PubMed

Related Articles An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling. Food Res Int. 2017 09;99(Pt 1):206-215 Authors: Estelles-Lopez L, Ropodi A, Pavlidis D, Fotopoulou J, Gkousari C, Peyrodie A, Panagou E, Nychas GJ, Mohareb F Abstract Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. PMID: 28784477 [PubMed - indexed for MEDLINE]

Related Articles UHPLC-PDA-ESI-TOF/MS metabolic profiling and antioxidant capacity of arabica and robusta coffee silverskin: Antioxidants vs phytotoxins. Food Res Int. 2017 09;99(Pt 1):155-165 Authors: Panusa A, Petrucci R, Lavecchia R, Zuorro A Abstract A deeper knowledge of the chemical composition of coffee silverskin (CS) is needed due to the growing interest in its use as a food additive or an ingredient of dietary supplements. Accordingly, the aim of this paper was to investigate the metabolic profile of aqueous extracts of two varieties of CS, Coffee arabica (CS-A), Coffee canephora var. robusta (CS-R) and of a blend of the two (CS-b) and to compare it to the profile of Coffee arabica green coffee (GC). Chlorogenic acids, caffeine, furokauranes, and atractyligenins, phytotoxins not previously detected in CS, were either identified or tentatively assigned. An unknown compound, presumably a carboxyatractyligenin glycoside was detected only in GC. Caffeine and chlorogenic acids were quantified while the content of furokauranes and atractyligens was estimated. GC and CS were also characterized in terms of total polyphenols and antioxidant capacity. Differences in the metabolites distribution, polyphenols and antioxidant capacity in GC and CS were detailed. PMID: 28784472 [PubMed - indexed for MEDLINE]

Related Articles Markers of Inflammation and Incident Breast Cancer Risk in the Women's Health Study. Am J Epidemiol. 2018 04 01;187(4):705-716 Authors: Tobias DK, Akinkuolie AO, Chandler PD, Lawler PR, Manson JE, Buring JE, Ridker PM, Wang L, Lee IM, Mora S Abstract Chronic inflammation may be a risk factor for the development and progression of breast cancer, yet it is unknown which inflammatory biomarkers and pathways are especially relevant. The present study included 27,071 participants (mean age = 54.5 years) in the Women's Health Study who were free of cancer and cardiovascular disease at enrollment (1992-1995), with baseline measures of 4 inflammatory biomarkers: high-sensitivity C-reactive protein, fibrinogen, N-acetyl side-chains of acute phase proteins, and soluble intercellular adhesion molecule-1. We used Cox proportional hazards regression models to evaluate associations between baseline concentrations of biomarkers and incident breast cancer, and adjusted for baseline and time-varying factors such as age and body mass index. Self-reported invasive breast cancer was confirmed against medical records for 1,497 incident cases (90% postmenopausal). We observed different patterns of risk depending on the inflammatory biomarker. There was a significant direct association between fibrinogen and breast cancer risk (for quintile 5 vs. quintile 1, adjusted hazard ratio = 1.25, 95% confidence interval: 1.03, 1.51; P for trend = 0.01). In contrast, soluble intercellular adhesion molecule-1 was inversely associated with breast cancer (for quintile 5 vs. quintile 1, adjusted hazard ratio = 0.79, 95% confidence interval: 0.66, 0.94; P for trend = 0.02). N-acetyl side-chains of acute phase proteins and high-sensitivity C-reactive protein were not associated with breast cancer. The complex association of chronic inflammation and breast cancer may be considered when formulating anti-inflammatory cancer prevention or intervention strategies. PMID: 28641369 [PubMed - indexed for MEDLINE]

71 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/05/28PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

44 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/05/24PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

44 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/05/24PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

23 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/05/23PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

33 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/05/22PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

33 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/05/22PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

26 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/05/21PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Related Articles Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer's risk factors among 1,111 cohort participants. Genet Epidemiol. 2019 May 18;: Authors: Darst BF, Lu Q, Johnson SC, Engelman CD Abstract Although Alzheimer's disease (AD) is highly heritable, genetic variants are known to be associated with AD only explain a small proportion of its heritability. Genetic factors may only convey disease risk in individuals with certain environmental exposures, suggesting that a multiomics approach could reveal underlying mechanisms contributing to complex traits, such as AD. We developed an integrated network to investigate relationships between metabolomics, genomics, and AD risk factors using Wisconsin Registry for Alzheimer's Prevention participants. Analyses included 1,111 non-Hispanic Caucasian participants with whole blood expression for 11,376 genes (imputed from dense genome-wide genotyping), 1,097 fasting plasma metabolites, and 17 AD risk factors. A subset of 155 individuals also had 364 fastings cerebral spinal fluid (CSF) metabolites. After adjusting each of these 12,854 variables for potential confounders, we developed an undirected graphical network, representing all significant pairwise correlations upon adjusting for multiple testing. There were many instances of genes being indirectly linked to AD risk factors through metabolites, suggesting that genes may influence AD risk through particular metabolites. Follow-up analyses suggested that glycine mediates the relationship between carbamoyl-phosphate synthase 1 and measures of cardiovascular and diabetes risk, including body mass index, waist-hip ratio, inflammation, and insulin resistance. Further, 38 CSF metabolites explained more than 60% of the variance of CSF levels of tau, a detrimental protein that accumulates in the brain of AD patients and is necessary for its diagnosis. These results further our understanding of underlying mechanisms contributing to AD risk while demonstrating the utility of generating and integrating multiple omics data types. PMID: 31104335 [PubMed - as supplied by publisher]

