Bet-hedging and division of labor: How phenotypic heterogeneity helps foodborne pathogens adapt to diverse environmental stresses.

ABSTRACTThe ability of bacteria to respond to changes in their environment is critical to their survival, allowing them to withstand stress, form complex communities, and induce virulence responses during host infection. A remarkable feature of many of these bacterial responses is that they are often variable across individual cells, despite occurring in an isogenic population exposed to a homogeneous environmental change, a phenomenon known as phenotypic heterogeneity. Phenotypic heterogeneity can enable bet-hedging or division of labor strategies that allow bacteria to survive fluctuating conditions. Investigating the significance of phenotypic heterogeneity in environmental transitions requires dynamic, single-cell data. Technical advances in quantitative single-cell measurements, imaging, and microfluidics have led to a surge of publications on this topic. Here, we review recent discoveries on single-cell bacterial responses to environmental transitions of various origins and complexities, from simple diauxic shifts to community behaviors in biofilm formation to virulence regulation during infection. We describe how these studies firmly establish that this form of heterogeneity is prevalent and a conserved mechanism by which bacteria cope with fluctuating conditions. We end with an outline of current challenges and future directions for the field. While it remains challenging to predict how an individual bacterium will respond to a given environmental input, we anticipate that capturing the dynamics of the process will begin to resolve this and facilitate rational perturbation of environmental responses for therapeutic and bioengineering purposes.

This study investigates how parents’ division of labor strategies are impacted by climate change in Bangladesh. Qualitative methodologies were employed in the present study to gather and analyze data. Sunamganj, Satkhira and Natore are three climate-vulnerable districts where in-depth interviews were conducted with married men and women with only child. Thematic analysis was also employed, utilizing qualitative data provided by the interviews. The findings demonstrate that division of labor tactics, however, differ according on the kind and severity of risks and difficulties a region encounters as well as how vulnerable it is to climate change. Furthermore, when the effects of climatic shocks worsen and women are primarily faced with the difficulties of performing household tasks, which makes them more agitated and nervous, they also anticipate their spouse to be more cooperative. However, mothers in areas vulnerable to cyclones and droughts prioritize their children's education since it can be a means of mitigating the harm caused by climate change, as it can guarantee their children's future stability and means of subsistence. The findings indicate that couples’ gender-based labor distribution strategies may need to be adjusted since climate change affects how men and women choose to take on parenting obligations.

