1. NMR and DGGE on faecal waters from UC and IBS patients Introduction Institute of Food Research, Norwich Research Park, Colney, Norwich, NR4 7UA, UK Results Conclusions We thank the Ministry of Higher Education of Saudi Arabia and the BBSRC as well as Louise Scovell and Jamieson Crawford from the Norfolk and Norwich University Hospital Univariate analysis highlighted several markers for UC (50 buckets/138 have significant p values) and IBS (37buckets/138). These include taurine, glucose, cadaverine, putrescine, lactate, 2- methylbutyrate, acetate and several more Hierarchical clustering displayed some degree of conservation in the individuals’ profile as the intra-individual variability was lower than the inter-variability for both NMR and DGGE data (data not shown). Strikingly, a high correlation is observed between the DGGE and the NMR data suggesting a strong link between the metabolite composition of the faecal waters and the microbial community detected by DGGE (see 1). It is possible to model of UC, IBS and the healthy groups but the UC group is more readily separated then the IBS (2) Samah Noor, Karyn Ridgway, Arjan Narbad, Ian. J. Colquhoun, Kate Kemsley, and Gwénaëlle Le Gall NMR assignment Nuclear Magnetic Resonance (NMR)-based metabolomics and PCR-Denaturating Gradient Gel Electrophoresis (DGGE) were used to investigate the changes in bacterial presence and activity in faecal extracts from UC and IBS patients compared to healthy controls The causes of ulcerative colitis (UC) and irritable bowel syndrome (IBS) are not fully understood, however, gut microbiota has been suggested as an important factor in the pathogenesis of these conditions. Consequently, the metabolic activity of gut bacteria might also be altered Variable width bucketing was used to quantify the metabolite signals from the 1D spectra of 124 samples (1-4 separate collections from 22 controls, 11 IBS patients and 13 UC patients). The data were subsequently analysed by univariate and multivariate analyses 1 H- 13 C HSQC NMR spectrum of faecal extracts acquired at 600 MHz. One UC spectrum (green) and one control spectrum (red) are displayed 1, Butyrate; 2, Propionate; 3, Valine; 4, Leucine; 5, Isoleucine; 6, Threonine; 7, Isobutyrate; 8, Iso valerate; 9, Valetate; 10, Caproate; 11, Heptanoate?; 12, Alanine; 13, Lysine; 14, Arginine; 15, Acetate; 16, Glutamate; 17, Aspartate; 18, Glycine; 19, 3-phenyl- propionate; 20, Phloretate; 21, Tyrosine; 22, Phenylalanine; 23, Trytophan; 24, Uridine; 25, Succinate; 26, Trimethylamine; 27, Glycerol; 28, Serine; 29, Taurine; 30, Cadaverine; 31, Putrescien; 32, Lactate; 33, Ethanol;34, Glucose; 35, 5- aminopentanoate; 36, Methionine;37, Glutamine; 38, 2-methylbutyrate; 39, Proline;40, Ethanolamine; 41, Choline; 42, 5-N-acetyl neuraminate; + cystine, fucose, galactose… 3. Selection of metabolite markers Taurine (3.25 ppm) p 2e-6 p 1e-4 Bile acids (0.71 ppm) PCR-DGGE (a) DGGE profiles of faecal samples obtained from IBS, UC and control subjects showing the number of bands corresponding to amplicons of the V3 region of 16S rRNA of faecal bacteria. (b) Box and whiskers plot presenting the median and range of band counts of all faecal samples taken from UC, IBS, and controls. (notches that do not overlap have different medians at the 5% significance level) 1. High correlation between DGGE and NMR profiles R=0.85 P<0.002 controls UC IBS Canonical Correlation Analysis (CVA) between NMR and DGGE data sets 2. UC and IBS patients separate from healthy volunteers CVA of combined (“fused”) DGGE and NMR (using 16 PLS factors; cross-validated by person; success rate of 67% comparable the PLS-LDA model) The study reveals that the DGGE and NMR-based metabolomics are two global approaches that can be combined to give a new insight of the biological events associated with irritable bowel diseases (IBD) There are differences in the microbial and metabolite compositions of faecal extracts from healthy volunteers, IBS and UC patients suggesting microflora might play a role in the disease aetiology The inter-individual variability is large. Larger cohorts could help better define the IBD conditions gwenaelle.legall@bbsrc.ac.uk