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As pathogens and other microbes become progressively resistant to antibiotics, there is a growing world-wide concern that their use for treating and preventing diseases is rapidly becoming less effective and unsustainable. An important mechanism in the evolution of antibiotic resistance is horizontal gene transfer (HGT), as it is one of the most effective ways of transferring information between bacteria. Until now, HGT has experimentally mainly been studied in single bacterial populations. Yet, bacterial pathogens often do not live in isolation. It is therefore important to understand the role of HGT in the spread of antimicrobial resistance in a community context. This will be crucial for elucidating the nature of polymicrobial infections, given that ecological interactions and the microbial context can greatly impact bacterial growth and antibiotic efficacy. In this project, we investigate the community conjugation rates of pathogens isolated from polymicrobial urinary tract infections (UTIs). My research will provide fundamental knowledge on HGT in infectious communities by identifying eco-evolutionary principles that govern the rate of HGT and the spread of antimicrobial resistance in polymicrobial communities.

ObjectivesUrinary tract infections (UTIs) caused by multidrug-resistant Escherichia coli have become a major medical concern. Old antibiotics such as fosfomycin have become an alternative therapeutic option due to their effectiveness and, as a result, fosfomycin is now used as a first-line drug for the treatment of UTIs in many countries. Despite low resistance rates, fosfomycin heteroresistance, defined as a phenomenon where subpopulations of bacteria are resistant to high antibiotic concentrations whereas most of the bacteria are susceptible, is an underestimated problem.MethodsThe frequency of heteroresistance in E. coli isolated from hospitalized patients in Brazil and its effect on susceptibility of E. coli in biofilms was studied and the isolates were molecularly characterized to reveal the mechanisms behind their fosfomycin heteroresistance using whole-genome sequencing.ResultsA higher frequency of fosfomycin heteroresistance compared with other studies was found. In biofilms, most heteroresistant isolates were less sensitive to fosfomycin than control isolates and showed overexpression of metabolic genes thereby increasing their survival rate. Molecular characterization showed that some resistant subpopulations derived from heteroresistant isolates had a defect in their fosfomycin uptake system caused by mutations in transporter and regulatory genes, whereas others overexpressed the murA gene. None to minor effects on bacterial fitness were observed. Oxidative stress protection, virulence and metabolic genes were differentially expressed in resistant subpopulations and heteroresistant isolates.ConclusionFrequent detection of heteroresistance in UTIs may play a role in the failure of antibiotic treatments and should therefore be more carefully diagnosed.

The limits of evolution have long fascinated biologists. However, the causes of evolutionary constraint have remained elusive due to a poor mechanistic understanding of studied phenotypes. Recently, a range of innovative approaches have leveraged mechanistic information on regulatory networks and cellular biology. These methods combine systems biology models with population and single-cell quantification and with new genetic tools, and they have been applied to a range of complex cellular functions and engineered networks. In this article, we review these developments, which are revealing the mechanistic causes of epistasis at different levels of biological organization¤mdash¤in molecular recognition, within a single regulatory network, and between different networks¤mdash¤providing first indications of predictable features of evolutionary constraint.

The behaviour of microbial communities depends on environmental factors and on the interactions of the community members. This is also the case for urinary tract infection (UTI) microbial communities. Here, we devise a computational approach that uses indices of complementarity and competition based on metabolic gene annotation to rapidly predict putative interactions between pair of organisms with the aim to explain pairwise growth effects. We apply our method to 66 genomes selected from online databases, which belong to 6 genera representing members of UTI communities. This resulted in a selection of metabolic pathways with high correlation for each pairwise combination between a complementarity index and the experimentally derived growth data. Our results indicated that Enteroccus spp. were most complemented in its metabolism by the other members of the UTI community. This suggests that the growth of Enteroccus spp. can potentially be enhanced by complementary metabolites produced by other community members. We tested a few putative predicted interactions by experimental supplementation of the relevant predicted metabolites. As predicted by our method, folic acid supplementation led to the increase in the population density of UTI Enterococcus isolates. Overall, we believe our method is a rapid initial in silico screening for the prediction of metabolic interactions in microbial communities.

The human body harbors numerous populations of microorganisms in various ecological niches. Some of these microbial niches, such as the human gut and the respiratory system, are well studied. One system that has been understudied is the urinary tract, primarily because it has been considered sterile in the absence of infection. Thanks to modern sequencing and enhanced culture techniques, it is now known that a urinary microbiota exists. The implication is that these species live as communities in the urinary tract, forming microbial ecosystems. However, the interactions between species in such an ecosystem remains unknown. Various studies in different parts of the human body have highlighted the ability of the pre-existing microbiota to alter the course of infection by impacting the pathogenicity of bacteria either directly or indirectly. For the urinary tract, the effect of the resident microbiota on uropathogens and the phenotypic microbial interactions is largely unknown. No studies have yet measured the response of uropathogens to the resident urinary bacteria. In this study, we investigate the interactions between uropathogens, isolated from elderly individuals suffering from UTIs, and bacteria isolated from the urinary tract of asymptomatic individuals using growth measurements in conditioned media. We observed that bacteria isolated from individuals with UTI-like symptoms and bacteria isolated from asymptomatic individuals can affect each other’s growth; for example, bacteria isolated from symptomatic individuals affect the growth of bacteria isolated from asymptomatic individuals more negatively than vice versa. Additionally, we show that Gram-positive bacteria alter the growth characteristics differently compared to Gram-negative bacteria. Our results are an early step in elucidating the role of microbial interactions in urinary microbial ecosystems that harbor both uropathogens and pre-existing microbiota.

