Land Ecosystems and Hydrology

The terrestrial biosphere is an integral component of the Earth Observing System (EOS) science objectives concerning climate change, hydrologic cycle change, and changes in terrestrial productivity. The fluxes o f CO2 and other greenhouse gases from the land surface influence the global circulation models directly, and changes in land cover change the land surface biophysical properties o f energy and mass exchange. Hydrologic cycle perturbations result from terrestrially-induced climate changes, and more directly from changes in land cover acting on surface hydrologic balances. Finally, both climate and hydrology jointly control biospheric productivity, the source o f food, fuel, and fiber for humankind. The role of the land system in each of these three topics is somewhat different, so this chapter is organized into the subtopics of Land-Climate, Land-Hydrology, and Land-Vegetation interactions (Figures 5.1, 5.2, and 5.3)

Signatures of ecological processes in microbial community time series

Background: Growth rates, interactions between community members, stochasticity, and immigration are important drivers of microbial community dynamics. In sequencing data analysis, such as network construction and community model parameterization, we make implicit assumptions about the nature of these drivers and thereby restrict model outcome. Despite apparent risk of methodological bias, the validity of the assumptions is rarely tested, as comprehensive procedures are lacking Here, we propose a classification scheme to determine the processes that gave rise to the observed time series and to enable better model selection.Results: We implemented a three-step classification scheme in R that first determines whether dependence between successive time steps (temporal structure) is present in the time series and then assesses with a recently developed neutrality test whether interactions between species are required for the dynamics. If the first and second tests confirm the presence of temporal structure and interactions, then parameters for interaction models are estimated. To quantify the importance of temporal structure, we compute the noise-type profile of the community, which ranges from black in case of strong dependency to white in the absence of any dependency. We applied this scheme to simulated time series generated with the Dirichlet-multinomial (DM) distribution, Hubbell's neutral model, the generalized Lotka-Volterra model and its discrete variant (the Ricker model), and a self organized instability model, as well as to human stool microbiota time series. The noise-type profiles for all but DM data clearly indicated distinctive structures. The neutrality test correctly classified all but DM and neutral time series as non-neutral. The procedure reliably identified time series for which interaction inference was suitable. Both tests were required, as we demonstrated that all structured time series, including those generated with the neutral model, achieved a moderate to high goodness of fit to the Ricker model.Conclusions: We present a fast and robust scheme to classify community structure and to assess the prevalence of interactions directly from microbial time series data. The procedure not only serves to determine ecological drivers of microbial dynamics, but also to guide selection of appropriate community models for prediction and follow-up analysis

The Microbiome Stress Project: Toward a Global Meta-Analysis of Environmental Stressors and Their Effects on Microbial Communities

Microbial community structure is highly sensitive to natural (e.g., drought, temperature, fire) and anthropogenic (e.g., heavy metal exposure, land-use change) stressors. However, despite an immense amount of data generated, systematic, cross-environment analyses of microbiome responses to multiple disturbances are lacking. Here, we present the Microbiome Stress Project, an open-access database of environmental and host-associated 16S rRNA amplicon sequencing studies collected to facilitate cross-study analyses of microbiome responses to stressors. This database will comprise published and unpublished datasets re-processed from the raw sequences into exact sequence variants using our standardized computational pipeline. Our database will provide insight into general response patterns of microbiome diversity, structure, and stability to environmental stressors. It will also enable the identification of cross-study associations between single or multiple stressors and specific microbial clades. Here, we present a proof-of-concept meta-analysis of 606 microbiomes (from nine studies) to assess microbial community responses to: (1) one stressor in one environment: soil warming across a variety of soil types, (2) a range of stressors in one environment: soil microbiome responses to a comprehensive set of stressors (incl. temperature, diesel, antibiotics, land use change, drought, and heavy metals), (3) one stressor across a range of environments: copper exposure effects on soil, sediment, activated-sludge reactors, and gut environments, and (4) the general trends of microbiome stressor responses. Overall, we found that stressor exposure significantly decreases microbiome alpha diversity and increases beta diversity (community dispersion) across a range of environments and stressor types. We observed a hump-shaped relationship between microbial community resistance to stressors (i.e., the average pairwise similarity score between the control and stressed communities) and alpha diversity. We used Phylofactor to identify microbial clades and individual taxa as potential bioindicators of copper contamination across different environments. Using standardized computational and statistical methods, the Microbiome Stress Project will leverage thousands of existing datasets to build a general framework for how microbial communities respond to environmental stress

Small but crucial : the novel small heat shock protein Hsp21 mediates stress adaptation and virulence in Candida albicans

Peer reviewedPublisher PD

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins.AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains

Identification of a small molecule yeast TORC1 inhibitor with a flow cytometry-based multiplex screen

TOR (target of rapamycin) is a serine/threonine kinase, evolutionarily conserved from yeast to human, which functions as a fundamental controller of cell growth. The moderate clinical benefit of rapamycin in mTOR-based therapy of many cancers favors the development of new TOR inhibitors. Here we report a high throughput flow cytometry multiplexed screen using five GFPtagged yeast clones that represent the readouts of four branches of the TORC1 signaling pathway in budding yeast. Each GFP-tagged clone was differentially color-coded and the GFP signal of each clone was measured simultaneously by flow cytometry, which allows rapid prioritization of compounds that likely act through direct modulation of TORC1 or proximal signaling components. A total of 255 compounds were confirmed in dose-response analysis to alter GFP expression in one or more clones. To validate the concept of the high throughput screen, we have characterized CID 3528206, a small molecule most likely to act on TORC1 as it alters GFP expression in all five GFP clones in an analogous manner to rapamycin. We have shown that CID 3528206 inhibited yeast cell growth, and that CID 3528206 inhibited TORC1 activity both in vitro and in vivo with EC50s of 150 nM and 3.9 μM, respectively. The results of microarray analysis and yeast GFP collection screen further support the notion that CID 3528206 and rapamycin modulate similar cellular pathways. Together, these results indicate that the HTS has identified a potentially useful small molecule for further development of TOR inhibitors

Nutrient Control of Yeast PKA Activity Involves Opposing Effects on Phosphorylation of the Bcy1 Regulatory Subunit

Kelch repeat proteins Gpb1 and Gpb2 control yeast PKA activity in response to nutrients by stimulating phosphorylation of the Bcy1 regulatory subunit. Gpb1 and Gpb2 function by blocking inhibition of Bcy1 phosphorylation by PKA catalytic subunits. Phosphorylated Bcy1 is more stable and is a more effective inhibitor of PKA activity

Multivariate curve resolution of time course microarray data

BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements