RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5–3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson–Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.     

Artificial leaf aids analysis of chlorophyll fluorescence and P700 absorbance in studies involving microalgae

Measuring chlorophyll fluorescence and P700 absorbance has been widely used to study photosynthesis in both terrestrial plants and algae. However, in order to apply these measurement techniques to study microalgae, a concentrated suspension of algae, which is usually prepared by centrifugation, is required. In this study, instead of using centrifugation, we concentrated microalgae on a nitrocellulose membrane using filtration to create an ‘artificial leaf’ before analysis. Overall, we were able to generate values of the appropriate photosynthetic parameters that were comparable to those obtained when chlorophyll fluorescence and P700 absorbance were measured following centrifugation. There were no statistically significant differences (P > 0.05) between the artificial leaf method and the traditional cuvette method for determining chlorophyll fluorescence or P700 absorbance at appropriate chlorophyll concentrations. We were also able to reduce background noise by using a filter membrane as a carrier. Therefore, an artificial leaf has the potential to be a valuable tool for phycologists interested in studying microalgal photosynthesis by enabling them to eliminate tedious centrifugation steps. In addition, fluorometers commonly used for studying the leaves of higher plants will also be suitable for studying microalgae.     

Functional prediction of hypothetical proteins in human adenoviruses

Assigning functional information to hypothetical proteins in virus genomes is crucial for gaining insight into their proteomes. Human adenoviruses are medium sized viruses that cause a range of diseases. Their genomes possess proteins with uncharacterized function known as hypothetical proteins. Using a wide range of protein function prediction servers, functional information was obtained about these hypothetical proteins. A comparison of functional information obtained from these servers revealed that some of them produced functional information, while others provided little functional information about these human adenovirus hypothetical proteins. The PFP, ESG, PSIPRED, 3d2GO, and ProtFun servers produced the most functional information regarding these hypothetical proteins.     

叶脉网络系统的构建和系统学意义研究进展

为了解国内外叶脉网络系统的研究状况,综述了基因、激素对叶脉网络系统发育的调控机理,并剖析了叶脉的功能和系统学意义,分析了光、温度、水和外力破坏等环境因子对叶脉密度、叶脉直径等结构性状的影响。同时,综合考量植物碳投入经济权衡,阐明了叶脉网络系统是在遗传控制基础上由环境与碳投入共同调控建成。最后,对植物叶脉网络系统研究中存在的问题与未来发展方向进行了展望。     

Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design

Computational protein design (CPD) predictions are highly dependent on the structure of the input template used. However, it is unclear how small differences in template geometry translate to large differences in stability prediction accuracy. Herein, we explored how structural changes to the input template affect the outcome of stability predictions by CPD. To do this, we prepared alternate templates by Rotamer Optimization followed by energy Minimization (ROM) and used them to recapitulate the stability of 84 protein G domain β1 mutant sequences. In the ROM process, side-chain rotamers for wild-type (WT) or mutant sequences are optimized on crystal or nuclear magnetic resonance (NMR) structures prior to template minimization, resulting in alternate structures termed ROM templates. We show that use of ROM templates prepared from sequences known to be stable results predominantly in improved prediction accuracy compared to using the minimized crystal or NMR structures. Conversely, ROM templates prepared from sequences that are less stable than the WT reduce prediction accuracy by increasing the number of false positives. These observed changes in prediction outcomes are attributed to differences in side-chain contacts made by rotamers in ROM templates. Finally, we show that ROM templates prepared from sequences that are unfolded or that adopt a nonnative fold result in the selective enrichment of sequences that are also unfolded or that adopt a nonnative fold, respectively. Our results demonstrate the existence of a rotamer bias caused by the input template that can be harnessed to skew predictions toward sequences displaying desired characteristics.     

Genomic prediction of seedling root length in maize (Zea mays L.)

