Periodic bottlenecks in experimental antibiotic resistance evolution of Pseudomonas aeruginosa

Over the past decades, the spread of antibiotic resistance among nosocomial bacterial pathogens has developed into a global problem. Population bottlenecks are an important factor for bacterial evolution. Their influence on antibiotic resistance evolution is however not yet fully understood. Bottlenecks are defined as a strong reduction of population size that can lower the population‘s genetic diversity drastically. Population bottlenecks frequently occur in nature and play a significant role in the evolutionary history of populations. Bacterial populations can evolve resistance by various adaptive paths. However, the serial bottlenecks experienced by bacteria both in nature and in experimental evolution influence the direction of adaptation. After surviving a narrow bottleneck, future adaptation is more likely influenced by selective sweeps and periodic selection, rendering the adaptive paths less predictable. In contrast, higher degrees of parallel evolution and clonal interference are expected in case of a wider bottleneck, as higher genetic diversity is likely maintained. In this thesis, I validated the influence of different bottleneck sizes at different levels of selectivity on the evolvability of resistance in populations of the pathogenic bacterium Pseudomonas aeruginosa (subclone PA14). Three different evolution experiments were performed to simulate single drug treatments with carbenicillin (beta-lactam), ciprofloxacin (quinolone) and gentamicin (aminoglycoside) against PA14, for approximately 100 generations. While high inhibitory concentrations selected for the highest resistance under large transfer sizes, the highest resistance in low inhibitory concentrations populations emerged when the transfer size was small. These different dynamics are reflected by mutational patterns in the evolving bacterial genomes. Even though the total number of mutations per population for each treatment depended on the treatment drug, the diversity of the most frequent mutations at the final growth season was higher for small transfer sizes than for large transfer sizes. Surprisingly, only few mutations have completely fixed by the final transfer. These results may indicate that clonal interference of de novo mutations occurs regularly at sub-inhibitory drug concentrations. Overall, my data suggests that bottlenecks, in combination with antibiotic-induced selective pressure, can be a key determinant of resistance evolution and can shape genetic diversity within and between populations

Semantic systems biology of prokaryotes : heterogeneous data integration to understand bacterial metabolism

