ALBERT

Beschreibung: Small differences in the sensitivity of stomatal conductance to light intensity on leaf surfaces may lead to large differences in total canopy transpiration ( E C ) with increasing canopy leaf area ( L ). Typically, the increase of L would more than compensate for the decrease of transpiration per unit of leaf area ( E L ), resulting in concurrent increase of E C . However, highly shade-intolerant species, such as Larix principis-rupprechtii Mayr., may be so sensitive to increased shading that such compensation is not complete. We hypothesized that in such a stand, windfall-induced spatial variation at a decameter scale would result in greatly reduced E L in patches of high L leading to lower E C than low competition patches of sparse canopy. We further hypothesized that quicker extraction of soil moisture in patches of lower competition will result in earlier onset of drought symptoms in these patches. Thus, patches of low L will transition from light to soil moisture as the factor dominating E L . This process should progressively homogenize E C in the stand even as the variation of soil moisture is increasing. We tested the hypotheses utilizing sap flux of nine trees, and associated environmental and stand variables. The results were consistent with only some of the expectations. Under non-limiting soil moisture, E L was very sensitive to the spatial variation of L , decreasing sharply with increasing L and associated decrease of mean light intensity on leaf surfaces. Thus, under the conditions of ample soil moisture maximum E C decreased with increasing patch-scale L . Annual E C and biomass production also decreased with L , albeit more weakly. Furthermore, variation of E C among patches decreased as average stand soil moisture declined between rain events. However, contrary to expectation, high L plots which transpired less showed a greater E L sensitivity to decreasing stand-scale soil moisture, suggesting a different mechanism than simple control by decreasing soil moisture. We offer potential explanations to the observed phenomenon. Our results demonstrate that spatial variation of L at decameter scale, even within relatively homogeneous, single-species, even-aged stands, can produce large variation of transpiration, soil moisture and biomass production and should be considered in 1-D soil–plant–atmosphere models.

Beschreibung: The association of DSIF and NELF with initiated RNA Polymerase II (Pol II) is the general mechanism for inducing promoter-proximal pausing of Pol II. However, it remains largely unclear how the paused Pol II is released in response to stimulation. Here, we show that the release of the paused Pol II is cooperatively regulated by multiple P-TEFbs which are recruited by bromodomain-containing protein Brd4 and super elongation complex (SEC) via different recruitment mechanisms. Upon stimulation, Brd4 recruits P-TEFb to Spt5/DSIF via a recruitment pathway consisting of Med1, Med23 and Tat-SF1, whereas SEC recruits P-TEFb to NELF-A and NELF-E via Paf1c and Med26, respectively. P-TEFb-mediated phosphorylation of Spt5, NELF-A and NELF-E results in the dissociation of NELF from Pol II, thereby transiting transcription from pausing to elongation. Additionally, we demonstrate that P-TEFb-mediated Ser2 phosphorylation of Pol II is dispensable for pause release. Therefore, our studies reveal a co-regulatory mechanism of Brd4 and SEC in modulating the transcriptional pause release by recruiting multiple P-TEFbs via a Mediator- and Paf1c-coordinated recruitment network.

Beschreibung: Motivation: Families with inherited diseases are widely used in Mendelian/complex disease studies. Owing to the advances in high-throughput sequencing technologies, family genome sequencing becomes more and more prevalent. Visualizing family genomes can greatly facilitate human genetics studies and personalized medicine. However, due to the complex genetic relationships and high similarities among genomes of consanguineous family members, family genomes are difficult to be visualized in traditional genome visualization framework. How to visualize the family genome variants and their functions with integrated pedigree information remains a critical challenge. Results: We developed the Family Genome Browser (FGB) to provide comprehensive analysis and visualization for family genomes. The FGB can visualize family genomes in both individual level and variant level effectively, through integrating genome data with pedigree information. Family genome analysis, including determination of parental origin of the variants, detection of de novo mutations, identification of potential recombination events and identical-by-decent segments, etc., can be performed flexibly. Diverse annotations for the family genome variants, such as dbSNP memberships, linkage disequilibriums, genes, variant effects, potential phenotypes, etc., are illustrated as well. Moreover, the FGB can automatically search de novo mutations and compound heterozygous variants for a selected individual, and guide investigators to find high-risk genes with flexible navigation options. These features enable users to investigate and understand family genomes intuitively and systematically. Availability and implementation: The FGB is available at http://mlg.hit.edu.cn/FGB/ . Contact: ydwang@hit.edu.cn .

Beschreibung: Human globin gene expression during development is modulated by transcription factors in a stage-dependent manner. However, the mechanisms controlling the process are still largely unknown. In this study, we found that a nuclear protein, LYAR (human homologue of mouse Ly-1 antibody reactive clone) directly interacted with the methyltransferase PRMT5 which triggers the histone H4 Arg3 symmetric dimethylation (H4R3me2s) mark. We found that PRMT5 binding on the proximal -promoter was LYAR-dependent. The LYAR DNA-binding motif (GGTTAT) was identified by performing CASTing (cyclic amplification and selection of targets) experiments. Results of EMSA and ChIP assays confirmed that LYAR bound to a DNA region corresponding to the 5'-untranslated region of the -globin gene. We also found that LYAR repressed human fetal globin gene expression in both K562 cells and primary human adult erythroid progenitor cells. Thus, these data indicate that LYAR acts as a novel transcription factor that binds the -globin gene, and is essential for silencing the -globin gene.

