Effects of elevated atmospheric carbon dioxide on amino acid and NH 4 + -N cycling in a temperate pine ecosystem

Rising atmospheric carbon dioxide (CO 2 ) is expected to increase forest productivity, resulting in greater carbon (C) storage in forest ecosystems. Because elevated atmospheric CO 2 does not increase nitrogen (N) use efficiency in many forest tree species, additional N inputs will be required to sustain increased net primary productivity (NPP) under elevated atmospheric CO 2 . We investigated the importance of free amino acids (AAs) as a source for forest N uptake at the Duke Forest Free Air CO 2 Enrichment (FACE) site, comparing its importance with that of better-studied inorganic N sources. Potential proteolytic enzyme activity was monitored seasonally, and individual AA concentrations were measured in organic horizon extracts. Potential free AA production in soils ranged from 190 to 690 nmol N g −1  h −1 and was greater than potential rates of soil NH 4 + production. Because of this high potential rate of organic N production, we determined (1) whether intact AA uptake occurs by Pinus taeda L., the dominant tree species at the FACE site, (2) if the rate of cycling of AAs is comparable with that of ammonium (NH 4 + ), and (3) if atmospheric CO 2 concentration alters the aforementioned N cycling processes. A field experiment using universally labeled ammonium ( 15 NH 4 + ) and alanine ( 13 C 3 H 7 15 NO 2 ) demonstrated that 15 N is more readily taken up by plants and heterotrophic microorganisms as NH 4 + . Pine roots and microbes take up on average 2.4 and two times as much NH 4 + 15 N compared with alanine 15 N 1 week after tracer application. N cycling through soil pools was similar for alanine and NH 4 + , with the greatest 15 N tracer recovery in soil organic matter, followed by microbial biomass, dissolved organic N, extractable NH 4 + , and fine roots. Stoichiometric analyses of 13 C and 15 N uptake demonstrated that both plants and soil microorganisms take up alanine directly, with a 13 C :  15 N ratio of 3.3 : 1 in fine roots and 1.5 : 1 in microbial biomass. Our results suggest that intact AA (alanine) uptake contributes substantially to plant N uptake in loblolly pine forests. However, we found no evidence supporting increased recovery of free AAs in fine roots under elevated CO 2 , suggesting plants will need to acquire additional N via other mechanisms, such as increased root exploration or increased N use efficiency.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73167/1/j.1365-2486.2007.01411.x.pd

Interaction Networks Are Driven by Community-Responsive Phenotypes in a Chitin-Degrading Consortium of Soil Microbes

Soil microorganisms provide key ecological functions that often rely on metabolic interactions between individual populations of the soil microbiome. To better understand these interactions and community processes, we used chitin, a major carbon and nitrogen source in soil, as a test substrate to investigate microbial interactions during its decomposition. Chitin was applied to a model soil consortium that we developed, “model soil consortium-2” (MSC-2), consisting of eight members of diverse phyla and including both chitin degraders and nondegraders. A multiomics approach revealed how MSC-2 community-level processes during chitin decomposition differ from monocultures of the constituent species. Emergent properties of both species and the community were found, including changes in the chitin degradation potential of Streptomyces species and organization of all species into distinct roles in the chitin degradation process. The members of MSC-2 were further evaluated via metatranscriptomics and community metabolomics. Intriguingly, the most abundant members of MSC-2 were not those that were able to metabolize chitin itself, but rather those that were able to take full advantage of interspecies interactions to grow on chitin decomposition products. Using a model soil consortium greatly increased our knowledge of how carbon is decomposed and metabolized in a community setting, showing that niche size, rather than species metabolic capacity, can drive success and that certain species become active carbon degraders only in the context of their surrounding community. These conclusions fill important knowledge gaps that are key to our understanding of community interactions that support carbon and nitrogen cycling in soil

Interaction Networks Are Driven by Community-Responsive Phenotypes in a Chitin-Degrading Consortium of Soil Microbes

Soil microorganisms provide key ecological functions that often rely on metabolic interactions between individual populations of the soil microbiome. To better understand these interactions and community processes, we used chitin, a major carbon and nitrogen source in soil, as a test substrate to investigate microbial interactions during its decomposition. Chitin was applied to a model soil consortium that we developed, “model soil consortium-2” (MSC-2), consisting of eight members of diverse phyla and including both chitin degraders and nondegraders. A multiomics approach revealed how MSC-2 community-level processes during chitin decomposition differ from monocultures of the constituent species. Emergent properties of both species and the community were found, including changes in the chitin degradation potential of Streptomyces species and organization of all species into distinct roles in the chitin degradation process. The members of MSC-2 were further evaluated via metatranscriptomics and community metabolomics. Intriguingly, the most abundant members of MSC-2 were not those that were able to metabolize chitin itself, but rather those that were able to take full advantage of interspecies interactions to grow on chitin decomposition products. Using a model soil consortium greatly increased our knowledge of how carbon is decomposed and metabolized in a community setting, showing that niche size, rather than species metabolic capacity, can drive success and that certain species become active carbon degraders only in the context of their surrounding community. These conclusions fill important knowledge gaps that are key to our understanding of community interactions that support carbon and nitrogen cycling in soi

