A functional perspective on phenotypic heterogeneity in microorganisms

Preexisting cell state rather than stochastic noise confers high or low infection susceptibility of human lung epithelial cells to adenovirus

Viruses display large variability across all stages of their life cycle, including entry, gene expression, replication, assembly, and egress. We previously reported that the immediate early adenovirus (AdV) E1A transcripts accumulate in human lung epithelial A549 cancer cells with high variability, mostly independent of the number of incoming viral genomes, but somewhat correlated to the cell cycle state at the time of inoculation. Here, we leveraged the classical Luria-Delbrück fluctuation analysis to address whether infection variability primarily arises from the cell state or stochastic noise. The E1A expression was measured by the expression of green fluorescent protein (GFP) from the endogenous E1A promoter in AdV-C5_E1A-FS2A-GFP and found to be highly correlated with the viral plaque formation, indicating reliability of the reporter virus. As an ensemble, randomly picked clonal A549 cell isolates displayed significantly higher coefficients of variation in the E1A expression than technical noise, indicating a phenotypic variability larger than noise. The underlying cell state determining infection variability was maintained for at least 9 weeks of cell cultivation. Our results indicate that preexisting cell states tune adenovirus infection in favor of the cell or the virus. These findings have implications for antiviral strategies and gene therapy applications.IMPORTANCEViral infections are known for their variability. Underlying mechanisms are still incompletely understood but have been associated with particular cell states, for example, the eukaryotic cell division cycle in DNA virus infections. A cell state is the collective of biochemical, morphological, and contextual features owing to particular conditions or at random. It affects how intrinsic or extrinsic cues trigger a response, such as cell division or anti-viral state. Here, we provide evidence that cell states with a built-in memory confer high or low susceptibility of clonal human epithelial cells to adenovirus infection. Results are reminiscent of the Luria-Delbrück fluctuation test with bacteriophage infections back in 1943, which demonstrated that mutations, in the absence of selective pressure prior to infection, cause infection resistance rather than being a consequence of infection. Our findings of dynamic cell states conferring adenovirus infection susceptibility uncover new challenges for the prediction and treatment of viral infections.

Phenotypic heterogeneity in bacteria: the rise of antibiotic persistence, clinical implications, and therapeutic opportunities

The rising incidence of antimicrobial resistance (AMR) and the diminishing antibiotics discovery pipeline have created an unprecedented scenario where minor infections could become untreatable. AMR phenomenon is genetically encoded, and various genetic determinants have been implicated in their emergence and spread. Nevertheless, several non-genetic phenomena are also involved in antibiotic treatment failure which requires a systematic investigation. It has been observed that in an isogenic population of bacteria, not all cells behave or respond the same way to an antibiotic, because of the inherent heterogeneity among them. This heterogeneity is not always heritable but rather phenotypic. Three distinct types of phenotypic heterogeneity, namely tolerance, persistence, and heteroresistance have been observed in bacteria having significant clinical implications influencing the treatment outcome. While tolerance is when a population can survive high doses of antibiotics without changing the minimum inhibitory concentration (MIC) of the drug, persistence occurs in a subpopulation of bacteria that can survive exposure to high antibiotic doses. In contrast, when a subpopulation shows a very high MIC in comparison to the rest of the population, the phenomenon is called heteroresistance. In this article, we have highlighted bacterial persistence with a focus on their emergence and the underlying molecular mechanisms. Moreover, we have tried to associate the genome-wide methylation status with that of the heterogeneity at a single-cell level that may explain the role of epigenetic mechanisms in the development of persistence.

Heterogeneous survival upon disinfection underlies evolution of increased tolerance

Disinfection is important to limit the spread of infections, but failure of disinfection may foster the evolution of antimicrobial resistance in bacteria. Persisters are phenotypically tolerant subpopulations that survive toxic stress longer than susceptible cells, leading to failure in treatments with antimicrobials and facilitating resistance evolution. To date, little is known about persistence in the context of disinfectants. The aim of this study was to investigate the influence of persisters on disinfection and to determine the consequences of disinfectant persistence for the evolution of increased tolerance to disinfectants. Disinfection kinetics with high temporal resolution were recorded for Escherichia coli exposed to the following six disinfectants: hydrogen peroxide (H2O2), glutaraldehyde (GTA), chlorhexidine (CHX), benzalkonium chloride (BAC), didecyldimethylammonium chloride (DDAC), and isopropanol (ISO). A mathematical model was used to infer the presence of persisters from the time-kill data. Time-kill kinetics for BAC, DDAC, and ISO were indicative of persisters, whereas no or weak evidence was found for H2O2, GTA, and CHX. When subjected to comparative experimental evolution under recurring disinfection, E. coli evolved increased tolerance to substances for which persisters were predicted (BAC and ISO), whereas adaptation failed for substances in which no persisters were predicted (GTA and CHX), causing extinction of exposed populations. Our findings have implications for the risk of disinfection failure, highlighting a potential link between persistence to disinfectants and the ability to evolve disinfectant survival mechanisms.

IMPORTANCE

Disinfection is key to control the spread of infections. But the application of disinfectants bears the risk to promote the evolution of reduced susceptibility to antimicrobials if bacteria survive the treatment. The ability of individual bacteria to survive disinfection can display considerable heterogeneity within isogenic populations and may be facilitated by tolerant persister subpopulations. Using time-kill kinetics and interpreting the data within a mathematical framework, we quantify heterogeneity and persistence in Escherichia coli when exposed to six different disinfectants. We find that the level of persistence, and with this the risk for disinfection failure, depends on the disinfectant. Importantly, evolution experiments under recurrent disinfection provide evidence that links the presence of persisters to the ability to evolve reduced susceptibility to disinfectants. This study emphasizes the impact of heterogeneity within bacterial populations on disinfection outcomes and the potential consequences for the evolution of antimicrobial resistances.

Loss of Gre factors leads to phenotypic heterogeneity and cheating in Escherichia coli populations under nitric oxide stress

Nitric oxide (·NO) is one of the toxic metabolites that bacteria can be exposed to within phagosomes. Gre factors, which are also known as transcript cleavage factors or transcription elongation factors, relieve back-tracked transcription elongation complexes by cleaving nascent RNAs, which allows transcription to resume after stalling. Here we discovered that loss of both Gre factors in Escherichia coli, GreA and GreB, significantly compromised ·NO detoxification due to ·NO-induced phenotypic heterogeneity in ΔgreAΔgreB populations, which did not occur in wild-type cultures. Under normal culturing conditions, both wild-type and ΔgreAΔgreB synthesized transcripts uniformly, whereas treatment with ·NO led to bimodal transcript levels in ΔgreAΔgreB that were unimodal in wild-type. Interestingly, exposure to another toxic metabolite of phagosomes, hydrogen peroxide (H2O2), produced analogous results. Furthermore, we showed that loss of Gre factors led to cheating under ·NO stress where transcriptionally deficient cells benefited from the detoxification activities of the transcriptionally proficient subpopulation. Collectively, these results show that loss of Gre factor activities produces phenotypic heterogeneity under ·NO and H2O2 stress that can yield cheating between subpopulations.IMPORTANCEToxic metabolite stress occurs in a broad range of contexts that are important to human health, microbial ecology, and biotechnology, whereas Gre factors are highly conserved throughout the bacterial kingdom. Here we discovered that loss of Gre factors in E. coli leads to phenotypic heterogeneity under ·NO and H2O2 stress, which we further show with ·NO results in cheating between subpopulations. Collectively, these data suggest that Gre factors play a role in coping with toxic metabolite stress, and that loss of Gre factors can produce cheating between neighbors.

