Merrifield C.A.,Imperial College London |
Lewis M.C.,University of Bristol |
Claus S.P.,Imperial College London |
Claus S.P.,University of Reading |
And 11 more authors.
Gut | Year: 2013
Background: The process of weaning causes a major shift in intestinal microbiota and is a critical period for developing appropriate immune responses in young mammals. Objective: To use a new systems approach to provide an overview of host metabolism and the developing immune system in response to nutritional intervention around the weaning period. Design: Piglets (n=14) were weaned onto either an egg-based or soya-based diet at 3 weeks until 7 weeks, when all piglets were switched onto a fish-based diet. Half the animals on each weaning diet received Bifidobacterium lactis NCC2818 supplementation from weaning onwards. Immunoglobulin production from immunologically relevant intestinal sites was quantified and the urinary 1H NMR metabolic profile was obtained from each animal at post mortem (11 weeks). Results: Different weaning diets induced divergent and sustained shifts in the metabolic phenotype, which resulted in the alteration of urinary gut microbial co-metabolites, even after 4 weeks of dietary standardisation. B lactis NCC2818 supplementation affected the systemic metabolism of the different weaning diet groups over and above the effects of diet. Additionally, production of gut mucosa-associated IgA and IgM was found to depend upon the weaning diet and on B lactis NCC2818 supplementation. Conclusion: The correlation of urinary 1H NMR metabolic profile with mucosal immunoglobulin production was demonstrated, thus confirming the value of this multiplatform approach in uncovering non-invasive biomarkers of immunity. This has clear potential for translation into human healthcare with the development of urine testing as a means of assessing mucosal immune status. This might lead to early diagnosis of intestinal dysbiosis and with subsequent intervention, arrest disease development. This system enhances our overall understanding of pathologies under supra-organismal control. Source
Mirnezami R.,Imperial College London |
Spagou K.,Imperial College London |
Vorkas P.A.,Imperial College London |
Lewis M.R.,Imperial College London |
And 10 more authors.
Molecular Oncology | Year: 2014
Matrix-assisted laser desorption ionisation imaging mass spectrometry (MALDI-MSI) is a rapidly advancing technique for intact tissue analysis that allows simultaneous localisation and quantification of biomolecules in different histological regions of interest. This approach can potentially offer novel insights into tumour microenvironmental (TME) biochemistry. In this study we employed MALDI-MSI to evaluate fresh frozen sections of colorectal cancer (CRC) tissue and adjacent healthy mucosa obtained from 12 consenting patients undergoing surgery for confirmed CRC. Specifically, we sought to address three objectives: (1) To identify biochemical differences between different morphological regions within the CRC TME; (2) To characterise the biochemical differences between cancerous and healthy colorectal tissue using MALDI-MSI; (3) To determine whether MALDI-MSI profiling of tumour-adjacent tissue can identify novel metabolic 'field effects' associated with cancer. Our results demonstrate that CRC tissue harbours characteristic phospholipid signatures compared with healthy tissue and additionally, different tissue regions within the CRC TME reveal distinct biochemical profiles. Furthermore we observed biochemical differences between tumour-adjacent and tumour-remote healthy mucosa. We have referred to this 'field effect', exhibited by the tumour locale, as cancer-adjacent metaboplasia (CAM) and this finding builds on the established concept of field cancerisation. © 2013 Federation of European Biochemical Societies. Source
Cloarec O.,Korrigan science Ltd.
Journal of Chemometrics | Year: 2011
This paper presents a modified version of the NIPALS algorithm for PLS regression with one single response variable. This version, denoted a CF-PLS, provides significant advantages over the standard PLS. First of all, it strongly reduces the over-fit of the regression. Secondly, R 2 for the null hypothesis follows a Beta distribution only function of the number of observations, which allows the use of a probabilistic framework to test the validity of a component. Thirdly, the models generated with CF-PLS have comparable if not better prediction ability than the models fitted with NIPALS. Finally, the scores and loadings of the CF-PLS are directly related to the R 2, which makes the model and its interpretation more reliable. Copyright © 2011 John Wiley & Sons, Ltd. Source
Fonville J.M.,Imperial College London |
Carter C.,University of Birmingham |
Cloarec O.,Imperial College London |
Cloarec O.,Korrigan science Ltd. |
And 4 more authors.
Analytical Chemistry | Year: 2012
Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) provides localized information about the molecular content of a tissue sample. To derive reliable conclusions from MSI data, it is necessary to implement appropriate processing steps in order to compare peak intensities across the different pixels comprising the image. Here, we review commonly used normalization methods, and propose a rational data processing strategy, for robust evaluation and modeling of MSI data. The approach includes newly developed heuristic methods for selecting biologically relevant peaks and pixels to reduce the size of a data set and remove the influence of the applied MALDI matrix. The methods are demonstrated on a MALDI MSI data set of a sagittal section of rat brain (4750 bins, m/z = 50-1000, 111 111× 185 pixels) and the proposed preferred normalization method uses the median intensity of selected peaks, which were determined to be independent of the MALDI matrix. This was found to effectively compensate for a range of known limitations associated with the MALDI process and irregularities in MS image sampling routines. This new approach is relevant for processing of all MALDI MSI data sets, and thus likely to have impact in biomarker profiling, preclinical drug distribution studies, and studies addressing underlying molecular mechanisms of tissue pathology. © 2011 American Chemical Society. Source
Cloarec O.,Korrigan science Ltd.
Journal of Chemometrics | Year: 2014
Over-fitting in multivariate regression is often viewed as the consequence of the number of variables. However, it is almost counterintuitive that the number of variables used to fit a regression model increases the risk of over-fitting instead of adding useful information. In this paper, we will be discussing the source of over-fitting and ways of reducing it during the computation of partial least squares (PLS) components. A close look at the linear algebra used for PLS component calculation will highlight hints of the origin of over-fitting. Simulation of multivariate datasets will explore the influence of noise, number of variables and complexity of the underlying latent variable structure on over-fitting. A tentative solution to overcome the identified problem will be presented, and a new PLS algorithm will be proposed. Finally, the properties of this new algorithm will be explored. © 2014 John Wiley & Sons, Ltd. Source