Arachidonic acid inhibition of the NLRP3 inflammasome is a mechanism to explain the anti-inflammatory effects of fasting.

Elevated interleukin (IL)-1ß levels, NLRP3 inflammasome activity, and systemic inflammation are hallmarks of chronic metabolic inflammatory syndromes, but the mechanistic basis for this is unclear. Here, we show that levels of plasma IL-1ß are lower in fasting compared to fed subjects, while the lipid arachidonic acid (AA) is elevated. Lipid profiling of NLRP3-stimulated mouse macrophages shows enhanced AA production and an NLRP3-dependent eicosanoid signature. Inhibition of cyclooxygenase by nonsteroidal anti-inflammatory drugs decreases eicosanoid, but not AA, production. It also reduces both IL-1ß and IL-18 production in response to NLRP3 activation. AA inhibits NLRP3 inflammasome activity in human and mouse macrophages. Mechanistically, AA inhibits phospholipase C activity to reduce JNK1 stimulation and hence NLRP3 activity. These data show that AA is an important physiological regulator of the NLRP3 inflammasome and explains why fasting reduces systemic inflammation and also suggests a mechanism to explain how nonsteroidal anti-inflammatory drugs work.

Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers.

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.

Metabolomics analysis of plasma samples of patients with fibromyalgia and electromagnetic sensitivity using GC-MS technique.

Fibromyalgia (FM) is a chronic and systemic condition that causes widespread chronic pain, asthenia, and muscle stiffness, as well as in some cases depression, anxiety, and disorders of the autonomic system. The exact causes that lead to the development of FM are still unknown today. In a percentage of individuals, the symptoms of FM are often triggered and/or exacerbated by proximity to electrical and electromagnetic devices. Plasma metabolomic profile of 54 patients with fibromyalgia and self-reported electromagnetic sensitivity (IEI-EMF) were compared to 23 healthy subjects using gas chromatography-mass spectrometry (GC-MS) coupled with multivariate statistical analysis techniques. Before the GC-MS analysis the plasma samples were extracted with a modified Folch method and then derivatized with methoxamine hydrochloride in pyridine solution and N-trimethylsilyltrifuoroacetamide. The combined analysis allowed to identify a metabolomic profile able of distinguishing IEI-EMF patients and healthy subjects. IEI-EMF patients were therefore characterized by the alteration of 19 metabolites involved in different metabolic pathways such as energy metabolism, muscle, and pathways related to oxidative stress defense and chronic pain. The results obtained in this study complete the metabolomic "picture" previously investigated on the same cohort of IEI-EMF patients with 1H-NMR spectroscopy, placing a further piece for better understanding the pathophysiological mechanisms in patients with IEI-EMF.

Metabolome-wide association study on ABCA7 indicates a role of ceramide metabolism in Alzheimer's disease.

Genome-wide association studies (GWASs) have identified genetic loci associated with the risk of Alzheimer's disease (AD), but the molecular mechanisms by which they confer risk are largely unknown. We conducted a metabolome-wide association study (MWAS) of AD-associated loci from GWASs using untargeted metabolic profiling (metabolomics) by ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). We identified an association of lactosylceramides (LacCer) with AD-related single-nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0 × 10-5 to 1.3 × 10-44). We showed that plasma LacCer concentrations are associated with cognitive performance and genetically modified levels of LacCer are associated with AD risk. We then showed that concentrations of sphingomyelins, ceramides, and hexosylceramides were altered in brain tissue from Abca7 knockout mice, compared with wild type (WT) (P = 0.049-1.4 × 10-5), but not in a mouse model of amyloidosis. Furthermore, activation of microglia increases intracellular concentrations of hexosylceramides in part through induction in the expression of sphingosine kinase, an enzyme with a high control coefficient for sphingolipid and ceramide synthesis. Our work suggests that the risk for AD arising from functional variations in ABCA7 is mediated at least in part through ceramides. Modulation of their metabolism or downstream signaling may offer new therapeutic opportunities for AD.

Metabolic Alteration in Plasma and Biopsies From Patients With IBD.

BACKGROUND: Inflammatory bowel diseases (IBD) are chronic inflammatory disorders of the gastrointestinal tract, with periods of latency alternating with phases of exacerbation, and include 2 forms: Crohn disease (CD) and ulcerative colitis (UC). Although the etiology of IBD is still unclear, the identification and understanding of pathophysiological mechanisms underlying IBD could reveal newly targeted intestinal alterations and determine therapeutic approaches. METHODS: In this study, by using gas chromatography-mass spectrometry, we characterized plasma and biopsies from the metabolomics profiles of patients with IBD compared with those of a control group. RESULTS: The results showed a different metabolomics profile between patients with CD (n = 50) and patients with UC (n = 82) compared with the control group (n = 51). Multivariate statistical analysis of the identified metabolites in CD and UC showed changes in energetic metabolism, and lactic acid and ornithine in particular were altered in both plasma and colon biopsies. Moreover, metabolic changes were evidenced between the normal ileum and colon tissues. These differences disappeared when we compared the inflamed ileum and colon tissues, suggesting a common metabolism. CONCLUSIONS: This study showed how the metabolomics profile could be a potential tool to identify intestinal alterations associated with IBD and may have application in precision medicine and for better defining the pathogenesis of the disease.

