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An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020 and classified into four levels of severity ranging from mild symptoms to severe ventilated cases. These samples were investigated using a combination of quantitative Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) platforms to give broad lipoprotein, lipidomic and amino acid, tryptophan-kynurenine pathway and biogenic amine pathway coverage. All platforms revealed highly significant differences in metabolite patterns between patients and controls (n=89) that had been collected prior to the COVID-19 pandemic. The total number of significant metabolites increased with severity with 344 out of the 1034 quantitative variables being common to all severity classes. Metabolic signatures showed a continuum of changes across the respiratory severity levels with the most significant and extensive changes being in the most severely affected patients. Even mildly affected respiratory patients showed multiple highly significant abnormal biochemical signatures reflecting serious metabolic deficiencies of the type observed in Post acute COVID -19 syndrome patients. The most severe respiratory patients had a high mortality (56.1%) and we found that we could predict mortality in this patient sub-group with high accuracy in some cases up to 61 days prior to death, based on a separate metabolic model, which highlighted a different set of metabolites to those defining the basic disease. Specifically, hexosylceramides (HCER 16:0, HCER 20:0, HCER 24:1, HCER 26:0, HCER 26:1) were markedly elevated in the non-surviving patient group (Cliff’s delta 0.91-0.95) and two phosphoethanolamines (PE.O 18:0/18:1, Cliff’s delta=-0.98 and PE.P 16:0/18:1, Cliff’s delta=-0.93) were markedly lower in the non-survivors. These results indicate that patient morbidity to mortality trajectories are determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these “high risk” patients in the early disease stages.&nbsp

Additional file 5: Supplementary methods. Methods for the animals studies, NMR metabolomics, liver transcriptomics, and 16S rRNA and shotgun metagenomics sequencing.

1. The metabolic fate and urinary excretion of 2-bromophenol, a phenolic metabolite of bromobenzene, was investigated in male Sprague–Dawley rats following single intraperitoneal doses at either 0, 100, or 200 mg/kg. 2. Urine was collected for seven days and samples analysed using 1 H NMR spectroscopy, inductively coupled plasma (ICP)MS, and UPLC-MS. 3. 1 H NMR spectroscopy of the urine samples showed that, at these doses, 2-bromophenol had little effect on endogenous metabolite profiles, supporting histopathology and clinical chemistry data, which showed no changes associated with the administration of 2-bromophenol in this study. 4. The use of ICP-MS provided a means for the selective detection and quantification of bromine-containing species and showed that between 15 and 30% of the dose was excreted via the urine over 7 days of the study for both the 100 and 200 mg doses, respectively. 5. The bulk of the excretion of Br-containing material had occurred by 8 h post administration. UPLC-MS of urine revealed a number of metabolites of 2-bromophenol, with 2-bromophenol glucuronide and 2-bromophenol sulphate identified as the major species. A number of minor hydroxylated metabolites were also detected as their glucuronide, sulphate, or O-methyl conjugates. There was no evidence for the production of reactive metabolites.

Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.

Summary: MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. Availability and Implementation: MetaboSignal is available from Bioconductor: https://bioconductor.org/packages/MetaboSignal/ Contact: m.dumas@imperial.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Additional file 1: Figure S1. ROC analysis of cross-validated scores from OPLS-DA models. (A) NMR analysis of urine; Tcv1 and Tcv2 represent the first and second PLS component when 2 PLS components were applied to separate 3 classes; (B) LC-MS analysis of urine; (C) bile acid analysis of urine; (D) NMR analysis of serum; (E) NMR analysis of feces. Figure S2. Relative metabolite levels in urine from cohorts 1 (A) and 2 (B), plasma from cohorts 3 (C) and 2 (D), and feces from cohort 1 (E). Metabolite levels are indicated by relative peak heights from the median fold normalized spectra of urine and feces and non-normalized serum spectra. Error bars are presented in SEM. a.u. stands for arbitrary unit. Figure S3. OPLS-DA cross-validated scores plots of urinary 1H NMR spectra of the RYGB and LGB patients from cohort 3 at pre-op (Q2Y=0.49; R2X=12.35%; R2Y=89.6%; p = 0.006), and 6-month post-op (Q2Y=0.44; R2X=30.66%; R2Y=95.2%; p = 0.038). The metabolites that significantly contributed to the classification of different time points are shown in Fig. 2C. Figure S4. OPLS-DA cross-validated scores plots of urinary reverse-phase liquid chromatography-mass spectrometry profiles of the bariatric patients from cohort 1 in ESI positive mode (Q2Y=0.46; R2X=24.8%; R2Y=73.4%; CVANOVA p = 2.9 x 10-14) and ESI negative mode (Q2Y=0.52; R2X=14%; R2Y=76.6%; CVANOVA p = 6.2 x 10-17). The identified metabolites that significantly contributed to the classification between pre-op (black) and 2-6 months post-op (green) are listed in the table. Positive correlation (r) represents higher relative concentrations of these metabolites at the post-op compared to pre-op. p[1] is loadings from the OPLS-DA models. Coefficient of variation (CV) was calculated based on the quality control (QC) samples to evaluate the analytical variation of the features. Figure S5. OPLS-DA cross-validated scores plots of urinary bile acid profiles of the RYGB patients from cohort 1 at pre-op (black), 2-6 months (green) and 1-2 years (orange) post-op (A. Q2Y=0.21; R2X=16.9%; R2Y=48.9%; CVANOVA p = 5.8x10-9). Bar plots of relative intensities of urinary bile acids identified from cohort 2 RYGB patients at pre-op (black) and 6 months post-op (green). Wilcoxon matched pairs signed rank test was used. ** 0.001< p

