Imperial College London

Dr Joram M. Posma PhD MSc B AS MRSC

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Lecturer in Cancer Informatics
 
 
 
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Contact

 

j.posma11 Website

 
 
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Location

 

101Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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46 results found

Wu Y, Posma JM, Holmes E, Chambers E, Frost G, Garcia Perez Iet al., 2021, Odd chain fatty acids are not robust biomarkers for dietary intake of fiber, Molecular Nutrition and Food Research, Vol: 65, Pages: 1-8, ISSN: 1613-4125

Prior investigation has suggested a positive association between increased colonic propionate production and circulating odd-chain fatty acids [(OCFAs; pentadecanoic acid (C15:0), heptadecanoic acid (C17:0)]. As the major source of propionate in humans is the microbial fermentation of dietary fiber, OCFAs have been proposed as candidate biomarkers of dietary fiber. The objective of this study is to critically assess the plausibility, robustness, reliability, dose-response, time-response aspects of OCFAs as potential biomarkers of fermentable fibers in two independent studies using a validated analytical method. OCFAs were first assessed in a fiber supplementation study, where 21 participants received 10g dietary fiber supplementation for 7 days with blood samples collected on the final day at a 420 minute study visit. OCFAs were then assessed in a highly controlled inpatient setting, which 19 participants consumed a high fiber (45.1g/day) and a low fiber diet (13.6g/day) for 4 days. Collectively in both studies, dietary intakes of fiber as fiber supplementations or having consumed a high fiber diet did not increase circulating levels of OCFAs. The dose and temporal relations were not observed. Current study has generated new insight on the utility of OCFAs as fiber biomarkers and highlighted the importance of critical assessment of candidate dietary biomarkers before application.

Journal article

Posma JM, Garcia-Perez I, Frost G, Aljuraiban GS, Chan Q, Van Horn L, Daviglus M, Stamler J, Holmes E, Elliott P, Nicholson JKet al., 2021, Nutriome-metabolome relationships provide insights into dietary intake and metabolism (vol 1, pg 426, 2020), NATURE FOOD, Vol: 2, Pages: 541-542

Journal article

Boubnovski Martell M, Chen M, Linton-Reid K, Posma JM, Copley S, Aboagye Eet al., 2021, [pre-print] Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs, Publisher: arXiv

Automated lobar segmentation allows regional evaluation of lung disease and is important for diagnosis and therapy planning. Advanced statistical workflows permitting such evaluation is a needed area within respiratory medicine; their adoption remains slow, with poor workflow accuracy. Diseased lung regions often produce high-density zones on CT images, limiting an algorithm's execution to specify damaged lobes due to oblique or lacking fissures. This impact motivated developing an improved machine learning method to segment lung lobes that utilises tracheobronchial tree information to enhance segmentation accuracy through the algorithm's spatial familiarity to define lobar extent more accurately. The method undertakes parallel segmentation of lobes and auxiliary tissues simultaneously by employing multi-task learning (MTL) in conjunction with V-Net-attention, a popular convolutional neural network in the imaging realm. In keeping with the model's adeptness for better generalisation, high performance was retained in an external dataset of patients with four distinct diseases: severe lung cancer, COVID-19 pneumonitis, collapsed lungs and Chronic Obstructive Pulmonary Disease (COPD), even though the training data included none of these cases. The benefit of our external validation test is specifically relevant since our choice includes those patients who have diagnosed lung disease with associated radiological abnormalities. To ensure equal rank is given to all segmentations in the main task we report the following performance (Dice score) on a per-segment basis: normal lungs 0.97, COPD 0.94, lung cancer 0.94, COVID-19 pneumonitis 0.94 and collapsed lung 0.92, all at p<0.05. Even segmenting lobes with large deformations on CT images, the model maintained high accuracy. The approach can be readily adopted in the clinical setting as a robust tool for radiologists.

Working paper

Barker GF, Pechlivanis A, Bello AT, Chrysostomou D, Mullish BH, Marchesi J, Posma JM, Kinross JM, Nicholson J, O'Keefe SJ, Li JVet al., 2021, Aa022 a high-fiber low-fat diet increases fecal levels of lithocholic acid derivative 3-ketocholanic acid, Digestive Disease Week, Publisher: W B SAUNDERS CO-ELSEVIER INC, Pages: S393-S394, ISSN: 0016-5085

Conference paper

Jordi M-P, Wellington A, Lubach G, Posma J, Coe C, Swann Jet al., 2021, Gut microbial and metabolic profiling reveal the lingering effects of infantile iron deficiency unless treated with iron, Molecular Nutrition and Food Research, Vol: 65, ISSN: 1613-4125

