An international network to monitor the structure, composition and dynamics of Amazonian forests (RAINFOR)

The Amazon basin is likely to be increasingly affected by environmental changes: higher temperatures, changes in precipitation, CO2 fertilization and habitat fragmentation. To examine the important ecological and biogeochemical consequences of these changes, we are developing an international network, RAINFOR, which aims to monitor forest biomass and dynamics across Amazonia in a co-ordinated fashion in order to understand their relationship to soil and climate. The network will focus on sample plots established by independent researchers, some providing data extending back several decades. We will also conduct rapid transect studies of poorly monitored regions. Field expeditions analysed local soil and plant properties in the first phase (2001–2002). Initial results suggest that the network has the potential to reveal much information on the continental-scale relations between forest and environment. The network will also serve as a forum for discussion between researchers, with the aim of standardising sampling techniques and methodologies that will enable Amazonian forests to be monitored in a coherent manner in the coming decades

Murine model for Fusarium oxysporum invasive fusariosis reveals organ-specific structures for dissemination and long-term persistence

Peer reviewedPublisher PD

Transcriptional profiling reveals the expression of novel genes in response to various stimuli in the human dermatophyte Trichophyton rubrum

<p>Abstract</p> <p>Background</p> <p>Cutaneous mycoses are common human infections among healthy and immunocompromised hosts, and the anthropophilic fungus <it>Trichophyton rubrum </it>is the most prevalent microorganism isolated from such clinical cases worldwide. The aim of this study was to determine the transcriptional profile of <it>T. rubrum </it>exposed to various stimuli in order to obtain insights into the responses of this pathogen to different environmental challenges. Therefore, we generated an expressed sequence tag (EST) collection by constructing one cDNA library and nine suppression subtractive hybridization libraries.</p> <p>Results</p> <p>The 1388 unigenes identified in this study were functionally classified based on the Munich Information Center for Protein Sequences (MIPS) categories. The identified proteins were involved in transcriptional regulation, cellular defense and stress, protein degradation, signaling, transport, and secretion, among other functions. Analysis of these unigenes revealed 575 <it>T. rubrum </it>sequences that had not been previously deposited in public databases.</p> <p>Conclusion</p> <p>In this study, we identified novel <it>T. rubrum </it>genes that will be useful for ORF prediction in genome sequencing and facilitating functional genome analysis. Annotation of these expressed genes revealed metabolic adaptations of <it>T. rubrum </it>to carbon sources, ambient pH shifts, and various antifungal drugs used in medical practice. Furthermore, challenging <it>T. rubrum </it>with cytotoxic drugs and ambient pH shifts extended our understanding of the molecular events possibly involved in the infectious process and resistance to antifungal drugs.</p

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c