Deciphering Chronometabolic Dynamics Through Metabolomics, Stable Isotope Tracers, And Genome-Scale Reaction Modeling

Synchrony across environmental cues, endogenous genetic clocks, sleep/wake cycles, and metabolism evoke physiological harmony for organismal health. Perturbation of this synchrony has been recently correlated with a growing list of pathologies, which is alarming given the ubiquity of sleep deprivation, mistimed light exposure, and altered eating schedules in modern society. Deeper insights into clocks, sleep, and metabolism are necessary to understand these outcomes. In this work, extensive metabolic profiles of circadian systems were obtained from the development of new liquid chromatography mass spectrometry (LC-MS) metabolomics methods. These methods were applied to Drosophila melanogaster to discern relative influences of environmental and genetic drivers of metabolic cycles. Unique sets of metabolites oscillated with 24-hour circadian periods under light:dark (LD) and constant darkness (DD) conditions, and ultradian rhythms were noted for clock mutant flies under LD, suggesting clock-independent metabolic cycles driven by environmental inputs. However, this metabolomic analysis does not fully capture the inherently dynamic nature of circadian metabolism. These LC-MS methods were adapted to analyze isotope enrichments from a novel 13C6 glucose injection platform in Drosophila. Metabolic flux cycles were noted from glucose carbons into serine, glutamine and reduced glutathione biosynthesis, and altered under sleep deprivation, demonstrating unique energy and redox demands in perturbed sleep/wake cycles. Global isotopolome shifts were most notable in WT flies after lights-on, suggesting a catabolic rush from glucose oxidation early in the active phase. As the scope of these isotope tracer-based metabolomic analyses expand, attributing labeling patterns to specific reactions requires consideration of genome-scale metabolic networks. A new computational approach was developed, called the IsoPathFinder, which uncovered biosynthetic paths from glucose to serine, and extends to glycine and glutathione production. Carbon flux into glutamine was predicted to occur through the TCA cycle, supported by enzyme thermodynamics and circadian expression datasets. This tool is presented as a new mechanism to simulate additional isotope tracer experiments, with broad applicability beyond circadian research. Collectively, a new set of analytical and computational tools are developed to both produce dynamic metabolomic data and improve data interpretability, with applications to uncover new chronometabolic connections

Analytical and computational methods towards a metabolic model of ageing in Caenorhabditis elegans

Human life expectancy is increasing globally. This has major socioeconomic implications, but also raises scientific questions about the biological bases of ageing and longevity. Research on appropriate model organisms, such as the nematode worm Caenorhabditis elegans, is a key component of answering these questions. Ageing is a complex phenomenon, with both environmental and genetic influences. Metabolomics, the analysis of all small molecules within a biological system, offers the ability to integrate these complex factors to help understand the role of metabolism in ageing. This thesis addresses the current lack of methods for C. elegans metabolite analysis, with a particular focus on combining analytical and computational approaches. As a first essential step, C. elegans metabolite extraction protocols for NMR, GC-MS and LC-MS based analysis were optimized. Several methods to improve the coverage, automatic annotation and data analysis steps of NMR and GC-MS are proposed. Next, stable isotope labelling was explored as a tool for C. elegans metabolomics. An automated stable isotope based workflow was developed, which identifies all biological, non-redundant features within a LC-MS acquisition and annotates them with molecular compositions. This demonstrated that the vast majority (> 99.5%) of detected features inside LC-MS metabolomics experiments are not of biological origin or redundant. This stable isotope workflow was then used to compare the metabolism of 24 different C. elegans mutant strains from different pathways (e.g. insulin signalling, TOR pathway, neuronal signalling), with differing levels of lifespan extension compared to wild-type worms. The biologically relevant features (metabolites) were detected and annotated, and compared across the mutants. Some metabolites were correlated with longevity across the mutant set, in particular, glycerophospholipids. This led to the formulation of a hypothesis, that lifespan extension in C. elegans requires increased activity of common downstream longevity effector mechanisms (autophagy, and mitochondrial biogenesis), that also involve subcellular compartmentation and hence membrane formation. This results in the alterations in lipid metabolism detected here.Open Acces

