Assessing the complex sponge microbiota: core, variable and species-specific bacterial communities in marine sponges

Marine sponges are well known for their associations with highly diverse, yet very specific and often highly similar microbiota. The aim of this study was to identify potential bacterial sub-populations in relation to sponge phylogeny and sampling sites and to define the core bacterial community. 16S ribosomal RNA gene amplicon pyrosequencing was applied to 32 sponge species from eight locations around the world's oceans, thereby generating 2567 operational taxonomic units (OTUs at the 97% sequence similarity level) in total and up to 364 different OTUs per sponge species. The taxonomic richness detected in this study comprised 25 bacterial phyla with Proteobacteria, Chloroflexi and Poribacteria being most diverse in sponges. Among these phyla were nine candidate phyla, six of them found for the first time in sponges. Similarity comparison of bacterial communities revealed no correlation with host phylogeny but a tropical sub-population in that tropical sponges have more similar bacterial communities to each other than to subtropical sponges. A minimal core bacterial community consisting of very few OTUs (97%, 95% and 90%) was found. These microbes have a global distribution and are probably acquired via environmental transmission. In contrast, a large species-specific bacterial community was detected, which is represented by OTUs present in only a single sponge species. The species-specific bacterial community is probably mainly vertically transmitted. It is proposed that different sponges contain different bacterial species, however, these bacteria are still closely related to each other explaining the observed similarity of bacterial communities in sponges in this and previous studies. This global analysis represents the most comprehensive study of bacterial symbionts in sponges to date and provides novel insights into the complex structure of these unique associations

Diversity and dynamics of bacterial communities in early life stages of the Caribbean coral Porites astreoides

In this study, we examine microbial communities of early developmental stages of the coral Porites astreoides by sequence analysis of cloned 16S rRNA genes, terminal restriction fragment length polymorphism (TRFLP), and fluorescence in situ hybridization (FISH) imaging. Bacteria are associated with the ectoderm layer in newly released planula larvae, in 4-day-old planulae, and on the newly forming mesenteries surrounding developing septa in juvenile polyps after settlement. Roseobacter clade-associated (RCA) bacteria and Marinobacter sp. are consistently detected in specimens of P. astreoides spanning three early developmental stages, two locations in the Caribbean and 3 years of collection. Multi-response permutation procedures analysis on the TRFLP results do not support significant variation in the bacterial communities associated with P. astreoides larvae across collection location, collection year or developmental stage. The results are the first evidence of vertical transmission (from parent to offspring) of bacteria in corals. The results also show that at least two groups of bacterial taxa, the RCA bacteria and Marinobacter, are consistently associated with juvenile P. astreoides against a complex background of microbial associations, indicating that some components of the microbial community are long-term associates of the corals and may impact host health and survival

Impacts of Poultry House Environment on Poultry Litter Bacterial Community Composition

Viral and bacterial pathogens are a significant economic concern to the US broiler industry and the ecological epicenter for poultry pathogens is the mixture of bedding material, chicken excrement and feathers that comprises the litter of a poultry house. This study used high-throughput sequencing to assess the richness and diversity of poultry litter bacterial communities, and to look for connections between these communities and the environmental characteristics of a poultry house including its history of gangrenous dermatitis (GD). Cluster analysis of 16S rRNA gene sequences revealed differences in the distribution of bacterial phylotypes between Wet and Dry litter samples and between houses. Wet litter contained greater diversity with 90% of total bacterial abundance occurring within the top 214 OTU clusters. In contrast, only 50 clusters accounted for 90% of Dry litter bacterial abundance. The sixth largest OTU cluster across all samples classified as an Arcobacter sp., an emerging human pathogen, occurring in only the Wet litter samples of a house with a modern evaporative cooling system. Ironically, the primary pathogenic clostridial and staphylococcal species associated with GD were not found in any house; however, there were thirteen 16S rRNA gene phylotypes of mostly Gram-positive phyla that were unique to GD-affected houses and primarily occurred in Wet litter samples. Overall, the poultry house environment appeared to substantially impact the composition of litter bacterial communities and may play a key role in the emergence of food-borne pathogens

APPRAISAL OF LONGEVITY AND CONSIDERATION OF A NEW PARAMETER

The advanced ages and good health of many of the surviving members of the longitudinal study known as “Operation Retirement” prompted the researchers to initiate a study of longevity. One hundred and eleven subjects were interviewed intensively (average age 80) and life‐data and information of psychological concern were collated concerning panel members and their siblings and ancestors. Age at death and causes of death were generally reliably reported from family records, variously reaching back through two or three generations. Methodological problems caused the investigators to seek a new parameter, which they have called the Longevity Quotient (LQ), in some ways analogous to the Age‐Resistance Quotient established in relation to the UQOR retirement recreation guidance tests. For this purpose, a tabulation of life‐expectancy at birth had to be compiled, beyond the earliest birth dates available in the official Australian records. This will be discussed, in the explanation of the formula: Longevity Quotient (LQ) = Age of death/Life Expectancy at birth × 100 Since the LQ cannot properly be estimated until death, the method has had to be applied tentatively in respect of panel members, but firmly in a comparison of IQs and LQs of deceased members of the panel