Publications (55) View all
-
Dataset: 2013-mBio-Zhou-stochastic assembly
-
Article: Stochastic assembly leads to alternative communities with distinct functions in a bioreactor microbial community.
[show abstract] [hide abstract]
ABSTRACT: ABSTRACT The processes and mechanisms of community assembly and its relationships to community functioning are central issues in ecology. Both deterministic and stochastic factors play important roles in shaping community composition and structure, but the connection between community assembly and ecosystem functioning remains elusive, especially in microbial communities. Here, we used microbial electrolysis cell reactors as a model system to examine the roles of stochastic assembly in determining microbial community structure and functions. Under identical environmental conditions with the same source community, ecological drift (i.e., initial stochastic colonization) and subsequent biotic interactions created dramatically different communities with little overlap among 14 identical reactors, indicating that stochastic assembly played dominant roles in determining microbial community structure. Neutral community modeling analysis revealed that deterministic factors also played significant roles in shaping microbial community structure in these reactors. Most importantly, the newly formed communities differed substantially in community functions (e.g., H production), which showed strong linkages to community structure. This study is the first to demonstrate that stochastic assembly plays a dominant role in determining not only community structure but also ecosystem functions. Elucidating the links among community assembly, biodiversity, and ecosystem functioning is critical to understanding ecosystem functioning, biodiversity preservation, and ecosystem management. IMPORTANCE Microorganisms are the most diverse group of life known on earth. Although it is well documented that microbial natural biodiversity is extremely high, it is not clear why such high diversity is generated and maintained. Numerous studies have established the roles of niche-based deterministic factors (e.g., pH, temperature, and salt) in shaping microbial biodiversity, the importance of stochastic processes in generating microbial biodiversity is rarely appreciated. Moreover, while microorganisms mediate many ecosystem processes, the relationship between microbial diversity and ecosystem functioning remains largely elusive. Using a well-controlled laboratory system, this study provides empirical support for the dominant role of stochastic assembly in creating variations of microbial diversity and the first explicit evidence for the critical role of community assembly in influencing ecosystem functioning. The results presented in this study represent important contributions to the understanding of the mechanisms, especially stochastic processes, involved in shaping microbial biodiversity.mBio 01/2013; 4(2). · 5.31 Impact Factor -
Article: Functional gene differences in soil microbial communities from conventional, low-input and organic farmlands.
[show abstract] [hide abstract]
ABSTRACT: Various agriculture management practices may have distinct influences on soil microbial communities and their ecological functions. In this study, we utilized GeoChip, a high throughput microarray-based technique containing approximately 28,000 probes for genes involved in nitrogen (N)/carbon (C)/sulfur (S)/phosphorus (P) cycles and other processes, to evaluate the potential functions of soil microbial communities under conventional (CT), low-input (LI) and organic (ORG) management systems at an agricultural research site in Michigan, USA. Compared to CT, a higher diversity of functional genes was observed in LI. The functional gene diversity in ORG did not differ significantly from either CT or LI. Genes encoding enzymes involved in C/N/P/S cycles were generally lower in CT than in LI or ORG, with the exceptions of genes in pathways for lignin degradation, methane generation/oxidation, and assimilatory N reduction that all remained unchanged. Canonical correlation analysis showed that selected soil (bulk density, pH, cation exchange capacity, total C, C/N ratio, NO(3)(-), NH(4)(+), available phosphorus and potassium contents) and crop (seed and whole biomass) variables could explain 69.5% of the variation of soil microbial community composition. Also, significant correlations were observed between NO(3)(-) concentration and denitrification genes, NH(4)(+) concentration and ammonification genes, plus N(2)O flux and denitrification genes, indicating a close linkage between soil N availability or process and associated functional genes.Applied and environmental microbiology 12/2012; · 3.69 Impact Factor -
Article: Shifts of functional gene representation in wheat rhizosphere microbial communities under elevated ozone.
[show abstract] [hide abstract]
ABSTRACT: Although the influence of ozone (O(3)) on plants has been well studied in agroecosystems, little is known about the effect of elevated O(3) (eO(3)) on soil microbial functional communities. Here, we used a comprehensive functional gene array (GeoChip 3.0) to investigate the functional composition, and structure of rhizosphere microbial communities of Yannong 19 (O(3)-sensitive) and Yangmai 16 (O(3)-relatively sensitive) wheat (Triticum aestivum L.) cultivars under eO(3). Compared with ambient O(3) (aO(3)), eO(3) led to an increase in soil pH and total carbon (C) percentages in grain and straw of wheat plants, and reduced grain weight and soil dissolved organic carbon (DOC). Based on GeoChip hybridization signal intensities, although the overall functional structure of rhizosphere microbial communities did not significantly change by eO(3) or cultivars, the results showed that the abundance of specific functional genes involved in C fixation and degradation, nitrogen (N) fixation, and sulfite reduction did significantly (P<0.05) alter in response to eO(3) and/or wheat cultivars. Also, Yannong 19 appeared to harbor microbial functional communities in the rhizosphere more sensitive in response to eO(3) than Yangmai 16. Additionally, canonical correspondence analysis suggested that the functional structure of microbial community involved in C cycling was largely shaped by soil and plant properties including pH, DOC, microbial biomass C, C/N ratio and grain weight. This study provides new insight into our understanding of the influence of eO(3) and wheat cultivars on soil microbial communities.The ISME Journal 11/2012; · 7.38 Impact Factor -
Article: Molecular ecological network analyses.
[show abstract] [hide abstract]
ABSTRACT: Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA). The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.BMC Bioinformatics 05/2012; 13:113. · 2.75 Impact Factor