Single-cell heterogeneity in ribosome content and the consequences for the growth laws

Across species and environments, the ribosome content of cell populations correlates with population growth rate. The robustness and universality of this correlation have led to its classification as a "growth law." This law has fueled theories about how evolution selects for microbial organisms that maximize their growth rate based on nutrient availability, and it has informed models about how individual cells regulate their growth rates and ribosomal content. However, due to methodological limitations, this growth law has rarely been studied at the level of individual cells. While populations of fast-growing cells tend to have more ribosomes than populations of slow-growing cells, it is unclear whether individual cells tightly regulate their ribosome content to match their environment. Here, we employ recent groundbreaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes, S. cerevisiae (a single-celled yeast and eukaryote) and B. subtilis (a bacterium and prokaryote). In both species, we observe significant variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells exhibiting transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive that they have evolved to do things other than maximize their rate of growth. Overall, these results indicate that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability and cannot be predicted by a Gaussian process model that assumes measurements are sampled from a normal distribution centered on the population average. This work encourages the expansion of growth law and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity. To this end, we provide extensive data and analysis of ribosomal and transcriptomic variation across thousands of single cells from multiple conditions, replicates, and species.

Intraspecific diversity of Erwinia amylovora strains from northern Algeria

BACKGROUND

Fire blight, caused by Erwinia amylovora, is the most destructive bacterial disease affecting plants in the Rosaceae family, leading to significant economic losses. In Algeria, this disease has been reported since 2010. This study aimed to investigate the origin of fire blight in Algeria, in order to increase knowledge of the epidemiology of this serious disease and contribute to its management. A comprehensive characterization of 18 E. amylovora isolates recovered from northern Algeria between 2016 and 2021 to evaluate their phenotypical and genotypical diversity was conducted.

RESULTS

Phenotypic differences, particularly in growth kinetics, virulence, and fatty acid profiles, allowed differentiation of strains into five groups, possibly indicating distinct introduction events. Genetic characterization revealed that only one strain lacked the ubiquitous plasmid pEA29, which is correlated with reduced virulence, while none harbored the pEI70 plasmid. Phylogenetic analysis using concatenated sequences of the recA, groEL, rpoS, ams, and hrpN genes grouped Algerian strains with those from a broadly prevalent clade. CRISPR genotyping identified a novel CR1 pattern and three genotypes, two of them previously unreported.

CONCLUSIONS

This study represents the first phenotypic, genetic, and phylogenetic investigation of E. amylovora strains in the region, and provides valuable information on the possible pathways of the introduction of this fire blight pathogen in northern Africa. The findings suggest one or more introduction events from a common ancestor, likely originating in northern Italy, followed by dispersal in various regions of Algeria.

Exploiting phenotypic heterogeneity to improve production of glutathione by yeast

BACKGROUND

Gene expression noise (variation in gene expression among individual cells of a genetically uniform cell population) can result in heterogenous metabolite production by industrial microorganisms, with cultures containing both low- and high-producing cells. The presence of low-producing individuals may be a factor limiting the potential for high yields. This study tested the hypothesis that low-producing variants in yeast cell populations can be continuously counter-selected, to increase net production of glutathione (GSH) as an exemplar product.

RESULTS

A counter-selection system was engineered in Saccharomyces cerevisiae based on the known feedback inhibition of gamma-glutamylcysteine synthetase (GSH1) gene expression, which is rate limiting for GSH synthesis: the GSH1 ORF and the counter-selectable marker GAP1 were expressed under control of the TEF1 and GSH-regulated GSH1 promoters, respectively. An 18% increase in the mean cellular GSH level was achieved in cultures of the engineered strain supplemented with D-histidine to counter-select cells with high GAP1 expression (i.e. low GSH-producing cells). The phenotype was non-heritable and did not arise from a generic response to D-histidine, unlike that with certain other test-constructs prepared with alternative markers.

CONCLUSIONS

The results corroborate that the system developed here improves GSH production by targeting low-producing cells. This supports the potential for exploiting end-product/promoter interactions to enrich high-producing cells in phenotypically heterogeneous populations, in order to improve metabolite production by yeast.

Single-cell phenotypic profiling and backtracing exposes and predicts clinically relevant subpopulations in isogenic Staphylococcus aureus communities

Isogenic bacterial cell populations are phenotypically heterogenous and may include subpopulations of antibiotic tolerant or heteroresistant cells. The reversibility of these phenotypes and lack of biomarkers to differentiate functionally different, but morphologically identical cells is a challenge for research and clinical detection. To overcome this, we present ´Cellular Phenotypic Profiling and backTracing (CPPT)´, a fluorescence-activated cell sorting platform that uses fluorescent probes to visualize and quantify cellular traits and connects this phenotypic profile with a cell´s experimentally determined fate in single cell-derived growth and antibiotic susceptibility analysis. By applying CPPT on Staphylococcus aureus we phenotypically characterized dormant cells, exposed bimodal growth patterns in colony-derived cells and revealed different culturability of single cells on solid compared to liquid media. We demonstrate that a fluorescent vancomycin conjugate marks cellular subpopulations of vancomycin-intermediate S. aureus with increased likelihood to survive antibiotic exposure, showcasing the value of CPPT for discovery of clinically relevant biomarkers.

The macrophage-bacterium mismatch in persister formation

Many pathogens are hard to eradicate, even in the absence of genetically detectable antimicrobial resistance mechanisms and despite proven antibiotic susceptibility. The fraction of clonal bacteria that temporarily elude effective antibiotic treatments is commonly known as 'antibiotic persisters.' Over the past decade, there has been a growing body of research highlighting the pivotal role played by the cellular host in the development of persisters. In parallel, this research has also sought to elucidate the molecular mechanisms underlying the formation of intracellular antibiotic persisters and has demonstrated a prominent role for the bacterial stress response. However, questions remain regarding the conditions leading to the formation of stress-induced persisters among a clonal population of intracellular bacteria and despite an ostensibly uniform environment. In this opinion, following a brief review of the current state of knowledge regarding intracellular antibiotic persisters, we explore the ways in which macrophage functional heterogeneity and bacterial phenotypic heterogeneity may contribute to the emergence of these persisters. We propose that the degree of mismatch between the macrophage permissiveness and the bacterial preparedness to invade and thrive intracellularly may explain the formation of stress-induced nonreplicating intracellular persisters.

