The evolutionary advantage of heritable phenotypic heterogeneity

Bacterial toxin-antitoxin systems: Novel insights on toxin activation across populations and experimental shortcomings

The alarming rise in hard-to-treat bacterial infections is of great concern to human health. Thus, the identification of molecular mechanisms that enable the survival and growth of pathogens is of utmost urgency for the development of more efficient antimicrobial therapies. In challenging environments, such as presence of antibiotics, or during host infection, metabolic adjustments are essential for microorganism survival and competitiveness. Toxin-antitoxin systems (TASs) consisting of a toxin with metabolic modulating activity and a cognate antitoxin that antagonizes that toxin are important elements in the arsenal of bacterial stress defense. However, the exact physiological function of TA systems is highly debatable and with the exception of stabilization of mobile genetic elements and phage inhibition, other proposed biological functions lack a broad consensus. This review aims at gaining new insights into the physiological effects of TASs in bacteria and exploring the experimental shortcomings that lead to discrepant results in TAS research. Distinct control mechanisms ensure that only subsets of cells within isogenic cultures transiently develop moderate levels of toxin activity. As a result, TASs cause phenotypic growth heterogeneity rather than cell stasis in the entire population. It is this feature that allows bacteria to thrive in diverse environments through the creation of subpopulations with different metabolic rates and stress tolerance programs.

Psychogenetic, genetic and epigenetic mechanisms in Candida auris: Role in drug resistance

In recent years, we are facing the challenge of drug resistance emergence in fungi. The availability of limited antifungals and development of multi-drug resistance in fungal pathogens has become a serious concern in the past years in the health sector. Although several cellular, molecular, and genetic mechanisms have been proposed to explain the drug resistance mechanism in fungi, but a complete understanding of the molecular and genetic mechanisms is still lacking. Besides the genetic mechanism, epigenetic mechanisms are pivotal in the fungal lifecycle and disease biology. However, very little is understood about the role of epigenetic mechanisms in the emergence of multi-drug resistance in fungi, especially in Candida auris (C. auris). The current narrative review summaries the clinical characteristics, genomic organization, and molecular/genetic/epigenetic mechanisms underlying the emergence of drug resistance in C. auris. A very few studies have attempted to evaluate the role of epigenetic mechanisms in C. auris. Furthermore, advanced genetic tools such as the CRISP-Cas9 system can be utilized to elucidate the epigenetic mechanisms and their role in the emergence of multi-drug resistance in C. auris.

Collective behavior and nongenetic inheritance allow bacterial populations to adapt to changing environments

Collective behaviors require coordination among a group of individuals. As a result, individuals that are too phenotypically different from the rest of the group can be left out, reducing heterogeneity, but increasing coordination. If individuals also reproduce, the offspring can have different phenotypes from their parent(s). This raises the question of how these two opposing processes-loss of diversity by collective behaviors and generation of it through growth and inheritance-dynamically shape the phenotypic composition of an isogenic population. We examine this question theoretically using collective migration of chemotactic bacteria as a model system, where cells of different swimming phenotypes are better suited to navigate in different environments. We find that the differential loss of phenotypes caused by collective migration is environment-dependent. With cell growth, this differential loss enables migrating populations to dynamically adapt their phenotype compositions to the environment, enhancing migration through multiple environments. Which phenotypes are produced upon cell division depends on the level of nongenetic inheritance, and higher inheritance leads to larger composition adaptation and faster migration at steady state. However, this comes at the cost of slower responses to new environments. Due to this trade-off, there is an optimal level of inheritance that maximizes migration speed through changing environments, which enables a diverse population to outperform a nondiverse one. Growing populations might generally leverage the selection-like effects provided by collective behaviors to dynamically shape their own phenotype compositions, without mutations.

Genomic diversity and antimicrobial resistance of Campylobacter spp. from humans and livestock in Nigeria

Background

Campylobacter spp. are zoonotic pathogens, ubiquitous and are found naturally as commensals in livestock from where they can be transmitted to humans directly or through animal products. The genomic diversity and antimicrobial resistance profile of Campylobacter was investigated with a focus on C. jejuni and C. coli in humans and livestock (poultry and cattle) from Nigeria.

Methods

586 human stool samples and 472 faecal samples from livestock were cultured for thermophilic Campylobacter species on modified charcoal cefoperazone deoxycholate agar (mCCDA). Culture in combination with whole genome sequencing identified and confirmed the presence of Campylobacter in humans and animals from the study area. Further analysis of the sequences was performed to determine multilocus sequence types and genetic determinants of antimicrobial resistance to fluoroquinolone, betalactam, tetracycline and macrolide classes of antimicrobials.

