PubMed

Related Articles Unraveling Asian Soybean Rust metabolomics using mass spectrometry and Molecular Networking approach. Sci Rep. 2020 Jan 10;10(1):138 Authors: Silva E, da Graça JP, Porto C, Martin do Prado R, Hoffmann-Campo CB, Meyer MC, de Oliveira Nunes E, Pilau EJ Abstract Asian Soybean Rust (ASR), caused by the biotrophic fungus Phakopsora pachyrhizi, is a devastating disease with an estimated crop yield loss of up to 90%. Yet, there is a nerf of information on the metabolic response of soybean plants to the pathogen Untargeted metabolomics and Global Natural Products Social Molecular Networking platform approach was used to explore soybean metabolome modulation to P. pachyrhizi infection. Soybean plants susceptible to ASR was inoculated with P. pachyrhizi spore suspension and non-inoculated plants were used as controls. Leaves from both groups were collected 14 days post-inoculation and extracted using different extractor solvent mixtures. The extracts were analyzed on an ultra-high performance liquid chromatography system coupled to high-definition electrospray ionization-mass spectrometry. There was a significant production of defense secondary metabolites (phenylpropanoids, terpenoids and flavonoids) when P. pachyrhizi infected soybean plants, such as putatively identified liquiritigenin, coumestrol, formononetin, pisatin, medicarpin, biochanin A, glyoceollidin I, glyoceollidin II, glyoceollin I, glyoceolidin II, glyoceolidin III, glyoceolidin IV, glyoceolidin VI. Primary metabolites (amino acids, peptides and lipids) also were putatively identified. This is the first report using untargeted metabolomics and GNPS-Molecular Networking approach to explore ASR in soybean plants. Our data provide insights into the potential role of some metabolites in the plant resistance to ASR, which could result in the development of resistant genotypes of soybean to P. pachyrhizi, and effective and specific products against the pathogen. PMID: 31924833 [PubMed - in process]

Related Articles Diagnosis of Bovine Respiratory Disease in feedlot cattle using blood 1H NMR metabolomics. Sci Rep. 2020 Jan 10;10(1):115 Authors: Blakebrough-Hall C, Dona A, D'occhio MJ, McMeniman J, González LA Abstract Current diagnosis methods for Bovine Respiratory Disease (BRD) in feedlots have a low diagnostic accuracy. The current study aimed to search for blood biomarkers of BRD using 1H NMR metabolomics and determine their accuracy in diagnosing BRD. Animals with visual signs of BRD (n = 149) and visually healthy (non-BRD; n = 148) were sampled for blood metabolomics analysis. Lung lesions indicative of BRD were scored at slaughter. Non-targeted 1H NMR metabolomics was used to develop predictive algorithms for disease classification using classification and regression trees. In the absence of a gold standard for BRD diagnosis, six reference diagnosis methods were used to define an animal as BRD or non-BRD. Sensitivity (Se) and specificity (Sp) were used to estimate diagnostic accuracy (Acc). Blood metabolomics demonstrated a high accuracy at diagnosing BRD when using visual signs of BRD (Acc = 0.85), however was less accurate at diagnosing BRD using rectal temperature (Acc = 0.65), lung auscultation score (Acc = 0.61) and lung lesions at slaughter as reference diagnosis methods (Acc = 0.71). Phenylalanine, lactate, hydroxybutyrate, tyrosine, citrate and leucine were identified as metabolites of importance in classifying animals as BRD or non-BRD. The blood metabolome classified BRD and non-BRD animals with high accuracy and shows potential for use as a BRD diagnosis tool. PMID: 31924818 [PubMed - in process]

