Effects of diet on resource utilization by a model human gut microbiota containing Bacteroides cellulosilyticus WH2, a symbiont with an extensive glycobiome

The human gut microbiota is an important metabolic organ, yet little is known about how its individual species interact, establish dominant positions, and respond to changes in environmental factors such as diet. In this study, gnotobiotic mice were colonized with an artificial microbiota comprising 12 sequenced human gut bacterial species and fed oscillating diets of disparate composition. Rapid, reproducible, and reversible changes in the structure of this assemblage were observed. Time-series microbial RNA-Seq analyses revealed staggered functional responses to diet shifts throughout the assemblage that were heavily focused on carbohydrate and amino acid metabolism. High-resolution shotgun metaproteomics confirmed many of these responses at a protein level. One member, Bacteroides cellulosilyticus WH2, proved exceptionally fit regardless of diet. Its genome encoded more carbohydrate active enzymes than any previously sequenced member of the Bacteroidetes. Transcriptional profiling indicated that B. cellulosilyticus WH2 is an adaptive forager that tailors its versatile carbohydrate utilization strategy to available dietary polysaccharides, with a strong emphasis on plant-derived xylans abundant in dietary staples like cereal grains. Two highly expressed, diet-specific polysaccharide utilization loci (PULs) in B. cellulosilyticus WH2 were identified, one with characteristics of xylan utilization systems. Introduction of a B. cellulosilyticus WH2 library comprising >90,000 isogenic transposon mutants into gnotobiotic mice, along with the other artificial community members, confirmed that these loci represent critical diet-specific fitness determinants. Carbohydrates that trigger dramatic increases in expression of these two loci and many of the organism's 111 other predicted PULs were identified by RNA-Seq during in vitro growth on 31 distinct carbohydrate substrates, allowing us to better interpret in vivo RNA-Seq and proteomics data. These results offer insight into how gut microbes adapt to dietary perturbations at both a community level and from the perspective of a well-adapted symbiont with exceptional saccharolytic capabilities, and illustrate the value of artificial communities

Computational Proteomics Using Network-Based Strategies

This thesis examines the productive application of networks towards proteomics, with a specific biological focus on liver cancer. Contempory proteomics (shot- gun) is plagued by coverage and consistency issues. These can be resolved via network-based approaches. The application of 3 classes of network-based approaches are examined: A traditional cluster based approach termed Proteomics Expansion Pipeline), a generalization of PEP termed Maxlink and a feature-based approach termed Proteomics Signature Profiling. PEP is an improvement on prevailing cluster-based approaches. It uses a state- of-the-art cluster identification algorithm as well as network-cleaning approaches to identify the critical network regions indicated by the liver cancer data set. The top PARP1 associated-cluster was identified and independently validated. Maxlink allows identification of undetected proteins based on the number of links to identified differential proteins. It is more sensitive than PEP due to more relaxed requirements. Here, the novel roles of ARRB1/2 and ACTB are identified and discussed in the context of liver cancer. Both PEP and Maxlink are unable to deal with consistency issues, PSP is the first method able to deal with both, and is termed feature-based since the network- based clusters it uses are predicted independently of the data. It is also capable of using real complexes or predicted pathway subnets. By combining pathways and complexes, a novel basis of liver cancer progression implicating nucleotide pool imbalance aggravated by mutations of key DNA repair complexes was identified. Finally, comparative evaluations suggested that pure network-based methods are vastly outperformed by feature-based network methods utilizing real complexes. This is indicative that the quality of current networks are insufficient to provide strong biological rigor for data analysis, and should be carefully evaluated before further validations.Open Acces

