The Role of Genomic Context in Bacterial Growth Homeostasis

The growth of bacteria is a complex but well-orchestrated dance involving the repetitive and reproducible production of their diverse cellular components in order to divide. A lot can go astray and therefore the cell has developed several strategies in order to ensure everything remains synchronized. This problem is only further complicated as the cells adjust their growth rate to their living conditions resulting in ripple effects throughout the cell physiology. One notable change is that as nutrient availability and quality increases so too does the average size and the concentration of ribosomes in the cell. The latter enables the production of the largest macromolecule faction in the cell (proteins) including the production of more ribosomes required to maintain the protein synthesis requirements. With the increase in volume of the cell comes a required increase in surface area, and a disbalance between these two would result in untenable levels of internal pressure. How then do bacteria ensure that volume growth is synchronized with the production of cell envelope components so that cell homeostasis is maintained, especially in the face of fluctuating growth rate? Genomic context is known to assist in co-regulation of genes thereby synchronizing them to respond to different cellular stimuli. As the bacterial genome is highly fluid, the existence of conserved genomic contexts suggests important loci of co-regulation. Could it be in these gene clusters that a possible link between growth and surface expansion is found? To answer this question this thesis undertook three missions, firstly we established a genome comparison tool (www.GenCoDB.org) that will take advantage of the ever-growing availability of bacterial genomes to assist us in the analysis, comparison, and quantification of genome contexts. This will rely on novel strategies in order to: accommodate the breadth of genome data available in a computationally efficient manner, reduce the effect of sampling bias that plague most bacterial datasets and ensure candidates are considered significant for their evolutionary context. The availability of GenCoDB is sure to facilitate genomic context research in the microbiology community and improve accessibility to non-bioinformatics to this wellspring of important biological data. With the swath of genomic neighbourhood data, we then sought to understand and analyse the evolution of conserved gene clusters in order to narrow down possible volume-surface regulating candidates. By tracking the evolution of gene clusters throughout the Bacteria kingdom we found that co-orientation is strongly conserved, however, this does not influence the subsequent context around the cluster nor the expansion of the cluster. We found that vertical transmission and not horizontal gene transfer was found to be the driving factor of gene cluster occurrence in chromosomes and that the origin and terminus are hotspots for cluster maintenance. Finally, we found that despite the apparent frequency of operon organization in gene clusters, gene clusters appear to be maintained due to other selective pressures such as within-cluster protein-protein interactions and the essential status of their genes. We suggest that operons are a consequence and not a cause co-localization over evolutionary time. We identified a single gene cluster candidate that met all the requirements we believe are required for cell growth homeostasis of synchronized surface and volume expansion. These requirements were a broad conservation within Bacteria, and a connection between ribosome-associated proteins (growth) with cell envelope synthesizes. In agreement with our evolution studies we found that whilst the cluster was co-regulated this did not appear to be the selective pressure that brought these different processes together. Instead we found a potential role of genomic channelling, linking the production of pyrimidines with the synthesis of the cell envelope which is reliant on the co-localization of this cluster. Together, this work will forward the understanding of chromosome evolution in Bacteria and the potential implications of genomic context in metabolite utilization. It challenges the roles that operons and horizontal gene transfer play in the long-term evolution of gene order and it provides a new quantitative and statistical resource providing access to over 1.9 million gene neighbourhoods

Biomarkers and Bioassays for Cardiovascular Diseases: Present and Future

Stratification of cardiac patients arriving at the emergency department is now being made according to the levels of acute cardiac biomarkers (i.e. cardiac troponin (cTn) or creatine kinase myocardial band (CK-MB)). Ongoing efforts are undertaken in an attempt to identify and validate additional cardiac biomarkers, for example, interleukin-6, soluble CD40L, and C-reactive protein, in order to further risk stratify patients with acute coronary syndrome. Several studies have also now shown an association of platelet transcriptome and genomic single nucleotide polymorphisms with myocardial infarction by using advanced genomic tools. A number of markers, such as myeloid-related protein 14 (MRP-14), cyclooxygenase-1 (COX-1), 5-lipoxygenase activating protein (FLAP), leukotriene A4 hydrolase (LTA4H) and myocyte enhancing factor 2A (MEF2A), have been linked to acute coronary syndromes, including myocardial infarction. In the future, these novel markers may pave the way toward personalized disease-prevention programs based on a person’s genomic, thrombotic and cardiovascular profiles. Current and future biomarkers and bioassays for identifying at-risk patients will be discussed in this review

Project Florida: Federated Learning Made Easy

We present Project Florida, a system architecture and software development kit (SDK) enabling deployment of large-scale Federated Learning (FL) solutions across a heterogeneous device ecosystem. Federated learning is an approach to machine learning based on a strong data sovereignty principle, i.e., that privacy and security of data is best enabled by storing it at its origin, whether on end-user devices or in segregated cloud storage silos. Federated learning enables model training across devices and silos while the training data remains within its security boundary, by distributing a model snapshot to a client running inside the boundary, running client code to update the model, and then aggregating updated snapshots across many clients in a central orchestrator. Deploying a FL solution requires implementation of complex privacy and security mechanisms as well as scalable orchestration infrastructure. Scale and performance is a paramount concern, as the model training process benefits from full participation of many client devices, which may have a wide variety of performance characteristics. Project Florida aims to simplify the task of deploying cross-device FL solutions by providing cloud-hosted infrastructure and accompanying task management interfaces, as well as a multi-platform SDK supporting most major programming languages including C++, Java, and Python, enabling FL training across a wide range of operating system (OS) and hardware specifications. The architecture decouples service management from the FL workflow, enabling a cloud service provider to deliver FL-as-a-service (FLaaS) to ML engineers and application developers. We present an overview of Florida, including a description of the architecture, sample code, and illustrative experiments demonstrating system capabilities

