HokB Monomerization and Membrane Repolarization Control Persister Awakening.

Every bacterial population harbors a small subpopulation of so-called persisters that are transiently antibiotic tolerant. These persisters are associated with the recalcitrance of chronic infections because they can recolonize the host after antibiotic removal. Although several effectors have been described to induce persistence, persister cell awakening is poorly understood. We previously reported that the toxin HokB induces persistence via pore formation, resulting in membrane depolarization and ATP leakage. We now delineate mechanisms responsible for the awakening of HokB-induced persisters. We show that HokB dimerization by the oxidoreductase DsbA is essential for pore formation and peptide stability. Pores are disassembled via DsbC-mediated monomerization, which targets HokB for DegQ-mediated degradation. Finally, pore disassembly allows membrane repolarization by the electron transport chain, supporting cells to resume growth. These results provide a detailed view of both the formation and awakening of HokB-induced persister cells.

Host plant physiological transformation and microbial population heterogeneity as important determinants of the Soft Rot Pectobacteriaceae-plant interactions.

Pectobacterium and Dickeya species belonging to the Soft Rot Pectobacteriaceae (SRP) are one of the most devastating phytopathogens. They degrade plant tissues by producing an arsenal of plant cell wall degrading enzymes. However, SRP-plant interactions are not restricted to the production of these "brute force" weapons. Additionally, these bacteria apply stealth behavior related to (1) manipulation of the host plant via induction of susceptible responses and (2) formation of heterogeneous populations with functionally specialized cells. Our review aims to summarize current knowledge on SRP-induced plant susceptible responses and on the heterogeneity of SRP populations. The review shows that SRP are capable of adjusting the host's hormonal balance, inducing host-mediated plant cell wall modification, promoting iron assimilation by the host, stimulating the accumulation of reactive oxygen species and host cell death, and activating the synthesis of secondary metabolites that are ineffective in limiting disease progression. By this means, SRP facilitate host plant susceptibility. During host colonization, SRP populations produce various functionally specialized cells adapted for enhanced virulence, increased resistance, motility, vegetative growth, or colonization of the vascular system. This enables SRP to perform self-contradictory tasks, which benefits a population's overall fitness in various environments, including host plants. Such stealthy tactical actions facilitate plant-SRP interactions and disease progression.

Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.

We consider the setting where (1) an internal study builds a linear regression model for prediction based on individual-level data, (2) some external studies have fitted similar linear regression models that use only subsets of the covariates and provide coefficient estimates for the reduced models without individual-level data, and (3) there is heterogeneity across these study populations. The goal is to integrate the external model summary information into fitting the internal model to improve prediction accuracy. We adapt the James-Stein shrinkage method to propose estimators that are no worse and are oftentimes better in the prediction mean squared error after information integration, regardless of the degree of study population heterogeneity. We conduct comprehensive simulation studies to investigate the numerical performance of the proposed estimators. We also apply the method to enhance a prediction model for patella bone lead level in terms of blood lead level and other covariates by integrating summary information from published literature.

Quantifying microbial robustness in dynamic environments using microfluidic single-cell cultivation.