ScopeIron deficiency (ID) compromises the health of infants worldwide. Although readily treated with iron, concerns remain about the persistence of some effects. Metabolic and gut microbial consequences of infantile ID were investigated in juvenile monkeys after natural recovery (pID) from iron deficiency or post‐treatment with iron dextran and B vitamins (pID+Fe).Methods and ResultsMetabolomic profiling of urine and plasma is conducted with 1H nuclear magnetic resonance (NMR) spectroscopy. Gut microbiota are characterized from rectal swabs by amplicon sequencing of the 16S rRNA gene. Urinary metabolic profiles of pID monkeys significantly differed from pID+Fe and continuously iron‐sufficient controls (IS) with higher maltose and lower amounts of microbial‐derived metabolites. Persistent differences in energy metabolism are apparent from the plasma metabolic phenotypes with greater reliance on anaerobic glycolysis in pID monkeys. Microbial profiling indicated higher abundances of Methanobrevibacter, Lachnobacterium, and Ruminococcus in pID monkeys and any history of ID resulted in a lower Prevotella abundance compared to the IS controls.ConclusionsLingering metabolic and microbial effects are found after natural recovery from ID. These long‐term biochemical derangements are not present in the pID+Fe animals emphasizing the importance of the early detection and treatment of early‐life ID to ameliorate its chronic metabolic effects.

Journal article

Posma JM, Stamler J, Garcia-Perez I, Chan Q, Wijeyesekera A, Daviglus M, Van Horn L, Holmes E, Nicholson J, Elliott Pet al., 2021, Urinary metabolic phenotype of blood pressure, 19TH INTERNATIONAL SHR SYMPOSIUM SHR, Publisher: Lippincott, Williams & Wilkins, Pages: E70-E70, ISSN: 0263-6352

Objective: Metabolic phenotyping (metabolomics) captures systems-level information on metabolic processes by simultaneously measuring hundreds of metabolites using spectroscopic techniques. Concentrations of these metabolites are affected by genetic (host, microbiome), environmental and dietary factors and may provide insights into biochemical pathways underlying raised blood pressure (BP) in populations.Design and method: Two separate, timed 24hr urine specimens were obtained from 2,031 women and men, aged 40–59, from 8 USA population samples in the INTERMAP Study. Proton Nuclear Magnetic Resonance (1H NMR) was used to characterize a urinary metabolic signature; this was unaffected by diurnal variability and sampling time as it captures end-products of metabolism over a 24hr period. Demographic, population, medical, lifestyle and anthropometric factors were accounted for in regression models to define a urinary metabolic phenotype associated with BP.Results: 29 structurally identified urinary metabolites covaried with systolic BP (SBP), after adjustment for demographic variables, and 18 metabolites with diastolic BP (DBP), with 16 metabolites overlapping between SBP and DBP. These included metabolites related to energy metabolism, renal function, diet and gut microbiota. After adjustment for medical and lifestyle covariates, 22/14 metabolites remained associated with SBP/DBP. Joint covariate-metabolite penalized regression models identified Body Mass Index, age and family history as most important contributors, with 14 metabolites, including gut microbial co-metabolites, also included in the model. Metabolites were mapped in a symbiotic metabolic reaction network, that includes reactions mediated by 3,344 commensal gut microbial species, to highlight affected pathways (Figure). Significant single nucleotide polymorphisms (SNPs) from genome-wide association studies on cardiometabolic risk factors were mapped to genes in this network. This revealed multiple sub

Conference paper

Hu Y, Sun S, Rowlands T, Beck T, Posma JMet al., 2021, Auto-CORPus: automated and consistent outputs from research publications, Publisher: bioRxiv

Motivation: The availability of improved natural language processing (NLP) algorithms and models enable researchers to analyse larger corpora using open source tools. Text mining of biomedical literature is one area for which NLP has been used in recent years with large untapped potential. However, in order to generate corpora that can be analyzed using machine learning NLP algorithms, these need to be standardized. Summarizing data from literature to be stored into databases typically requires manual curation, especially for extracting data from result tables. Results: We present here an automated pipeline that cleans HTML files from biomedical literature. The output is a single JSON file that contains the text for each section, table data in machine-readable format and lists of phenotypes and abbreviations found in the article. We analyzed a total of 2,441 Open Access articles from PubMed Central, from both Genome-Wide and Metabolome-Wide Association Studies, and developed a model to standardize the section headers based on the Information Artifact Ontology. Extraction of table data was developed on PubMed articles and fine-tuned using the equivalent publisher versions. Availability: The Auto-CORPus package is freely available with detailed instructions from Github at https://github.com/jmp111/AutoCORPus/.