Small molecule communication of Legionella: the ins and outs of autoinducer and nitric oxide signaling

SUMMARYLegionella pneumophila is a Gram-negative environmental bacterium, which survives in planktonic form, colonizes biofilms, and infects protozoa. Upon inhalation of Legionella-contaminated aerosols, the opportunistic pathogen replicates within and destroys alveolar macrophages, thereby causing a severe pneumonia termed Legionnaires' disease. Gram-negative bacteria employ low molecular weight organic compounds as well as the inorganic gas nitric oxide (NO) for cell-cell communication. L. pneumophila produces, secretes, and detects the α-hydroxyketone compound Legionella autoinducer-1 (LAI-1, 3-hydroxypentadecane-4-one). LAI-1 is secreted by L. pneumophila in outer membrane vesicles and not only promotes communication among bacteria but also triggers responses from eukaryotic cells. L. pneumophila detects NO through three different receptors, and signaling through the volatile molecule translates into fluctuations of the intracellular second messenger cyclic-di-guanylate monophosphate. The LAI-1 and NO signaling pathways are linked via the pleiotropic transcription factor LvbR. In this review, we summarize current knowledge about inter-bacterial and inter-kingdom signaling through LAI-1 and NO by Legionella species.

Label-free single-cell antimicrobial susceptibility testing in droplets with concentration gradient generation

Bacterial communities exhibit significant heterogeneity, resulting in the emergence of specialized phenotypes that can withstand antibiotic exposure. Unfortunately, the existence of subpopulations resistant to antibiotics often goes unnoticed during treatment initiation. Thus, it is crucial to consider the concept of single-cell antibiotic susceptibility testing (AST) to tackle bacterial infections. Nevertheless, its practical application in clinical settings is hindered by its inability to conduct AST efficiently across a wide range of antibiotics and concentrations. This study introduces a droplet-based microfluidic platform designed for rapid single-cell AST by creating an antibiotic concentration gradient. The advantage of a microfluidic platform is achieved by executing bacteria and antibiotic mixing, cell encapsulation, incubation, and enumeration of bacteria in a seamless workflow, facilitating susceptibility testing of each antibiotic. Firstly, we demonstrate the rapid determination of minimum inhibitory concentration (MIC) of several antibiotics with Gram-negative E. coli and Gram-positive S. aureus, which enables us to bypass the time-consuming bacteria cultivation, speeding up the AST in 3 h from 1 to 2 days of conventional methods. Additionally, we assess 10 clinical isolates including methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant Staphylococcus aureus (MDRSA) against clinically important antibiotics for analyzing the MIC, compared to the gold standard AST method from the United States Clinical and Laboratory Standards Institute (CLSI), which becomes available only after 48 h. Furthermore, by monitoring single cells within individual droplets, we have found a spectrum of resistance levels among genetically identical cells, revealing phenotypic heterogeneity within isogenic populations. This discovery not only advances clinical diagnostics and treatment strategies but also significantly contributes to the field of antibiotic stewardship, underlining the importance of our approach in addressing bacterial resistance.

Unravelling the Roles of Bacterial Nanomachines Bistability in Pathogens' Life Cycle

Bacterial nanomachines represent remarkable feats of evolutionary engineering, showcasing intricate molecular mechanisms that enable bacteria to perform a diverse array of functions essential to persist, thrive, and evolve within ecological and pathological niches. Injectosomes and bacterial flagella represent two categories of bacterial nanomachines that have been particularly well studied both at the molecular and functional levels. Among the diverse functionalities of these nanomachines, bistability emerges as a fascinating phenomenon, underscoring their dynamic and complex regulation as well as their contribution to shaping the bacterial community behavior during the infection process. In this review, we examine two closely related bacterial nanomachines, the type 3 secretion system, and the flagellum, to explore how the bistability of molecular-scale devices shapes the bacterial eco-pathological life cycle.

Extreme makeover: the incredible cell membrane adaptations of extremophiles to harsh environments

The existence of life beyond Earth has long captivated humanity, and the study of extremophiles-organisms surviving and thriving in extreme environments-provides crucial insights into this possibility. Extremophiles overcome severe challenges such as enzyme inactivity, protein denaturation, and damage of the cell membrane by adopting several strategies. This feature article focuses on the molecular strategies extremophiles use to maintain the cell membrane's structure and fluidity under external stress. Key strategies include homeoviscous adaptation (HVA), involving the regulation of lipid composition, and osmolyte-mediated adaptation (OMA), where small organic molecules protect the lipid membrane under stress. Proteins also have direct and indirect roles in protecting the lipid membrane. Examining the survival strategies of extremophiles provides scientists with crucial insights into how life can adapt and persist in harsh conditions, shedding light on the origins of life. This article examines HVA and OMA and their mechanisms in maintaining membrane stability, emphasizing our contributions to this field. It also provides a brief overview of the roles of proteins and concludes with recommendations for future research directions.

Biofilms as more than the sum of their parts: lessons from developmental biology

Although our understanding of both bacterial cell physiology and the complex behaviors exhibited by bacterial biofilms is expanding rapidly, we cannot yet sum the behaviors of individual cells to understand or predict biofilm behavior. This is both because cell physiology in biofilms is different from planktonic growth and because cell behavior in biofilms is spatiotemporally patterned. We use developmental biology as a guide to examine this phenotypic patterning, discussing candidate cues that may encode spatiotemporal information and possible roles for phenotypic patterning in biofilms. We consider other questions that arise from the comparison between biofilm and eukaryotic development, including what defines normal biofilm development and the nature of biofilm cell types and fates. We conclude by discussing what biofilm development can tell us about developmental processes, emphasizing the additional challenges faced by bacteria in biofilm development compared with their eukaryotic counterparts.

Strain heterogeneity in a non-pathogenic Aspergillus fungus highlights factors associated with virulence

Fungal pathogens exhibit extensive strain heterogeneity, including variation in virulence. Whether closely related non-pathogenic species also exhibit strain heterogeneity remains unknown. Here, we comprehensively characterized the pathogenic potentials (i.e., the ability to cause morbidity and mortality) of 16 diverse strains of Aspergillus fischeri, a non-pathogenic close relative of the major pathogen Aspergillus fumigatus. In vitro immune response assays and in vivo virulence assays using a mouse model of pulmonary aspergillosis showed that A. fischeri strains varied widely in their pathogenic potential. Furthermore, pangenome analyses suggest that A. fischeri genomic and phenotypic diversity is even greater. Genomic, transcriptomic, and metabolic profiling identified several pathways and secondary metabolites associated with variation in virulence. Notably, strain virulence was associated with the simultaneous presence of the secondary metabolites hexadehydroastechrome and gliotoxin. We submit that examining the pathogenic potentials of non-pathogenic close relatives is key for understanding the origins of fungal pathogenicity.

Evolution of a bistable genetic system in fluctuating and nonfluctuating environments

Epigenetic mechanisms can generate bacterial lineages capable of spontaneously switching between distinct phenotypes. Currently, mathematical models and simulations propose epigenetic switches as a mechanism of adaptation to deal with fluctuating environments. However, bacterial evolution experiments for testing these predictions are lacking. Here, we exploit an epigenetic switch in Salmonella enterica, the opvAB operon, to show clear evidence that OpvAB bistability persists in changing environments but not in stable conditions. Epigenetic control of transcription in the opvAB operon produces OpvABOFF (phage-sensitive) and OpvABON (phage-resistant) cells in a reversible manner and may be interpreted as an example of bet-hedging to preadapt Salmonella populations to the encounter with phages. Our experimental observations and computational simulations illustrate the adaptive value of epigenetic variation as an evolutionary strategy for mutation avoidance in fluctuating environments. In addition, our study provides experimental support to game theory models predicting that phenotypic heterogeneity is advantageous in changing and unpredictable environments.

