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A mass sea urchin die-off in the Caribbean Sea in the 1980s may have resulted from a single-cell protist called a scuticociliate. 

Over the past two decades, researchers have searched for methods to better understand the relationship between coral hosts and their microbiomes. Data on how coral-associated bacteria are involved in their host’s responses to stressors that cause bleaching, disease, and other deleterious effects can elucidate how they may mediate, ameliorate, and exacerbate interactions between the coral and the surrounding environment. At the same time tracking coral bacteria dynamics can reveal previously undiscovered mechanisms of coral resilience, acclimatization, and evolutionary adaptation. Although modern techniques have reduced the cost of conducting high-throughput sequencing of coral microbes, to explore the composition, function, and dynamics of coral-associated bacteria, it is necessary that the entire procedure, from collection to sequencing, and subsequent analysis be carried out in an objective and effective way. Corals represent a difficult host with which to work, and unique steps in the process of microbiome assessment are necessary to avoid inaccuracies or unusable data in microbiome libraries, such as off-target amplification of host sequences. Here, we review, compare and contrast, and recommend methods for sample collection, preservation, and processing (e.g., DNA extraction) pipelines to best generate 16S amplicon libraries with the aim of tracking coral microbiome dynamics. We also discuss some basic quality assurance and general bioinformatic methods to analyze the diversity, composition, and taxonomic profiles of the microbiomes. This review aims to be a generalizable guide for researchers interested in starting and modifying the molecular biology aspects of coral microbiome research, highlighting best practices and tricks of the trade. 

Filamentous viruses are hypothesized to play a role in stony coral tissue loss disease (SCTLD) through infection of the endosymbiotic dinoflagellates (Family Symbiodiniaceae) of corals. To evaluate this hypothesis, it is critical to understand the global distribution of filamentous virus infections across the genetic diversity of Symbiodiniaceae hosts. Using transmission electron microscopy, we demonstrate that filamentous virus-like particles (VLPs) are present in over 60% of Symbiodiniaceae cells (genusCladocopium) within Pacific corals (Acropora hyacinthus,Porites c.f. lobata); these VLPs are more prevalent in Symbiodiniaceae of in situ colonies experiencing heat stress. Symbiodiniaceae expelled fromA. hyacinthusalso contain filamentous VLPs, and these cells are more degraded than theirin hospitecounterparts. Similar to VLPs reported from SCTLD-affected Caribbean reefs, VLPs range from ~150 to 1500 nm in length and 16–37 nm in diameter and appear to constitute various stages in a replication cycle. Finally, we demonstrate that SCTLD-affected corals containing filamentous VLPs are dominated by diverse Symbiodiniaceae lineages from the generaBreviolum, Cladocopium, andDurusdinium. Although this study cannot definitively confirm or refute the role of filamentous VLPs in SCTLD, it demonstrates that filamentous VLPs are not solely observed in SCTLD-affected corals or reef regions, nor are they solely associated with corals dominated by members of a particular Symbiodiniaceae genus. We hypothesize that filamentous viruses are a widespread, common group that infects Symbiodiniaceae. Genomic characterization of these viruses and empirical tests of the impacts of filamentous virus infection on Symbiodiniaceae and coral colonies should be prioritized.

 

Chronically high levels of inorganic nutrients have been documented in Florida’s coral reefs and are linked to increased prevalence and severity of coral bleaching and disease. Naturally disease-resistant genotypes of the staghorn coralAcropora cervicornisare rare, and it is unknown whether prolonged exposure to acute or chronic high nutrient levels will reduce the disease tolerance of these genotypes. Recently, the relative abundance of the bacterial genusAquarickettsiawas identified as a significant indicator of disease susceptibility inA. cervicornis, and the abundance of this bacterial species was previously found to increase under chronic and acute nutrient enrichment. We therefore examined the impact of common constituents of nutrient pollution (phosphate, nitrate, and ammonium) on microbial community structure in a disease-resistant genotype with naturally low abundances ofAquarickettsia.We found that although this putative parasite responded positively to nutrient enrichment in a disease-resistant host, relative abundances remained low (< 0.5%). Further, while microbial diversity was not altered significantly after 3 weeks of nutrient enrichment, 6 weeks of enrichment was sufficient to shift microbiome diversity and composition. Coral growth rates were also reduced by 6 weeks of nitrate treatment compared to untreated conditions. Together these data suggest that the microbiomes of disease-resistantA. cervicornismay be initially resistant to shifts in microbial community structure, but succumb to compositional and diversity alterations after more sustained environmental pressure. As the maintenance of disease-resistant genotypes is critical for coral population management and restoration, a complete understanding of how these genotypes respond to environmental stressors is necessary to predict their longevity.

 

Abstract Nutrient pollution is linked to coral disease susceptibility and severity, but the mechanism behind this effect remains underexplored. A recently identified bacterial species, ‘Ca. Aquarickettsia rohweri,’ is hypothesized to parasitize the Caribbean staghorn coral, Acropora cervicornis, leading to reduced coral growth and increased disease susceptibility. Aquarickettsia rohweri is hypothesized to assimilate host metabolites and ATP and was previously demonstrated to be highly nutrient-responsive. As nutrient enrichment is a pervasive issue in the Caribbean, this study examined the effects of common nutrient pollutants (nitrate, ammonium, and phosphate) on a disease-susceptible genotype of A. cervicornis. Microbial diversity was found to decline over the course of the experiment in phosphate-, nitrate-, and combined-treated samples, and quantitative PCR indicated that Aquarickettsia abundance increased significantly across all treatments. Only treatments amended with phosphate, however, exhibited a significant shift in Aquarickettsia abundance relative to other taxa. Furthermore, corals exposed to phosphate had significantly lower linear extension than untreated or nitrate-treated corals after 3 weeks of nutrient exposure. Together these data suggest that while experimental tank conditions, with an elevated nutrient regime associated with coastal waters, increased total bacterial abundance, only the addition of phosphate significantly altered the ratios of Aquarickettsia compared to other members of the microbiome. 

