Search

Funder: Alzheimer's Society; Id: http://dx.doi.org/10.13039/501100000320, Funder: Medical Research Council; Id: http://dx.doi.org/10.13039/501100007155, Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late-onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late-onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over-represented across the AD multi-omics data sets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations.

Analysis of oxylipins by liquid chromatography mass spectrometry (LC/MS) is challenging because of the small mass range occupied by this diverse lipid class, the presence of numerous structural isomers, and their low abundance in biological samples. Although highly sensitive LC/MS/MS methods are commonly used, further separation is achievable by using drift tube ion mobility coupled with high-resolution mass spectrometry (DTIM-MS). Herein, we present a combined analytical and computational method for the identification of oxylipins and fatty acids. We use a reversed-phase LC/DTIM-MS workflow able to profile and quantify (based on chromatographic peak area) the oxylipin and fatty acid content of biological samples while simultaneously acquiring full scan and product ion spectra. The information regarding accurate mass, collision-cross-section values in nitrogen (DTCCSN2), and retention times of the species found are compared to an internal library of lipid standards as well as the LIPID MAPS Structure Database by using specifically developed processing tools. Features detected within the DTCCSN2 and m/ z ranges of the analyzed standards are flagged as oxylipin-like species, which can be further characterized using drift-time alignment of product and precursor ions distinctive of DTIM-MS. This not only helps identification by reducing the number of annotations from LIPID MAPS but also guides discovery studies of potentially novel species. Testing the methodology on Salmonella enterica serovar Typhimurium-infected murine bone-marrow-derived macrophages and thrombin activated human platelets yields results in agreement with literature. This workflow has also annotated features as potentially novel oxylipins, confirming its ability in providing further insights into lipid analysis of biological samples.

Lipids are a large and highly diverse family of biomolecules, which play essential structural, storage and signalling roles in cells and tissues. Although traditional mass spectrometry (MS) approaches used in lipidomics are highly sensitive and selective, lipid analysis remains challenging due to the chemical diversity of lipid structures, multiple isobaric species and incomplete separation using many forms of chromatography. Ion mobility (IM) separates ions in the gas phase based on their physicochemical properties. Addition of IM to the traditional lipidomic workflow both enhances separation of complex lipid mixtures, beneficial for lipid identification, and improves isomer resolution. Herein, we discuss the recent developments in IM-MS for lipidomics.

Background: Thyroid cancer is the most common endocrine malignancy, with papillary thyroid carcinoma (PTC) being the most common (85&ndash, 90%) among all the different types of thyroid carcinomas. Cancer cells show metabolic alterations and, due to their rapid proliferation, an accumulation of reactive oxygen species, playing a fundamental role in cancer development and progression. Currently, the crosstalk among thyrocytes metabolism, redox balance and oncogenic mutations remain poorly characterized. The aim of this study was to investigate the interplay among metabolic alterations, redox homeostasis and oncogenic mutations in PTC-derived cells. Methods: Metabolic and redox profile, glutamate-cysteine ligase, glutaminase-1 and metabolic transporters were evaluated in PTC-derived cell lines with distinguished genetic background (TPC-1, K1 and B-CPAP), as well as in an immortalized thyroid cell line (Nthy-ori3-1) selected as control. Results: PTC-derived cells, particularly B-CPAP cells, harboring BRAF, TP53 and human telomerase reverse transcriptase (hTERT) mutation, displayed an increase of metabolites and transporters involved in energetic pathways. Furthermore, all PTC-derived cells showed altered redox homeostasis, as reported by the decreased antioxidant ratios, as well as the increased levels of intracellular oxidant species. Conclusion: Our findings confirmed the pivotal role of the metabolism and redox state regulation in the PTC biology. Particularly, the most perturbed metabolic phenotypes were found in B-CPAP cells, which are characterized by the most aggressive genetic background.