BACKGROUND: Microorganisms must respond to changes in their environment. Analysing the robustness of functions (i.e. performance stability) to such dynamic perturbations is of great interest in both laboratory and industrial settings. Recently, a quantification method capable of assessing the robustness of various functions, such as specific growth rate or product yield, across different conditions, time frames, and populations has been developed for microorganisms grown in a 96-well plate. In micro-titer-plates, environmental change is slow and undefined. Dynamic microfluidic single-cell cultivation (dMSCC) enables the precise maintenance and manipulation of microenvironments, while tracking single cells over time using live-cell imaging. Here, we combined dMSCC and a robustness quantification method to a pipeline for assessing performance stability to changes occurring within seconds or minutes. RESULTS: Saccharomyces cerevisiae CEN.PK113-7D, harbouring a biosensor for intracellular ATP levels, was exposed to glucose feast-starvation cycles, with each condition lasting from 1.5 to 48 min over a 20 h period. A semi-automated image and data analysis pipeline was developed and applied to assess the performance and robustness of various functions at population, subpopulation, and single-cell resolution. We observed a decrease in specific growth rate but an increase in intracellular ATP levels with longer oscillation intervals. Cells subjected to 48 min oscillations exhibited the highest average ATP content, but the lowest stability over time and the highest heterogeneity within the population. CONCLUSION: The proposed pipeline enabled the investigation of function stability in dynamic environments, both over time and within populations. The strategy allows for parallelisation and automation, and is easily adaptable to new organisms, biosensors, cultivation conditions, and oscillation frequencies. Insights on the microbial response to changing environments will guide strain development and bioprocess optimisation.

The impact of austerity on children: Uncovering effect heterogeneity by political, economic, and family factors in low- and middle-income countries.

Which children are most vulnerable when their government imposes austerity? Research tends to focus on either the political-economic level or the family level. Using a sample of nearly two million children in 67 countries, this study synthesizes theories from family sociology and political science to examine the heterogeneous effects on child poverty of economic shocks following the implementation of an International Monetary Fund (IMF) program. To discover effect heterogeneity, we apply machine learning to policy evaluation. We find that children's average probability of falling into poverty increases by 14 percentage points. We find substantial effect heterogeneity, with family wealth and governments' education spending as the two most important moderators. In contrast to studies that emphasize the vulnerability of low-income families, we find that middle-class children face an equally high risk of poverty. Our results show that synthesizing family and political factors yield deeper knowledge of how economic shocks affect children.

Microfabrication and its use in investigating fungal biology.

Advances in microfabrication technology, and its increasing accessibility, allow us to explore fungal biology as never before. By coupling molecular genetics with fluorescence live-cell imaging in custom-designed chambers, we can now probe single yeast cell responses to changing conditions over a lifetime, characterise population heterogeneity and investigate its underlying causes. By growing filamentous fungi in complex physical environments, we can identify cross-species commonalities, reveal species-specific growth responses and examine physiological differences relevant to diverse fungal lifestyles. As affordability and expertise broadens, microfluidic platforms will become a standard technique for examining the role of fungi in cross-kingdom interactions, ranging from rhizosphere to microbiome to interconnected human organ systems. This review brings together the perspectives already gained from studying fungal biology in microfabricated systems and outlines their potential in understanding the role of fungi in the environment, health and disease.

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity.

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.

Simultaneous selection and incorporation of consistent external aggregate information.

Interest has grown in synthesizing participant level data of a study with relevant external aggregate information. Several efficient and flexible procedures have been developed under the assumption that the internal study and the external sources concern the same population. This homogeneity condition, albeit commonly being imposed, is hard to check due to limitedly available external information in aggregate data forms. Bias may be introduced when the assumption is violated. In this article, we propose a penalized likelihood approach that avoids undesirable bias by simultaneously selecting and synthesizing consistent external aggregate information. The proposed approach provides a general framework which incorporate consistent external information from heterogeneous study populations as long as the conditional distribution of the dependent variable under investigation is same and differences in the independent variable distributions are properly accounted for via a semi-parametric density ratio model. The proposed approach also properly accounts for the sampling errors in the external information. A two-step estimator and an optimization algorithm are proposed for computation. We establish the selection and estimation consistency and the asymptotic normality of the two-step estimator. The proposed approach is illustrated with an analysis of gestational weight gain management studies.

Impact of mixing insufficiencies on L-phenylalanine production with an Escherichia coli reporter strain in a novel two-compartment bioreactor.

