Exploring deep phylogenies using protein structure : a dissertation submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Biochemistry, Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand

Recent times have seen an exponential growth in protein sequence and structure data. The most popular way of characterising newly determined protein sequences is to compare them to well characterised sequences and predict the function of novel sequences based on homology. This practice has been highly successful for a majority of proteins. However, these sequence based methods struggle with certain deeply diverging proteins and hence cannot always recover evolutionary histories. Another feature of proteins, namely their structures, has been shown to retain evolutionary signals over longer time scales compared to the respective sequences that encode them. The structure therefore presents an opportunity to uncover the evolutionary signal that otherwise escapes conventional sequence-based methods. Structural phylogenetics refers to the comparison of protein structures to extract evolutionary relationships. The area of structural phylogenetics has been around for a number of years and multiple approaches exist to delineate evolutionary relationships from protein structures. However, once the relationships have been recovered from protein structural data, no methods exist, at present, to verify the robustness of these relationships. Because of the nature of the structural data, conventional sequence-based methods, e.g. bootstrapping, cannot be applied. This work introduces the first ever use of a molecular dynamics (MD)-based bootstrap method, which can add a measure of significance to the relationships inferred from the structure-based analysis. This work begins in Chapter 2 by thoroughly investigating the use of a protein structural comparison metric Qscore, which has previously been used to generate structural phylogenies, and highlights its strengths and weaknesses. The mechanistic exploration of the structural comparison metric reveals a size difference limit of no more than 5-10% in the sizes of protein structures being compared for accurate phylogenetic inference to be made. Chapter 2 also explores the MD-based bootstrap method to offer an interpretation of the significance values recovered. Two protein structural datasets, one relatively more conserved at the sequence level than the other and with different levels of structural conservation are used as controls to simplify the interpretation of the statistics recovered from the MD-based bootstrap method. Chapter 3 then sees the application of the Qscore metric to the aminoacyl-tRNA synthetases. The aminoacyl-tRNA synthetases are believed to have been present at the dawn of life, making them one of the most ancient protein families. Due to the important functional role they play, these proteins are conserved at both sequence and structural levels and well-characterised using both sequence and structure-based comparative methods. This family therefore offered inferences which could be informed with structural analysis using an automated method. Successful recovery of known relationships raised confidence in the ability of structural phylogenetic analysis based on Qscore to detect evolutionary signals. In Chapter 4, a structural phylogeny was created for a protein structural dataset presenting either the histone fold or its ancestral precursor. This structural dataset comprised of proteins that were significantly diverged at a sequence level, however shared a common structural motif. The structural phylogeny recovered the split between bacterial and non-bacterial proteins. Furthermore, TATA protein associated factors were found to have multiple points of origin. Moreover, some mismatch was found between the classifications of these proteins between SCOP and PFam, which also did not agree with the results from this work. Using the structural phylogeny a model outlining the evolution of these proteins was proposed. The structural phylogeny of the Ferritin-like superfamily has previously been generated using the Qscore metric and supported qualitatively. Chapter 5 recovers the structural phylogeny of the Ferritin-like superfamily and finds quantitative support for the inferred relationships from the first ever implementation of the MD-based bootstrap method. The use of the MD-based bootstrap method simultaneously allows for the resolution of polytomies in structural databases. Some limitations of the MD-based bootstrap method, highlighted in Chapter 2, are revisited in Chapter 5. This work indicates that evolutionary signals can be successfully extracted from protein structures for deeply diverging proteins and that the MD-based bootstrap method can be used to gauge the robustness of relationships inferred

Cost estimation alongside a multi-regional, multi-country randomized trial of antenatal ultrasound in five low-and-middle-income countries

