Differential signal sensitivities can contribute to the stability of multispecies bacterial communities.

BACKGROUND: Bacterial species present in multispecies microbial communities often react to the same chemical signal but at vastly different concentrations. The existence of different response thresholds with respect to the same signal molecule has been well documented in quorum sensing which is one of the best studied inter-cellular signalling mechanisms in bacteria. The biological significance of this phenomenon is still poorly understood, and cannot be easily studied in nature or in laboratory models. The aim of this study is to establish the role of differential signal response thresholds in stabilizing microbial communities. RESULTS: We tested binary competition scenarios using an agent-based model in which competing bacteria had different response levels with respect to signals, cooperation factors or both, respectively. While in previous scenarios fitter species outcompete slower growing competitors, we found that stable equilibria could form if the fitter species responded to a higher chemical concentration level than the slower growing competitor. We also found that species secreting antibiotic could form a stable community with other competing species if antibiotic production started at higher response thresholds. CONCLUSIONS: Microbial communities in nature rely on the stable coexistence of species that necessarily differ in their fitness. We found that differential response thresholds provide a simple and elegant way for keeping slower growing species within the community. High response thresholds can be considered as self-restraint of the fitter species that allows metabolically useful but slower growing species to remain within a community, and thereby the metabolic repertoire of the community will be maintained. REVIEWERS: This article was reviewed by Michael Gromiha, Sebastian Maurer-Stroh, István Simon and L. Aravind

A KLF6-driven transcriptional network links lipid homeostasis and tumour growth in renal carcinoma.

Transcriptional networks are critical for the establishment of tissue-specific cellular states in health and disease, including cancer. Yet, the transcriptional circuits that control carcinogenesis remain poorly understood. Here we report that Kruppel like factor 6 (KLF6), a transcription factor of the zinc finger family, regulates lipid homeostasis in clear cell renal cell carcinoma (ccRCC). We show that KLF6 supports the expression of lipid metabolism genes and promotes the expression of PDGFB, which activates mTOR signalling and the downstream lipid metabolism regulators SREBF1 and SREBF2. KLF6 expression is driven by a robust super enhancer that integrates signals from multiple pathways, including the ccRCC-initiating VHL-HIF2A pathway. These results suggest an underlying mechanism for high mTOR activity in ccRCC cells. More generally, the link between super enhancer-driven transcriptional networks and essential metabolic pathways may provide clues to the mechanisms that maintain the stability of cell identity-defining transcriptional programmes in cancer.CRU

NOTCH-mediated non-cell autonomous regulation of chromatin structure during senescence.

Senescent cells interact with the surrounding microenvironment achieving diverse functional outcomes. We have recently identified that NOTCH1 can drive 'lateral induction' of a unique senescence phenotype in adjacent cells by specifically upregulating the NOTCH ligand JAG1. Here we show that NOTCH signalling can modulate chromatin structure autonomously and non-autonomously. In addition to senescence-associated heterochromatic foci (SAHF), oncogenic RAS-induced senescent (RIS) cells exhibit a massive increase in chromatin accessibility. NOTCH signalling suppresses SAHF and increased chromatin accessibility in this context. Strikingly, NOTCH-induced senescent cells, or cancer cells with high JAG1 expression, drive similar chromatin architectural changes in adjacent cells through cell-cell contact. Mechanistically, we show that NOTCH signalling represses the chromatin architectural protein HMGA1, an association found in multiple human cancers. Thus, HMGA1 is involved not only in SAHFs but also in RIS-driven chromatin accessibility. In conclusion, this study identifies that the JAG1-NOTCH-HMGA1 axis mediates the juxtacrine regulation of chromatin architecture

Locality versus globality in bacterial signalling: can local communication stabilize bacterial communities?

