Doctor of Philosophy Thesis Thomas Payne Professor Jeremy Nicholson
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Doctor of Philosophy Thesis Thomas Payne Novel biomarkers of renal transplant failure/dysfunction via spectroscopic phenotyping Professor Jeremy Nicholson Professor Nadey Hakim Department of Surgery & Cancer Imperial College London Sir Alexander Fleming Building South Kensington Campus London SW7 2AZ 2012–2015 Declaration of Originality I certify that this thesis, and the research to which it refers, are the product of my own work, and that any ideas or quotations from the work of other people, published or otherwise, are fully acknowledged in accordance with standard scientific practice. 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. Acknowledgements First, I would like to thank both supervisors, Professor Nicholson and Professor Hakim, for allowing me this PhD opportunity and engage within their research focus, with particular gratitude for all the guidance and encouragement they have shown towards me. I would then like to express my greatest appreciation to Anisha Wijeyesekera and the 2012 STRATiGRAD cohort for all their support, effort and kindness – Torben Kimhofer, Goncalo Correia, Frances Jackson, Arnaud Wolfer, Adam Beech and Richmond Bergner. Their endless inspiration and belief have motivated me through the hard times of dissolution, and for these reasons I will be sad to leave. I wish them the very best of luck in the future. Finally, I would like to add recognition to Caroline Sands, Claire Merrifield, Anthony Dona and Maria Gomez Romero for their kind help and support throughout this unique experience. PhD. Thomas Payne 1 Abstract Successful renal transplantation not only improves patients’ quality and duration of life, but also confers a substantial economic healthcare cost saving. With the growing burden of end-stage renal disease and the requirement for renal replacement therapy, strategies to augment transplant success and subsequent graft survival become more vital than ever. Herein, an objective means of characterising renal function across the transplant journey, and appropriately stratifying in accordance to individual contingencies/factors (including the early detection of renal dysfunction), based on metabolism is explored. Patient pairs, recipients and donors, were metabolically phenotyped prior to (24 h) and post (days 1–5) transplantation using a multi-platform analytical approach (i.e., Nuclear Magnetic Resonance Spectroscopy (NMR) and Mass Spectrometry (MS)) of urine and plasma (n = 50). Using advanced statistics, the resulting metabolic profiles were subsequently modelled, and related to multiple clinical phenotypes (and outcomes), to increase the understanding of molecular changes/signatures across transplantation, capturing valuable information pertinent to transplant type, cause, co-morbidity, modality, immunology and complication (p-value < 0.05) – over donors as well as recipients. An attempt to then develop predictive algorithms for the early detection of renal dysfunction was preliminary defined within the confines of the study design, where integrated NMR and MS metabolic data improved patient stratification for complications over clinical measures (receiver operator characteristic area under curve over 0.900) and potentially replace current measures. While prospective/multicentre studies are imperative for subsequent real-world adoption (qualification/validation), the work conducted herein encompassed much of the first stage of marker development – discovery – where metabolic phenotyping renal transplantation has provided a deeper characterisation of patient journeys with new insights into multiple contingencies/factors (including complication). Such findings infer the value of metabolic phenotyping to augment and potentially replace current measures and methods to better inform decision making in the clinic on the individual/precision level. PhD. Thomas Payne 2 Abbreviations 1D One dimensional PE Phosphatidylethanolamine 2D Two dimensional PG Phosphatidylglycerol a.u. Arbitrary unit PI Phosphatidylinositol AUC Area under curve PLS Partial least squares OR projection to latent structures CI Confidence interval PO Post-operative COSY Correlation spectroscopy ppm Parts per million CPMG Carr–Purcell–Meiboom–Gill PQN Probabilistic quotient normalisation CV Coefficient of variation PR Pre-operative (/relative standard deviation) DA Discriminant analysis PS Phosphatidylserine DSA Donor-specific antibody PUFA Polyunsaturated fatty acid EM Expectation-maximization QC Quality control ESI Electrospray ionization ROC Receiver operator characteristic ESRD End-stage renal disease SOM Self-organising map FT Fourier transformation STOCSY Statistical total correlation spectroscopy HLA Human leukocyte antigen TCA Tricarboxylic acid (/ Krebs) HMBC Heteronuclear multiple-bond TIC Total ion current