Adaptive Prediction As a Strategy in Microbial Infections

PLoS Pathogens, May 2022

Sascha Brunke, Bernhard Hube

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Adaptive Prediction As a Strategy in Microbial Infections

Hube B (2014) Adaptive Prediction As a Strategy in Microbial Infections. PLoS Pathog 10(10): e1004356. doi:10.1371/journal.ppat.1004356 Adaptive Prediction As a Strategy in Microbial Infections Sascha Brunke 0 1 Bernhard Hube 0 1 Joseph Heitman, Duke University Medical Center, United States of America 0 Funding: This work was funded by the Hans Knoell Institute and German Federal Ministry of Education and Health (BMBF) Germany, FKZ: 01EO1002 - Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript 1 1 Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology, Hans Kno ll Institute (HKI) , Jena, Germany , 2 Center for Sepsis Control and Care, Jena University Hospital , Jena, Germany , 3 Friedrich Schiller University , Jena , Germany Microorganisms need to sense and respond to constantly changing microenvironments, and adapt their transcriptome, proteome, and metabolism accordingly to survive [1]. However, microbes sometimes react in a way which does not make immediate biological sense in light of the current environmentfor example, by up-regulating an iron acquisition system in times of metal abundance. The reason for this seemingly nonsensical behavior can lie in the microbe's ability to predict a coming change in conditions by cues from the current environment. If the microbe (pre-)adapts accordingly, it will increase its fitness and chances of survival under subsequent selection pressures-a concept known as adaptive prediction (Figure 1) [2]. In metazoans with complex neural network architecture, the capacity to anticipate changes in the environment is understandable. It can be achieved in a single multicellular organism, e.g., by classical conditioning. In unicellular organisms, however, this type of learning normally requires generations of selection pressure to connect one predictor to a coming condition. - A good example for adaptive prediction comes from the gut bacterium Escherichia coli. In this microbe, an increase in temperature elicits a transcriptional response typical for low oxygen levels [3]. This makes biological sense, as the increase in temperature can indicate the bacteriums arrival in the gut, where oxygen will soon become limiting. Interestingly, this predictive function can be disrupted if temperature and oxygen levels are dissociated over evolutionary timescales. In a laboratory microevolution experiment with a reversed temperatureoxygen relationship (i.e., high temperature is followed by high oxygen), Tagkopoulos et al. obtained E. coli strains where the predictive quality of temperature for oxygen was largely lost [3]. Similarly, maltose utilization genes are activated in E. coli upon exposure to lactose, reflecting the sequential abundance of these sugars in the gut [2]. Again, disruption of this sequence over hundreds of generations was able to abolish this adaptive prediction in vitro [2]. These two examples show how strongly an evolved adaptive prediction response can impact microbial fitness. As many pathogens are gut-associated, similar patterns can be found in pathogenic enteric bacteria. The enterohemorrhagic E. coli (EHEC) serotype O157:H7, for example, can use the presence of bile as a signal to induce transcription of iron acquisition genes, independent of actual iron levels [4]. This can be useful in the iron-sequestering environment of the small intestine, where bile abounds. On the other hand, pathogenicity-island encoded genes that are specifically expressed at later stages of the intestinal passage by EHECs were found to be repressed by bile in the upper part of the small intestine [4]. Many other enteric bacteria, like Salmonella, Shigella, and Vibrio spp. also use bile as a signal to regulate virulence programs, which are biologically unlinked to bile salts but are advantageous at later stages in their mammalian hosts (reviewed in [5]). Vibrio cholerae is also known to induce genes late in its infection cycle that are of no immediate use in the host. These genes, for example those involved in chitin binding and degradation, should benefit the bacteria only after they are released into the aquatic environment where crustaceans provide ample chitin [6]although it is tempting to speculate that chitin degradation may play an additional role in competition with resident fungi in the gut. In summary, sensing certain host-specific factors can herald changing conditions, and pathogens can use these signals in their (pre-)adaptation to the host or for transition from the host. Candida albicans is a fungal pathogen that can transit from a commensal state in the gut to an aggressive pathogen that invades tissue and disseminates via the bloodstream. Tissue invasion is linked to a specific morphology change, the yeast-to-hypha transition (Figure 2). The hyp (...truncated)


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Sascha Brunke, Bernhard Hube. Adaptive Prediction As a Strategy in Microbial Infections, PLoS Pathogens, 2014, 10, DOI: https://doi.org/10.1371/journal.ppat.1004356