Identification of the Type Eleven Secretion System (T11SS) and Characterization of T11SS-dependent Effector Proteins

Host-associated microbes live in dangerous environments as a result of host immune killing, nutrient provisioning, and physiological conditions. Bacteria have evolved a host of surface and secreted proteins to help interact with this host environment and overcome nutrient limitation. The studies included within this dissertation describe the identification of a novel bacterial secretion system which has evolved to transport these symbiosis mediating proteins. This system, termed the type eleven secretion system (T11SS), is present throughout the Gram negative phylum Proteobacteria, including many human pathogens such as Neisseria meningitidis, Acinetobacter baumanii, Haemophilus haemolyticus, and Proteus vulgaris. Furthermore, these studies describe how novel cargo proteins of this secretion system were identified and characterized using molecular biology and physicochemical techniques. Chapter 1 establishes the importance of nematode model systems in researching symbiosis, highlighting how research in entomopathogenic nematodes identified the first T11S. Chapters 2 and 3 use a T11SS-dependent hemophore named hemophilin and its transporter protein to demonstrate T11SS secretion and its mechanisms of cargo specificity. Chapter 3 also explores the role of hemophilin within the nematode symbiont X. nematophila in surviving heme starvation and facilitating nematode fitness. Chapter 4 demonstrates that the lipidated symbiosis factor NilC is surface exposed by the T11SS NilB and uses a combination of metabolomics, proteomics, and lectin library analysis to describe the role of NilC in colonization. Chapter 5 describes a protocol for bioinformatically controlling genome co-occurrence analyses and utilizes this technique to demonstrate significant co-occurrence of T11SS with metal uptake pathways, single carbon metabolism, and mobile genetic elements. Additionally, this protocol allowed prediction of 141 T11SS-dependent cargo falling into 10 distinct architectures, including never before seen T11SS-dependent adhesins and glycoproteins. Finally, Chapter 6 summarizes our findings and contextualizes how the T11SS plays essential roles in host-microbe association in mutualistic bacteria and pathogenic bacteria alike

Advancing a systems cell-free metabolic engineering approach to natural product synthesis and discovery

Next generation DNA sequencing has led to an accumulation of a putative biosynthetic gene clusters for many natural product classes of interest. In vivo extraction and heterologous expression do not have sufficient throughput to validate predicted enzyme functions and inform future annotations. Further, engineering the production of new natural products is laborious and limited by the trade-offs between cell growth and product synthesis. Conversely, cell-free platforms, particularly those capable of cell-free protein synthesis (CFPS), facilitate rapid screening of enzyme function and prototyping of metabolic pathways. However, the protein content and metabolic activity of many cell-free systems are poorly defined, increasing variability between lysates and impeding systematic engineering. Here, the strength of untargeted peptidomics as an enabling tool for the engineering of cell-free systems is established based upon its ability to measure both global protein abundances and newly synthesized peptides. Synthesis of peptide natural products was found to be more robust in purified enzyme CFPS systems compared to crude lysates; however, non-specific peptide degradation, detected through peptidomics, remains a concern. Crude cell-free systems were determined be better suited to small molecule production, due to the extensive metabolic networks they were found to possess. Perturbations of these networks, carried out through changes to growth media, were observed through shotgun proteomics and informed engineering of phenol biosynthesis in a crude Escherichia coli lysate. Implementing shotgun proteomics as an analytical tool for cell-free systems will increase reproducibility and further the development of a platform for high-throughput functional genomics and metabolic engineering

