Understanding and Engineering the Factors That Control Fatty Acid Biosynthesis in Escherichia Coli

Total Page:16

File Type:pdf, Size:1020Kb

Understanding and Engineering the Factors That Control Fatty Acid Biosynthesis in Escherichia Coli UNDERSTANDING AND ENGINEERING THE FACTORS THAT CONTROL FATTY ACID BIOSYNTHESIS IN ESCHERICHIA COLI DANIELLE GALLAGHER IMPERIAL COLLEGE LONDON, DEPARTMENT OF LIFE SCIENCES THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY OF THE UNIVERSITY OF LONDON, IMPERIAL COLLEGE LONDON 2018 I declare that this thesis is my own work and that it has not been submitted anywhere for any award. Where other sources of information have been used, they have been acknowledged. 1 Copyright Declaration The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work 2 ABSTRACT This research identified and characterised several genetic and environmental factors that contribute to controlling the metabolic flux through fatty acid synthesis (FAS) in Escherichia coli, to enable high yield in production of fatty acids (FA) which can be further processed towards industrially relevant commodity products. Due to their essentiality to growth, high energy and carbon investment, E. coli have evolved several mechanisms that tightly coordinate FAS with phospholipid synthesis and the energy status of the cell. This makes the outcome of systematic engineering manipulations difficult to predict, but is an area of research that benefits greatly from computationally driven solutions that can be gained from modelling metabolism. In order to model metabolism effectively, parameters of the kinetic system must be obtained and enable subsequent associations with in vivo metabolism and physiology. To adequately make this association, mathematical formulas describing biochemical pathways must account for the control and regulation the system is subject to, so that metabolic engineers can design towards specific phenotypes and product yield. This work investigated FAS regulation under a range of perturbations and how the system responds in vivo to these changes, to gain insight into the control and regulations that are in place during fluctuating conditions. Furthermore, the experiments of this study found adaptive evolution to FA to be a promising strategy in complementing a directed engineering approach in E. coli, in order to adapt to the burden that presents itself during FA overproduction. As a directed approach towards FA overproduction, three novel bypass routes to malonyl-CoA production were also investigated, and found to improve in vivo rates of malonyl-CoA. However, improving this rate-limiting step alone was not sufficient for to overcome the native regulatory and energetic limitations that are present in FAS. The bypass routes were therefore combined with specific process optimisations identified during this research, which was found to improve yield compared to non-optimised cultivations. 3 ACKNOWLEDGEMENTS First and foremost, I would like to thank my supervisor Dr Patrik Jones, for giving me the opportunity to work in the MME lab, for his collaboration and the discussions that went into shaping this research and for the support it took to complete. Thanks to Paulina for the lab chats, Queenie for being a kind and funny bench mate, John and Ian for reading and giving valuable feedback on the thesis and being supportive in answering many questions over the years. My thanks also go to James Mansfield for his support and guidance during the work conducted at the bioreactor suite, and Mark Bennet for technical support of the LC-MS work. I am thankful to my PRP panel Prof Anne Dell and Prof Mark Isalan for their invaluable feedback in the beginning and throughout the milestones of this PhD. To my student supervisions over the last three years for their invaluable discussions around this research. Special thanks to Dr. Danielle Belgrave for being a wonderful mentor and friend to me at Imperial, and for the inspiring lunch time retreats. I am thankful for my family who have shaped me as a person, and given me my perspective on life. Páidín, Mícheál and Alex, thank you for the happy memories and the humour that kept me going here. I am so lucky and proud to have you as my brothers. My mother Mary, thank you for everything you’ve given and done for me over my whole life, for supporting, guiding and consoling me over the last three years - for being my friend and for motivating me. My father Danny, thank you for always encouraging my interests, for not letting me give up, and for giving me a healthy dose of ambition my whole life. I could never thank you enough for giving me the foundations to pursue this career. To my other family, my framily in London and Ireland, thank you for the canal cans, Toy Shows, critical masses, emergency pints, birthdays, Christmas curries, soul- soothing brunches and stitch and bitch nights. Thanks for keeping it light and for simply being around over the years. Your friendships have meant the world to me. Thanks to Lia my furry writing companion for the cuddles and scratches. 4 Finally, an infinite gratitude and love to Jimmer, mo chroí, mo solas ’s mo shaol. Thank you for brightening my life and bringing me so much joy. I could not have finished this PhD without you. The words of this thesis are dedicated to you, our life together and our future. Is tú mo chúis <3 5 TABLE OF CONTENTS Abstract .............................................................................................................................. 3 Acknowledgements ............................................................................................................ 