Clostridium difficile is a bacterial pathogen of global significance that is a major cause of antibiotic-associated diarrhea. Antibiotics deplete the indigenous gut microbiota and change the metabolic environment in the gut to one favoring C. difficile growth. Here we used metabolomics and transcriptomics to define the gut environment after antibiotics and during the initial stages of C. difficile colonization and infection. We show that amino acids, in particular, proline and branched-chain amino acids, and carbohydrates decrease in abundance over time and that C. difficile gene expression is consistent with their utilization by the bacterium in vivo. We employed an integrated approach to analyze the metabolome and transcriptome to identify associations between metabolites and transcripts. This highlighted the importance of key nutrients in the early stages of colonization, and the data provide a rationale for the development of therapies based on the use of bacteria that specifically compete for nutrients that are essential for C. difficile colonization and disease.

Human immunodeficiency virus (HIV)-infected patients are at an increased risk of co-infection with human papilloma virus (HPV), and subsequent malignancies such as oral cancer. To determine the role of HIV-associated immune suppression on HPV persistence and pathogenesis, and to investigate the mechanisms underlying the modulation of HPV infection and oral cancer by HIV, we developed a mathematical model of HIV/HPV co-infection. Our model captures known immunological and molecular features such as impaired HPV-specific effector T helper 1 (Th1) cell responses, and enhanced HPV infection due to HIV. We used the model to determine HPV prognosis in the presence of HIV infection, and identified conditions under which HIV infection alters HPV persistence in the oral mucosa system. The model predicts that conditions leading to HPV persistence during HIV/HPV co-infection are the permissive immune environment created by HIV and molecular interactions between the two viruses. The model also determines when HPV infection continues to persist in the short run in a co-infected patient undergoing antiretroviral therapy. Lastly, the model predicts that, under efficacious antiretroviral treatment, HPV infections will decrease in the long run due to the restoration of CD4+ T cell numbers and protective immune responses.

One contribution of 9 to a theme issue 'Multiscale dynamics of infectious diseases'.Many pathogens are able to replicate or survive in abiotic environments. Disease transmission models that include environmental reservoirs and environment-to-host transmission have used a variety of functional forms and modelling frameworks without a clear connection to pathogen ecology or space and time scales. We present a conceptual framework to organize microparasites based on the role that abiotic environments play in their lifecycle. Mean-field and individual-based models for environmental transmission are analysed and compared. We show considerable divergence between both modelling approaches when conditions do not facilitate well mixing and for pathogens with fast dynamics in the environment. We conclude with recommendations for modelling environmentally transmitted pathogens based on the pathogen lifecycle and time and spatial scales of the host-pathogen system under consideration.

Antimicrobial drugs administered systemically may cause the emergence and dissemination of antimicrobial resistance among enteric bacteria. To develop logical, research-based recommendations for food animal veterinarians, we must understand how to maximize antimicrobial drug efficacy while minimizing risk of antimicrobial resistance. Our objective is to evaluate the effect of two approved dosing regimens of enrofloxacin (a single high dose or three low doses) on Escherichia coli in cattle. We look specifically at bacteria above and below the epidemiological cutoff (ECOFF), above which the bacteria are likely to have an acquired or mutational resistance to enrofloxacin. We developed a differential equation model for the antimicrobial drug concentrations in plasma and colon, and bacteria populations in the feces. The model was fit to animal data of drug concentrations in the plasma and colon obtained using ultrafiltration probes. Fecal E. coli counts and minimum inhibitory concentrations were measured for the week after receiving the antimicrobial drug. We predict that the antimicrobial susceptibility of the bacteria above the ECOFF pre-treatment strongly affects the composition of the bacteria following treatment. Faster removal of the antimicrobial drugs from the colon throughout the study leads to improved clearance of bacteria above the ECOFF in the low dose regimen. If we assume a fitness cost is associated with bacteria above the ECOFF, the increased fitness costs leads to reduction of bacteria above the ECOFF in the low dose study. These results suggest the initial E. coli susceptibility is a strong indicator of how steers respond to antimicrobial drug treatment. Fig 1. ODE schematic. Schematic of the mathematical model for enrofloxacin and ciprofloxacin concentrations throughout the gastrointestinal tract (GIT) of the steer and the effects on the E. coli population. The compartments S, P, and C are antimicrobial concentration in the steer while the compartments E and R describe the bacteria population in the feces.

