Tremacron hongheense,a New Species of Gesneriaceaefrom Southeastern Yunnan,China

A new species of Gesneriaceae from Honghe County, Southeastern Yunnan, China, Tremacron hongheense WH. Chen & YM. Shui, is described and illustrated. The new species is similar to Tremacron forrestii Craib, but differs by its leaf blade adaxially sparsely long setose (vs. densely white appressed pubescent and sparsely rusty brown villous), corolla tube outside short white glandular (vs. nearly glabrous), corolla lobes red and thickening at apex, especially adaxial lip (vs. yellow and not thickening), stamens 16-18cm long (vs. 04-12cm long), staminode 05-14cm long (vs. 02-04cm long).     

Effects of grazing on plant species diversity and carbon partitioning in semiarid rangelands of northeastern China

Grasslands are one of the most widespread landscapes worldwide, covering approximately one-fifth of the world’s land surface, where grazing is a common practice. How carbon storage responds to grazing in steppes remains poorly understood. We quantified the effects of grazing on community composition and species diversity, and carbon storage in two typical grasslands of northeastern China, one in Horqin and the other one in Hulunbeier. In both grasslands, grazing did not influence plant species diversity. However, it substantially decreased aboveground carbon by 31% and 54% in Horqin and Hulunbeier, respectively. Fenced and grazing treatments showed a similar belowground carbon at both locations. The predominant carbon pool in the study grassland ecosystem was found in the upper 100 cm soil depth, from 98.2 to 99.1% of the total carbon storage. There were no significant effects of grazing on soil carbon neither in the whole profile nor in the uppermost 20 cm soil depth in the two study grasslands. Studies on the effects of varying rangeland management, such as region disparity and grazing systems, may have important consequences on species diversity and carbon partitioning, and thus on rangeland stability and ecosystem functioning.     

Peak plasma interleukin-6 and other peripheral markers of inflammation in the first week of ischaemic stroke correlate with brain infarct volume, stroke severity and long-term outcome

Background

Cerebral ischaemia initiates an inflammatory response in the brain and periphery. We assessed the relationship between peak values of plasma interleukin-6 (IL-6) in the first week after ischaemic stroke, with measures of stroke severity and outcome.

Methods

Thirty-seven patients with ischaemic stroke were prospectively recruited. Plasma IL-6, and other markers of peripheral inflammation, were measured at pre-determined timepoints in the first week after stroke onset. Primary analyses were the association between peak plasma IL-6 concentration with both modified Rankin score (mRS) at 3 months and computed tomography (CT) brain infarct volume.

Results

Peak plasma IL-6 concentration correlated significantly (p < 0.001) with CT brain infarct volume (r = 0.75) and mRS at 3 months (r = 0.72). It correlated similarly with clinical outcome at 12 months or stroke severity. Strong associations were also noted between either peak plasma C-reactive protein (CRP) concentration or white blood cell (WBC) count, and all outcome measures.

Conclusions

These data provide evidence that the magnitude of the peripheral inflammatory response is related to the severity of acute ischaemic stroke, and clinical outcome.     

Carbon sequestration in Synechococcus Sp.: from molecular machines to hierarchical modeling

The U.S. Department of Energy recently announced the first five grants for the Genomes to Life (GTL) Program. The goal of this program is to "achieve the most far-reaching of all biological goals: a fundamental, comprehensive, and systematic understanding of life." While more information about the program can be found at the GTL website (www.doegenomestolife.org), this paper provides an overview of one of the five GTL projects funded, "Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling." This project is a combined experimental and computational effort emphasizing developing, prototyping, and applying new computational tools and methods to elucidate the biochemical mechanisms of the carbon sequestration of Synechococcus Sp., an abundant marine cyanobacteria known to play an important role in the global carbon cycle. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO(2) are important terms in the global environmental response to anthropogenic atmospheric inputs of CO(2) and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. The project includes five subprojects: an experimental investigation, three computational biology efforts, and a fifth which deals with addressing computational infrastructure challenges of relevance to this project and the Genomes to Life program as a whole. Our experimental effort is designed to provide biology and data to drive the computational efforts and includes significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Our computational efforts include coupling molecular simulation methods with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes and developing a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. Furthermore, given that the ultimate goal of this effort is to develop a systems-level of understanding of how the Synechococcus genome affects carbon fixation at the global scale, we will develop and apply a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, because the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats, we have also established a companion computational infrastructure to support this effort as well as the Genomes to Life program as a whole.     

