Soil bacterial diversity is associated with human population density in urban greenspaces

Author Posting. © The Author(s), 2018. This is the author's version of the work. It is posted here by permission of American Chemical Society for personal use, not for redistribution. The definitive version was published in Environmental Science and Technology 52 (2018): 5115–5124, doi:10.1021/acs.est.7b06417.Urban greenspaces provide extensive ecosystem services, including pollutant remediation, water management, carbon maintenance, and nutrient cycling. However, while the urban soil microbiota underpin these services, we still have limited understanding of the factors that influence their distribution. We characterized soil bacterial communities from turf-grasses associated with urban parks, streets and residential sites across a major urban environment, including a gradient of human population density. Bacterial diversity was significantly positively correlated with the population density; and species diversity was greater in park and street soils, compared to residential soils. Population density and greenspace type also led to significant differences in the microbial community composition that was also significantly correlated with soil pH, moisture and texture. Co-occurrence network analysis revealed that microbial guilds in urban soils were well correlated. Abundant soil microbes in high density population areas had fewer interactions, while abundant bacteria in high moisture soils had more interactions. These results indicate the significant influence of changes in urban demographics and land-use on soil microbial communities. As urbanization is rapidly growing across the planet, it is important to improve our understanding of the consequences of urban zoning on the soil microbiota.This study is supported by the Earth Microbiome Project (http://www.earthmicrobiome.org/) and the China Scholarship Council (http://en.csc.edu.cn/).2019-04-0

Wheat rhizosphere harbors a less complex and more stable microbial co-occurrence pattern than bulk soil

© The Author(s), 2018. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Soil Biology and Biochemistry 125 (2018): 251-260, doi:10.1016/j.soilbio.2018.07.022.The rhizosphere harbors complex microbial communities, whose dynamic associations are considered critical for plant growth and health but remain poorly understood. We constructed co-occurrence networks for archaeal, bacterial and fungal communities associated with the rhizosphere and bulk soil of wheat fields on the North China Plain. Rhizosphere co-occurrence networks had fewer nodes, edges, modules and lower density, but maintained more robust structure compared with bulk soil, suggesting that a less complex topology and more stable co-occurrence pattern is a feature for wheat rhizosphere. Bacterial and fungal communities followed a power-law distribution, while the archaeal community did not. Soil pH and microbial diversity were significantly correlated with network size and connectivity in both rhizosphere and bulk soils. Keystone species that played essential roles in network structure were predicted to maintain a flexible generalist metabolism, and had fewer significant correlations with environmental variables, especially in the rhizosphere. These results indicate that distinct microbial co-occurrence patterns exist in wheat rhizosphere, which could be associated with variable agricultural ecosystem properties.This work was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB15010101) and the National Program on Key Basic Research Project (2014CB954002).2020-07-2

Improving automated model reconstruction across phylogenetically diverse genome-scale metabolic models

info:eu-repo/semantics/publishedVersio

Automated pathway curation and improving metabolic model reconstruction based on phylogenetic analysis of pathway conservation

ICSB 2017 - 18th International Conference on Systems BiologyMetabolic models generated by automated reconstruction pipelines are widely used for high-throughput prediction of microbial phenotypes. However, the generation of accurate in-silico phenotype predictions based solely on genomic data continues to be a challenge as metabolic models often require extensive gapfilling in order to produce biomass. As a result, the true physiological profile of an organism can be altered by the addition of non-native biochemical pathways or reactions during the gapfilling process. In this study, we constructed draft genome-scale metabolic models for ~1000 diverse set of reference microbial genomes currently available in GenBank, and we decomposed these models into a set of classical biochemical pathways. We then determine the extent to which each pathway is either consistently present or absent in each region of the phylogenetic tree, and we study the degree of conservation in the specific steps where gaps exist in each pathway across a phylogenetic neighborhood. Based on this analysis, we improved the reliability of our gapfilling algorithms, which in turn, improved the reliability of our models in predicting auxotrophy. This also resulted in improvements to the genome annotations underlying our models. We validated our improved auxotrophy predictions using growth condition data collected for a diverse set of organisms. Our improved gapfilling algorithm will be available for use within the DOE Knowledgebase (KBase) platform (https://kbase.us).info:eu-repo/semantics/publishedVersio

Computing and applying atomic regulons to understand gene expression and regulation

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2016.01819/full#supplementary-materialUnderstanding gene function and regulation is essential for the interpretation prediction and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets Atomic Regulons ARs represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here we describe an approach for inferring ARs that leverages large-scale expression data sets gene context and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness CLR analysis finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms we computed ARs for Shewanella oneidensis Pseudomonas aeruginosa Thermus thermophilus and Staphylococcus aureus each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.JF acknowledges funding from [SFRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) PhD program. CH and PW were supported by the National Science Foundation under grant number EFRI-MIKS-1137089. RT was supported by the Genomic Science Program (GSP), Office of Biological and Environmental Research (OBER), U.S. Department of Energy(DOE),and his work is a contribution of the Pacific North west National Laboratory (PNNL) Foundational Scientific Focus Area. This work was partially supported by an award from the National Science Foundation to MD, AB, NT, and RO (NSFABI-0850546). This work was also supported by the United States National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Service [Contract No. HHSN272201400027C]