Stable Isotope Probing-nanoFTIR for Quantitation of Cellular Metabolism and Observation of Growth-Dependent Spectral Features

This study utilizes nanoscale Fourier transform infrared spectroscopy (nanoFTIR) to perform stable isotope probing (SIP) on individual bacteria cells cultured in the presence of 13C-labelled glucose. SIP-nanoFTIR simultaneously quantifies single-cell metabolism through infrared spectroscopy and acquires cellular morphological information via atomic force microscopy. The redshift of the amide I peak corresponds to the isotopic enrichment of newly synthesized proteins. These observations of single-cell translational activity are comparable to those of conventional methods, examining bulk cell numbers. Observing cells cultured under conditions of limited carbon, SIP- nanoFTIR is used to identify environmentally-induced changes in metabolic heterogeneity and cellular morphology. Individuals outcompeting their neighboring cells will likely play a disproportionately large role in shaping population dynamics during adverse conditions or environmental fluctuations. Additionally, SIP-nanoFTIR enables the spectroscopic differentiation of specific cellular growth phases. During cellular replication, subcellular isotope distribution becomes more homogenous, which is reflected in the spectroscopic features dependent on the extent of 13C-13C mode coupling or to specific isotopic symmetries within protein secondary structures. As SIP-nanoFTIR captures single-cell metabolism, environmentally-induced cellular processes, and subcellular isotope localization, this technique offers widespread applications across a variety of disciplines including microbial ecology, biophysics, biopharmaceuticals, medicinal science, and cancer research.

Hosts manipulate lifestyle switch and pathogenicity heterogeneity of opportunistic pathogens in the single-cell resolution

Host-microbe interactions are virtually bidirectional, but how the host affects their microbiome is poorly understood. Here, we report that the host is a critical modulator to regulate the lifestyle switch and pathogenicity heterogeneity of the opportunistic pathogens Serratia marcescens utilizing the Drosophila and bacterium model system. First, we find that Drosophila larvae efficiently outcompete S. marcescens and typically drive a bacterial switch from pathogenicity to commensalism toward the fly. Furthermore, Drosophila larvae reshape the transcriptomic and metabolic profiles of S. marcescens characterized by a lifestyle switch. More importantly, the host alters pathogenicity and heterogeneity of S. marcescens in the single-cell resolution. Finally, we find that larvae-derived AMPs are required to recapitulate the response of S. marcescens to larvae. Altogether, our findings provide an insight into the pivotal roles of the host in harnessing the life history and heterogeneity of symbiotic bacterial cells, advancing knowledge of the reciprocal relationships between the host and pathogen.

Direct observation correlates NFκB cRel in B cells with activating and terminating their proliferative program

Antibody responses require the proliferative expansion of B cells controlled by affinity-dependent signals. Yet, proliferative bursts are heterogeneous, varying between 0 and 8 divisions in response to the same stimulus. NFκB cRel is activated in response to immune stimulation in B cells and is genetically required for proliferation. Here, we asked whether proliferative heterogeneity is controlled by natural variations in cRel abundance. We developed a fluorescent reporter mTFP1-cRel for the direct observation of cRel in live proliferating B cells. We found that cRel is heterogeneously distributed among naïve B cells, which are enriched for high expressors in a heavy-tailed distribution. We found that high cRel expressors show faster activation of the proliferative program, but do not sustain it well, with population expansion decaying earlier. With a mathematical model of the molecular network, we showed that cRel heterogeneity arises from balancing positive feedback by autoregulation and negative feedback by its inhibitor IκBε, confirmed by mouse knockouts. Using live-cell fluorescence microscopy, we showed that increased cRel primes B cells for early proliferation via higher basal expression of the cell cycle driver cMyc. However, peak cMyc induction amplitude is constrained by incoherent feedforward regulation, decoding the fold change of cRel activity to terminate the proliferative burst. This results in a complex nonlinear, nonmonotonic relationship between cRel expression and the extent of proliferation. These findings emphasize the importance of direct observational studies to complement gene knockout results and to learn about quantitative relationships between biological processes and their key regulators in the context of natural variations.

Fluid flow drives phenotypic heterogeneity in bacterial growth and adhesion on surfaces

Bacteria often thrive in surface-attached communities, where they can form biofilms affording them multiple advantages. In this sessile form, fluid flow is a key component of their environments, renewing nutrients and transporting metabolic products and signaling molecules. It also controls colonization patterns and growth rates on surfaces, through bacteria transport, attachment and detachment. However, the current understanding of bacterial growth on surfaces neglects the possibility that bacteria may modulate their division behavior as a response to flow. Here, we employed single-cell imaging in microfluidic experiments to demonstrate that attached Escherichia coli cells can enter a growth arrest state while simultaneously enhancing their adhesion underflow. Despite utilizing clonal populations, we observed a non-uniform response characterized by bistable dynamics, with co-existing subpopulations of non-dividing and actively dividing bacteria. As the proportion of non-dividing bacteria increased with the applied flow rate, it resulted in a reduction in the average growth rate of bacterial populations on flow-exposed surfaces. Dividing bacteria exhibited asymmetric attachment, whereas non-dividing counterparts adhered to the surface via both cell poles. Hence, this phenotypic diversity allows bacterial colonies to combine enhanced attachment with sustained growth, although at a reduced rate, which may be a significant advantage in fluctuating flow conditions.

A deterministic, c-di-GMP-dependent program ensures the generation of phenotypically similar, symmetric daughter cells during cytokinesis

Phenotypic heterogeneity in bacteria can result from stochastic processes or deterministic programs. The deterministic programs often involve the versatile second messenger c-di-GMP, and give rise to daughter cells with different c-di-GMP levels by deploying c-di-GMP metabolizing enzymes asymmetrically during cell division. By contrast, less is known about how phenotypic heterogeneity is kept to a minimum. Here, we identify a deterministic c-di-GMP-dependent program that is hardwired into the cell cycle of Myxococcus xanthus to minimize phenotypic heterogeneity and guarantee the formation of phenotypically similar daughter cells during division. Cells lacking the diguanylate cyclase DmxA have an aberrant motility behaviour. DmxA is recruited to the cell division site and its activity is switched on during cytokinesis, resulting in a transient increase in the c-di-GMP concentration. During cytokinesis, this c-di-GMP burst ensures the symmetric incorporation and allocation of structural motility proteins and motility regulators at the new cell poles of the two daughters, thereby generating phenotypically similar daughters with correct motility behaviours. Thus, our findings suggest a general c-di-GMP-dependent mechanism for minimizing phenotypic heterogeneity, and demonstrate that bacteria can ensure the formation of dissimilar or similar daughter cells by deploying c-di-GMP metabolizing enzymes to distinct subcellular locations.

Single-cell level LasR-mediated quorum sensing response of Pseudomonas aeruginosa to pulses of signal molecules

Quorum sensing (QS) is a communication form between bacteria via small signal molecules that enables global gene regulation as a function of cell density. We applied a microfluidic mother machine to study the kinetics of the QS response of Pseudomonas aeruginosa bacteria to additions and withdrawals of signal molecules. We traced the fast buildup and the subsequent considerably slower decay of a population-level and single-cell-level QS response. We applied a mathematical model to explain the results quantitatively. We found significant heterogeneity in QS on the single-cell level, which may result from variations in quorum-controlled gene expression and protein degradation. Heterogeneity correlates with cell lineage history, too. We used single-cell data to define and quantitatively characterize the population-level quorum state. We found that the population-level QS response is well-defined. The buildup of the quorum is fast upon signal molecule addition. At the same time, its decay is much slower following signal withdrawal, and the quorum may be maintained for several hours in the absence of the signal. Furthermore, the quorum sensing response of the population was largely repeatable in subsequent pulses of signal molecules.

