A Preliminary Characterization and Assessment of Mesophotic Octocoral Microbiomes in the Western Gulf of Mexico

Mesophotic Coral Ecosystems are highly diverse and productive ecosystems in the western Gulf of Mexico which are composed of, in part, by octocorals (subclass Octocorallia). Despite their importance as foundational organisms octocorals are an understudied group in this region, with little known about their microbial community. Ninety-eight Octocoral samples collected from the western and northwestern Gulf of Mexico were sequenced using 16S rRNA sequencing to characterize their microbial communities. The sequenced microbiomes were generally low in diversity composed of a few core microbial taxa. Octocoral group was the main driver of microbiome composition as opposed to depth, season, region, and reef type. The effect of sequencing depth on a subset of 24 samples was examined, and showed low sequencing depth was sufficient to capture microbiome community trends. This microbiome data may be used to improve our understanding of the biology of octocorals in the Gulf of Mexico and contribute to conservation efforts in the future

Clustering the autisms using glutamate synapse protein interaction networks from cortical and hippocampal tissue of seven mouse models

Abstract Background Autism spectrum disorders (ASDs) are a heterogeneous group of behaviorally defined disorders and are associated with hundreds of rare genetic mutations and several environmental risk factors. Mouse models of specific risk factors have been successful in identifying molecular mechanisms associated with a given factor. However, comparisons among different models to elucidate underlying common pathways or to define clusters of biologically relevant disease subtypes have been complicated by different methodological approaches or different brain regions examined by the labs that developed each model. Here, we use a novel proteomic technique, quantitative multiplex co-immunoprecipitation or QMI, to make a series of identical measurements of a synaptic protein interaction network in seven different animal models. We aim to identify molecular disruptions that are common to multiple models. Methods QMI was performed on 92 hippocampal and cortical samples taken from seven mouse models of ASD: Shank3B, Shank3Δex4-9, Ube3a2xTG, TSC2, FMR1, and CNTNAP2 mutants, as well as E12.5 VPA (maternal valproic acid injection on day 12.5 post-conception). The QMI panel targeted a network of 16 interacting, ASD-linked, synaptic proteins, probing 240 potential co-associations. A custom non-parametric statistical test was used to call significant differences between ASD models and littermate controls, and Hierarchical Clustering by Principal Components was used to cluster the models using mean log2 fold change values. Results Each model displayed a unique set of disrupted interactions, but some interactions were disrupted in multiple models. These tended to be interactions that are known to change with synaptic activity. Clustering revealed potential relationships among models and suggested deficits in AKT signaling in Ube3a2xTG mice, which were confirmed by phospho-western blots. Conclusions These data highlight the great heterogeneity among models, but suggest that high-dimensional measures of a synaptic protein network may allow differentiation of subtypes of ASD with shared molecular pathology