Metabolic profiling of aortic stenosis and hypertrophic cardiomyopathy identifies mechanistic contrasts in substrate utilization.

Aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM) are distinct disorders leading to left ventricular hypertrophy (LVH), but whether cardiac metabolism substantially differs between these in humans remains to be elucidated. We undertook an invasive (aortic root, coronary sinus) metabolic profiling in patients with severe AS and HCM in comparison with non-LVH controls to investigate cardiac fuel selection and metabolic remodeling. These patients were assessed under different physiological states (at rest, during stress induced by pacing). The identified changes in the metabolome were further validated by metabolomic and orthogonal transcriptomic analysis, in separately recruited patient cohorts. We identified a highly discriminant metabolomic signature in severe AS in all samples, regardless of sampling site, characterized by striking accumulation of long-chain acylcarnitines, intermediates of fatty acid transport across the inner mitochondrial membrane, and validated this in a separate cohort. Mechanistically, we identify a downregulation in the PPAR-α transcriptional network, including expression of genes regulating fatty acid oxidation (FAO). In silico modeling of ß-oxidation demonstrated that flux could be inhibited by both the accumulation of fatty acids as a substrate for mitochondria and the accumulation of medium-chain carnitines which induce competitive inhibition of the acyl-CoA dehydrogenases. We present a comprehensive analysis of changes in the metabolic pathways (transcriptome to metabolome) in severe AS, and its comparison to HCM. Our results demonstrate a progressive impairment of ß-oxidation from HCM to AS, particularly for FAO of long-chain fatty acids, and that the PPAR-α signaling network may be a specific metabolic therapeutic target in AS.

Fecal microbiota and volatile metabolome pattern alterations precede late-onset meningitis in preterm neonates.

OBJECTIVE: The fecal microbiota and metabolome are hypothesized to be altered before late-onset neonatal meningitis (LOM), in analogy to late-onset sepsis (LOS). The present study aimed to identify fecal microbiota composition and volatile metabolomics preceding LOM. METHODS: Cases and gestational age-matched controls were selected from a prospective, longitudinal preterm cohort study (born <30 weeks' gestation) at nine neonatal intensive care units. The microbial composition (16S rRNA sequencing) and volatile metabolome (gas chromatography-ion mobility spectrometry (GC-IMS) and GC-time-of-flight-mass spectrometry (GC-TOF-MS)), were analyzed in fecal samples 1-10 days pre-LOM. RESULTS: Of 1397 included infants, 21 were diagnosed with LOM (1.5%), and 19 with concomitant LOS (90%). Random Forest classification and MaAsLin2 analysis found similar microbiota features contribute to the discrimination of fecal pre-LOM samples versus controls. A Random Forest model based on six microbiota features accurately predicts LOM 1-3 days before diagnosis with an area under the curve (AUC) of 0.88 (n=147). Pattern recognition analysis by GC-IMS revealed an AUC of 0.70-0.76 (P<0.05) in the three days pre-LOM (n=92). No single discriminative metabolites were identified by GC-TOF-MS (n=66). CONCLUSION: Infants with LOM could be accurately discriminated from controls based on preclinical microbiota composition, while alterations in the volatile metabolome were moderately associated with preclinical LOM.

Cross-species transcriptomics identifies obesity associated genes between human and mouse studies.