The goal of this thesis is to improve the prediction of genotype to phenotypeassociations with a focus on metabolic phenotypes of prokaryotes. This goal isachieved through data integration, which in turn required the development ofsupporting solutions based on semantic web technologies. Chapter 1 providesan introduction to the challenges associated to data integration. Semantic webtechnologies provide solutions to some of these challenges and the basics ofthese technologies are explained in the Introduction. Furthermore, the ba-sics of constraint based metabolic modeling and construction of genome scalemodels (GEM) are also provided. The chapters in the thesis are separated inthree related topics: chapters 2, 3 and 4 focus on data integration based onheterogeneous networks and their application to the human pathogen M. tu-berculosis; chapters 5, 6, 7, 8 and 9 focus on the semantic web based solutionsto genome annotation and applications thereof; and chapter 10 focus on thefinal goal to associate genotypes to phenotypes using GEMs. Chapter 2 provides the prototype of a workflow to efficiently analyze in-formation generated by different inference and prediction methods. This me-thod relies on providing the user the means to simultaneously visualize andanalyze the coexisting networks generated by different algorithms, heteroge-neous data sets, and a suite of analysis tools. As a show case, we have ana-lyzed the gene co-expression networks of M. tuberculosis generated using over600 expression experiments. Hereby we gained new knowledge about theregulation of the DNA repair, dormancy, iron uptake and zinc uptake sys-tems. Furthermore, it enabled us to develop a pipeline to integrate ChIP-seqdat and a tool to uncover multiple regulatory layers. In chapter 3 the prototype presented in chapter 2 is further developedinto the Synchronous Network Data Integration (SyNDI) framework, whichis based on Cytoscape and Galaxy. The functionality and usability of theframework is highlighted with three biological examples. We analyzed thedistinct connectivity of plasma metabolites in networks associated with highor low latent cardiovascular disease risk. We obtained deeper insights froma few similar inflammatory response pathways in Staphylococcus aureus infec-tion common to human and mouse. We identified not yet reported regulatorymotifs associated with transcriptional adaptations of M. tuberculosis.In chapter 4 we present a review providing a systems level overview ofthe molecular and cellular components involved in divalent metal homeosta-sis and their role in regulating the three main virulence strategies of M. tu-berculosis: immune modulation, dormancy and phagosome escape. With theuse of the tools presented in chapter 2 and 3 we identified a single regulatorycascade for these three virulence strategies that respond to limited availabilityof divalent metals in the phagosome. The tools presented in chapter 2 and 3 achieve data integration throughthe use of multiple similarity, coexistence, coexpression and interaction geneand protein networks. However, the presented tools cannot store additional(genome) annotations. Therefore, we applied semantic web technologies tostore and integrate heterogeneous annotation data sets. An increasing num-ber of widely used biological resources are already available in the RDF datamodel. There are however, no tools available that provide structural overviewsof these resources. Such structural overviews are essential to efficiently querythese resources and to assess their structural integrity and design. There-fore, in chapter 5, I present RDF2Graph, a tool that automatically recoversthe structure of an RDF resource. The generated overview enables users tocreate complex queries on these resources and to structurally validate newlycreated resources. Direct functional comparison support genotype to phenotype predictions.A prerequisite for a direct functional comparison is consistent annotation ofthe genetic elements with evidence statements. However, the standard struc-tured formats used by the public sequence databases to present genome an-notations provide limited support for data mining, hampering comparativeanalyses at large scale. To enable interoperability of genome annotations fordata mining application, we have developed the Genome Biology OntologyLanguage (GBOL) and associated infrastructure (GBOL stack), which is pre-sented in chapter 6. GBOL is provenance aware and thus provides a consistentrepresentation of functional genome annotations linked to the provenance.The provenance of a genome annotation describes the contextual details andderivation history of the process that resulted in the annotation. GBOL is mod-ular in design, extensible and linked to existing ontologies. The GBOL stackof supporting tools enforces consistency within and between the GBOL defi-nitions in the ontology. Based on GBOL, we developed the genome annotation pipeline SAPP (Se-mantic Annotation Platform with Provenance) presented in chapter 7. SAPPautomatically predicts, tracks and stores structural and functional annotationsand associated dataset- and element-wise provenance in a Linked Data for-mat, thereby enabling information mining and retrieval with Semantic Webtechnologies. This greatly reduces the administrative burden of handling mul-tiple analysis tools and versions thereof and facilitates multi-level large scalecomparative analysis. In turn this can be used to make genotype to phenotypepredictions. The development of GBOL and SAPP was done simultaneously. Duringthe development we realized that we had to constantly validated the data ex-ported to RDF to ensure coherence with the ontology. This was an extremelytime consuming process and prone to error, therefore we developed the Em-pusa code generator. Empusa is presented in chapter 8. SAPP has been successfully used to annotate 432 sequenced Pseudomonas strains and integrate the resulting annotation in a large scale functional com-parison using protein domains. This comparison is presented in chapter 9.Additionally, data from six metabolic models, nearly a thousand transcrip-tome measurements and four large scale transposon mutagenesis experimentswere integrated with the genome annotations. In this way, we linked gene es-sentiality, persistence and expression variability. This gave us insight into thediversity, versatility and evolutionary history of the Pseudomonas genus, whichcontains some important pathogens as well some useful species for bioengi-neering and bioremediation purposes. Genome annotation can be used to create GEM, which can be used to betterlink genotypes to phenotypes. Bio-Growmatch, presented in chapter 10, istool that can automatically suggest modification to improve a GEM based onphenotype data. Thereby integrating growth data into the complete processof modelling the metabolism of an organism. Chapter 11 presents a general discussion on how the chapters contributedthe central goal. After which I discuss provenance requirements for data reuseand integration. I further discuss how this can be used to further improveknowledge generation. The acquired knowledge could, in turn, be used to de-sign new experiments. The principles of the dry-lab cycle and how semantictechnologies can contribute to establish these cycles are discussed in chapter11. Finally a discussion is presented on how to apply these principles to im-prove the creation and usability of GEM’s.</p

Ancestral Sequence Reconstructions of Stator Proteins of the Bacterial Flagellar Motor

The bacterial flagellar motor (BFM) is a bidirectional nanomachine that confers motility to several bacteria. The BFM is powered by ion transfer across the cell membrane through its stator. The stator consists of two membrane proteins: MotA and MotB in proton (H+)-powered motors or PomA and PomB in sodium (Na+)-powered motors. Over the years, several parts of the BFM have been resolved using numerous mutagenesis studies and different microscopic techniques. However, the entire structure of the BFM, its ion selection mechanism, the functional roles of each structural residue, and how its complexity evolves and adapts over time are not completely known. In this thesis, we used ancestral sequence reconstruction (ASR) to study the evolutionary history and roles of the key structural residues of the stator complex of the BFM. First, we reconstructed and synthesised thirteen combined transmembrane (TM) and plug domains of ancestral MotBs (MotB-ASRs) to test previously hypothesised critical motifs for the ion-selectivity of BFM. The results showed that all resurrected MotB-ASRs were functional and restored motility with the contemporary E. coli MotA in a stator-deleted strain. In addition, all MotB-ASRs exhibited Na+-independent motility in different ionic conditions, suggesting that the synthesised MotB-ASRs were more likely to be proton-powered. Secondly, we reconstructed and synthesised ten complete ancient MotAs (MotA-ASRs) to study the role of the key structural residues of MotA in BFM function. We identified that four of the ten MotA-ASRs were functional and restored motility in combination with contemporary E. coli MotB and several previously synthesised MotB-ASRs. The functional MotA-ASRs also showed Na+-independent motility in different ionic conditions, like our MotB-ASRs. Additionally, the resurrected MotA-ASRs provided evidence of several variable regions of MotA and revealed 30 conserved residues that were essential for flagellar function. Lastly, we screened two novel motility inhibitors, HM2-16F and BB2-50F, and characterised their anti-motility activity on multiple strains and stator types. We also optimised and developed new high-resolution assays for the phenotypic study of stator function to verify the targets of the motility inhibitors. Our results confirmed that these compounds inhibited bacterial swimming but did not target the stator. In summary, this thesis shows the use of ASR as a tool to study the stator proteins of the BFM