Structural Investigation of Snake Venom Proteins by Mass Spectrometry

Snake venoms are a rich and complex source of bioactive proteins and peptides. The proteomic variability of snake venoms introduces fascinating and complex investigations from a venom adaptational perspective, and the potency and specificity of these venom proteins lend promising potential for therapeutic applications. However, a significant knowledge gap exists in the proteomic and higher-order structural understanding of venom proteins, which poses a challenge for successful applications. The research in this thesis is focussed on probing ecological and structural biology questions surrounding snake venoms of medical importance from a fundamental protein structural level using mass spectrometry (MS)-based proteomics and native MS. This work contributes towards bridging the knowledge gap between venom protein structure and potential applications, and further expands knowledge of venom diversity. The venom composition of the Australian tiger snake Notechis scutatus was studied using a shotgun proteomics approach from five different geographical populations in response to the polymorphic and widespread geographical diversity exhibited by this species. Analysis of the five venom proteomes established a high degree of diversity in the various toxin groups identified in each population, and in particular, significant variations in relative abundance of 3 finger-toxins appeared to be the greatest distinction across the five venoms. Venom proteomic variations between populations may be due to a diet prey-type influence although climate, seasonal, and intrinsic variabilities must also be considered. Quaternary structures of various venom proteins from a repertoire of medically significant venoms including Collett’s snake Pseudechis colletti, the forest cobra Naja melanoleuca, and the puff adder Bitis arietans were explored for the first time. Using a combined approach of proteomics, native and denatured MS, a 117 kDa non-covalent dimer of a minor toxin component L-amino acid oxidase in the P. colletti venom and a 60 kDa tetramer of a major toxin group C-type lectin in the B. arietans venom were identified amongst other components. A targeted, higher-order structural characterisation of phospholipase A2s (PLA2) in P. colletti venom by combined native and denatured MS analyses revealed a variety of monomeric, highly modified PLA2s. Furthermore, a 27.7 kDa covalently-linked PLA2 dimer was identified in P. colletti venom for the first time by MS, and these PLA2 species were also found to adopt a highly compact and spherical geometry based on ion mobility measurements of collision cross section. Importantly, further exploration of the catalytic efficiencies of the monomeric and dimeric forms of PLA2 using a MS-based PLA2 enzyme assay revealed that dimeric PLA2 possessed substantially greater bioactivity than monomeric PLA2. This highlights the significance of quaternary structures in augmenting biological activity, and emphasises the importance of understanding higher-order protein interactions in venoms.Thesis (MPhil.) -- University of Adelaide, School of Physical Sciences, 202

Classification and Automatic Annotation of Tandem Repeat Proteins in RepeatsDB

Protein tandem repeats are crucial structural elements in various biological processes, playing essential roles in cell adhesion, protein-protein interactions, and molecular recognition. These repetitive regions have sparked considerable interest in structural biology and bioinformatics, leading to the development of specialized resources like RepeatsDB. RepeatsDB is a comprehensive, curated database of annotated tandem repeat protein structures, offering a valuable resource for researchers. In this study, we systematically analyzed protein tandem repeats in RepeatsDB, with a primary focus on Alpha-Solenoids and Beta-Propellers, to enhance the existing classification system and provide a more profound understanding of protein tandem repeats. Our investigation commenced with an initial statistical analysis to elucidate the diversity and population status of distinct repeat groups within the database, as well as their respective degree of annotation. This approach proved instrumental in addressing the challenges associated with numerous entries that had a missing annotation. We conducted a structural analysis using pairwise structural alignment and explored dimensionality reduction and visualization techniques to uncover novel structural relationships. These findings improved our understanding of protein structural comparisons and informed a refined classification system. We utilized the density-based clustering algorithm, DBSCAN, to establish structural similarity ranges for Clan members and provide computational support for defining Clan boundaries. This method proved effective in detecting outlier entries and refining existing clans, leading to the proposal of new repeat groups. Additionally, we implemented a supervised classification experiment using the K-Nearest Neighbors (KNN) algorithm, which facilitated the automatic annotation of previously unannotated entries. This study introduces an automatic annotation methodology that significantly improves the performance of RepeatsDB curators and can be extended to other bioinformatics applications. The findings contribute to a more comprehensive understanding of protein tandem repeats and offer valuable insights for future research in structural biology and bioinformatics.Abstract Protein tandem repeats are crucial structural elements in various biological processes, playing essential roles in cell adhesion, protein-protein interactions, and molecular recognition. These repetitive regions have sparked considerable interest in structural biology and bioinformatics, leading to the development of specialized resources like RepeatsDB. RepeatsDB is a comprehensive, curated database of annotated tandem repeat protein structures, offering a valuable resource for researchers. In this study, we systematically analyzed protein tandem repeats in RepeatsDB, with a primary focus on Alpha-Solenoids and Beta-Propellers, to enhance the existing classification system and provide a more profound understanding of protein tandem repeats. Our investigation commenced with an initial statistical analysis to elucidate the diversity and population status of distinct repeat groups within the database, as well as their respective degree of annotation. This approach proved instrumental in addressing the challenges associated with numerous entries that had a missing annotation. We conducted a structural analysis using pairwise structural alignment and explored dimensionality reduction and visualization techniques to uncover novel structural relationships. These findings improved our understanding of protein structural comparisons and informed a refined classification system. We utilized the density-based clustering algorithm, DBSCAN, to establish structural similarity ranges for Clan members and provide computational support for defining Clan boundaries. This method proved effective in detecting outlier entries and refining existing clans, leading to the proposal of new repeat groups. Additionally, we implemented a supervised classification experiment using the K-Nearest Neighbors (KNN) algorithm, which facilitated the automatic annotation of previously unannotated entries. This study introduces an automatic annotation methodology that significantly improves the performance of RepeatsDB curators and can be extended to other bioinformatics applications. The findings contribute to a more comprehensive understanding of protein tandem repeats and offer valuable insights for future research in structural biology and bioinformatics