Related Articles Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure. Sci Rep. 2020 Jan 10;10(1):130 Authors: Marcinkiewicz-Siemion M, Kaminski M, Ciborowski M, Ptaszynska-Kopczynska K, Szpakowicz A, Lisowska A, Jasiewicz M, Tarasiuk E, Kretowski A, Sobkowicz B, Kaminski KA Abstract The metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially clinically useful diagnostic panel for HFrEF. The study included 67 chronic HFrEF patients (left ventricular ejection fraction-LVEF 24.3 ± 5.9%) and 39 controls without the disease. Fasting serum samples were fingerprinted by liquid chromatography-mass spectrometry. Feature selection based on random-forest models fitted to resampled data and followed by linear modelling, resulted in selection of eight metabolites (uric acid, two isomers of LPC 18:2, LPC 20:1, deoxycholic acid, docosahexaenoic acid and one unknown metabolite), demonstrating their predictive value in HFrEF. The accuracy of a model based on metabolites panel was comparable to BNP (0.85 vs 0.82), as verified on the test set. Selected metabolites correlated with clinical, echocardiographic and functional parameters. The combination of two innovative tools (metabolomics and machine-learning methods), both unrestrained by the gaps in the current knowledge, enables identification of a novel diagnostic panel. Its diagnostic value seems to be comparable to BNP. Large scale, multi-center studies using validated targeted methods are crucial to confirm clinical utility of proposed markers. PMID: 31924803 [PubMed - in process]

Related Articles An unsupervised learning approach to identify novel signatures of health and disease from multimodal data. Genome Med. 2020 Jan 10;12(1):7 Authors: Shomorony I, Cirulli ET, Huang L, Napier LA, Heister RR, Hicks M, Cohen IV, Yu HC, Swisher CL, Schenker-Ahmed NM, Li W, Nelson KE, Brar P, Kahn AM, Spector TD, Caskey CT, Venter JC, Karow DS, Kirkness EF, Shah N Abstract BACKGROUND: Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures. METHODS: We collected 1385 data features from diverse modalities, including metabolome, microbiome, genetics, and advanced imaging, from 1253 individuals and from a longitudinal validation cohort of 1083 individuals. We utilized a combination of unsupervised machine learning methods to identify multimodal biomarker signatures of health and disease risk. RESULTS: Our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers. Stratification of individuals based on the signatures of these biomarkers identified distinct subsets of individuals with similar health statuses. Subset membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and body mass index. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We identified potential early signatures for hypertension and a poor metabolic health outcome. Additionally, we found novel associations between a uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family. CONCLUSIONS: Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages-an essential step towards personalized, preventative health risk assessment. PMID: 31924279 [PubMed - in process]

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 2020/01/11PubMed 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.

22 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 2020/01/10PubMed 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.

A synthetic lethal drug combination mimics glucose deprivation-induced cancer cell death in the presence of glucose. J Biol Chem. 2019 Dec 30;: Authors: Joly JH, Delfarah A, Phung PS, Parrish S, Graham NA Abstract Metabolic reprogramming in cancer cells can increase their dependence on metabolic substrates such as glucose. As such, the vulnerability of cancer cells to glucose deprivation creates an attractive opportunity for therapeutic intervention. Because it is not possible to starve tumors of glucose in vivo, here we sought to identify the mechanisms in glucose deprivation-induced cancer cell death and then designed inhibitor combinations to mimic glucose deprivation-induced cell death. Using metabolomic profiling, we found that cells undergoing glucose deprivation-induced cell death exhibited dramatic accumulation of intracellular L-cysteine and its oxidized dimer, L-cystine, and depletion of the antioxidant glutathione. Building on this observation, we show that glucose deprivation-induced cell death is driven not by lack of glucose, but rather by L-cystine import. Following glucose deprivation, the import of L-cystine and its subsequent reduction to L-cysteine depleted both NADPH and glutathione pools, thereby allowing toxic accumulation of reactive oxygen species. Consistent with this model, we found that the glutamate/cystine antiporter xCT is required for increased sensitivity to glucose deprivation. We searched for glycolytic enzymes whose expression is essential for survival of cancer cells with high xCT expression and identified glucose transporter type 1 (GLUT1). Testing a drug combination that co-targeted GLUT1 and glutathione synthesis, we found that this combination induces synthetic lethal cell death in high xCT-expressing cell lines susceptible to glucose deprivation. These results indicate that co-targeting GLUT1 and glutathione synthesis may offer a potential therapeutic approach for targeting tumors dependent on glucose for survival. PMID: 31914417 [PubMed - as supplied by publisher]