16S rRNA gene profiling (amplicon sequencing) is a popular technique for understanding host-associated and environmental microbial communities. Most protocols for sequencing amplicon libraries follow a standardized pipeline that can differ slightly depending on laboratory facility and user. Given that the same variable region of the 16S gene is targeted, it is generally accepted that sequencing output from differing protocols are comparable and this assumption underlies our ability to identify universal patterns in microbial dynamics through meta-analyses. However, discrepant results from a combined 16S rRNA gene dataset prepared by two labs whose protocols differed only in DNA polymerase and sequencing platform led us to scrutinize the outputs and challenge the idea of confidently combining them for standard microbiome analysis. Using technical replicates of reef-building coral samples from two species, Montipora aequituberculata and Porites lobata , we evaluated the consistency of alpha and beta diversity metrics between data resulting from these highly similar protocols. While we found minimal variation in alpha diversity between platform, significant differences were revealed with most beta diversity metrics, dependent on host species. These inconsistencies persisted following removal of low abundance taxa and when comparing across higher taxonomic levels, suggesting that bacterial community differences associated with sequencing protocol are likely to be context dependent and difficult to correct without extensive validation work. The results of this study encourage caution in the statistical comparison and interpretation of studies that combine rRNA gene sequence data from distinct protocols and point to a need for further work identifying mechanistic causes of these observed differences. 

Microbiomes are essential features of holobionts, providing their hosts with key metabolic and functional traits like resistance to environmental disturbances and diseases. In scleractinian corals, questions remain about the microbiome's role in resistance and resilience to factors contributing to the ongoing global coral decline and whether microbes serve as a form of holobiont ecological memory. To test if and how coral microbiomes affect host health outcomes during repeated disturbances, we conducted a large‐scale (32 exclosures, 200 colonies, and 3 coral species sampled) and long‐term (28 months, 2018–2020) manipulative experiment on the forereef of Mo'orea, French Polynesia. In 2019 and 2020, this reef experienced the two most severe marine heatwaves on record for the site. Our experiment and these events afforded us the opportunity to test microbiome dynamics and roles in the context of coral bleaching and mortality resulting from these successive and severe heatwaves. We report unique microbiome responses to repeated heatwaves inAcropora retusa,Porites lobata, andPocilloporaspp., which included: microbiome acclimatization inA. retusa, and both microbiome resilience to the first marine heatwave and microbiome resistance to the second marine heatwave inPocilloporaspp. Moreover, observed microbiome dynamics significantly correlated with coral species‐specific phenotypes. For example, bleaching and mortality inA. retusaboth significantly increased with greater microbiome beta dispersion and greater Shannon Diversity, whileP. lobatacolonies had different microbiomes across mortality prevalence. Compositional microbiome changes, such as changes to proportions of differentially abundant putatively beneficial to putatively detrimental taxa to coral health outcomes during repeated heat stress, also correlated with host mortality, with higher proportions of detrimental taxa yielding higher mortality inA. retusa. This study reveals evidence for coral species‐specific microbial responses to repeated heatwaves and, importantly, suggests that host‐dependent microbiome dynamics may provide a form of holobiont ecological memory to repeated heat stress.

 

ABSTRACT Shotgun metagenomic sequencing has transformed our understanding of microbial community ecology. However, preparing metagenomic libraries for high-throughput DNA sequencing remains a costly, labor-intensive, and time-consuming procedure, which in turn limits the utility of metagenomes. Several library preparation procedures have recently been developed to offset these costs, but it is unclear how these newer procedures compare to current standards in the field. In particular, it is not clear if all such procedures perform equally well across different types of microbial communities or if features of the biological samples being processed (e.g., DNA amount) impact the accuracy of the approach. To address these questions, we assessed how five different shotgun DNA sequence library preparation methods, including the commonly used Nextera Flex kit, perform when applied to metagenomic DNA. We measured each method’s ability to produce metagenomic data that accurately represent the underlying taxonomic and genetic diversity of the community. We performed these analyses across a range of microbial community types (e.g., soil, coral associated, and mouse gut associated) and input DNA amounts. We find that the type of community and amount of input DNA influence each method’s performance, indicating that careful consideration may be needed when selecting between methods, especially for low-complexity communities. However, the cost-effective preparation methods that we assessed are generally comparable to the current gold-standard Nextera DNA Flex kit for high-complexity communities. Overall, the results from this analysis will help expand and even facilitate access to metagenomic approaches in future studies. IMPORTANCE Metagenomic library preparation methods and sequencing technologies continue to advance rapidly, allowing researchers to characterize microbial communities in previously underexplored environmental samples and systems. However, widely accepted standardized library preparation methods can be cost-prohibitive. Newly available approaches may be less expensive, but their efficacy in comparison to standardized methods remains unknown. In this study, we compared five different metagenomic library preparation methods. We evaluated each method across a range of microbial communities varying in complexity and quantity of input DNA. Our findings demonstrate the importance of considering sample properties, including community type, composition, and DNA amount, when choosing the most appropriate metagenomic library preparation method. 

ABSTRACT A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts.