Background and Aims

The ability to simulate plant competition accurately is essential for plant functional type (PFT)-based models used in climate-change studies, yet gaps and uncertainties remain in our understanding of the details of the competition mechanisms and in ecosystem responses at a landscape level. This study examines secondary succession in a temperate deciduous forest in eastern China with the aim of determining if competition between tree types can be explained by differences in leaf ecophysiological traits and growth allometry, and whether ecophysiological traits and habitat spatial configurations among PFTs differentiate their responses to climate change.

Methods

A temperate deciduous broadleaved forest in eastern China was studied, containing two major vegetation types dominated by Quercus liaotungensis (OAK) and by birch/poplar (Betula platyphylla and Populus davidiana; BIP), respectively. The Terrestrial Ecosystem Simulator (TESim) suite of models was used to examine carbon and water dynamics using parameters measured at the site, and the model was evaluated against long-term data collected at the site.

Key Results

Simulations indicated that a higher assimilation rate for the BIP vegetation than OAK led to the former's dominance during early successional stages with relatively low competition. In middle/late succession with intensive competition for below-ground resources, BIP, with its lower drought tolerance/resistance and smaller allocation to leaves/roots, gave way to OAK. At landscape scale, predictions with increased temperature extrapolated from existing weather records resulted in increased average net primary productivity (NPP; +19 %), heterotrophic respiration (+23 %) and net ecosystem carbon balance (+17 %). The BIP vegetation in higher and cooler habitats showed 14 % greater sensitivity to increased temperature than the OAK at lower and warmer locations.

Conclusions

Drought tolerance/resistance and morphology-related allocation strategy (i.e. more allocation to leaves/roots) played key roles in the competition between the vegetation types. The overall site-average impacts of increased temperature on NPP and carbon stored in plants were found to be positive, despite negative effects of increased respiration and soil water stress, with such impacts being more significant for BIP located in higher and cooler habitats.

PLIN4 is a member of the PAT family of lipid storage droplet (LSD) proteins. Associations between seven single nucleotide polymorphisms (SNPs) at human PLIN4 with obesity related phenotypes were investigated using meta-analysis followed by a determination if these phenotypes are modulated by interactions between PLIN4 SNPs and dietary PUFA. Samples consisted of subjects from two populations of European ancestry. We demonstrated association of rs8887 with anthropometrics. Meta-analysis demonstrated significant interactions between the rs8887 minor allele with PUFA n3 modulating anthropometrics. rs884164 showed interaction with both n3 and n6 PUFA modulating anthropometric and lipid phenotypes. In silico analysis of the PLIN4 3′UTR sequence surrounding the rs8887 minor A allele predicted a seed site for the human microRNA-522 (miR-522), suggesting a functional mechanism. Our data showed that a PLIN4 3′UTR luciferase reporter carrying the A allele of rs8887 was reduced in response to miR-522 mimics compared to the G allele. These results suggest variation at the PLIN4 locus, and its interaction with PUFA as a modulator of obesity related phenotypes, acts in part through creation of a miR-522 regulatory site.

FLIGHT (http://flight.icr.ac.uk/) is an online resource compiling data from high-throughput Drosophila in vivo and in vitro RNAi screens. FLIGHT includes details of RNAi reagents and their predicted off-target effects, alongside RNAi screen hits, scores and phenotypes, including images from high-content screens. The latest release of FLIGHT is designed to enable users to upload, analyze, integrate and share their own RNAi screens. Users can perform multiple normalizations, view quality control plots, detect and assign screen hits and compare hits from multiple screens using a variety of methods including hierarchical clustering. FLIGHT integrates RNAi screen data with microarray gene expression as well as genomic annotations and genetic/physical interaction datasets to provide a single interface for RNAi screen analysis and datamining in Drosophila.

The title mol­ecule, C12H14ClN2O3PS, has a cis configuration with respect to the C=N bond. Inter­molecular C—H⋯O inter­actions inter­connect the mol­ecules into chains along the c axis. The chains are further connected into a two-dimensional network parallel to the bc plane by weak π–π inter­actions between adjacent aromatic rings (centroid–centroid distance = 3.772Å).

Members of the protein kinase family are amongst the most commonly mutated genes in human cancer, and both mutated and activated protein kinases have proved to be tractable targets for the development of new anticancer therapies The MoKCa database (Mutations of Kinases in Cancer, http://strubiol.icr.ac.uk/extra/mokca) has been developed to structurally and functionally annotate, and where possible predict, the phenotypic consequences of mutations in protein kinases implicated in cancer. Somatic mutation data from tumours and tumour cell lines have been mapped onto the crystal structures of the affected protein domains. Positions of the mutated amino-acids are highlighted on a sequence-based domain pictogram, as well as a 3D-image of the protein structure, and in a molecular graphics package, integrated for interactive viewing. The data associated with each mutation is presented in the Web interface, along with expert annotation of the detailed molecular functional implications of the mutation. Proteins are linked to functional annotation resources and are annotated with structural and functional features such as domains and phosphorylation sites. MoKCa aims to provide assessments available from multiple sources and algorithms for each potential cancer-associated mutation, and present these together in a consistent and coherent fashion to facilitate authoritative annotation by cancer biologists and structural biologists, directly involved in the generation and analysis of new mutational data.

pSTIING () is a new publicly accessible web-based application and knowledgebase featuring 65 228 distinct molecular associations (comprising protein–protein, protein–lipid, protein–small molecule interactions and transcriptional regulatory associations), ligand–receptor–cell type information and signal transduction modules. It has a particular major focus on regulatory networks relevant to chronic inflammation, cell migration and cancer. The web application and interface provide graphical representations of networks allowing users to combine and extend transcriptional regulatory and signalling modules, infer molecular interactions across species and explore networks via protein domains/motifs, gene ontology annotations and human diseases. pSTIING also supports the direct cross-correlation of experimental results with interaction information in the knowledgebase via the CLADIST tool associated with pSTIING, which currently analyses and clusters gene expression, proteomic and phenotypic datasets. This allows the contextual projection of co-expression patterns onto prior network information, facilitating the identification of functional modules in physiologically relevant systems.

FLIGHT () is a new database designed to help researchers browse and cross-correlate data from large-scale RNAi studies. To date, the majority of these functional genomic screens have been carried out using Drosophila cell lines. These RNAi screens follow 100 years of classical Drosophila genetics, but have already revealed their potential by ascribing an impressive number of functions to known and novel genes. This has in turn given rise to a pressing need for tools to simplify the analysis of the large amount of phenotypic information generated. FLIGHT aims to do this by providing users with a gene-centric view of screen results and by making it possible to cluster phenotypic data to identify genes with related functions. Additionally, FLIGHT provides microarray expression data for many of the Drosophila cell lines commonly used in RNAi screens. This, together with information about cell lines, protocols and dsRNA primer sequences, is intended to help researchers design their own cell-based screens. Finally, although the current focus of FLIGHT is Drosophila, the database has been designed to facilitate the comparison of functional data across species and to help researchers working with other systems navigate their way through the fly genome.