Additional file 1: Figure S1. ROC analysis of cross-validated scores from OPLS-DA models. (A) NMR analysis of urine; Tcv1 and Tcv2 represent the first and second PLS component when 2 PLS components were applied to separate 3 classes; (B) LC-MS analysis of urine; (C) bile acid analysis of urine; (D) NMR analysis of serum; (E) NMR analysis of feces. Figure S2. Relative metabolite levels in urine from cohorts 1 (A) and 2 (B), plasma from cohorts 3 (C) and 2 (D), and feces from cohort 1 (E). Metabolite levels are indicated by relative peak heights from the median fold normalized spectra of urine and feces and non-normalized serum spectra. Error bars are presented in SEM. a.u. stands for arbitrary unit. Figure S3. OPLS-DA cross-validated scores plots of urinary 1H NMR spectra of the RYGB and LGB patients from cohort 3 at pre-op (Q2Y=0.49; R2X=12.35%; R2Y=89.6%; p = 0.006), and 6-month post-op (Q2Y=0.44; R2X=30.66%; R2Y=95.2%; p = 0.038). The metabolites that significantly contributed to the classification of different time points are shown in Fig. 2C. Figure S4. OPLS-DA cross-validated scores plots of urinary reverse-phase liquid chromatography-mass spectrometry profiles of the bariatric patients from cohort 1 in ESI positive mode (Q2Y=0.46; R2X=24.8%; R2Y=73.4%; CVANOVA p = 2.9 x 10-14) and ESI negative mode (Q2Y=0.52; R2X=14%; R2Y=76.6%; CVANOVA p = 6.2 x 10-17). The identified metabolites that significantly contributed to the classification between pre-op (black) and 2-6 months post-op (green) are listed in the table. Positive correlation (r) represents higher relative concentrations of these metabolites at the post-op compared to pre-op. p[1] is loadings from the OPLS-DA models. Coefficient of variation (CV) was calculated based on the quality control (QC) samples to evaluate the analytical variation of the features. Figure S5. OPLS-DA cross-validated scores plots of urinary bile acid profiles of the RYGB patients from cohort 1 at pre-op (black), 2-6 months (green) and 1-2 years (orange) post-op (A. Q2Y=0.21; R2X=16.9%; R2Y=48.9%; CVANOVA p = 5.8x10-9). Bar plots of relative intensities of urinary bile acids identified from cohort 2 RYGB patients at pre-op (black) and 6 months post-op (green). Wilcoxon matched pairs signed rank test was used. ** 0.001< p