Results

From the human stool samples tested, 50 (9%) were positive of which 33 (66%) were C. jejuni, 14 (28%) were C. coli while 3 (6%) were C. hyointestinalis. In livestock, 132 (28%) were positive. Thirty one (7%) were C. jejuni while 101 (21%) were C. coli. Whole genome sequencing and MLST of the isolates revealed a total of 32 sequence types (STs) identified from 47 human isolates while 48 STs were identified in 124 isolates from livestock indicating a population which was overall, genetically diverse with a few more dominant strains. The antimicrobial resistance profiles of the isolates indicated a higher prevalence of resistance in Campylobacter isolated from livestock than in humans. Generally, resistance was greatest for betalactams (42%) closely followed by fluoroquinolones (41%), tetracyclines (15%) and lastly macrolides (2%). Multidrug resistance to three or more antimicrobials was observed in 24 (13%) isolates from humans (n = 1, 4%) and chicken (n = 23, 96%).

Conclusions

This study has further contributed information about the epidemiology, genetic diversity and antimicrobial resistance profile of thermophilic Campylobacter in Nigeria.

Cell-of-Origin and Genetic, Epigenetic, and Microenvironmental Factors Contribute to the Intra-Tumoral Heterogeneity of Pediatric Intracranial Ependymoma

Intra-tumoral heterogeneity (ITH) is a complex multifaceted phenomenon that posits major challenges for the clinical management of cancer patients. Genetic, epigenetic, and microenvironmental factors are concurrent drivers of diversity among the distinct populations of cancer cells. ITH may also be installed by cancer stem cells (CSCs), that foster unidirectional hierarchy of cellular phenotypes or, alternatively, shift dynamically between distinct cellular states. Ependymoma (EPN), a molecularly heterogeneous group of tumors, shows a specific spatiotemporal distribution that suggests a link between ependymomagenesis and alterations of the biological processes involved in embryonic brain development. In children, EPN most often arises intra-cranially and is associated with an adverse outcome. Emerging evidence shows that EPN displays large intra-patient heterogeneity. In this review, after touching on EPN inter-tumoral heterogeneity, we focus on the sources of ITH in pediatric intra-cranial EPN in the framework of the CSC paradigm. We also examine how single-cell technology has shed new light on the complexity and developmental origins of EPN and the potential impact that this understanding may have on the therapeutic strategies against this deadly pediatric malignancy.

Memory shapes microbial populations

Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory—the ability to track and condition the time to switch to a different phenotypic state—can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments.

While being genetically the same, a population of cells can show phenotypic variability even under homogeneous environments. Often advantageous under heterogeneous environments, this phenotypic heterogeneity is highly relevant in the studies of antibiotic resistance evolution and cancer resurgence. Numerous theoretical models exist applying a simple model of phenotypic switching. Experimental measurements on phenotypic heterogeneity have increased in precision over the past decade, and the simple models are inadequate to explain the new observations. In this paper, we explore the role of cellular memory as a crucial component of phenotypic switching. We see that memory helps account for the hitherto unexplained observations and fundamentally extend our understanding of phenotypic heterogeneity.

Phenotypic-dependent variability and the emergence of tolerance in bacterial populations

Ecological and evolutionary dynamics have been historically regarded as unfolding at broadly separated timescales. However, these two types of processes are nowadays well-documented to intersperse much more tightly than traditionally assumed, especially in communities of microorganisms. Advancing the development of mathematical and computational approaches to shed novel light onto eco-evolutionary problems is a challenge of utmost relevance. With this motivation in mind, here we scrutinize recent experimental results showing evidence of rapid evolution of tolerance by lag in bacterial populations that are periodically exposed to antibiotic stress in laboratory conditions. In particular, the distribution of single-cell lag times-i.e., the times that individual bacteria from the community remain in a dormant state to cope with stress-evolves its average value to approximately fit the antibiotic-exposure time. Moreover, the distribution develops right-skewed heavy tails, revealing the presence of individuals with anomalously large lag times. Here, we develop a parsimonious individual-based model mimicking the actual demographic processes of the experimental setup. Individuals are characterized by a single phenotypic trait: their intrinsic lag time, which is transmitted with variation to the progeny. The model-in a version in which the amplitude of phenotypic variations grows with the parent's lag time-is able to reproduce quite well the key empirical observations. Furthermore, we develop a general mathematical framework allowing us to describe with good accuracy the properties of the stochastic model by means of a macroscopic equation, which generalizes the Crow-Kimura equation in population genetics. Even if the model does not account for all the biological mechanisms (e.g., genetic changes) in a detailed way-i.e., it is a phenomenological one-it sheds light onto the eco-evolutionary dynamics of the problem and can be helpful to design strategies to hinder the emergence of tolerance in bacterial communities. From a broader perspective, this work represents a benchmark for the mathematical framework designed to tackle much more general eco-evolutionary problems, thus paving the road to further research avenues.

A Mathematical Study of the Influence of Hypoxia and Acidity on the Evolutionary Dynamics of Cancer

Hypoxia and acidity act as environmental stressors promoting selection for cancer cells with a more aggressive phenotype. As a result, a deeper theoretical understanding of the spatio-temporal processes that drive the adaptation of tumour cells to hypoxic and acidic microenvironments may open up new avenues of research in oncology and cancer treatment. We present a mathematical model to study the influence of hypoxia and acidity on the evolutionary dynamics of cancer cells in vascularised tumours. The model is formulated as a system of partial integro-differential equations that describe the phenotypic evolution of cancer cells in response to dynamic variations in the spatial distribution of three abiotic factors that are key players in tumour metabolism: oxygen, glucose and lactate. The results of numerical simulations of a calibrated version of the model based on real data recapitulate the eco-evolutionary spatial dynamics of tumour cells and their adaptation to hypoxic and acidic microenvironments. Moreover, such results demonstrate how nonlinear interactions between tumour cells and abiotic factors can lead to the formation of environmental gradients which select for cells with phenotypic characteristics that vary with distance from intra-tumour blood vessels, thus promoting the emergence of intra-tumour phenotypic heterogeneity. Finally, our theoretical findings reconcile the conclusions of earlier studies by showing that the order in which resistance to hypoxia and resistance to acidity arise in tumours depend on the ways in which oxygen and lactate act as environmental stressors in the evolutionary dynamics of cancer cells.

Predictive Analytics for Care and Management of Patients With Acute Diseases: Deep Learning-Based Method to Predict Crucial Complication Phenotypes

BACKGROUND

Acute diseases present severe complications that develop rapidly, exhibit distinct phenotypes, and have profound effects on patient outcomes. Predictive analytics can enhance physicians' care and management of patients with acute diseases by predicting crucial complication phenotypes for a timely diagnosis and treatment. However, effective phenotype predictions require several challenges to be overcome. First, patient data collected in the early stages of an acute disease (eg, clinical data and laboratory results) are less informative for predicting phenotypic outcomes. Second, patient data are temporal and heterogeneous; for example, patients receive laboratory tests at different time intervals and frequencies. Third, imbalanced distributions of patient outcomes create additional complexity for predicting complication phenotypes.

OBJECTIVE

To predict crucial complication phenotypes among patients with acute diseases, we propose a novel, deep learning-based method that uses recurrent neural network-based sequence embedding to represent disease progression while considering temporal heterogeneities in patient data. Our method incorporates a latent regulator to alleviate data insufficiency constraints by accounting for the underlying mechanisms that are not observed in patient data. The proposed method also includes cost-sensitive learning to address imbalanced outcome distributions in patient data for improved predictions.

METHODS

From a major health care organization in Taiwan, we obtained a sample of 10,354 electronic health records that pertained to 6545 patients with peritonitis. The proposed method projects these temporal, heterogeneous, and clinical data into a substantially reduced feature space and then incorporates a latent regulator (latent parameter matrix) to obviate data insufficiencies and account for variations in phenotypic expressions. Moreover, our method employs cost-sensitive learning to further increase the predictive performance.

RESULTS

We evaluated the efficacy of the proposed method for predicting two hepatic complication phenotypes in patients with peritonitis: acute hepatic encephalopathy and hepatorenal syndrome. The following three benchmark techniques were evaluated: temporal multiple measurement case-based reasoning (MMCBR), temporal short long-term memory (T-SLTM) networks, and time fusion convolutional neural network (CNN). For acute hepatic encephalopathy predictions, our method attained an area under the curve (AUC) value of 0.82, which outperforms temporal MMCBR by 64%, T-SLTM by 26%, and time fusion CNN by 26%. For hepatorenal syndrome predictions, our method achieved an AUC value of 0.64, which is 29% better than that of temporal MMCBR (0.54). Overall, the evaluation results show that the proposed method significantly outperforms all the benchmarks, as measured by recall, F-measure, and AUC while maintaining comparable precision values.

CONCLUSIONS

The proposed method learns a short-term temporal representation from patient data to predict complication phenotypes and offers greater predictive utilities than prevalent data-driven techniques. This method is generalizable and can be applied to different acute disease (illness) scenarios that are characterized by insufficient patient clinical data availability, temporal heterogeneities, and imbalanced distributions of important patient outcomes.

