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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. 

A detailed evaluation of eight bacterial isolates from floral nectar and animal visitors to flowers shows evidence that they represent three novel species in the genus Acinetobacter . Phylogenomic analysis shows the closest relatives of these new isolates are Acinetobacter apis , Acinetobacter boissieri and Acinetobacter nectaris , previously described species associated with floral nectar and bees, but high genome-wide sequence divergence defines these isolates as novel species. Pairwise comparisons of the average nucleotide identity of the new isolates compared to known species is extremely low (<83 %), thus confirming that these samples are representative of three novel Acinetobacter species, for which the names Acinetobacter pollinis sp. nov., Acinetobacter baretiae sp. nov. and Acinetobacter rathckeae sp. nov. are proposed. The respective type strains are SCC477 T (=TSD-214 T =LMG 31655 T ), B10A T (=TSD-213 T =LMG 31702 T ) and EC24 T (=TSD-215 T =LMG 31703 T =DSM 111781 T ). 

The symbiont “Candidatus Aquarickettsia rohweri” infects a diversity of aquatic hosts. In the threatened Caribbean coral, Acropora cervicornis, Aquarickettsia proliferates in response to increased nutrient exposure, resulting in suppressed growth and increased disease susceptibility and mortality of coral. This study evaluated the extent, as well as the ecology and evolution of Aquarickettsia infecting threatened corals, Ac. cervicornis, and Ac. palmata and their hybrid (“Ac. prolifera”). Aquarickettsia was found in all acroporids, with coral host and geographic location impacting the infection magnitude. Phylogenomic and genome-wide single-nucleotide variant analysis of Aquarickettsia found phylogenetic clustering by geographic region, not by coral taxon. Analysis of Aquarickettsia fixation indices suggests multiple sequential infections of the same coral colony are unlikely. Furthermore, relative to other Rickettsiales species, Aquarickettsia is undergoing positive selection, with Florida populations experiencing greater positive selection relative to other Caribbean locations. This may be due in part to Aquarickettsia proliferating in response to greater nutrient stress in Florida, as indicated by greater in situ replication rates in these corals. Aquarickettsia was not found to significantly codiversify with either the coral animal or the coral’s algal symbiont (Symbiodinium “fitti”). Quantitative PCR analysis showed that gametes, larvae, recruits, and juveniles from susceptible, captive-reared coral genets were not infected with Aquarickettsia. Thus, horizontal transmission of Aquarickettsia via coral mucocytes or an unidentified host is more likely. The prevalence of Aquarickettsia in Ac. cervicornis and its high abundance in the Florida coral population suggests that coral disease mitigation efforts focus on preventing early infection via horizontal transmission.