BACKGROUND: Fundamentally defined by an imbalance in energy consumption and energy expenditure, obesity is a significant risk factor of several musculoskeletal conditions including osteoarthritis (OA). High-fat diets and sedentary lifestyle leads to increased adiposity resulting in systemic inflammation due to the endocrine properties of adipose tissue producing inflammatory cytokines and adipokines. We previously showed serum levels of specific adipokines are associated with biomarkers of bone remodelling and cartilage volume loss in knee OA patients. Whilst more recently we find the metabolic consequence of obesity drives the enrichment of pro-inflammatory fibroblast subsets within joint synovial tissues in obese individuals compared to those of BMI defined 'health weight'. As such this present study identifies obesity-associated genes in OA joint tissues which are conserved across species and conditions. METHODS: The study utilised 6 publicly available bulk and single-cell transcriptomic datasets from human and mice studies downloaded from Gene Expression Omnibus (GEO). Machine learning models were employed to model and statistically test datasets for conserved gene expression profiles. Identified genes were validated in OA tissues from obese and healthy weight individuals using quantitative PCR method (N = 38). Obese and healthy-weight patients were categorised by BMI > 30 and BMI between 18 and 24.9 respectively. Informed consent was obtained from all study participants who were scheduled to undergo elective arthroplasty. RESULTS: Principal component analysis (PCA) was used to investigate the variations between classes of mouse and human data which confirmed variation between obese and healthy populations. Differential gene expression analysis filtered on adjusted p-values of p < 0.05, identified differentially expressed genes (DEGs) in mouse and human datasets. DEGs were analysed further using area under curve (AUC) which identified 12 genes. Pathway enrichment analysis suggests these genes were involved in the biosynthesis and elongation of fatty acids and the transport, oxidation, and catabolic processing of lipids. qPCR validation found the majority of genes showed a tendency to be upregulated in joint tissues from obese participants. Three validated genes, IGFBP2 (p = 0.0363), DOK6 (0.0451) and CASP1 (0.0412) were found to be significantly different in obese joint tissues compared to lean-weight joint tissues. CONCLUSIONS: The present study has employed machine learning models across several published obesity datasets to identify obesity-associated genes which are validated in joint tissues from OA. These results suggest obesity-associated genes are conserved across conditions and may be fundamental in accelerating disease in obese individuals. Whilst further validations and additional conditions remain to be tested in this model, identifying obesity-associated genes in this way may serve as a global aid for patient stratification giving rise to the potential of targeted therapeutic interventions in such patient subpopulations.

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.

Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.

Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

Spatially restricted tumour-associated and host-associated immune drivers correlate with the recurrence sites of pancreatic cancer.

OBJECTIVE: Most patients with pancreatic ductal adenocarcinoma (PDAC) will experience recurrence after resection. Here, we investigate spatially organised immune determinants of PDAC recurrence. DESIGN: PDACs (n=284; discovery cohort) were classified according to recurrence site as liver (n=93/33%), lung (n=49/17%), local (n=31/11%), peritoneal (n=38/13%) and no-recurrence (n=73/26%). Spatial compartments were identified by fluorescent imaging as: pancytokeratin (PanCK)+CD45- (tumour cells); CD45+PanCK- (leucocytes) and PanCK-CD45- (stromal cells), followed by transcriptomic (72 genes) and proteomic analysis (51 proteins) for immune pathway targets. Results from next-generation sequencing (n=194) were integrated. Finally, 10 tumours from each group underwent immunophenotypic analysis by multiplex immunofluorescence. A validation cohort (n=109) was examined in parallel. RESULTS: No-recurrent PDACs show high immunogenicity, adaptive immune responses and are rich in pro-inflammatory chemokines, granzyme B and alpha-smooth muscle actin+ fibroblasts. PDACs with liver and/or peritoneal recurrences display low immunogenicity, stemness phenotype and innate immune responses, whereas those with peritoneal metastases are additionally rich in FAP+ fibroblasts. PDACs with local and/or lung recurrences display interferon-gamma signalling and mixed adaptive and innate immune responses, but with different leading immune cell population. Tumours with local recurrences overexpress dendritic cell markers whereas those with lung recurrences neutrophilic markers. Except the exclusive presence of RNF43 mutations in the no-recurrence group, no genetic differences were seen. The no-recurrence group exhibited the best, whereas liver and peritoneal recurrences the poorest prognosis. CONCLUSIONS: Our findings demonstrate distinct inflammatory/stromal responses in each recurrence group, which might affect dissemination patterns and patient outcomes. These findings may help to inform personalised adjuvant/neoadjuvant and surveillance strategies in PDAC, including immunotherapeutic modalities.

