Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens

Accuracy of prediction of yet-to-be observed phenotypes for food conversion rate (FCR) in broilers was studied in a genome-assisted selection context. Data consisted of FCR measured on the progeny of 394 sires with SNP information. A Bayesian regression model (Bayes A) and a semi-parametric approach (Reproducing kernel Hilbert Spaces regression, RKHS) using all available SNPs (p = 3481) were compared with a standard linear model in which future performance was predicted using pedigree indexes in the absence of genomic data. The RKHS regression was also tested on several sets of pre-selected SNPs (p = 400) using alternative measures of the information gain provided by the SNPs. All analyses were performed using 333 genotyped sires as training set, and predictions were made on 61 birds as testing set, which were sons of sires in the training set. Accuracy of prediction was measured as the Spearman correlation (r¯S) between observed and predicted phenotype, with its confidence interval assessed through a bootstrap approach. A large improvement of genome-assisted prediction (up to an almost 4-fold increase in accuracy) was found relative to pedigree index. Bayes A and RKHS regression were equally accurate (r¯S = 0.27) when all 3481 SNPs were included in the model. However, RKHS with 400 pre-selected informative SNPs was more accurate than Bayes A with all SNPs.     

Incomplete posttranslational prohormone modifications in hyperactive neuroendocrine cells

Background  

In black-background-adapted Xenopus laevis, the intermediate pituitary melanotrope cells are hyperactive, producing large amounts of their major secretory cargo proopiomelanocortin (POMC, representing ~80% of all newly synthesised proteins), whereas in white-adapted frogs these cells are only basally active. Here we explored in the hyperactive and basally active melanotrope cells the capacity for posttranslational POMC processing events in the secretory pathway.     

GITR signaling potentiates airway hyperresponsiveness by enhancing Th2 cell activity in a mouse model of asthma

Background

Anxiety and depression are common and treatable risk factors for re-hospitalisation and death in patients with COPD. The degree of lung function impairment does not sufficiently explain anxiety and depression. The BODE index allows a functional classification of COPD beyond FEV₁. The aim of this cross-sectional study was (1) to test whether the BODE index is superior to the GOLD classification for explaining anxious and depressive symptoms; and (2) to assess which components of the BODE index are associated with these psychological aspects of COPD.

Methods

COPD was classified according to the GOLD stages based on FEV_1%predicted in 122 stable patients with COPD. An additional four stage classification was constructed based on the quartiles of the BODE index. The hospital anxiety and depression scale was used to assess anxious and depressive symptoms.

Results

The overall prevalence of anxious and depressive symptoms was 49% and 52%, respectively. The prevalence of anxious symptoms increased with increasing BODE stages but not with increasing GOLD stages. The prevalence of depressive symptoms increased with both increasing GOLD and BODE stages. The BODE index was superior to FEV_1%predicted for explaining anxious and depressive symptoms. Anxious symptoms were explained by dyspnoea. Depressive symptoms were explained by both dyspnoea and reduced exercise capacity.

Conclusion

The BODE index is superior to the GOLD classification for explaining anxious and depressive symptoms in COPD patients. These psychological consequences of the disease may play a role in future classification systems of COPD.     

An interactive tool for visualization of relationships between gene expression profiles

Background  

Application of phenetic methods to gene expression analysis proved to be a successful approach. Visualizing the results in a 3-dimentional space may further enhance these techniques.     

Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins

Background  

We describe Distill, a suite of servers for the prediction of protein structural features: secondary structure; relative solvent accessibility; contact density; backbone structural motifs; residue contact maps at 6, 8 and 12 Angstrom; coarse protein topology. The servers are based on large-scale ensembles of recursive neural networks and trained on large, up-to-date, non-redundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of C _α traces for short proteins (up to 200 amino acids).     

The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies

Background  

Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers.     

The use of citrullinated peptides and proteins for the diagnosis of rheumatoid arthritis

The presence or absence of antibodies to citrullinated peptides/proteins (ACPA) is an important parameter that helps a clinician set a diagnosis of early rheumatoid arthritis and, hence, initiate treatment. There are several commercial tests available to measure ACPA levels, although it can be difficult to decide what the best test for a given clinical question is. We analyzed literature data in which the diagnostic and other properties of various ACPA tests are compared. The results show that for diagnostic purposes the CCP2 test has the highest specificity, the highest sensitivity in stratified studies and the highest positive predictive value. For the prediction of future joint destruction the CCP2, MCV, and CCP3 tests may be used. The ability to predict the likelihood of not achieving sustained disease-modifying antirheumatic drug-free remission was highest for the CCP2 test. Finally, the levels of anti-CCP2 and anti-CCP3 (and possibly anti-mutated citrullinated vimentin) in rheumatoid arthritis patients are not significantly influenced by TNFα blocking agents.     

Ways and means of coping with uncertainties of the relationship of the genetic blue print to protein structure and function in the cell

As one of the disciplines of systems biology, proteomics is central to enabling the elucidation of protein function within the cell; furthermore, the question of how to deduce protein structure and function from the genetic readout has gained new significance. This problem is of particular relevance for proteins engaged in cell signalling. In dealing with this question, I shall critically comment on the reliability and predictability of transmission and translation of the genetic blue print into the phenotype, the protein. Based on this information, I will then evaluate the intentions and goals of today's proteomics and gene-networking and appraise their chances of success. Some of the themes commented on in this publication are explored in greater detail with particular emphasis on the historical roots of concepts and techniques in my forthcoming book, published in German: Von Molekülen zu Zellen. 100 Jahre experimentelle Biologie. Betrachtungen eines Biochemikers.