IgA subclasses have different effector functions associated with distinct glycosylation profiles. Nat Commun. 2020 Jan 08;11(1):120 Authors: Steffen U, Koeleman CA, Sokolova MV, Bang H, Kleyer A, Rech J, Unterweger H, Schicht M, Garreis F, Hahn J, Andes FT, Hartmann F, Hahn M, Mahajan A, Paulsen F, Hoffmann M, Lochnit G, Muñoz LE, Wuhrer M, Falck D, Herrmann M, Schett G Abstract Monomeric serum immunoglobulin A (IgA) can contribute to the development of various autoimmune diseases, but the regulation of serum IgA effector functions is not well defined. Here, we show that the two IgA subclasses (IgA1 and IgA2) differ in their effect on immune cells due to distinct binding and signaling properties. Whereas IgA2 acts pro-inflammatory on neutrophils and macrophages, IgA1 does not have pronounced effects. Moreover, IgA1 and IgA2 have different glycosylation profiles, with IgA1 possessing more sialic acid than IgA2. Removal of sialic acid increases the pro-inflammatory capacity of IgA1, making it comparable to IgA2. Of note, disease-specific autoantibodies in patients with rheumatoid arthritis display a shift toward the pro-inflammatory IgA2 subclass, which is associated with higher disease activity. Taken together, these data demonstrate that IgA effector functions depend on subclass and glycosylation, and that disturbances in subclass balance are associated with autoimmune disease. PMID: 31913287 [PubMed - in process]

Metabolomics and adductomics of newborn bloodspots to retrospectively assess the early-life exposome. Curr Opin Pediatr. 2020 Jan 06;: Authors: Petrick LM, Uppal K, Funk WE Abstract PURPOSE OF REVIEW: Exposomics studies can measure health-relevant chemical exposures during a lifetime and estimate the 'internal' environment. However, sampling limitations make these features difficult to capture directly during the critical neonatal time period. RECENT FINDINGS: We review the use of newborn dried bloodspots (DBS) archived from newborn screening programs for exposomic analysis in epidemiological children's health studies. Emerging 'omics technologies such as adductomics and metabolomics have been adapted for DBS analysis, and these technologies can now provide valuable etiological information on the complex interplay between exposures, biological response, and population phenotypes. SUMMARY: Adductomics and metabolomics of DBS can provide robust measurements for retrospective epidemiological investigations. With extensive bioarchiving programs in the United States and other countries, DBS are poised to substantially aid epidemiological studies, particularly for rare and low-frequency childhood diseases and disorders. PMID: 31913157 [PubMed - as supplied by publisher]

Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nat Commun. 2020 Jan 07;11(1):39 Authors: Hagenbeek FA, Pool R, van Dongen J, Draisma HHM, Jan Hottenga J, Willemsen G, Abdellaoui A, Fedko IO, den Braber A, Visser PJ, de Geus EJCN, Willems van Dijk K, Verhoeven A, Suchiman HE, Beekman M, Slagboom PE, van Duijn CM, BBMRI Metabolomics Consortium, Harms AC, Hankemeier T, Bartels M, Nivard MG, Boomsma DI Abstract Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes. PMID: 31911595 [PubMed - in process]