Time-dependent metabolic phenotyping of inflammatory dysregulation

A rich and functional description of a patient health status is the fundamental basis for the personalisation of treatment and the targeting of interventions. The function of inflammation in the healing process as well as its involvement in most major diseases is well established, yet the specific mechanism by which it contributes to the pathogenesis is still not fully understood. If conditions arising from a dysregulation of the inflammatory process are to be treated before they become irreversible, a novel understanding of these pathologies must be achieved and a stratification of patients based on their inflammatory status undertaken. The work presented in this thesis aims to deliver new analytical and statistical approaches to support the investigation of the time-dependent dysregulation of inflammation. Lipid mediators have been described as exerting a major role in the initiation and regulation of the inflammatory response, yet analytical platforms for their large-scale characterisation in human biofluids are lacking. This thesis reports the validation of an assay for the simultaneous quantification of pro- and anti-inflammatory signalling molecules in multiple human biofluids. The coverage of the assay in each biofluid is subsequently established, characterising inflammatory signalling across biological compartments. A second study explores the assay’s applicability in a clinical context; investigating the relationship between lipid mediators, current clinical markers of inflammation and post-operative complications. Characterising the interplay between signalling and regulatory networks is key to understanding a living system’s response to perturbations, yet few statistical approaches are suited for the detection of time-dependent patterns in short and irregularly sampled longitudinal datasets. This thesis reports the development of a statistical approach to support the identification of altered time-trajectories in such studies. The method’s wide applicability is subsequently demonstrated on two investigations covering the diversity of metabolic phenotyping data generation platforms. This thesis is a proof of concept for the characterisation of patient-specific inflammatory status in a clinical context and the identification of altered time-dependent patterns. Both analytical and statistical developments have been motivated by the needs of real world applications and provide a template for the characterisation and analysis of the molecular basis for treatment.Open Acces

Marine Toxins from Harmful Algae and Seafood Safety

The rapid expansion of aquaculture around the world is increasingly being impacted by toxins produced by harmful marine microalgae, which threaten the safety of seafood. In addition, ocean climate change is leading to changing patterns in the distribution of toxic dinoflagellates and diatoms which produce these toxins. New approaches are being developed to monitor for harmful species and the toxins they produce. This Special Issue covers pioneering research on harmful marine microalgae and their toxins, including the identification of species and toxins; the development of new chemical and biological techniques to identify and monitor species and toxins; the uptake of marine biotoxins in seafood and marine ecosystems; and the distribution and abundance of toxins, particularly in relation to climate change

Compound-Specific Isotope Analysis of Amino Acids in Biological Tissues: Applications in Forensic Entomology, Food Authentication and Soft-Biometrics in Humans

In this work we demonstrate the power of compound-specific isotope analysis (CSIA) to analyze proteinaceous biological materials in three distinct forensic applications, including: 1) linking necrophagous blow flies in different life stages to their primary carrion diet; 2) identifying the harvesting area of oysters for food authentication purposes; and 3) the ability to predict biometric traits about humans from their hair. In the first application, we measured the amino-acid-level fractionation that occurs at each major life stage of Calliphora vicina (Robineau-Desvoidy) (Diptera: Calliphoridae) blow flies. Adult blow flies oviposited on raw pork muscle, beef muscle, or chicken liver. Larvae, pupae and adult blow flies from each carrion were selected for amino acid CSIA. Canonical discriminant analysis showed that flies were correctly classified to specific carrion types in 100% (original rules) and 96.8% (leave-one-out cross-validation [LOOCV]) of cases. Regarding life stages, we obtained 100% and 71% of correct classification in original rules and LOOCV, respectively. Most of the essential amino acids did not significantly change between life stages (at 95% CI). However, some non-essential (Ala, Ser, and Glu) and conditionally essential amino acids (Gly and Pro) were isotopically depleted in the adult stage. Except for the essential amino acids, the amino acids in larvae and pupae were enriched in 13C and adult blow flies were depleted in 13C relative to the carrion on which they fed. These results make it possible to exclude potential sources of carrion as larval food. In addition, amino-acid-specific IRMS could help inform entomologists whether a fly has just arrived from another location to feed on a corpse or has emerged from a pupa whose feedstock was the corpse. Regarding the source inference of oysters, we investigated the bulk, amino-acid compound-specific stable isotopes, cadmium and lead concentrations of the popular Eastern oyster, Crassostrea virginica. This species has been one of the most popular species for the oyster harvesting business in the United States, despite its claimed reduced availability due to excessive harvesting and some parasitic diseases. The results from specimens collected from different Gulf of Mexico bays were subjected to multivariate statistical analysis to assess whether we could predict the oysters’ harvest area. Our results indicate that the combination of trace elements and isotope ratios can predict geographic provenance of oysters with greater than 70% correct classification using LOOCV, which is superior to using only CSIA or only trace elements. The δ13C values of serine and glycine could also discriminate between two adjacent harvest areas within the same Apalachicola bay. One of these areas is fishable in the winter season and the other is fishable in the summer season, so the ability to differentiate oysters from these two areas is a valuable capability for the Florida Department of Agriculture, which is responsible for enforcement. The use of chemical signatures to identify harvest areas is a valuable tool to protect consumers from food fraud, food-borne diseases and to help regulatory agencies enforce harvesting regulations. Finally, we describe the use of amino-acid CSIA and amino acid quantitation of scalp hair of American individuals to predict soft biometrics in humans. We measured the isotope ratios and respective quantities of 13 amino acid peaks. Correlation analysis of the multivariate data provided the degree of correlation between essential and non-essential amino acids with factors such sex and age of the hair donors. The isotope ratios of each amino acid were first corrected for the extent of C4-based carbon in the diet to reveal relationships between metabolic or phenotypic factors and the isotope ratios of 13C in the amino acids in the hair shafts. Multivariate analysis revealed that the sex of a donor could be correctly predicted with cross-validated accuracies of 80% and 89% using the isotope ratios or quantities of amino acids, respectively. The continuous dependent variables of donor age and body mass index (BMI) were also predicted using the amino acid isotope ratios or quantities, but the predictions were not as reliable as for sex determination. Unexpectedly, the δ13C values of hair reflected the frequency of alcohol consumption in two groups of subjects

