The TNFα receptor TNFRSF1A and genes encoding the amiloride-sensitive sodium channel ENaC as modulators in cystic fibrosis

The CFTR mutations in cystic fibrosis (CF) lead to ion transport anomalities which predispose to chronic infection and inflammation of CF airways as the major determinants for morbidity and mortality in CF. Discordant clinical phenotypes of siblings with identical CFTR mutations and the large variability of clinical manifestations of patients who are homozygous for the most common mutation F508del suggest that both environment and genes other than CFTR contribute substantially to CF disease. The prime candidates for genetic modifiers in CF are elements of host defence such as the TNFα receptor and of ion transport such as the amiloride-sensitive epithelial sodium channel ENaC, both of which are encoded side by side on 12p13 (TNFRSF1A, SCNN1A) and 16p12 (SCNN1B, SCNN1G). Thirty-seven families with F508del-CFTR homozygous siblings exhibiting extreme clinical phenotypes that had been selected from the 467 pairs of the European CF Twin and Sibling Study were genotyped at 12p13 and 16p12 markers. The ENaC was identified as a modulator of CF by transmission disequilibrium at SCNN1G and association with CF phenotype intrapair discordance at SCNN1B. Family-based and case-control analyses and sequencing of SCNN1A and TNFRSF1A uncovered an association of the TNFRSF1A intron 1 haplotype with disease severity. Carriers of risk haplotypes were underrepresented suggesting a strong impact of both loci on survival. The finding that TNFRSF1A, SCNN1B and SCNN1G are clinically relevant modulators of CF disease supports current concepts that the depletion of airway surface liquid and inadequate host inflammatory responses trigger pulmonary disease in CF.Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

Signal peptide cleavage site prediction

A series of reports - and extracts of reports - from the Freedom of Information Conference, 6-7 July, 2000, New York Academy of Medicine. The conference was sponsored by BioMed Central, to promote debate about the communication and validation of biomedical research published on the internet. Details of the meeting and all presentations are available in full online at http://biomedcentral.com/info/conference.asp

     

Amino Acid Usage Is Asymmetrically Biased in AT- and GC-Rich Microbial Genomes

Introduction

Genomic base composition ranges from less than 25% AT to more than 85% AT in prokaryotes. Since only a small fraction of prokaryotic genomes is not protein coding even a minor change in genomic base composition will induce profound protein changes. We examined how amino acid and codon frequencies were distributed in over 2000 microbial genomes and how these distributions were affected by base compositional changes. In addition, we wanted to know how genome-wide amino acid usage was biased in the different genomes and how changes to base composition and mutations affected this bias. To carry this out, we used a Generalized Additive Mixed-effects Model (GAMM) to explore non-linear associations and strong data dependences in closely related microbes; principal component analysis (PCA) was used to examine genomic amino acid- and codon frequencies, while the concept of relative entropy was used to analyze genomic mutation rates.

Results

We found that genomic amino acid frequencies carried a stronger phylogenetic signal than codon frequencies, but that this signal was weak compared to that of genomic %AT. Further, in contrast to codon usage bias (CUB), amino acid usage bias (AAUB) was differently distributed in AT- and GC-rich genomes in the sense that AT-rich genomes did not prefer specific amino acids over others to the same extent as GC-rich genomes. AAUB was also associated with relative entropy; genomes with low AAUB contained more random mutations as a consequence of relaxed purifying selection than genomes with higher AAUB.

Conclusion

Genomic base composition has a substantial effect on both amino acid- and codon frequencies in bacterial genomes. While phylogeny influenced amino acid usage more in GC-rich genomes, AT-content was driving amino acid usage in AT-rich genomes. We found the GAMM model to be an excellent tool to analyze the genomic data used in this study.     

Genomic comparisons of <Emphasis Type="Italic">Brucella</Emphasis> spp. and closely related bacteria using base compositional and proteome based methods

Background  

Classification of bacteria within the genus Brucella has been difficult due in part to considerable genomic homogeneity between the different species and biovars, in spite of clear differences in phenotypes. Therefore, many different methods have been used to assess Brucella taxonomy. In the current work, we examine 32 sequenced genomes from genus Brucella representing the six classical species, as well as more recently described species, using bioinformatical methods. Comparisons were made at the level of genomic DNA using oligonucleotide based methods (Markov chain based genomic signatures, genomic codon and amino acid frequencies based comparisons) and proteomes (all-against-all BLAST protein comparisons and pan-genomic analyses).     

An environmental signature for 323 microbial genomes based on codon adaptation indices

Background

Codon adaptation indices (CAIs) represent an evolutionary strategy to modulate gene expression and have widely been used to predict potentially highly expressed genes within microbial genomes. Here, we evaluate and compare two very different methods for estimating CAI values, one corresponding to translational codon usage bias and the second obtained mathematically by searching for the most dominant codon bias.

Results

The level of correlation between these two CAI methods is a simple and intuitive measure of the degree of translational bias in an organism, and from this we confirm that fast replicating bacteria are more likely to have a dominant translational codon usage bias than are slow replicating bacteria, and that this translational codon usage bias may be used for prediction of highly expressed genes. By analyzing more than 300 bacterial genomes, as well as five fungal genomes, we show that codon usage preference provides an environmental signature by which it is possible to group bacteria according to their lifestyle, for instance soil bacteria and soil symbionts, spore formers, enteric bacteria, aquatic bacteria, and intercellular and extracellular pathogens.

Conclusion

The results and the approach described here may be used to acquire new knowledge regarding species lifestyle and to elucidate relationships between organisms that are far apart evolutionarily.     

Prediction of highly expressed genes in microbes based on chromatin accessibility

Background  

It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed genes in microbial genomes. We compare these predictions with those based on codon adaptation index (CAI) values, and also with experimental data for 6 different microbial genomes, with a particular interest in experimental data from Escherichia coli. Moreover, position preference is examined further in 328 sequenced microbial genomes.     

Analysis of biological samples using prompt gamma radions induced by 14.7-MeV neutrons

We investigate a method for determining the elemental composition of biological samples that uses prompt gamma rays induced by 14.7-MeV neutrons. Alpha particles are produced simultaneously with the neutrons, which exit opposite the alpha detector through the vacuum chamber wall. The sample under investigation is irradiated and emits gamma radiations in a spectral energy distribution characteristic of the material. Barium-fluoride (BaF₂) and high-purity germanium (HPGe) gamma detectors view the sample and record the spectrum of gamma radiation.