12 research outputs found
The ABCs of membrane transporters in health and disease (SLC series): introduction
The field of transport biology has steadily grown over the past decade and is now recognized as playing an important role in manifestation and treatment of disease. The SLC (solute carrier) gene series has grown to now include 52 families and 395 transporter genes in the human genome. A list of these genes can be found at the HUGO Gene Nomenclature Committee (HGNC) website (see www.genenames.org/genefamilies/SLC). This special issue features mini-reviews for each of these SLC families written by the experts in each field. The existing online resource for solute carriers, the Bioparadigms SLC Tables (www.bioparadigms.org), has been updated and significantly extended with additional information and cross-links to other relevant databases, and the nomenclature used in this database has been validated and approved by the HGNC. In addition, the Bioparadigms SLC Tables functionality has been improved to allow easier access by the scientific community. This introduction includes: an overview of all known SLC and "non-SLC" transporter genes; a list of transporters of water soluble vitamins; a summary of recent progress in the structure determination of transporters (including GLUT1/SLC2A1); roles of transporters in human diseases and roles in drug approval and pharmaceutical perspectives
Recommended from our members
Amino Acid Dysregulation Metabotypes: Potential Biomarkers for Diagnosis and Individualized Treatment for Subtypes of Autism Spectrum Disorder
BackgroundAutism spectrum disorder (ASD) is behaviorally and biologically heterogeneous and likely represents a series of conditions arising from different underlying genetic, metabolic, and environmental factors. There are currently no reliable diagnostic biomarkers for ASD. Based on evidence that dysregulation of branched-chain amino acids (BCAAs) may contribute to the behavioral characteristics of ASD, we tested whether dysregulation of amino acids (AAs) was a pervasive phenomenon in individuals with ASD. This is the first article to report results from the Children's Autism Metabolome Project (CAMP), a large-scale effort to define autism biomarkers based on metabolomic analyses of blood samples from young children.MethodsDysregulation of AA metabolism was identified by comparing plasma metabolites from 516 children with ASD with those from 164 age-matched typically developing children recruited into the CAMP. ASD subjects were stratified into subpopulations based on shared metabolic phenotypes associated with BCAA dysregulation.ResultsWe identified groups of AAs with positive correlations that were, as a group, negatively correlated with BCAA levels in ASD. Imbalances between these two groups of AAs identified three ASD-associated amino acid dysregulation metabotypes. The combination of glutamine, glycine, and ornithine amino acid dysregulation metabotypes identified a dysregulation in AA/BCAA metabolism that is present in 16.7% of the CAMP subjects with ASD and is detectable with a specificity of 96.3% and a positive predictive value of 93.5% within the ASD subject cohort.ConclusionsIdentification and utilization of metabotypes of ASD can lead to actionable metabolic tests that support early diagnosis and stratification for targeted therapeutic interventions
2-Azetidinone Cholesterol Absorption Inhibitors: Structure−Activity Relationships on the Heterocyclic Nucleus
Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children
<div><p>Background</p><p>The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age.</p><p>Objectives</p><p>To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment.</p><p>Methods</p><p>Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD.</p><p>Results</p><p>A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set.</p><p>Conclusions</p><p>This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment recommendations.</p></div
Classifier performance metrics based on predictions on the independent 21-sample validation set, showing the feature sets with the highest accuracy.
<p>Feature No. corresponds to the number of the ordered, ranked VIP features that were evaluated. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112445#pone.0112445.s003" target="_blank">Table S3</a> shows the results for all feature sets.</p><p>Classifier performance metrics based on predictions on the independent 21-sample validation set, showing the feature sets with the highest accuracy.</p
A breakdown of the numbers of features resulting from filtering and annotation processes, based on molecular formula.
<p>This table also helps to illustrate the orthogonality and contribution of each of the 5 analytical platforms. Molecular formulae are being used here only to approximate the method orthogonality, since any given molecular formula may be associated with multiple chemical structures. *These annotations were confirmed in the GCMS platform and the formula were confirmed by using the KEGG database instead of the FBF procedure used in the 4 LCMS platforms.</p><p>A breakdown of the numbers of features resulting from filtering and annotation processes, based on molecular formula.</p
Confirmed metabolites.
<p>Statistically significant metabolites from the 61-sample training set with chemical structures confirmed by LC-HRMS-MS or GC-MS.</p><p>Confirmed metabolites.</p
Performance of the SVM and PLS models.
<p>Average AUC and accuracy of the (a) SVM and (b) PLS models containing different numbers of features. The bar graphs show the number of optimal models which were derived from recursive feature elimination process that was included in the resampling process for the indicated number of features.</p
Classification modeling process.
<p>A three-layer nested cross-validation approach was applied using both PLS-DA and SVM modeling methods to determine significant features capable of classifying children with ASD from TD children. The 179 features of the training set were analyzed using a leave-one-group-out cross-validation loop as described. The results from this cross-validation process were used to estimate model performance and create a robust feature VIP score index to rank the ASD vs TD classification importance of each of the 179 features. These feature ranks were used to evaluate the performance of the molecular signature using an independent validation set.</p