The evolution of mechanisms to produce phenotypic heterogeneity in microorganisms

Advanced Nosema bombycis Spore Identification: Single-Cell Raman Spectroscopy Combined with Self-Attention Mechanism-Guided Deep Learning

Nosema bombycis (Nb) has been considered a dangerous pathogen, which can spread rapidly through free spores. Nowadays, pebrine disease caused by Nb spores is a serious threat to silkworms, causing huge economic losses in both the silk industry and agriculture every year. Thus, how to accurately identify living Nb spores at a single-cell level is greatly demanded. In this work, we proposed a novel approach to accurately and conveniently identify Nb spores using single-cell Raman spectroscopy and a self-attention mechanism (SAM)-guided convolutional neural network (CNN) framework. With the assistance of SAM and data augmentation methods, an optimal CNN model can not only efficiently extract spectral feature information but also construct potential relationships of global spectral features. Compared with the case without both SAM and data augmentation, the average prediction accuracy of Nb spores from nine different Bombyx mori larvae can be significantly developed by almost 18%, from original 83.93 ± 4.88% to 99.27 ± 0.25%. To visualize the individual classification weight, a local feature extraction strategy named blocking individual Raman bands was proposed. According to the relative weight, these four Raman bands located at 1658, 1458, 1127, and 849 cm-1, mainly contribute to the high prediction accuracy of 99.27 ± 0.25%. It is worth noting that these Raman bands were also highlighted by the weight curve of SAM, indicating that the four Raman bands proposed by our optimal CNN model are reliable. Our findings clearly show that single-cell Raman spectroscopy combined with SAM-mediated CNN configuration has great potential in performing early diagnosis of Nb spores and monitoring pebrine disease.

Phenotypic heterogeneity follows a growth-viability tradeoff in response to amino acid identity

In their natural environments, microorganisms mainly operate at suboptimal growth conditions with fluctuations in nutrient abundance. The resulting cellular adaptation is subject to conflicting tasks: growth or survival maximisation. Here, we study this adaptation by systematically measuring the impact of a nitrogen downshift to 24 nitrogen sources on cellular metabolism at the single-cell level. Saccharomyces lineages grown in rich media and exposed to a nitrogen downshift gradually differentiate to form two subpopulations of different cell sizes where one favours growth while the other favours viability with an extended chronological lifespan. This differentiation is asymmetrical with daughter cells representing the new differentiated state with increased viability. We characterise the metabolic response of the subpopulations using RNA sequencing, metabolic biosensors and a transcription factor-tagged GFP library coupled to high-throughput microscopy, imaging more than 800,000 cells. We find that the subpopulation with increased viability is associated with a dormant quiescent state displaying differences in MAPK signalling. Depending on the identity of the nitrogen source present, differentiation into the quiescent state can be actively maintained, attenuated, or aborted. These results establish amino acids as important signalling molecules for the formation of genetically identical subpopulations, involved in chronological lifespan and growth rate determination.

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.

Programming Dynamic Division of Labor Using Horizontal Gene Transfer

The metabolic engineering of microbes has broad applications, including biomanufacturing, bioprocessing, and environmental remediation. The introduction of a complex, multistep pathway often imposes a substantial metabolic burden on the host cell, restraining the accumulation of productive biomass and limiting pathway efficiency. One strategy to alleviate metabolic burden is the division of labor (DOL) in which different subpopulations carry out different parts of the pathway and work together to convert a substrate into a final product. However, the maintenance of different engineered subpopulations is challenging due to competition and convoluted interstrain population dynamics. Through modeling, we show that dynamic division of labor (DDOL), which we define as the DOL between indiscrete populations capable of dynamic and reversible interchange, can overcome these limitations and enable the robust maintenance of burdensome, multistep pathways. We propose that DDOL can be mediated by horizontal gene transfer (HGT) and use plasmid genomics to uncover evidence that DDOL is a strategy utilized by natural microbial communities. Our work suggests that bioengineers can harness HGT to stabilize synthetic metabolic pathways in microbial communities, enabling the development of robust engineered systems for deployment in a variety of contexts.

Revisiting the Multifaceted Roles of Bacteriocins : The Multifaceted Roles of Bacteriocins

Bacteriocins are gene-encoded antimicrobial peptides produced by bacteria. These peptides are heterogeneous in terms of structure, antimicrobial activities, biosynthetic clusters, and regulatory mechanisms. Bacteriocins are widespread in nature and may contribute to microbial diversity due to their capacity to target specific bacteria. Primarily studied as food preservatives and therapeutic agents, their function in natural settings is however less known. This review emphasizes the ecological significance of bacteriocins as multifunctional peptides by exploring bacteriocin distribution, mobility, and their impact on bacterial population dynamics and biofilms.

Monitoring Phenotype Heterogeneity at the Single-Cell Level within Bacillus Populations Producing Poly-3-hydroxybutyrate by Label-Free Super-resolution Infrared Imaging

Phenotypic heterogeneity is commonly found among bacterial cells within microbial populations due to intrinsic factors as well as equipping the organisms to respond to external perturbations. The emergence of phenotypic heterogeneity in bacterial populations, particularly in the context of using these bacteria as microbial cell factories, is a major concern for industrial bioprocessing applications. This is due to the potential impact on overall productivity by allowing the growth of subpopulations consisting of inefficient producer cells. Monitoring the spread of phenotypes across bacterial cells within the same population at the single-cell level is key to the development of robust, high-yield bioprocesses. Here, we discuss the novel development of optical photothermal infrared (O-PTIR) spectroscopy to probe phenotypic heterogeneity within Bacillus strains by monitoring the production of the bioplastic poly-3-hydroxybutyrate (PHB) at the single-cell level. Measurements obtained on single-point and in imaging mode show significant variability in the PHB content within bacterial cells, ranging from whether or not a cell produces PHB to variations in the intragranular biochemistry of PHB within bacterial cells. Our results show the ability of O-PTIR spectroscopy to probe PHB production at the single-cell level in a rapid, label-free, and semiquantitative manner. These findings highlight the potential of O-PTIR spectroscopy in single-cell microbial metabolomics as a whole-organism fingerprinting tool that can be used to monitor the dynamic of bacterial populations as well as for understanding their mechanisms for dealing with environmental stress, which is crucial for metabolic engineering research.

