Local Genomic Adaptation of Coral Reef-Associated Microbiomes to Gradients of Natural Variability and Anthropogenic Stressors

Holobionts are species-specific associations between macro- and microorganisms. On coral reefs, the benthic coverage of coral and algal holobionts varies due to natural and anthropogenic forcings. Different benthic macroorganisms are predicted to have specific microbiomes. In contrast, local environmental factors are predicted to select for specific metabolic pathways in microbes. To reconcile these two predictions, we hypothesized that adaptation of microbiomes to local conditions is facilitated by the horizontal transger of genes responsible for specific metabolic capabilities. To test this hypothesis, microbial metagenomes were sequenced from 22 coral reefs at 11 Line Islands in the central Pacific that together span a wide range of biogeochemical and anthropogenic influences. Consistent with our hypothesis, the percent cover of major benthic functional groups significantly correlated with particular microbial taxa. Reefs with higher coral cover had a coral microbiome with higher abundances of Alphaproteobacteria (such as Rhodobacterales and Sphingomonadales), whereas microbiomes of algae-dominated reefs had higher abundances of Gammaproteobacteria (such as Alteromonadales, Pseudomonadales, and Vibrionales), Betaproteobacteria, and Bacteriodetes. In contrast to taxa, geography was the strongest predictor of microbial community metabolism. Microbial communities on reefs with higher nutrient availability (e.g., equatorial upwelling zones) were enriched in genes involved in nutrient-related metabolisms (e.g., nitrate and nitrite ammonification, Ton/Tol transport, etc.). On reefs further from the equator, microbes had more genes encoding chlorophyll biosynthesis and photosystems I/II. These results support the hypothesis that core microbiomes are determined by holobiont macroorganisms, and that those core taxa adapt to local conditions by selecting for advantageous metabolic genes

The GAAS Metagenomic Tool and Its Estimations of Viral and Microbial Average Genome Size in Four Major Biomes

Metagenomic studies characterize both the composition and diversity of uncultured viral and microbial communities. BLAST-based comparisons have typically been used for such analyses; however, sampling biases, high percentages of unknown sequences, and the use of arbitrary thresholds to find significant similarities can decrease the accuracy and validity of estimates. Here, we present Genome relative Abundance and Average Size (GAAS), a complete software package that provides improved estimates of community composition and average genome length for metagenomes in both textual and graphical formats. GAAS implements a novel methodology to control for sampling bias via length normalization, to adjust for multiple BLAST similarities by similarity weighting, and to select significant similarities using relative alignment lengths. In benchmark tests, the GAAS method was robust to both high percentages of unknown sequences and to variations in metagenomic sequence read lengths. Re-analysis of the Sargasso Sea virome using GAAS indicated that standard methodologies for metagenomic analysis may dramatically underestimate the abundance and importance of organisms with small genomes in environmental systems. Using GAAS, we conducted a meta-analysis of microbial and viral average genome lengths in over 150 metagenomes from four biomes to determine whether genome lengths vary consistently between and within biomes, and between microbial and viral communities from the same environment. Significant differences between biomes and within aquatic sub-biomes (oceans, hypersaline systems, freshwater, and microbialites) suggested that average genome length is a fundamental property of environments driven by factors at the sub-biome level. The behavior of paired viral and microbial metagenomes from the same environment indicated that microbial and viral average genome sizes are independent of each other, but indicative of community responses to stressors and environmental conditions

Microbes versus fish : the bioenergetics of coral reef systems

Metabolic rate refers to the rate by which chemical energy is converted into biological energy and used for either maintenance of existing structure or production of new biomass. The Metabolic Theory of Ecology (MTE) predicts the metabolic rate of individual organisms based on the observation that most variation in an individual's metabolic rate can be explained by body size and temperature. The objective of this dissertation was to investigate the bioenergetics of coral reef systems using MTE. My hypothesis was that human activities alter the energy budget of the reef system, specifically by altering the allocation of metabolic energy between microbes and macrobes. I found that in reef systems, even a small increase in microbial biomass can result in substantial changes in whole system rates of energy and materials flux. By comparison, relatively large reductions in fish biomass, affect the system bioenergetics to a lesser degree. The percentage of the combined fish and microbial predicted metabolic rate that is microbial, a.k.a. the microbialization score, was used as a metric for assessing and comparing reef health. My results demonstrated a strong positive correlation between reef microbialization scores and human impact. Regardless of oceanographic context, the microbialization score was a powerful metric for assessing the level of human impact a reef system is experiencing. The process of microbialization was further examined by assessing the effects of human activity on the relative roles of heterotrophic and autotrophic microbes. I found that shifts in microbial trophic structure change both the magnitude and efficiency of energy flow. Specifically, there was a significant increase in the ratio of autotrophic to heterotrophic microbes with human impact, which was also related to an increase in the mass specific energy requirements (W g-1) of the microbial community. I am proposing that microbialization is actually a mechanism of reef resilience that dampens the effects of both overfishing and eutrophication. In conclusion, this research sheds new light on the effects that rising human impact has on the bioenergetics of coral reef systems and adds to our current understanding of the mechanism(s) that underlie reef system degradatio