The Role of the Gut Microbiome In Bone and Joint

Osteoporosis and osteoarthritis affect millions of people worldwide every year. Osteoporosis related fractures totaled 8.9 million worldwide annually and osteoarthritis affects over 30 million people in the US alone. Recently, the gut microbiome has been identified as a factor that can influence chronic conditions associated with bone and joint disease such as obesity, diabetes, metabolic syndrome, inflammatory bowel diseases, and malnutrition. Though the gut microbiome is studied extensively in relation to metabolic diseases and disorders, the role of the gut microbiome in the development and progression of bone and joint disease is largely unexplored. Recent evidence suggests that the gut microbiome can influence bone mass, however no studies have determined if the mechanical performance of the bone is influenced by the gut microbiome. Therefore, first, we characterize how alterations to the gut microbiome can influence whole bone mechanical performance at skeletal maturity. We evaluate alterations in the gut microbiome caused by genotypic alteration and chronic treatment with antibiotics. Our results demonstrate that disruption of the gut microbiome with antibiotics is associated with reductions in cortical bone mass and whole bone strength, as well as drastic shifts in the composition of the gut microbiome. Furthermore, the changes in whole bone strength are greater than can be explained by the associated changes in bone mass and geometry, suggesting impaired bone tissue material properties in mice with an altered gut microbiome due to genotypic alteration and chronic antibiotic treatment. Next, we evaluate the changes in bone tissue composition caused by alterations in the gut microbiome. Additionally, we investigate how the functional profile of the gut microbiome can influence bone tissue material properties through several potential pathways: 1) regulation of nutrient and vitamin absorption/synthesis; 2) regulation of the immune system; 3) translocation of bacterial products. Our results demonstrate that disruption of the gut microbiome with antibiotics causes changes in bone mineral crystallinity, and that the effect is different per mouse genotype. Furthermore, we show that the functional capacity of the gut microbiome is dramatically altered in mice treated with antibiotics. A pathway involving vitamin K, a factor important for bone health, and associated with fracture risk, is suspected as changes in microbial gene pathways for vitamin K synthesis are disrupted leading to reduced vitamin K levels in organs. Last, we evaluate how alterations in the gut microbiome may influence the development and severity of load-induced osteoarthritis. Here we investigate obesity and metabolic syndrome, two conditions associated with an altered gut microbiome and an increased risk of developing osteoarthritis (OA). We use a mouse model of metabolic syndrome dependent on the gut microbiome, a mouse model of severe obesity/diabetes, and an in vivo non-invasive loading model to induce osteoarthritis-like pathology. Our results demonstrate that metabolic syndrome in the current mouse model does not increase load-induced cartilage damage, while severe obesity leads to increases in cartilage damage, though only after a prolonged loading period. The increased cartilage damage in severely obese mice is associated with increased adiposity, systemic inflammation, and bacterial lipopolysaccharide. We also demonstrate that disruption of the gut microbiome in the metabolic syndrome mice is associated with decreased load-induced cartilage damage, as well as changes in subchondral bone properties. Together, the current work suggests the gut microbiome influences both the structure and composition of bone, and can influence the development of osteoarthritis. The current work helps to establish a promising foundation for future lines of investigation evaluating how the gut microbiome influences bone and joint and suggests that there may be a future use for manipulating the gut microbiome in therapies to treat and prevent bone and joint disease

Advances in Host Plant and Rhizobium Genomics to Enhance Symbiotic Nitrogen Fixation in Grain Legumes

Legumes form symbiotic relationship with root-nodule, rhizobia. The nitrogen (N2) fixed by legumes is a renewable source and of great importance to agriculture. Symbiotic nitrogen fixation (SNF) is constrained by multiple stresses and alleviating them would improve SNF contribution to agroecosystems. Genetic differences in adaptation tolerance to various stresses are known in both host plant and rhizobium. The discovery and use of promiscuous germplasm in soybean led to the release of high-yielding cultivars in Africa. High N2-fixing soybean cultivars are commercially grown in Australia and some countries in Africa and South America and those of pea in Russia. SNF is a complex trait, governed by multigenes with varying effects. Few major quantitative trait loci (QTL) and candidate genes underlying QTL are reported in grain and model legumes. Nodulating genes in model legumes are cloned and orthologs determined in grain legumes. Single nucleotide polymorphism (SNP) markers from nodulation genes are available in common bean and soybean. Genomes of chickpea, pigeonpea, and soybean; and genomes of several rhizobium species are decoded. Expression studies revealed few genes associated with SNF in model and grain legumes. Advances in host plant and rhizobium genomics are helping identify DNA markers to aid breeding of legume cultivars with high symbiotic efficiency. A paradigm shift is needed by breeding programs to simultaneously improve host plant and rhizobium to harness the strength of positive symbiotic interactions in cultivar development. Computation models based on metabolic reconstruction pathways are providing greater insights to explore genotype–phenotype relationships in SNF. Models to simulate the response of N2 fixation to a range of environmental variables and crop growth are assisting researchers to quantify SNF for efficient and sustainable agricultural production systems. Such knowledge helps identifying bottlenecks in specific legume–rhizobia systems that could be overcome by legume breeding to enhance SNF. This review discusses the recent developments to improve SNF and productivity of grain legumes

Discovery of Pseudomonas Natural Products Involved in the Biological Control of Potato Pathogens

Potato common scab and late blight, caused by Streptomyces scabies and Phytophthora infestans, respectively, are serious diseases affecting one of the world’s largest and most important food crops. The lack of stable interventions has shifted recent focus toward biological control (biocontrol) methods. Pseudomonas isolates have shown significant promise as bacterial biocontrol agents, occurring in soils worldwide with high inter-strain diversity and potential for natural product biosynthesis. This thesis details investigations into the biosynthetic potential of environmental Pseudomonas strains isolated from a potato field, with a focus on discovering novel natural products active against plant pathogens. Investigations focused on a strain showing strong biocontrol phenotypes, Ps652. Initially, this strain showed strong inhibition of phytopathogens but with few biosynthetic gene clusters (BGCs) identified by common methods. A variety of methods were used to identify the determinants of the strong biocontrol phenotype shown by this strain, including activity-guided isolation of natural products and transposon mutagenesis. 3,7-dihydroxytropolone (3,7-HT) is reported here as being produced by a Pseudomonas isolate for the first time. 3,7-HT shows improved activity towards Streptomyces scabies compared to 7-hydroxytropolone, but does not fully explain activity of Ps652 against P. infestans. Additionally, investigations were made into putative RiPP BGCs containing DUF692 proteins in environmental Pseudomonas isolates Ps706 and Ps708. These BGCs appeared associated with phytopathogen inhibition in previous work, and were studied here using bioinformatics, gene deletions, and heterologous expression approaches