CACONET: a novel classification framework for microbial correlation networks.

MOTIVATION: Existing microbiome-based disease prediction relies on the ability of machine learning methods to differentiate disease from healthy subjects based on the observed taxa abundance across samples. Despite numerous microbes have been implicated as potential biomarkers, challenges remain due to not only the statistical nature of microbiome data but also the lack of understanding of microbial interactions which can be indicative of the disease. RESULTS: We propose CACONET (classification of Compositional-Aware COrrelation NETworks), a computational framework that learns to classify microbial correlation networks and extracts potential signature interactions, taking as input taxa relative abundance across samples and their health status. By using Bayesian compositional-aware correlation inference, a collection of posterior correlation networks can be drawn and used for graph-level classification, thus incorporating uncertainty in the estimates. CACONET then employs a deep learning approach for graph classification, achieving excellent performance metrics by exploiting the correlation structure. We test the framework on both simulated data and a large real-world dataset pertaining to microbiome samples of colorectal cancer (CRC) and healthy subjects, and identify potential network substructure characteristic of CRC microbiota. CACONET is customizable and can be adapted to further improve its utility. AVAILABILITY AND IMPLEMENTATION: CACONET is available at https://github.com/yuanwxu/corr-net-classify. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabotypes of flavan-3-ol colonic metabolites after cranberry intake: elucidation and statistical approaches.

PURPOSE: Extensive inter-individual variability exists in the production of flavan-3-ol metabolites. Preliminary metabolic phenotypes (metabotypes) have been defined, but there is no consensus on the existence of metabotypes associated with the catabolism of catechins and proanthocyanidins. This study aims at elucidating the presence of different metabotypes in the urinary excretion of main flavan-3-ol colonic metabolites after consumption of cranberry products and at assessing the impact of the statistical technique used for metabotyping. METHODS: Data on urinary concentrations of phenyl-γ-valerolactones and 3-(hydroxyphenyl)propanoic acid derivatives from two human interventions has been used. Different multivariate statistics, principal component analysis (PCA), cluster analysis, and partial least square-discriminant analysis (PLS-DA), have been considered. RESULTS: Data pre-treatment plays a major role on resulting PCA models. Cluster analysis based on k-means and a final consensus algorithm lead to quantitative-based models, while the expectation-maximization algorithm and clustering according to principal component scores yield metabotypes characterized by quali-quantitative differences in the excretion of colonic metabolites. PLS-DA, together with univariate analyses, has served to validate the urinary metabotypes in the production of flavan-3-ol metabolites and to confirm the robustness of the methodological approach. CONCLUSIONS: This work proposes a methodological workflow for metabotype definition and highlights the importance of data pre-treatment and clustering methods on the final outcomes for a given dataset. It represents an additional step toward the understanding of the inter-individual variability in flavan-3-ol metabolism. TRIAL REGISTRATION: The acute study was registered at clinicaltrials.gov as NCT02517775, August 7, 2015; the chronic study was registered at clinicaltrials.gov as NCT02764749, May 6, 2016.

Investigating the potential of a prematurely aged immune phenotype in severely injured patients as predictor of risk of sepsis.