BACKGROUND: The omnipresence of population heterogeneity in industrial bioprocesses originates from prevailing dynamic bioprocess conditions, which promote differences in the expression of cellular characteristics. Despite the awareness, the concrete consequences of this phenomenon remain poorly understood. RESULTS: Therefore, for the first time, a L-phenylalanine overproducing Escherichia coli quadruple reporter strain was established for monitoring of general stress response, growth behavior, oxygen limitation and product formation of single cells based on mTagBFP2, mEmerald, CyOFP1, and mCardinal2 expression measured by flow cytometry. This strain was applied for the fed-batch production of L-phenylalanine from glycerol and ammonia in a stirred-tank bioreactor at homogeneous conditions compared to the same process in a novel two-compartment bioreactor. This two-compartment bioreactor consists of a stirred-tank bioreactor with an initial volume of 0.9 L (homogeneous zone) with a coiled flow inverter with a fixed working volume of 0.45 L as a bypass (limitation zone) operated at a mean hydraulic residence time of 102 s. The product formation was similar in both bioreactor setups with maximum L-phenylalanine concentrations of 21.1 ± 0.6 g L-1 demonstrating the consistency of this study's microbial L-phenylalanine production. However, cell growth was vulnerable to repetitive exposure to the dynamically changing conditions in the two-compartment bioreactor with maximum biomass yields reduced by 21%. The functionality of reporter molecules was approved in the stirred-tank bioreactor cultivation, in which expressed fluorescence levels of all four markers were in accordance with respective process state variables. Additional evaluation of the distributions on single-cell level revealed the presence of population heterogeneity in both bioprocesses. Especially for the marker of the general stress response and the product formation, the corresponding histograms were characterized by bimodal shapes and broad distributions. These phenomena were pronounced particularly at the beginning and the end of the fed-batch process. CONCLUSIONS: The here shown findings confirm multiple reporter strains to be a noninvasive tool for monitoring cellular characteristics and identifying potential subpopulations in bioprocesses. In combination with experiments in scale-down setups, these can be utilized for a better physiological understanding of bioprocesses and support future scale-up procedures.

Multilevel modelling for measuring interaction of effects between multiple categorical variables: An illustrative application using risk factors for preeclampsia.

BACKGROUND: Measuring multiple and higher-order interaction effects between multiple categorical variables proves challenging. OBJECTIVES: To illustrate a multilevel modelling approach to studying complex interactions. METHODS: We apply a two-level random-intercept linear regression to a binary outcome for individuals (level-1) nested within strata (level-2) defined by all observed combinations of multiple categorical exposure variables. As a pedagogic application, we analyse 36 strata defined by five risk factors of preeclampsia (parity, previous preeclampsia, chronic hypertension, multiple pregnancies, body mass index category) among 652,603 women in the Swedish Medical Birth Registry between 2002 and 2010. RESULTS: The absolute risk of preeclampsia was 4% but was predicted to vary from 1% to 44% across strata. The stratum discriminatory accuracy was 30% according to the variance partition coefficient (VPC) and 0.73 according to the area under the receiver operating characteristic curve (AUC). While the risk heterogeneity across strata was primarily due to the main effects of the categories defining the strata, 5% of the variation was attributable to their two- and higher-way interaction effects. One stratum presented a positive interaction, and two strata presented negative interaction. CONCLUSIONS: Multilevel modelling is an innovative tool for identifying and analysing higher-order interaction effects. Further work is needed to explore how this approach can best be applied to making causal inferences.

Structural transition of parenthood among Chinese nulliparous couples with planned pregnancies, 2013-2019.