Related Articles Correction to: Assessing the effect of nitisinone induced hypertyrosinaemia on monoamine neurotransmitters in brain tissue from a murine model of alkaptonuria using mass spectrometry imaging. Metabolomics. 2019 May 18;15(5):81 Authors: Davison AS, Strittmatter N, Sutherland H, Hughes AT, Hughes J, Bou-Gharios G, Milan AM, Goodwin RJA, Ranganath LR, Gallagher JA Abstract The original publication of this article contained an incorrect version that did not include some final reviewers' suggestions, was inadvertently received for production and published. The original article has been corrected. PMID: 31104147 [PubMed - in process]

Related Articles Integrated Regulation of HuR by Translation Repression and Protein Degradation Determines Pulsatile Expression of p53 Under DNA Damage. iScience. 2019 May 04;15:342-359 Authors: Guha A, Ahuja D, Das Mandal S, Parasar B, Deyasi K, Roy D, Sharma V, Willard B, Ghosh A, Ray PS Abstract Expression of tumor suppressor p53 is regulated at multiple levels, disruption of which often leads to cancer. We have adopted an approach combining computational systems modeling with experimental validation to elucidate the translation regulatory network that controls p53 expression post DNA damage. The RNA-binding protein HuR activates p53 mRNA translation in response to UVC-induced DNA damage in breast carcinoma cells. p53 and HuR levels show pulsatile change post UV irradiation. The computed model fitted with the observed pulse of p53 and HuR only when hypothetical regulators of synthesis and degradation of HuR were incorporated. miR-125b, a UV-responsive microRNA, was found to represses the translation of HuR mRNA. Furthermore, UV irradiation triggered proteasomal degradation of HuR mediated by an E3-ubiquitin ligase tripartite motif-containing 21 (TRIM21). The integrated action of miR-125b and TRIM21 constitutes an intricate control system that regulates pulsatile expression of HuR and p53 and determines cell viability in response to DNA damage. PMID: 31103853 [PubMed - as supplied by publisher]

Related Articles Integrative transcriptomics, proteomics, and metabolomics data analysis exploring the injury mechanism of ricin on human lung epithelial cells. Toxicol In Vitro. 2019 May 16;: Authors: Xu N, Dong M, Wang Y, Chang Y, Wan J, Zhu W, Wang J, Liu W Abstract Ricin (RT) is a plant toxin belonging to the family of type II ribosome-inactivating protein with high bioterrorism potential. Aerosol RT exposure is the most lethal route, but its mechanism of injury needs further investigation. In the present study, we performed a comprehensive transcriptomics, proteomics and metabolomics analysis on the potential mechanism of injury caused by RT on human lung epithelial cells. In total, 5872 genes, 187 proteins, and 143 metabolites were shown to be significantly changed in human lung epithelial cells after RT treatment. Molecular function, pathway, and network analyses, the genes and proteins regulated in RT-treated cells were mainly attributed to fatty acid metabolism, arginine and proline metabolism and ubiquitin-mediated proteolysis pathway. Furthermore, a comprehensive analysis of transcripts, proteins, and metabolites was performed. The results revealed the correlated genes, proteins, and metabolic pathways regulated in metabolic pathways, amino acid metabolism, transcription and energy metabolism. These genes, proteins, and metabolites involved in these dis-regulated pathways may provide a more targeted and credible direction to study the mechanism of RT injury on human lung epithelial cells. This study provides large-scale omics data that can be used to develop a new strategy for the prevention, rapid diagnosis, and treatment of RT poisoning, especially of RT aerosol. PMID: 31103672 [PubMed - as supplied by publisher]

