Quantitative studies of bacterial chemotaxis and microbial population dynamics

Abstract. Understanding how the Earth's biogeochemical cycles respond to environmental change is a prerequisite for the prediction and mitigation of the effects of anthropogenic perturbations. Microbial populations mediate key steps in these cycles, yet they are often crudely represented in biogeochemical models. Here, we show that microbial population dynamics can qualitatively affect the response of biogeochemical cycles to environmental change. Using simple and generic mathematical models, we find that nutrient limitations on microbial population growth can lead to regime shifts, in which the redox state of a biogeochemical cycle changes dramatically as the availability of a redox-controlling species, such as oxygen or acetate, crosses a threshold (a "tipping point"). These redox regime shifts occur in parameter ranges that are relevant to the present-day sulfur cycle in the natural environment and the present-day nitrogen cycle in eutrophic terrestrial environments. These shifts may also have relevance to iron cycling in the iron-containing Proterozoic and Archean oceans. We show that redox regime shifts also occur in models with physically realistic modifications, such as additional terms, chemical states, or microbial populations. Our work reveals a possible new mechanism by which regime shifts can occur in nutrient-cycling ecosystems and biogeochemical cycles, and highlights the importance of considering microbial population dynamics in models of biogeochemical cycles.

Division synchrony and the dynamics of microbial populations: A size-specific model

Effects of cell motility and chemotaxis on microbial population growth

Microbial population dynamics on leaves in time and space are a function of immigration, emigration, growth, and death. Insight into the relative significance of each population process to the generation of specific dynamics for individual microorganisms is necessary to understanding the ecology and life history strategy of the microorganism and to developing effective control strategies. Additionally, information on the significance of within-leaf versus extra-leaf processes to the generation of phyllosphere dynamics is important to determining the range of spatial scales over which a population should be studied. Unfortunately, such information is difficult to obtain due to the lack of effective methodologies for distinguishing these processes within phyllosphere populations. Future research efforts should focus on the quantification of immigration, emigration, growth, and death relative to the population dynamics of phyllosphere microorganisms.

Microbial communities thrive in virtually every habitat on Earth and are essential to the function of diverse ecosystems. Most microbial habitats are not spatially continuous and well-mixed, but rather composed, at the microscale, of many isolated or semi-isolated local patches of different sizes, resulting in partitioning of microbial populations into discrete local populations. The impact of this spatial fragmentation on population dynamics is not well-understood. Here, we study how such variably sized micro-habitat patches affect the growth dynamics of clonal microbial populations and how dynamics in individual patches dictate those of the metapopulation. To investigate this, we developed the μ-SPLASH, an ecology-on-a-chip platform, enabling the culture of microbes in microscopic landscapes comprised of thousands of microdroplets, with a wide range of sizes. Using the μ-SPLASH, we cultured the model bacteria Escherichia coli and based on time-lapse microscopy, analyzed the population dynamics within thousands of individual droplets. Our results reveal that growth curves substantially vary with droplet size. Although growth rates generally increase with drop size, reproductive success and the time to approach carrying capacity, display non-monotonic patterns. Combining μ-SPLASH experiments with computational modeling, we show that these patterns result from both stochastic and deterministic processes, and demonstrate the roles of initial population density, patchiness, and patch size distribution in dictating the local and metapopulation dynamics. This study reveals basic principles that elucidate the effects of habitat fragmentation and population partitioning on microbial population dynamics. These insights deepen our understanding of natural microbial communities and have significant implications for microbiome engineering.