BACKGROUND: The postponement of parenthood is a global public health issue that has received attention of many public health experts. However, few studies have investigated the postponement in marriage age, marriage and conception interval, and pregnancy age in terms of demographic and regional heterogenicities. METHODS: This is a cross-sectional, registry-based study, and a total of 13 894 601 nulliparous couples who participated in the National Free Pre-Pregnancy Check-ups Project and became pregnant during 2013-2019 were included. We calculated annual percentage change and forest plots for marriage age, marriage and conception interval, and pregnancy age. RESULTS: Late marriage (marriage age ≥ 35 years), long marriage and conception interval (marriage and conception interval ≥ 2 years), and advanced pregnancy (pregnancy age ≥ 35 years) increased from 1.20%, 22.01%, and 1.88% in 2013 to 1.69%, 32.75%, and 2.79% in 2019, respectively. The corresponding annual percentage changes were 6.55%, 8.44%, and 8.17%. Participants without higher education had a higher annual percentage change, but comparable prevalence for long marriage and conception interval with participants with higher education. Participants residing in second- or new first-tier cities, and the northeast of China who had a higher prevalence of parenthood postponement also had higher corresponding annual percentage changes. CONCLUSIONS: Structural postponement of parenthood with demographic and regional heterogenicities was observed among Chinese nulliparous couples with planned pregnancies during 2013-2019. Inclusive and comprehensive parenting support should be developed and implemented in mainland China to minimize the negative health effects arising from the postponement, especially for couples without higher education and living in new first/second-tier cities or the northeast China.

Emergence of Phenotypically Distinct Subpopulations Is a Factor in Adaptation of Recombinant Saccharomyces cerevisiae under Glucose-Limited Conditions.

Cells cultured in a nutrient-limited environment can undergo adaptation, which confers improved fitness under long-term energy limitation. We have shown previously how a recombinant Saccharomyces cerevisiae strain, producing a heterologous insulin product, under glucose-limited conditions adapts over time at the average population level. Here, we investigated this adaptation at the single-cell level by application of fluorescence-activated cell sorting (FACS) and showed that the following three apparent phenotypes underlie the adaptive response observed at the bulk level: (i) cells that drastically reduced insulin production (23%), (ii) cells with reduced enzymatic capacity in central carbon metabolism (46%), and (iii) cells that exhibited pseudohyphal growth (31%). We speculate that the phenotypic heterogeneity is a result of different mechanisms to increase fitness. Cells with reduced insulin productivity have increased fitness by reducing the burden of the heterologous insulin production, and the populations with reduced enzymatic capacity of the central carbon metabolism and pseudohyphal growth have increased fitness toward the glucose-limited conditions. The results highlight the importance of considering population heterogeneity when studying adaptation and evolution. IMPORTANCE The yeast Saccharomyces cerevisiae is an attractive microbial host for industrial production and is used widely for manufacturing, e.g., pharmaceuticals. Chemostat cultivation mode is an efficient cultivation strategy for industrial production processes as it ensures a constant, well-controlled cultivation environment. Nevertheless, both the production of a heterologous product and the constant cultivation environment in the chemostat impose a selective pressure on the production organism, which may result in adaptation and loss of productivity. The exact mechanisms behind the observed adaptation and loss of performance are often unidentified. We used a recombinant S. cerevisiae strain producing heterologous insulin and investigated the adaptation occurring during chemostat growth at the single-cell level. We showed that three apparent phenotypes underlie the adaptive response observed at the bulk level in the chemostat. These findings highlight the importance of considering population heterogeneity when studying adaptation in industrial bioprocesses.

Pursuing sources of heterogeneity in modeling clustered population.

Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are many candidate predictors, it is not only of interest to identify the predictors that are associated with the outcome, but also to distinguish the true sources of heterogeneity, that is, to identify the predictors that have different effects among the clusters and thus are the true contributors to the formation of the clusters. We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously. We develop an efficient algorithm and show that our approach can achieve both estimation and selection consistency. Simulation studies further demonstrate the effectiveness of our method under various practical scenarios. Three applications are presented, namely, an imaging genetics study for linking genetic factors and brain neuroimaging traits in Alzheimer's disease, a public health study for exploring the association between suicide risk among adolescents and their school district characteristics, and a sport analytics study for understanding how the salary levels of baseball players are associated with their performance and contractual status.

Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach.

