Dynamic Computational Model of Symptomatic Bacteremia to Inform Bacterial Separation Treatment Requirements

A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice

Simple Summary

Computational modeling of bacterial infection is an attractive way to simulate infection scenarios. In the long-term, such models could be used to identify factors that make individuals more susceptible to infection, or how interference with bacterial growth influences the course of bacterial infection. This study used different mouse infection models (immunocompetent, lacking a microbiota, and immunodeficient models) to develop a basic mathematical model of a Yersinia enterocolitica gastrointestinal infection. We showed that our model can reflect our findings derived from mouse infections, and we demonstrated how crucial the exact knowledge about parameters influencing the population dynamics is. Still, we think that computational models will be of great value in the future; however, to foster the development of more complex models, we propose the broad implementation of the interdisciplinary training of mathematicians and biologists.

Abstract

The complex interplay of a pathogen with its virulence and fitness factors, the host’s immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001.

Inertial-based Fluidic Platform for Rapid Isolation of Blood-borne Pathogens

INTRODUCTION

Bacterial sepsis is a life-threatening disease and a significant clinical problem caused by host responses to a microbial infection. Sepsis is a leading cause of death worldwide and, importantly, a significant cause of morbidity and mortality in combat settings, placing a considerable burden on military personnel and military health budgets. The current method of treating sepsis is restricted to pathogen identification, which can be prolonged, and antibiotic administration, which is, initially, often suboptimal. The clinical trials that have been performed to evaluate bacterial separation as a sepsis therapy have been unsuccessful, and new approaches are needed to address this unmet clinical need.

MATERIALS AND METHODS

An inertial-based, scalable spiral microfluidic device has been created to overcome these previous deficiencies through successful separation of infection-causing pathogens from the bloodstream, serving as a proof of principle for future adaptations. Fluorescent imaging of fluorescent microspheres mimicking the sizes of bacteria cells and blood cells as well as fluorescently stained Acinetobacter baumannii were used to visualize flow within the spiral. The particles were imaged when flowing at a constant volumetric rate of 0.2 mL min-1 through the device. The same device was functionalized with colistin and exposed to flowing A. baumannii at 0.2 mL h-1.

RESULTS

Fluorescent imaging within the channel under a constant volumetric flow rate demonstrated that smaller, bacteria-sized microspheres accumulated along the inner wall of the channel, whereas larger blood cell-sized microspheres accumulated within the center of the channel. Additionally, fluorescently stained A. baumannii displayed accumulation along the channel walls in agreement with calculated performance. Nearly 106 colony-forming units of A. baumannii were extracted with 100% capture efficiency from flowing phosphate-buffered saline at 0.2 mL h-1 in this device; this is at least one order of magnitude more bacteria than present in the blood of a human at the onset of sepsis.

CONCLUSIONS

This type of bacterial separation device potentially provides an ideal approach for treating soldiers in combat settings. It eliminates the need for immediate pathogen identification and determination of antimicrobial susceptibility, making it suitable for rapid use within low-resource environments. The overall simplicity and durability of this design also supports its broad translational potential to improve military mortality rates and overall patient outcomes.

Immunoinformatics and Vaccine Development: An Overview

The use of vaccines have resulted in a remarkable improvement in global health. It has saved several lives, reduced treatment costs and raised the quality of animal and human lives. Current traditional vaccines came empirically with either vague or completely no knowledge of how they modulate our immune system. Even at the face of potential vaccine design advance, immune-related concerns (as seen with specific vulnerable populations, cases of emerging/re-emerging infectious disease, pathogens with complex lifecycle and antigenic variability, need for personalized vaccinations, and concerns for vaccines' immunological safety -specifically vaccine likelihood to trigger non-antigen-specific responses that may cause autoimmunity and vaccine allergy) are being raised. And these concerns have driven immunologists toward research for a better approach to vaccine design that will consider these challenges. Currently, immunoinformatics has paved the way for a better understanding of some infectious disease pathogenesis, diagnosis, immune system response and computational vaccinology. The importance of this immunoinformatics in the study of infectious diseases is diverse in terms of computational approaches used, but is united by common qualities related to host–pathogen relationship. Bioinformatics methods are also used to assign functions to uncharacterized genes which can be targeted as a candidate in vaccine design and can be a better approach toward the inclusion of women that are pregnant into vaccine trials and programs. The essence of this review is to give insight into the need to focus on novel computational, experimental and computation-driven experimental approaches for studying of host–pathogen interactions and thus making a case for its use in vaccine development.

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

Magnetic Extraction of Acinetobacter baumannii Using Colistin-Functionalized γ-Fe2O3/Au Core/Shell Composite Nanoclusters

Acinetobacter baumannii is a Gram-negative bacterium of increasing concern due to its virulence and persistence in combat and healthcare environments. The incidence of both community-acquired and nosocomial A. baumannii infections is on the rise in foreign and domestic healthcare facilities. Treatment options are limited due to the acquisition of multidrug resistance to the few effective antibiotics. Currently, the most effective pharmaceutically based treatment for multidrug-resistant A. baumannii infections is the antibiotic colistin (polymyxin E). To minimize side effects associated with administration of colistin or other toxic antimicrobial agents, we propose the development of a nanotechnology-mediated treatment strategy. In this design-based effort, colistin-functionalized multilayered, inorganic, magnetoplasmonic nanoconstructs were fabricated to bind to the surface of A. baumannii. This result, for the first time, demonstrates a robust, pharmaceutical-based motif for high affinity, composite nanoparticulates targeting the A. baumannii surface. The antibiotic-activated nanomaterials demonstrated cytocompatibility with human cells and no acute bacterial toxicity at nanoparticle to bacterial concentrations <10 000:1. The magnetomotive characteristics of the nanomaterial enabled magnetic extraction of the bacteria. In a macroscale environment, maximal separation efficiencies exceeding 38% were achieved. This result demonstrates the potential for implementation of this technology into micro- or mesofluidic-based separation environments to enhance extraction efficiencies. The future development of such a mesofluidic-based, nanotechnology-mediated platform is potentially suitable for adjuvant therapies to assist in the treatment of sepsis.