Working paper

Brignardello J, Fountana S, Posma JM, Chambers ES, Nicholson JK, Wist J, Frost G, Garcia-Perez I, Holmes Eet al., 2021, Characterization of diet-dependent temporal changes in circulating short-chain fatty acid concentrations: A randomized crossover dietary trial, The American Journal of Clinical Nutrition, ISSN: 0002-9165

Background: Production of Short-chain fatty acids (SCFAs) from food is a complex and dynamic saccharolytic fermentation process mediated by both human and gut microbial factors. SCFA production and knowledge of the relationship between SCFA profiles and dietary patterns is lacking. Objective: Temporal changes in SCFA levels in response to two contrasting diets were investigated using a novel GC-MS method.Design: Samples were obtained from a randomized, controlled, crossover trial designed to characterize the metabolic response to four diets. Participants (n=19) undertook these diets during an inpatient stay (of 72-h). Serum samples were collected 2-h after breakfast (AB), lunch (AL) and dinner (AD) on day 3 and a fasting sample (FA) was obtained on day 4. 24-h urine samples were collected on day 3. In this sub-study, samples from the two extreme diets representing a diet with high adherence to WHO healthy eating recommendations and a typical Western diet were analyzed using a bespoke GC-MS method developed to detect and quantify 10 SCFAs and precursors in serum and urine samples. Results: Considerable inter-individual variation in serum SCFA concentrations was observed across all time points and temporal fluctuations were observed for both diets. Although the sample collection timing exerted a greater magnitude of effect on circulating SCFA concentrations, the unhealthy diet was associated with a lower concentration of acetic acid (FA: coefficient=-17.0; standard error (SE)=5.8; p-trend=0.00615), 2-methylbutyric acid (AL: coefficient=-0.1; SE=0.028; p-trend=4.13x10-4 and AD: coefficient =-0.1; SE:=0.028; p-trend=2.28x10-3) and 2-hydroxybutyric acid (FA: coefficient=-15.8; standard error=5.11; p-trend: 4.09x10-3). In contrast lactic acid was significantly higher in the unhealthy diet (AL: coefficient=750.2; standard error=315.2; p-trend=0.024 and AD: coefficient=1219.3; standard error=322.6; p-trend: 8.28x10-4). Conclusion: The GC-MS method allowed robust mapping of

Journal article

Penney N, Barton W, Posma J, Darzi A, Frost G, Cotter P, Holmes E, Shanahan F, O Sullivan O, Garcia Perez Iet al., 2020, Investigating the role of diet and exercise in gut microbe-hostcometabolism, mSystems, Vol: 5, Pages: 1-16, ISSN: 2379-5077

We investigated the individual and combined effects of diet and physical exercise on metabolism and the gut microbiome to establish how these lifestyle factors influence host-microbiome cometabolism. Urinary and fecal samples were collected from athletes and less active controls. Individuals were further classified according to an objective dietary assessment score of adherence to healthy dietary habits according to WHO guidelines, calculated from their proton nuclear magnetic resonance (1H-NMR) urinary profiles. Subsequent models were generated comparing extremes of dietary habits, exercise, and the combined effect of both. Differences in metabolic phenotypes and gut microbiome profiles between the two groups were assessed. Each of the models pertaining to diet healthiness, physical exercise, or a combination of both displayed a metabolic and functional microbial signature, with a significant proportion of the metabolites identified as discriminating between the various pairwise comparisons resulting from gut microbe-host cometabolism. Microbial diversity was associated with a combination of high adherence to healthy dietary habits and exercise and was correlated with a distinct array of microbially derived metabolites, including markers of proteolytic activity. Improved control of dietary confounders, through the use of an objective dietary assessment score, has uncovered further insights into the complex, multifactorial relationship between diet, exercise, the gut microbiome, and metabolism. Furthermore, the observation of higher proteolytic activity associated with higher microbial diversity indicates that increased microbial diversity may confer deleterious as well as beneficial effects on the host.

Journal article

Garcia Perez I, Posma JM, Serrano Contreras JI, Boulange C, Chan Q, Frost G, Stamler J, Elliott P, Lindon J, Holmes E, Nicholson Jet al., 2020, Identifying unknown metabolites using NMR-based metabolic profiling techniques, Nature Protocols, Vol: 15, Pages: 2538-2567, ISSN: 1750-2799

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes, but is hindered by a lack of automated annotation and standardised methods for structure elucidation of candidate disease biomarkers. Here, we describe a system for identifying molecular species derived from NMR spectroscopy based metabolic phenotyping studies, with detailed info on sample preparation, data acquisition, and data modelling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as STOCSY, STORM and RED-STORM to identify other signals in the NMR spectra relating to the same molecule. It also utilizes 2D-NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multidimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take two or three days. This approach to biomarker discovery is efficient, cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. Finally, it requires basic understanding of Matlab in order to perform statistical spectroscopic tools and analytical skills to perform Solid Phase Extraction, LC-fraction collection, LC-NMR-MS and 1D and 2D NMR experiments.

Journal article

Posma JM, Garcia Perez I, Frost G, Aljuraiban G, Chan Q, Van Horn L, Daviglus M, Stamler J, Holmes E, Elliott P, Nicholson Jet al., 2020, Nutriome-metabolome relationships provide insights into dietary intake and metabolism, Nature Food, Vol: 1, Pages: 426-436, ISSN: 2662-1355

Dietary assessment traditionally relies on self-reported data which are often inaccurate and may result in erroneous diet-disease risk associations. We illustrate how urinary metabolic phenotyping can be used as alternative approach for obtaining information on dietary patterns. We used two multi-pass 24-hr dietary recalls, obtained on two occasions on average three weeks apart, paired with two 24-hr urine collections from 1,848 U.S. individuals; 67 nutrients influenced the urinary metabotype measured with ¹H-NMR spectroscopy characterized by 46 structurally identified metabolites. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living U.S. cohort and replicated in an independent U.K. cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers are associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to unhealthy metabolic phenotype and hence give entry points for prevention and intervention strategies.

Journal article

Garcia Perez I, Posma JM, Chambers E, Mathers J, Draper J, Beckmann M, Nicholson J, Holmes E, Frost Get al., 2020, Dietary metabotype modelling predicts individual responses to dietary interventions, Nature Food, Vol: 1, Pages: 355-364, ISSN: 2662-1355

Habitual consumption of poor quality diets is linked directly to risk factors for many non-communicable disease. This has resulted in the vast majority of countries globally and the World Health Organisation developing policies for healthy eating to reduce the prevalence of non communicable disease in the population. However, there is mounting evidence of variability in individual metabolic responses to any dietary intervention. We have developed a method for applying a pipeline for understanding inter-individual differences in response to diet, based on coupling data from highly-controlled dietary studies with deep metabolic phenotyping. In this feasibility study, we create an individual Dietary Metabotype Score (DMS) that embodies inter-individual variability in dietary response and captures consequent dynamic changes in concentrations of urinary metabolites. We find an inverse relationship between the DMS and blood glucose concentration. There is also a relationship between the DMS and urinary metabolic energy loss. Furthermore we employ a metabolic entropy approach to visualize individual and collective responses to dietary. Potentially, the DMS offers a method to target and to enhance dietary response at an individual level therefore reducing burden of non communicable diseases at a population level.

Journal article

Andreas NJ, Roy RB, Gomez-Romero M, Horneffer-van der Sluis V, Lewis MR, Camuzeaux SSM, Jimenez B, Posma JM, Tientcheu L, Egere U, Sillah A, Togun T, Holmes E, Kampmann Bet al., 2020, Performance of metabonomic serum analysis for diagnostics in paediatric tuberculosis, Scientific Reports, Vol: 10, Pages: 1-11, ISSN: 2045-2322

We applied a metabonomic strategy to identify host biomarkers in serum to diagnose paediatric tuberculosis (TB) disease. 112 symptomatic children with presumptive TB were recruited in The Gambia and classified as bacteriologically-confirmed TB, clinically diagnosed TB, or other diseases. Sera were analysed using 1H nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Multivariate data analysis was used to distinguish patients with TB from other diseases. Diagnostic accuracy was evaluated using Receiver Operating Characteristic (ROC) curves. Model performance was tested in a validation cohort of 36 children from the UK. Data acquired using 1H NMR demonstrated a sensitivity, specificity and Area Under the Curve (AUC) of 69% (95% confidence interval [CI], 56–73%), 83% (95% CI, 73–93%), and 0.78 respectively, and correctly classified 20% of the validation cohort from the UK. The most discriminatory MS data showed a sensitivity of 67% (95% CI, 60–71%), specificity of 86% (95% CI, 75–93%) and an AUC of 0.78, correctly classifying 83% of the validation cohort. Amongst children with presumptive TB, metabolic profiling of sera distinguished bacteriologically-confirmed and clinical TB from other diseases. This novel approach yielded a diagnostic performance for paediatric TB comparable to that of Xpert MTB/RIF and interferon gamma release assays.