Metabolic profiling of chronic obstructive pulmonary disease model rats and the interventional effects of HuaTanJiangQi decoction using UHPLC-Q-TOF/MSE. J Pharm Biomed Anal. 2019 Dec 24;180:113078 Authors: Fang W, Li C, Wu Q, Yao Z, Wu J, Huang P, Wang D, Li Z Abstract The occurrence of chronic obstructive pulmonary disease (COPD) will lead to physiological and pathological variations and endogenous metabolic disorders. A traditional Chinese medicine formula, HuaTanJiangQi decoction (HTJQ), exhibits an unambiguous therapeutic effect on COPD in China. Nevertheless, the mechanism of its therapeutic effect on COPD is not clear. With this purpose, pulmonary function, histopathological and the inflammatory factors in bronchoalveolar lavage fluid (BALF) in rats model of COPD were investigated. Then, ultra high-performance liquid chromatography quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) analysis and multivariate statistical analysis were used to further reveal the mechanism of HTJQ therapeutic effect on COPD via metabolomics study. The results showed that the characteristics of lung tissues were significantly reversed, the concentration of LTB4 and LTC4 were gradually decreased, and the lung function began to recover after HTJQ treatment. These typical indicators of COPD in HTJQ intervention group were reversed similar to the control group, suggested that HTJQ has a therapeutic effect on COPD. Moreover, 32 dysregulated metabolites, including Thromboxane a2, Sphingosine 1-phosphate, PC(18:2(9Z,12Z)/18:1(11Z)), Leukotriene B4, Glutathione, Arachidonic acid, Sphingosylphosphocholine acid, N-Acetyl-leukotriene e4, Lysopc(18:1(11Z)), L-Cysteine, and Guanosine diphosphate. All the altered metabolites were associated with the onset and development of COPD, and involved in glycerophospholipid metabolism, sphingolipid metabolism, glutathione metabolism, and arachidonic acid metabolism, which were significantly changed in rats model with COPD. Generally, these findings provide a systematic view of metabolic changes linked to the onset and development of COPD, also indicated that HTJQ could provide satisfactory therapeutic effects on COPD and metabolomics study can be utilized to further understand the molecular mechanisms. PMID: 31911286 [PubMed - as supplied by publisher]

Ginsenoside Rg1 and the control of inflammation implications for the therapy of Type 2 diabetes: A review of scientific findings and call for further research. Pharmacol Res. 2020 Jan 03;:104630 Authors: Alolga RN, Nuer-Allornuvor GF, Kuugbee ED, Yin X, Ma G Abstract The incidence of type 2 diabetes (T2D) is gradually assuming pandemic proportions, leaving in its trail increased morbidity and mortality. This trend is mainly credited to the adoption of unhealthy lifestyles resulting in increased cases of overweightness and obesity. Traditionally, T2D is considered a metabolic disorder epitomized by prolonged elevated levels of glucose due to insulin resistance and/or decreased insulin secretion resulting from pancreatic β-cells dysfunction. Our current understanding of the disease implicates the adipose tissue in the induction of low-grade chronic inflammation which in turn initiates a cascade of anti- and pro-inflammatory responses by the immune system ultimately damaging the β-cells of the pancreas. The central role of inflammation in the initiation and progress of T2D is now receiving a lot of attention. This review gives an overview of the centrality of inflammation in the pathogenesis of T2D and focuses on the therapeutic potential of ginsenoside Rg1. This review is borne out of the hypothesis that, if inflammation is an absolute precondition to T2D initiation and progress, then attenuation of inflammation should hold therapeutic promise. In line with this, we highlight the anti-diabetic, hepatoprotective and neuroprotective effects of ginsenoside Rg1 among others and proffer suggestions for future studies. PMID: 31911245 [PubMed - as supplied by publisher]