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract How phenotypically distinct states in isogenic cell populations appear and stably co-exist remains unresolved. We find that within a mature, clonal yeast colony developing in low glucose, cells arrange into metabolically disparate cell groups. Using this system, we model and experimentally identify metabolic constraints sufficient to drive such self-assembly. Beginning in a uniformly gluconeogenic state, cells exhibiting a contrary, high pentose phosphate pathway activity state, spontaneously appear and proliferate, in a spatially constrained manner. Gluconeogenic cells in the colony produce and provide a resource, which we identify as trehalose. Above threshold concentrations of external trehalose, cells switch to the new metabolic state and proliferate. A self-organized system establishes, where cells in this new state are sustained by trehalose consumption, which thereby restrains other cells in the trehalose producing, gluconeogenic state. Our work suggests simple physico-chemical principles that determine how isogenic cells spontaneously self-organize into structured assemblies in complimentary, specialized states. eLife digest Under certain conditions, single-celled microbes such as yeast and bacteria form communities of many cells. In some cases, the cells in these communities specialize to perform specific roles. By specializing, these cells may help the whole community to survive in difficult environments. These co-dependent communities have some similarities to how cells specialize and work together in larger living things – like animals or plants – that in some cases can contain trillions of cells. Research has already identified the genes involved in creating communities from a population of identical cells. It is less clear how cells within these communities become specialized to different roles. The budding yeast Saccharomyces cerevisiae can help to reveal how genetic and environmental factors contribute to cell communities. By growing yeast in conditions with a low level of glucose, Varahan et al. were able to form cell communities. The communities contained some specialized cells with a high level of activity in a biochemical system called the pentose phosphate pathway (PPP). This is unusual in low-glucose conditions. Further examination showed that many cells in the community produce a sugar called trehalose and, in parts of the community where trehalose levels are high, cells switch to the high PPP state and gain energy from processing trehalose. These findings suggest that the availability of a specific nutrient (in this case, trehalose), which can be made by the cells themselves, is a sufficient signal to trigger specialization of cells. This shows how simple biochemistry can drive specialization and organization of cells. Certain infections are caused by cell communities called biofilms. These findings could also contribute to new approaches to preventing biofilms. This knowledge could in turn reveal how complex multi-cellular organisms evolved, and it may also be relevant to studies looking into the development of cancer. Introduction During the course of development, groups of isogenic cells often form spatially organized, interdependent communities. The emergence of such phenotypically heterogeneous, spatially constrained sub-populations of cells is considered a requisite first step towards multicellularity. Here, clonal cells proliferate and differentiate into phenotypically distinct cells that stably coexist, and organize spatially with distinct patterns and shapes (Newman, 2016; Niklas, 2014). Through such collective behavior, groups of cells can maintain orientation, stay together, and specialize in different tasks through the division of labor, while remaining organized with intricate spatial arrangements (Ackermann, 2015; Newman, 2016). In both eukaryotic and prokaryotic microbes, such organization into structured, isogenic but phenotypically heterogeneous communities, is widely prevalent, and also reversible (Ackermann, 2015). Such phenotypic heterogeneity within groups of clonal cells enables several microbes to persist in fluctuating environments, thereby providing an adaptive benefit for the cell community (Wolf et al., 2005; Thattai and van Oudenaarden, 2004). A well studied example of spatially organized, phenotypically heterogeneous groups of cells comes from the Dictyostelid social amoeba, which upon starvation transition from individual protists to collective cellular aggregates that go on to form slime-molds, or fruiting bodies (Bonner, 1949; Du et al., 2015; Kaiser, 1986). Indeed, most microbes show some such complex, heterogeneous cell behavior, for example in the extensive spatial organization within clonal bacterial biofilms and swarms (Kearns et al., 2004; Kolter, 2007), or in the individuality exhibited in Escherichia coli populations (Spudich and Koshland, 1976). Despite its popular perception as a unicellular microbe, natural isolates of the budding yeast, Saccharomyces cerevisiae, also form phenotypically heterogeneous, multicellular communities (Cáp et al., 2012; Koschwanez et al., 2011; Palková and Váchová, 2016; Ratcliff et al., 2012; Váchová and Palková, 2018; Veelders et al., 2010; Wloch-Salamon et al., 2017). However, despite striking descriptions on the nature and development of phenotypically heterogeneous states within groups of cells, the rules governing the emergence and maintenance of new phenotypic states within isogenic cell populations remain unclear. Current studies emphasize genetic and epigenetic changes that are required to maintain phenotypic heterogeneity within a cell population (Ackermann, 2015; Sneppen et al., 2015). In particular, many studies emphasize stochastic gene expression changes that can drive phenotypic heterogeneity (Süel et al., 2007; Ackermann, 2015; Balázsi et al., 2011; Blake et al., 2003). Further, groups of cells can produce adhesion molecules to bring themselves together (Halfmann et al., 2012; Halme et al., 2004; Octavio et al., 2009; Váchová and Palková, 2018), or support possible co-dependencies (such as commensal or mutual dependencies on shared resources) within the populations (Ackermann, 2015). Such studies now provide insight into why such heterogeneous cell groups might exist, and what the evolutionary benefits might be. However, an underlying biochemical logic to explain how distinct, specialized cell states can emerge and persist in the first place is