Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways

Background

Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways.

Results

We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks.

Conclusion

The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.

     

Reproductive Allocation in Three Macrophyte Species from Different Lakes with Variable Eutrophic Conditions

Reproductive allocation is a key process in the plant life cycle and aquatic plants exhibit great diversity in their reproductive systems. In the present study, we conduct a field investigation of three aquatic macrophytes: Stuckenia pectinata, Myriophyllum spicatum, and Potamogeton perfoliatus. Our results showed that widespread species, including S. pectinata and M. spicatum had greater plasticity in their allocation patterns in the form of increased sexual and asexual reproduction, and greater potential to set seeds and increase fitness in more eutrophic environments. P. perfoliatus also exhibited a capacity to adopt varied sexual reproductive strategies such as setting more offspring for the future, although only in clear conditions with low nutrient levels. Our results establish strategies and mechanisms of some species for tolerating and surviving in varied eutrophic lake conditions.     

Over-expression of miR166a inhibits cotyledon formation in somatic embryos and promotes lateral root development in seedlings of Larix leptolepis

Somatic embryo (SE) regeneration is an ideal experimental system to realize rapid propagation of excellent clones and genetic improvement for perennial gymnosperms. In the present study, genes encoding the miRNA166 precursor were identified and LamiR166a was successfully transformed into the gymnosperm Larix leptolepis (L. leptolepis) and five LamiR166a over-expressed embryonic cell lines were screened out as stable embryo masses. As expected, the targets of miR166a, LaHDZ31-34, were all down-regulated in transgenic lines according to qRT-PCR results. The results showed that the percentage of normal SEs with 4–7 cotyledons was 77.0?% in wild type (WT) lines, but was reduced to 60.3?% in the pSuper::MIR166a lines with “cup-shaped” embryos comprised 7.0?% of WT and 20.7?% of transgenic embryos. Microscopic observation further showed that the intermediate region surrounded by the cotyledons was larger than in the control, with no upward bulge of the shoot apical meristem (SAM). The expression pattern of the two meristem marker genes CLAVATA (CLV) and WUSCHEL-related homeobox (WOX) were investigated. The results showed that the expression levels of WOX were three times higher in transgenic lines than in WT samples, which suggest that miR166a may indirectly regulate SAM development by directly affecting WOX expression. Besides, overexpression of LamiR166a clearly increased the rooting rate and promoted lateral root formation in L. leptolepis seedlings. These results may provide new insights into the regulatory role of miR166 in gymnosperms, and also new applications for forestry production in practice.     

Functional role of metalloproteins in genome stability

Cells contain a large number of metalloproteins that commonly harbor at least one metal ion cofactor. In metalloproteins, metal ions are usually coordinated by oxygen, sulfur, or nitrogen centers belonging to amino acid residues in the protein. The presence of the metal ion in metalloproteins allows them to take part in diverse biological processes, such as genome stability, metabolic catalysis, and cell cycle progression. Clinically, alteration of the function of metalloproteins in mammals is genetically associated with diseases characterized by DNA damage and repair defects. The present review focuses on the current perspectives of metal ion homeostasis in different organisms and summarizes the most recent understanding on magnesium, copper, iron, and manganese-containing proteins and their functional involvement in the maintenance of genome stability.     

Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.