A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies

Comparative functional genomics offers a powerful approach to study species evolution. To date, the majority of these studies have focused on the transcriptome in mammalian and yeast phylogenies. Here, we present a novel multi-species proteomic dataset and a computational pipeline to systematically compare the protein levels across multiple plant species. Globally we find that protein levels diverge according to phylogenetic distance but is more constrained than the mRNA level. Module-level comparative analysis of groups of proteins shows that proteins that are more highly expressed tend to be more conserved. To interpret the evolutionary patterns of conservation and divergence, we develop a novel network-based integrative analysis pipeline that combines publicly available transcriptomic datasets to define co-expression modules. Our analysis pipeline can be used to relate the changes in protein levels to different species-specific phenotypic traits. We present a case study with the rhizobia-legume symbiosis process that supports the role of autophagy in this symbiotic association

Novel strategies for the identification of biomarkers of non-Hodgkin lymphoma: evidence from the European Prospective Investigation into Cancer (EPIC)

Non-Hodgkin’s Lymphomas (NHL) represent the eighth most common cancer in Western Europe. Yet despite their widespread prevalence and high mortality rate relatively little is known about the aetiology of these hematological malignancies. Consequently NHL represents an ideal candidate for the discovery of biomarkers lying along the causal pathway. Such biomarkers would allow the improved identification of risk factors and high risk individuals, as well as an enhanced understanding of lymphomageneisis. However, to date there has been little progress in determining validated predictive biomarkers of NHL. This thesis attempts to address some of the issues that have previously hampered the study of NHL through novel strategies of biomarker identification utilising novel methodologies, technologies and statistical techniques. The thesis comprises a nested case-control study within the European Prospective investigation into Cancer (EPIC) cohort and is split into two parts: the ‘validation of biomarkers’ and the ‘integration of biomarkers’. The most exciting finding was the identification of a novel biomarker for Follicular lymphoma based on the t(14;18) translocation which comprises a previously unknown pre-disease condition. Although no other predictive biomarkers were identified this work represents a ‘proof-of-principle’ for the use profile regression in the study of highly dimensional complex datasets, and the possibility of using mass-spectrometry derived metabolic profiles in the study of lymphoma. Part two of the thesis confirmed that the use of the ‘meet-in-the-middle’ approach was a valuable and feasible method for studying the complete causal pathway from risk factor to disease. Together these results highlight potential avenues for further study of NHL and confirm the utility of a number of novel strategies that can aid such work. Additionally it informs on some of the likely challenges that will be involved.Open Acces

Using an In Vitro-In Vivo Correlation for the ‘Bioequivalence by Design’ Development of an Immediate Release Carbamazepine Product

The quality of a drug product may be characterized by the consistency with which its indicated clinical effect, and safety profile, is experienced by the patient. The concept that such quality should be built into a product is at the core of the United States Food and Drug Administration’s (FDA) quality by design (QbD) initiative. This vision for pharmaceutical product development emphasizes the risk-based identification of critical quality attributes (CQA) which summarize a product’s performance, the efficient refinement of critical product/process parameters (CPP) that can affect such attributes, and the systematic development of CPP limits, which assure appropriate performance of CQAs. For a tableted drug product, a cornerstone CQA is dissolution. Often, a formulation and/or manufacturing process can change during a patient’s course of treatment, potentially jeopardizing the consistent performance of the drug product. Regulatory agencies typically require that sponsors demonstrate how the generic/post-change product is bioequivalent to the reference/pre-change product. While in vivo clinical trials are one strategy for demonstrating this, sponsors typically prefer in vitro dissolution tests as an alternative. During these in vitro test, the F2 metric is commonly used to assess dissolution profile similarity. This work sought to compare the F2 method with an alternative method, on the basis of errors in bioequivalence. The alternative method was based on the use of a physiologically based in vitro-in vivo correlation (PB-IVIVC) model that had been nested within a clinical trial simulation platform. The PB-IVIVC method provides a direct link from dissolution performance to clinical performance. Thus, when it is used to refine a CPP-vs-dissolution response surface, based on the performance of a reference product, the assurance of clinically defined bioequivalence can be directly built into a model tablet system. The model drug product for this work was an immediate release carbamazepine tablet. Carbamazepine was selected as the model active pharmaceutical ingredient because it has a narrow therapeutic index and is designated as a class II compound (i.e. high permeability, low solubility) according to the FDA’s biopharmaceutics classification system (BCS). As such, this compound is identified within the FDA’s scale-up and post-approval change guidance as possessing elevated risk for biononequivalence when changes are imposed to its formulation and/or manufacturing process. After gathering single dose in vitro-in vivo data from the literature, the construction of the PB-IVIVC began according to a two-step process. Here, the respective parameters for the rate and extent of each product’s absorption were calculated using classical pharmacokinetic modeling and then regressed against each product’s rate and extent of dissolution. Next, the classically defined clearance parameter was replaced using a physiologically based clearance model. This allowed routinely available population pharmacokinetic data to be combined with first principles of human physiology, for the mechanistic prediction of intersubject variability via correlated Monte Carlo simulations. This PB-IVIVC was then used to not only define the CPP ranges for the model carbamazepine tablet system that would directly provide for bioequivalent performance but to perform a post hoc assessment of the CPP ranges conferred by the use of F2 statistic. Ultimately, the results showed that when the product’s CPPs were refined using the F2 statistic the was a higher risk of biononequivalence was higher when compared to a product that had been refined using the PB-IVIVC. It is intended that this work support the movement of product/process optimization practices away from methods that result in rigid factors of unknown clinical significance, and towards those that are focused on efficiently achieving specific clinical objectives

Stable isotope sourcing using sampling

Stable isotope sourcing is used to estimate proportional contributions of sources to a mixture, such as in the analysis of animal diets, plant nutrient use, geochemistry, pollution, and forensics. We focus on animal ecology because of the particular complexities due to the process of digestion and assimilation. Parameter estimation has been a challenge because there are often many sources and few isotopes leading to an underconstrained linear system for the diet probability vector. This dissertation offers three primary contributions to the mixing model community. (1) We detail and provide an R implementation of a better algorithm (SISUS) for representing possible solutions in the underconstrained case (many sources, few isotopes) when no variance is considered (Phillips and Gregg, 2003). (2) We provide general methods for performing frequentist estimation in the perfectly-constrained case using the delta method and the bootstrap, which extends previous work applying the delta method to two- and three-source problems (Phillips and Gregg, 2001). (3) We propose two Bayesian models, the implicit representation model estimating the population mean diet through the mean mixture isotope ratio, and the explicit representation model estimating the population mean diet through mixture-specific diets given individual isotope ratios. Secondary contributions include (4) estimation using summaries from the literature in lieu of observation-level data, (5) multiple methods for incorporating isotope ratio discrimination (fractionation) in the analysis, (6) the use of measurement error to account for and partition more uncertainty, (7) estimation improvements by pooling multiple estimates, and (8) detailing scenarios when one model is preferred over another. We show that the Bayesian explicit representation model provides more precise diet estimates than other models when measurement error is small and informed by the necessary calibration measurements