largely absent. This is particularly so for isogenic (and therefore putatively identical) groups of cells in seemingly uniform environments. In essence, are there simple chemical or physical constraints, derived from existing biochemical rules and limitations, that explain the emergence and maintenance of heterogeneous phenotypic states of groups of clonal cells in space and over time? Contrastingly, a common theme occurs in nearly all described examples of phenotypically heterogeneous, isogenic groups of cells. This is a requirement of some ‘metabolic stress’ or nutrient limitation that is necessary for the emergence of phenotypic heterogeneity and spatial organization, typically in the form of metabolically inter-dependent cells (Ackermann, 2015; Campbell et al., 2016; Cáp et al., 2012; Johnson et al., 2012; Liu et al., 2015). This idea has been explored experimentally, where approaches that systems-engineer metabolic dependencies between non-isogenic cells can result in interdependent populations that constitute mixed communities (Campbell et al., 2016; Campbell et al., 2015; Embree et al., 2015; Wintermute and Silver, 2010). These findings suggest that biochemical constraints derived from metabolism may determine the nature of phenotypic heterogeneity, and the spatial organization of cells in distinct states within the population. Therefore, if we can understand what these biochemical constraints are, and discern how metabolic states can be altered through these constraints, this may address how genetically identical cells can self-organize into distinct states. In this study, using clonal yeast cells, we experimentally and theoretically show how metabolic constraints imposed on a population of isogenic cells can determine the production, accumulation, and utilization of a specific, shared resource. The selective utilization of this resource enables the spontaneous emergence and persistence of cells exhibiting a counter-intuitive metabolic state, with spatial organization. These metabolic constraints create inherent threshold effects, enabling some cells to switch to new metabolic states, while restraining other cells to the original state which produces the resource. This thereby drives the overall self-organization of cells into specialized, spatially ordered communities. Finally, this group of spatially organized, metabolically distinct cells confer a collective growth advantage to the community of cells, rationalizing why such spatial self-organization of cells into distinct metabolic states benefits the cell community. Results Cells within S. cerevisiae colonies exhibit ordered metabolic specialization Using a well-studied S. cerevisiae isolate as a model (Reynolds and Fink, 2001), we established a simple system to study the formation of a clonal colony with irregular morphology. On 2% agar plates containing a complex rich medium with low glucose concentrations, S. cerevisiae forms rugose colonies with distinct architecture, after ~5–6 days (Figure 1A). Such colonies do not form in the typical, high (1–2%) glucose medium used for yeast growth (Figure 1A). Thus, as previously well established (Granek and Magwene, 2010; Reynolds and Fink, 2001), glucose limitation (with other nutrients such as amino acids being non-limiting) drives this complex colony architecture formation. Currently, the description of such colonies is limited to this external rugose morphology, and does not describe the phenotypic states of cells and/or any spatial organization in the colony. With only such a description, as observed in Figure 1A, the mature colony surface has an internal circle and some radial streaks near the periphery. We carried out a more detailed observation of entire colonies under a microscopic bright-field (using a 4x lens). Here, we unexpectedly noticed what appeared to be distinct internal patterning, and apparent spatial organization of cells within the colony (Figure 1B). As categorized purely based on these observed differences in visual optical density (‘dark’ or ‘light’), regions between the colony center and periphery had optically dense (dark) networks spanning the circumference of the colony, interspersed with optically rare regions. In contrast, the periphery of the mature colony appeared entirely light (Figure 1B). Based simply on these optical traits alone, we categorized cells present in these regions of the colony as dark cells and light cells (Figure 1B). At this point, our description is visual and qualitative, and does not imply any other difference in the cells in either region. However, this visual description is both robust and simple, and hence we use this nomenclature for the remainder of this manuscript. Figure 1 with 2 supplements see all Download asset Open asset Cells within S.cerevisiae colonies exhibit ordered metabolic specialization. (A) Low glucose is required for rugose colonies to develop. The panel shows the morphology of mature yeast colonies in rich medium, with supplemented glucose as the sole variable. Scale bar: 2 mm. (B) Reconstructed bright-field images of a mature wild-type colony. Within the colony, a network of dark and bright regions is clearly visible, as classified based purely on optical density. We classify the cells in the dark region as dark cells, and in the peripheral light region as light cells. Scale bar: 2 mm. (C) Spatial distribution of mCherry fluorescence across a colony, indicating the activity of (i) a reporter for hexokinase (HXK1) activity, or (ii) a gluconeogenesis dependent reporter (PCK1), in two different colonies. The percentage of fluorescent cells (in isolated light and dark cells from the respective colonies) were also estimated by flow cytometry, and is shown as bar graphs. Scale bar: 2 mm. Also see Figure 1—figure supplement 1A–B and Figure 1—figure supplement 2A for more information. (D) Western blot based detection of proteins involved in gluconeogenesis (Fbp1p and Pck1p), or associated with increased gluconeogenic activity (Icl1p), in isolated dark or light cells. The blot is representative of three biological replicates (n = 3). Also see Figure 1—figure supplement 2B for more information. (E) Comparative steady-state amounts of trehalose and glycogen (as gluconeogenesis end point metabolites), in light and dark cells (n = 3). Statistical significance was calculated using unpaired t test (*** indicates p<0.001) and error bars represent standard deviation. Since these structured colonies form only in glucose-limited conditions, we hypothesized