Related Articles Metabolomics workflow for lung cancer: Discovery of biomarkers. Clin Chim Acta. 2019 May 16;: Authors: Tang Y, Li Z, Lazar L, Fang Z, Tang C, Zhao J Abstract Lung cancer is one of the most common cancers in the world. Due to the limitations of current diagnostic techniques and methods, most lung cancers are diagnosed at the advanced stage, which is not conducive to early treatment. The rise of metabolomics has provided new ideas for the early diagnosis of lung cancer. As a method for the comprehensive analysis of endogenous metabolites of the biological system, metabolomics has shown significant application potential for the early diagnosis and individualized treatment of various cancers including lung cancers. Via advanced analytical techniques and bioinformatics tools, the metabolome was excavated to find biomarkers related to cancer and its prognosis. In this review, the research methods and workflow of metabolomics are summarized, with an emphasis on the recent discovery of biomarkers and major metabolic pathways for lung cancers. PMID: 31103622 [PubMed - as supplied by publisher]

Related Articles Targeting bioactive compounds in natural extracts - Development of a comprehensive workflow combining chemical and biological data. Anal Chim Acta. 2019 Sep 06;1070:29-42 Authors: Ory L, Nazih EH, Daoud S, Mocquard J, Bourjot M, Margueritte L, Delsuc MA, Bard JM, Pouchus YF, Bertrand S, Roullier C Abstract In natural product drug discovery, several strategies have emerged to highlight specifically bioactive compound(s) within complex mixtures (fractions or crude extracts) using metabolomics tools. In this area, a great deal of interest has raised among the scientific community on strategies to link chemical profiles and associated biological data, leading to the new field called "biochemometrics". This article falls into this emerging research by proposing a complete workflow, which was divided into three major steps. The first one consists in the fractionation of the same extract using four different chromatographic stationary phases and appropriated elution conditions to obtain five fractions for each column. The second step corresponds to the acquisition of chemical profiles using HPLC-HRMS analysis, and the biological evaluation of each fraction. The last step evaluates the links between the relative abundances of molecules present in fractions (peak area) and the global bioactivity level observed for each fraction. To this purpose, an original bioinformatics script (encoded with R Studio software) using the combination of four statistical models (Spearman, F-PCA, PLS, PLS-DA) was here developed leading to the generation of a "Super list" of potential bioactive compounds together with a predictive score. This strategy was validated by its application on a marine-derived Penicillium chrysogenum extract exhibiting antiproliferative activity on breast cancer cells (MCF-7 cells). After the three steps of the workflow, one main compound was highlighted as responsible for the bioactivity and identified as ergosterol. Its antiproliferative activity was confirmed with an IC50 of 0.10 μM on MCF-7 cells. The script efficiency was further demonstrated by comparing the results obtained with a different recently described approach based on NMR profiling and by virtually modifying the data to evaluate the computational tool behaviour. This approach represents a new and efficient tool to tackle some of the bottlenecks in natural product drug discovery programs. PMID: 31103165 [PubMed - in process]

Related Articles Rhizosphere microbiomes diverge among Populus trichocarpa plant-host genotypes and chemotypes, but it depends on soil origin. Microbiome. 2019 May 18;7(1):76 Authors: Veach AM, Morris R, Yip DZ, Yang ZK, Engle NL, Cregger MA, Tschaplinski TJ, Schadt CW Abstract BACKGROUND: Plants have developed defense strategies for phytopathogen and herbivore protection via coordinated metabolic mechanisms. Low-molecular weight metabolites produced within plant tissues, such as salicylic acid, represent one such mechanism which likely mediates plant - microbe interactions above and below ground. Salicylic acid is a ubiquitous phytohormone at low levels in most plants, yet are concentrated defense compounds in Populus, likely acting as a selective filter for rhizosphere microbiomes. We propagated twelve Populus trichocarpa genotypes which varied an order of magnitude in salicylic acid (SA)-related secondary metabolites, in contrasting soils from two different origins. After four months of growth, plant properties (leaf growth, chlorophyll content, and net photosynthetic rate) and plant root metabolomics specifically targeting SA metabolites were measured via GC-MS. In addition, rhizosphere microbiome composition was measured via Illumina MiSeq sequencing of 16S and ITS2 rRNA-genes. RESULTS: Soil origin was the primary filter causing divergence in bacterial/archaeal and fungal communities with plant genotype secondarily influential. Both bacterial/archaeal and fungal evenness varied between soil origins and bacterial/archaeal diversity and evenness correlated with at least one SA metabolite (diversity: populin; evenness: total phenolics). The production of individual salicylic acid derivatives that varied by host genotype resulted in compositional differences for bacteria /archaea (tremuloidin) and fungi (salicylic acid) within one soil origin (Clatskanie) whereas soils from Corvallis did not illicit microbial compositional changes due to salicylic acid derivatives. Several dominant bacterial (e.g., Betaproteobacteria, Acidobacteria, Verrucomicrobia, Chloroflexi, Gemmatimonadete, Firmicutes) and one fungal phyla (Mortierellomycota) also correlated with specific SA secondary metabolites; bacterial phyla exhibited more negative interactions (declining abundance with increasing metabolite concentration) than positive interactions. CONCLUSIONS: These results indicate microbial communities diverge most among soil origin. However, within a soil origin, bacterial/archaeal communities are responsive to plant SA production within greenhouse-based rhizosphere microbiomes. Fungal microbiomes are impacted by root SA-metabolites, but overall to a lesser degree within this experimental context. These results suggest plant defense strategies, such as SA and its secondary metabolites, may partially drive patterns of both bacterial/archaeal and fungal taxa-specific colonization and assembly. PMID: 31103040 [PubMed - in process]

