Korrigan science Ltd.

Maidenhead, United Kingdom

Korrigan science Ltd.

Maidenhead, United Kingdom
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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.


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.


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.


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.


Lewis M.R.,Imperial College London | Pearce J.T.M.,Imperial College London | Spagou K.,Imperial College London | Green M.,Waters Corporation | And 16 more authors.
Analytical Chemistry | Year: 2016

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided. © 2016 American Chemical Society.


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.


Jimenez B.,Imperial College London | Mirnezami R.,Imperial College London | Kinross J.,Imperial College London | Cloarec O.,Imperial College London | And 7 more authors.
Journal of Proteome Research | Year: 2013

Colorectal cancer (CRC) is a major cause of morbidity and mortality in developed countries. Despite operative advances and the widespread adoption of combined-modality treatment, the 5-year survival rarely exceeds 60%. Improving our understanding of the biological processes involved in CRC development and progression will help generate new diagnostic and prognostic approaches. Previous studies have identified altered metabolism as a common feature in carcinogenesis, and quantitative measurement of this altered activity (metabonomics/metabolomics) has the potential to generate novel metabolite-based biomarkers for CRC diagnosis, staging and prognostication. In the present study we applied high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy to analyze metabolites in intact tumor samples (n = 83) and samples of adjacent mucosa (n = 87) obtained from 26 patients undergoing surgical resection for CRC. Orthogonal partial least-squares discriminant analysis (OPLS-DA) of metabolic profiles identified marked biochemical differences between cancer tissue and adjacent mucosa (R2 = 0.72; Q2 = 0.45; AUC = 0.91). Taurine, isoglutamine, choline, lactate, phenylalanine, tyrosine (increased concentrations in tumor tissue) together with lipids and triglycerides (decreased concentrations in tumor tissue) were the most discriminant metabolites between the two groups in the model. In addition, tumor tissue metabolic profiles were able to distinguish between tumors of different T- and N-stages according to TNM classification. Moreover, we found that tumor-adjacent mucosa (10 cm from the tumor margin) harbors unique metabolic field changes that distinguish tumors according to T- and N-stage with higher predictive capability than tumor tissue itself and are accurately predictive of 5-year survival (AUC = 0.88), offering a highly novel means of tumor classification and prognostication in CRC. © 2012 American Chemical Society.


PubMed | Korrigan science Ltd., Imperial College London, Somerset House, University of Reading and 3 more.
Type: Journal Article | Journal: The ISME journal | Year: 2015

The postnatal environment, including factors such as weaning and acquisition of the gut microbiota, has been causally linked to the development of later immunological diseases such as allergy and autoimmunity, and has also been associated with a predisposition to metabolic disorders. We show that the very early-life environment influences the development of both the gut microbiota and host metabolic phenotype in a porcine model of human infants. Farm piglets were nursed by their mothers for 1 day, before removal to highly controlled, individual isolators where they received formula milk until weaning at 21 days. The experiment was repeated, to create two batches, which differed only in minor environmental fluctuations during the first day. At day 1 after birth, metabolic profiling of serum by (1)H nuclear magnetic resonance spectroscopy demonstrated significant, systemic, inter-batch variation which persisted until weaning. However, the urinary metabolic profiles demonstrated that significant inter-batch effects on 3-hydroxyisovalerate, trimethylamine-N-oxide and mannitol persisted beyond weaning to at least 35 days. Batch effects were linked to significant differences in the composition of colonic microbiota at 35 days, determined by 16 S pyrosequencing. Different weaning diets modulated both the microbiota and metabolic phenotype independently of the persistent batch effects. We demonstrate that the environment during the first day of life influences development of the microbiota and metabolic phenotype and thus should be taken into account when interrogating experimental outcomes. In addition, we suggest that intervention at this early time could provide metabolic rescue for at-risk infants who have undergone aberrant patterns of initial intestinal colonisation.


PubMed | Korrigan science Ltd., Imperial College London, University of Oxford and Waters Corporation
Type: Journal Article | Journal: Analytical chemistry | Year: 2016

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.

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