Glutathione regulates transforming growth factor-beta-stimulated collagen production in fibroblasts

Transforming growth factor-beta (TGF-beta) is a potent fibrogenic cytokine. The molecular mechanism underlying TGF-beta fibrogenesis, however, has not been completely elucidated. In this study, we showed that TGF beta decreased the intracellular GSH content in murine embryo fibroblasts (NIH 3T3), which was followed by an increase in collagen I mRNA content and collagen protein production. Prevention of GSH depletion with N-acetylcysteine (NAC), GSH, or GSH ester abrogated TGF-beta-stimulated collagen production, whereas a decrease in intracellular GSH content with L-buthionine-S,R-sulfoximine, an inhibitor of de novo GSH synthesis, enhanced TGF-beta-stimulated collagen production. These results suggest that GSH depletion induced by TGF-beta may mediate TGF-beta-stimulated collagen production. In addition, we showed that TGF-beta stimulated superoxide production and increased release of H2O2 from the cells, whereas GSH ester decreased basal and TGF-beta + glucose oxidase-stimulated H2O2 release. H2O2, exogenously added or continuously generated by glucose oxidase, enhanced TGF-beta-stimulated collagen production, whereas suppression of superoxide production by diphenyliodonium, an NAD(P)H oxidase inhibitor, blocked TGF-beta-stimulated collagen production. These data further suggest that reactive oxygen species are involved in TGF-beta-stimulated collagen production and that the effect of GSH depletion on TGF-beta-stimulated collagen production may be mediated by facilitating reactive oxygen species signaling.     

A computational method for assessing peptide- identification reliability in tandem mass spectrometry analysis with SEQUEST

High-throughput protein identification in mass spectrometry is predominantly achieved by first identifying tryptic peptides by a database search and then by combining the peptide hits for protein identification. One of the popular tools used for the database search is SEQUEST. Peptide identification is carried out by selecting SEQUEST hits above a specified threshold, the value of which is typically chosen empirically in an attempt to separate true identifications from false ones. These SEQUEST scores are not normalized with respect to the composition, length and other parameters of the peptides. Furthermore, there is no rigorous reliability estimate assigned to the protein identifications derived from these scores. Hence, the interpretation of SEQUEST hits generally requires human involvement, making it difficult to scale up the identification process for genome-scale applications. To overcome these limitations, we have developed a method, which combines a neural network and a statistical model, for normalizing SEQUEST scores, and also for providing a reliability estimate for each SEQUEST hit. This method improves the sensitivity and specificity of peptide identification compared to the standard filtering procedure used in the SEQUEST package, and provides a basis for estimating the reliability of protein identifications.     

1H NMR metabolomics reveals increased glutaminolysis upon overexpression of NSD3s or Pdp3 in Saccharomyces cerevisiae

NSD3s, the proline-tryptophan-tryptophan-proline (PWWP) domain-containing, short isoform of the human oncoprotein NSD3, displays high transforming properties. Overexpression of human NSD3s or the yeast protein Pdp3 in Saccharomyces cerevisiae induces similar metabolic changes, including increased growth rate and sensitivity to oxidative stress, accompanied by decreased oxygen consumption. Here, we set out to elucidate the biochemical pathways leading to the observed metabolic phenotype by analyzing the alterations in yeast metabolome in response to NSD3s or Pdp3 overexpression using ¹H nuclear magnetic resonance (NMR) metabolomics. We observed an increase in aspartate and alanine, together with a decrease in arginine levels, on overexpression of NSD3s or Pdp3, suggesting an increase in the rate of glutaminolysis. In addition, certain metabolites, including glutamate, valine, and phosphocholine were either NSD3s or Pdp3 specific, indicating that additional metabolic pathways are adapted in a protein-dependent manner. The observation that certain metabolic pathways are differentially regulated by NSD3s and Pdp3 suggests that, despite the structural similarity between their PWWP domains, the two proteins act by unique mechanisms and may recruit different downstream signaling complexes. This study establishes for the first time a functional link between the human oncoprotein NSD3s and cancer metabolic reprogramming.