BACKGROUND: Traumatic injury elicits a hyperinflammatory response and remodelling of the immune system leading to immuneparesis. This study aimed to evaluate whether traumatic injury results in a state of prematurely aged immune phenotype to relate this to clinical outcomes and a greater risk of developing additional morbidities post-injury. METHODS AND FINDINGS: Blood samples were collected from 57 critically injured patients with a mean Injury Severity Score (ISS) of 26 (range 15-75 years), mean age of 39.67 years (range 20-84 years), and 80.7% males, at days 3, 14, 28 and 60 post-hospital admission. 55 healthy controls (HC), mean age 40.57 years (range 20-85 years), 89.7% males were also recruited. The phenotype and frequency of adaptive immune cells were used to calculate the IMM-AGE score, an indicator of the degree of phenotypic ageing of the immune system. IMM-AGE was elevated in trauma patients at an early timepoint (day 3) in comparison with healthy controls (p < 0.001), driven by an increase in senescent CD8 T cells (p < 0.0001), memory CD8 T cells (p < 0.0001) and regulatory T cells (p < 0.0001) and a reduction in naïve CD8 T cells (p < 0.001) and overall T cell lymphopenia (p < 0 .0001). These changes persisted to day 60. Furthermore, the IMM-AGE scores were significantly higher in trauma patients (mean score 0.72) that developed sepsis (p = 0.05) in comparison with those (mean score 0.61) that did not. CONCLUSIONS: The profoundly altered peripheral adaptive immune compartment after critical injury can be used as a potential biomarker to identify individuals at a high risk of developing sepsis and this state of prematurely aged immune phenotype in biologically young individuals persists for up to two months post-hospitalisation, compromising the host immune response to infections. Reversing this aged immune system is likely to have a beneficial impact on short- and longer-term outcomes of trauma survivors.

Breastfeeding promotes early neonatal regulatory T-cell expansion and immune tolerance of non-inherited maternal antigens.

BACKGROUND: Breastfeeding is associated with long-term health benefits, such as a lower incidence of childhood infections, asthma, obesity and autoimmune disorders. However, little is known regarding how the maternal and neonatal immune systems interact after parturition when the neonate receives nutrition from maternal breast milk. METHODS: We undertook a comparative analysis of immune repertoire and function at birth and 3 weeks of age in a cohort of 38 term neonates born by caesarean section grouped according to feeding method (breast milk versus formula). We used flow cytometry to study the immune phenotype in neonatal and maternal blood samples and mixed lymphocyte reactions to establish the proliferation response of neonatal versus maternal lymphocytes and vice versa. The microbiome of neonatal stool samples was also investigated using 16S rRNA sequencing. RESULTS: We show that the proportion of regulatory T cells (Tregs) increases in this period and is nearly twofold higher in exclusively breastfed neonates compared with those who received formula milk only. Moreover, breastfed neonates show a specific and Treg-dependent reduction in proliferative T-cell responses to non-inherited maternal antigens (NIMA), associated with a reduction in inflammatory cytokine production. We also observed the enrichment of short chain fatty acid producing taxa (Veillonella and Gemella) in stool samples of exclusively breastfed neonates. CONCLUSIONS: These data indicate that exposure of the neonate to maternal cells through breastfeeding acts to drive the maturation of Tregs and 'tolerizes' the neonate towards NIMA.

Unique diagnostic signatures of concussion in the saliva of male athletes: the Study of Concussion in Rugby Union through MicroRNAs (SCRUM).

OBJECTIVE: To investigate the role of salivary small non-coding RNAs (sncRNAs) in the diagnosis of sport-related concussion. METHODS: Saliva was obtained from male professional players in the top two tiers of England's elite rugby union competition across two seasons (2017-2019). Samples were collected preseason from 1028 players, and during standardised head injury assessments (HIAs) at three time points (in-game, post-game, and 36-48 hours post-game) from 156 of these. Samples were also collected from controls (102 uninjured players and 66 players sustaining a musculoskeletal injury). Diagnostic sncRNAs were identified with next generation sequencing and validated using quantitative PCR in 702 samples. A predictive logistic regression model was built on 2017-2018 data (training dataset) and prospectively validated the following season (test dataset). RESULTS: The HIA process confirmed concussion in 106 players (HIA+) and excluded this in 50 (HIA-). 32 sncRNAs were significantly differentially expressed across these two groups, with let-7f-5p showing the highest area under the curve (AUC) at 36-48 hours. Additionally, a combined panel of 14 sncRNAs (let-7a-5p, miR-143-3p, miR-103a-3p, miR-34b-3p, RNU6-7, RNU6-45, Snora57, snoU13.120, tRNA18Arg-CCT, U6-168, U6-428, U6-1249, Uco22cjg1,YRNA_255) could differentiate concussed subjects from all other groups, including players who were HIA- and controls, immediately after the game (AUC 0.91, 95% CI 0.81 to 1) and 36-48 hours later (AUC 0.94, 95% CI 0.86 to 1). When prospectively tested, the panel confirmed high predictive accuracy (AUC 0.96, 95% CI 0.92 to 1 post-game and AUC 0.93, 95% CI 0.86 to 1 at 36-48 hours). CONCLUSIONS: SCRUM, a large prospective observational study of non-invasive concussion biomarkers, has identified unique signatures of concussion in saliva of male athletes diagnosed with concussion.

Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer.

Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.

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.

Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference.

Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a number of different methods have been proposed to infer the structure of a GRN, there are large discrepancies among the different inference algorithms they adopt, rendering their meaningful comparison challenging. In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of a GRN directly from data and computationally validated our results. Both methods were applied to different gene expression datasets resulting from inflammatory bowel disease (IBD), pancreatic ductal adenocarcinoma (PDAC), and acute myeloid leukaemia (AML) studies. For each case, gene regulators were successfully identified. For example, for the case of the IBD dataset, the UGT1A family genes were identified as key regulators while upon analysing the PDAC dataset, the SULF1 and THBS2 genes were depicted. We further demonstrate that an ensemble-based approach, that combines the output of the MIDER and PLSNET algorithms, can infer the structure of a GRN from data with higher accuracy. We have also estimated the number of the samples required for potential future validation studies. Here, we presented our proposed analysis framework that caters not only to candidate regulator genes prediction for potential validation experiments but also an estimation of the number of samples required for these experiments.

Chemical Characterization of Capsule-Brewed Espresso Coffee Aroma from the Most Widespread Italian Brands by HS-SPME/GC-MS.

Coffee capsules market is on the rise as it allows access to a wide selection of coffee, differing in taste and brand. However, few data about the chemical characterization of the capsule-brewed coffee aroma are available. In this work, an untargeted approach using headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS) and combined to chemometrics was performed to study and compare aroma profile from 65 capsule-brewed espresso coffees (ECs) commercialized by five of the most representative brands in Italy. Volatile profiles obtained from ECs were subjected to multivariate statistical analysis, which generally did not show a significant variability among coffees belonging to the same brand, except for those modified after the addition of specific flavor additives or aromatic substances (such as caramel, chocolate, etc.). Similarities may be related to the starting coffee brew or the processing method, which is likely the same for each individual brand. Additionally, partial least squares discriminant analysis (PLS-DA) showed that capsules from a specific brand contain the highest concentration of pyrazines, thus characterized by an intense and characteristic aroma, and a stronger note than those from the other brands. This study supports that the chemical analysis in conjunction with chemometric tools is a useful approach for assessing flavor quality, even if the need remains to identify volatile markers of high-quality beverages.

-Omics biomarker identification pipeline for translational medicine.