The use of cultured primary hepatocytes is of great importance in the pharmaco-toxicological testing of chemical compounds, and represents an alternative to dosing regimes conducted in immortalised cell lines or in vivo testing. By stabilising the epigenome, the histone deacetylase inhibitor TSA (trichostatin A) has been shown to preserve hepatocyte xenobiotic metabolic capacity after isolation, however the effects of TSA on endogenous metabolism are to date unknown. Using an NMR-based metabonomics approach we have investigated the impact of TSA treatment on the global profile and individual metabolite levels in hepatocytes to evaluate if the metabolome was stabilised and the normal metabolic phenotype retained in this model. Aqueous soluble metabolites were extracted from isolated rat hepatocytes after 24 and 72 hours exposure to TSA (25?M) together with time-matched controls and measured by 1H NMR spectroscopy. Multivariate analysis of the global profiles revealed a clear effect of TSA-treatment and discrimination between different time points. Metabolic differences between time points in the TSA treated group were less than those observed in the control, suggesting that TSA reduced the time-related effect on metabolism. Considering individual metabolites, TSA treatment was associated with decreased amounts of glycerophosphocholine (GPC), 3-hydroxybutyric acid (3HBA) and adenosine, an increase in glycogen and slowed the decrease of glutathione (GSH) over time. The effect on intracellular glycogen (glucose store) and the ketone body 3HBA (ketosis product) may indicate a change in energy metabolism. Effects on adenosine metabolism are consistent with other reported events following TSA exposure, specifically down-regulation of ecto-5'-nucleotidase. The effect on GSH suggests that bioavailability is higher in TSA treated cells: this would have implications in many metabolic processes and specifically phase II conjugation of xenobiotics. Levels of choline metabolites are frequently altered as cells are transformed to an immortalised or tumourigenic phenotype, and so the effect of TSA on GPC may reflect cell cycle arrest and preservation of the differentiated metabolic state. Collectively our data suggest that TSA treatment reduces the loss of a normal metabolic phenotype in cultured primary hepatocytes, improving the model as a tool to study endogenous liver metabolism and potentially affecting the accuracy of all biological assays in this system.

Over the last few decades, mass spectrometry imaging (MSI) has gained increasing interest as an analytical tool for the analysis of spatial molecular patterns in samples of interest. Because of its untargeted and high-throughput nature, MSI data often consists of hundreds to thousand spectral peak images. Statistical analysis of this type of data has been extensively used to investigate the relationship between the observed molecular patterns and the local properties of the sample. For example, supervised learning techniques can be employed to segment the tissue specimens into histologically relevant areas. In general, these regions must be identified by an expert histopathologist by visual inspection of the optical image of the tissue specimen; for instance, by employing haematoxylin and eosin (H&E) staining. Using this approach, lists of significantly up/down-regulated ions in the various tissue regions can be identified by univariate or multivariate statistical modelling. Unfortunately, these methods cannot be used when there is no certainty about a direct relationship between histological characteristics and molecular patterns. This situation is typical of analyses where underlying models of the local molecular interactions are unknown, such as in cancerous tissue. In these cases, different data analysis tools must be employed. Unsupervised data analysis provides a class of techniques and models that can identify data patterns without requiring external information common to objects of the same class. The purpose of these methods is to identify statistical properties common to subsets of the analysed data and to generate a partition based on these. In the case of MSI, unsupervised methods usually employed fall into the category of clustering. The purpose of these methods is to generate a partition of the measured spectra, accordingly to the similarity between their ion patterns, and assign these to the corresponding pixels, thus providing a molecular-based segmentation of the sample. However, the lack of a ground truth makes the clustering challenging, requiring various strategies for validating the quality of the observed results. Furthermore, the unprocessed MSI data often contains signals which are the result of the specific analytical technique used to extract the molecular content of the sample of interest. For instance, MALDI contains signals associated with the chemical matrix used to enhance the ion desorption, or, in the case of DESI, solvent-related signals are present in the final dataset. It is evident that these sample-unrelated signals can interfere with the results of the unsupervised analysis. For this reason, a series of filters, published in an R package, called SPatially aUTomatic deNoising for Ims toolKit (SPUTNIK), are presented. The filters aim to identify and remove spectral signals that are not likely to be sample-related. The results show that this approach not only significantly reduces the size of the data, but also improves the quality of the clustering results. Subsequently, the benefit of dimensionality reduction (DR) techniques in determining the optimal number of clusters in large MSI data is investigated. It is shown that although standard linear methods, such as principal component analysis (PCA), cannot provide an accurate and comprehensive picture of the statistical properties of the data, deep-learning-based (highly non-linear) methods reveal the presence of groups of mutually similar spectra. Additionally, the benefit of using a 3-dimensional (3D) tissue specimen for generating robust unsupervised partitions of the data is presented. Finally, the information contained in MSI data about the spatial localisation of the detected molecules is exploited for the identification of groups of highly co-localised ions. Using the hypothesis that groups of highly co-localised molecules can be an expression of local metabolism, differences in the ion co-localisation patterns between metastatic and non-metastatic colorectal cancer are identified. Open Access