Journal article

Ocvirk S, Wilson AS, Posma JM, Li JV, Koller KR, Day GM, Flanagan CA, Otto JE, Sacco PE, Sacco FD, Sapp FR, Wilson AS, Newton K, Brouard F, DeLany JP, Behnning M, Appolonia CN, Soni D, Bhatti F, Methé B, Fitch A, Morris A, Gaskins HR, Kinross J, Nicholson JK, Thomas TK, O'Keefe SJDet al., 2019, A prospective cohort analysis of gut microbial co-metabolism in Alaska Native and rural African people at high and low risk of colorectal cancer, American Journal of Clinical Nutrition, Vol: 111, Pages: 406-419, ISSN: 0002-9165

BACKGROUND: Alaska Native (AN) people have the world's highest recorded incidence of sporadic colorectal cancer (CRC) (∼91:100,000), whereas rural African (RA) people have the lowest risk (<5:100,000). Previous data supported the hypothesis that diet affected CRC risk through its effects on the colonic microbiota that produce tumor-suppressive or -promoting metabolites. OBJECTIVES: We investigated whether differences in these metabolites may contribute to the high risk of CRC in AN people. METHODS: A cross-sectional observational study assessed dietary intake from 32 AN and 21 RA healthy middle-aged volunteers before screening colonoscopy. Analysis of fecal microbiota composition by 16S ribosomal RNA gene sequencing and fecal/urinary metabolites by 1H-NMR spectroscopy was complemented with targeted quantification of fecal SCFAs, bile acids, and functional microbial genes. RESULTS: Adenomatous polyps were detected in 16 of 32 AN participants, but not found in RA participants. The AN diet contained higher proportions of fat and animal protein and less fiber. AN fecal microbiota showed a compositional predominance of Blautia and Lachnoclostridium, higher microbial capacity for bile acid conversion, and low abundance of some species involved in saccharolytic fermentation (e.g., Prevotellaceae, Ruminococcaceae), but no significant lack of butyrogenic bacteria. Significantly lower concentrations of tumor-suppressive butyrate (22.5 ± 3.1 compared with 47.2 ± 7.3 SEM µmol/g) coincided with significantly higher concentrations of tumor-promoting deoxycholic acid (26.7 ± 4.2 compared with 11 ± 1.9 µmol/g) in AN fecal samples. AN participants had lower quantities of fecal/urinary metabolites than RA participants and metabolite profiles correlated with the abundance of distinct microbial genera in feces. The main microbial and metabolic CRC-associated markers were not significantly altered in

Journal article

Wilson T, Garcia-Perez I, Posma JM, Lloyd AJ, Chambers ES, Tailliart K, Zubair H, Beckmann M, Mathers JC, Holmes E, Frost G, Draper Jet al., 2019, Spot and cumulative urine samples are suitable replacements for 24-hour urine collections for objective measures of dietary exposure in adults using metabolite biomarkers, Journal of Nutrition, Vol: 149, Pages: 1692-1700, ISSN: 0022-3166

BACKGROUND: Measurement of multiple food intake exposure biomarkers in urine may offer an objective method for monitoring diet. The potential of spot and cumulative urine samples that have reduced burden on participants as replacements for 24-h urine collections has not been evaluated. OBJECTIVE: The aim of this study was to determine the utility of spot and cumulative urine samples for classifying the metabolic profiles of people according to dietary intake when compared with 24-h urine collections in a controlled dietary intervention study. METHODS: Nineteen healthy individuals (10 male, 9 female, aged 21-65 y, BMI 20-35 kg/m2) each consumed 4 distinctly different diets, each for 1 wk. Spot urine samples were collected ∼2 h post meals on 3 intervention days/wk. Cumulative urine samples were collected daily over 3 separate temporal periods. A 24-h urine collection was created by combining the 3 cumulative urine samples. Urine samples were analyzed with metabolite fingerprinting by both high-resolution flow infusion electrospray mass spectrometry (FIE-HRMS) and proton nuclear magnetic resonance spectroscopy (1H-NMR). Concentrations of dietary intake biomarkers were measured with liquid chromatography triple quadrupole mass spectrometry and by integration of 1H-NMR data. RESULTS: Cross-validation modeling with 1H-NMR and FIE-HRMS data demonstrated the power of spot and cumulative urine samples in predicting dietary patterns in 24-h urine collections. Particularly, there was no significant loss of information when post-dinner (PD) spot or overnight cumulative samples were substituted for 24-h urine collections (classification accuracies of 0.891 and 0.938, respectively). Quantitative analysis of urine samples also demonstrated the relation between PD spot samples and 24-h urines for dietary exposure biomarkers. CONCLUSIONS: We conclude that PD spot urine samples are suitable replacements for 24-h urine collections. Alternatively, cumulative samples collected overn