Background: Improving maternal health has been a primary goal of international health agencies for many years, with the aim of reducing maternal and child deaths and improving access to antenatal care (ANC) services, particularly in low-and-middle-income countries (LMICs). Health interventions with these aims have received more attention from a clinical effectiveness perspective than for cost impact and economic efficiency.Methods: We collected data on resource use and costs as part of a large, multi-country study assessing the use of routine antenatal screening ultrasound (US) with the aim of considering the implications for economic efficiency. We assessed typical antenatal outpatient and hospital-based (facility) care for pregnant women, in general, with selective complication-related data collection in women participating in a large maternal health registry and clinical trial in five LMICs. We estimated average costs from a facility/health system perspective for outpatient and inpatient services. We converted all country-level currency cost estimates to 2015 United States dollars (USD). We compared average costs across countries for ANC visits, deliveries, higher-risk pregnancies, and complications, and conducted sensitivity analyses.Results: Our study included sites in five countries representing different regions. Overall, the relative cost of individual ANC and delivery-related healthcare use was consistent among countries, generally corresponding to country-specific income levels. ANC outpatient visit cost estimates per patient among countries ranged from 15 to 30 USD, based on average counts for visits with and without US. Estimates for antenatal screening US visits were more costly than non-US visits. Costs associated with higher-risk pregnancies were influenced by rates of hospital delivery by cesarean section (mean per person delivery cost estimate range: 25-65 USD).Conclusions: Despite substantial differences among countries in infrastructures and health system capacity, there were similarities in resource allocation, delivery location, and country-level challenges. Overall, there was no clear suggestion that adding antenatal screening US would result in either major cost savings or major cost increases. However, antenatal screening US would have higher training and maintenance costs. Given the lack of clinical effectiveness evidence and greater resource constraints of LMICs, it is unlikely that introducing antenatal screening US would be economically efficient in these settings--on the demand side (i.e., patients) or supply side (i.e., healthcare providers).Trial registration: Trial number: NCT01990625 (First posted: November 21, 2013 on https://clinicaltrials.gov )

ACKNOWLEDGMENT

though the proposed method is sound, it is not a complete method, i.e., there are pairs of equivalent circuits for which it cannot prove equivalence. The method can be used as an effective preprocessing step for a general method such as [10]. It is interesting to note that for some synthesis steps, the method is complete. This is, e.g., the case for circuits optimized with combinational synthesis techniques, and also for retimed circuits. The proposed method assumes that an initial state is designated for both circuits. This initial state is used in two ways: It acts as a reference point to allow the detection of antivalent signals, and it is used to calculate the initial partition „H of the set p. The approach of [4] shows that the assumption of a designated initial state can be weakened. It should be possible to extend their work such that it also applies to the method presented in this paper

Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits.

Mitochondria (MT), the major site of cellular energy production, are under dual genetic control by 37 mitochondrial DNA (mtDNA) genes and numerous nuclear genes (MT-nDNA). In the CHARGEmtDNA+ Consortium, we studied genetic associations of mtDNA and MT-nDNA associations with body mass index (BMI), waist-hip-ratio (WHR), glucose, insulin, HOMA-B, HOMA-IR, and HbA1c. This 45-cohort collaboration comprised 70,775 (insulin) to 170,202 (BMI) pan-ancestry individuals. Validation and imputation of mtDNA variants was followed by single-variant and gene-based association testing. We report two significant common variants, one in MT-ATP6 associated (p ≤ 5E-04) with WHR and one in the D-loop with glucose. Five rare variants in MT-ATP6, MT-ND5, and MT-ND6 associated with BMI, WHR, or insulin. Gene-based meta-analysis identified MT-ND3 associated with BMI (p ≤ 1E-03). We considered 2,282 MT-nDNA candidate gene associations compiled from online summary results for our traits (20 unique studies with 31 dataset consortia's genome-wide associations [GWASs]). Of these, 109 genes associated (p ≤ 1E-06) with at least 1 of our 7 traits. We assessed regulatory features of variants in the 109 genes, cis- and trans-gene expression regulation, and performed enrichment and protein-protein interactions analyses. Of the identified mtDNA and MT-nDNA genes, 79 associated with adipose measures, 49 with glucose/insulin, 13 with risk for type 2 diabetes, and 18 with cardiovascular disease, indicating for pleiotropic effects with health implications. Additionally, 21 genes related to cholesterol, suggesting additional important roles for the genes identified. Our results suggest that mtDNA and MT-nDNA genes and variants reported make important contributions to glucose and insulin metabolism, adipocyte regulation, diabetes, and cardiovascular disease