Cardiometabolic disorders (CMDs) are a major public health problem worldwide. The main goal of this thesis is to characterize the genetic architecture of CMD-related metabolites in a Lebanese cohort. In order to maximise the extraction of meaningful biological information from this dataset, an important part of this thesis focuses on the evaluation and subsequent improvement of the standard methods currently used for molecular epidemiology studies. First, I describe MetaboSignal, a novel network-based approach to explore the genetic regulation of the metabolome. Second, I comprehensively compare the recovery of metabolic information in the different 1H NMR strategies routinely used for metabolic profiling of plasma (standard 1D, spin-echo and JRES). Third, I describe a new method for dimensionality reduction of 1H NMR datasets prior to statistical modelling. Finally, I use all this methodological knowledge to search for molecular biomarkers of CMDs in a Lebanese population. Metabolome-wide association analyses identified a number of metabolites associated with CMDs, as well as several associations involving N-glycan units from acute-phase glycoproteins. Genetic mapping of these metabolites validated previously reported gene-metabolite associations, and revealed two novel loci associated with CMD-related metabolites. Collectively, this work contributes to the ongoing efforts to characterize the molecular mechanisms underlying complex human diseases. Open Access

A rich and functional description of a patient health status is the fundamental basis for the personalisation of treatment and the targeting of interventions. The function of inflammation in the healing process as well as its involvement in most major diseases is well established, yet the specific mechanism by which it contributes to the pathogenesis is still not fully understood. If conditions arising from a dysregulation of the inflammatory process are to be treated before they become irreversible, a novel understanding of these pathologies must be achieved and a stratification of patients based on their inflammatory status undertaken. The work presented in this thesis aims to deliver new analytical and statistical approaches to support the investigation of the time-dependent dysregulation of inflammation. Lipid mediators have been described as exerting a major role in the initiation and regulation of the inflammatory response, yet analytical platforms for their large-scale characterisation in human biofluids are lacking. This thesis reports the validation of an assay for the simultaneous quantification of pro- and anti-inflammatory signalling molecules in multiple human biofluids. The coverage of the assay in each biofluid is subsequently established, characterising inflammatory signalling across biological compartments. A second study explores the assay’s applicability in a clinical context; investigating the relationship between lipid mediators, current clinical markers of inflammation and post-operative complications. Characterising the interplay between signalling and regulatory networks is key to understanding a living system’s response to perturbations, yet few statistical approaches are suited for the detection of time-dependent patterns in short and irregularly sampled longitudinal datasets. This thesis reports the development of a statistical approach to support the identification of altered time-trajectories in such studies. The method’s wide applicability is subsequently demonstrated on two investigations covering the diversity of metabolic phenotyping data generation platforms. This thesis is a proof of concept for the characterisation of patient-specific inflammatory status in a clinical context and the identification of altered time-dependent patterns. Both analytical and statistical developments have been motivated by the needs of real world applications and provide a template for the characterisation and analysis of the molecular basis for treatment. Open Access

In recent years, increasingly efficient molecular biology techniques created new opportunities to harness large-scale repositories of biological material collected in epidemiological studies; however, methods to manipulate and analyse the wealth of information thus generated have lagged behind. The introductory chapter of this thesis presents the multifaceted field of ‘computational epidemiology’ from the perspectives of molecular biology, measurement theory, and statistical modelling. Focusing on measurement of DNA methylation levels, the author also reviews the state of the art, proposes novel pre-processing methods and evaluation frameworks, and provides recommendations for genome-wide studies of DNA methylation levels using Illumina Infinium® HumanMethylation450 BeadChips. The remaining chapters, in the form of three self-contained scientific articles, cover applications on the following topics: (i) DNA methylation differences associated with internal migration patterns within Italy; (ii) associations of DNA methylation profiles with adiposity measures, targeted gene expression, biomarkers of lipid and glucose metabolism, and risk of developing three obesity-associated diseases; (iii) associations of a dietary score with blood pressure, and with urinary metabolites as characterised by NMR spectroscopy. The thesis is concluded with general remarks and the presentation of some open problems that offer potential for future research. Open Access