BACKGROUND: Translational medicine (TM) is an emerging domain that aims to facilitate medical or biological advances efficiently from the scientist to the clinician. Central to the TM vision is to narrow the gap between basic science and applied science in terms of time, cost and early diagnosis of the disease state. Biomarker identification is one of the main challenges within TM. The identification of disease biomarkers from -omics data will not only help the stratification of diverse patient cohorts but will also provide early diagnostic information which could improve patient management and potentially prevent adverse outcomes. However, biomarker identification needs to be robust and reproducible. Hence a robust unbiased computational framework that can help clinicians identify those biomarkers is necessary. METHODS: We developed a pipeline (workflow) that includes two different supervised classification techniques based on regularization methods to identify biomarkers from -omics or other high dimension clinical datasets. The pipeline includes several important steps such as quality control and stability of selected biomarkers. The process takes input files (outcome and independent variables or -omics data) and pre-processes (normalization, missing values) them. After a random division of samples into training and test sets, Least Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods are applied to identify the most important features representing potential biomarker candidates. The penalization parameters are optimised using 10-fold cross validation and the process undergoes 100 iterations and a combinatorial analysis to select the best performing multivariate model. An empirical unbiased assessment of their quality as biomarkers for clinical use is performed through a Receiver Operating Characteristic curve and its Area Under the Curve analysis on both permuted and real data for 1000 different randomized training and test sets. We validated this pipeline against previously published biomarkers. RESULTS: We applied this pipeline to three different datasets with previously published biomarkers: lipidomics data by Acharjee et al. (Metabolomics 13:25, 2017) and transcriptomics data by Rajamani and Bhasin (Genome Med 8:38, 2016) and Mills et al. (Blood 114:1063-1072, 2009). Our results demonstrate that our method was able to identify both previously published biomarkers as well as new variables that add value to the published results. CONCLUSIONS: We developed a robust pipeline to identify clinically relevant biomarkers that can be applied to different -omics datasets. Such identification reveals potentially novel drug targets and can be used as a part of a machine-learning based patient stratification framework in the translational medicine settings.

Inter-individual variability in the production of flavan-3-ol colonic metabolites: preliminary elucidation of urinary metabotypes.

PURPOSE: There is much information on the bioavailability of (poly)phenolic compounds following acute intake of various foods. However, there are only limited data on the effects of repeated and combined exposure to specific (poly)phenol food sources and the inter-individual variability in their bioavailability. This study evaluated the combined urinary excretion of (poly)phenols from green tea and coffee following daily consumption by healthy subjects in free-living conditions. The inter-individual variability in the production of phenolic metabolites was also investigated. METHODS: Eleven participants consumed both tablets of green tea and green coffee bean extracts daily for 8 weeks and 24-h urine was collected on five different occasions. The urinary profile of phenolic metabolites and a set of multivariate statistical tests were used to investigate the putative existence of characteristic metabotypes in the production of flavan-3-ol microbial metabolites. RESULTS: (Poly)phenolic compounds in the green tea and green coffee bean extracts were absorbed and excreted after simultaneous consumption, with green tea resulting in more inter-individual variability in urinary excretion of phenolic metabolites. Three metabotypes in the production of flavan-3-ol microbial metabolites were tentatively defined, characterized by the excretion of different amounts of trihydroxyphenyl-γ-valerolactones, dihydroxyphenyl-γ-valerolactones, and hydroxyphenylpropionic acids. CONCLUSIONS: The selective production of microbiota-derived metabolites from flavan-3-ols and the putative existence of characteristic metabotypes in their production represent an important development in the study of the bioavailability of plant bioactives. These observations will contribute to better understand the health effects and individual differences associated with consumption of flavan-3-ols, arguably the main class of flavonoids in the human diet.

Metabolomics and Lipidomics Study of Mouse Models of Type 1 Diabetes Highlights Divergent Metabolism in Purine and Tryptophan Metabolism Prior to Disease Onset.

With the increase in incidence of type 1 diabetes (T1DM), there is an urgent need to understand the early molecular and metabolic alterations that accompany the autoimmune disease. This is not least because in murine models early intervention can prevent the development of disease. We have applied a liquid chromatography (LC-) and gas chromatography (GC-) mass spectrometry (MS) metabolomics and lipidomics analysis of blood plasma and pancreas tissue to follow the progression of disease in three models related to autoimmune diabetes: the nonobese diabetic (NOD) mouse, susceptible to the development of autoimmune diabetes, and the NOD-E (transgenic NOD mice that express the I-E heterodimer of the major histocompatibility complex II) and NOD-severe combined immunodeficiency (SCID) mouse strains, two models protected from the development of diabetes. All three analyses highlighted the metabolic differences between the NOD-SCID mouse and the other two strains, regardless of diabetic status indicating that NOD-SCID mice are poor controls for metabolic changes in NOD mice. By comparing NOD and NOD-E mice, we show the development of T1DM in NOD mice is associated with changes in lipid, purine, and tryptophan metabolism, including an increase in kynurenic acid and a decrease in lysophospholipids, metabolites previously associated with inflammation.

