A comparative description of mitochondrial DNA differentiation in selected avian and other vertebrate genera

Levels of mitochondrial DNA (mtDNA) sequence divergence between species within each of several avian (Anas, Aythya, Dendroica, Melospiza, and Zonotrichia) and nonavian (Lepomis and Hyla) vertebrate genera were compared. An analysis of digestion profiles generated by 13-18 restriction endonucleases indicates little overlap in magnitude of mtDNA divergence for the avian versus nonavian taxa examined. In 55 interspecific comparisons among the avian congeners, the fraction of identical fragment lengths (F) ranged from 0.26 to 0.96 (F = 0.46), and, given certain assumptions, these translate into estimates of nucleotide sequence divergence (p) ranging from 0.007 to 0.088; in 46 comparisons among the fish and amphibian congeners, F values ranged from 0.00 to 0.36 (F = 0.09), yielding estimates of P greater than 0.070. The small mtDNA distances among avian congeners are associated with protein-electrophoretic distances (D values) less than approximately 0.2, while the mtDNA distances among assayed fish and amphibian congeners are associated with D values usually greater than 0.4. Since the conservative pattern of protein differentiation previously reported for many avian versus nonavian taxa now appears to be paralleled by a conservative pattern of mtDNA divergence, it seems increasingly likely that many avian species have shared more recent common ancestors than have their nonavian taxonomic counterparts. However, estimates of avian divergence times derived from mtDNA- and protein-calibrated clocks cannot readily be reconciled with some published dates based on limited fossil remains. If the earlier paleontological interpretations are valid, then protein and mtDNA evolution must be somewhat decelerated in birds. The empirical and conceptual issues raised by these findings are highly analogous to those in the long-standing debate about rates of molecular evolution and times of separation of ancestral hominids from African apes.      

Eosinophil-induced release of acetylcholine from differentiated cholinergic nerve cells

One immunological component of asthma is believed to be the interaction of eosinophils with parasympathetic cholinergic nerves and a consequent inhibition of acetylcholine muscarinic M2 receptor activity, leading to enhanced acetylcholine release and bronchoconstriction. Here we have used an in vitro model of cholinergic nerve function, the human IMR32 cell line, to study this interaction. IMR32 cells, differentiated in culture for 7 days, expressed M2 receptors. Cells were radiolabeled with [3H]choline and electrically stimulated. The stimulation-induced release of acetylcholine was prevented by the removal of Ca2+. The muscarinic M1/M2 receptor agonist arecaidine reduced the release of acetylcholine after stimulation (to 82 +/- 2% of control at 10(-7) M), and the M2 receptor antagonist AF-DX 116 increased it (to 175 +/- 23% of control at 10(-5) M), indicating the presence of a functional M2 receptor that modulated acetylcholine release. When human eosinophils were added to IMR32 cells, they enhanced acetylcholine release by 36 +/- 10%. This effect was prevented by inhibitors of adhesion of the eosinophils to the IMR32 cells. Pretreatment of IMR32 cells with 10 mM carbachol, to desensitize acetylcholine receptors, prevented the potentiation of acetylcholine release by eosinophils or AF-DX 116. Acetylcholine release was similarly potentiated (by up to 45 +/- 7%) by degranulation products from eosinophils that had been treated with N-formyl-methionyl-leucyl-phenylalanine or that had been in contact with IMR32 cells. Contact between eosinophils and IMR32 cells led to an initial increase in expression of M2 receptors, whereas prolonged exposure reduced M2 receptor expression.     