BceAB-type antibiotic resistance transporters appear to act by target protection of cell wall synthesis

Resistance against cell wall-active antimicrobial peptides in bacteria is often mediated by transporters. In low GC-content Gram-positive bacteria, a common type of such transporters are BceAB-like systems, which frequently provide high-level resistance against peptide antibiotics that target intermediates of the lipid II cycle of cell wall synthesis. How a transporter can offer protection from drugs that are active on the cell surface, however, has presented researchers with a conundrum. Multiple theories have been discussed, ranging from removal of the peptides from the membrane, internalisation of the drug for degradation, to removal of the cellular target rather than the drug itself. To resolve this much-debated question, we here investigated the mode-of-action of the transporter BceAB of Bacillus subtilis. We show that it does not inactivate or import its substrate antibiotic bacitracin. Moreover, we present evidence that the critical factor driving transport activity is not the drug itself, but instead the concentration of drug-target complexes in the cell. Our results, together with previously reported findings, lead us to propose that BceAB-type transporters act by transiently freeing lipid II cycle intermediates from the inhibitory grip of antimicrobial peptides, and thus provide resistance through target protection of cell wall synthesis. Target protection has so far only been reported for resistance against antibiotics with intracellular targets, such as the ribosome. However, this mechanism offers a plausible explanation for the use of transporters as resistance determinants against cell wall-active antibiotics in Gram-positive bacteria where cell wall synthesis lacks the additional protection of an outer membrane

BceAB-type antibiotic resistance transporters appear to act by target protection of cell wall synthesis

Resistance against cell wall-active antimicrobial peptides in bacteria is often mediated by transporters. In low GC-content Gram-positive bacteria, a common type of such transporters are BceAB-like systems, which frequently provide high-level resistance against peptide antibiotics that target intermediates of the lipid II cycle of cell wall synthesis. How a transporter can offer protection from drugs that are active on the cell surface, however, has presented researchers with a conundrum. Multiple theories have been discussed, ranging from removal of the peptides from the membrane, internalisation of the drug for degradation, to removal of the cellular target rather than the drug itself. To resolve this much-debated question, we here investigated the mode-of-action of the transporter BceAB of Bacillus subtilis. We show that it does not inactivate or import its substrate antibiotic bacitracin. Moreover, we present evidence that the critical factor driving transport activity is not the drug itself, but instead the concentration of drug-target complexes in the cell. Our results, together with previously reported findings, lead us to propose that BceAB-type transporters act by transiently freeing lipid II cycle intermediates from the inhibitory grip of antimicrobial peptides, and thus provide resistance through target protection of cell wall synthesis. Target protection has so far only been reported for resistance against antibiotics with intracellular targets, such as the ribosome. However, this mechanism offers a plausible explanation for the use of transporters as resistance determinants against cell wall-active antibiotics in Gram-positive bacteria where cell wall synthesis lacks the additional protection of an outer membrane

From modules to networks: A systems-­level analysis of the bacitracin stress response in <i>Bacillus subtilis</i>

Bacterial resistance against antibiotics often involves multiple mechanisms that are interconnected to ensure robust protection. So far, the knowledge about underlying regulatory features of those resistance networks is sparse, since they can hardly be determined by experimentation alone. Here, we present the first computational approach to elucidate the interplay between multiple resistance modules against a single antibiotic and how regulatory network structure allows the cell to respond to and compensate for perturbations of resistance. Based on the response of Bacillus subtilis toward the cell wall synthesis-inhibiting antibiotic bacitracin, we developed a mathematical model that comprehensively describes the protective effect of two well-studied resistance modules (BceAB and BcrC) on the progression of the lipid II cycle. By integrating experimental measurements of expression levels, the model accurately predicts the efficacy of bacitracin against the B. subtilis wild type as well as mutant strains lacking one or both of the resistance modules. Our study reveals that bacitracin-induced changes in the properties of the lipid II cycle itself control the interplay between the two resistance modules. In particular, variations in the concentrations of UPP, the lipid II cycle intermediate that is targeted by bacitracin, connect the effect of the BceAB transporter and the homeostatic response via BcrC to an overall resistance response. We propose that monitoring changes in pathway properties caused by a stressor allows the cell to fine-tune deployment of multiple resistance systems and may serve as a cost-beneficial strategy to control the overall response toward this stressor

Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation

We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the private examples demonstrated in the prompt. We propose a novel algorithm that generates synthetic few-shot demonstrations from the private dataset with formal differential privacy (DP) guarantees, and show empirically that it can achieve effective ICL. We conduct extensive experiments on standard benchmarks and compare our algorithm with non-private ICL and zero-shot solutions. Our results demonstrate that our algorithm can achieve competitive performance with strong privacy levels. These results open up new possibilities for ICL with privacy protection for a broad range of applications