SummaryThe microbial population dynamics on apples cv. Golden Delicious were analysed every 15 days between bud and harvest in a fully replicated experiment in northern Spain in 1994 and 1995. The total microbial populations varied with developmental stage, and with prevailing climatic conditions. The predominant mycroflora were the filamentous fungiCladosporiumandAlternariaspp. and white and pink yeasts. Other genera isolated included mainly species ofEpicoccum, FusariumandAcremonium.However, the most important post‐harvest pathogensPenicillium expansumandBotrytis cinereawere seldom isolated from ripening apples. Maximum total filamentous fungal populations occurred after fruit set and during early ripening [2 × 104cfu (colony‐forming units) g‐1approximately] while those of bacteria were maximum at bud stage (3.5 × 105and 3.0 × 104cfu g‐1in 1994 and 1995 respectively). White yeasts were more numerous than pink yeasts. Endophytic infection of apple buds byAlternariaspp., responsible for core rot, was found in almost all bud tissue. By contrast,Cladosporiumspp. were initially isolated later from 12.5–50% of tissue samples during blooming and fruit set. The impact of a four‐spray fungicide regime during apple development significantly decreased the total filamentous fungal populations in both years, and that ofCladosporiumspp. in 1994. However, bacterial populations were often higher on apples from fungicide‐treated plots. Fungicide sprays decreased populations ofCladosporium, Alternariaand white yeasts for a maximum of up to 15–30 days after application. Fungicide application had little effect on endophytic infection of apples byAlternariaspp. between bud and harvest.

Microbial population dynamics were investigated during start-up and during periods of overload conditions in anaerobic co-digesters treating municipal solid waste and sewage sludge. Changes in community structure were monitored using ribosomal RNA-based oligonucleotide probe hybridization to measure the abundance of syntrophic propionate-oxidizing bacteria (SPOB), saturated fatty acid-beta-oxidizing syntrophs (SFAS), and methanogens. These changes were linked to traditional performance parameters such as biogas production and volatile fatty acid (VFA) concentrations. Digesters with high levels of Archaea started up successfully. Methanosaeta concilii was the dominant aceticlastic methanogen in these systems. In contrast, digesters that experienced a difficult start-up period had lower levels of Archaea with proportionally more abundant Methanosarcina spp. Syntrophic propionate-oxidizing bacteria and saturated fatty acid-beta-oxidizing syntrophs were present at low levels in all digesters, and SPOB appeared to play a role in stabilizing propionate levels during start-up of one digester. Digesters with a history of poor performance tolerated a severe organic overload event better than digesters that had previously performed well. It is hypothesized that higher levels of SPOB and SFAS and their methanogenic partners in previously unstable digesters are responsible for this behavior.

Uninoculated wines are regarded as having improved mouthfeel and texture and more complex flavor profiles when compared to wines inoculated with commercial S. cerevisiae strains. Uninoculated fermentation involves a complex microbial succession of yeasts and bacteria during fermentation. Microbial population dynamics are affected by several factors that can ultimately determine if a particular species or strain contributes to wine aroma and flavor. In this work, we have studied the effect of aeration, a common winemaking practice, on the yeast microbiota during uninoculated Chardonnay wine fermentation. The timing of aeration and then aeration intensity were evaluated across two successive vintages. While the timing of aeration significantly impacted fermentation efficiency across oxygen treatments, different levels of aeration intensity only differed when compared to the non-aerated control ferments. Air addition increased the viable cell population size of yeast from the genera Hanseniaspora, Lachancea, Metschnikowia and Torulaspora in both vintages. While in 2019, a high relative abundance was found for Hanseniaspora species in aerated ferments, in 2020, T. delbrueckii was visibly more abundant than other species in response to aeration. Accompanying the observed differences in yeast community structure, the chemical profile of the finished wines was also different across the various aeration treatments. However, excessive aeration resulted in elevated concentrations of ethyl acetate and acetic acid, which would likely have a detrimental effect on wine quality. This work demonstrates the role of aeration in shaping yeast population dynamics and modulating a volatile profile in uninoculated wines, and highlights the need for careful air addition to avoid a negative sensory impact on wine flavor and aroma.