Journal article

Lahiri S, Kim H, Garcia-Perez I, Reza MM, Martin KA, Kundu P, Cox LM, Selkrig J, Posma JM, Zhang H, Padmanabhan P, Moret C, Gulyás B, Blaser MJ, Auwerx J, Holmes E, Nicholson J, Wahli W, Pettersson Set al., 2019, The gut microbiota influences skeletal muscle mass and function in mice, Science Translational Medicine, Vol: 11, ISSN: 1946-6234

The functional interactions between the gut microbiota and the host are important for host physiology, homeostasis, and sustained health. We compared the skeletal muscle of germ-free mice that lacked a gut microbiota to the skeletal muscle of pathogen-free mice that had a gut microbiota. Compared to pathogen-free mouse skeletal muscle, germ-free mouse skeletal muscle showed atrophy, decreased expression of insulin-like growth factor 1, and reduced transcription of genes associated with skeletal muscle growth and mitochondrial function. Nuclear magnetic resonance spectrometry analysis of skeletal muscle, liver, and serum from germ-free mice revealed multiple changes in the amounts of amino acids, including glycine and alanine, compared to pathogen-free mice. Germ-free mice also showed reduced serum choline, the precursor of acetylcholine, the key neurotransmitter that signals between muscle and nerve at neuromuscular junctions. Reduced expression of genes encoding Rapsyn and Lrp4, two proteins important for neuromuscular junction assembly and function, was also observed in skeletal muscle from germ-free mice compared to pathogen-free mice. Transplanting the gut microbiota from pathogen-free mice into germ-free mice resulted in an increase in skeletal muscle mass, a reduction in muscle atrophy markers, improved oxidative metabolic capacity of the muscle, and elevated expression of the neuromuscular junction assembly genes <jats:italic>Rapsyn</jats:italic> and <jats:italic>Lrp4</jats:italic>. Treating germ-free mice with short-chain fatty acids (microbial metabolites) partly reversed skeletal muscle impairments. Our results suggest a role for the gut microbiota in regulating skeletal muscle mass and function in mice.</jats:p>

Journal article

Rodriguez-Martinez A, Ayala R, Posma JM, Harvey N, Jiménez B, Sonomura K, Sato T-A, Matsuda F, Zalloua P, Gauguier D, Nicholson JK, Dumas M-Eet al., 2019, pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra, Bioinformatics, Vol: 35, Pages: 1916-1922, ISSN: 1367-4803

Motivation: Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA ("pJRES Binning Algorithm"), which aims to extend the applicability of SRV to pJRES spectra. Results: The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building. Availability: The algorithm is implemented using the MWASTools R/Bioconductor package. Supplementary information: Supplementary data are available at Bioinformatics online.

Journal article

Neves AL, Rodriguez-Martinez A, Ayala R, Posma JM, Abellona U MR, Chilloux J, Nicholson JK, Dumas M-E, Hoyles Let al., 2019, A network-based data-mining approach to investigate indole-related microbiota-host co-metabolism, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>Indoles have been shown to play a significant role in cardiometabolic disorders. While some individual bacterial species are known to produce indole-adducts, to our best knowledge no studies have made use of publicly available genome data to identify prokaryotes, specifically those associated with the human gut microbiota, contributing to the indole metabolic network.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Here, we propose a computational strategy, comprising the integration of KEGG and BLAST, to identify prokaryote-specific metabolic reactions relevant for the production of indoles, as well as to predict new members of the human gut microbiota potentially involved in these reactions. By identifying relevant prokaryotic species for further validation studies<jats:italic>in vitro</jats:italic>, this strategy represents a useful approach for those interrogating the metabolism of other gut-derived microbial metabolites relevant to human health.</jats:p></jats:sec><jats:sec><jats:title>Availability</jats:title><jats:p>All R scripts and files (gut microbial dataset, FASTA protein sequences, BLASTP output files) are available from<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/AndreaRMICL/Microbial_networks">https://github.com/AndreaRMICL/Microbial_networks</jats:ext-link>.</jats:p></jats:sec><jats:sec><jats:title>Contact</jats:title><jats:p>ARM:<jats:email>andrea.rodriguez-martinez13@imperial.ac.uk</jats:email>; LH:<jats:email>lesley.hoyles@ntu.ac.uk</jats:email>.</jats:p></jats:sec>

Working paper

Boulange CL, Rood IM, Posma JM, Lindon JC, Holmes E, Wetzels JFM, Deegens JKJ, Kaluarachchi MRet al., 2019, NMR and MS urinary metabolic phenotyping in kidney diseases is fit-for-purpose in the presence of a protease inhibitor, Molecular Omics, Vol: 15, Pages: 39-49, ISSN: 2515-4184