4 Table of contents ................................................................................................................ 6 List of figures .................................................................................................................... 10 List of tables ..................................................................................................................... 17 Abbreviations ................................................................................................................... 17 1 General Introduction ..................................................................................................... 20 1.1 Motivation and background .................................................................................... 20 1.2 Microbial commodity chemical production ............................................................. 26 1.3 Renewable fatty acid synthesis ............................................................................... 29 1.4 Fatty acid biosynthesis and degradation pathways in E. coli .................................... 31 1.5 The regulation of fatty acid synthesis in E. coli ........................................................ 35 1.6 Phospholipid and cell membrane synthesis ............................................................. 41 1.7 The contribution of central carbon metabolism toward fatty acid synthesis ........... 43 1.8 The use of predictive modelling to guide and inform fatty acid research ................. 46 1.9 Current practices and yields in engineering E. coli for fatty acid overproduction ..... 49 1.10 Objective of research ............................................................................................ 53 2 Materials and Methods ................................................................................................. 55 2.1 Media preparation and buffers ............................................................................... 55 2.2 Microbiological techniques ..................................................................................... 56 Sterile technique ............................................................................................................... 56 Frozen glycerol stocks ....................................................................................................... 56 Strains ............................................................................................................................... 56 Batch cultivation conditions ............................................................................................. 57 Continuous cultivation in 1.5L bioreactor ........................................................................ 58 Continuous cultivation in turbidostats ............................................................................. 59 Preparation of competent cells ........................................................................................ 60 Transformation of E. coli .................................................................................................. 60 6 Quantification of cell culture density ............................................................................... 60 Growth assays ................................................................................................................... 61 2.3 Molecular biological techniques .............................................................................. 61 Plasmids ............................................................................................................................ 61 Primers .............................................................................................................................
Recommended publications
  • A Study on Hierarchical Clustering for Identifying Asthma Endotypes (J4R/ Volume 03 / Issue 05 / 004)
    Journal for Research | Volume 03| Issue 05 | July 2017 ISSN: 2395-7549 A Study on Hierarchical Clustering for Identifying Asthma Endotypes S. Poorani Dr. P. Balasubramanie Assistant Professor Professor Department of Computer Technology Department of Computer Technology Kongu Engineering College, Perundurai, India Kongu Engineering College, Perundurai, India N. Sasipriyaa Assistant Professor Department of Computer Science and Engineering Kongu Engineering College, Perundurai, India Abstract Large multitudes of medical data are attained due to advancement in recent technology. These large volumes comprise valuable information which helps for diagnosing diseases. Data mining techniques can be used to extract useful patterns from these mass data. It provides a user- oriented approach to the novel and hidden patterns in the data. One of the major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis data. Healthcare system needs an automated tool that identifies and disseminates relevant healthcare information. Asthma is one of the dominant disease all over the world. Identifying subtypes of the disease will help in the treatment and prevention approaches. In this paper a clustering model is proposed based on observable symptoms of asthma. The hierarchical clustering technique is used to group the samples based on important characteristics of asthma. Keywords: Data Mining, Hierarchical Clustering, Asthma, Health Care _______________________________________________________________________________________________________ I. INTRODUCTION There are 300 million asthmatics worldwide with 1/10th of those living in India. A recent review about epidemiological studies showed that the asthma prevalence among children was 7.24%[1]. Recently the Global Initiative for Asthma (GINA) defines asthma as a heterogeneous disease usually characterized by chronic airway inflammation.