Characterization of Macrophage Galactose-type Lectin (MGL) ligands in colorectal cancer cell lines. Biochim Biophys Acta Gen Subj. 2020 Jan 03;:129513 Authors: Pirro M, Rombouts Y, Stella A, Neyrolles O, Burlet-Schiltz O, van Vliet SJ, de Ru AH, Mohammed Y, Wuhrer M, van Veelen PA, Hensbergen PJ Abstract BACKGROUND: The Ca2+-dependent C-type lectin receptor Macrophage Galactose-type Lectin (MGL) is highly expressed by tolerogenic dendritic cells (DC) and macrophages. MGL exhibits a high binding specificity for terminal alpha- and beta-linked GalNAc residues found in Tn, sTn and LacdiNAc antigens. These glycan epitopes are often overexpressed in colorectal cancer (CRC), and, as such, MGL can be used to discriminate tumor from the corresponding healthy tissues. Moreover, the high expression of MGL ligands is associated with poor disease-free survival in stage III of CRC tumors. Nonetheless, the glycoproteins expressed by tumor cells that are recognized by MGL have hitherto remained elusive. METHODS: Using a panel of three CRC cell lines (HCT116, HT29 and LS174T), recapitulating CRC diversity, we performed FACS staining and pull-down assays using a recombinant soluble form of MGL (and a mutant MGL as control) combined with mass spectrometry-based (glyco)proteomics. RESULTS: HCT116 and HT29, but not LS174T, are high MGL-binding CRC cell lines. On these cells, the major cell surface binding proteins are receptors (e.g. MET, PTK7, SORL1, PTPRF) and integrins (ITGB1, ITGA3). From these proteins, several N- and/or O-glycopeptides were identified, of which some carried either a LacdiNAc or Tn epitope. CONCLUSIONS: We have identified cell surface MGL-ligands on CRC cell lines. GENERAL SIGNIFICANCE: Advances in (glyco)proteomics have led to identification of candidate key mediators of immune-evasion and tumor growth in CRC. PMID: 31911241 [PubMed - as supplied by publisher]

Bayesian multiple hypotheses testing in compositional analysis of untargeted metabolomic data. Anal Chim Acta. 2020 Feb 08;1097:49-61 Authors: de Sousa J, Vencálek O, Hron K, Václavík J, Friedecký D, Adam T Abstract Clinical metabolomics aims at finding statistically significant differences in metabolic statuses of patient and control groups with the intention of understanding pathobiochemical processes and identification of clinically useful biomarkers of particular diseases. After the raw measurements are integrated and pre-processed as intensities of chromatographic peaks, the differences between controls and patients are evaluated by both univariate and multivariate statistical methods. The traditional univariate approach relies on t-tests (or their nonparametric alternatives) and the results from multiple testing are misleadingly compared merely by p-values using the so-called volcano plot. This paper proposes a Bayesian counterpart to the widespread univariate analysis, taking into account the compositional character of a metabolome. Since each metabolome is a collection of some small-molecule metabolites in a biological material, the relative structure of metabolomic data, which is inherently contained in ratios between metabolites, is of the main interest. Therefore, a proper choice of logratio coordinates is an essential step for any statistical analysis of such data. In addition, a concept of b-values is introduced together with a Bayesian version of the volcano plot incorporating distance levels of the posterior highest density intervals from zero. The theoretical background of the contribution is illustrated using two data sets containing samples of patients suffering from 3-hydroxy-3-methylglutaryl-CoA lyase deficiency and medium-chain acyl-CoA dehydrogenase deficiency. To evaluate the stability of the proposed method as well as the benefits of the compositional approach, two simulations designed to mimic a loss of samples and a systematical measurement error, respectively, are added. PMID: 31910969 [PubMed - in process]