Consequences of Lipid Remodeling of Adipocyte Membranes Being Functionally Distinct from Lipid Storage in Obesity.

Obesity is a complex disorder where the genome interacts with diet and environmental factors to ultimately influence body mass, composition, and shape. Numerous studies have investigated how bulk lipid metabolism of adipose tissue changes with obesity and, in particular, how the composition of triglycerides (TGs) changes with increased adipocyte expansion. However, reflecting the analytical challenge posed by examining non-TG lipids in extracts dominated by TGs, the glycerophospholipid composition of cell membranes has been seldom investigated. Phospholipids (PLs) contribute to a variety of cellular processes including maintaining organelle functionality, providing an optimized environment for membrane-associated proteins, and acting as pools for metabolites (e.g. choline for one-carbon metabolism and for methylation of DNA). We have conducted a comprehensive lipidomic study of white adipose tissue in mice which become obese either through genetic modification (ob/ob), diet (high fat diet), or a combination of the two, using both solid phase extraction and ion mobility to increase coverage of the lipidome. Composition changes in seven classes of lipids (free fatty acids, diglycerides, TGs, phosphatidylcholines, lyso-phosphatidylcholines, phosphatidylethanolamines, and phosphatidylserines) correlated with perturbations in one-carbon metabolism and transcriptional changes in adipose tissue. We demonstrate that changes in TGs that dominate the overall lipid composition of white adipose tissue are distinct from diet-induced alterations of PLs, the predominant components of the cell membranes. PLs correlate better with transcriptional and one-carbon metabolism changes within the cell, suggesting that the compositional changes that occur in cell membranes during adipocyte expansion have far-reaching functional consequences. Data are available at MetaboLights under the submission number: MTBLS1775.

A Comprehensive UHPLC Ion Mobility Quadrupole Time-of-Flight Method for Profiling and Quantification of Eicosanoids, Other Oxylipins, and Fatty Acids.

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.

A randomized 3-way crossover study indicates that high-protein feeding induces de novo lipogenesis in healthy humans.

BACKGROUNDDietary changes have led to the growing prevalence of type 2 diabetes and nonalcoholic fatty liver disease. A hallmark of both disorders is hepatic lipid accumulation, derived in part from increased de novo lipogenesis. Despite the popularity of high-protein diets for weight loss, the effect of dietary protein on de novo lipogenesis is poorly studied. We aimed to characterize the effect of dietary protein on de novo lipid synthesis.METHODSWe use a 3-way crossover interventional study in healthy males to determine the effect of high-protein feeding on de novo lipogenesis, combined with in vitro models to determine the lipogenic effects of specific amino acids. The primary outcome was a change in de novo lipogenesis-associated triglycerides in response to protein feeding.RESULTSWe demonstrate that high-protein feeding, rich in glutamate, increases de novo lipogenesis-associated triglycerides in plasma (1.5-fold compared with control; P < 0.0001) and liver-derived very low-density lipoprotein particles (1.8-fold; P < 0.0001) in samples from human subjects (n = 9 per group). In hepatocytes, we show that glutamate-derived carbon is incorporated into triglycerides via palmitate. In addition, supplementation with glutamate, glutamine, and leucine, but not lysine, increased triglyceride synthesis and decreased glucose uptake. Glutamate, glutamine, and leucine increased activation of protein kinase B, suggesting that induction of de novo lipogenesis occurs via the insulin signaling cascade.CONCLUSIONThese findings provide mechanistic insight into how select amino acids induce de novo lipogenesis and insulin resistance, suggesting that high-protein feeding to tackle diabetes and obesity requires greater consideration.FUNDINGThe research was supported by UK Medical Research Council grants MR/P011705/1, MC_UP_A090_1006 and MR/P01836X/1. JLG is supported by the Imperial Biomedical Research Centre, National Institute for Health Research (NIHR).

Crosstalk between Metabolic Alterations and Altered Redox Balance in PTC-Derived Cell Lines.

Background: Thyroid cancer is the most common endocrine malignancy, with papillary thyroid carcinoma (PTC) being the most common (85⁻90%) 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.

KniMet: a pipeline for the processing of chromatography-mass spectrometry metabolomics data.

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.

Author Correction: Cross sectional evaluation of the gut-microbiome metabolome axis in an Italian cohort of IBD patients.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

The potential of Ion Mobility Mass Spectrometry for high-throughput and high-resolution lipidomics.

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.

Italian cohort of patients affected by inflammatory bowel disease is characterised by variation in glycerophospholipid, free fatty acids and amino acid levels.

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.