Energetic differences between bacterioplankton trophic groups and coral reef resistance

Coral reefs are among the most productive and diverse marine ecosystems on the Earth. They are also particularly sensitive to changing energetic requirements by different trophic levels. Microbialization specifically refers to the increase in the energetic metabolic demands of microbes relative to macrobes and is significantly correlated with increasing human influence on coral reefs. In this study, metabolic theory of ecology is used to quantify the relative contributions of two broad bacterioplankton groups, autotrophs and heterotrophs, to energy flux on 27 Pacific coral reef ecosystems experiencing human impact to varying degrees. The effective activation energy required for photosynthesis is lower than the average energy of activation for the biochemical reactions of the Krebs cycle, and changes in the proportional abundance of these two groups can greatly affect rates of energy and materials cycling. We show that reef-water communities with a higher proportional abundance of microbial autotrophs expend more metabolic energy per gram of microbial biomass. Increased energy and materials flux through fast energy channels (i.e. water-column associated microbial autotrophs) may dampen the detrimental effects of increased heterotrophic loads (e.g. coral disease) on coral reef systems experiencing anthropogenic disturbance

Assessing Coral Reefs on a Pacific-Wide Scale Using the Microbialization Score

The majority of the world’s coral reefs are in various stages of decline. While a suite of disturbances (overfishing, eutrophication, and global climate change) have been identified, the mechanism(s) of reef system decline remain elusive. Increased microbial and viral loading with higher percentages of opportunistic and specific microbial pathogens have been identified as potentially unifying features of coral reefs in decline. Due to their relative size and high per cell activity, a small change in microbial biomass may signal a large reallocation of available energy in an ecosystem; that is the microbialization of the coral reef. Our hypothesis was that human activities alter the energy budget of the reef system, specifically by altering the allocation of metabolic energy between microbes and macrobes. To determine if this is occurring on a regional scale, we calculated the basal metabolic rates for the fish and microbial communities at 99 sites on twenty-nine coral islands throughout the Pacific Ocean using previously established scaling relationships. From these metabolic rate predictions, we derived a new metric for assessing and comparing reef health called the microbialization score. The microbialization score represents the percentage of the combined fish and microbial predicted metabolic rate that is microbial. Our result

Local genomic adaptation of coral reef-associated microbiomes to gradients of natural variability and anthropogenic stressors

Holobionts are species-specific associations between macro- and microorganisms. On coral reefs, the benthic coverage of coral and algal holobionts varies due to natural and anthropogenic forcings. Different benthic macroorganisms are predicted to have specific microbiomes. In contrast, local environmental factors are predicted to select for specific metabolic pathways in microbes. To reconcile these two predictions, we hypothesized that adaptation of microbiomes to local conditions is facilitated by the horizontal transfer of genes responsible for specific metabolic capabilities. To test this hypothesis, microbial metagenomes were sequenced from 22 coral reefs at 11 Line Islands in the central Pacific that together span a wide range of biogeochemical and anthropogenic influences. Consistent with our hypothesis, the percent cover of major benthic functional groups significantly correlated with particular microbial taxa. Reefs with higher coral cover had a coral microbiome with higher abundances of Alphaproteobacteria (such as Rhodobacterales and Sphingomonadales), whereas microbiomes of algae-dominated reefs had higher abundances of Gammaproteobacteria (such as Alteromonadales, Pseudomonadales, and Vibrionales), Betaproteobacteria, and Bacteriodetes. In contrast to taxa, geography was the strongest predictor of microbial community metabolism. Microbial communities on reefs with higher nutrient availability (e.g., equatorial upwelling zones) were enriched in genes involved in nutrient-related metabolisms (e.g., nitrate and nitrite ammonification, Ton/Tol transport, etc.). On reefs further from the equator, microbes had more genes encoding chlorophyll biosynthesis and photosystems I/II. These results support the hypothesis that core microbiomes are determined by holobiont macroorganisms, and that those core taxa adapt to local conditions by selecting for advantageous metabolic genes

Survey data and calculated values for 29 islands in the Pacific, grouped by region.

<p>Predicted metabolic rates are basal rates. NPP = net primary production. Colors identify each island group in the figures.</p

Measures of energy use versus metrics of primary production.

<p>(<b>a</b>) Non-linear regression analysis of the combined fish + microbes predicted metabolic rate versus net primary production (NPP) for the 29 surveyed islands. NPP was derived from satellite data using the Vertically Generalized Production Model (VGPM). (y = 0.00008x+0.0012; R² = 0.21) (<b>b</b>) Non-linear regression analysis of the combined fish + microbes predicted metabolic rate versus nearshore chl<i>a</i> concentrations at the 29 surveyed islands (y = 0.54x+0.01; R² = 0.08) (<b>c</b>) Microbialization scores versus NPP derived from satellite data using the VGPM for the 29 surveyed islands. (<b>d</b>) Microbialization scores versus nearshore chl<i>a</i> concentrations at the 29 surveyed islands (y = 171.5x+29.7; R² = 0.22). Colors are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043233#pone-0043233-g002" target="_blank">Fig. 2</a>. For island abbreviations see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043233#pone-0043233-t001" target="_blank">Table 1</a>.</p