Nephrotic syndrome with idiopathic membranous nephropathy as a major contributor, is characterized by proteinuria, hypoalbuminemia and oedema. Diagnosis is based on renal biopsy and the condition is treated using immunosuppressive drugs; however nephrotic syndrome treatment efficacy varies among patients. Multi-omic urine analyses can discover new markers of nephrotic syndrome that can be used to develop personalized treatments. For proteomics, a protease inhibitor (PI) is sometimes added at sample collection to conserve proteins but its impact on urine metabolic phenotyping needs to be evaluated. Urine from controls (n = 4) and idiopathic membranous nephropathy (iMN) patients (n = 6) were collected with and without PI addition and analysed using 1H NMR spectroscopy and UPLC-MS. PI-related data features were observed in the 1H NMR spectra but their removal followed by a median fold change normalisation, eliminated the PI contribution. PI-related metabolites in UPLC-MS data had limited effect on metabolic patterns specific to iMN. When using an appropriate data processing pipeline, PI-containing urine samples are appropriate for 1H NMR and MS metabolic profiling of patients with nephrotic syndrome.

Journal article

Chan Q, Lau C-HE, Gibson R, Chekmeneva E, Correia GDS, Loo R, Ebbels TM, Posma JM, Dyer AR, Miura K, Ueshima H, Zhao L, Daviglus ML, Elliott P, Stamler J, Holmes E, Van Horn Let al., 2019, Relationships of Dietary and Supplement Magnesium Intake and Its Urinary Metabolomic Biomarkers With Blood Pressure: The INTERMAP Study, Scientific Sessions of the American-Heart-Association on Epidemiology and Prevention/Lifestyle and Cardiometabolic Health, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322

Conference paper

Posma JM, 2018, Metabolic profiling, Encyclopedia of Bioinformatics and Computational Biology, Editors: Cannataro, Gaeta, Gribskov, Asif Khan, Nakai, Ranganathan, Schonbach

Metabolic profiling involves the measurement of metabolites, small molecules under 1 kDa in weight, in biological fluids such as plasma/serum and urine or in tissue samples using high-throughput spectroscopic and spectrometric technologies. It contributes phenotypic information which cannot be directly obtained from other omic platforms that can be associated with disease diagnosis and prognosis, and provide mechanistic understanding of disease aetiology and drug treatments. This Chapter provides background on the data generated by Nuclear Magnetic Resonance spectroscopy and hyphenated Mass Spectrometry techniques, bioinformatic and statistical methods used to analyse these data and discusses bottlenecks in data processing, analysis and interpretation.

Book chapter

Posma JM, 2018, Multivariate statistical methods for metabolic phenotyping, The Handbook of Metabolic Phenotyping, Editors: Lindon, Holmes, Nicholson, Publisher: Elsevier

The nature of metabolic phenotyping data, where typically more variables are measured than samples are available, requires careful application of statistical methods in order to be able to make meaningful inferences from the data. This Chapter describes different aspects of the multivariate modelling of this type of data, including data transformations and partitioning, unsupervised algorithms for dimension reduction, supervised algorithms for classification, clustering and regression, metrics and methods for obtaining unbiased prediction error estimates of predictive models and statistical spectroscopy tools used for biomarker identification. It focusses on describing methods routinely applied in the field as well as discussing methods that, as computational advancements are made, are poised to become more widely applied to metabolic phenotyping data.

Book chapter

Rodriguez-Martinez A, Ayala R, Posma J, Dumas Met al., 2018, Exploring the Genetic Landscape of Metabolic Phenotypes with MetaboSignal, Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.], ISSN: 1934-3396

Journal article

Posma JM, Garcia Perez I, Ebbels TMD, Lindon JC, Stamler J, Elliott P, Holmes E, Nicholson Jet al., 2018, Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data, Journal of Proteome Research, Vol: 17, Pages: 1586-1595, ISSN: 1535-3893

Metabolism is altered by genetics, diet, disease status, environment and many other factors. Modelling 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) is 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.

Journal article

Garcia-Perez I, Posma JM, Gibson R, Chambers ES, Holmes E, Frost Get al., 2017, Modernazing dietary assessment by use of metabolic profiling, Endocrine Abstracts

Journal article

Garcia Perez I, Posma JM, Gibson R, Chambers ES, Nicholson JK, Holmes E, Frost Get al., 2017, Modernizing dietary assessment, IUNS 21st ICN International Congress of Nutrition, Publisher: Karger Publishers, Pages: 286-287, ISSN: 1421-9697

Conference paper

Rodriguez-Martinez A, Posma JM, Ayala R, Harvey N, Jimenez B, Neves AL, Lindon JC, Sonomura K, Sato T-A, Matsuda F, Zalloua P, Gauguier D, Nicholson JK, Dumas M-Eet al., 2017, J-Resolved (1)H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis., Analytical Chemistry, Vol: 89, Pages: 11405-11412, ISSN: 0003-2700

(1)H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D (1)H NMR spectra, we have investigated the use of 2D J-resolved (JRES) (1)H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the J-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows.