With the increasing availability of data in the public domain, there has been a growing interest in exploiting information from external sources to improve the analysis of smaller scale studies. An emerging challenge in the era of big data is that the subject-level data are high dimensional, but the external information is at an aggregate level and of a lower dimension. Moreover, heterogeneity and uncertainty in the auxiliary information are often not accounted for in information synthesis. In this paper, we propose a unified framework to summarize various forms of aggregated information via estimating equations and develop a penalized empirical likelihood approach to incorporate such information in logistic regression. When the homogeneity assumption is violated, we extend the method to account for population heterogeneity among different sources of information. When the uncertainty in the external information is not negligible, we propose a variance estimator adjusting for the uncertainty. The proposed estimators are asymptotically more efficient than the conventional penalized maximum likelihood estimator and enjoy the oracle property even with a diverging number of predictors. Simulation studies show that the proposed approaches yield higher accuracy in variable selection compared with competitors. We illustrate the proposed methodologies with a pediatric kidney transplant study.

Effect of Non-homogeneous Mixing and Asymptomatic Individuals on Final Epidemic Size and Basic Reproduction Number in a Meta-Population Model.

To uncover the effective interventions during the pandemic period, a novel mathematical model, which incorporates separate compartments for incubation and asymptomatic individuals, has been developed in this paper. On the basis of a general mixing, final size relation and next-generation matrix are derived for a meta-population model by introducing the matrix blocking. The final size ([Formula: see text]) and the basic reproduction number ([Formula: see text]) are no longer a simple monotonous relationship. The analytical results of heterogeneity illustrate that activity is more sensitive than the others. And the proportion of asymptomatic individuals is a key factor for final epidemic size compared to the regulatory factor. Furthermore, the impact of preferential contact level on [Formula: see text] and [Formula: see text] is comparatively complex. The isolation can effectively reduce the final size, which further verifies its effectiveness. When vaccination is considered, the mixing methods maybe influence the doses of vaccination used and its effective. Moreover, using the present predictive model, we can provide the valuable reference about identifying the ideal strategies to curb the pandemic disease.

Insights into adult atopic dermatitis heterogeneity derived from circulating biomarker profiling in patients with moderate-to-severe disease.

Atopic dermatitis (AD) is a heterogeneous systemic inflammatory skin disease associated with dysregulated immune responses, barrier dysfunction and activated sensory nerves. To characterize circulating inflammatory profiles and underlying systemic disease heterogeneity within AD patients, blood samples from adult patients (N = 123) with moderate-to-severe AD in a phase 2 study of baricitinib (JAHG) were analysed. Baseline levels of 131 markers were evaluated using high-throughput and ultrasensitive proteomic platforms, patient clusters were generated based on these peripheral markers. We implemented a novel cluster reproducibility method to validate cluster outcomes within our study and used publicly available AD biomarker data set (73 markers, N = 58 patients) to validate our findings. Cluster reproducibility analysis demonstrated best consistency for 2 clusters by k-means, reproducibility of this clustering outcome was validated in an independent patient cohort. These unique JAHG patient subgroups either possessed elevated pro-inflammatory mediators, notably TNFß, MCP-3 and IL-13, among a variety of immune responses (high inflammatory) or lower levels of inflammatory biomarkers (low inflammatory). The high inflammatory subgroup was associated with greater baseline disease severity, demonstrated by greater EASI, SCORAD Index, Itch NRS and DLQI scores, compared with low inflammatory subgroup. African-American patients were predominantly associated with the high inflammatory subgroup and increased baseline disease severity. In patients with moderate-to-severe AD, heterogeneity was identified by the detection of 2 disease subgroups, differential clustering amongst ethnic groups and elevated pro-inflammatory mediators extending beyond traditional polarized immune responses. Therapeutic strategies targeting multiple pro-inflammatory cytokines may be needed to address this heterogeneity.

Effect of population heterogeneity on herd immunity and on vaccination decision making process.