Nine Things Genomics Can Tell Us About Candida auris

Candida auris is a recently emerged multidrug-resistant fungal pathogen causing severe illness in hospitalized patients. C. auris is most closely related to a few environmental or rarely observed but cosmopolitan Candida species. However, C. auris is unique in the concern it is generating among public health agencies for its rapid emergence, difficulty to treat, and the likelihood for further and more extensive outbreaks and spread. To date, five geographically distributed and genetically divergent lineages have been identified, none of which includes isolates that were collected prior to 1996. Indeed, C. auris' ecological niche(s) and emergence remain enigmatic, although a number of hypotheses have been proposed. Recent genomic and transcriptomic work has also identified a variety of gene and chromosomal features that may have conferred C. auris with several important clinical phenotypes including its drug-resistance and growth at high temperatures. In this review we discuss nine major lines of enquiry into C. auris that big-data technologies and analytical approaches are beginning to answer.

In Silico implementation of evolutionary paradigm in therapy design: Towards anti-cancer therapy as Darwinian process

In here presented in silico study we suggest a way how to implement the evolutionary principles into anti-cancer therapy design. We hypothesize that instead of its ongoing supervised adaptation, the therapy may be constructed as a self-sustaining evolutionary process in a dynamic fitness landscape established implicitly by evolving cancer cells, microenvironment and the therapy itself. For these purposes, we replace a unified therapy with the 'therapy species', which is a population of heterogeneous elementary therapies, and propose a way how to turn the toxicity of the elementary therapy into its fitness in a way conforming to evolutionary causation. As a result, not only the therapies govern the evolution of different cell phenotypes, but the cells' resistances govern the evolution of the therapies as well. We illustrate the approach by the minimalistic ad hoc evolutionary model. Its results indicate that the resistant cells could bias the evolution towards more toxic elementary therapies by inhibiting the less toxic ones. As the evolutionary causation of cancer drug resistance has been intensively studied for a few decades, we refer to cancer as a special case to illustrate purely theoretical analysis.

(In)exhaustible Suppliers for Evolution? Epistatic Selection Tunes the Adaptive Potential of Nongenetic Inheritance

Nongenetic inheritance media-from methyl-accepting cytosines to culture-tend to mutate more frequently than DNA sequences. Whether this makes them inexhaustible suppliers for adaptive evolution will depend on the effect of nongenetic mutations (hereafter, epimutations) on fitness-related traits. Here we investigate how these effects might themselves evolve, specifically whether natural selection may set boundaries to the adaptive potential of nongenetic inheritance media because of their higher mutability. In our model, the genetic and epigenetic contributions to a nonneutral phenotype are controlled by an epistatic modifier locus, which evolves under the combined effects of drift and selection. We show that a pure genetic control evolves when the environment is stable-provided that the population is large-such that the phenotype becomes robust to frequent epimutations. When the environment fluctuates, however, selection on the modifier locus also fluctuates and can overall produce a large nongenetic contribution to the phenotype, especially when the epimutation rate matches the rate of environmental variation. We further show that selection on the modifier locus is generally insensitive to recombination, meaning it is mostly direct, that is, not relying on subsequent effects in future generations. These results suggest that unstable inheritance media might significantly contribute to fitness variation of traits subject to highly variable selective pressures but little to traits responding to scarcely variable aspects of the environment. More generally, our study demonstrates that the rate of mutation and the adaptive potential of any inheritance media should not be seen as independent properties.

Evolutionary Rescue Through Partly Heritable Phenotypic Variability

Environmental variation is commonplace, but unpredictable. Populations that encounter a deleterious environment can sometimes avoid extinction by rapid evolutionary adaptation. Phenotypic variability, whereby a single genotype can express multiple different phenotypes, might play an important role in rescuing such populations from extinction. This type of evolutionary bet-hedging need not confer a direct benefit to a single individual, but it may increase the chance of long-term survival of a lineage. Here, we develop a population genetic model to explore how partly heritable phenotypic variability influences the probability of evolutionary rescue and the mean duration of population persistence in changing environments. We find that the probability of population persistence depends nonmonotonically on the degree of phenotypic heritability between generations: some heritability can help avert extinction, but too much heritability removes any benefit of phenotypic variability. Partly heritable phenotypic variation is particularly advantageous when it extends the persistence time of a declining population and thereby increases the chance of rescue via beneficial mutations at linked loci. We discuss the implications of these results in the context of therapies designed to eradicate populations of pathogens or aberrant cellular lineages.

Links between evolutionary processes and phenotypic robustness in microbes

The costs and benefits of random phenotypic heterogeneity in microbes have been vigorously debated and experimental tested for decades; yet, this conversation is largely independent from discussion of phenotypic robustness in other disciplines. In this review I connect microbial examples of stochasticity with studies on the ecological and population-genetic consequences of phenotypic variability. These topics illustrate the complexity of selection pressures on phenotypic robustness and provide inspiration that this complexity can be parsed with theoretical advances and the experimental power of microbial systems.