    [Show full text]
  • Towards Using AI to Augment Human Support in Digital Mental Healthcare
    Towards Using AI to Augment Human Support in Digital Mental Healthcare Prerna Chikersal Abstract School of Computer Science To address the need for more access to, and increase Carnegie Mellon University, US the effectiveness of, mental health treatment, internet- [email protected] delivered psychotherapy programs such as iCBT are shown to achieve clinical outcomes comparable to face- Gavin Doherty to-face therapy. While offering ubiquitous access to School of Computer Science & Statistics healthcare, a key concern of digital therapy programs is Trinity College Dublin, IRL to sustain users’ engagement with the treatment to [email protected] attain desired benefits. Research has demonstrated that including a trained ‘human supporter’ to the digital Anja Thieme mental health ecosystem can provide useful guidance Healthcare Intelligence and motivation to its users, and lead to more effective Microsoft Research Cambridge, UK outcomes than unsupported interventions. Within this [email protected] context, we describe early research that makes use of machine learning (ML) approaches to better understand how the behaviors of these human supporters may benefit the mental health outcomes of clients; and how such effects could be maximized. We discuss new opportunities for augmenting human support through personalization, and the related ethical challenges. Author Keywords Mental health; digital behavioral intervention; ethics; responsible AI; machine learning; NLP; data mining. CSS Concepts •Human-centered computing → Human computer Submission
    [Show full text]
  • Atopic Dermatitis and Respiratory Allergy: What Is the Link
    Curr Derm Rep (2015) 4:221–227 DOI 10.1007/s13671-015-0121-6 ATOPIC DERMATITIS (C FLOHR, SECTION EDITOR) Atopic Dermatitis and Respiratory Allergy: What is the Link Danielle C. M. Belgrave1,2 & Angela Simpson1 & Iain E. Buchan2 & Adnan Custovic1 Published online: 28 September 2015 # Springer Science+Business Media New York 2015 Abstract Understanding the aetiology and progression of Introduction atopic dermatitis and respiratory allergy may elucidate early preventative and management strategies aimed towards reduc- Understanding the aetiology and progression of atopic derma- ing the global burden of asthma and allergic disease. In this titis and respiratory allergy may elucidate early preventative article, we review the current opinion concerning the link and management strategies aimed towards reducing the global between atopic dermatitis and the subsequent progression of burden of asthma and allergic disease [1, 2]. There is increas- respiratory allergies during childhood and into early adoles- ing interest in determining the causes of atopic dermatitis and cence. Advances in machine learning and statistical method- respiratory allergies, especially in the light of the increasing ology have facilitated the discovery of more refined defini- prevalence and economic burden associated with these condi- tions of phenotypes for identifying biomarkers. Understand- tions. Globally, up to 20 % of children suffer from atopic ing the role of atopic dermatitis in the development of respi- dermatitis [3]. According to the 2014 Global Asthma Report, ratory allergy may ultimately allow us to determine more ef- it is estimated that 334 million people have asthma with 14 % fective treatment strategies, thus reducing the patient and eco- of the world’s children and 8.6 % of young adults exhibiting nomic burden associated with these conditions.
    [Show full text]
  • Pediatric Pulmonology, on Pediatric Pulmonology, International Congress Th 15 ISSN 8755-6863 PEDIATRIC
    PEDIATRIC PULMONOLOGY PEDIATRIC VOLUME 51 • SUPPLEMENT 43 • JUNE 2016 PEDIATRIC PULMONOLOGY Volume 51 • Supplement 43 • June 2016 Proceedings S1 Foreword S2 Postgraduate Course on LFT S6 Keynote Speaker S8 I. Plenary Sessions S21 II. Topic Sessions S57 III. Young Investigator Oral Communications S60 IV. Posters PEDIATRIC PULMONOLOGY S90 Index Volume 51 Volume • Supplement 43 15th International Congress on Pediatric Pulmonology, Naples, Italy, June 23–26, 2016 • June 2016 June Pages S1–S92 Pages ISSN 8755-6863 ONLINEhttp://mc.manuscriptcentral.com/ppul SUBMISSION AND PEER REVIEW PEDIATRIC PULMONOLOGY Editor-in-Chief: THOMAS MURPHY, Watertown, MA USA Deputy Editor: TERRY NOAH, Chapel Hill, NC USA Associate Editors: ANNE CHANG, Brisbane, Queensland, Australia STEPHANIE DAVIS, Indianapolis, IN USA ALEXANDER MOELLER, Zurich, Switzerland KUNLING SHEN, Beijing, China PAUL STEWART, Chapel Hill, NC USA STEVEN TURNER, Aberdeen, Scotland, United Kingdom Topic Editors: JUDITH VOYNOW, Richmond, VA USA © 2016 Wiley Periodicals, Inc. All rights reserved. No part of this publication may be reproduced, stored or transmitted in any form or by any means without the prior permission in writing from the copyright holder. Authorization to copy items for internal and personal use is granted by the HEATHER ZAR, Cape Town, South Africa copyright holder for libraries and other users registered with their local Reproduction Rights Organization (RRO), e.g. Copyright Clearance Center (CCC), 222 Rosewood Drive, Danvers, MA 01923, USA (www.copyright.com), provided the appropriate fee is paid directly to the RRO. This consent Managing Editor: CARLENE RUMMERY, Winnipeg, MB Canada does not extend to other kinds of copying such as copying for general distribution, for advertising or promotional purposes, for creating new collective works or for resale.