We study the influence of population heterogeneity on herd immunity level and on individual's vaccination decision making process. We first formulate the mathematical model in a population with two subgroups, based on different activity levels or different susceptibilities. The herd immunity threshold is derived and discussed. It is calculated that the required vaccine coverage level for herd immunity in a heterogeneous mixing population can be varied significantly. The required vaccine coverage level is lower than the classical herd immunity level, if the vaccine coverage level in the more active group or more susceptible group is higher than the other subgroup. It is suggested that the classical herd immunity levels can be misleading in the process of planning mass vaccination programs. The analysis is further extended to study the population with more subgroups. We then study the formal vaccination games to simulate the process of vaccination decision making, in either homogeneous or heterogeneous mixing populations. It is proved that the Nash equilibrium in the vaccination game is not unique if population heterogeneity is considered. Moreover, herd immunity is not achieved if individuals are solely driven by self-interests.

Synthesizing external aggregated information in the penalized Cox regression under population heterogeneity.

Synthesizing external aggregated information has been proven useful in improving estimation efficiency when conducting statistical analysis using a limited amount of data. In this paper, we develop a unified framework for combining information from high-dimensional individual-level data and potentially low-dimensional external aggregate data under the Cox model. We summarize various forms of external aggregated information by population estimating equations and propose a penalized empirical likelihood approach to borrow information from these estimating equations. The proposed methods possess the flexibility to handle the case where individual-level data and external aggregate data are from heterogeneous populations. Specifically, a penalized empirical likelihood ratio test is developed to check for the potential heterogeneity, and a semiparametric density ratio model is postulated to account for the heterogeneity. Moreover, we study the impact of uncertainty in the auxiliary information on the efficiency gain and propose a modified variance estimator to adjust for the uncertainty. The proposed estimators enjoy the oracle property and are asymptotically more efficient than the penalized partial likelihood estimator that does not exploit the external aggregated information. Simulation studies show improvement in both estimation efficiency and variable selection over the competitors. The proposed approaches are applied to the analysis of a pediatric kidney transplant study for illustration.

Modeling population heterogeneity from microbial communities to immune response in cells.

Heterogeneity is universally observed in all natural systems and across multiple scales. Understanding population heterogeneity is an intriguing and attractive topic of research in different disciplines, including microbiology and immunology. Microbes and mammalian immune cells present obviously rather different system-specific biological features. Nevertheless, as typically occurs in science, similar methods can be used to study both types of cells. This is particularly true for mathematical modeling, in which key features of a system are translated into algorithms to challenge our mechanistic understanding of the underlying biology. In this review, we first present a broad overview of the experimental developments that allowed observing heterogeneity at the single cell level. We then highlight how this "data revolution" requires the parallel advancement of algorithms and computing infrastructure for data processing and analysis, and finally present representative examples of computational models of population heterogeneity, from microbial communities to immune response in cells.

Metabolic Cross-talk Between Human Bronchial Epithelial Cells and Internalized Staphylococcus aureus as a Driver for Infection.

Staphylococcus aureus is infamous for causing recurrent infections of the human respiratory tract. This is a consequence of its ability to adapt to different niches, including the intracellular milieu of lung epithelial cells. To understand the dynamic interplay between epithelial cells and the intracellular pathogen, we dissected their interactions over 4 days by mass spectrometry. Additionally, we investigated the dynamics of infection through live cell imaging, immunofluorescence and electron microscopy. The results highlight a major role of often overlooked temporal changes in the bacterial and host metabolism, triggered by fierce competition over limited resources. Remarkably, replicating bacteria reside predominantly within membrane-enclosed compartments and induce apoptosis of the host within ∼24 h post infection. Surviving infected host cells carry a subpopulation of non-replicating bacteria in the cytoplasm that persists. Altogether, we conclude that, besides the production of virulence factors by bacteria, it is the way in which intracellular resources are used, and how host and intracellular bacteria subsequently adapt to each other that determines the ultimate outcome of the infectious process.