Polymicrobial infections:The influence of the ecological community on the development of infections

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.Polymicrobial infections are caused by a microbial community. The microbes in the community can affect the interaction with the host, pathogens or both. Ecological interactions between individual microbes can affect their growth, virulence and sen-sitivity to antibiotics. In addition, interactions between a microbe and its host can lead to increased survival of other microbes, such as pathogens, by for exam-ple decreasing the immune responses or damaging host cells. Such interactions potentially increase the complexity of infections, which can have conse-quences for antibiotic treatments. Therefore, it is rel-evant to approach polymicrobial infections from an ecological perspective

The good and the bad:Ecological interaction measurements between the urinary microbiota and uropathogens

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

Iron regulates contrasting toxicity of uropathogenic <i>Escherichia coli</i> in macrophages and epithelial cells

By far most urinary tract infections are caused by genetically diverse uropathogenic Escherichia coli (UPEC). Knowledge of the virulence mechanisms of UPEC is critical for drug development, but most studies focus on only a single strain of UPEC. In this study, we compared the virulence mechanisms of four antibiotic-resistant and highly pathogenic UPEC isolates in human blood monocyte-derived macrophages and a bladder epithelial cell (BEC) line: ST999, ST131, ST1981 and ST95. We found that while non-pathogenic E. coli strains are efficiently killed by macrophages in bactericidal single membrane vacuoles, the UPEC strains survive within double-membrane vacuoles. On side-by-side comparison, we found that whereas ST999 only carries Fe3+ importers, ST95 carries both Fe2+ and Fe3+ importers and the toxins haemolysin and colibactin. Moreover, we found that ST999 grows in the Fe3+ rich vacuoles of BECs and macrophages with concomitant increased expression of haem receptor chuA and the hydrogen peroxide sensor oxyR. In contrast, ST95 produces toxins in iron-depleted conditions similar to that of the urinary tract. Whereas ST95 also persist in the iron rich vacuoles of BECs, it produces colibactin in response to low Fe3+ contributing to macrophage death. Thus, iron regulates the contrasting toxicities of UPEC strains in macrophages and bladder epithelial cells due to low and high labile iron concentrations, respectively

Using functional annotations to study pairwise interactions in urinary tract infection communities

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

Fast identification of <i>Escherichia coli</i> in urinary tract infections using a virulence gene based PCR approach in a novel thermal cycler

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

Optimality and evolution of transcriptionally regulated gene expression

<p>Abstract</p> <p>Background</p> <p>How transcriptionally regulated gene expression evolves under natural selection is an open question. The cost and benefit of gene expression are the driving factors. While the former can be determined by gratuitous induction, the latter is difficult to measure directly.</p> <p>Results</p> <p>We addressed this problem by decoupling the regulatory and metabolic function of the <it>Escherichia coli lac </it>system, using an inducer that cannot be metabolized and a carbon source that does not induce. Growth rate measurements directly identified the induced expression level that maximizes the metabolism benefits minus the protein production costs, without relying on models. Using these results, we established a controlled mismatch between sensing and metabolism, resulting in sub-optimal transcriptional regulation with the potential to improve by evolution. Next, we tested the evolutionary response by serial transfer. Constant environments showed cells evolving to the predicted expression optimum. Phenotypes with decreased expression emerged several hundred generations later than phenotypes with increased expression, indicating a higher genetic accessibility of the latter. Environments alternating between low and high expression demands resulted in overall rather than differential changes in expression, which is explained by the concave shape of the cross-environmental tradeoff curve that limits the selective advantage of altering the regulatory response.</p> <p>Conclusions</p> <p>This work indicates that the decoupling of regulatory and metabolic functions allows one to directly measure the costs and benefits that underlie the natural selection of gene regulation. Regulated gene expression is shown to evolve within several hundreds of generations to optima that are predicted by these costs and benefits. The results provide a step towards a quantitative understanding of the adaptive origins of regulatory systems.</p

Predicting evolution using regulatory architecture

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