Metabolomics applied to diabetes-lessons from human population studies.

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.

Metabolomic profile in hyperthyroid patients before and after antithyroid drug treatment: Correlation with thyroid hormone and TSH concentration.

Hyperthyroidism (HT) is characterized by an intense metabolic impact which affects the lipid, carbohydrate and amino acids metabolism, with increased resting energy expenditure and thermogenesis. Metabolomics is a new comprehensive technique that allows to capture an instant metabolic picture of an organism, reflecting peculiar molecular and pathophysiological states. The aim of the present prospective study was to identify a distinct metabolomic profile in HT patients using 1H NMR spectroscopy before and after antithyroid drug treatment. This prospective study included 15 patients (10 female, 5 male) who were newly diagnosed hyperthyroidism. A nuclear magnetic resonance (1H NMR) based analysis was performed on plasma samples from the same patients at diagnosis (HypT0) and when they achieved euthyroidism (HypT1). The case groups were compared with a control group of 26 healthy volunteers (C). Multivariate statistical analysis was performed with Partial Least Squares-Discriminant Analysis (PLS-DA). PLS-DA identified a distinct metabolic profile between C and untreated hyperthyroid patients (R2X 0.638, R2Y 0.932, Q2 0.783). Interestingly, a significant difference was also found between C and euthyroid patients after treatment (R2X 0.510, R2Y 0.838, Q2 0.607), while similar cluster emerged comparing HypT0vs HypT1 patients. This study shows that metabolomic profile is deeply influenced by hyperthyroidism and this alteration persists after normalization of thyrotropin (TSH) and free thyroid hormone (FT3, FT4) concentration. This suggests that TSH, FT3 and FT4 assays may not be insufficient to detect long lasting peripheral effects of the thyroid hormones action. Further studies are needed to clarify whether and to what extent the evaluation of metabolomics profile may provide relevant information in the clinical management of hyperthyroidism.

Cross sectional evaluation of the gut-microbiome metabolome axis in an Italian cohort of IBD patients.

Inflammatory bowel disease (IBD) is a chronic inflammatory disease of the gastrointestinal tract of uncertain origin, which includes ulcerative colitis (UC) and Crohn's disease (CD). The composition of gut microbiota may change in IBD affected individuals, but whether dysbiosis is the cause or the consequence of inflammatory processes in the intestinal tissue is still unclear. Here, the composition of the microbiota and the metabolites in stool of 183 subjects (82 UC, 50 CD, and 51 healthy controls) were determined. The metabolites content and the microbiological profiles were significantly different between IBD and healthy subjects. In the IBD group, Firmicutes, Proteobacteria, Verrucomicrobia, and Fusobacteria were significantly increased, whereas Bacteroidetes and Cyanobacteria were decreased. At genus level Escherichia, Faecalibacterium, Streptococcus, Sutterella and Veillonella were increased, whereas Bacteroides, Flavobacterium, and Oscillospira decreased. Various metabolites including biogenic amines, amino acids, lipids, were significantly increased in IBD, while others, such as two B group vitamins, were decreased in IBD compared to healthy subjects. This study underlines the potential role of an inter-omics approach in understanding the metabolic pathways involved in IBD. The combined evaluation of metabolites and fecal microbiome can be useful to discriminate between healthy subjects and patients with IBD.

Metabolomic analysis identifies altered metabolic pathways in Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic, demyelinating disease that affects the central nervous system and is characterized by a complex pathogenesis and difficult management. The identification of new biomarkers would be clinically useful for more accurate diagnoses and disease monitoring. Metabolomics, the identification of small endogenous molecules, offers an instantaneous molecular snapshot of the MS phenotype. Here the metabolomic profiles (utilizing plasma from patients with MS) were characterized with a Gas cromatography-mass spectrometry-based platform followed by a multivariate statistical analysis and comparison with a healthy control (HC) population. The obtained partial least square discriminant analysis (PLS-DA) model identified and validated significant metabolic differences between individuals with MS and HC (R2X=0.223, R2Y=0.82, Q2=0.562; p<0.001). Among discriminant metabolites phosphate, fructose, myo-inositol, pyroglutamate, threonate, l-leucine, l-asparagine, l-ornithine, l-glutamine, and l-glutamate were correctly identified, and some resulted as unknown. A receiver operating characteristic (ROC) curve with AUC 0.84 (p=0.01; CI: 0.75-1) generated with the concentrations of the discriminant metabolites, supported the strength of the model. Pathway analysis indicated asparagine and citrulline biosynthesis as the main canonical pathways involved in MS. Changes in the citrulline biosynthesis pathway suggests the involvement of oxidative stress during neuronal damage. The results confirmed metabolomics as a useful approach to better understand the pathogenesis of MS and to provide new biomarkers for the disease to be used together with clinical data.

Using Molecular Initiating Events To Generate 2D Structure-Activity Relationships for Toxicity Screening.

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.

Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes.

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 from in silico target 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.

Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis.

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.