Identification of RISC-associated microRNAs and their targets during CD8⁺ T cell activation

MicroRNAs (miRNAs) are short (~22 nucleotide long) single-stranded noncoding RNAs that regulate gene expression post-transcriptionally in the RNA-induced silencing complex (RISC). miRNAs play an important role in immune cell function and affect many aspects of T cell immunity. Activation of naive T cells induces dramatic changes in the expression of miRNAs and RISC-associated proteins. We studied these changes in expression of miRNAs in CD8+ T cells using the OT-I transgenic T-cell receptor (TCR) mouse model, in which all T cells are CD8+ and respond to ovalbumin peptides. Upon in vitro activation, we saw dynamic changes in the expression of individual miRNAs, which were influenced by whether the T cells responded to high or low affinity peptides and whether they were differentiating to effector or memory cells. It was recently shown that in naive T cells, miRNAs are predominantly found in a low molecular weight (LMW) RISC composed of Argonaute (Ago)-proteins and miRNAs. Upon activation of T cells, biologically active miRNAs interacting with their target messenger RNAs (mRNAs) were shown to redistribute to a high molecular weight (HMW) RISC, which additionally contains RNA metabolism factors and Ago-interacting proteins such as GW182. We followed the development of HMW and LMW complexes in activated CD8⁺ T cells in order to determine their role and to identify the miRNAs and their targets present in both. We confirmed that GW182 protein was induced upon CD8⁺ T cell activation and associated with Ago-2, forming HMW complexes. To study the distribution of miRNAs between HMW and LMW RISC, we undertook small RNA sequencing of the associated miRNAs. From these data we identified specific miRNAs that were enriched in HMW RISC in activated CD8⁺ T cells. We also found that miRNA abundance did not always reflect its association with HMW RISC. Lastly, to discover miRNA targets, we used a novel method called cross-linking, ligation and sequencing of hybrids (CLASH), which directly identifies miRNAs and their targets by immunoprecipitation of RISC and RNA sequencing. From these data we found potential novel targets for key miRNAs in CD8⁺ T cells. Expanding our knowledge of the role of miRNAs in T cell activation beyond observations of miRNA expression changes, by focusing on biologically active miRNAs and their targets in HMW RISC will deepen our understanding of the mechanism of action of miRNAs as well as the signalling pathways surrounding T cell activation

Exploring the chemical space of post-translationally modified peptides in Streptomyces with machine learning

The ongoing increase in antimicrobial resistance combined with the low discovery of novel antibiotics is a serious threat to our health care. Genome mining has given new potential to the field of natural product discovery, as thousands of biosynthetic gene clusters (BGCs) are discovered for which the natural product is not known.Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent a highly diverse class of natural products. The large number of different modifications that can be applied to a RiPP results in a large variety of chemical structures, but also stems from a large genetic variety in BGCs. As a result, no single method can effectively mine for all RiPP BGCs, making it an interesting source for new molecules.In this thesis, new methods are explored to mine genomes for the BGCs of novel RiPP variants, with a focus on discovering RiPPs that have new modifications. RRE-Finder is a new tool for the detection of RiPP Recognition Elements, domains that are often found in RiPP BGCs. DecRiPPter is another tool that employs machine learning models to discover new RiPP precursor genes encoded in the genomes. Both tools can be used to prioritize novel RiPP BGCs. Two candidate BGCs are characterized, one of which could be shown to specify a new RiPP, validating the approach.Grant 731.014.206 (Syngenopep, TKI Chemie) from the Dutch Research Council (NWO)Microbial Biotechnolog

Development of 3D Bioartificial Human Tissue Models of Periprosthetic Shoulder Joint Infection

Periprosthetic joint infection (PJI) is a devastating and costly post-surgical complication that is not well understood due to the scarcity of physiologically representative experimental models. This thesis outlines the development of two 3D bioartificial human tissue models designed to study the cellular and biochemical interactions between primary fibroblasts from the shoulder capsule (SC) and infectious microorganisms. Using the Fibroblast-Bacteria Co-culture in 3D Collagen model, we demonstrated a global gene repression of metabolic and homeostatic processes in SC fibroblasts following 48 hours of co-culture with Cutibacterium acnes – the most common microbial cause of PJI in the shoulder. These cellular changes coincided with an increase in pro-inflammatory signaling. The Shoulder-Joint Implant Mimetic (S-JIM) model generated a range of oxygen levels (\u3c 0.3% to 21% O2) that accurately represents the different microenvironments present across connective tissue layers in a shoulder joint. Electron microscopy images confirmed that the hypoxic conditions generated in the core of the S-JIM supported the anaerobic proliferation of C. acnes, but this microbial expansion resulted in the death of adjacent SC fibroblasts after 96 hours of co-culture. Using the S-JIM, we demonstrated the bactericidal effectiveness of direct vancomycin prophylaxis against C. acnes and confirmed the possibility of differentiating between healthy host tissues and C. acnes-infected tissues using mass spectrometry