and ultimately consumers to effectively monitor and assess the welfare state of production animals. Proteomics is the science that studies the proteins existing in a given tissue or fluid. In this review we address this topic by showing clear examples where proteomics has been used to study stress-induced changes at various levels. We adopt a multi-species (cattle, swine, small ruminants, poultry, fish and shellfish) approach under the effect of various stress inducers (handling, transport, management, nutritional, thermal and exposure to pollutants) clearly demonstrating how proteomics and systems biology are key elements to the study of stress and welfare in farm animals and powerful tools for animal welfare, health and productivity

Skeletal muscle metabolomics and blood biochemistry analysis reveal metabolic changes associated with dietary amino acid supplementation in dairy calves

The effects of different amino acid (AA) supplementations of milk protein-based milk replacers in pre-ruminant calves from 3 days to 7 weeks of age were studied. Animals were divided into 4 groups: Ctrl) Control group fed with milk protein-based milk replacer without supplementation; GP) supplementation with 0.1% glycine and 0.3% proline; FY) supplementation with 0.2% phenylalanine and 0.2% tyrosine; MKT) supplementation with 0.62% lysine, 0.22% methionine and 0.61% threonine. For statistical analysis, t-test was used to compare AA-supplemented animals to the Ctrl group. At week 7, body weight and average daily gain (ADG) were measured and blood samples and skeletal muscle biopsies were taken. Blood biochemistry analytes related to energy metabolism were determined and it was shown that MKT group had higher serum creatinine and higher plasma concentration of three supplemented AAs as well as arginine compared with the Ctrl group. GP group had similar glycine/proline plasma concentration compared with the other groups while in FY group only plasma phenylalanine concentration was higher compared with Control. Although the AA supplementations in the GP and FY groups did not affect average daily gain and metabolic health profile from serum, the metabolome analysis from skeletal muscle biopsy revealed several differences between the GP-FY groups and the Ctrl-MKT groups, suggesting a metabolic adaptation especially in GP and FY groupsinfo:eu-repo/semantics/publishedVersio