    [Show full text]
  • Neurips 2020 Workshop Book
    NeurIPS 2020 Workshop book Schedule Highlights Generated Fri Dec 18, 2020 Workshop organizers make last-minute changes to their schedule. Download this document again to get the lastest changes, or use the NeurIPS mobile application. Dec. 10, 2020 None Topological Data Analysis and Beyond Rieck, Chazal, Krishnaswamy, Kwitt, Natesan Ramamurthy, Umeda, Wolf Dec. 11, 2020 None Privacy Preserving Machine Learning - PriML and PPML Joint Edition Balle, Bell, Bellet, Chaudhuri, Gascon, Honkela, Koskela, Meehan, Ohrimenko, Park, Raykova, Smart, Wang, Weller None Tackling Climate Change with ML Dao, Sherwin, Donti, Kuntz, Kaack, Yusuf, Rolnick, Nakalembe, Monteleoni, Bengio None Meta-Learning Wang, Vanschoren, Grant, Schwarz, Visin, Clune, Calandra None OPT2020: Optimization for Machine Learning Paquette, Schmidt, Stich, Gu, Takac None Advances and Opportunities: Machine Learning for Education Garg, Heffernan, Meyers None Differential Geometry meets Deep Learning (DiffGeo4DL) Bose, Mathieu, Le Lan, Chami, Sala, De Sa, Nickel, Ré, Hamilton None Workshop on Dataset Curation and Security Baracaldo Angel, Bisk, Blum, Curry, Dickerson, Goldblum, Goldstein, Li, Schwarzschild None Machine Learning for Health (ML4H): Advancing Healthcare for All Hyland, Schmaltz, Onu, Nosakhare, Alsentzer, Chen, McDermott, Roy, Akera, Kiyasseh, Falck, Adams, Bica, Bear Don't Walk IV, Sarkar, Pfohl, Beam, Beaulieu-Jones, Belgrave, Naumann None Learning Meaningful Representations of Life (LMRL.org) Wood, Marks, Jones, Dieng, Aspuru-Guzik, Kundaje, Engelhardt, Liu, Boyden, Lindorff-Larsen,
    [Show full text]
  • Ds3-Datascience-Polytechniqu…
    Machine Learning for Personalised Healthcare: Advances and Challenges Danielle Belgrave Researcher Healthcare Machine Learning @DaniCMBelg My Research Patient 1 Patient 2 Patient 3 Patient 4 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hours) Latent Variable Modelling Longitudinal Data Analysis Missing Data X Y Z Patient-Centric Approach Causality Multidisciplinary Top 8 Challenges: DS in Healthcare 1. Address some of the technical challenges facing the community of machine learning for doing impactful healthcare research 2. Presentation of current solutions 3. Steps for future research Challenge # 1: Estimating Treatment Effects Intervention Population is split into 2 Outcomes for both groups by random allocation groups are measured Patient Group Control = Cured = Still Diseased Supervised Learning Intervention Control p1 = proportion in the intervention group who are cured p2 proportion in the control group who are cured H0: p1 - p2 = 0 e vs H1: p1 - p2 ≠ 0 Mean: p1 - p2 푝 (1−푝 ) 푝 (1−푝 ) = Cured = Not cured Variance: 1 1 + 2 2 푛1 푛2 푝 − 푝 Assume well-labelled groups Test Statistic: Z = 1 2 1 1 푝(1−푝)( + ) Machine recognises a new example 푛1 푛2 Classification, regression Challenge #2: Heterogeneous Populations Patient Group Same diagnosis same prescription Understanding Heterogeneity Drug NOT toxic Drug toxic but and beneficial Patient Group NOT beneficial Same diagnosis same prescription Drug toxic but Drug NOT toxic and beneficial NOT beneficial Supervised Learning Unsupervised Learning is not Enough Not cured Cured Patient Group Assume
    [Show full text]
  • Ascertaining Patterns of Asthma Symptoms in Childhood Using Machine Learning Methods
    Ascertaining Patterns of Asthma Symptoms in Childhood using Machine Learning Methods A thesis submitted to The University of Manchester for the degree of Doctor of Philosophy In the Faculty of Biology, Medicine and Health. 2019 Matea Deliu School of Health Sciences Division of Informatics, Imaging and Data Science Blank page 2 Table of Contents List of Tables ................................................................................................................... 6 List of Figures .................................................................................................................. 8 Abstract .......................................................................................................................... 9 Declaration .....................................................................................................................11 Copyright Statement.......................................................................................................11 Acknowledgements ........................................................................................................12 About the Author ...........................................................................................................13 1.1 Candidate Degrees ........................................................................................................... 13 1.2 Research Interests ............................................................................................................ 13 1.3 Publications ....................................................................................................................
