Transfer aleurone cells inSetaria lutescens (Gramineae)

Summary Typical aleurone cells occur around the periphery of the caryopsis. These cells are tabular with moderately thick walls and lack cell wall ingrowths. Transfer aleurone cells only occur adjacent to the placental vascular bundle, which supplies the developing embryo and endosperm. These specialized aleurone cells are approximately columnar, with thick walls bearing ingrowths on the outer radial and outer tangential walls. The wall ingrowths of transfer aleurone cells appear similar to those of transfer cells previously described and quite likely also function in short-distance transport of substances.Journal paper No. J-6737 of the Iowa Agricultural and Home Economics Experiment Station, Ames, Iowa. Project No. 1685.     

Using genetic algorithms to select most predictive protein features

Many important characteristics of proteins such as biochemical activity and subcellular localization present a challenge to machine-learning methods: it is often difficult to encode the appropriate input features at the residue level for the purpose of making a prediction for the entire protein. The problem is usually that the biophysics of the connection between a machine-learning method's input (sequence feature) and its output (observed phenomenon to be predicted) remains unknown; in other words, we may only know that a certain protein is an enzyme (output) without knowing which region may contain the active site residues (input). The goal then becomes to dissect a protein into a vast set of sequence-derived features and to correlate those features with the desired output. We introduce a framework that begins with a set of global sequence features and then vastly expands the feature space by generically encoding the coexistence of residue-based features. It is this combination of individual features, that is the step from the fractions of serine and buried (input space 20 + 2) to the fraction of buried serine (input space 20 * 2) that implicitly shifts the search space from global feature inputs to features that can capture very local evidence such as a the individual residues of a catalytic triad. The vast feature space created is explored by a genetic algorithm (GA) paired with neural networks and support vector machines. We find that the GA is critical for selecting combinations of features that are neither too general resulting in poor performance, nor too specific, leading to overtraining. The final framework manages to effectively sample a feature space that is far too large for exhaustive enumeration. We demonstrate the power of the concept by applying it to prediction of protein enzymatic activity.     

Cell cycle kinases predicted from conserved biophysical properties

Machine-learning techniques can classify functionally related proteins where homology-transfer as well as sequence and structure motifs fail. Here, we present a method that aimed at complementing homology-transfer in the identification of cell cycle control kinases from sequence alone. First, we identified functionally significant residues in cell cycle proteins through their high sequence conservation and biophysical properties. We then incorporated these residues and their features into support vector machines (SVM) to identify new kinases and more specifically to differentiate cell cycle kinases from other kinases and other proteins. As expected, the most informative residues tend to be highly conserved and tend to localize in the ATP binding regions of the kinases. Another observation confirmed that ATP binding regions are typically not found on the surface but in partially buried sites, and that this fact is correctly captured by accessibility predictions. Using these highly conserved, semi-buried residues and their biophysical properties, we could distinguish cell cycle S/T kinases from other kinase families at levels around 70-80% accuracy and 62-81% coverage. An application to the entire human proteome predicted at least 97 human proteins with limited previous annotations to be candidates for cell cycle kinases.     

Insects had it first: surfactants as a defence against predators

Insects have evolved an astonishing array of defences to ward off enemies. Well known and widespread is the regurgitation of oral secretion (OS), fluid that repels attacking predators. In herbivores, the effectiveness of OS has been ascribed so far to the presence of deterrent secondary metabolites sequestered from the host plant. This notion implies, however, that generalists experience less protection on plants with low amounts of secondary metabolites or with compounds ineffective against potential enemies. Resolving the dilemma, we describe a novel defence mechanism that is independent of deterrents as it relies on the intrinsic detergent properties of the OS. The OS of Spodoptera exigua (and other species) was found to be highly amphiphilic and well capable of wetting the hydrophobic cuticle of predatory ants. As a result, affected ants stopped attacking and engaged in extensive cleansing. The presence of surfactants was sufficient to explain the defensive character of herbivore OS. We hypothesize that detergency is a common but unrecognized mode of defence, which provides a base level of protection that may or may not be further enhanced by plant-derived deterrents. Our study also proves that insects 'invented' the use of defensive surfactants long before modern agriculture had started applying them as insecticides.     

Inheritance of fertility in broiler chickens

Background

The fertility of a chicken''s egg is a trait which depends on both the hen that lays the egg and on her mate. It is also known that fertility of an individual changes over the laying period.

Methods

Longitudinal models including both random genetic and permanent environmental effects of both the female and her male mate were used to model the proportion of fertile eggs in a pedigree broiler population over the ages 29-54 weeks.

Results

Both the male and the female contribute to variation in fertility. Estimates of heritability of weekly records were typically 7% for female and 10% for male contributions to fertility. Repeatability estimates ranged from 24 to 33%, respectively. The estimated genetic variance remained almost constant for both sexes over the laying period and the genetic correlations between different ages were close to 1.0. The permanent environment components increased substantially towards the end of the analyzed period, and correlations between permanent environment effects at different ages declined with increasing age difference The heritability of mean fertility over the whole laying period was estimated at 13% for females and 17% for males. A small positive correlation between genetic effects for male and female fertility was found.

Conclusion

Opportunities to improve fertility in broiler stocks by selection on both sexes exist and should have an impact throughout the laying period.     

Structural genomics is the largest contributor of novel structural leverage

The Protein Structural Initiative (PSI) at the US National Institutes of Health (NIH) is funding four large-scale centers for structural genomics (SG). These centers systematically target many large families without structural coverage, as well as very large families with inadequate structural coverage. Here, we report a few simple metrics that demonstrate how successfully these efforts optimize structural coverage: while the PSI-2 (2005-now) contributed more than 8% of all structures deposited into the PDB, it contributed over 20% of all novel structures (i.e. structures for protein sequences with no structural representative in the PDB on the date of deposition). The structural coverage of the protein universe represented by today’s UniProt (v12.8) has increased linearly from 1992 to 2008; structural genomics has contributed significantly to the maintenance of this growth rate. Success in increasing novel leverage (defined in Liu et al. in Nat Biotechnol 25:849–851, 2007) has resulted from systematic targeting of large families. PSI’s per structure contribution to novel leverage was over 4-fold higher than that for non-PSI structural biology efforts during the past 8 years. If the success of the PSI continues, it may just take another ~15 years to cover most sequences in the current UniProt database.