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

The negotiation of discourse relations in context: co-constructing degrees of overtness

AbstractThis paper examines the linguistic realization of discourse relations in the discourse genre of commentary. Based on a “bare” source text from which all extraclausal constituents had been removed, the linguistic realization of discourse relations is compared across three dyadically co-constructed experimental texts. The methodological framework integrates Segmented Discourse Representation Theory with functional grammar and psycholinguistic models of discourse processing, and describes discourse relations (DRs) that tend to be indexed implicitly only, that is, via intraclausal cues, and others that tend to be indexed with a mix of intra- and extraclausal cues. Continuation and Result; Continuation and Elaboration; and Elaboration, Explanation, and Background show some overlap in their definitions, and that is why their realizations may remain vague unless they are supplemented with extraclausal cues. Salience can account for the overspecifications observed, while underspecifications may be accounted for in terms of cognitive economy.</jats:p

High-mobility organic thin-film transistors based on a small-molecule semiconductor deposited in vacuum and by solution shearing

The small-molecule organic semiconductor 2,9-di-decyl-dinaphtho-[2,3-b: 2',3'-f]-thieno[3,2-b]-thiophene (C-10-DNTT) was used to fabricate bottom-gate, top-contact thin-film transistors (TFTs) in which the semiconductor layer was prepared either by vacuum deposition or by solution shearing. The maximum effective charge-carrier mobility of TFTs with vacuum-deposited C-10-DNTT is 8.5 cm(2)/V s for a nominal semiconductor thickness of 10 nm and a substrate temperature during the semiconductor deposition of 80 degrees C. Scanning electron microscopy analysis reveals the growth of small, isolated islands that begin to coalesce into a flat conducting layer when the nominal thickness exceeds 4 nm. The morphology of the vacuum-deposited semiconductor layers is dominated by tall lamellae that are formed during the deposition, except at very high substrate temperatures. Atomic force microscopy and X-ray diffraction measurements indicate that the C-10-DNTT molecules stand approximately upright with respect to the substrate surface, both in the flat conducting layer near the surface and within the lamellae. Using the transmission line method on TFTs with channel lengths ranging from 10 to 100 mu m, a relatively small contact resistance of 0.33 k Omega cm was determined. TFTs with the C-10-DNTT layer prepared by solution shearing exhibit a pronounced anisotropy of the electrical performance: TFTs with the channel oriented parallel to the shearing direction have an average carrier mobility of (2.8 +/- 0.3) cm(2)/V s, while TFTs with the channel oriented perpendicular to the shearing direction have a somewhat smaller average mobility of (1.3 +/- 0.1) cm(2)/V s. (C) 2013 Elsevier B.V. All rights reserved

Deep learning predicts microbial interactions from self-organized spatiotemporal patterns

Microbial communities organize into spatial patterns that are largely governed by interspecies interactions. This phenomenon is an important metric for understanding community functional dynamics, yet the use of spatial patterns for predicting microbial interactions is currently lacking. Here we propose supervised deep learning as a new tool for network inference. An agent-based model was used to simulate the spatiotemporal evolution of two interacting organisms under diverse growth and interaction scenarios, the data of which was subsequently used to train deep neural networks. For small-size domains (100 mm x 100 mm) over which interaction coefficients are assumed to be invariant, we obtained fairly accurate predictions, as indicated by an average R2 value of 0.84. In application to relatively larger domains (450 mm x 450 mm) where interaction coefficients are varying in space, deep learning models correctly predicted spatial distributions of interaction coefficients without any additional training. Lastly, we evaluated our model against real biological data obtained using Pseudomonas fluorescens and Escherichia coli co-cultures treated with polymeric chitin or N-acetylglucosamine, the hydrolysis product of chitin. While P. fluorescens can utilize both substrates for growth, E. coli lacked the ability to degrade chitin. Consistent with our expectations, our model predicted context-dependent interactions across two substrates, i.e., degrader-cheater relationship on chitin polymers and competition on monomers. The combined use of the agent-based model and machine learning algorithm successfully demonstrates how to infer microbial interactions from spatially distributed data, presenting itself as a useful tool for the analysis of more complex microbial community interaction