Multi-omic serum biomarkers for prognosis of disease progression in prostate cancer. J Transl Med. 2020 Jan 07;18(1):10 Authors: Kiebish MA, Cullen J, Mishra P, Ali A, Milliman E, Rodrigues LO, Chen EY, Tolstikov V, Zhang L, Panagopoulos K, Shah P, Chen Y, Petrovics G, Rosner IL, Sesterhenn IA, McLeod DG, Granger E, Sarangarajan R, Akmaev V, Srinivasan A, Srivastava S, Narain NR, Dobi A Abstract BACKGROUND: Predicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease. The objective of our study was to identify prostate cancer prognostic markers in patients 'sera using a multi-omics discovery platform. METHODS: Pre-surgical serum samples collected from a longitudinal, racially diverse, prostate cancer patient cohort (N = 382) were examined. Linear Regression and Bayesian computational approaches integrated with multi-omics, were used to select markers to predict biochemical recurrence (BCR). BCR-free survival was modeled using unadjusted Kaplan-Meier estimation curves and multivariable Cox proportional hazards analysis, adjusted for key pathologic variables. Receiver operating characteristic (ROC) curve statistics were used to examine the predictive value of markers in discriminating BCR events from non-events. The findings were further validated by creating a training set (N = 267) and testing set (N = 115) from the cohort. RESULTS: Among 382 patients, 72 (19%) experienced a BCR event in a median follow-up time of 6.9 years. Two proteins-Tenascin C (TNC) and Apolipoprotein A1V (Apo-AIV), one metabolite-1-Methyladenosine (1-MA) and one phospholipid molecular species phosphatidic acid (PA) 18:0-22:0 showed a cumulative predictive performance of AUC = 0.78 [OR (95% CI) = 6.56 (2.98-14.40), P < 0.05], in differentiating patients with and without BCR event. In the validation set all four metabolites consistently reproduced an equivalent performance with high negative predictive value (NPV; > 80%) for BCR. The combination of pTstage and Gleason score with the analytes, further increased the sensitivity [AUC = 0.89, 95% (CI) = 4.45-32.05, P < 0.05], with an increased NPV (0.96) and OR (12.4) for BCR. The panel of markers combined with the pathological parameters demonstrated a more accurate prediction of BCR than the pathological parameters alone in prostate cancer. CONCLUSIONS: In this study, a panel of serum analytes were identified that complemented pathologic patient features in predicting prostate cancer progression. This panel offers a new opportunity to complement current prognostic markers and to monitor the potential impact of primary treatment versus surveillance on patient oncological outcome. PMID: 31910880 [PubMed - in process]

Phosphatidylcholine PC ae C44:6 in cerebrospinal fluid is a sensitive biomarker for bacterial meningitis. J Transl Med. 2020 Jan 07;18(1):9 Authors: de Araujo LS, Pessler K, Sühs KW, Novoselova N, Klawonn F, Kuhn M, Kaever V, Müller-Vahl K, Trebst C, Skripuletz T, Stangel M, Pessler F Abstract BACKGROUND: The timely diagnosis of bacterial meningitis is of utmost importance due to the need to institute antibiotic treatment as early as possible. Moreover, the differentiation from other causes of meningitis/encephalitis is critical because of differences in management such as the need for antiviral or immunosuppressive treatments. Considering our previously reported association between free membrane phospholipids in cerebrospinal fluid (CSF) and CNS involvement in neuroinfections we evaluated phosphatidylcholine PC ae C44:6, an integral constituent of cell membranes, as diagnostic biomarker for bacterial meningitis. METHODS: We used tandem mass spectrometry to measure concentrations of PC ae C44:6 in cell-free CSF samples (n = 221) from patients with acute bacterial meningitis, neuroborreliosis, viral meningitis/encephalitis (herpes simplex virus, varicella zoster virus, enteroviruses), autoimmune neuroinflammation (anti-NMDA-receptor autoimmune encephalitis, multiple sclerosis), facial nerve and segmental herpes zoster (shingles), and noninflammatory CNS disorders (Bell's palsy, Tourette syndrome, normal pressure hydrocephalus). RESULTS: PC ae C44:6 concentrations were significantly higher in bacterial meningitis than in all other diagnostic groups, and were higher in patients with a classic bacterial meningitis pathogen (e.g. Streptococcus pneumoniae, Neisseria meningitidis, Staphylococcus aureus) than in those with less virulent or opportunistic pathogens as causative agents (P = 0.026). PC ae C44:6 concentrations were only moderately associated with CSF cell count (Spearman's ρ = 0.45; P = 0.009), indicating that they do not merely reflect neuroinflammation. In receiver operating characteristic curve analysis, PC ae C44:6 equaled CSF cell count in the ability to distinguish bacterial meningitis from viral meningitis/encephalitis and autoimmune CNS disorders (AUC 0.93 both), but had higher sensitivity (91% vs. 41%) and negative predictive value (98% vs. 89%). A diagnostic algorithm comprising cell count, lactate and PC ae C44:6 had a sensitivity of 97% (specificity 87%) and negative predictive value of 99% (positive predictive value 61%) and correctly diagnosed three of four bacterial meningitis samples that were misclassified by cell count and lactate due to low values not suggestive of bacterial meningitis. CONCLUSIONS: Increased CSF PC ae C44:6 concentrations in bacterial meningitis likely reflect ongoing CNS cell membrane stress or damage and have potential as additional, sensitive biomarker to diagnose bacterial meningitis in patients with less pronounced neuroinflammation. PMID: 31910875 [PubMed - in process]

