Ovarian cycling and reproductive state shape the vaginal microbiota in wild baboons

Background: The vaginal microbiome is an important site of bacterial-mammalian symbiosis. This symbiosis is currently best characterized for humans, where lactobacilli dominate the microbial community and may help defend women against infectious disease. However, lactobacilli do not dominate the vaginal microbiota of any other mammal studied to date, raising key questions about the forces that shape the vaginal microbiome in non-human mammals. Results: We used Illumina sequencing of the bacterial 16S rRNA gene to investigate variation in the taxonomic composition of the vaginal microbiota in 48 baboons (Papio cynocephalus), members of a well-studied wild population in Kenya. Similar to prior studies, we found that the baboon vaginal microbiota was not dominated by lactobacilli. Despite this difference, and similar to humans, reproductive state was the dominant predictor of baboon vaginal microbiota, with pregnancy, postpartum amenorrhea, and ovarian cycling explaining 18% of the variance in community composition. Furthermore, among cycling females, a striking 39% of variance in community composition was explained by ovarian cycle phase, with an especially distinctive microbial community around ovulation. Peri-ovulatory females exhibited the highest relative abundance of lactic acid-producing bacteria compared to any other phase, with a mean relative abundance of 44%. To a lesser extent, sexual behavior, especially a history of shared sexual partners, also predicted vaginal microbial similarity between baboons. Conclusions: Despite striking differences in their dominant microbes, both human and baboon vaginal microbiota exhibit profound changes in composition in response to reproductive state, ovarian cycle phase, and sexual behavior. We found major shifts in composition during ovulation, which may have implications for disease risk and conception success. These findings highlight the need for future studies to account for fine-scale differences in reproductive state, particularly differences between the various phases of the ovarian cycle. Overall, our work contributes to an emerging understanding of the forces that explain intra- and inter-individual variation in the mammalian vaginal microbiome, with particular emphasis on its role in host health and disease risk

DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 reference manual

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 developers manual.

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes

baboon_metadata_alpha_diversity

Metadata, number of reads, estimated sample coverage, and alpha diversity metrics (Shannon's diversity index and OTU richness) for each vaginal sample included in this study

Data from: Ovarian cycling and reproductive state shape the vaginal microbiota in wild baboons

Background: The vaginal microbiome is an important site of bacterial-mammalian symbiosis. This symbiosis is currently best characterized for humans, where lactobacilli dominate the microbial community and may help defend women against infectious disease. However, lactobacilli do not dominate the vaginal microbiota of any other mammal studied to date, raising key questions about the forces that shape the vaginal microbiome in non-human mammals. Results: We used Illumina sequencing of the bacterial 16S rRNA gene to investigate variation in the taxonomic composition of the vaginal microbiota in 48 baboons (Papio cynocephalus), members of a well-studied wild population in Kenya. Similar to prior studies, we found that the baboon vaginal microbiota was not dominated by lactobacilli. Despite this difference, and similar to humans, reproductive state was the dominant predictor of baboon vaginal microbiota, with pregnancy, postpartum amenorrhea, and ovarian cycling explaining 18% of the variance in community composition. Furthermore, among cycling females, a striking 39% of variance in community composition was explained by ovarian cycle phase, with an especially distinctive microbial community around ovulation. Peri-ovulatory females exhibited the highest relative abundance of lactic acid-producing bacteria compared to any other phase, with a mean relative abundance of 44%. To a lesser extent, sexual behavior, especially a history of shared sexual partners, also predicted vaginal microbial similarity between baboons. Conclusions: Despite striking differences in their dominant microbes, both human and baboon vaginal microbiota exhibit profound changes in composition in response to reproductive state, ovarian cycle phase, and sexual behavior. We found major shifts in composition during ovulation, which may have implications for disease risk and conception success. These findings highlight the need for future studies to account for fine-scale differences in reproductive state, particularly differences between the various phases of the ovarian cycle. Overall, our work contributes to an emerging understanding of the forces that explain intra- and inter-individual variation in the mammalian vaginal microbiome, with particular emphasis on its role in host health and disease risk

Additional file 4: of Ovarian cycling and reproductive state shape the vaginal microbiota in wild baboons

Full account of statistical analysis performed in R. (PDF 3480 kb

Additional file 2: of Ovarian cycling and reproductive state shape the vaginal microbiota in wild baboons

Supplementary Tables S1-S5. (XLSX 65 kb

Additional file 3: of Ovarian cycling and reproductive state shape the vaginal microbiota in wild baboons

Supplementary Figures S1-S8. (DOCX 1129 kb

Microbial Community Dynamics during Acetate Biostimulation of RDX-Contaminated Groundwater

Biostimulation of groundwater microbial communities (e.g., with carbon sources) is a common approach to achieving in situ bioremediation of organic pollutants (e.g., explosives). We monitored a field-scale approach to remediate the explosive RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine) in an aquifer near the Iowa Army Ammunition Plant in Middletown, IA. The purpose of the study was to gain insight into the effect of biostimulation on the microbial community. Biostimulation with acetate led to the onset of RDX reduction at the site, which was most apparent in monitoring well MW309. Based on previous laboratory experiments, we hypothesized that RDX degradation and metabolite production would correspond to enrichment of one or more Fe­(III)-reducing bacterial species. Community DNA from MW309 was analyzed with 454 pyrosequencing and terminal restriction fragment length polymorphism. Production of RDX metabolites corresponded to a microbial community shift from primarily Fe­(III)-reducing Betaproteobacteria to a community dominated by Fe­(III)-reducing Deltaproteobacteria (Geobacteraceae in particular) and Bacteroidetes taxa. This data provides a firsthand field-scale microbial ecology context to in situ RDX bioremediation using modern sequencing techniques that will inform future biostimulation applications