that dissecting the expected metabolic requirements during glucose limitation might reveal drivers of this internal organization within the colony. The expected metabolic requirements of cells growing in glucose limited conditions are as follows: first, all cells would be expected to have constitutively high expression of the high-affinity hexokinase (Hxk1p) (Lobo and Maitra, 1977; Rodríguez et al., 2001). Further, during glucose-limited growth, all cells are expected to carry out extensive gluconeogenesis, as the default metabolic state (Broach, 2012; Haarasilta and Oura, 1975; Yin et al., 2000). Indeed, we confirmed this second expectation by measuring the amounts of the gluconeogenic enzymes Pck1 (phosphoenolpyruvate carboxykinase) and Fbp1 (fructose-1,6-bisphosphatase), in short-term (4–5 hr) liquid cultures of log-phase cells growing in either high glucose medium, or in the same glucose-limited medium we used for colony growth. Expectedly, we observed very high amounts of these gluconeogenic enzymes in cells growing in glucose-limited medium (Figure 1—figure supplement 1A), reiterating that even in well-mixed glucose-limited, cells are in a strongly gluconeogenic state. In order to now examine the mature colony and dissecting expected metabolic requirements in these conditions, we first designed visual indicators for these metabolic hallmarks of yeast cell growth in low glucose. We engineered two different fluorescent reporters, one dependent on HXK1 expression (mCherry under the HXK1 gene promoter), and the second on PCK1 expression as an indicator of gluconeogenic activity (mCherry under the PCK1 gene promoter) (Figure 1—figure supplement 1B). Cells carrying these reporters were seeded to develop into colonies, and the expression levels of these reporters were monitored in the mature, rugose colony (5–6 days). Expectedly, the HXK1-promoter dependent reporter showed constitutive, high expression in all cells across the entire colony (Figure 1C). Contrastingly, only the dark cells exhibited high gluconeogenesis reporter activity (Figure 1C). Notably, the light cells entirely lacked detectable gluconeogenic reporter activity (Figure 1C). To better quantify this phenomenon, cells were dissected out from dark or light regions respectively (under the light microscope, using a fine needle), and the percentage of fluorescent cells in each region was measured using flow cytometry. Based on flow cytometric readouts,~80% of the isolated dark cells showed strong fluorescence for the gluconeogenic reporter, while ~97% of the light cells were non-fluorescent for gluconeogenic activity (Figure 1C, Figure 1—figure supplement 1C). This spatial distribution of gluconeogenic activity is shown as a quantitative heat-map histogram overlaid on the entire colony, in Figure 1—figure supplement 2A. Since this observation was based solely on reporter activity, in order to more directly examine this observation, we estimated native protein amounts of enzymes associated with gluconeogenesis (Pck1, Fbp1, and Icl1- Isocitrate lyase from the glyoxylate shunt) in isolated light cells and dark cells. Only the dark cells showed expression of the gluconeogenic enzymes (Figure 1D, Figure 1—figure supplement 2B). Finally, we measured steady-state amounts of trehalose and glycogen within dark and light cells, using these metabolites as unambiguous biochemical readouts of the end-point biochemical outputs of gluconeogenesis (François et al., 1991). We observed that the dark cells had substantially higher amounts of both trehalose and glycogen (Figure 1E), indicating greater gluconeogenic activity in these cells. Collectively, these results strikingly reveal that intracellular gluconeogenic activity is spatially restricted to specific regions, resulting in a distinct pattern of metabolically specialized zones within the colony. Cells organize into spatially restricted, contrary metabolic states within the colony In the given nutrient conditions of low glucose, gluconeogenesis is an expected, constitutive metabolic process, essential for cells. This can therefore be considered as a necessary, permitted metabolic state in this condition. Paradoxically, in these mature colonies, gluconeogenic activity was spatially restricted to only within the dark cell region, with no discernible gluconeogenic activity in the cells located in the light region. This absence of gluconeogenic activity in these light cells, while concomitant with a constitutively high level of hexokinase activity, therefore poses a biochemical paradox. What might the metabolic state of these light cells be? To quickly address this using a crude but useful readout, we compared the ability of freshly isolated light and dark cells to proliferate in both gluconeogenic (low glucose), and non-gluconeogenic (high glucose) growth conditions. For simplicity, isolated light cells and dark cells were inoculated either into a medium where gluconeogenesis is essential (2% ethanol +glycerol as a sole carbon source), or in high (2%) glucose medium where cells rely on high glycolytic and pentose phosphate pathway (PPP) activity, and initial cell proliferation was monitored. Here, cells that had been growing in high glucose were used as a control. Expectedly, the dark cells grew robustly and reached significantly higher cell numbers (0D600) compared to the light cells in the gluconeogenic condition (Figure 2A). Conversely, light cells grew robustly when transferred to the high glucose medium, as compared to the dark cells (Figure 2A). While this was an overly simple, and not definitive experiment, counter-intuitively, this result suggested that despite being in a low-glucose environment, the light cells were well suited for growth in high glucose, and therefore might be in a metabolic state suited for growth in glucose. We therefore decided to more systematically investigate this phenomenon. Figure 2 with 1 supplement see all Download asset Open asset Cells organize into spatially restricted, contrary metabolic states within the colony. (A) Comparative immediate growth of isolated light cells and dark cells, transferred to a ‘gluconeogenic medium’ (2% ethanol as carbon source), or a ‘glycolytic medium’ (2% glucose as carbon source), based on increased absorbance (OD600) in culture. Wild-type cells growing in liquid medium (2% glucose) in log phase (i.e. in a glycolytic state) were used as controls for growth comparison (n = 3). (B) A schematic showing metabolic flow in glycolysis and the pentose