Extensive DNA mimicry by the ArdA anti-restriction protein and its role in the spread of antibiotic resistance

The ardA gene, found in many prokaryotes including important pathogenic species, allows associated mobile genetic elements to evade the ubiquitous Type I DNA restriction systems and thereby assist the spread of resistance genes in bacterial populations. As such, ardA contributes to a major healthcare problem. We have solved the structure of the ArdA protein from the conjugative transposon Tn916 and find that it has a novel extremely elongated curved cylindrical structure with defined helical grooves. The high density of aspartate and glutamate residues on the surface follow a helical pattern and the whole protein mimics a 42-base pair stretch of B-form DNA making ArdA by far the largest DNA mimic known. Each monomer of this dimeric structure comprises three alpha–beta domains, each with a different fold. These domains have the same fold as previously determined proteins possessing entirely different functions. This DNA mimicry explains how ArdA can bind and inhibit the Type I restriction enzymes and we demonstrate that 6 different ardA from pathogenic bacteria can function in Escherichia coli hosting a range of different Type I restriction systems.     

Comparison of classification methods for detecting associations between SNPs and chick mortality

Multi-category classification methods were used to detect SNP-mortality associations in broilers. The objective was to select a subset of whole genome SNPs associated with chick mortality. This was done by categorizing mortality rates and using a filter-wrapper feature selection procedure in each of the classification methods evaluated. Different numbers of categories (2, 3, 4, 5 and 10) and three classification algorithms (naïve Bayes classifiers, Bayesian networks and neural networks) were compared, using early and late chick mortality rates in low and high hygiene environments. Evaluation of SNPs selected by each classification method was done by predicted residual sum of squares and a significance test-related metric. A naïve Bayes classifier, coupled with discretization into two or three categories generated the SNP subset with greatest predictive ability. Further, an alternative categorization scheme, which used only two extreme portions of the empirical distribution of mortality rates, was considered. This scheme selected SNPs with greater predictive ability than those chosen by the methods described previously. Use of extreme samples seems to enhance the ability of feature selection procedures to select influential SNPs in genetic association studies.     

Adverse environmental conditions influence age-related innate immune responsiveness

Background-  

The innate immune system plays an important role in the recognition and induction of protective responses against infectious pathogens, whilst there is increasing evidence for a role in mediating chronic inflammatory diseases at older age. Despite indications that environmental conditions can influence the senescence process of the adaptive immune system, it is not known whether the same holds true for the innate immune system. Therefore we studied whether age-related innate immune responses are similar or differ between populations living under very diverse environmental conditions.     

Sequencing the genome of the Atlantic salmon (<Emphasis Type="Italic">Salmo salar</Emphasis>)

The International Collaboration to Sequence the Atlantic Salmon Genome (ICSASG) will produce a genome sequence that identifies and physically maps all genes in the Atlantic salmon genome and acts as a reference sequence for other salmonids.     

Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors

Background

Adenocarcinomas of the tongue are rare and represent the minority (20 to 25%) of salivary gland tumors affecting the tongue. We investigated the utility of massively parallel sequencing to characterize an adenocarcinoma of the tongue, before and after treatment.

Results

In the pre-treatment tumor we identified 7,629 genes within regions of copy number gain. There were 1,078 genes that exhibited increased expression relative to the blood and unrelated tumors and four genes contained somatic protein-coding mutations. Our analysis suggested the tumor cells were driven by the RET oncogene. Genes whose protein products are targeted by the RET inhibitors sunitinib and sorafenib correlated with being amplified and or highly expressed. Consistent with our observations, administration of sunitinib was associated with stable disease lasting 4 months, after which the lung lesions began to grow. Administration of sorafenib and sulindac provided disease stabilization for an additional 3 months after which the cancer progressed and new lesions appeared. A recurring metastasis possessed 7,288 genes within copy number amplicons, 385 genes exhibiting increased expression relative to other tumors and 9 new somatic protein coding mutations. The observed mutations and amplifications were consistent with therapeutic resistance arising through activation of the MAPK and AKT pathways.

Conclusions

We conclude that complete genomic characterization of a rare tumor has the potential to aid in clinical decision making and identifying therapeutic approaches where no established treatment protocols exist. These results also provide direct in vivo genomic evidence for mutational evolution within a tumor under drug selection and potential mechanisms of drug resistance accrual.     

The anti-cyclic citrullinated peptide response in tuberculosis patients is not citrulline-dependent and sensitive to treatment

Introduction  

Patients with tuberculosis (TB) frequently produce anti-citrullinated protein antibodies (ACPA). The objective of this study is to characterize the citrulline-dependence of the ACPA reactivity in sera of patients with mycobacterium infections.     

CodonTest: Modeling Amino Acid Substitution Preferences in Coding Sequences

Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes.