New Types of Dissipative Streaming Instabilities

Two new, previously unknown types of dissipative streaming instabilities (DSI) are substantiated. They follow from new approach, which allows solving in general form the classical problem of an initial perturbation development for streaming instabilities (SI). SI is caused by relative motion of the streams of plasma components. With an increase in level of dissipation SI transforms into a DSI. The transformation occurs because dissipation serves as a channel for energy removal for the growth of the negative energy wave of the stream. Until recently, only one type of DSI was known. Its maximal growth rate depends on the beam density nb and the collision frequency ν in the plasma as ∼nb/ν. All types of conventional beam-plasma instabilities (Cherenkov, cyclotron, etc.) transform into it. The solution of the problem of the initial perturbation development in systems with weak beam-plasma coupling leads to a new type of DSI. With an increase in the level of dissipation, the instability in these systems transforms to the new DSI. Its maximal growth rate is ∼nb/ν. The second new DSI develops in beam-plasma waveguide with over-limiting current of e-beam. Its growth rate ∼nb/ν. In addition, the solutions of abovementioned problem provide much information about SI and DSI, significant part of which is unavailable by other methods.

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator and predicted maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity; extensions allow estimates from 16S rRNA sequences alone as well as weighted community estimates from metagenomes. We compared the growth rates of cultivated and uncultivated organisms to illustrate how culture collections are strongly biased toward organisms capable of rapid growth. Finally, we found that organisms naturally group into two growth classes and observed a bias in growth predictions for extremely slow-growing organisms. These observations ultimately led us to suggest evolutionary definitions of oligotrophy and copiotrophy based on the selective regime an organism occupies. We found that these growth classes are associated with distinct selective regimes and genomic functional potentials.

Environmental and Physiological Factors Affecting High-Throughput Measurements of Bacterial Growth

ABSTRACT Bacterial growth under nutrient-rich and starvation conditions is intrinsically tied to the environmental history and physiological state of the population. While high-throughput technologies have enabled rapid analyses of mutant libraries, technical and biological challenges complicate data collection and interpretation. Here, we present a framework for the execution and analysis of growth measurements with improved accuracy over that of standard approaches. Using this framework, we demonstrate key biological insights that emerge from consideration of culturing conditions and history. We determined that quantification of the background absorbance in each well of a multiwell plate is critical for accurate measurements of maximal growth rate. Using mathematical modeling, we demonstrated that maximal growth rate is dependent on initial cell density, which distorts comparisons across strains with variable lag properties. We established a multiple-passage protocol that alleviates the substantial effects of glycerol on growth in carbon-poor media, and we tracked growth rate-mediated fitness increases observed during a long-term evolution of Escherichia coli in low glucose concentrations. Finally, we showed that growth of Bacillus subtilis in the presence of glycerol induces a long lag in the next passage due to inhibition of a large fraction of the population. Transposon mutagenesis linked this phenotype to the incorporation of glycerol into lipoteichoic acids, revealing a new role for these envelope components in resuming growth after starvation. Together, our investigations underscore the complex physiology of bacteria during bulk passaging and the importance of robust strategies to understand and quantify growth. IMPORTANCE How starved bacteria adapt and multiply under replete nutrient conditions is intimately linked to their history of previous growth, their physiological state, and the surrounding environment. While automated equipment has enabled high-throughput growth measurements, data interpretation and knowledge gaps regarding the determinants of growth kinetics complicate comparisons between strains. Here, we present a framework for growth measurements that improves accuracy and attenuates the effects of growth history. We determined that background absorbance quantification and multiple passaging cycles allow for accurate growth rate measurements even in carbon-poor media, which we used to reveal growth-rate increases during long-term laboratory evolution of Escherichia coli. Using mathematical modeling, we showed that maximum growth rate depends on initial cell density. Finally, we demonstrated that growth of Bacillus subtilis with glycerol inhibits the future growth of most of the population, due to lipoteichoic acid synthesis. These studies highlight the challenges of accurate quantification of bacterial growth behaviors.