phosphate pathway (PPP), and also illustrating the synthesis of nucleotides (dependent upon pentose phosphate pathway). TKL1 controls an important step in the PPP, and is strongly induced during high PPP flux. (C) Spatial distribution of mCherry fluorescence across a colony, based on the activity of a PPP- dependent reporter. Scale bar: 2 mm. Also see Figure 1—figure supplement 1A and Figure 2—figure supplement 1A. (D) LC-MS/MS based metabolite analysis, using exogenously added 13C Glucose, to compare flux of 13C Glucose into the PPP metabolite ribulose-5-phosphate (R-5-P), in light and dark cells. The red circles represent 13C labeled carbon atoms (n = 3). Also see Figure 2—figure supplement 1B. (E) Comparative metabolic-flux based analysis comparing 15N incorporation into newly synthesized nucleotides, in dark and light cells. Also see S2, and Materials and methods. (F) Light cells and dark cells isolated from a 7 day old wild-type complex colony re-form indistinguishable mature colonies when re-seeded onto fresh agar plates, and allowed to develop for 7 days. Scale bar = 2 mm. Statistical significance was calculated using unpaired t test (*** indicates p<0.001, ** indicates p<0.01) and error bars represent standard deviation. In the presence of glucose, yeast cells typically show high glycolytic and PPP activities, as part of the Crabtree (analogous to the Warburg) effect (Crabtree, 1929; De Deken, 1966; Figure 2B). Therefore, if the light cells in the colony were indeed behaving as though present in more glucose-replete conditions, they should exhibit high PPP To test we first designed a fluorescent reporter (mCherry under the of the 1 et al., gene Figure 1—figure supplement and monitored reporter activity across the mature colony. Indeed, only the light cells exhibited high activity (Figure Figure 2—figure supplement 1A). This spatial of high PPP activity across the colony is also shown as an overlaid quantitative heat-map histogram in Figure 2—figure supplement 1A. we directly the of these light cells exhibiting high PPP For we a based metabolic flux to the flux towards PPP in light and dark cells. Light and dark cells isolated from colonies were with glucose metabolites and the incorporation of this carbon into the PPP ribulose-5-phosphate and was measured by liquid The amounts of these labeled PPP were compared between the two cell or Notably, light cells significantly higher levels of 13C labeled glucose into PPP metabolites compared to the dark cells (Figure and Figure 2—figure supplement and see 1 for showing that the light cells are in a high PPP activity state. Finally, we if other biochemical end-point outputs high PPP were also high in the light cells. synthesis is a of PPP activity and The carbon of newly synthesized nucleotides is derived from the PPP, while the comes from amino acids and Figure and see 1 for We metabolic to in light and dark cells, as an end-point collective of high PPP activity with amino Light and dark cells, isolated from colonies were with a and incorporation of this into nucleotides was measured by liquid Light cells had higher flux into compared to the dark cells (Figure and see 1 for together, we find that light cells exhibit metabolic hallmarks of cells growing in glucose-replete conditions, increased PPP activity, and increased Thus, in the spatially organized colony, the light cells and dark cells have contrary metabolic states. This is despite the expectation that the gluconeogenic state, exhibited by the dark cells, is the metabolic state in the given growth conditions. 1 used for LC-MS/MS 15N has all has all has all has all and sugar 13C has all of which are the phosphate the phosphate the phosphate the phosphate the phosphate the phosphate Notably, the light cells or dark cells, when isolated and as a new colony, both develop into complex colonies (Figure This that these phenotypic differences between the light and dark cells are and do not genetic Collectively, these data reveal that cells within the colony organize into spatially metabolically specialized regions. Within these regions, cells exhibit metabolic states. of these states, where cells have high PPP activity, is counter-intuitive and be sustained given the external nutrient A model suggests constraints for the emergence and organization of cells in metabolic states What the emergence and spatial organization of a group of cells, in these contrary metabolic what can explain the emergence and proliferation of the light cells, which exhibit this counter-intuitive metabolic state, while the colony a of cells in the dark To address we a This model simple derived from our to the formation of a colony of and cells. The model was its was only to find a of that is sufficient to produce the overall spatial and of cell states observed in the colonies. The the model was not to all possible that explain this phenomenon. The model should only for both the emergence of light cells, as well as spatial organization with dark cells. Such a model could therefore suggest constraints that determine the emergence of light cells, and the organization of the colony with the observed organization, which can be experimentally While this we a of that be based on our data (Figure This (i) the dark cells to a light state, (ii) the of some resource by dark cells, which may be by the cells, for this resource, of this resource, and of cell division are (Figure we a of for groups of cells within the colony (Figure Here, each is either or by a group of cells see Materials and methods for we the simplicity, in order to colony to colonies) by that the either of all light or all dark cells. This is a that was At each step of all the shown in Figure are across the spatial using the (Figure In such an (i) all cells all nutrients in while glucose concentrations are (ii) dark cells and in the given conditions, dark cells produce a as a of existing metabolic state, this resource the and is dark cells switch to the light state if sufficient resource is present and the resource when can the light state cells, which can if there is an in the the resource is not present in that the light cells switch to dark cells. for Finally, this of a shared resource is the emergence of light cells from dark can only if the nutrient enables a switch to the new metabolic state. Figure with 2 supplements see all Download asset Open asset A model suggests constraints for the emergence and organization of cells in metabolic states. (A) based on into developing a