Beschreibung: Motivation: Discovering drug’s Anatomical Therapeutic Chemical (ATC) classification rules at molecular level is of vital importance to understand a vast majority of drugs action. However, few studies attempt to annotate drug’s potential ATC-codes by computational approaches. Results: Here, we introduce drug-target network to computationally predict drug’s ATC-codes and propose a novel method named NetPredATC. Starting from the assumption that drugs with similar chemical structures or target proteins share common ATC-codes, our method, NetPredATC, aims to assign drug’s potential ATC-codes by integrating chemical structures and target proteins. Specifically, we first construct a gold-standard positive dataset from drugs’ ATC-code annotation databases. Then we characterize ATC-code and drug by their similarity profiles and define kernel function to correlate them. Finally, we use a kernel method, support vector machine, to automatically predict drug’s ATC-codes. Our method was validated on four drug datasets with various target proteins, including enzymes, ion channels, G-protein couple receptors and nuclear receptors. We found that both drug’s chemical structure and target protein are predictive, and target protein information has better accuracy. Further integrating these two data sources revealed more experimentally validated ATC-codes for drugs. We extensively compared our NetPredATC with SuperPred, which is a chemical similarity-only based method. Experimental results showed that our NetPredATC outperforms SuperPred not only in predictive coverage but also in accuracy. In addition, database search and functional annotation analysis support that our novel predictions are worthy of future experimental validation. Conclusion: In conclusion, our new method, NetPredATC, can predict drug’s ATC-codes more accurately by incorporating drug-target network and integrating data, which will promote drug mechanism understanding and drug repositioning and discovery. Availability: NetPredATC is available at http://doc.aporc.org/wiki/NetPredATC . Contact: ycwang@nwipb.cas.cn or ywang@amss.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Beschreibung: The O-antigens of all Shigella flexneri serotypes, except serotype 6, share a linear tetrasaccharide repeat composed of one N -acetylglucosamine and three l -rhamnose residues, and differences between the serotypes are due to modification of various monosaccharide residues with glucosyl and/or O-acetyl and/or phosphoethanolamine (PEtN) groups. Plasmid-borne opt (formerly lpt - O ) gene encoding a PEtN transferase which modifies the O-antigens of S. flexneri serotype X, 4a and Y strains and converts the hosts into MASF IV-1 (E1037) positive "variant" (v) Xv, 4av and Yv serotypes, respectively. In this study, we showed that the opt -carrying plasmid pSFxv_2 can transform strains of all S. flexneri serotypes (1–6) to confer them with the MASF IV-1 epitope recognized by monoclonal antibody MASF IV-1 and typing antiserum IV. The transformants possessed modified O-antigens with a PEtN group(s) at position 3 of one or two rhamnose residues. In some serotypes, the PEtN modification competed or/and interfered with glucosylation and O-acetylation at the same or its neighboring sugar residue. We also showed that the plasmid pSFxv_2 is mobilizable to other S. flexneri strains by conjugation. Although pSFxv_2-harboring S. flexneri strains found in clinical infections are restricted to serotypes Xv, 4av, Yv and, possibly, 6v, our results demonstrate a high potential of dissemination of this plasmid in S. flexneri and emergence of new S. flexneri serotypes.

Beschreibung: Motivation: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein–protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel drug targets. Results: Here, we propose a machine learning method to predict PPI targets in a genomic-wide scale. Specifically, we develop a computational method, named as PrePPItar, to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs. First, we survey the databases and manually construct a gold-standard positive dataset for drug and PPI interactions. This effort leads to a dataset with 227 associations among 63 PPIs and 113 FDA-approved drugs and allows us to build models to learn the association rules from the data. Second, we characterize drugs by profiling in chemical structure, drug ATC-code annotation, and side-effect space and represent PPI similarity by a symmetrical S-kernel based on protein amino acid sequence. Then the drugs and PPIs are correlated by Kronecker product kernel. Finally, a support vector machine (SVM), is trained to predict novel associations between drugs and PPIs. We validate our PrePPItar method on the well-established gold-standard dataset by cross-validation. We find that all chemical structure, drug ATC-code, and side-effect information are predictive for PPI target. Moreover, we can increase the PPI target prediction coverage by integrating multiple data sources. Follow-up database search and pathway analysis indicate that our new predictions are worthy of future experimental validation. Conclusion: In conclusion, PrePPItar can serve as a useful tool for PPI target discovery and provides a general heterogeneous data integrative framework. Availability and implementation: PrePPItar is available at http://doc.aporc.org/wiki/PrePPItar . Contact: ycwang@nwipb.cas.cn or ywang@amss.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.