Related Articles High temperature-induced proteomic and metabolomic profiles of a thermophilic Bacillus manusensis isolated from the deep-sea hydrothermal field of Manus Basin. J Proteomics. 2019 May 15;:103380 Authors: Sun QL, Sun YY, Zhang J, Luan ZD, Lian C, Liu SQ, Yu C Abstract Thermophiles are organisms that grow optimally at 50 °C-80 °C and studies on the survival mechanisms of thermophiles have drawn great attention. Bacillus manusensis S50-6 is the type strain of a new thermophilic species isolated from hydrothermal vent in Manus Basin. In this study, we examined the growth and global responses of S50-6 to high temperature on molecular level using multi-omics method (genomics, proteomics, and metabolomics). S50-6 grew optimally at 50 °C (Favorable, F) and poorly at 65 °C (Non-Favorable, NF); it formed spores at F but not at NF condition. At NF condition, S50-6 formed long filaments containing undivided cells. A total of 1621 proteins were identified at F and NF conditions, and 613 proteins were differentially expressed between F and NF. At NF condition, proteins of glycolysis, rRNA mature and modification, and DNA/protein repair were up-regulated, whereas proteins of sporulation and amino acid/nucleotide metabolism were down-regulated. Consistently, many metabolites associated with amino acid and nucleotide metabolic processes were down-regulated at NF condition. Our results revealed molecular strategies of deep-sea B. manusensis to survive at unfavorable high temperature and provided new insights into the thermotolerant mechanisms of thermophiles. SIGNIFICANCE: In this study, we systematically characterized the genomic, proteomic and metabolomic profiles of a thermophilic deep-sea Bacillus manusensis under different temperatures. Based on these analysis, we propose a model delineating the global responses of B. manusensis to unfavorable high temperature. Under unfavorable high temperature, glycolysis is a more important energy supply pathway; protein synthesis is subjected to more stringent regulation by increased tRNA modification; protein and DNA repair associated proteins are enhanced in production to promote heat survival. In contrast, energy-costing pathways, such as sporulation, are repressed, and basic metabolic pathways, such as amino acid and nucleotide metabolisms, are slowed down. Our results provide new insights into the thermotolerant mechanisms of thermophilic Bacillus. PMID: 31102757 [PubMed - as supplied by publisher]

Related Articles Metabolome Wide Association Study of serum DDT and DDE in Pregnancy and Early Postpartum. Reprod Toxicol. 2019 May 15;: Authors: Hu X, Li S, Cirrilo P, Krigbaum N, Tran V, Ishikawa T, La Merrill MA, Jones DP, Cohn B Abstract The advancement of high-resolution metabolomics (HRM) and metabolome-wide-association study (MWAS) enables the readout of environmental effects in human specimens. We used HRM to understand DDT-induced alterations of in utero environment and potential health effects. Endogenous metabolites were measured in 397 maternal perinatal serum samples collected during 1959-1967 in the Child Health and Development Studies (CHDS) and in 16 maternal postnatal serum samples of mice treated with or without DDT. MWAS was performed to assess associations between metabolites and p,p'-DDT, o,p'-DDT and p,p'-DDE levels, followed by pathway analysis. Distinct metabolic profiles were found with p,p'-DDT and p,p'-DDE. Amino acids such arginine had a strong association with p,p'-DDT and o,p'-DDT in both women and mice, whereas lipids and acyl-carnitine intermediates were found exclusively associated with p,p'-DDE in CHDS women indicating mitochondrial impairment. It suggests that the role of serine and fatty acid metabolism on the causal disease pathway should be examined. PMID: 31102720 [PubMed - as supplied by publisher]

Related Articles Capturing the complex interplay between drugs and the intestinal microbiome. Clin Pharmacol Ther. 2019 May 18;: Authors: Birer C, Wright ES Abstract Predicting drug interactions, disposition, and side effects is central to the practice of clinical pharmacology. Until recently, the human microbiome has been an underappreciated player in the dynamics of drug metabolism. It is now clear that humans are 'superorganisms' with about tenfold more microbial cells than human cells and harboring an immense diversity of microbial enzymes. Owing to the advent of new technologies, we are beginning to understand the human microbiome's impact on clinical pharmacology. This article is protected by copyright. All rights reserved. PMID: 31102465 [PubMed - as supplied by publisher]