Genotypes with extreme phenotypes are valuable for studying ‘difficult’ quantitative traits. Genomic prediction (GP) might allow the identification of such extremes by phenotyping a training population of limited size and predicting genotypes with extreme phenotypes in large sequences of germplasm collections. We tested this approach employing seedling root traits in maize and the extensively genotyped Ames Panel. A training population made up of 384 inbred lines from the Ames Panel was phenotyped by extracting root traits from images using the software program aria . A ridge regression best linear unbiased prediction strategy was used to train a GP model. Genomic estimated breeding values for the trait ‘total root length’ (TRL) were predicted for 2431 inbred lines, which had previously been genotyped by sequencing. Selections were made for 100 extreme TRL lines and those with the predicted longest or shortest TRL were validated for TRL and other root traits. The two predicted extreme groups with regard to TRL were significantly different (P = 0.0001). The difference in predicted means for TRL between groups was 145.1 cm and 118.7 cm for observed means, which were significantly different (P = 0.001). The accuracy of predicting the rank between 1 and 200 of the validation population based on TRL (longest to shortest) was determined using a Spearman correlation to be ρ = 0.55. Taken together, our results support the idea that GP may be a useful approach for identifying the most informative genotypes in sequenced germplasm collections to facilitate experiments for quantitative inherited traits.     

Modelling the genetic architecture of flowering time control in barley through nested association mapping

Background

Barley, globally the fourth most important cereal, provides food and beverages for humans and feed for animal husbandry. Maximizing grain yield under varying climate conditions largely depends on the optimal timing of flowering. Therefore, regulation of flowering time is of extraordinary importance to meet future food and feed demands. We developed the first barley nested association mapping (NAM) population, HEB-25, by crossing 25 wild barleys with one elite barley cultivar, and used it to dissect the genetic architecture of flowering time.

Results

Upon cultivation of 1,420 lines in multi-field trials and applying a genome-wide association study, eight major quantitative trait loci (QTL) were identified as main determinants to control flowering time in barley. These QTL accounted for 64% of the cross-validated proportion of explained genotypic variance (p_G). The strongest single QTL effect corresponded to the known photoperiod response gene Ppd-H1. After sequencing the causative part of Ppd-H1, we differentiated twelve haplotypes in HEB-25, whereof the strongest exotic haplotype accelerated flowering time by 11 days compared to the elite barley haplotype. Applying a whole genome prediction model including main effects and epistatic interactions allowed predicting flowering time with an unmatched accuracy of 77% of cross-validated p_G.

Conclusions

The elaborated causal models represent a fundamental step to explain flowering time in barley. In addition, our study confirms that the exotic biodiversity present in HEB-25 is a valuable toolbox to dissect the genetic architecture of important agronomic traits and to replenish the elite barley breeding pool with favorable, trait-improving exotic alleles.

     

Specific Activation of Insulin-like Growth Factor-1 Receptor by Ginsenoside Rg5 Promotes Angiogenesis and Vasorelaxation

Ginsenoside Rg5 is a compound newly synthesized during the steaming process of ginseng; however, its biological activity has not been elucidated with regard to endothelial function. We found that Rg5 stimulated in vitro angiogenesis of human endothelial cells, consistent with increased neovascularization and blood perfusion in a mouse hind limb ischemia model. Rg5 also evoked vasorelaxation in aortic rings isolated from wild type and high cholesterol-fed ApoE^−/− mice but not from endothelial nitric-oxide synthase (eNOS) knock-out mice. Angiogenic activity of Rg5 was highly associated with a specific increase in insulin-like growth factor-1 receptor (IGF-1R) phosphorylation and subsequent activation of multiple angiogenic signals, including ERK, FAK, Akt/eNOS/NO, and G_i-mediated phospholipase C/Ca²⁺/eNOS dimerization pathways. The vasodilative activity of Rg5 was mediated by the eNOS/NO/cGMP axis. IGF-1R knockdown suppressed Rg5-induced angiogenesis and vasorelaxation by inhibiting key angiogenic signaling and NO/cGMP pathways. In silico docking analysis showed that Rg5 bound with high affinity to IGF-1R at the same binding site of IGF. Rg5 blocked binding of IGF-1 to its receptor with an IC₅₀ of ∼90 nmol/liter. However, Rg5 did not induce vascular inflammation and permeability. These data suggest that Rg5 plays a novel role as an IGF-1R agonist, promoting therapeutic angiogenesis and improving hypertension without adverse effects in the vasculature.