Effect of Salt Stress on Mutation and Genetic Architecture for Fitness Components in Saccharomyces cerevisiae

Mutations shape genetic architecture and thus influence the evolvability, adaptation and diversification of populations. Mutations may have different and even opposite effects on separate fitness components, and their rate of origin, distribution of effects and variance-covariance structure may depend on environmental quality. We performed an approximately 1,500-generation mutation-accumulation (MA) study in diploids of the yeast Saccharomyces cerevisiae in stressful (high-salt) and normal environments (50 lines each) to investigate the rate of input of mutational variation (Vm) as well as the mutation rate and distribution of effects on diploid and haploid fitness components, assayed in the normal environment. All four fitness components in both MA treatments exhibited statistically significant mutational variance and mutational heritability. Compared to normal-MA, salt stress increased the mutational variance in growth rate by more than sevenfold in haploids derived from the MA lines. This increase was not detected in diploid growth rate, suggesting masking of mutations in the heterozygous state. The genetic architecture arising from mutation (M-matrix) differed between normal and salt conditions. Salt stress also increased environmental variance in three fitness components, consistent with a reduction in canalization. Maximum-likelihood analysis indicated that stress increased the genomic mutation rate by approximately twofold for maximal growth rate and sporulation rate in diploids and for viability in haploids, and by tenfold for maximal growth rate in haploids, but large confidence intervals precluded distinguishing these values between MA environments. We discuss correlations between fitness components in diploids and haploids and compare the correlations between the two MA environmental treatments.

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator, and implement this estimator in an easy-to-use R package (gRodon), which outperforms the state-of-the-art growth estimator in multiple settings, including in a community context where we implement a novel species abundance correction for metagenomes. Additionally, we estimate maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity. We provide these compiled maximal growth rates in a publicly-available database (EGGO), which we use to illustrate how culture collections show a strong bias towards organisms capable of rapid growth. We demonstrate how this database can be used to propagate maximal growth rate predictions to organisms for which we lack genomic information, on the basis of 16S rRNA sequence alone. Finally, we observe a bias in growth predictions for extremely slow-growing organisms, ultimately leading us to suggest a novel evolutionary definition of oligotrophy based on the selective regime an organism occupies.SignificanceDespite the wide perception that microbes have rapid growth rates, many environments like seawater and soil are often dominated by microorganisms that can only grow very slowly. Our knowledge about growth is necessarily biased towards easily culturable organisms, which turn out to be those that tend to grow fast, because microbial growth rates have traditionally been measured using lab growth experiments. But how are potential growth rates distributed in nature? We developed a tool to predict maximum growth rate from an organism’s genome sequence (gRodon). We predicted the growth rates of over 200,000 organisms and compiled these predictions in a publicly-available database (EGGO), which illustrates how current collections of cultured microbes are strongly biased towards fast-growing organisms.

Environmental and physiological factors affecting high-throughput measurements of bacterial growth

Bacterial growth in nutrient-rich and starvation conditions is intrinsically tied to the environmental history and physiological state of the population. While high-throughput technologies have enabled rapid analyses of mutant libraries, technical and biological challenges complicate data collection and interpretation. Here, we present a framework for the execution and analysis of growth measurements with improved accuracy over standard approaches. Using this framework, we demonstrate key biological insights that emerge from consideration of culturing conditions and history. We determined that quantification of the background absorbance in each well of a multi-well plate is critical for accurate measurements of maximal growth rate. Using mathematical modeling, we demonstrated that maximal growth rate is dependent on initial cell density, which distorts comparisons across strains with variable lag properties. We established a multiple-passage protocol that alleviates the substantial effects of glycerol on growth in carbon-poor media, and we tracked growth rate-mediated fitness increases observed during a long-term evolution of Escherichia coli in low glucose concentrations. Finally, we showed that growth of Bacillus subtilis in the presence of glycerol induces a long lag in the next passage due to inhibition of a large fraction of the population. Transposon mutagenesis linked this phenotype to the incorporation of glycerol into lipoteichoic acids, revealing a new role for these envelope components in resuming growth after starvation. Together, our investigations underscore the complex physiology of bacteria during bulk passaging and the importance of robust strategies to understand and quantify growth.Abstract ImportanceHow starved bacteria adapt to and multiply in replete nutrient conditions is intimately linked to their history of previous growth, their physiological state, and the surrounding environment. While automated equipment has enabled high-throughput growth measurements, data interpretation and knowledge gaps regarding the determinants of growth kinetics complicate comparisons between strains. Here, we present a framework for growth measurements that improves accuracy and attenuates the effects of growth history. We determined that background absorbance quantification and multiple passaging cycles allows for accurate growth-rate measurements even in carbon-poor media, which we used to reveal growth-rate increases during long-term laboratory evolution of Escherichia coli. Using mathematical modeling, we showed that maximum growth rate depends on initial cell density. Finally, we demonstrated that growth of Bacillus subtilis with glycerol inhibits the future growth of most of the population, due to lipoteichoic-acid synthesis. These studies highlight the challenges of accurate quantification of bacterial growth behaviors.

Calycomorphotria hydatis gen. nov., sp. nov., a novel species in the family Planctomycetaceae with conspicuous subcellular structures

A novel strain belonging to the family Planctomycetaceae, designated V22T, was isolated from sediment of a seawater fish tank in Braunschweig, Germany. The isolate forms pink colonies on solid medium and displays common characteristics of planctomycetal strains, such as division by budding, formation of rosettes, a condensed nucleoid and presence of crateriform structures and fimbriae. Unusual invaginations of the cytoplasmic membrane and filamentous putative cytoskeletal elements were observed in thin sections analysed by transmission electron microscopy. Strain V22T is an aerobic heterotroph showing optimal growth at 30 °C and pH 8.5. During laboratory cultivations, strain V22T reached generation times of 10 h (maximal growth rate of 0.069 h−1). Its genome has a size of 5.2 Mb and a G + C content of 54.9%. Phylogenetically, the strain represents a novel genus and species in the family Planctomycetaceae, order Planctomycetales, class Planctomycetia. We propose the name Calycomorphotria hydatis gen. nov., sp. nov. for the novel taxon, represented by the type strain V22T (DSM 29767T = LMG 29080T).

Existence and Stability of Stationary States of a Reaction–Diffusion-Advection Model for Two Competing Species

It is well known that the research of two species in the Lotka–Volterra competition system could create very interesting dynamics. In our paper, we investigate the global dynamical behavior of a classic Lotka–Volterra competition system by studying the steady states and corresponding stability by mainly employing the methods of monotone dynamical systems theory, Lyapunov–Schmidt reduction and spectral theory and so on. It illustrates that the dynamical behavior substantially relies on certain variable of the maximal growth rate. Furthermore, we obtain that one of the semi-trivial steady state solutions is a global attractor in some special cases. In biology, these results show that both of the species do not coexist and the mutant forces the extinction of resident species under some condition for two similar species system.

Aureliella helgolandensis gen. nov., sp. nov., a novel Planctomycete isolated from a jellyfish at the shore of the island Helgoland

A novel planctomycetal strain, designated Q31aT, was isolated from a jellyfish at the shore of the island Helgoland in the North Sea. The strain forms lucid white colonies on solid medium and displays typical characteristics of planctomycetal strains, such as division by budding, formation of rosettes, presence of crateriform structures, extracellular matrix or fibre and a holdfast structure. Q31aT is mesophilic (temperature optimum 27 °C), neutrophilic (pH optimum 7.5), aerobic and heterotrophic. A maximal growth rate of 0.017 h− 1 (generation time of 41 h) was observed. Q31aT has a genome size of 8.44 Mb and a G + C content of 55.3%. Phylogenetically, the strain represents a novel genus and species in the recently introduced family Pirellulaceae, order Pirellulales, class Planctomycetia. We propose the name Aureliella helgolandensis gen. nov., sp. nov. for the novel species, represented by Q31aT (= DSM 103537T = LMG 29700T) as the type strain.

Community-level respiration of prokaryotic microbes may rise with global warming

Understanding how the metabolic rates of prokaryotes respond to temperature is fundamental to our understanding of how ecosystem functioning will be altered by climate change, as these micro-organisms are major contributors to global carbon efflux. Ecological metabolic theory suggests that species living at higher temperatures evolve higher growth rates than those in cooler niches due to thermodynamic constraints. Here, using a global prokaryotic dataset, we find that maximal growth rate at thermal optimum increases with temperature for mesophiles (temperature optima $$\lesssim 4{5}\ ^{\circ }$$≲45∘C), but not thermophiles ($$\gtrsim 4{5}\ ^{\circ }$$≳45∘C). Furthermore, short-term (within-day) thermal responses of prokaryotic metabolic rates are typically more sensitive to warming than those of eukaryotes. Because climatic warming will mostly impact ecosystems in the mesophilic temperature range, we conclude that as microbial communities adapt to higher temperatures, their metabolic rates and therefore, biomass-specific CO$${}_{2}$$2 production, will inevitably rise. Using a mathematical model, we illustrate the potential global impacts of these findings.