Highly Multiplexed Spatial Transcriptomics in Bacteria

Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial mRNA. To overcome this challenge, we combine 1000-fold volumetric expansion with multiplexed error robust fluorescence in situ hybridization (MERFISH) to create bacterial-MERFISH. This method enables high-throughput, spatially resolved profiling of thousands of operons within individual bacteria. Using bacterial-MERFISH, we dissect the response of E. coli to carbon starvation, systematically map subcellular RNA organization, and chart the adaptation of a gut commensal B. thetaiotaomicron to micron-scale niches in the mammalian colon. We envision bacterial-MERFISH will be broadly applicable to the study of bacterial single-cell heterogeneity in diverse, spatially structured, and native environments.

Communication leads to bacterial heterogeneous adaptation to changing conditions in partial nitrification reactor

Recently, it is reported that bacterial communication coordinates the whole consortia to jointly resist the adverse environments. Here, we found the bacterial communication inevitably distinguished bacterial adaptation among different species in partial nitrification reactor under decreasing temperatures. We operated a partial nitrification reactor under temperature gradient from 30 °C to 5 °C and found the promotion of bacterial communication on adaptation of ammonia-oxidizing bacteria (AOB) was greater than that of nitrite-oxidizing bacteria (NOB). Signal pathways with single-component sensing protein in AOB can regulate more genes involved in bacterial adaptation than that with two-component sensing protein in NOB. The negative effects of bacterial communication, which were seriously ignored, have been highlighted, and Clp regulator downstream diffusible signal factor (DSF) based signal pathways worked as transcription activators and inhibitors of adaptation genes in AOB and NOB respectively. Bacterial communication can induce differential adaptation through influencing bacterial interactions. AOB inclined to cooperate with DSF synthesis bacteria as temperature declined, however, cooperation between NOB and DSF synthesis bacteria inclined to get weakening. According to the regulatory effects of signal pathways, bacterial survival strategies for self-protection were revealed. This study hints a potential way to govern niche differentiation in the microbiota by bacterial communication, contributing to forming an efficient artificial ecosystem.

Feedback between stochastic gene networks and population dynamics enables cellular decision-making

Phenotypic selection occurs when genetically identical cells are subject to different reproductive abilities due to cellular noise. Such noise arises from fluctuations in reactions synthesizing proteins and plays a crucial role in how cells make decisions and respond to stress or drugs. We propose a general stochastic agent-based model for growing populations capturing the feedback between gene expression and cell division dynamics. We devise a finite state projection approach to analyze gene expression and division distributions and infer selection from single-cell data in mother machines and lineage trees. We use the theory to quantify selection in multi-stable gene expression networks and elucidate that the trade-off between phenotypic switching and selection enables robust decision-making essential for synthetic circuits and developmental lineage decisions. Using live-cell data, we demonstrate that combining theory and inference provides quantitative insights into bet-hedging-like response to DNA damage and adaptation during antibiotic exposure in Escherichia coli.

An overview of Parafrankia (Nod+/Fix+) and Pseudofrankia (Nod+/Fix-) interactions through genome mining and experimental modeling in co-culture and co-inoculation of Elaeagnus angustifolia

In many frankia, the ability to nodulate host plants (Nod+) and fix nitrogen (Fix+) is a common strategy. However, some frankia within the Pseudofrankia genus lack one or two of these traits. This phenomenon has been consistently observed across various actinorhizal nodule isolates, displaying Nod- and/or Fix- phenotypes. Yet, the mechanisms supporting the colonization and persistence of these inefficient frankia within nodules, both with and without symbiotic strains (Nod+/Fix+), remain unclear. It is also uncertain whether these associations burden or benefit host plants. This study delves into the ecological interactions between Parafrankia EUN1f and Pseudofrankia inefficax EuI1c, isolated from Elaeagnus umbellata nodules. EUN1f (Nod+/Fix+) and EuI1c (Nod+/Fix-) display contrasting symbiotic traits. While the prediction suggests a competitive scenario, the absence of direct interaction evidence implies that the competitive advantage of EUN1f and EuI1c is likely contingent on contextual factors such as substrate availability and the specific nature of stressors in their respective habitats. In co-culture, EUN1f outperforms EuI1c, especially under specific conditions, driven by its nitrogenase activity. Iron-depleted conditions favor EUN1f, emphasizing iron's role in microbial competition. Both strains benefit from host root exudates in pure culture, but EUN1f dominates in co-culture, enhancing its competitive traits. Nodulation experiments show that host plant preferences align with inoculum strain abundance under nitrogen-depleted conditions, while consistently favoring EUN1f in nitrogen-supplied media. This study unveils competitive dynamics and niche exclusion between EUN1f and EuI1c, suggesting that host plant may penalize less effective strains and even all strains. These findings highlight the complex interplay between strain competition and host selective pressure, warranting further research into the underlying mechanisms shaping plant-microbe-microbe interactions in diverse ecosystems.

IMPORTANCE

While Pseudofrankia strains typically lack the common traits of ability to nodulate the host plant (Nod-) and/or fix nitrogen (Fix-), they are still recovered from actinorhizal nodules. The enigmatic question of how and why these unconventional strains establish themselves within nodule tissue, thriving either alongside symbiotic strains (Nod+/Fix+) or independently, while considering potential metabolic costs to the host plant, remains a perplexing puzzle. This study endeavors to unravel the competitive dynamics between Pseudofrankia inefficax strain EuI1c (Nod+/Fix-) and Parafrankia strain EU1Nf (Nod+/Fix+) through a comprehensive exploration of genomic data and empirical modeling, conducted both in controlled laboratory settings and within the host plant environment.

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Space and genealogy determine inter-individual differences in siderophore gene expression in bacterial colonies

Heterogeneity in gene expression is common among clonal cells in bacteria, although the sources and functions of variation often remain unknown. Here, we track cellular heterogeneity in the bacterium Pseudomonas aeruginosa during colony growth by focusing on siderophore gene expression (pyoverdine versus pyochelin) important for iron nutrition. We find that the spatial position of cells within colonies and non-genetic yet heritable differences between cell lineages are significant sources of cellular heterogeneity, while cell pole age and lifespan have no effect. Regarding functions, our results indicate that cells adjust their siderophore investment strategies along a gradient from the colony center to its edge. Moreover, cell lineages with below-average siderophore investment benefit from lineages with above-average siderophore investment, presumably due to siderophore sharing. Our study highlights that single-cell experiments with dual gene expression reporters can identify sources of gene expression variation of interlinked traits and offer explanations for adaptive benefits in bacteria.