    [Show full text]
  • Elsevier Editorial System(Tm) for the Lancet Respiratory Medicine Manuscript Draft
    Elsevier Editorial System(tm) for The Lancet Respiratory Medicine Manuscript Draft Manuscript Number: THELANCETRM-D-17-00681R2 Title: Lung function trajectories from pre-school age to adulthood and their associations with early life factors: a retrospective analysis of three population-based birth cohort studies. Article Type: Article (Original Research) Keywords: Respiratory function tests; Forced expiratory volume; Child development; Lung diseases, obstructive; Genetics Corresponding Author: Professor John Henderson, Corresponding Author's Institution: University of Bristol First Author: Danielle Belgrave Order of Authors: Danielle Belgrave; Raquel Granell; Steve w Turner; John A Curtin; Iain E Buchan; Peter N LeSouëf; Angela Simpson; John Henderson; Adnan Custovic Manuscript Region of Origin: UNITED KINGDOM Abstract: Background: Maximal lung function in early adulthood is an important determinant of mortality and COPD. We investigated whether there are distinct trajectories of lung function during childhood, and whether these extend to adulthood and infancy. Methods: To ascertain trajectories of forced expiratory volume in 1 second (FEV1), we studied two population-based birth cohorts with repeat spirometry from childhood into early adulthood (1046 and 1390 participants). To ascertain whether trajectories extend to early life, we used a third cohort with repeat lung function measures in infancy (V′maxFRC) and childhood (FEV1; n=196). We identified trajectories using latent profile modelling. We created an allele score to investigate genetic associations of trajectories, and constructed a multivariable model to identify their early-life predictors. Results: We identified four childhood FEV1 trajectories: Persistently High; Normal; Below Average; and Persistently Low. Persistently Low trajectory (~5% of participants) was associated with persistent wheezing and asthma throughout follow-up.
    [Show full text]
  • Challenges in Using Hierarchical Clustering to Identify Asthma Subtypes: Choosing the Variables and Variable Transformation Mate
    Challenges in using hierarchical clustering to identify asthma subtypes: choosing the variables and variable transformation Matea Deliu, University of Manchester, Health e-Research Centre, Manchester Tolga Yavuz, University of Manchester, Manchester Matthew Sperrin and Umit Sahiner, Imperial College London, London Danielle Belgrave and Cansin Sackesen Adnan Custovic Omer Kalayci, Haceteppe University, Ankara Introduction The use of unsupervised clustering has identified different subtypes of asthma. Choosing the variables to input into the clustering algorithm is one of the important considerations. The majority of previous studies selected variables based on expert advice, whilst others used dimension reduction techniques such as principal component analysis (PCA). We aimed to compare the results of unsupervised clustering when using raw variables, or variables transformed using dimensionality reduction techniques. Methods We recruited 613 asthmatics aged 6–23 years (Ankara, Turkey). We conducted extensive phenotyping, with 49 variables including demographic data, sensitization, lung function, medication, peripheral eosinophilia, and markers of asthma severity. We performed hierarchical clustering (HC) using: (1) all variables and (2) PCA-transformed variables. Results PCA revealed 5 components describing atopy and variations in asthma severity, which were then used to infer cluster assignment. The optimal HC solution in both PCA-transformed and raw untransformed data identified 5- clusters which were not identical. Both identified mild asthma with good lung function, severe atopic asthma and late- onset atopic mild asthma. Clustering without PCA identified early-onset severe atopic asthma and late-onset atopic asthma with high BMI, whilst early onset non-atopic mild asthma in females was identified in HC with PCA. However, cluster stability was poor.
