Neutral Evolution and Dispersal Limitation Produce Biogeographic Patterns in Microcystis aeruginosa Populations of Lake Systems

Intraspecific divergence within Microcystis aeruginosa mediates the dynamics of freshwater harmful algal blooms under climate warming scenarios

Intraspecific biodiversity can have ecosystem-level consequences and may affect the accuracy of ecological forecasting. For example, rare genetic variants may have traits that prove beneficial under future environmental conditions. The cyanobacterium responsible for most freshwater harmful algal blooms worldwide, Microcystis aeruginosa, occurs in at least three types. While the dominant type occurs in eutrophic environments and is adapted to thrive in nutrient-rich conditions, two additional types have recently been discovered that inhabit oligotrophic and eutrophic environments and have genomic adaptations for survival under nutrient limitation. Here, we show that these oligotrophic types are widespread throughout the Eastern USA. By pairing an experimental warming study with gene expression analyses, we found that the eutrophic type may be most susceptible to climate warming. In comparison, oligotrophic types maintained their growth better and persisted longer under warming. As a mechanistic explanation for these patterns, we found that oligotrophic types responded to warming by widespread elevated expression of heat shock protein genes. Reduction of nutrient loading has been a historically effective mitigation strategy for controlling harmful algal blooms. Our results suggest that climate warming may benefit oligotrophic types of M. aeruginosa, potentially reducing the effectiveness of such mitigation efforts. In-depth study of intraspecific variation may therefore improve forecasting for understanding future whole ecosystem dynamics.

Biogeographic patterns in populations of marine Pseudoalteromonas atlantica isolates

Intra-specific genomic diversity is well documented in microbes. The question, however, remains whether natural selection or neutral evolution is the major contributor to this diversity. We undertook this study to estimate genomic diversity in Pseudoalteromonas atlantica populations and whether the diversity, if present, could be attributed to environmental factors or distance effects. We isolated and sequenced twenty-three strains of P. atlantica from three geographically distant deep marine basins and performed comparative genomic analyses to study the genomic diversity of populations among these basins. Average nucleotide identity followed a strictly geographical pattern. In two out of three locations, the strains within the location exhibited >99.5% identity, whereas, among locations, the strains showed <98.11% identity. Phylogenetic and pan-genome analysis also reflected the biogeographical separation of the strains. Strains from the same location shared many accessory genes and clustered closely on the phylogenetic tree. Phenotypic diversity between populations was studied in ten out of twenty-three strains testing carbon and nitrogen source utilization and osmotolerance. A genetic basis for phenotypic diversity could be established in most cases but was apparently not influenced by local environmental conditions. Our study suggests that neutral evolution may have a substantial role in the biodiversity of P. atlantica.

Hydrological and soil physiochemical variables determine the rhizospheric microbiota in subtropical lakeshore areas

Background

Due to intensive sluice construction and other human disturbances, lakeshore vegetation has been destroyed and ecosystems greatly changed. Rhizospheric microbiota constitute a key part of a functioning rhizosphere ecosystem. Maintaining rhizosphere microbial diversity is a central, critical issue for sustaining these rhizospheric microbiota functions and associated ecosystem services. However, the community composition and abiotic factors influencing rhizospheric microbiota in lakeshore remain largely understudied.

Methods

The spatiotemporal composition of lakeshore rhizospheric microbiota and the factors shaping them were seasonally investigated in three subtropical floodplain lakes (Lake Chaohu, Lake Wuchang, and Lake Dahuchi) along the Yangtze River in China through 16S rRNA amplicon high-throughput sequencing.

Results

Our results showed that four archaeal and 21 bacterial phyla (97.04 ± 0.25% of total sequences) dominated the rhizospheric microbiota communities of three lakeshore areas. Moreover, we uncovered significant differences among rhizospheric microbiota among the lakes, seasons, and average submerged depths. The Acidobacteria, Actinobacteria, Bacteroidetes, Bathyarchaeota, Gemmatimonadetes, and Proteobacteria differed significantly among the three lakes, with more than half of these dominant phyla showing significant changes in abundance between seasons, while the DHVEG-6, Ignavibacteriae, Nitrospirae, Spirochaetes, and Zixibacteria varied considerably across the average submerged depths (n = 58 sites in total). Canonical correspondence analyses revealed that the fluctuation range of water level and pH were the most important factors influencing the microbial communities and their dominant microbiota, followed by total nitrogen, moisture, and total phosphorus in soil. These results suggest a suite of hydrological and soil physiochemical variables together governed the differential structuring of rhizospheric microbiota composition among different lakes, seasons, and sampling sites. This work thus provides valuable ecological information to better manage rhizospheric microbiota and protect the vegetation of subtropical lakeshore areas.