Characterization of individual spores of two biological insecticides, Bacillus thuringiensis and Lysinibacillus sphaericus, in response to glutaraldehyde using single-cell optical approaches

Bacillus thuringiensis (Bt) and Lysinibacillus sphaericus (Ls) are the most widely used microbial insecticides. Both encounter unfavorable environmental factors and pesticides in the field. Here, the responses of Bt and Ls spores to glutaraldehyde were characterized using Raman spectroscopy and differential interference contrast imaging at the single-cell level. Bt spores were more sensitive to glutaraldehyde than Ls spores under prolonged exposure: <1.0% of Bt spores were viable after 10 min of 0.5% (v/v) glutaraldehyde treatment, compared to ~ 20% of Ls spores. The Raman spectra of glutaraldehyde-treated Bt and Ls spores were almost identical to those of untreated spores; however, the germination process of individual spores was significantly altered. The time to onset of germination, the period of rapid Ca2+-2,6-pyridinedicarboxylic acid (CaDPA) release, and the period of cortex hydrolysis of treated Bt spores were significantly longer than those of untreated spores, with dodecylamine germination being particularly affected. Similarly, the germination of treated Ls spores was significantly prolonged, although the prolongation was less than that of Bt spores. Although the interiors of Bt and Ls spores were undamaged and CaDPA did not leak, proteins and structures involved in spore germination could be severely damaged, resulting in slower and significantly prolonged germination. This study provides insights into the impact of glutaraldehyde on bacterial spores at the single cell level and the variability in spore response to glutaraldehyde across species and populations.

Dynamical model of antibiotic responses linking expression of resistance genes to metabolism explains emergence of heterogeneity during drug exposures

Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.

Phenotypic heterogeneity in Streptomyces colonies

Streptomyces are a large genus of multicellular bacteria best known for their prolific production of bioactive natural products. In addition, they play key roles in the mineralisation of insoluble resources, such as chitin and cellulose. Because of their multicellular mode of growth, colonies of interconnected hyphae extend over a large area that may experience different conditions in different parts of the colony. Here, we argue that within-colony phenotypic heterogeneity can allow colonies to simultaneously respond to divergent inputs from resources or competitors that are spatially and temporally dynamic. We discuss causal drivers of heterogeneity, including competitors, precursor availability, metabolic diversity and division of labour, that facilitate divergent phenotypes within Streptomyces colonies. We discuss the adaptive causes and consequences of within-colony heterogeneity, highlight current knowledge (gaps) and outline key questions for future studies.

Dissecting cell heterogeneities in bacterial biofilms and their implications for antibiotic tolerance

Bacterial biofilms consist of large, self-formed aggregates where resident bacteria can exhibit very different physiological states and phenotypes. This heterogeneity of cell types is crucial for many structural and functional emergent properties of biofilms. Consequently, it becomes essential to understand what drives cells to differentiate and how they achieve it within the three-dimensional landscape of the biofilms. Here, we discuss recent advances in comprehending two forms of cell heterogeneity that, while recognized to coexist within biofilms, have proven challenging to distinguish. These two forms include cell heterogeneity arising as a consequence of bacteria physiologically responding to resource gradients formed across the biofilms and cell-to-cell phenotypic heterogeneity, which emerges locally within biofilm subzones among neighboring bacteria due to stochastic variations in gene expression. We describe the defining features and concepts related to both forms of cell heterogeneity and discuss their implications, with a particular focus on antibiotic tolerance.

Dynamic nesting of Anaplasma marginale in the microbial communities of Rhipicephalus microplus

Interactions within the tick microbiome involving symbionts, commensals, and tick-borne pathogens (TBPs) play a pivotal role in disease ecology. This study explored temporal changes in the microbiome of Rhipicephalus microplus, an important cattle tick vector, focusing on its interaction with Anaplasma marginale. To overcome limitations inherent in sampling methods relying on questing ticks, which may not consistently reflect pathogen presence due to variations in exposure to infected hosts in nature, our study focused on ticks fed on chronically infected cattle. This approach ensures continuous pathogen exposure, providing a more comprehensive understanding of the nesting patterns of A. marginale in the R. microplus microbiome. Using next-generation sequencing, microbiome dynamics were characterized over 2 years, revealing significant shifts in diversity, composition, and abundance. Anaplasma marginale exhibited varying associations, with its increased abundance correlating with reduced microbial diversity. Co-occurrence networks demonstrated Anaplasma's evolving role, transitioning from diverse connections to keystone taxa status. An integrative approach involving in silico node removal unveils the impact of Anaplasma on network stability, highlighting its role in conferring robustness to the microbial community. This study provides insights into the intricate interplay between the tick microbiome and A. marginale, shedding light on potential avenues for controlling bovine anaplasmosis through microbiome manipulation.

Plasmid-mediated phenotypic noise leads to transient antibiotic resistance in bacteria

The rise of antibiotic resistance is a critical public health concern, requiring an understanding of mechanisms that enable bacteria to tolerate antimicrobial agents. Bacteria use diverse strategies, including the amplification of drug-resistance genes. In this paper, we showed that multicopy plasmids, often carrying antibiotic resistance genes in clinical bacteria, can rapidly amplify genes, leading to plasmid-mediated phenotypic noise and transient antibiotic resistance. By combining stochastic simulations of a computational model with high-throughput single-cell measurements of blaTEM-1 expression in Escherichia coli MG1655, we showed that plasmid copy number variability stably maintains populations composed of cells with both low and high plasmid copy numbers. This diversity in plasmid copy number enhances the probability of bacterial survival in the presence of antibiotics, while also rapidly reducing the burden of carrying multiple plasmids in drug-free environments. Our results further support the tenet that multicopy plasmids not only act as vehicles for the horizontal transfer of genetic information between cells but also as drivers of bacterial adaptation, enabling rapid modulation of gene copy numbers. Understanding the role of multicopy plasmids in antibiotic resistance is critical, and our study provides insights into how bacteria can transiently survive lethal concentrations of antibiotics.

Phenotypic Patterning through Copy Number Adaptation to Environmental Gradients

We recently described a paradigm for engineering bacterial adaptation using plasmids coupled to the same origin of replication. In this study, we use plasmid coupling to generate spatially separated and phenotypically distinct populations in response to heterogeneous environments. Using a custom microfluidic device, we continuously tracked engineered populations along induced gradients, enabling an in-depth analysis of the spatiotemporal dynamics of plasmid coupling. Our observations reveal a pronounced phenotypic separation within 4 h exposure to an opposing gradient of AHL and arabinose. Additionally, by modulating the burden strength balance between coupled plasmids, we demonstrate the inherent limitations and tunability of this system. Intriguingly, phenotypic separation persists for an extended time, hinting at a biophysical spatial retention mechanism reminiscent of natural speciation processes. Complementing our experimental data, mathematical models provide invaluable insights into the underlying mechanisms and guide optimization of plasmid coupling for prospective applications of environmental copy number adaptation engineering across separated domains.

Spectral analysis and sorting of microbial organisms using a spectral sorter

This chapter discusses the problems related to the application of conventional flow cytometers to microbiology. To address some of those limitations, the concept of spectral flow cytometry is introduced and the advantages over conventional flow cytometry for bacterial sorting are presented. We demonstrate by using ThermoFisher's Bigfoot spectral sorter where the spectral signatures of different stains for staining bacteria are demonstrated with an example of performing unmixing on spectral datasets. In addition to the Bigfoot's spectral analysis, the special biosafety features of this instrument are discussed. Utilizing these biosafety features, the sorting and patterning at the single cell level is optimized using non-pathogenic bacteria. Finally, the chapter is concluded by presenting a novel, label free, non-destructive, and rapid phenotypic method called Elastic Light Scattering (ELS) technology for identification of the patterned bacterial cells based on their unique colony scatter patterns.