Environmental triggers for geosmin and 2-MIB production in drinking water reservoirs

The presence of taste and odour compounds (T&O) in drinking water lead to numerous complaints to water companies worldwide. Geosmin and 2-MIB are common T&O compounds, with Cyanobacteria being the primary biological source in drinking water reservoirs. Both compounds have low odour thresholds in humans and require expensive additional treatment. This thesis used molecular and statistical analysis of water from Welsh Water reservoirs, to provide a framework for predicting and monitoring T&O events and understanding their causes. Elevated T&O concentrations were confined to warmer months, except for a one geosmin event in winter 2019. There was no correlation between cyanobacterial abundance and T&O concentrations, but qPCR analysis based on eDNA sampling demonstrated that geosmin synthase (geoA) was a suitable proxy for predicting geosmin concentrations. Abundances of geoA and 2-MIB cyclase (mic) were significantly non-linearly associated with high ammonium-to-nitrate ratios, identifying thresholds for heightened T&O risk. The ratio of total inorganic nitrogen to total phosphorous was significantly non-linearly associated with increases in geoA. Increased geoA was also significantly negatively associated with temperature and dissolved reactive silicate in all reservoirs. Next-generation sequencing of bacterial and algal communities showed that community compositions clustered according to T&O concentrations. Bacterial and algal co-occurrence networks uncovered significant positive and negative associations, highlighting cyanospheres in all reservoirs. Random Forest models were developed for geosmin (Alaw) and 2-MIB (Pentwyn) using significantly co-occurring taxa exposing indicative T&O taxa and the probable Cyanobacteria causing the T&O. Cyanobacteria had more negative than positive associations in their cyanospheres. This thesis illustrates the importance of nutrient ratios in triggering potential geosmin and 2-MIB events. It also indicates that Cyanobacteria subjected to environmental stress (negative biotic interactions and low temperatures) increase their T&O-production. These findings provide a useful framework for water monitoring to enable the prediction and possible prevention of T&O events

An Evaluation of the Effects of Marine Oil-spills, Remediation Strategies, and Shipwrecks on Microbial Community Structure and Succession

The evaluation of how Bacteria respond to oil-contamination, and the application of dispersants and biosurfactants, in North Sea seawater microcosms is the focus of Chapter Two. Analysis revealed that dispersants and biosurfactants, which significantly reduced the interfacial tension between oil and water, significantly increased growth of obligate hydrocarbonoclastic bacteria (OHCB) in 24 hours, translating into significantly enhanced alkane-biodegradation. Early sampling of microcosms revealed how the OHCB Oleispira, hitherto considered a psychrophile, can dominate bacterial communities at the relatively high temperature of 16ºC. Bacterial response to oil-pollution is examined further in Chapter Three, where an in situ oil-slick is compared to a chemically dispersed oil-slick in the North Sea. Results suggest a lack of hydrocarbon-degrading bacteria (HCB) growth, even in samples with measurable hydrocarbons, could potentially be attributed to phosphorous limitation. Whilst the Ecological Index of Hydrocarbon Exposure, which quantifies the proportion of a bacterial community with hydrocarbon-biodegradation potential, revealed an extremely low score, highlighting a limited capacity for the environment, at the time of sampling, to naturally attenuate oil. Analysis of sediments contaminated by the Agia Zoni II oil-spill (Greece, 2017), in Chapter Four, demonstrated significant growth of HCB five-days post-oil-spill. Whilst the relative abundance of HCB declined as oil was removed, a legacy effect was observed, with the OHCB Alcanivorax and Cycloclasticus persisting for several months after the oil-spill. Finally, analysis of sediments around a North Sea shipwreck (HMS Royal Oak), in Chapter Five, revealed low levels of pyrogenic polycyclic aromatic hydrocarbons and little evidence of HCB, indicating sediments showed no long-term impact by previous oil-pollution from the shipwreck. This thesis not only advances our understanding of microbial response to oil-spills, remediation strategies, and shipwrecks, in a range of marine environments, but also highlights the importance of harnessing such knowledge and data to advance post-incident monitoring guidelines and models