In nature, the complexity and structure of microbial communities varies widely, ranging from a few species to thousands of species, and from highly structured to highly unstructured communities. Here, we describe the identity and functional capacity of microbial populations within distinct layers of a pristine, marine-derived, meromictic (stratified) lake (Ace Lake) in Antarctica. Nine million open reading frames were analyzed, representing microbial samples taken from six depths of the lake size fractionated on sequential 3.0, 0.8 and 0.1 lm filters, and including metaproteome data from matching 0.1 lm filters. We determine how the interactions of members of this highly structured and moderately complex community define the biogeochemical fluxes throughout the entire lake. Our view is that the health of this delicate ecosystem is dictated by the effects of the polar light cycle on the dominant role of green sulfur bacteria in primary production and nutrient cycling, and the influence of viruses/phage and phage resistance on the cooperation between members of the microbial community right throughout the lake. To test our assertions, and develop a framework applicable to other microbially driven ecosystems, we developed a mathematical model that describes how cooperation within a microbial system is impacted by periodic fluctuations in environmental parameters on key populations of microorganisms. Our study reveals a mutualistic structure within the microbial community throughout the lake that has arisen as the result of mechanistic interactions between the physico-chemical parameters and the selection of individual members of the community. By exhaustively describing and modelling interactions in Ace Lake, we have developed an approach that may be applicable to learning how environmental perturbations affect the microbial dynamics in more complex aquatic systems.

BackgroundHumans host individually unique skin microbiota, suggesting that microbiota traces transferred from skin to surfaces could serve as forensic markers analogous to fingerprints. While it is known that individuals leave identifiable microbiota traces on surfaces, it is not clear for how long these traces persist. Moreover, as skin and surface microbiota change with time, even persistent traces may lose their forensic potential as they would cease to resemble the microbiota of the person who left them. We followed skin and surface microbiota within households for four seasons to determine whether accurate microbiota-based matching of individuals to their households could be achieved across long time delays.ResultsWhile household surface microbiota traces could be matched to the correct occupant or occupants with 67% accuracy, accuracy decreased substantially when skin and surface samples were collected in different seasons, and particularly when surface samples were collected long after skin samples. Most OTUs persisted on skin or surfaces for less than one season, indicating that OTU loss was the major cause of decreased matching accuracy. OTUs that were more useful for individual identification persisted for less time and were less likely to be deposited from skin to surface, suggesting a trade-off between the longevity and identifying value of microbiota traces.ConclusionsWhile microbiota traces have potential forensic value, unlike fingerprints they are not static and may degrade in a way that preferentially erases features useful in identifying individuals.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-016-0209-7) contains supplementary material, which is available to authorized users.

Metagenomic samples from oceans around the globe were used to examine the biogeography of the dominant marine heterotrophic bacterial clade, SAR11. Analysis uncovers evidence of adaptive radiation in response to environmental parameters, particularly temperature.

Heterotrophic marine bacteria play key roles in remineralizing organic matter generated from primary production. However, far more is known about which groups are dominant than about the cellular processes they perform in order to become dominant. In the Southern Ocean, eukaryotic phytoplankton are the dominant primary producers. In this study we used metagenomics and metaproteomics to determine how the dominant bacterial and archaeal plankton processed bloom material. We examined the microbial community composition in 14 metagenomes and found that the relative abundance of Flavobacteria (dominated by Polaribacter) was positively correlated with chlorophyll a fluorescence, and the relative abundance of SAR11 was inversely correlated with both fluorescence and Flavobacteria abundance. By performing metaproteomics on the sample with the highest relative abundance of Flavobacteria (Newcomb Bay, East Antarctica) we defined how Flavobacteria attach to and degrade diverse complex organic material, how they make labile compounds available to Alphaproteobacteria (especially SAR11) and Gammaproteobacteria, and how these heterotrophic Proteobacteria target and utilize these nutrients. The presence of methylotrophic proteins for archaea and bacteria also indicated the importance of metabolic specialists. Overall, the study provides functional data for the microbial mechanisms of nutrient cycling at the surface of the coastal Southern Ocean.

Subway systems are indispensable for urban societies, but microbiological characteristics of subway aerosols are relatively unknown. Previous studies investigating microbial compositions in subways employed methodologies that underestimated the diversity of microbial exposure for commuters, with little focus on factors governing subway air microbiology, which may have public health implications. Here, a culture-independent approach unraveling the bacterial diversity within the urban subway network in Hong Kong is presented. Aerosol samples from multiple subway lines and outdoor locations were collected. Targeting the 16S rRNA gene V4 region, extensive taxonomic diversity was found, with the most common bacterial genera in the subway environment among those associated with skin. Overall, subway lines harbored different phylogenetic communities based on ␣-and ␤-diversity comparisons, and closer inspection suggests that each community within a line is dependent on architectural characteristics, nearby outdoor microbiomes, and connectedness with other lines. Microbial diversities and assemblages also varied depending on the day sampled, as well as the time of day, and changes in microbial communities between peak and nonpeak commuting hours were attributed largely to increases in skin-associated genera in peak samples. Microbial diversities within the subway were influenced by temperature and relative humidity, while carbon dioxide levels showed a positive correlation with abundances of commuter-associated genera. This Hong Kong data set and communities from previous studies conducted in the United States formed distinct community clusters, indicating that additional work is required to unravel the mechanisms that shape subway microbiomes around the globe.