Strain heterogeneity in a non-pathogenic fungus highlights factors contributing to virulence

Fungal pathogens exhibit extensive strain heterogeneity, including variation in virulence. Whether closely related non-pathogenic species also exhibit strain heterogeneity remains unknown. Here, we comprehensively characterized the pathogenic potentials (i.e., the ability to cause morbidity and mortality) of 16 diverse strains of Aspergillus fischeri, a non-pathogenic close relative of the major pathogen Aspergillus fumigatus. In vitro immune response assays and in vivo virulence assays using a mouse model of pulmonary aspergillosis showed that A. fischeri strains varied widely in their pathogenic potential. Furthermore, pangenome analyses suggest that A. fischeri genomic and phenotypic diversity is even greater. Genomic, transcriptomic, and metabolomic profiling identified several pathways and secondary metabolites associated with variation in virulence. Notably, strain virulence was associated with the simultaneous presence of the secondary metabolites hexadehydroastechrome and gliotoxin. We submit that examining the pathogenic potentials of non-pathogenic close relatives is key for understanding the origins of fungal pathogenicity.

Bet hedging in a unicellular microalga

Understanding how organisms have adapted to persist in unpredictable environments is a fundamental goal in biology. Bet hedging, an evolutionary adaptation observed from microbes to humans, facilitates reproduction and population persistence in randomly fluctuating environments. Despite its prevalence, empirical evidence in microalgae, crucial primary producers and carbon sinks, is lacking. Here, we report a bet-hedging strategy in the unicellular microalga Haematococcus pluvialis. We show that isogenic populations reversibly diversify into heterophenotypic mobile and non-mobile cells independently of environmental conditions, likely driven by stochastic gene expression. Mobile cells grow faster but are stress-sensitive, while non-mobile cells prioritise stress resistance over growth. This is due to shifts from growth-promoting activities (cell division, photosynthesis) to resilience-promoting processes (thickened cell wall, cell enlargement, aggregation, accumulation of antioxidant and energy-storing compounds). Our results provide empirical evidence for bet hedging in a microalga, indicating the potential for adaptation to current and future environmental conditions and consequently conservation of ecosystem functions.

Multipad agarose plate: a rapid and high-throughput approach for antibiotic susceptibility testing

We describe a phenotypic antibiotic susceptibility testing (AST) method that can provide an eightfold speed-up in turnaround time compared with the current clinical standard by leveraging advances in microscopy and single-cell imaging. A newly developed growth plate containing 96 agarose pads, termed the multipad agarose plate (MAP), can be assembled at low cost. Pads can be prepared with dilution series of antibiotics. Bacteria are seeded on the pads and automatically imaged using brightfield microscopy, with a fully automated segmentation pipeline quantifying microcolony formation and growth rate. Using a test set of nine antibiotics with very different targets, we demonstrate that accurate minimum inhibitory concentration (MIC) measurements can be performed based on the growth rate of microcolonies within 3 h of incubation with the antibiotic when started from exponential phase. Faster, reliable and high-throughput methods for AST, such as MAP, could improve patient care by expediting treatment initiation and alleviating the burden of antimicrobial resistance.

Quantifying Dynamic Phenotypic Heterogeneity in Resistant Escherichia coli under Translation-Inhibiting Antibiotics

Understanding the phenotypic heterogeneity of antibiotic-resistant bacteria following treatment and the transitions between different phenotypes is crucial for developing effective infection control strategies. The study expands upon previous work by explicating chloramphenicol-induced phenotypic heterogeneities in growth rate, gene expression, and morphology of resistant Escherichia coli using time-lapse microscopy. Correlating the bacterial growth rate and cspC expression, four interchangeable phenotypic subpopulations across varying antibiotic concentrations are identified, surpassing the previously described growth rate bistability. Notably, bacterial cells exhibiting either fast or slow growth rates can concurrently harbor subpopulations characterized by high and low gene expression levels, respectively. To elucidate the mechanisms behind this enhanced heterogeneity, a concise gene expression network model is proposed and the biological significance of the four phenotypes is further explored. Additionally, by employing Hidden Markov Model fitting and integrating the non-equilibrium landscape and flux theory, the real-time data encompassing diverse bacterial traits are analyzed. This approach reveals dynamic changes and switching kinetics in different cell fates, facilitating the quantification of observable behaviors and the non-equilibrium dynamics and thermodynamics at play. The results highlight the multi-dimensional heterogeneous behaviors of antibiotic-resistant bacteria under antibiotic stress, providing new insights into the compromised antibiotic efficacy, microbial response, and associated evolution processes.

Microfluidic approaches in microbial ecology

Microbial life is at the heart of many diverse environments and regulates most natural processes, from the functioning of animal organs to the cycling of global carbon. Yet, the study of microbial ecology is often limited by challenges in visualizing microbial processes and replicating the environmental conditions under which they unfold. Microfluidics operates at the characteristic scale at which microorganisms live and perform their functions, thus allowing for the observation and quantification of behaviors such as growth, motility, and responses to external cues, often with greater detail than classical techniques. By enabling a high degree of control in space and time of environmental conditions such as nutrient gradients, pH levels, and fluid flow patterns, microfluidics further provides the opportunity to study microbial processes in conditions that mimic the natural settings harboring microbial life. In this review, we describe how recent applications of microfluidic systems to microbial ecology have enriched our understanding of microbial life and microbial communities. We highlight discoveries enabled by microfluidic approaches ranging from single-cell behaviors to the functioning of multi-cellular communities, and we indicate potential future opportunities to use microfluidics to further advance our understanding of microbial processes and their implications.

Emerging tools for uncovering genetic and transcriptomic heterogeneities in bacteria

Bacterial communities display an astonishing degree of heterogeneities among their constituent cells across both the genomic and transcriptomic levels, giving rise to diverse social interactions and stress-adaptation strategies indispensable for proliferating in the natural environment (Ackermann in Nat Rev Microbiol 13:497-508, 2015). Our knowledge about bacterial heterogeneities and their physiological ramifications critically depends on our ability to unambiguously resolve the genetic and phenotypic states of the individual cells that make up the population. In this short review, I highlight several recently developed methods for studying bacterial heterogeneities, primarily focusing on single-cell techniques based on advanced sequencing and microscopy technologies. I will discuss the working principle of each technique as well as the types of problems each technique is best positioned to address. With significant improvements in resolution and throughput, these emerging tools together offer unprecedented and complementary views of various types of heterogeneities found within bacterial populations, paving the way for mechanistic dissections and systematic interventions in laboratory and clinical settings.