Journal article

Rodriguez Martinez A, Posma JM, Ayala R, Neves AL, Anwar M, Petretto E, Emanueli C, Gauguier D, Nicholson JK, Dumas Met al., 2017, MWASTools: an R/Bioconductor package for metabolome-wide association studies, Bioinformatics, Vol: 34, Pages: 890-892, ISSN: 1367-4803

Summary: MWASTools is an R package designed to provide an integrated pipeline to analyze metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of MWAS results.Availability: The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/

Journal article

Garcia Perez I, Posma JM, Gibson R, Chambers ES, Hansen TH, Vestergaard H, Hansen T, Beckmann M, Pedersen O, Elliott P, Stamler J, Nicholson JK, Draper J, Mathers JC, Holmes E, Frost Get al., 2017, Objective assessment of dietary patterns using metabolic phenotyping: a randomized, controlled, crossover trial, The Lancet Diabetes & Endocrinology, Vol: 5, Pages: 184-195, ISSN: 2213-8587

Background: The burden of non-communicable diseases, such as obesity, diabetes, coronary heart disease and cancer, can be reduced by the consumption of healthy diets. Accurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, many of them influenced by food intake. We aim to classify people according to dietary behaviour and enhance dietary reporting using metabolic profiling of urine.Methods: To develop metabolite models from 19 healthy volunteers who attended a clinical research unit for four day periods on four occasions. We used the World Health Organisation’s healthy eating guidelines (increase fruits, vegetables, wholegrains, dietary fibre and decrease fats, sugars, and salt) to develop four dietary interventions lasting for four days each that ranged from a diet associated with a low to high risk of developing non-communicable disease. Urine samples were measured by 1H-NMR spectroscopy. This study is registered as an International Standard Randomized Controlled Trial, number ISRCTN 43087333. INTERMAP U.K. (n=225) and a healthy-eating Danish cohort (n=66) were used as free-living validation datasets.Findings: There was clear separation between the urinary metabolite profiles of the four diets. We also demonstrated significant stepwise differences in metabolite levels between the lowest and highest metabolic risk diets and developed metabolite models for each diet. Application of the derived metabolite models to independent cohorts confirmed the association between urinary metabolic and dietary profiles in INTERMAP (P<0•001) and the Danish cohort (P<0•001).Interpretation: Urinary metabolite models, developed in a highly controlled environment, can classify groups of free-living people into consumers of dietary profiles associated with lower or higher non-communicable disease risk based on multivariate m

Journal article

Posma JM, Garcia Perez I, Heaton JC, Burdisso P, Mathers JC, Draper J, Lewis M, Lindon JC, Frost G, Holmes E, Nicholson JKet al., 2017, An integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers, Analytical Chemistry, Vol: 89, Pages: 3300-3309, ISSN: 1086-4377

A major purpose of exploratory metabolic profiling is for the identification of molecular species that are statistically associated with specific biological or medical outcomes; unfortunately the structure elucidation process of unknowns is often a major bottleneck in this process. We present here new holistic strategies that combine different statistical spectroscopic and analytical techniques to improve and simplify the process of metabolite identification. We exemplify these strategies using study data collected as part of a dietary intervention to improve health and which elicits a relatively subtle suite of changes from complex molecular profiles. We identify three new dietary biomarkers related to the consumption of peas (N-methyl nicotinic acid), apples (rhamnitol) and onions (N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide) that can be used to enhance dietary assessment and assess adherence to diet. As part of the strategy, we introduce a new probabilistic statistical spectroscopy tool, RED-STORM (Resolution EnhanceD SubseT Optimization by Reference Matching), that uses 2D J-resolved ¹H-NMR spectra for enhanced information recovery using the Bayesian paradigm to extract a subset of spectra with similar spectral signatures to a reference. RED-STORM provided new information for subsequent experiments (e.g. 2D-NMR spectroscopy, Solid-Phase Extraction, Liquid Chromatography prefaced Mass Spectrometry) used to ultimately identify an unknown compound. In summary, we illustrate the benefit of acquiring J-resolved experiments alongside conventional 1D ¹H-NMR as part of routine metabolic profiling in large datasets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resource.

Journal article

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