Combining Molecular Observations and Microbial Ecosystem Modeling: A Practical Guide

Advances in technologies for molecular observation are leading to novel types of data, including gene, transcript, protein, and metabolite levels, which are fundamentally different from the types traditionally compared with microbial ecosystem models, such as biomass (e.g., chlorophyll a) and nutrient concentrations. A grand challenge is to use these data to improve predictive models and use models to explain observed patterns. This article presents a framework that aligns observations and models along the dimension of abstraction or biological organization-from raw sequences to ecosystem patterns for observations, and from sequence simulators to ecological theory for models. It then reviews 16 studies that compared model results with molecular observations. Molecular data can and are being combined with microbial ecosystem models, but to keep up with and take advantage of the full scope of observations, models need to become more mechanistically detailed and complex, which is a technical and cultural challenge for the ecological modeling community.

Fresh Ideas Bloom in Gut Healthcare to Cross-Fertilize Lake Management

Harmful bacteria may be the most significant threat to human gut and lake ecosystem health, and they are often managed using similar tools, like poisoning with antibiotics or algicides. Out-of-the-box thinking in human microbiome engineering is leading to novel methods, like engineering bacteria to kill pathogens, "persuade" them not to produce toxins, or "mop up" their toxins. The bacterial agent can be given a competitive edge via an exclusive nutrient, and they can be engineered to commit suicide once their work is done. Viruses can kill pathogens with specific DNA sequences or knock out their antibiotic resistance genes using CRISPR technology. Some of these ideas may work for lakes. We critically review novel methods for managing harmful bacteria in the gut from the perspective of managing toxic cyanobacteria in lakes, and discuss practical aspects such as modifying bacteria using genetic engineering or directed evolution, mass culturing and controlling the agents. A key knowledge gap is in the ecology of strains, like toxigenic vs nontoxigenic Microcystis, including allelopathic and Black Queen interactions. Some of the "gut methods" may have future potential for lakes, but there presently is no substitute for established management approaches, including reducing N and P nutrient inputs, and mitigating climate change.

Circadian clock helps cyanobacteria manage energy in coastal and high latitude ocean

The circadian clock coordinates cellular functions over the diel cycle in many organisms. The molecular mechanisms of the cyanobacterial clock are well characterized, but its ecological role remains a mystery. We present an agent-based model of Synechococcus (harboring a self-sustained, bona fide circadian clock) that explicitly represents genes (e.g., kaiABC), transcripts, proteins, and metabolites. The model is calibrated to data from laboratory experiments with wild type and no-clock mutant strains, and it successfully reproduces the main observed patterns of glycogen metabolism. Comparison of wild type and no-clock mutant strains suggests a main benefit of the clock is due to energy management. For example, it inhibits glycogen synthesis early in the day when it is not needed and energy is better used for making the photosynthesis apparatus. To explore the ecological role of the clock, we integrate the model into a dynamic, three-dimensional global circulation model that includes light variability due to seasonal and diel incident radiation and vertical extinction. Model output is compared with field data, including in situ gene transcript levels. We simulate cyanobaceria with and without a circadian clock, which allows us to quantify the fitness benefit of the clock. Interestingly, the benefit is weakest in the low latitude open ocean, where Prochlorococcus (lacking a self-sustained clock) dominates. However, our attempt to experimentally validate this testable prediction failed. Our study provides insights into the role of the clock and an example for how models can be used to integrate across multiple levels of biological organization.

Carbon limitation drives GC content evolution of a marine bacterium in an individual-based genome-scale model

An important unanswered question in evolutionary genomics is the source of considerable variation of genomic base composition (GC content) even among organisms that share one habitat. Evolution toward GC-poor genomes has been considered a major adaptive pathway in the oligotrophic ocean, but GC-rich bacteria are also prevalent and highly successful in this environment. We quantify the contribution of multiple factors to the change of genomic GC content of Ruegeria pomeroyi DSS-3, a representative and GC-rich member in the globally abundant Roseobacter clade, using an agent-based model. The model simulates 2 × 10⁸ cells, which allows random genetic drift to act in a realistic manner. Each cell has a whole genome subject to base-substitution mutation and recombination, which affect the carbon and nitrogen requirements of DNA and protein pools. Nonsynonymous changes can be functionally deleterious. Together, these factors affect the growth and fitness. Simulations show that experimentally determined mutation bias toward GC is not sufficient to build the GC-rich genome of DSS-3. While nitrogen availability has been repeatedly hypothesized to drive the evolution of GC content in marine bacterioplankton, our model instead predicts that DSS-3 and its ancestors have been evolving in environments primarily limited by carbon.