Can you sequence ecology? Metagenomics of adaptive diversification

Eco-evolutionary responses of the microbial loop to surface ocean warming and consequences for primary production

Predicting the response of ocean primary production to climate warming is a major challenge. One key control of primary production is the microbial loop driven by heterotrophic bacteria, yet how warming alters the microbial loop and its function is poorly understood. Here we develop an eco-evolutionary model to predict the physiological response and adaptation through selection of bacterial populations in the microbial loop and how this will impact ecosystem function such as primary production. We find that the ecophysiological response of primary production to warming is driven by a decrease in regenerated production which depends on nutrient availability. In nutrient-poor environments, the loss of regenerated production to warming is due to decreasing microbial loop activity. However, this ecophysiological response can be opposed or even reversed by bacterial adaptation through selection, especially in cold environments: heterotrophic bacteria with lower bacterial growth efficiency are selected, which strengthens the "link" behavior of the microbial loop, increasing both new and regenerated production. In cold and rich environments such as the Arctic Ocean, the effect of bacterial adaptation on primary production exceeds the ecophysiological response. Accounting for bacterial adaptation through selection is thus critically needed to improve models and projections of the ocean primary production in a warming world.

From sequence to information

Today massive amounts of sequenced metagenomic and metatranscriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding of biological systems analysed from different perspectives is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data are of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data have been successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects.

This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.

Protocols for Investigating the Host-tissue Distribution, Transmission-mode, and Effect on the Host Fitness of a Densovirus in the Cotton Bollworm

Many novel viruses have been discovered in animal hosts using next-generation sequencing technologies. Previously, we reported a mutualistic virus, Helicoverpa armigera densovirus (HaDV2), in a lepidopteran species, the cotton bollworm, Helicoverpa armigera (Hubner). Here, we describe the protocols that are currently used to study the effect of HaDV2 on its host. First, we establish a HaDV2-free cotton bollworm colony from a single breeding pair. Then, we orally inoculate some neonate larval offspring with HaDV2-containing filtered liquid to produce two colonies with the same genetic background: one HaDV2-infected, the other uninfected. A protocol to compare life table parameters (e.g., larval, pupal, and adult periods and fecundity) between the HaDV2-infected and -uninfected individuals is also presented, as are the protocols for determining the host-tissue distribution and transmission efficiency of HaDV2. These protocols would also be suitable for investigating the effects of other orally transmitted viruses on their insect hosts, lepidopteran hosts in particular.

Parallel Mutations Result in a Wide Range of Cooperation and Community Consequences in a Two-Species Bacterial Consortium

Multi-species microbial communities play a critical role in human health, industry, and waste remediation. Recently, the evolution of synthetic consortia in the laboratory has enabled adaptation to be addressed in the context of interacting species. Using an engineered bacterial consortium, we repeatedly evolved cooperative genotypes and examined both the predictability of evolution and the phenotypes that determine community dynamics. Eight Salmonella enterica serovar Typhimurium strains evolved methionine excretion sufficient to support growth of an Escherichia coli methionine auxotroph, from whom they required excreted growth substrates. Non-synonymous mutations in metA, encoding homoserine trans-succinylase (HTS), were detected in each evolved S. enterica methionine cooperator and were shown to be necessary for cooperative consortia growth. Molecular modeling was used to predict that most of the non-synonymous mutations slightly increase the binding affinity for HTS homodimer formation. Despite this genetic parallelism and trend of increasing protein binding stability, these metA alleles gave rise to a wide range of phenotypic diversity in terms of individual versus group benefit. The cooperators with the highest methionine excretion permitted nearly two-fold faster consortia growth and supported the highest fraction of E. coli, yet also had the slowest individual growth rates compared to less cooperative strains. Thus, although the genetic basis of adaptation was quite similar across independent origins of cooperative phenotypes, quantitative measurements of metabolite production were required to predict either the individual-level growth consequences or how these propagate to community-level behavior.

From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems

Microorganisms and the viruses that infect them are the most numerous biological entities on Earth and enclose its greatest biodiversity and genetic reservoir. With strength in their numbers, these microscopic organisms are major players in the cycles of energy and matter that sustain all life. Scientists have only scratched the surface of this vast microbial world through culture-dependent methods. Recent developments in generating metagenomes, large random samples of nucleic acid sequences isolated directly from the environment, are providing comprehensive portraits of the composition, structure, and functioning of microbial communities. Moreover, advances in metagenomic analysis have created the possibility of obtaining complete or nearly complete genome sequences from uncultured microorganisms, providing important means to study their biology, ecology, and evolution. Here we review some of the recent developments in the field of metagenomics, focusing on the discovery of genetic novelty and on methods for obtaining uncultured genome sequences, including through the recycling of previously published datasets. Moreover we discuss how metagenomics has become a core scientific tool to characterize eco-evolutionary patterns of microbial ecosystems, thus allowing us to simultaneously discover new microbes and study their natural communities. We conclude by discussing general guidelines and challenges for modeling the interactions between uncultured microorganisms and viruses based on the information contained in their genome sequences. These models will significantly advance our understanding of the functioning of microbial ecosystems and the roles of microbes in the environment.

