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Gut microbiota
reference. LoA 2: MS/MS precursor and product ions or 1D +2D NMR chemical shifts and multiplicity match to a reference database or literature to putatively annotate compound. LoA 3: chemical shift ( δ ) and multiplicity matches a reference database to tentatively assign the compound (for further details see online supplementary methods). Twitter Follow Orla O ’ Sullivan @OrlaOS Acknowledgements The authors express gratitude to all participants for the donation of time and samples, in particular staff and players at the Irish Rugby Football Union. Fiona Fouhy for insight provided into the library preparation of metagenomic sequencing. The authors thank the Imperial-National Institute for Health Research (NIHR) Clinical Phenome Centre for support. Contributors WB prepared DNA samples for metagenomic sequencing. OOS and WB processed and analysed the metagenomic data. EH, IGP and NCP performed metabolomic processing and statistical analysis thereof. FS, PDC, OOS and WB devised experimental design and approach. FS, PDC, OC, OOS, MGM, EH, NCP and WB wrote manuscript. Results discussed by all authors. Funding This research was funded by Science Foundation Ireland in the form of a centre grant (APC Microbiome Institute Grant Number SFI/12/RC/2273). Research in the Cotter laboratory is funded by SFI through the PI award, ‘ Obesibiotics ’ (11/PI/ 1137). OOS and WB are funded by Science Foundation Ireland through a Starting Investigator Research Grant award (13/SIRG/2160). Nicholas Penney is funded by the Diabetes Research and Wellness Foundation through the Sutherland-Earl Clinical Research Fellowship 2015. The centre is supported by the NIHR Imperial Biomedical Research Centre based at Imperial College Healthcare National Health Service (NHS) Trust and Imperial College London. Disclaimer The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Competing interests FS is a founder shareholder in Atlantia Food Clinical Trials, Tucana Health and Alimentary Health. He is director of the APC Microbiome Institute , a research centre funded in part by Science Foundation Ireland (APC/SFI/ 12/RC/2273) and which is/has recently been in receipt of research grants from Abbvie, Alimentary Health, Cremo, Danone, Janssen, Friesland Campina, General Mills, Kerry, MeadJohnson, Nutricia, 4D pharma and Second Genome, Sigmoid pharma. Ethics approval Cork Clinical Research Ethics Committee. Provenance and peer review Not commissioned; externally peer reviewed. Data sharing statement In conformation of data accessibility protocol, metagenomic raw sequence data from this study are deposited in EMBL BNucleotide Sequence Database (ENA) (http://www.ebi.ac.uk/ena/data/), accession number PRJEB15388. REFERENCES 1 Harkin A. Muscling in on depression. N Engl J Med 2014;371:2333 – 4. 2 Benatti FB, Pedersen BK. Exercise as an anti-in fl ammatory therapy for rheumatic diseases-myokine regulation. Nat Rev Rheumatol 2015;11:86 – 97. 3 Hawley JA, Krook A. Metabolism: one step forward for exercise. Nat Rev Endocrinol 2016;12:7 – 8. 4 Hoffman-Goetz L, Pervaiz N, Packer N, et al . Freewheel training decreases pro- and increases anti-in fl ammatory cytokine expression in mouse intestinal lymphocytes. Brain Behav Immun 2010;24:1105 – 15. 5 Barton W, Shanahan F, Cotter PD, et al . The metabolic role of the microbiota. Clin Liver Dis 2015;5:91 – 3. 6 Szuhany KL, Bugatti M, Otto MW. A meta-analytic review of the effects of exercise on brain-derived neurotrophic factor. J Psychiatr Res 2015;60:56 – 64. 7 Ryan SM, Nolan YM. Neuroin fl ammation negatively affects adult hippocampal neurogenesis and cognition: can exercise compensate? Neurosci Biobehav Rev 2016;61:121 – 31. 8 Johannesson E, Simrén M, Strid H, et al . Physical activity improves symptoms in irritable bowel syndrome: a randomized controlled trial. Am J Gastroenterol 2011;106:915 – 22. 9 Robsahm TE, Aagnes B, Hjårtaker A, et al . Body mass index, physical activity, and colorectal cancer by anatomical subsites: a systematic review and meta-analysis of cohort studies. Eur J Cancer Prev 2013;22:492 – 505. 10 Schwingshackl L, Missbach B, Dias S, et al . Impact of different training modalities on glycaemic control and blood lipids in patients with type 2 diabetes: a systematic review and network meta-analysis. Diabetologia 2014;57:1789 – 97. 11 Biswas A, Oh PI, Faulkner GE, et al . Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis. Ann Intern Med 2015;162:123 – 32.