BACKGROUND: Inflammatory bowel disease is a group of pathologies characterised by chronic inflammation of the intestine and an unclear aetiology. Its main manifestations are Crohn's disease and ulcerative colitis. Currently, biopsies are the most used diagnostic tests for these diseases and metabolomics could represent a less invasive approach to identify biomarkers of disease presence and progression. OBJECTIVES: The lipid and the polar metabolite profile of plasma samples of patients affected by inflammatory bowel disease have been compared with healthy individuals with the aim to find their metabolomic differences. Also, a selected sub-set of samples was analysed following solid phase extraction to further characterise differences between pathological samples. METHODS: A total of 200 plasma samples were analysed using drift tube ion mobility coupled with time of flight mass spectrometry and liquid chromatography for the lipid metabolite profile analysis, while liquid chromatography coupled with triple quadrupole mass spectrometry was used for the polar metabolite profile analysis. RESULTS: Variations in the lipid profile between inflammatory bowel disease and healthy individuals were highlighted. Phosphatidylcholines, lyso-phosphatidylcholines and fatty acids were significantly changed among pathological samples suggesting changes in phospholipase A2 and arachidonic acid metabolic pathways. Variations in the levels of cholesteryl esters and glycerophospholipids were also found. Furthermore, a decrease in amino acids levels suggests mucosal damage in inflammatory bowel disease. CONCLUSIONS: Given good statistical results and predictive power of the model produced in our study, metabolomics can be considered as a valid tool to investigate inflammatory bowel disease., This study was funded by Agilent Technologies, Regione Autonoma della Sardegna (L.R.7/2007, Grant Number F71J12001180002), and the Medical Research Council UK (Grant Number MR/P011705/1).

The 'classical' distribution of type 2 diabetes (T2D) across the globe is rapidly changing and it is no longer predominantly a disease of middle-aged/elderly adults of western countries, but it is becoming more common through Asia and the Middle East, as well as increasingly found in younger individuals. This global altered incidence of T2D is most likely associated with the spread of western diets and sedentary lifestyles, although there is still much debate as to whether the increased incidence rates are due to an overconsumption of fats, sugars or more generally high-calorie foods. In this context, understanding the interactions between genes of risk and diet and how they influence the incidence of T2D will help define the causative pathways of the disease. This review focuses on the use of metabolomics in large cohort studies to follow the incidence of type 2 diabetes in different populations. Such approaches have been used to identify new biomarkers of pre-diabetes, such as branch chain amino acids, and associate metabolomic profiles with genes of known risk in T2D from large scale GWAS studies. As the field develops, there are also examples of meta-analysis across metabolomics cohort studies and cross-comparisons with different populations to allow us to understand how genes and diet contribute to disease risk. Such approaches demonstrate that insulin resistance and T2D have far reaching metabolic effects beyond raised blood glucose and how the disease impacts systemic metabolism.

INTRODUCTION: Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow. OBJECTIVES: Merge in the same platform the steps required for metabolomics data processing. METHODS: KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform. RESULTS: The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation. CONCLUSION: KniMet provides the user with a local, modular and customizable workflow for the processing of both GC-MS and LC-MS open profiling data.

Molecular initiating events (MIEs) can be boiled down to chemical interactions. Chemicals that interact must have intrinsic properties that allow them to exhibit this behavior, be these properties stereochemical, electronic, or otherwise. In an attempt to discover some of these chemical characteristics, we have constructed structural alert-style structure-activity relationships (SARs) to computationally predict MIEs. This work utilizes chemical informatics approaches, searching the ChEMBL database for molecules that bind to a number of pharmacologically important human toxicology targets, including G-protein coupled receptors, enzymes, ion channels, nuclear receptors, and transporters. By screening these compounds to find common 2D fragments and combining this approach with a good understanding of the literature, bespoke 2D structural alerts have been written. These SARs form the beginning of a tool for screening novel chemicals to establish the kind of interactions that they may be able to make in humans. These SARs have been run through an internal validation to test their quality, and the results of this are also discussed. MIEs have proven to be difficult to find and characterize, but we believe we have taken a key first step with this work.

Traditional Chinese medicine (TCM) still needs more scientific rationale to be proven for it to be accepted further in the West. We are now in the position to propose computational hypotheses for the mode-of-actions (MOAs) of 45 TCM therapeutic action (sub)classes fromin silicotarget prediction algorithms, whose target was later annotated with Kyoto Encyclopedia of Genes and Genomes pathway, and to discover the relationship between them by generating a hierarchical clustering. The results of 10,749 TCM compounds showed 183 enriched targets and 99 enriched pathways from Estimation Score ≤ 0 and ≥ 5% of compounds/targets in a (sub)class. The MOA of a (sub)class was established from supporting literature. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase nonreceptor type 2 (PTPN2) and digestive system such as mineral absorption. We found two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif, which agreed with the important treatment principle of TCM, “the foundation of acquired constitution” that includes spleen and stomach. In short, the TCM (sub)classes, in many cases share similar targets/pathways despite having different indications.

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein-ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature.

BACKGROUND: An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. RESULTS: The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. CONCLUSION: Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.