Commentary: A host-produced quorum-sensing autoinducer controls a phage lysis-lysogeny decision

With the recent publication by Silpe and Bassler (2019), considering phage detection of a bacterial quorum-sensing (QS) autoinducer, we now have as many as five examples of phage-associated intercellular communication (Table 1). Each potentially involves ecological inferences by phages as to concentrations of surrounding phage-infected or uninfected bacteria. While the utility of phage detection of bacterial QS molecules may at first glance appear to be straightforward, we suggest in this commentary that the underlying ecological explanation is unlikely to be simple

The pharmacokinetic–pharmacodynamic modelling framework as a tool to predict drug resistance evolution

Pharmacokinetic–pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms

Long lived transients in gene regulation

Gene expression is regulated by the set of transcription factors (TFs) that bind to the promoter. The ensuing regulating function is often represented as a combinational logic circuit, where output (gene expression) is determined by current input values (promoter bound TFs) only. However, the simultaneous arrival of TFs is a strong assumption, since transcription and translation of genes introduce intrinsic time delays and there is no global synchronisation among the arrival times of different molecular species at their targets. We present an experimentally implementable genetic circuit with two inputs and one output, which in the presence of small delays in input arrival, exhibits qualitatively distinct population-level phenotypes, over timescales that are longer than typical cell doubling times. From a dynamical systems point of view, these phenotypes represent long-lived transients: although they converge to the same value eventually, they do so after a very long time span. The key feature of this toy model genetic circuit is that, despite having only two inputs and one output, it is regulated by twenty-three distinct DNA-TF configurations, two of which are more stable than others (DNA looped states), one promoting and another blocking the expression of the output gene. Small delays in input arrival time result in a majority of cells in the population quickly reaching the stable state associated with the first input, while exiting of this stable state occurs at a slow timescale. In order to mechanistically model the behaviour of this genetic circuit, we used a rule-based modelling language, and implemented a grid-search to find parameter combinations giving rise to long-lived transients. Our analysis shows that in the absence of feedback, there exist path-dependent gene regulatory mechanisms based on the long timescale of transients. The behaviour of this toy model circuit suggests that gene regulatory networks can exploit event timing to create phenotypes, and it opens the possibility that they could use event timing to memorise events, without regulatory feedback. The model reveals the importance of (i) mechanistically modelling the transitions between the different DNA-TF states, and (ii) employing transient analysis thereof

Epistatic Interactions in the Arabinose Cis-Regulatory Element

Changes in gene expression are an important mode of evolution; however, the proximate mechanism of these changes is poorly understood. In particular, little is known about the effects of mutations within cis binding sites for transcription factors, or the nature of epistatic interactions between these mutations. Here, we tested the effects of single and double mutants in two cis binding sites involved in the transcriptional regulation of the Escherichia coli araBAD operon, a component of arabinose metabolism, using a synthetic system. This system decouples transcriptional control from any posttranslational effects on fitness, allowing a precise estimate of the effect of single and double mutations, and hence epistasis, on gene expression. We found that epistatic interactions between mutations in the araBAD cis-regulatory element are common, and that the predominant form of epistasis is negative. The magnitude of the interactions depended on whether the mutations are located in the same or in different operator sites. Importantly, these epistatic interactions were dependent on the presence of arabinose, a native inducer of the araBAD operon in vivo, with some interactions changing in sign (e.g., from negative to positive) in its presence. This study thus reveals that mutations in even relatively simple cis-regulatory elements interact in complex ways such that selection on the level of gene expression in one environment might perturb regulation in the other environment in an unpredictable and uncorrelated manner

Spike detection with Hidden Semi-Markov Event Sequence Models in EEG data

Zsfassung in dt. SpracheEpilepsie gehört zu den häufigsten neurologischen Krankheiten weltweit und wird verursacht durch exzessives Feuern von Nervenzellen im Gehirn. Die betroffenen Hirnregionen und die daraus resultierenden Erscheinungsformen der Krankheit variieren sehr stark, was sowohl die Diagnose, als auch die Behandlung erheblich erschwert. Das wichtigste Hilfsmittel stellt das Elektroenzephalogramm (EEG) dar, indem es epileptogene Muster sichtbar, welche entweder mit einem bestimmten Typ der Krankheit in Verbindung gebracht werden können oder möglicherweise sogar auf einen spezifischen Ursprungsort, den epileptischen Fokus, hindeuten. Patienten bei denen ein epileptischer Fokus ausgemacht werden kann, stellen möglichen Kandidaten für eine Operation dar, vor allem wenn Medikamente nicht zur Anfallsfreiheit führen oder schwere Nebenwirkungen hervorrufen.Eine manuelle Durchsicht und Beurteilung von EEG Daten erfordern viel Zeit und Erfahrung, weshalb eine automatische Detektion eine vorteilhafte Alternative darstellen könnte. Im Folgenden wird ein Überblick über das große Angebot an verschiedenen Algorithmen, welche über die Jahre studiert wurden, gegeben. Das EpiScan Modul, das zur online Anfallsdetektion dient, und die mögliche Einführung der Chirplet Transformation in das Modul, werden im Detail beschrieben. Der Hauptteil beschäftigt sich aber mit einem Spikedetektionssystem das auf einem statistischen Modell, dem Hidden Semi-Markov Event Sequence Model (HSMESM), basiert. Der Hauptvorteil dieses Ansatzes besteht in der statistischen Modellierung der Dauer der möglichen Zustände (im Falle von Spikes: Anstieg, Spitze und Abfall). In der Testphase zeigte der Algorithmus bessere Resultate als die jetzige Methode bezüglich der Detektion von unterschiedlichen Spikemorphologien und -weiten. Variation der Dauer des Spikes bis hin zu steilen Wellen konnten erkannt werden ohne dass der ursprüngliche Parametersatz angepasst werden musste oder falsche Alarme ausgelöst wurden. Sogar ungewöhnliche Formen, wie beispielsweise Doppelspitzen, stellten kein Problem für das HSMESM Modell dar. Außerdem wurde die Fähigkeit des HSMESM Ansatzes andere epileptiforme EEG Aktivitäten, wie etwa Polyspikes und Vertexwellen, zu erkennen, ebenfalls getestet und zeigt vielversprechende Ergebnisse. Speziell für Polyspikes wurden aber in manchen Fällen mehr falsche Alarme ausgegeben. Dieses Thema könnte durch Anpassung des Vorverarbeitungsprozesses behoben werden, da das Fenster, das in der Erstellung der Eventsequenz verwendet wird, zu groß ist, um geeignete Events für die Detektion von Polyspikes zu generieren.Epilepsy is one of the most common neurological disorders world-wide caused by excessive neuronal discharges in the brain. The affected regions of the brain and the resulting manifestations of the disease are very diverse and render diagnosis as well as treatment difficult. The main tool represents the electroencephalography (EEG) by visualizing epileptic patterns that can be either associated with a specific type of epilepsy or even show the area of origin, called epileptic focus. Patients that have a distinct epileptic focus are possible candidates for epilepsy surgery especially if anti-epileptic drugs fail to make them seizure free or result in serious adverse reactions. As manual EEG viewing requires both, a high amount of experience and time, automated detection procedures appear to be provide valuable alternatives in this field. A short summary of the wide range of different algorithmic approaches that have been studied over the years are presented. The EpiScan module for online seizure detection with possible introduction of chirplet transform to the method is discussed in more depth. However, the main part focuses on a spike detection system based on statistical means through Hidden Semi-Markov Event Sequence Models (HSMESM). The main advantage of this approach lies with the statistical modeling of the duration probabilities of the possible states (for spikes: Rise, Peak and Fall). During testing the algorithm provided better results than the current approach regarding detection of different spike morphologies and width.Variations in duration of the spike up to sharp waves could be recognized without adjustment of the parameter set or triggering of false alarms and even unusual shapes, like double-peaks, did not pose a problem to the HSMESM model. In addition, the ability of the HSMESM approach to detect other epileptiform EEG activities, such as polyspikes and vertex waves, was examined as well and showed promising outcomes.However, especially for polyspikes more false alarms were elicited in some cases. This issue could be addressed by adaption of the preprocessing as the windows used for building the event sequence are too large to yield appropriate events for polyspike detection.9

IST Austria Thesis

Decades of studies have revealed the mechanisms of gene regulation in molecular detail. We make use of such well-described regulatory systems to explore how the molecular mechanisms of protein-protein and protein-DNA interactions shape the dynamics and evolution of gene regulation. i) We uncover how the biophysics of protein-DNA binding determines the potential of regulatory networks to evolve and adapt, which can be captured using a simple mathematical model. ii) The evolution of regulatory connections can lead to a significant amount of crosstalk between binding proteins. We explore the effect of crosstalk on gene expression from a target promoter, which seems to be modulated through binding competition at non-specific DNA sites. iii) We investigate how the very same biophysical characteristics as in i) can generate significant fitness costs for cells through global crosstalk, meaning non-specific DNA binding across the genomic background. iv) Binding competition between proteins at a target promoter is a prevailing regulatory feature due to the prevalence of co-regulation at bacterial promoters. However, the dynamics of these systems are not always straightforward to determine even if the molecular mechanisms of regulation are known. A detailed model of the biophysical interactions reveals that interference between the regulatory proteins can constitute a new, generic form of system memory that records the history of the input signals at the promoter. We demonstrate how the biophysics of protein-DNA binding can be harnessed to investigate the principles that shape and ultimately limit cellular gene regulation. These results provide a basis for studies of higher-level functionality, which arises from the underlying regulation

Phenotypic flux: The role of physiology in explaining the conundrum of bacterial persistence amid phage attack

Bacteriophages, the viruses of bacteria, have been studied for over a century. They were not only instrumental in laying the foundations of molecular biology, but they are also likely to play crucial roles in shaping our biosphere and may offer a solution to the control of drug-resistant bacterial infections. However, it remains challenging to predict the conditions for bacterial eradication by phage predation, sometimes even under well-defined laboratory conditions, and, most curiously, if the majority of surviving cells are genetically phage-susceptible. Here, I propose that even clonal phage and bacterial populations are generally in a state of continuous 'phenotypic flux', which is caused by transient and nongenetic variation in phage and bacterial physiology. Phenotypic flux can shape phage infection dynamics by reducing the force of infection to an extent that allows for coexistence between phages and susceptible bacteria. Understanding the mechanisms and impact of phenotypic flux may be key to providing a complete picture of phage-bacteria coexistence. I review the empirical evidence for phenotypic variation in phage and bacterial physiology together with the ways they have been modeled and discuss the potential implications of phenotypic flux for ecological and evolutionary dynamics between phages and bacteria, as well as for phage therapy.ISSN:2057-157

Assessing the relative importance of bacterial resistance, persistence and hyper-mutation for antibiotic treatment failure

To curb the rising threat of antimicrobial resistance, we need to understand the routes to antimicrobial treatment failure. Bacteria can survive treatment by using both genetic and phenotypic mechanisms to diminish the effect of antimicrobials. We assemble empirical data showing that, for example, Pseudomonas aeruginosa infections frequently contain persisters, transiently non-growing cells unaffected by antibiotics (AB) and hyper-mutators, mutants with elevated mutation rates, and thus higher probability of genetic resistance emergence. Resistance, persistence and hyper-mutation dynamics are difficult to disentangle experimentally. Hence, we use stochastic population modelling and deterministic fitness calculations to investigate the relative importance of genetic and phenotypic mechanisms for immediate treatment failure and establishment of prolonged, chronic infections. We find that persistence causes 'hidden' treatment failure with very low cell numbers if antimicrobial concentrations prevent growth of genetically resistant cells. Persister cells can regrow after treatment is discontinued and allow for resistance evolution in the absence of AB. This leads to different mutational routes during treatment and relapse of an infection. By contrast, hyper-mutation facilitates resistance evolution during treatment, but rarely contributes to treatment failure. Our findings highlight the time and concentration dependence of different bacterial mechanisms to escape AB killing, which should be considered when designing 'failure-proof' treatments.ISSN:0080-4649ISSN:0950-1193ISSN:1471-2954ISSN:0962-845

The pharmacokinetic–pharmacodynamic modelling framework as a tool to predict drug resistance evolution:This article is part of the Microbial Evolution collection.

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.ISSN:1350-0872ISSN:1465-208