Urine metabolomics signatures in reversible cerebral vasoconstriction syndrome. Cephalalgia. 2020 Jan 07;:333102419897621 Authors: Hsu WH, Wang SJ, Chao YM, Chen CJ, Wang YF, Fuh JL, Chen SP, Lin YL Abstract BACKGROUND: The pathophysiology of reversible cerebral vasoconstriction syndrome is unclear. An unbiased systems-based approach might help to illustrate the metabolite profiling and underlying pathophysiology. METHODS: Urine samples were collected from reversible cerebral vasoconstriction syndrome patients and matched controls recruited in Taipei Veterans General Hospital. 1H-Nuclear magnetic resonance was used to initially explore the metabolic profile, and liquid chromatography tandem mass spectrometry was then used to identify metabolic alterations in reversible cerebral vasoconstriction syndrome. Untargeted metabolite screening was randomly performed on 10 reversible cerebral vasoconstriction syndrome patients and 10 control subjects in the discovery phase. The selected untargeted metabolites were further validated on 47 reversible cerebral vasoconstriction syndrome patients during their ictal stage (with 40 of them having remission samples) and 47 controls in the replication phase. RESULTS AND CONCLUSION: Six metabolites-hippurate, citrate, 1,3,7-trimethyluric acid, ascorbic acid, D-glucurono-6,3-lactone, and D-threo-isocitric acid-with t-test derived p-value < 0.05 and VIP score >1, were identified as potential urine signatures that can well distinguish reversible cerebral vasoconstriction syndrome subjects at ictal stage from controls. Among them, citrate, hippurate, ascorbic acid, and D-glucurono-6,3-lactone were significantly lower, and 1,3,7-trimethyluric acid and D-threo-isocitric acid were higher in reversible cerebral vasoconstriction syndrome patients. Of these, four selected metabolites, citrate, D-glucurono-6,3-lactone, ascorbic acid, and 1,3,7-trimethyluric acid, returned to normal levels in remission. These metabolites are related to pathways associated with free radical scavenging, with the hub molecules being associated with endothelial dysfunction or sympathetic overactivity. Whether these metabolites and their implicated networks play a role in the pathogenesis of reversible cerebral vasoconstriction syndrome remains to be confirmed. PMID: 31910660 [PubMed - as supplied by publisher]

Related Articles Proteome Analysis Using Gel-LC-MS/MS. Curr Protoc Protein Sci. 2019 06;96(1):e93 Authors: Goldman AR, Beer LA, Tang HY, Hembach P, Zayas-Bazan D, Speicher DW Abstract This article describes processing of protein samples using 1D SDS gels prior to protease digestion for proteomics workflows that subsequently utilize reversed-phase nanocapillary ultra-high-pressure liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS). The resulting LC-MS/MS data are used to identify peptides and thereby infer proteins present in samples ranging from simple mixtures to very complex proteomes. Bottom-up proteome studies usually involve quantitative comparisons across several or many samples. For either situation, 1D SDS gels represent a simple, widely available technique that can be used to either fractionate complex proteomes or rapidly clean up low microgram samples with minimal losses. After gel separation and staining/destaining, appropriate gel slices are excised, and in-gel reduction, alkylation, and protease digestion are performed. Digests are then processed for LC-MS/MS analysis. Protocols are described for either sample fractionation with high-throughput processing of many samples or simple cleanup without fractionation. An optional strategy is to conduct in-solution reduction and alkylation prior to running gels, which is advantageous when a large number of samples will be separated into large numbers of fractions. Optimization of trypsin digestion parameters and comparison to in-solution protease digestion are also described. © 2019 by John Wiley & Sons, Inc. PMID: 31180188 [PubMed - indexed for MEDLINE]

29 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 2020/01/08PubMed 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.

24 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 2020/01/07PubMed 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.