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Abstract Background Anopheles darlingi is the most important malaria vector in the Neotropics. An understanding of A. darlingi's population structure and contemporary gene flow patterns is necessary if vector populations are to be successfully controlled. We assessed population genetic structure and levels of differentiation based on 1,376 samples from 31 localities throughout the Peruvian and Brazilian Amazon and Central America using 5–8 microsatellite loci. Results We found high levels of polymorphism for all of the Amazonian populations (mean RS = 7.62, mean HO = 0.742), and low levels for the Belize and Guatemalan populations (mean RS = 4.3, mean HO = 0.457). The Bayesian clustering analysis revealed five population clusters: northeastern Amazonian Brazil, southeastern and central Amazonian Brazil, western and central Amazonian Brazil, Peruvian Amazon, and the Central American populations. Within Central America there was low non-significant differentiation, except for between the populations separated by the Maya Mountains. Within Amazonia there was a moderate level of significant differentiation attributed to isolation by distance. Within Peru there was no significant population structure and low differentiation, and some evidence of a population expansion. The pairwise estimates of genetic differentiation between Central America and Amazonian populations were all very high and highly significant (FST = 0.1859 – 0.3901, P < 0.05). Both the DA and FST distance-based trees illustrated the main division to be between Central America and Amazonia. Conclusion We detected a large amount of population structure in Amazonia, with three population clusters within Brazil and one including the Peru populations. The considerable differences in Ne among the populations may have contributed to the observed genetic differentiation. All of the data suggest that the primary division within A. darlingi corresponds to two white gene genotypes between Amazonia (genotype 1) and Central America, parts of Colombia and Venezuela (genotype 2), and are in agreement with previously published mitochondrial COI gene sequences interpreted as incipient species. Overall, it appears that two main factors have contributed to the genetic differentiation between the population clusters: physical distance between the populations and the differences in effective population sizes among the subpopulations.

Antarctica polynyas support intense phytoplankton blooms, impacting their environment by a substantial depletion of inorganic carbon and nutrients. These blooms are dominated by the colony-forming haptophyte Phaeocystis antarctica and they are accompanied by a distinct bacterial population. Yet, the ecological role these bacteria may play in P. antarctica blooms awaits elucidation of their functional gene pool and of the geochemical activities they support. Here, we report on a metagenome (~160 million reads) analysis of the microbial community associated with a P. antarctica bloom event in the Amundsen Sea polynya (West Antarctica). Genomes of the most abundant Bacteroidetes and Proteobacteria populations have been reconstructed and a network analysis indicates a strong functional partitioning of these bacterial taxa. Three of them (SAR92, and members of the Oceanospirillaceae and Cryomorphaceae) are found in close association with P. antarctica colonies. Distinct features of their carbohydrate, nitrogen, sulfur and iron metabolisms may serve to support mutualistic relationships with P. antarctica. The SAR92 genome indicates a specialization in the degradation of fatty acids and dimethylsulfoniopropionate (compounds released by P. antarctica) into dimethyl sulfide, an aerosol precursor. The Oceanospirillaceae genome carries genes that may enhance algal physiology (cobalamin synthesis). Finally, the Cryomorphaceae genome is enriched in genes that function in cell or colony invasion. A novel pico-eukaryote, Micromonas related genome (19.6 Mb, ~94% completion) was also recovered. It contains the gene for an anti-freeze protein, which is lacking in Micromonas at lower latitudes. These draft genomes are representative for abundant microbial taxa across the Southern Ocean surface. [ABSTRACT FROM AUTHOR]