Bacterial phenotypic heterogeneity through the lens of single-cell RNA sequencing

Bacterial transcription is not monolithic. Microbes exist in a wide variety of cell states that help them adapt to their environment, acquire and produce essential nutrients, and engage in both competition and cooperation with their neighbors. While we typically think of bacterial adaptation as a group behavior, where all cells respond in unison, there is often a mixture of phenotypic responses within a bacterial population, where distinct cell types arise. A primary phenomenon driving these distinct cell states is transcriptional heterogeneity. Given that bacterial mRNA transcripts are extremely short-lived compared to eukaryotes, their transcriptional state is closely associated with their physiology, and thus the transcriptome of a bacterial cell acts as a snapshot of the behavior of that bacterium. Therefore, the application of single-cell transcriptomics to microbial populations will provide novel insight into cellular differentiation and bacterial ecology. In this review, we provide an overview of transcriptional heterogeneity in microbial systems, discuss the findings already provided by single-cell approaches, and plot new avenues of inquiry in transcriptional regulation, cellular biology, and mechanisms of heterogeneity that are made possible when microbial communities are analyzed at single-cell resolution.

Effect of glyphosate, its metabolite AMPA, and the glyphosate formulation Roundup® on brown trout (Salmo trutta f. fario) gut microbiome diversity

Glyphosate is used worldwide as a compound of pesticides and is detectable in many environmental compartments. It enters water bodies primarily through drift from agricultural areas so that aquatic organisms are exposed to this chemical, especially after rain events. Glyphosate is advertised and sold as a highly specific herbicide, which interacts with the EPSP synthase, an enzyme of the shikimate metabolism, resulting in inhibition of the synthesis of vital aromatic amino acids. However, not only plants but also bacteria can possess this enzyme so that influences of glyphosate on the microbiomes of exposed organisms cannot be excluded. Those influences may result in subtle and long-term effects, e.g., disturbance of the symbiotic interactions of bionts with microorganisms of their microbiomes. Mechanisms how the transformation product aminomethylphosphonic acid (AMPA) of glyphosate might interfere in this context have not understood so far. In the present study, molecular biological fingerprinting methods showed concentration-dependent effects of glyphosate and AMPA on fish microbiomes. In addition, age-dependent differences in the composition of the microbiomes regarding abundance and diversity were detected. Furthermore, the effect of exposure to glyphosate and AMPA was investigated for several fish pathogens of gut microbiomes in terms of their gene expression of virulence factors associated with pathogenicity. In vitro transcriptome analysis with the fish pathogen Yersinia ruckeri revealed that it is questionable whether the observed effect on the microbiome is caused by the intended mode of action of glyphosate, such as the inhibition of EPSP synthase activity.

Variation in growth rates between cultures hinders the cultivation of ammonia-oxidizing bacteria

Ammonia-oxidizing bacteria, key players in the nitrogen cycle, have been the focus of extensive research. Numerous novel species have been isolated and their growth dynamics were studied. Despite these efforts, controlling their growth to obtain diverse physiological findings remains a challenge. These bacteria often fail to grow, even under optimal conditions. This unpredictable growth pattern could be viewed as a survival strategy. Understanding this heterogeneous behavior could enhance our ability to culture these bacteria. In this study, the variation in the growth rate was quantified for the ammonia-oxidizing bacterium Nitrosomonas mobilis Ms1. Our findings revealed significant growth rate variation under low inoculum conditions. Interestingly, higher cell densities resulted in more stable cultures. A comparative analysis of three Nitrosomonas species showed a correlation between growth rate variation and culture failure. The greater the variation in growth rate, the higher the likelihood of culture failure.

Emergent antibiotic persistence in a spatially structured synthetic microbial mutualism

Antibiotic persistence (heterotolerance) allows a subpopulation of bacteria to survive antibiotic-induced killing and contributes to the evolution of antibiotic resistance. Although bacteria typically live in microbial communities with complex ecological interactions, little is known about how microbial ecology affects antibiotic persistence. Here, we demonstrated within a synthetic two-species microbial mutualism of Escherichia coli and Salmonella enterica that the combination of cross-feeding and community spatial structure can emergently cause high antibiotic persistence in bacteria by increasing the cell-to-cell heterogeneity. Tracking ampicillin-induced death for bacteria on agar surfaces, we found that E. coli forms up to 55 times more antibiotic persisters in the cross-feeding coculture than in monoculture. This high persistence could not be explained solely by the presence of S. enterica, the presence of cross-feeding, average nutrient starvation, or spontaneous resistant mutations. Time-series fluorescent microscopy revealed increased cell-to-cell variation in E. coli lag time in the mutualistic co-culture. Furthermore, we discovered that an E. coli cell can survive antibiotic killing if the nearby S. enterica cells on which it relies die first. In conclusion, we showed that the high antibiotic persistence phenotype can be an emergent phenomenon caused by a combination of cross-feeding and spatial structure. Our work highlights the importance of considering spatially structured interactions during antibiotic treatment and understanding microbial community resilience more broadly.

Single-Molecule Fluorescent In Situ Hybridization (smFISH) for RNA Detection in Bacteria

In this chapter, we describe in detail how to perform a successful smFISH experiment and how to quantify mRNA transcripts in bacterial cells. The flexibility of the method allows for straightforward adaptation to different bacterial species and experimental conditions. Thanks to the feasibility of the approach, the method can easily be adapted by other laboratories. Finally, we believe that this method has a great potential to generate insights into the complicated life of bacteria.

Identification of a novel heterozygous PTH1R variant in a Chinese family with incomplete penetrance

BACKGROUND

Mutations in PTH1R are associated with Jansen-type metaphyseal chondrodysplasia (JMC), Blomstrand osteochondrodysplasia (BOCD), Eiken syndrome, enchondroma, and primary failure of tooth eruption (PFE). Inheritance of the PTH1R gene can be either autosomal dominant or autosomal recessive, indicating the complexity of the gene. Our objective was to identify the phenotypic differences in members of a family with a novel PTH1R mutation.

METHODS

The proband was a 13-year, 6-month-old girl presenting with short stature, abnormal tooth eruption, skeletal dysplasia, and midface hypoplasia. The brother and father of the proband presented with short stature and abnormal tooth eruption. High-throughput sequencing was performed on the proband, and the variant was confirmed in the proband and other family members by Sanger sequencing. Amino acid sequence alignment was performed using ClustalX software. Three-dimensional structures were analyzed and displayed using the I-TASSER website and PyMOL software.

RESULTS

High-throughput genome sequencing and Sanger sequencing validation showed that the proband, her father, and her brother all carried the PTH1R (NM_000316) c.1393G>A (p.E465K) mutation. The c.1393G>A (p.E465K) mutation was novel, as it has not been reported in the literature database. According to the American College of Medical Genetics and Genomics (ACMG) guidelines, the p.E465K variant was considered to have uncertain significance. Biological information analysis demonstrated that this identified variant was highly conserved and highly likely pathogenic.

CONCLUSIONS

We identified a novel heterozygous mutation in the PTH1R gene leading to clinical manifestations with incomplete penetrance that expands the spectrum of known PTH1R mutations.

Genome-Scale Analysis of the Structure and Function of RNA Pathways and Networks in Pseudomonas aeruginosa

In recent years, several genome-wide approaches based on RNA sequencing (RNA-seq) have been developed. These methods allow a comprehensive and dynamic view of the structure and function of the multi-layered RNA pathways and networks. Many of these approaches, including the promising one of single-cell transcriptome analysis, have been successfully applied to Pseudomonas aeruginosa. However, we are only at the beginning because only a few surrounding conditions have been considered. Here, we aim to illustrate the different types of approaches based on RNA-seq that will lead us in the future to a better understanding of the dynamics of RNA biology in P. aeruginosa.