The ability of the immune system to clear pathogens is limited during chronic virus infections where potent long-lived plasma and memory B-cells are produced only after germinal center B-cells undergo many rounds of somatic hypermutations. In this paper, we investigate the mechanisms of germinal center B-cell formation by developing mathematical models for the dynamics of B-cell somatic hypermutations. We use the models to determine how B-cell selection and competition for T follicular helper cells and antigen influences the size and composition of germinal centers in acute and chronic infections. We predict that the T follicular helper cells are a limiting resource in driving large numbers of somatic hypermutations and present possible mechanisms that can revert this limitation in the presence of non-mutating and mutating antigen.

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e17554 Background: The ability to understand and predict at the time of diagnosis the trajectories of prostate cancer patients is critical for deciding the appropriate treatment plan. Evidence-based approaches for outcome prediction include predictive machine learning algorithms that harness health record data. Methods: All our analyses used the Veterans Affairs Clinical Data Warehouse (CDW). We included all individuals with a non-metastatic (early stage) prostate cancer diagnosis between 2002 and 2017 as documented in the CDW cancer registry (N = 111351). Our predictors were demographics (age at diagnosis, race), disease staging parameters abstracted at diagnosis ( Stage grouping AJCC, Gleason score, SEER summary stage) and prostate specific antigen (PSA) laboratory values in the last 5 years prior to diagnosis (last value, the value before last, average, minimum, maximum, rate of the change of the last 2 PSAs and density). The predicted outcome was disease progression at 2 years (N = 3469) and 5 years (N = 6325) defined as metastasis - taking either Abiraterone, Sipuleucel-T, Enzalutamide or Radium 223, registry cancer related death or PSA > 50. We used 4 different machine learning classifiers to train prediction models: random forest, k-nearest neighbor, decision trees, and xgboost all with hyper parameter optimization. For testing, we used two approaches: (1) 20% sample held out at the beginning of the study, and (2) stratified test/train split on the remaining data. Results: The table below shows the performance of the best classifier, xgboost. The top five predictors of disease progression were the last PSA, Gleason Score, maximum PSA, age at diagnosis, and SEER summary stage. The last PSA had a significantly higher contribution than the other predictors. More than one PSA value is important for prediction, emphasizing the need for investigating the PSA trajectory in the period before diagnosis. The models are overall very robust going from outcome at 2 years compared to 5 years. Conclusions: A machine learning based xgboost classifier can be integrated in clinical decision support at diagnosis, to robustly predict disease progression at 2 and 5 years. [Table: see text]

Strain-specific plasma cells are capable of producing neutralizing antibodies that are essential for clearance of challenging pathogens. These neutralizing antibodies also function as a main defense against disease establishment in a host. However, when a rapidly mutating pathogen infects a host, successful control of the invasion requires shifting the production of plasma cells from strain-specific to broadly reactive. In this study, we develop a mathematical model of germinal center dynamics and use it to predict the events that lead to improved breadth of the plasma cell response. We examine scenarios that lead to germinal centers that are composed of B-cells that come from a single strain-specific clone, a single broadly reactive clone or both clones. We find that the initial B-cell clonal composition, T-follicular helper cell signaling, increased rounds of productive somatic hypermutation, and B-cell selection strength are among the mechanisms differentiating between strain-specific and broadly reactive plasma cell production during infections. Understanding the contribution of these factors to emergence of breadth may assist in boosting broadly reactive plasma cells production.