Bacterial Strain Diversity Within Wounds

Significance: Rare bacterial taxa (taxa of low relative frequency) are numerous and ubiquitous in virtually any sample-including wound samples. In addition, even the high-frequency genera and species contain multiple strains. These strains, individually, are each only a small fraction of the total bacterial population. Against the view that wounds contain relatively few kinds of bacteria, this newly recognized diversity implies a relatively high rate of migration into the wound and the potential for diversification during infection. Understanding the biological and medical importance of these numerous taxa is an important new element of wound microbiology. Recent Advances: Only recently have these numerous strains been discovered; the technology to detect, identify, and characterize them is still in its infancy. Multiple strains of both gram-negative and gram-positive bacteria have been found in a single wound. In the few cases studied, the distribution of the bacteria suggests microhabitats and biological interactions. Critical Issues: The distribution of the strains, their phenotypic diversity, and their interactions are still largely uncharacterized. The technologies to investigate this level of genomic detail are still developing and have not been largely deployed to investigate wounds. Future Directions: As advanced metagenomics, single-cell genomics, and advanced microscopy develop, the study of wound microbiology will better address the complex interplay of numerous individually rare strains with both the host and each other.

Synthetic microbial ecosystems for biotechnology

Most highly controlled and specific applications of microorganisms in biotechnology involve pure cultures. Maintaining single strain cultures is important for industry as contaminants can reduce productivity and lead to longer "down-times" during sterilisation. However, microbes working together provide distinct advantages over pure cultures. They can undertake more metabolically complex tasks, improve efficiency and even expand applications to open systems. By combining rapidly advancing technologies with ecological theory, the use of microbial ecosystems in biotechnology will inevitably increase. This review provides insight into the use of synthetic microbial communities in biotechnology by applying the engineering paradigm of measure, model, manipulate and manufacture, and illustrate the emerging wider potential of the synthetic ecology field. Systems to improve biofuel production using microalgae are also discussed.

Comparative genomic analysis reveals 2-oxoacid dehydrogenase complex lipoylation correlation with aerobiosis in archaea

Metagenomic analyses have advanced our understanding of ecological microbial diversity, but to what extent can metagenomic data be used to predict the metabolic capacity of difficult-to-study organisms and their abiotic environmental interactions? We tackle this question, using a comparative genomic approach, by considering the molecular basis of aerobiosis within archaea. Lipoylation, the covalent attachment of lipoic acid to 2-oxoacid dehydrogenase multienzyme complexes (OADHCs), is essential for metabolism in aerobic bacteria and eukarya. Lipoylation is catalysed either by lipoate protein ligase (LplA), which in archaea is typically encoded by two genes (LplA-N and LplA-C), or by a lipoyl(octanoyl) transferase (LipB or LipM) plus a lipoic acid synthetase (LipA). Does the genomic presence of lipoylation and OADHC genes across archaea from diverse habitats correlate with aerobiosis? First, analyses of 11,826 biotin protein ligase (BPL)-LplA-LipB transferase family members and 147 archaeal genomes identified 85 species with lipoylation capabilities and provided support for multiple ancestral acquisitions of lipoylation pathways during archaeal evolution. Second, with the exception of the Sulfolobales order, the majority of species possessing lipoylation systems exclusively retain LplA, or either LipB or LipM, consistent with archaeal genome streamlining. Third, obligate anaerobic archaea display widespread loss of lipoylation and OADHC genes. Conversely, a high level of correspondence is observed between aerobiosis and the presence of LplA/LipB/LipM, LipA and OADHC E2, consistent with the role of lipoylation in aerobic metabolism. This correspondence between OADHC lipoylation capacity and aerobiosis indicates that genomic pathway profiling in archaea is informative and that well characterized pathways may be predictive in relation to abiotic conditions in difficult-to-study extremophiles. Given the highly variable retention of gene repertoires across the archaea, the extension of comparative genomic pathway profiling to broader metabolic and homeostasis networks should be useful in revealing characteristics from metagenomic datasets related to adaptations to diverse environments.

The genotypic view of social interactions in microbial communities

Dense and diverse microbial communities are found in many environments. Disentangling the social interactions between strains and species is central to understanding microbes and how they respond to perturbations. However, the study of social evolution in microbes tends to focus on single species. Here, we broaden this perspective and review evolutionary and ecological theory relevant to microbial interactions across all phylogenetic scales. Despite increased complexity, we reduce the theory to a simple null model that we call the genotypic view. This states that cooperation will occur when cells are surrounded by identical genotypes at the loci that drive interactions, with genetic identity coming from recent clonal growth or horizontal gene transfer (HGT). In contrast, because cooperation is only expected to evolve between different genotypes under restrictive ecological conditions, different genotypes will typically compete. Competition between two genotypes includes mutual harm but, importantly, also many interactions that are beneficial to one of the two genotypes, such as predation. The literature offers support for the genotypic view with relatively few examples of cooperation between genotypes. However, the study of microbial interactions is still at an early stage. We outline the logic and methods that help to better evaluate our perspective and move us toward rationally engineering microbial communities to our own advantage.