Computational predictions provide insights into the biology of TAL effector target sites.

Transcription activator-like (TAL) effectors are injected into host plant cells by Xanthomonas bacteria to function as transcriptional activators for the benefit of the pathogen. The DNA binding domain of TAL effectors is composed of conserved amino acid repeat structures containing repeat-variable diresidues (RVDs) that determine DNA binding specificity. In this paper, we present TALgetter, a new approach for predicting TAL effector target sites based on a statistical model. In contrast to previous approaches, the parameters of TALgetter are estimated from training data computationally. We demonstrate that TALgetter successfully predicts known TAL effector target sites and often yields a greater number of predictions that are consistent with up-regulation in gene expression microarrays than an existing approach, Target Finder of the TALE-NT suite. We study the binding specificities estimated by TALgetter and approve that different RVDs are differently important for transcriptional activation. In subsequent studies, the predictions of TALgetter indicate a previously unreported positional preference of TAL effector target sites relative to the transcription start site. In addition, several TAL effectors are predicted to bind to the TATA-box, which might constitute one general mode of transcriptional activation by TAL effectors. Scrutinizing the predicted target sites of TALgetter, we propose several novel TAL effector virulence targets in rice and sweet orange. TAL-mediated induction of the candidates is supported by gene expression microarrays. Validity of these targets is also supported by functional analogy to known TAL effector targets, by an over-representation of TAL effector targets with similar function, or by a biological function related to pathogen infection. Hence, these predicted TAL effector virulence targets are promising candidates for studying the virulence function of TAL effectors. TALgetter is implemented as part of the open-source Java library Jstacs, and is freely available as a web-application and a command line program.

Assessing the carbon balance of circumpolar Arctic tundra using remote sensing and process modeling.

This paper reviews the current status of using remote sensing and process-based modeling approaches to assess the contemporary and future circumpolar carbon balance of Arctic tundra, including the exchange of both carbon dioxide and methane with the atmosphere. Analyses based on remote sensing approaches that use a 20-year data record of satellite data indicate that tundra is greening in the Arctic, suggesting an increase in photosynthetic activity and net primary production. Modeling studies generally simulate a small net carbon sink for the distribution of Arctic tundra, a result that is within the uncertainty range of field-based estimates of net carbon exchange. Applications of process-based approaches for scenarios of future climate change generally indicate net carbon sequestration in Arctic tundra as enhanced vegetation production exceeds simulated increases in decomposition. However, methane emissions are likely to increase dramatically, in response to rising soil temperatures, over the next century. Key uncertainties in the response of Arctic ecosystems to climate change include uncertainties in future fire regimes and uncertainties relating to changes in the soil environment. These include the response of soil decomposition and respiration to warming and deepening of the soil active layer, uncertainties in precipitation and potential soil drying, and distribution of wetlands. While there are numerous uncertainties in the projections of process-based models, they generally indicate that Arctic tundra will be a small sink for carbon over the next century and that methane emissions will increase considerably, which implies that exchange of greenhouse gases between the atmosphere and Arctic tundra ecosystems is likely to contribute to climate warming.

A plant's perspective of extremes: terrestrial plant responses to changing climatic variability.

We review observational, experimental, and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied, although potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heat-waves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational, and/or modeling studies have the potential to overcome important caveats of the respective individual approaches.