The problem of procured abortion and of its possible legal liberalization has become more or less everywhere the subject of impassioned discussions. These debates would be less grave were it not a question of human life, a primordial value, which must be protected and promoted. Everyone understands this, although many look for reasons, even against all evidence, to promote the use of abortion. ... The Church is too conscious of the fact that it belongs to her vocation to defend man against everything that could disintegrate or lessen his dignity to remain silent on such a topic.

We implement an agent-based simulation of the response threshold model of reproductive division of labor. Ants in our simulation must perform two tasks in their environment: forage and reproduce. The colony is capable of allocating ant resources to these roles using different division of labor strategies via genetic architectures and plasticity mechanisms. We find that the deterministic allocation strategy of the response threshold model is more robust than the probabilistic allocation strategy. The deterministic allocation strategy is also capable of evolving complex solutions to colony problems like niche construction and recovery from the loss of the breeding caste. In addition, plasticity mechanisms had both positive and negative influence on the emergence of reproductive division of labor. The combination of plasticity mechanisms has an additive and sometimes emergent impact.

Bacteria mostly live in collectives, where social interactions are prevalent. In biofilms, groups of surface-attached matrix-bound microbial cells exist and interact. Recent studies demonstrate that experimental evolution can be applied to biofilms to investigate and monitor the alterations in social interactions within the spatial structures. Here, experimental evolution approaches were employed to examine the evolutionary interplay of social activities in relation to the production of the biofilm matrix in pellicle biofilms of Bacillus subtilis, a plant beneficial soil-dwelling bacterium. The significant discoveries in this work are: 1)Biofilms are adequate models to study and monitor the origin and development of social interactions as these provide a tractable system to understand the impact of spatial distribution on microbial evolution. 2)Experimental evolution of biofilms facilitates diversification in clonal populations and results in complex interactions involving cooperative and exploitative behaviours. 3)Evolution of competitive interactions between matrix producers and non-producers results in an enhanced competitive advantage of the non-producers in the biofilm via the release of bacteriophages. 4)The exopolysaccharide (EPS) and protein components (TasA) of B. subtilis biofilms are costly public goods that facilitate the division of labour that is optimal at the genotypic level. 5)Genetic division of labour of costly public goods, EPS and TasA, is diminished by the arose of individuality during experimental evolution that selects for novel biofilm traits. 6)Experimental evolution of matrix producers in the presence of cheaters leads to an alteration in phenotypic heterogeneity that creates an anti-cheating mechanism within the biofilm. The findings presented here demonstrate how interactions in spatially structured environments become diverse, adaptive, maintained, and/or developed into unique biofilm traits, thus, contributing to Sociomicrobiology.