    [Show full text]
  • Features of Asthma Which Provide Meaningful Insights for Understanding the Disease Heterogeneity
    Received: 20 May 2017 | Revised: 31 July 2017 | Accepted: 15 August 2017 DOI: 10.1111/cea.13014 ORIGINAL ARTICLE Epidemiology of Allergic Disease Features of asthma which provide meaningful insights for understanding the disease heterogeneity M. Deliu1 | T. S. Yavuz2,3 | M. Sperrin1 | D. Belgrave6 | U. M. Sahiner5 | C. Sackesen4,5 | O. Kalayci5 | A. Custovic6 1Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Summary Health, University of Manchester, Background: Data-driven methods such as hierarchical clustering (HC) and principal Manchester, UK component analysis (PCA) have been used to identify asthma subtypes, with incon- 2Department of Pediatric Allergy, Gulhane School of Medicine, Ankara, Turkey sistent results. 3Department of Paediatric Allergy, Objective: To develop a framework for the discovery of stable and clinically mean- Children’s Hospital, University of Bonn, Bonn, Germany ingful asthma subtypes. 4School of Medicine, Pediatric Allergy Unit, Methods: We performed HC in a rich data set from 613 asthmatic children, using Koc University, Istanbul, Turkey 45 clinical variables (Model 1), and after PCA dimensionality reduction (Model 2). 5Pediatric Allergy and Asthma Unit, Hacettepe University School of Medicine, Clinical experts then identified a set of asthma features/domains which informed Ankara, Turkey clusters in the two analyses. In Model 3, we reclustered the data using these 6 Department of Medicine, Section of features to ascertain whether this improved the discovery process. Paediatrics, Imperial College London, London, UK Results: Cluster stability was poor in Models 1 and 2. Clinical experts highlighted four asthma features/domains which differentiated the clusters in two models: age Correspondence Adnan Custovic, Department of Medicine, of onset, allergic sensitization, severity, and recent exacerbations.
    [Show full text]
  • Advances in Data Science 2018: Final Speakers & Discussion Themes
    Advances in Data Science 2018: Final Speakers & Discussion Themes Author, Gwern Hywel A Data Science Foundation Blog March 2018 --------------------------------------------------- www.datascience.foundation Data Science Foundation Data Science Foundation, Atlantic Business Centre, Atlantic Street, Altrincham, WA14 5NQ Tel: 0161 926 3670 Email:[email protected] Web: www.datascience.foundation Registered in England and Wales 4th June 2015, Registered Number 9624670 Copyright 2016 - 2017 Data Science Foundation Advances in Data Science Conference 2018 Speakers & Discussion Themes Date: Monday 21st May - Tuesday 22nd May 2018 Venue: Museum of Science & Industry, Liverpool Road, Manchester, M3 4FP The Data Science Institute invites you to join us at Advances in Data Science 2018, a two- day conference exploring data science's potential to support societal well-being. Jointly organised by The University of Manchester's Data Science Institute and the Cathie Marsh Institute for Social Research, Advances in Data Science 2018 is set to build upon the success of the 2017 conference in May 2017. With our keynote speakers confirmed, the international line-up comprises of world-leading academic and industry experts in data science and social sciences. Join us for this unique opportunity to learn about the latest global research in societal well-being, share knowledge and innovation, and expand your own network by connecting with world-class researchers, analysts and industry leaders. Key Applications Focusing on Gaussian processes, Deep
    [Show full text]
  • Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies
    Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies Danielle C. M. Belgrave1,2.*, Raquel Granell3., Angela Simpson1., John Guiver4., Christopher Bishop4., Iain Buchan2., A. John Henderson3., Adnan Custovic1. 1 Centre for Respiratory Medicine and Allergy, Institute of Inflammation and Repair, University of Manchester and University Hospital of South Manchester, Manchester, United Kingdom, 2 Centre for Health Informatics, Institute of Population Health, University of Manchester, Manchester, United Kingdom, 3 School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom, 4 Microsoft Research Cambridge, Cambridge, United Kingdom Abstract Background: The term ‘‘atopic march’’ has been used to imply a natural progression of a cascade of symptoms from eczema to asthma and rhinitis through childhood. We hypothesize that this expression does not adequately describe the natural history of eczema, wheeze, and rhinitis during childhood. We propose that this paradigm arose from cross-sectional analyses of longitudinal studies, and may reflect a population pattern that may not predominate at the individual level. Methods and Findings: Data from 9,801 children in two population-based birth cohorts were used to determine individual profiles of eczema, wheeze, and rhinitis and whether the manifestations of these symptoms followed an atopic march pattern. Children were assessed at ages 1, 3, 5, 8, and 11 y. We used Bayesian machine learning methods to identify distinct latent classes based on individual profiles of eczema, wheeze, and rhinitis. This approach allowed us to identify groups of children with similar patterns of eczema, wheeze, and rhinitis over time. Using a latent disease profile model, the data were best described by eight latent classes: no disease (51.3%), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%).
    [Show full text]