Metagenomic statistical and bioinformatic analysis Delivered raw FASTQ sequence fi les were quality checked as follows: contaminating sequences of human origin were fi rst removed through the NCBI Best Match Tagger (BMTagger). Poor-quality and duplicate read removal, as well as trimming was implemented using a combination of SAM (sequence align- ment map) and Picard tools. Processing of raw sequence data produced a total of 2 803 449 392 fi ltered reads with a mean read count of 32 598 248.74 (±10 639 447 SD) per each of the 86 samples. These re fi ned reads were then subjected to func- tional pro fi ling by the most recent iteration of the Human Microbiome Project Uni fi ed Metabolic Analysis Network (HUMAnN2 V.0.5.0) pipeline. 44 The functional pro fi ling per- formed by HUMAnN2 composed tabulated fi les of microbial metabolic pathway abundance and coverage derived from the Metacyc database. 45 Microbial pathway data were statistically analysed in the R software environment (V.3.2.2) (for further details see online supplementary methods) (R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2012). 2015. http://www.R-project.org). All presented p values were corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate (pFDR) method. 46 Metabolic pro fi ling Urine and faecal samples were prepared for metabonomic ana- lysis as previously described. 47 48 Using established methods, urine samples underwent 1 H-NMR, RP and HILIC chromatog- raphy pro fi ling experiments. Faecal samples underwent 1 H-NMR, HILIC and bile acid UPLC-MS pro fi ling experiments and GC-MS-targeted SCFA analysis. 48 – 50 After data preprocessing, 51 the resulting 1 H-NMR and LC-MS data sets were imported into SIMCA 14.1 (Umetrics) to conduct multivariate statistical analysis. PCA, followed by OPLS-DA, was performed to examine the data sets and to observe clustering in the results according to the prede fi ned classes. The OPLS-DA models in this study were established based on one PLS component and one orthogonal component. Unit variance scaling was applied to 1 H-NMR data, Pareto scaling was applied to MS data. The fi t and predictability of the models obtained were determined by the R 2 Y and Q 2 Y values, respectively. Signi fi cant metabolites were obtained from LC-MS OPLS-DA models through division of the regression coef fi cients by the jack-knife interval SE to give an estimate of the t-statistic. Variables with a t-statistic ≥ 1.96 (z-score, corresponding to the 97.5 percentile) were considered signi fi cant. Signi fi cant metabo- lites were obtained from 1 H-NMR OPLS-DA models after inves- tigating correlations with correlation coef fi cients values higher than 0.4. Univariate statistical analysis (Mann-Whitney U test) was used to examine the SCFA data set. p values were adjusted for multiple testing using the pFDR method. Con fi rmation of metabolite identities in the NMR data was obtained using 1D 1 HNMR and 2D 1 H- 1 HNMR and 1 H- 13 C NMR experiments. In addition, statistical tools such as SubseT Optimization by Reference Matching (STORM) and Statistical TOtal Correlation SpectroscopY (STOCSY) were also applied. 52 53 Con fi rmation of metabolites identities in the LC-MS data was obtained using tandem MS (MS/MS) on selected target ions. Metabolite identi fi cation was characterised by a level of assignment (LoA) score that describes how the identi fi cation was made. 54 The levels used were as follows: LoA 1: identi fi ed compound, con fi rmed by comparison to an authentic chemical
BartonW, et al . Gut 2017; 0 :1 – 9. doi:10.1136/gutjnl-2016-313627
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