Using anaerobic/aerobic sequencing batch reactors (SBRs) it was found that pre-fermentation of influent glucose resulted in a microbial population capable of enhanced biological phosphorus removal (EBPR). Batch tests indicated the C1-C5 carboxylic acids, except propionate, typically improved phosphorus removal. Branched molecules were superior to their linear isomers. The C1-C5 alcohols did not affect removal. Glucose, propionate, and an amino-acid rich substrate were detrimental. Using NMR spectroscopy it was observed that intracellular forms and locations of phosphorus did not change regardless of the substrate received. Polyphosphate (polyP) was present throughout the cells at the end of aerobiosis. It then degraded to inorganic phosphate via a zero-order enzymatic reaction concentrated at the cell membrane. An anaerobic/aerobic SBR receiving starch, rather than glucose fermentation products, showed only marginal EBPR and did not respond to carboxylic acids or other substrates in batch tests. Pseudomonas and Bacillus were numerous in the glucose system but were not isolated from the starch system. Aeromonas were dominant in the starch system. Although the glucose system showed better phosphorus removal than the starch system, it also showed greater variability. Phosphorus removal varied in a chaotic, but bounded, manner, probably due to population dynamics.

Microbial communities abound with examples of complex social interactions that shape microbial ecosystems. One particularly striking example is microbial cooperation via the secretion of public goods. It has been suggested by theory, and recently demonstrated experimentally, that microbial population dynamics and the evolutionary dynamics of cooperative social genes take place with similar timescales, and are linked to each other via an eco-evolutionary feedback loop. We overview this recent evidence, and discuss the possibility that a third process may be also part of this loop: phenotypic dynamics. Complex social strategies may be implemented at the single-cell level by means of gene regulatory networks. Thus gene expression plasticity or stochastic gene expression, both of which may occur with a timescale of one to a few generations, can potentially lead to a three-way coupling between behavioral dynamics, population dynamics, and evolutionary dynamics

Population Dynamics: Mathematic Models of Population, Development, and Natural Resources

Dynamics of free-living nitrogen-fixing bacterial populations in antagonistic conditions

We have developed a substrate-induced growth response (SIGR) method for quantifying activity and population dynamics of microorganisms involved in bioremediation processes in soil and bioreactors. The biomass of organisms that can mineralize a given chemical can be estimated based on the concentration of that chemical needed to induce the growth of the standing population. Estimates of population size are obtained by using nonlinear regression techniques to fit a simple model of microbial population dynamics to biodegradation curves. Using this approach we obtain estimates of values for parameters such as initial population size and growth rate of organisms carrying out biodegradative processes. Our approach was validated by comparing model parameter estimates with independent estimates of the same parameters from the same bioremediation systems. Examples studied include pentachlorophenol degraders introduced into soil and 2,4-dinitrophenol degrading organisms in a bioreactor.