Molecular microbial ecology investigations often employ large marker gene datasets, for example, ribosomal RNAs, to represent the occurrence of single-cell genomes in microbial communities. Massively parallel DNA sequencing technologies enable extensive surveys of marker gene libraries that sometimes include nearly identical sequences. Computational approaches that rely on pairwise sequence alignments for similarity assessment and de novo clustering with de facto similarity thresholds to partition high-throughput sequencing datasets constrain fine-scale resolution descriptions of microbial communities. Minimum Entropy Decomposition (MED) provides a computationally efficient means to partition marker gene datasets into 'MED nodes', which represent homogeneous operational taxonomic units. By employing Shannon entropy, MED uses only the information-rich nucleotide positions across reads and iteratively partitions large datasets while omitting stochastic variation. When applied to analyses of microbiomes from two deep-sea cryptic sponges Hexadella dedritifera and Hexadella cf. dedritifera, MED resolved a key Gammaproteobacteria cluster into multiple MED nodes that are specific to different sponges, and revealed that these closely related sympatric sponge species maintain distinct microbial communities. MED analysis of a previously published human oral microbiome dataset also revealed that taxa separated by less than 1% sequence variation distributed to distinct niches in the oral cavity. The information theory-guided decomposition process behind the MED algorithm enables sensitive discrimination of closely related organisms in marker gene amplicon datasets without relying on extensive computational heuristics and user supervision. [ABSTRACT FROM AUTHOR]

Delineating differences in gut microbiomes of human and animal hosts contributes towards understanding human health and enables new strategies for detecting reservoirs of waterborne human pathogens. We focused upon Blautia, a single microbial genus that is important for nutrient assimilation as preliminary work suggested host-related patterns within members of this genus. In our dataset of 57 M sequence reads of the V6 region of the 16S ribosomal RNA gene in samples collected from seven host species, we identified 200 high-resolution taxonomic units within Blautia using oligotyping. Our analysis revealed 13 host-specific oligotypes that occurred exclusively in fecal samples of humans (three oligotypes), swine (six oligotypes), cows (one oligotype), deer (one oligotype), or chickens (two oligotypes). We identified an additional 171 oligotypes that exhibited differential abundance patterns among all the host species. Blautia oligotypes in the human population obtained from sewage and fecal samples displayed remarkable continuity. Oligotypes from only 10 Brazilian human fecal samples collected from individuals in a rural village encompassed 97% of all Blautia oligotypes found in a Brazilian sewage sample from a city of three million people. Further, 75% of the oligotypes in Brazilian human fecal samples matched those in US sewage samples, implying that a universal set of Blautia strains may be shared among culturally and geographically distinct human populations. Such strains can serve as universal markers to assess human fecal contamination in environmental samples. Our results indicate that host-specificity and host-preference patterns of organisms within this genus are driven by host physiology more than dietary habits. [ABSTRACT FROM AUTHOR]

Most DNA-based microbial source tracking (MST) approaches target host-associated organisms within the order Bacteroidales, but the gut microbiota of humans and other animals contain organisms from an array of other taxonomic groups that might provide indicators of fecal pollution sources. To discern between human and nonhuman fecal sources, we compared the V6 regions of the 16S rRNA genes detected in fecal samples from six animal hosts to those found in sewage (as a proxy for humans). We focused on 10 abundant genera and used oligotyping, which can detect subtle differences between rRNA gene sequences from ecologically distinct organisms. Our analysis showed clear patterns of differential oligotype distributions between sewage and animal samples. Over 100 oligotypes of human origin occurred preferentially in sewage samples, and 99 human oligotypes were sewage specific. Sequences represented by the sewage-specific oligotypes can be used individually for development of PCR-based assays or together with the oligotypes preferentially associated with sewage to implement a signature-based approach. Analysis of sewage from Spain and Brazil showed that the sewage-specific oligotypes identified in U.S. sewage have the potential to be used as global alternative indicators of human fecal pollution. Environmental samples with evidence of prior human fecal contamination had consistent ratios of sewage signature oligotypes that corresponded to the trends observed for sewage. Our methodology represents a promising approach to identifying new bacterial taxa for MST applications and further highlights the potential of the family Lachnospiraceae to provide human-specific markers. In addition to source tracking applications, the patterns of the fine-scale population structure within fecal taxa suggest a fundamental relationship between bacteria and their hosts. [ABSTRACT FROM AUTHOR]