Topology of Reticulate Evolution

The standard representation of evolutionary relationships is a bifurcating tree. However, many types of genetic exchange, collectively referred to as reticulate evolution, involve processes that cannot be modeled as trees. Increasing genomic data has pointed to the prevalence of reticulate processes, particularly in microorganisms, and underscored the need for new approaches to capture and represent the scale and frequency of these events. This thesis contains results from applying new techniques from applied and computational topology, under the heading topological data analysis, to the problem of characterizing reticulate evolution in molecular sequence data. First, we develop approaches for analyzing sequence data using topology. We propose new topological constructions specific to molecular sequence data that generalize standard constructions such as Vietoris-Rips. We draw on previous work in phylogenetic networks and use homology to provide a quantitative measure of reticulate events. We develop methods for performing statistical inference using topological summary statistics. Next, we apply our approach to several types of molecular sequence data. First, we examine the mosaic genome structure in phages. We recover inconsistencies in existing morphology-based taxonomies, use a network approach to construct a genome-based representation of phage relationships, and identify conserved gene families within phage populations. Second, we study influenza, a common human pathogen. We capture widespread patterns of reassortment, including nonrandom cosegregation of segments and barriers to subtype mixing. In contrast to traditional influenza studies, which focus on the phylogenetic branching patterns of only the two surface-marker proteins, we use whole-genome data to represent influenza molecular relationships. Using this representation, we identify unexpected relationships between divergent influenza subtypes. Finally, we examine a set of pathogenic bacteria. We use two sources of data to measure rates of reticulation in both the core genome and the mobile genome across a range of species. Network approaches are used to represent the population of S. aureus and analyze the spread of antibiotic resistance genes. The presence of antibiotic resistance genes in the human microbiome is investigated

Life during Dormancy: Genetic Regulation in Fission Yeast Spores and in Killifish Diapause

Dormant stages allow organisms to survive in life-threatening environments for extended periods of time. Dormancy is characterised by a reversible arrest of cell replication and increased stress resistance. In addition, dormancy involves reprogramming of gene expression and energy metabolism from a mode of proliferation and development to a mode of suspended growth, possibly including suspended ageing. Here I study the spores of fission yeast and diapause embryos of turquoise killifish to reveal similarities in transcriptome and proteome changes during dormancy. Moreover, I uncover some conservation in the genetic regulation of dormancy and halted ageing across different organisms and dormant stages, including yeast quiescence and worm dauer stages. In particular, I find ribosomal proteins and autophagy play critical roles in supporting dormancy in yeast and killifish. Supporting this result, functional analysis using Barcode sequencing of the genome-wide deletion library for fission yeast identifies ribosomal proteins and autophagy as important factors for survival during dormancy. Furthermore, while traditionally it has been assumed that spores in yeast and diapause in killifish equate to suspension biological activity, I find that both dormant states can respond to environmental or physiological triggers by altering their gene-expression programmes. Specifically, this response includes proteomic and transcriptomic changes to heat stress as well as changes with the chronological passing of time following their formation. While some of these genetic changes mimic non-dormant yeast stress responses, they differ from expression signatures observed during ageing. This finding is consistent with the idea that dormant yeast and killifish cells are not ageing in the same manner as non- dormant cells, or that ageing is even suspended during dormancy. Finally, as dormant stages are a state of suspended activity, events occurring during the dormant stage are not thought to affect the organism in post-dormant cells. Intriguingly, I find that the stress experienced during dormancy and the duration the organism stays in dormancy is ‘remembered’ and can affect post-dormancy recovery in both yeast and killifish. This phenomenon is evidenced by changes in gene expression profiles. I also observe subtle differences in stress resistance and chronological lifespan in germinates from stressed or old spores. This is exhibited by a type of hormesis where sub-lethal stress during dormancy might confer a slight lifespan extension in the post-dormant state. These new insights transform our understanding of “dormant states” and the implication of dormancy to post-dormancy stress survival and ageing