Foreword. Preface. Contributors. Part I Concepts and Methods in Bacterial Population Genetics. 1 The Coalescent of Bacterial Populations. 1.1 Background and Motivation. 1.2 Population Reproduction Models. 1.3 Time and the Effective Population Size. 1.4 The Genealogy of a Sample of Size n. 1.5 From Coalescent Time to Real Time. 1.6 Mutations. 1.7 Demography. 1.8 Recombination and Gene Conversion. 1.9 Summary. 2 Linkage, Selection, and the Clonal Complex. 2.1 Introduction Historical Overview. 2.2 Recombination, Linkage, and Substructure. 2.3 Neutrality versus Selection. 2.4 Clustering Techniques. 3 Sequence-Based Analysis of Bacterial Population Structures. 3.1 Introduction. 3.2 Alignments. 3.3 Phylogenetic Methods. 3.4 Measures of Uncertainty. 3.5 Beyond the Tree Model. 4 Genetic Recombination and Bacterial Population Structure. 4.1 Introduction. 4.2 Constraints on LGT. 4.3 Infl uences of LGT on Sequence Analyses. 4.4 The Detection of Individual LGT Events. 4.5 The Estimation of Homologous Recombination Rates. 4.6 Properly Accounting for LGT During Sequence Analyses. 4.7 Questions Relating Directly to LGT. 5 Statistical Methods for Detecting the Presence of Natural Selection in Bacterial Populations. 5.1 Introduction. 5.2 Natural Selection. 5.3 Statistical Methods for Detecting the Presence of Natural Selection. 5.4 Statistical Methods for Bacterial Populations. 5.5 An Example. 5.6 Discussion and Perspective. 6 Demographic Infl uences on Bacterial Population Structure. 6.1 Bacterial Population Size. 6.2 Measures of Genetic Diversity. 6.3 The Concept of Effective Population Size. 6.4 Inferring Past Demography from Genetic Sequence Data. 6.5 Population Subdivision. 6.6 What is a Bacterial Population? 6.7 Conclusion. 7 Population Genomics of Bacteria. 7.1 Introduction. 7.2 Classical Bacterial Population Genetics. 7.3 The Genomics Era. 7.4 Bacterial Population Genomics. 7.5 Next-Gen Bacterial Population Genomics. 7.6 Next-Gen Genomics Technology. 7.7 Next-Gen Genomic Data Analysis. 7.8 Conclusions/Future Prospects. 8 The Use of MLVA and SNP Analysis to Study the Population Genetics of Pathogenic Bacteria. 8.1 Introduction. 8.2 MLVA and Other DNA Fragment-Based Methods. 8.3 SNP and DNA Sequence-Based Methods. 8.4 Conclusion. Part II Population Genetics of Select Bacterial Pathogens. 9 Population Genetics of Bacillus: Phylogeography of Anthrax in North America. 9.1 Introduction. 9.2 History of Anthrax in North America. 9.3 The Anthrax Districts after 1944. 9.4 Molecular Genotyping of B. anthracis. 9.5 Genotypes within the Anthrax Districts in North America. 9.6 Phylogenetic Resolution within the WNA Lineage. 9.7 Phylogeographic Resolution within the Ames Lineage. 9.8 Additional B. anthracis Genotypes in North America. 9.9 Conclusions. 10 Population Genetics of Campylobacter. 10.1 Introduction. 10.2 Human Infection. 10.3 Genetic Structure. 10.4 Models of Campylobacter Evolution. 10.5 Clades and Species. 10.6 Conclusion. 11 Population Genetics of Enterococcus. 11.1 Introduction. 11.2 Antibiotic Resistance. 11.3 Vancomycin Resistance. 11.4 VRE: A Zoonosis or Not? 11.5 Population Structure and Genetic Evolution: Similarities and Differences Between E. faecium and E. faecalis. 11.6 What Is Driving GD in E. faecium and E. faecalis? 11.7 The Accessory Genome of E. faecium and E. faecalis. 11.8 Summary, Conclusions, and Future Perspectives. 12 Population Biology of Lyme Borreliosis Spirochetes. 12.1 Introduction. 12.2 Genome Organization of LB Spirochetes. 12.3 Genotyping of LB Spirochetes and Phylogenetic Tools. 12.4 Population Biology and Evolution of LB Spirochetes. 12.5 Do LB Species Exist? 12.6 Future Research Avenues. 13 Population Genetics of Neisseria meningitidis. 13.1 Introduction. 13.2 A Brief History of Typing of Meningococci. 13.3 Species Separation. 13.4 Sampling Strategies. 13.5 The Clonal Complexes of Meningococci. 13.6 Forces Shaping the Meningococcal Metalineage. 13.7 Virulence, a Mysterious Trait. 13.8 Population Effect of Meningococcal Vaccines. 13.9 Antibiotic Resistance and Meningococcal Lineages. 13.10 Concluding Remarks. 14 Population Genetics of Pathogenic Escherichia coli. 14.1 Introduction. 14.2 E. coli Population Genetics: Clonal or not Clonal? 14.3 The E. coli Phylogenetic Structure. 14.4 The Evolutionary History of a Host-Specifi c Obligate Pathogen: The Shigella and EIEC Case Study. 14.5 What Makes You an Opportunistic Pathogen? 14.6 The Virulence Resistance Trade-off. 14.7 Concluding Remarks. 15 Population Genetics of Salmonella: Selection for Antigenic Diversity. 15.1 Introduction. 15.2 Generation Timescale Diversifi cation. 15.3 Antigenic Diversity in Salmonella. 15.4 Why Are Diverse H and O Antigens Maintained in Salmonella? 15.5 Conclusions. 16 Population Genetics of Staphylococcus. 16.1 Introduction. 16.2 Overview of The Staphylococcal Population Structure. 16.3 Staphylococcal Population Structure in Specific Disease Contexts. 16.4 Origin and Maintenance of Staphylococcal Genetic Variation. 16.5 Macroevolutionary Considerations and Concluding Remarks. Appendix 1 Diversity and Differentiation. 17 Population Genetics of Streptococcus. 17.1 Habitats, Transmission, and Disease. 17.2 Classical Strain Typing. 17.3 Multilocus Sequence Typing (MLST) Based on Housekeeping Genes. 17.4 Species Boundaries and Gene Flow. 17.5 Niche-driving Genes. 17.6 Bacterial Population Dynamics and Selection. 17.7 Machinery of Genetic Change, Revisited. 18 Population Genetics of Vibrios. 18.1 Introduction. 18.2 V. cholerae. 18.3 V. parahaemolyticus . 18.4 V. vulnificus. 18.5 Conclusions. References. Index.