Genetical genomics of quality related traits in potato tubers using proteomics.

BACKGROUND: Recent advances in ~omics technologies such as transcriptomics, metabolomics and proteomics along with genotypic profiling have permitted the genetic dissection of complex traits such as quality traits in non-model species. To get more insight into the genetic factors underlying variation in quality traits related to carbohydrate and starch metabolism and cold sweetening, we determined the protein content and composition in potato tubers using 2D-gel electrophoresis in a diploid potato mapping population. Upon analyzing we made sure that the proteins from the patatin family were excluded to ensure a better representation of the other proteins. RESULTS: We subsequently performed pQTL analyses for all other proteins with a sufficient representation in the population and established a relationship between proteins and 26 potato tuber quality traits (e.g. flesh colour, enzymatic discoloration) by co-localization on the genetic map and a direct correlation study of protein abundances and phenotypic traits. Over 1643 unique protein spots were detected in total over the two harvests. We were able to map pQTLs for over 300 different protein spots some of which co-localized with traits such as starch content and cold sweetening. pQTLs were observed on every chromosome although not evenly distributed over the chromosomes. The largest number of pQTLs was found for chromosome 8 and the lowest for chromosome number 10. For some 20 protein spots multiple QTLs were observed. CONCLUSIONS: From this analysis, hotspot areas for protein QTLs were identified on chromosomes three, five, eight and nine. The hotspot on chromosome 3 coincided with a QTL previously identified for total protein content and had more than 23 pQTLs in the region from 70 to 80 cM. Some of the co-localizing protein spots associated with some of the most interesting tuber quality traits were identified, albeit far less than we had anticipated at the onset of the experiments.

Integration of multi-omics data for prediction of phenotypic traits using random forest.

BACKGROUND: In order to find genetic and metabolic pathways related to phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and LC-MS, proteomics data and a selected set of tuber quality phenotypic data from a diploid segregating mapping population of potato. In this study we present an approach to integrate these ~ omics data sets for the purpose of predicting phenotypic traits. This gives us networks of relatively small sets of interrelated ~ omics variables that can predict, with higher accuracy, a quality trait of interest. RESULTS: We used Random Forest regression for integrating multiple ~ omics data for prediction of four quality traits of potato: tuber flesh colour, DSC onset, tuber shape and enzymatic discoloration. For tuber flesh colour beta-carotene hydroxylase and zeaxanthin epoxidase were ranked first and forty-fourth respectively both of which have previously been associated with flesh colour in potato tubers. Combining all the significant genes, LC-peaks, GC-peaks and proteins, the variation explained was 75 %, only slightly more than what gene expression or LC-MS data explain by themselves which indicates that there are correlations among the variables across data sets. For tuber shape regressed on the gene expression, LC-MS, GC-MS and proteomics data sets separately, only gene expression data was found to explain significant variation. For DSC onset, we found 12 significant gene expression, 5 metabolite levels (GC) and 2 proteins that are associated with the trait. Using those 19 significant variables, the variation explained was 45 %. Expression QTL (eQTL) analyses showed many associations with genomic regions in chromosome 2 with also the highest explained variation compared to other chromosomes. Transcriptomics and metabolomics analysis on enzymatic discoloration after 5 min resulted in 420 significant genes and 8 significant LC metabolites, among which two were putatively identified as caffeoylquinic acid methyl ester and tyrosine. CONCLUSIONS: In this study, we made a strategy for selecting and integrating multiple ~ omics data using random forest method and selected representative individual peaks for networks based on eQTL, mQTL or pQTL information. Network analysis was done to interpret how a particular trait is associated with gene expression, metabolite and protein data.