<p>Abstract</p> <p>Background</p> <p>Microbial consortia are a major form of life; however their stability conditions are poorly understood and are often explained in terms of species-specific defence mechanisms (secretion of extracellular matrix, antimicrobial compounds, siderophores, etc.). Here we propose a hypothesis that the primarily local nature of intercellular signalling can be a general mechanism underlying the stability of many forms of microbial communities.</p> <p>Presentation of the hypothesis</p> <p>We propose that a large microbial community can be pictured as a theatre of spontaneously emerging, partially overlapping, locally recruited microcommunities whose members interact primarily among themselves, via secreted (signalling) molecules or cell-cell contacts. We hypothesize that stability in an open environment relies on a predominantly local steady state of intercellular communication which ensures that i) deleterious mutants or strains can be excluded by a localized collapse, while ii) microcommunities harbouring useful traits can persist and/or spread even in the absence of specific protection mechanisms.</p> <p>Testing the hypothesis</p> <p>Some elements of this model can be tested experimentally by analyzing the behaviour of synthetic consortia composed of strains having well-defined communication systems and devoid of specific defence mechanisms. Supporting evidence can be obtained by <it>in silico </it>simulations.</p> <p>Implications of the hypothesis</p> <p>The hypothesis provides a framework for a systematic comparison of bacterial community behavior in open and closed environments. The model predicts that local signalling may enable multispecies communities to colonize open, structured environments. On the other hand, a confined niche or a host may be more likely to be colonized by a bacterial mono-species community, and local communication here provides a control against spontaneously arising cheaters, provided that survival depends on cooperation.</p> <p>Reviewers</p> <p>This article was reviewed by G. Jékely, L. Aravind and E. Szathmáry (nominated by F. Eisenhaber)</p

Modeling bacterial quorum sensing in open and closed environments : potential discrepancies between agar plate and culture flask experiments

Quorum sensing (QS) is a process of bacterial communication and cooperation mediated by the release of jointly exploited signals and "public goods" into the environment. There are conflicting reports on the behavior of mutants deficient in the release of these materials. Namely, mutants that appear perfectly viable and capable of outgrowing wild type cells in a closed model system such as a culture flask, may not be viable or invasive on open surfaces such as agar plates. Here we show via agent-based computational simulations that this apparent discrepancy is due to the difference between open and closed systems. We suggest that the experimental difference is due to the fact that wild type cells can easily saturate a well-mixed culture flask with signals and public goods so QS will be not necessary after a certain time point. As a consequence, QS-deficient mutants can continue to grow even after the wild type population has vanished. This phenomenon is not likely to occur in open environments including open surfaces and agar plate models. In other words, even if QS is required for survival, QS deficient mutants may grow faster initially in short term laboratory experiments or computer simulations, while only WT cells appear stable over longer time scales, especially when adaptation to changing environments is important

Sharing.

<p>Competition of species A and B that can utilize each other’s signals, public goods and nutrients to a varying extent. <i>a</i> = signal sharing, <i>b</i> = public goods sharing, <i>c</i> = nutrient sharing. Left: regions of co-colocalizing communities (i.e. segregation coefficient is below 0.5, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057947#s4" target="_blank">Methods</a>). Right: Relative fitness of the mixed communities (shaded area on the left) as a function of food sharing (top curve). RF>1 indicates that both species grow faster in a community than alone. Bottom curve: relative fitness of non-colocalizing communities. The values are the average of 10 calculations, error bars represent the standard deviation of the mean.</p

Stability of Multispecies Bacterial Communities: Signaling Networks May Stabilize Microbiomes

<div><p>Multispecies bacterial communities can be remarkably stable and resilient even though they consist of cells and species that compete for environmental resources. <i>In silico</i> models suggest that common signals released into the environment may help selected bacterial species cluster at common locations and that sharing of public goods (i.e. molecules produced and released for mutual benefit) can stabilize this coexistence. In contrast, unilateral eavesdropping on signals produced by a potentially invading species may protect a community by keeping invaders away from limited resources. Shared bacterial signals, such as those found in quorum sensing systems, may thus play a key role in fine tuning competition and cooperation within multi-bacterial communities. We suggest that in addition to metabolic complementarity, signaling dynamics may be important in further understanding complex bacterial communities such as the human, animal as well as plant microbiomes.</p> </div

Exploitation.

<p>Species B exploits the QS system (signals, public goods) and nutrients of species A. This provides a fitness advantage to the exploiter species B in the entire parameter range. Left: Regions of the parameter space represent either competitive exclusion or competitive segregation. Right: Fitness of the two species relative to growing alone, as a function of nutrient sharing. Relative fitness = 1 in the top curve indicates that the growth of species B is not hampered by the competition. The values are the average of 10 calculations, error bars represent the standard deviation of the mean.</p