Disentangling the growth curve of microbial culture

Many researchers have studied the population dynamics of microbe of microbes as a typical example of population dynamics. The Monod equation, which mainly focuses on the growth and stationary phases, is used when plotting a growth curve. However, the growth potential in the late stage of culture has been overlooked. Previous studies considered the direct degradation of products to the limiting substrate. In this study, we considered microbial growth during the stationary phase, which enables us to describe the dynamics precisely. The microbes were divided into two populations: one grew by consuming the limiting substrate and the other degraded the products by metabolism. According to the numerical analysis of our model, microbes may choose one of two strategies: one consumes substrates and expands quickly, and the other grows slowly while cleaning up the environment in which they thrive. Furthermore, we found three types of microbial growth depending on their ability to detect metabolite accumulation. Using experimentally measured data, this model can estimate the dynamics of cell density, the substrates, and the metabolites used. The model's disentangling of growth curves offers novel interpretive possibilities for culture system dynamics.

The reciprocal changes in dominant species with complete metabolic functions explain the decoupling phenomenon of microbial taxonomic and functional composition in a grassland

The decoupling of microbial functional and taxonomic components refers to the phenomenon that a drastic change in microbial taxonomic composition leads to no or only a gentle change in functional composition. Although many studies have identified this phenomenon, the mechanisms underlying it are still unclear. Here we demonstrate, using metagenomics data from a steppe grassland soil under different grazing and phosphorus addition treatments, that there is no “decoupling” in the variation of taxonomic and metabolic functional composition of the microbial community within functional groups at species level. In contrast, the high consistency and complementarity between the abundance and functional gene diversity of two dominant species made metabolic functions unaffected by grazing and phosphorus addition. This complementarity between the two dominant species shapes a bistability pattern that differs from functional redundancy in that only two species cannot form observable redundancy in a large microbial community. In other words, the “monopoly” of metabolic functions by the two most abundant species leads to the disappearance of functional redundancy. Our findings imply that for soil microbial communities, the impact of species identity on metabolic functions is much greater than that of species diversity, and it is more important to monitor the dynamics of key dominant microorganisms for accurately predicting the changes in the metabolic functions of the ecosystems.

Bacterial Quorum Sensing Allows Graded and Bimodal Cellular Responses to Variations in Population Density

Quorum sensing (QS) is a mechanism of cell-cell communication that connects gene expression to environmental conditions (e.g., cell density) in many bacterial species, mediated by diffusible signal molecules. Current functional studies focus on qualitatively distinct QS ON/OFF states. In the context of density sensing, this view led to the adoption of a "quorum" analogy in which populations sense when they are above a sufficient density (i.e., "quorate") to efficiently turn on cooperative behaviors. This framework overlooks the potential for intermediate, graded responses to shifts in the environment. In this study, we tracked QS-regulated protease (lasB) expression and showed that Pseudomonas aeruginosa can deliver a graded behavioral response to fine-scale variation in population density, on both the population and single-cell scales. On the population scale, we saw a graded response to variation in population density (controlled by culture carrying capacity). On the single-cell scale, we saw significant bimodality at higher densities, with separate OFF and ON subpopulations that responded differentially to changes in density: a static OFF population of cells and increasing intensity of expression among the ON population of cells. Together, these results indicate that QS can tune gene expression to graded environmental change, with no critical cell mass or "quorum" at which behavioral responses are activated on either the individual-cell or population scale. In an infection context, our results indicate there is not a hard threshold separating a quorate "attack" mode from a subquorate "stealth" mode. IMPORTANCE Bacteria can be highly social, controlling collective behaviors via cell-cell communication mechanisms known as quorum sensing (QS). QS is now a large research field, yet a basic question remains unanswered: what is the environmental resolution of QS? The notion of a threshold, or "quorum," separating coordinated ON and OFF states is a central dogma in QS, but recent studies have shown heterogeneous responses at a single cell scale. Using Pseudomonas aeruginosa, we showed that populations generate graded responses to environmental variation through shifts in the proportion of cells responding and the intensity of responses. In an infection context, our results indicate that there is not a hard threshold separating a quorate "attack" mode and a subquorate "stealth" mode.

The evolution of division of labour in structured and unstructured groups

Recent theory has overturned the assumption that accelerating returns from individual specialisation are required to favour the evolution of division of labour. Yanni et al., 2020, showed that topologically constrained groups, where cells cooperate with only direct neighbours such as for filaments or branching growths, can evolve a reproductive division of labour even with diminishing returns from individual specialisation. We develop a conceptual framework and specific models to investigate the factors that can favour the initial evolution of reproductive division of labour. We find that selection for division of labour in topologically constrained groups: (1) is not a single mechanism to favour division of labour-depending upon details of the group structure, division of labour can be favoured for different reasons; (2) always involves an efficiency benefit at the level of group fitness; and (3) requires a mechanism of coordination to determine which individuals perform which tasks. Given that such coordination must evolve prior to or concurrently with division of labour, this could limit the extent to which topological constraints favoured the initial evolution of division of labour. We conclude by suggesting experimental designs that could determine why division of labour is favoured in the natural world.