Polymicrobiële infecties worden veroorzaakt door een microbiële gemeenschap. De microben in de gemeenschap kunnen invloed hebben op een infec-tie door interactie met de gastheer, de pathogenen of met beide. Microben kunnen ecologische interacties met elkaar aangaan, waardoor ze elkaars groei, viru-lentie of gevoeligheid voor antibiotica veranderen. Daarnaast kunnen interacties tussen een microbe en de gastheer zorgen voor een betere overleving van een andere microbe, zoals een pathogeen, door bij-voorbeeld het verminderen van immuunreacties of het schaden van gastheercellen. Hierdoor worden infecties complexer, wat mogelijk gevolgen heeft voor antibioticabehandelingen. Het is daarom rele-vant om polymicrobiële infecties vanuit een ecolo-gisch perspectief te benaderen.

The topography of the adaptive landscape is a major determinant of the course of evolution. In this review we use the adaptive landscape metaphor to highlight the effect of ecology on evolution. We describe how ecological interactions modulate the shape of the adaptive landscape, and how this affects adaptive constraints. We focus on microbial communities as model systems. Copyright (C) EPLA, 2018

Bacteria and other microbes play a crucial role in human health and disease. Medicine and clinical microbiology have traditionally attempted to identify the etiological agents that causes disease, and how to eliminate them. Yet this traditional paradigm is becoming inadequate for dealing with a changing disease landscape. Major challenges to human health are noncommunicable chronic diseases, often driven by altered immunity and inflammation, and persistent communicable infections whose agents harbor antibiotic resistance. It is increasingly recognized that microbe-microbe interactions, as well as human-microbe interactions are important. Here, we review the “Evolutionary Medicine” framework to study how microbial communities influence human health. This approach aims to predict and manipulate microbial influences on human health by integrating ecology, evolutionary biology, microbiology, bioinformatics and clinical expertise. We focus on the potential promise of evolutionary medicine to address three key challenges: 1) detecting microbial transmission; 2) predicting antimicrobial resistance; 3) understanding microbe-microbe and human-microbe interactions in health and disease, in the context of the microbiome.

Bacteriën leven vaak samen in complexe gemeenschappen. Inzicht in deze microbiële ecosystemen is essentieel om ingrijpen mogelijk te maken als deze ecosystemen leiden tot ziekte. Bacteriën reageren niet alleen op invloeden van de gastheer, maar beïnvloeden ook elkaar, wat mogelijk verstrekkende gevolgen heeft voor het verloop van de ziekte. Hier beschrijven we dat klinische isolaten in een polymicrobiële infectie kunnen worden beschouwd als ecosystemen. Vaak hebben deze ecosystemen eigenschappen die de isolaten in afzondering niet hebben; zo kunnen ze virulenter zijn of resistenter tegen antibiotica. We benadrukken daarom dat ook voor infecties geldt: het geheel is meer dan de som der delen.

Uropathogenic Escherichia coli (UPEC) is the most common causal agent of urinary tract infections (UTIs) in humans. Currently, clinical detection methods take hours (dipsticks) to days (culturing methods), limiting rapid intervention. As an alternative, the use of molecular methods could improve speed and accuracy, but their applicability is complicated by high genomic variability within UPEC strains. Here, we describe a novel PCR-based method for the identification of E. coli in urine. Based on in silico screening of UPEC genomes, we selected three UPEC-specific genes predicted to be involved in pathogenesis (c3509, c3686 (yrbH) and chuA), and one E. coli-specific marker gene (uidA). We validated the method on 128 clinical (UTI) strains. Despite differential occurrences of these genes in uropathogenic E. coli, the method, when using multi-gene combinations, specifically detected the target organism across all samples. The lower detection limit, assessed with model UPEC strains, was approximately 104 CFU/ml. Additionally, the use of this method in a novel ultrafast PCR thermal cycler (Nextgen PCR) allowed a detection time from urine sampling to identification of only 52 min. This is the first study that uses such defined sets of marker genes for the detection of E. coli in UTIs. In addition, we are the first to demonstrate the potential of the Nextgen thermal cycler. Our E. coli identification method has the potential to be a rapid, reliable and inexpensive alternative for traditional methods.

Communities of bacteria derived from polymicrobial urinary tract infections (UTIs) together with commensal residents in the urobiome can be viewed as small ecosystems. By measuring pair-wise interactions we obtained a unique insight in the ecological interactions of these microbiome members. We find that many of these bacterial interactions affect the immediate tolerance to antibiotics, as well as their ability to evolve antibiotic resistance. This shows that microbial community composition affects the ecology and the evolution of infectious bacterial consortia.