LRB-Net: Improving VQA via division of labor strategy and multimodal classifiers

Phenotypic Heterogeneity in Bacterial Quorum Sensing Systems

Bacterial quorum sensing and phenotypic heterogeneity: how the collective shapes the individual.

Enterococci have become opportunistic pathogens and are deemed important hospital-acquired pathogens. Enterococci , especially Enterococcus faecalis and E. faecium , can cause various life-threatening infections, including urinary tract infections, bacteremia, nosocomial infections, and endocarditis. The widespread distribution of enterococci in various environments, including soil, water, and the gastrointestinal tracts of humans and animals, increases the likelihood of contaminating food products during different stages of production, processing, and handling. They become resistant to most antibiotic groups, including glycopeptides. Enterococci are used as probiotics in some countries and help in the ripening of some foods, like cheese. The extensive use of antibiotics in human treatments or livestock not only increases antibiotic resistance genes (ARGs) but also promotes the spread and transfer of ARGs among microorganisms. The spreading of vancomycin-resistant enterococci (VRE) is regarded as the most serious threat among acquired antibiotic resistance, and the spreading of VRE genes between animals and humans is possible. Bacteriophages are promising, effective antibacterial agents that can eliminate harmful bacteria without affecting the beneficial microbiota. Phage-derived endolysins can be used as a food preservative to control the growth of pathogens and extend the food shelf life. Several phages have been characterized against clinical enterococci, and recently, the use of phages and their endolysins against enterococci in food has emerged due to the possible threat of enterococci as a food-borne bacteria. Although there is a growing number of articles related to the treatment of infection, especially for E. faecium and E. faecalis , using phage and endolysin, the number of works focused on food is less abundant. In this review, we aimed to show the risk of enterococci in food and the role of phages and their endolysins in controlling enterococcal growth and ensuring food safety. • Enterococci probiotics may spread antimicrobial resistance and virulence genes. • Vancomycin-resistant enterococci can spread via livestock and food to humans. • Phages act as biocontrol agents to prevent and eliminate foodborne pathogens. • Endolysins are enzymes derived from phages and considered promising natural food biopreservatives.

Previous studies showed that β-thalassemia is a monogenic disease caused by β-globin gene mutations.The epidemiological and genetic research have found that β-thalassemia has significant genetic and phenotypic heterogeneity,which imply the possibility of other latent pathopoiesis genes,in addition to the β-globin gene (HBB gene ). Latest researches put forward the notion of β-thalassemia modifier genes.They can influence the expression,synthesis and stability of globin,and thus affect the balance of α and non-α globin chains.Modifier genes and their gene polymorphism are associated with the clinical phenotypes and deserve further investigation.New findings should be helpful to the understanding of etiology and mechanisms of the β-thalassemia,offer more potential therapeutic target genes,and provide a guidance to clinical medicine,gene therapy and personalized medicine. Key words: β-thalassemia; Modifier genes

Familial Cardiomyopathy Is Associated With A Significantly Worse Prognosis Than Non-familial Cardiomyopathy.

Endoglin-deficient Mice, a Unique Model to Study Hereditary Hemorrhagic Telangiectasia

Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and continue to proliferate. While the genetic basis of resistance to many drugs is relatively well characterised, non-genetic factors are much less understood. Here we investigate the role of non-genetic, phenotypic heterogeneity in the response of glioblastoma cancer cells to the drug temozolomide (TMZ) often used to treat this type of cancer. Using a combination of live imaging, machine-learning image analysis and agent-based modelling, we show that even if all cells share the same genetic background, individual cells respond differently to TMZ. We quantitatively characterise this response by measuring the doubling time, lifespan, cell cycle phase, area and motility of cells, and determine how these quantities correlate with each other as well as between the mother and daughter cell. We also show that these responses do not correlate with the cellular level of the enzyme MGMT which has been implicated in the response to TMZ.

Significance of intronic and synonymous MYBPC3 gene variations in hypertrophic cardiomyopathy