A metabolome‐based core hybridisation strategy for the prediction of rice grain weight across environments

Marker‐based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F₁ hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted (r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 (r = 0.72). Our study demonstrates that the TGW of F₁ hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate‐adaptive varieties.     

microRNA-222 Attenuates Mitochondrial Dysfunction During Transmissible Gastroenteritis Virus Infection

1. Download : Download high-res image (93KB)
2. Download : Download full-size image

Highlights

• •microRNA-222 attenuates TGEV-induced mitochondrial dysfunction.
• •microRNA-222 downregulates THBS1 and CD47.
• •THBS1 is the target of microRNA-222 during TGEV infection.
• •THBS1 and CD47 increase mitochondrial Ca²⁺ level and reduced mitochondrial membrane potential (MMP).

     

Quantitative Proteomics of the Mitotic Chromosome Scaffold Reveals the Association of BAZ1B with Chromosomal Axes,

1. Download : Download high-res image (93KB)
2. Download : Download full-size image

Highlights

• •Quantitative proteomics of mitotic chromosome scaffold isolated from chicken DT40 cells.
• •BAZ1B identified in the isolated mitotic chromosome scaffold localizes to mitotic chromosome axes.
• •BAZ1B knockout caused prophase delay because of altered chromosome condensation timing and impaired mitosis progression.
• •BAZ1B knockout did not affect prometaphase chromosome structure.

     

MotifAnalyzer‐PDZ: A computational program to investigate the evolution of PDZ‐binding target specificity

Recognition of short linear motifs (SLiMs) or peptides by proteins is an important component of many cellular processes. However, due to limited and degenerate binding motifs, prediction of cellular targets is challenging. In addition, many of these interactions are transient and of relatively low affinity. Here, we focus on one of the largest families of SLiM‐binding domains in the human proteome, the PDZ domain. These domains bind the extreme C‐terminus of target proteins, and are involved in many signaling and trafficking pathways. To predict endogenous targets of PDZ domains, we developed MotifAnalyzer‐PDZ, a program that filters and compares all motif‐satisfying sequences in any publicly available proteome. This approach enables us to determine possible PDZ binding targets in humans and other organisms. Using this program, we predicted and biochemically tested novel human PDZ targets by looking for strong sequence conservation in evolution. We also identified three C‐terminal sequences in choanoflagellates that bind a choanoflagellate PDZ domain, the Monsiga brevicollis SHANK1 PDZ domain (mbSHANK1), with endogenously‐relevant affinities, despite a lack of conservation with the targets of a homologous human PDZ domain, SHANK1. All three are predicted to be signaling proteins, with strong sequence homology to cytosolic and receptor tyrosine kinases. Finally, we analyzed and compared the positional amino acid enrichments in PDZ motif‐satisfying sequences from over a dozen organisms. Overall, MotifAnalyzer‐PDZ is a versatile program to investigate potential PDZ interactions. This proof‐of‐concept work is poised to enable similar types of analyses for other SLiM‐binding domains (e.g., MotifAnalyzer‐Kinase). MotifAnalyzer‐PDZ is available at http://motifAnalyzerPDZ.cs.wwu.edu .     

Helianthus maximiliani and species fine‐scale spatial pattern affect diversity interactions in reconstructed tallgrass prairies

相似文献   

Computational methods in protein structure prediction

This review presents the advances in protein structure prediction from the computational methods perspective. The approaches are classified into four major categories: comparative modeling, fold recognition, first principles methods that employ database information, and first principles methods without database information. Important advances along with current limitations and challenges are presented.     

Urbanization and Mismatch with Protected Areas Place the Conservation of a Threatened Species at Risk

In this work, we report the predicted distribution of the threatened fluminense swallowtail butterfly, Parides ascanius (Cramer 1775), and correlate it to the presence of urban and protected areas within its range. The distribution was modeled using a genetic algorithm. The predicted distribution of the fluminense swallowtail shows high agreement within Rio de Janeiro state, in a near-continuous strip of 2,038,253 ha along the coastal lowlands, 17.8 percent of which is within urban areas. Only 8.7 percent (178,187 ha) of the remaining (nonurban) predicted model overlapped at least partially with protected areas (19 in all). Almost half of these protected areas also overlapped with urban areas, resulting in an additional loss of 58,751 ha. In seven of 19 protected areas, the distribution of P. ascanius was predicted by less than 50 percent of the models; five of the remaining protected areas are less restrictive reserves. Despite the wide distribution predicted by the models, only two of the observed occurrence points matched the predicted distribution within protected areas. Modeling threatened species distribution is a useful tool for highlighting gaps in networks of protected areas and should aid in planning to fill these gaps. However, in several developing countries with high biodiversity, there is insufficient basic biological information for many threatened species. In these cases, prospecting field studies are urgently needed.     

Components of metabolic syndrome predicting diabetes: no role of inflammation or dyslipidemia

Objective: The diagnostic criteria and the clinical usefulness of the metabolic syndrome (MetSy) are currently questioned. The objective was to describe the structure of MetSy and to evaluate its components for prediction of diabetes type 2 (T2DM). Research Methods and Procedures: This was a case‐referent study nested within a population‐based health survey. Among 33,336 participants, we identified 177 initially non‐diabetic individuals who developed T2DM after 0.1 to 10.5 years (mean, 5.4 years), and, for each diabetes case, two referents matched for sex, age, and year of health survey. Baseline variables included oral glucose tolerance test, BMI, blood pressure, blood lipids, adipokines, inflammatory markers, insulin resistance, and β‐cell function. Exploratory and confirmative factor analyses were applied to hypothesize the structure of the MetSy. The prediction of T2DM by the different factors was evaluated by multivariate logistic regression analysis. Results: A hypothetical five‐factor model of intercorrelated composite factors was generated. The inflammation, dyslipidemia, and blood pressure factors were predicitive only in univariate analysis. In multivariable analyses, two factors independently and significantly predicted T2DM: an obesity/insulin resistance factor and a glycemia factor. The composite factors did not improve the prediction of T2DM compared with single variables. Among the original variables, fasting glucose, proinsulin, BMI, and blood pressure values were predictive of T2DM. Discussion: Our data support the concept of a MetSy, and we propose five separate clusters of components. The inflammation and dyslipidemia factors were not independently associated with diabetes risk. In contrast, obesity and accompanying insulin resistance and β‐cell decompensation seem to be two core perturbations promoting and predicting progression to T2DM.