Representing the function and sensitivity of coastal interfaces in earth system models

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ward, N. D., Megonigal, J. P., Bond-Lamberty, B., Bailey, V. L., Butman, D., Canuel, E. A., Diefenderfer, H., Ganju, N. K., Goni, M. A., Graham, E. B., Hopkinson, C. S., Khangaonkar, T., Langley, J. A., McDowell, N. G., Myers-Pigg, A. N., Neumann, R. B., Osburn, C. L., Price, R. M., Rowland, J., Sengupta, A., Simard, M., Thornton, P. E., Tzortziou, M., Vargas, R., Weisenhorn, P. B., & Windham-Myers, L. Representing the function and sensitivity of coastal interfaces in earth system models. Nature Communications, 11(1), (2020): 2458, doi:10.1038/s41467-020-16236-2.Between the land and ocean, diverse coastal ecosystems transform, store, and transport material. Across these interfaces, the dynamic exchange of energy and matter is driven by hydrological and hydrodynamic processes such as river and groundwater discharge, tides, waves, and storms. These dynamics regulate ecosystem functions and Earth’s climate, yet global models lack representation of coastal processes and related feedbacks, impeding their predictions of coastal and global responses to change. Here, we assess existing coastal monitoring networks and regional models, existing challenges in these efforts, and recommend a path towards development of global models that more robustly reflect the coastal interface.Funding for this work was provided by Pacific Northwest National Laboratory (PNNL) Laboratory Directed Research & Development (LDRD) as part of the Predicting Ecosystem Resilience through Multiscale Integrative Science (PREMIS) Initiative. PNNL is operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830. Additional support to J.P.M. was provided by the NSF-LTREB program (DEB-0950080, DEB-1457100, DEB-1557009), DOE-TES Program (DE-SC0008339), and the Smithsonian Institution. This manuscript was motivated by discussions held by co-authors during a three-day workshop at PNNL in Richland, WA: The System for Terrestrial Aquatic Research (STAR) Workshop: Terrestrial-Aquatic Research in Coastal Systems. The authors thank PNNL artist Nathan Johnson for preparing the figures in this manuscript and Terry Clark, Dr. Charlette Geffen, and Dr. Nancy Hess for their aid in organizing the STAR workshop. The authors thank all workshop participants not listed as authors for their valuable insight: Lihini Aluwihare (contributed to biogeochemistry discussions and development of concept for Fig. 3), Gautam Bisht (contributed to modeling discussion), Emmett Duffy (contributed to observational network discussions), Yilin Fang (contributed to modeling discussion), Jeremy Jones (contributed to biogeochemistry discussions), Roser Matamala (contributed to biogeochemistry discussions), James Morris (contributed to biogeochemistry discussions), Robert Twilley (contributed to biogeochemistry discussions), and Jesse Vance (contributed to observational network discussions). A full report on the workshop discussions can be found at https://www.pnnl.gov/publications/star-workshop-terrestrial-aquatic-research-coastal-systems

A communal catalogue reveals Earth's multiscale microbial diversity

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.Peer reviewe

A communal catalogue reveals Earth’s multiscale microbial diversity

Our growing awareness of the microbial world’s importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity

Substrate Utilization by Micrococcus Luteus: Biodegradation of Pyridine for Metabolic Modeling

The bacterium Micrococcus luteus was grown on three substrates: glucose, acetate, and pyridine; all maintained at a molar C: N ratio of 5:1 Optical density, pH measurements, substrate, and ammonium concentrations were monitored at regular intervals. The N-heterocycle pyridine is a by-product of coal gasification and oil shale that has high water solubility and mobility through the soil; leading to surface and groundwater contamination. M. luteus was able to grow on all substrates and riboflavin production was apparent in all treatments. Glucose, the most widely used energy reserve, produced acidic conditions during log phase growth, and supported the most biomass production. Acetate surprisingly produced alkaline conditions during log phase growth. Pyridine was oxidized for energy by M luteus and as the pyridine concentration decreased, the ammonium concentration increased. The ring N was released to the medium as ammonium or incorporated into biomass. M luteus utilized pyridine as a carbon, nitrogen, and energy source similar to glucose and acetate treatments amended with ammonium. The metabolism of N-heterocycles remains poorly understood, but with transcriptomic analysis and metabolic modeling, more information on the metabolic pathways of pyridine degradation can be obtained. More studies of this organism will be necessary to elucidate the degradative pathways for pyridine; which can lead to a better understanding of N-heterocycle-degrading microorganisms

Data from: Modeling central metabolism and energy biosynthesis across microbial life

Background: Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles Results: To overcome this challenge, we developed methods and tools (http://coremodels.mcs.anl.gov) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80%) of our models were found to have some type of aerobic ETC, whereas 5,100 (62%) have an anaerobic ETC, and 1,279 (15%) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70%) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30%) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. Conclusions: We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes