12 research outputs found
Gut Microbiota Composition Modulates the Magnitude and Quality of Germinal Centers during Plasmodium Infections
Gut microbiota composition is associated with human and rodent Plasmodium infections, yet the mechanism by which gut microbiota affects the severity of malaria remains unknown. Humoral immunity is critical in mediating the clearance of Plasmodium blood stage infections, prompting the hypothesis that mice with gut microbiota-dependent decreases in parasite burden exhibit better germinal center (GC) responses. In support of this hypothesis, mice with a low parasite burden exhibit increases in GC B cell numbers and parasite-specific antibody titers, as well as better maintenance of GC structures and a more targeted, qualitatively different antibody response. This enhanced humoral immunity affects memory, as mice with a low parasite burden exhibit robust protection against challenge with a heterologous, lethal Plasmodium species. These results demonstrate that gut microbiota composition influences the biology of spleen GCs as well as the titer and repertoire of parasite-specific antibodies, identifying potential approaches to develop optimal treatments for malaria
Temporospatial shifts within commercial laboratory mouse gut microbiota impact experimental reproducibility
Experimental reproducibility in mouse models is impacted by both genetics and environment. The generation of reproducible data is critical for the biomedical enterprise and has become a major concern for the scientific community and funding agencies alike. Among the factors that impact reproducibility in experimental mouse models is the variable composition of the microbiota in mice supplied by different commercial vendors. Less attention has been paid to how the microbiota of mice supplied by a particular vendor might change over time.
Results
In the course of conducting a series of experiments in a mouse model of malaria, we observed a profound and lasting change in the severity of malaria in mice infected with Plasmodium yoelii; while for several years mice obtained from a specific production suite of a specific commercial vendor were able to clear the parasites effectively in a relatively short time, mice subsequently shipped from the same unit suffered much more severe disease. Gut microbiota analysis of frozen cecal samples identified a distinct and lasting shift in bacteria populations that coincided with the altered response of the later shipments of mice to infection with malaria parasites. Germ-free mice colonized with cecal microbiota from mice within the same production suite before and after this change followed by Plasmodium infection provided a direct demonstration that the change in gut microbiota profoundly impacted the severity of malaria. Moreover, spatial changes in gut microbiota composition were also shown to alter the acute bacterial burden following Salmonella infection, and tumor burden in a lung tumorigenesis model.
Conclusion
These changes in gut bacteria may have impacted the experimental reproducibility of diverse research groups and highlight the need for both laboratory animal providers and researchers to collaborate in determining the methods and criteria needed to stabilize the gut microbiota of animal breeding colonies and research cohorts, and to develop a microbiota solution to increase experimental rigor and reproducibility
Return on Investment from Industrial Energy Efficiency: Evidence from Developing Countries
Return on investment from industrial energy efficiency: evidence from developing countries
Genomes of ubiquitous marine and hypersaline Hydrogenovibrio, Thiomicrorhabdus, and Thiomicrospira spp. encode a diversity of mechanisms to sustain chemolithoautotrophy in heterogeneous environments.
Chemolithoautotrophic bacteria from the genera Hydrogenovibrio, Thiomicrorhabdus, and Thiomicrospira are common, sometimes dominant, isolates from sulfidic habitats including hydrothermal vents, soda and salt lakes, and marine sediments. Their genome sequences confirm their membership in a deeply branching clade of the Gammaproteobacteria. Several adaptations to heterogeneous habitats are apparent. Their genomes include large numbers of genes for sensing and responding to their environment (EAL- and GGDEF-domain proteins, and methyl-accepting chemotaxis proteins) despite their small sizes (2.1 - 3.1 Mbp). An array of sulfur-oxidizing complexes are encoded, likely to facilitate these organisms' use of multiple forms of reduced sulfur as electron donors. Hydrogenase genes are present in some taxa, including group 1d and 2b hydrogenases in Hydrogenovibrio marinus and H. thermophilus MA2-6, acquired via horizontal gene transfer. In addition to high-affinity cbb3cytochrome c oxidase, some also encode cytochrome bd-type quinol oxidase or ba3-type cytochrome c oxidase, which could facilitate growth under different oxygen tensions, or maintain redox balance. Carboxysome operons are present in most, with genes downstream encoding transporters from four evolutionarily distinct families, which may act with the carboxysomes to form CO2concentrating mechanisms. These adaptations to habitat variability likely contribute to the cosmopolitan distribution of these organisms. This article is protected by copyright. All rights reserved
Integrative modelling reveals mechanisms linking productivity and plant species richness
How ecosystem productivity and species richness are interrelated is one of the most debated subjects in the history of ecology1. Decades of intensive study have yet to discern the actual mechanisms behind observed global patterns2, 3. Here, by integrating the predictions from multiple theories into a single model and using data from 1,126 grassland plots spanning five continents, we detect the clear signals of numerous underlying mechanisms linking productivity and richness. We find that an integrative model has substantially higher explanatory power than traditional bivariate analyses. In addition, the specific results unveil several surprising findings that conflict with classical models4, 5, 6, 7. These include the isolation of a strong and consistent enhancement of productivity by richness, an effect in striking contrast with superficial data patterns. Also revealed is a consistent importance of competition across the full range of productivity values, in direct conflict with some (but not all) proposed models. The promotion of local richness by macroecological gradients in climatic favourability, generally seen as a competing hypothesis8, is also found to be important in our analysis. The results demonstrate that an integrative modelling approach leads to a major advance in our ability to discern the underlying processes operating in ecological systems
Integrative modelling reveals mechanisms linking productivity and plant species richness
The Influence of Vegetation Height Heterogeneity on Forest and Woodland Bird Species Richness across the United States
BioTIME:a database of biodiversity time series for the Anthropocene
Abstract
Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.
Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.
Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km² (158 cm²) to 100 km² (1,000,000,000,000 cm²).
Time period and grain: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.
Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
Software format: .csv and .SQL