The difficulty involved in quantifying biogeochemically significant microbes in marine sediments limits our ability to assess interspecific interactions, population turnover times, and niches of uncultured taxa. We incubated surface sediments from Cape Lookout Bight, North Carolina, USA, anoxically at 21°C for 122 days. Sulfate decreased until day 68, after which methane increased, with hydrogen concentrations consistent with the predicted values of an electron donor exerting thermodynamic control. We measured turnover times using two relative quantification methods, quantitative PCR (qPCR) and the product of 16S gene read abundance and total cell abundance (FRAxC, which stands for "fraction of read abundance times cells"), to estimate the population turnover rates of uncultured clades. Most 16S rRNA reads were from deeply branching uncultured groups, and ∼98% of 16S rRNA genes did not abruptly shift in relative abundance when sulfate reduction gave way to methanogenesis. Uncultured Methanomicrobiales and Methanosarcinales increased at the onset of methanogenesis with population turnover times estimated from qPCR at 9.7 ± 3.9 and 12.6 ± 4.1 days, respectively. These were consistent with FRAxC turnover times of 9.4 ± 5.8 and 9.2 ± 3.5 days, respectively. Uncultured Syntrophaceae, which are possibly fermentative syntrophs of methanogens, and uncultured Kazan-3A-21 archaea also increased at the onset of methanogenesis, with FRAxC turnover times of 14.7 ± 6.9 and 10.6 ± 3.6 days. Kazan-3A-21 may therefore either perform methanogenesis or form a fermentative syntrophy with methanogens. Three genera of sulfate-reducing bacteria, Desulfovibrio, Desulfobacter, and Desulfobacterium, increased in the first 19 days before declining rapidly during sulfate reduction. We conclude that population turnover times on the order of days can be measured robustly in organic-rich marine sediment, and the transition from sulfate-reducing to methanogenic conditions stimulates growth only in a few clades directly involved in methanogenesis, rather than in the whole microbial community.IMPORTANCE Many microbes cannot be isolated in pure culture to determine their preferential growth conditions and predict their response to changing environmental conditions. We created a microcosm of marine sediments that allowed us to simulate a diagenetic profile using a temporal analog for depth. This allowed for the observation of the microbial community population dynamics caused by the natural shift from sulfate reduction to methanogenesis. Our research provides evidence for the population dynamics of uncultured microbes as well as the application of a novel method of turnover rate analysis for individual taxa within a mixed incubation, FRAxC, which stands for "fraction of read abundance times cells," which was verified by quantitative PCR. This allows for the calculation of population turnover times for microbes in a natural setting and the identification of uncultured clades involved in geochemical processes.