correlation spectroscopy J-RES J-resolved spectroscopy ULOQ Upper limit of quantification LLOQ Lowest limit of quantification UPLC Ultra performance liquid chromatography LPC Lyso-phosphatidylcholine UV Unit variance LPE Lyso-phosphatidylethanolamine VIP Variable importance in projection MS Mass spectrometry NMR Nuclear magnetic resonance OPLS Orthogonal projection to latent structures PC Phosphatidylcholine PCA Principal component analysis PhD. Thomas Payne 3 Contents 1. Introduction 1.1. Renal transplantation 8 1.2. Metabolic phenotyping 11 1.3. Metabolic applications in renal transplantation 17 1.4. Aims 21 1.5. Hypothesis 21 2. Methods & Materials 2.1. Patient cohort & sample collection 22 2.2. NMR acquisition 24 2.3. MS acquisition 27 2.4. Data analysis 28 2.4.1. Pre-processing 28 2.4.1.1. NMR data 29 2.4.1.2. MS data 32 2.4.1.3. Scaling 35 2.4.2. Multivariate chemometrics 36 2.4.2.1. Unsupervised methods 37 2.4.2.2. Supervised methods 38 2.4.2.3. Model evaluation/validation 40 2.4.3. Cluster analysis 41 2.4.3.1. Distances 41 2.4.3.2. Algorithms 41 2.4.3.3. SOM 42 2.4.3.4. Model evaluation/validation 43 2.4.4. Statistical spectroscopy 43 2.4.4.1. STOCSY 43 2.4.4.2. Non-experimental spectral manipulation 45 2.4.5. Spectroscopic curve fitting 47 2.4.6. Univariate statistics 48 2.4.6.1. Pairwise comparison 49 2.4.6.2. Linear regression (mixed effects) 49 2.4.7. ROC 50 3. Metabolic Profiling Using NMR Spectroscopy PhD. Thomas Payne 4 3.1. Summary 52 3.2. Aims 52 3.3. Methods & materials 53 3.3.1. Sample preparation 53 3.3.2. 1D NMR analysis 53 3.3.3. 2D NMR analysis 54 3.3.4. PCA 55 3.3.5. Statistical spectroscopy 55 3.3.6. Curve fitting 55 3.3.7. Pairwise comparison (non-parametric & parametric) 55 3.3.8. Correlation & clustering 55 3.3.9. PLS (single- & multi-block) 56 3.3.10. OPLS & O2PLS 56 3.4. Results – Urinary NMR spectroscopy 56 3.4.1. Urinary high-resolution NMR analysis 56 3.5.1.1. Donors 57 3.5.1.2. Recipients 65 3.4.2. Urinary targeted NMR analysis 69 3.5.2.1. Donors 70 3.5.2.2. Recipients 73 3.5. Results – Plasma NMR spectroscopy 80 3.5.1. Clinical creatinine agreement 80 3.5.2. Plasma targeted NMR analysis 81 3.6.3.1. Donors 81 3.6.3.2. Recipients 85 3.6. Discussion 91 4. Metabolic Profiling Using MS 4.1. Summary 94 4.2. Aims 94 4.3. Methods & materials 95 4.3.1. Untargeted lipidomic MS 95 4.4.1.1. Sample preparation 95 4.4.1.2. Acquisition 95 4.4.1.3. Processing 96 PhD. Thomas Payne 5 4.4.1.4. Identification 97 4.3.2. Targeted oxylipin MS 97 4.4.1.1. Sample preparation 97 4.4.1.2. Acquisition 99 4.4.1.3. Processing 99 4.3.3. SOM 99 4.3.4. PCA 100 4.3.5. Pairwise comparison (non-parametric & parametric) 100 4.3.6. Correlation & clustering 100 4.3.7. PLS (single- & multi-block) 100 4.3.8. OPLS & O2PLS 101 4.4. Results – Plasma lipidomics 101 4.4.1. Positive mode 101 4.4.2. Negative mode 112 4.5. Results – Plasma oxylipins 123 4.5.1. Donors 125 4.5.2. Recipients 129 4.6. Discussion 134 5. Clinical Data & Integration 5.1. Summary 139 5.2. Aims 139 5.3. Methods & materials 140 5.3.1. Correlation & clustering 140 5.3.2. Pairwise comparison (non-parametric & parametric) 140 5.3.3. Linear regression (mixed effects) 140 5.3.4. PCA 140 5.3.5. Supervised PLS (single- & multi-block) 141 5.3.6. OPLS & O2PLS 141 5.4. Results – Clinical measures 141 5.4.1. Demographics 141 5.4.2. Univariate analysis 144 5.4.3. Multivariate analysis 154 5.5. Results – Metabolic integration 159 5.5.1. Unsupervised 159 PhD. Thomas Payne 6 5.5.2. Supervised 161 5.5.3. Multi-marker ROC 165 5.6. Discussion 167 6. Discussion/Conclusion 169 7. References Appendix I PhD. Thomas Payne 7 1. Introduction 1.1. Renal transplantation From the first successful kidney transplantation at the beginning of the 1950s, to execution of the first laparoscopic live-donor nephrectomy in the mid 1990s (and more recently finger-assisted nephrectomy), continual advances have made renal transplantation todays’ most successful and widespread organ transplant operation 1. Despite this success, profound challenges remain still that must be overcome in order to refine the procedure to continually contend with the ever changing, modern-day pressures of society, especially in relation to modern technological advances. The kidney is one of the most highly differentiated organs, with close to 30 different cell types, that governs complex physiologic processes from endocrine functions, regulation of blood pressure and intraglomerular hemodynamics, solute and water transport, acid-base balance and waste removal 2. Enclosed by the capsule, nephrons positioned in the cortex and medulla deliver the majority of the kidney’s functional capacity (Cortical and Juxtamedullary) – glomerular filtration of water and solutes (e.g., sodium chloride, potassium, bicarbonate, glucose, amino acids, etc), primarily through a hydrostatic pressure gradient, followed by various sequential reabsorption and secretion events.