Cyclin-dependent kinase inhibitors enhance the resolution of inflammation by promoting inflammatory cell apoptosis

Apoptosis is essential for clearance of potentially injurious inflammatory cells and subsequent efficient resolution of inflammation. Here we report that human neutrophils contain functionally active cyclin-dependent kinases (CDKs), and that structurally diverse CDK inhibitors induce caspase-dependent apoptosis and override powerful anti-apoptosis signals from survival factors such as granulocyte-macrophage colony-stimulating factor (GM-CSF). We show that the CDK inhibitor R-roscovitine (Seliciclib or CYC202) markedly enhances resolution of established neutrophil-dependent inflammation in carrageenan-elicited acute pleurisy, bleomycin-induced lung injury, and passively induced arthritis in mice. In the pleurisy model, the caspase inhibitor zVAD-fmk prevents R-roscovitine-enhanced resolution of inflammation, indicating that this CDK inhibitor augments inflammatory cell apoptosis. We also provide evidence that R-roscovitine promotes apoptosis by reducing concentrations of the anti-apoptotic protein Mcl-1. Thus, CDK inhibitors enhance the resolution of established inflammation by promoting apoptosis of inflammatory cells, thereby demonstrating a hitherto unrecognized potential for the treatment of inflammatory disorders.     

Selection against genetic defects in conservation schemes while controlling inbreeding

We studied different genetic models and evaluation systems to select against a genetic disease with additive, recessive or polygenic inheritance in genetic conservation schemes. When using optimum contribution selection with a restriction on the rate of inbreeding (ΔF) to select against a disease allele, selection directly on DNA-genotypes is, as expected, the most efficient strategy. Selection for BLUP or segregation analysis breeding value estimates both need 1–2 generations more to halve the frequency of the disease allele, while these methods do not require knowledge of the disease mutation at the DNA level. BLUP and segregation analysis methods were equally efficient when selecting against a disease with single gene or complex polygene inheritance, i.e. knowledge about the mode of inheritance of the disease was not needed for efficient selection against the disease. Smaller schemes or schemes with a more stringent restriction on ΔF needed more generations to halve the frequency of the disease alleles or the fraction of diseased animals. Optimum contribution selection maintained ΔF at its predefined level, even when selection of females was at random. It is argued that in the investigated small conservation schemes with selection against a genetic defect, control of ΔF is very important.     

Comparisons of the molecular evolutionary process at rbcL and ndhF in the grass family (Poaceae)

We examine rate heterogeneity among evolutionary lineages of the grass family at two plasmid loci, ndhF and rbcL, and we introduce a method to determine whether patterns of rate heterogeneity are correlated between loci. We show both that rates of synonymous evolution are heterogeneous among grass lineages and that are heterogeneity is correlated between loci at synonymous sites. At nonsynonymous sites, the pattern of rate heterogeneity is not correlated between loci, primarily due to an aberrant pattern of rate heterogeneity at nonsynonymous sites of rbcL. We compare patterns of synonymous rate heterogeneity to predictors based on the generation time effect and the speciation rate hypotheses. Although there is some evidence for generation time effects, neither generation time effects nor speciation rates appear to be sufficient to explain patterns of rate heterogeneity in the grass plastid sequences.      

The Accuracy of Computerized Adaptive Testing in Heterogeneous Populations: A Mixture Item-Response Theory Analysis

Background

Computerized adaptive testing (CAT) utilizes latent variable measurement model parameters that are typically assumed to be equivalently applicable to all people. Biased latent variable scores may be obtained in samples that are heterogeneous with respect to a specified measurement model. We examined the implications of sample heterogeneity with respect to CAT-predicted patient-reported outcomes (PRO) scores for the measurement of pain.

Methods

A latent variable mixture modeling (LVMM) analysis was conducted using data collected from a heterogeneous sample of people in British Columbia, Canada, who were administered the 36 pain domain items of the CAT-5D-QOL. The fitted LVMM was then used to produce data for a simulation analysis. We evaluated bias by comparing the referent PRO scores of the LVMM with PRO scores predicted by a “conventional” CAT (ignoring heterogeneity) and a LVMM-based “mixture” CAT (accommodating heterogeneity).

Results

The LVMM analysis indicated support for three latent classes with class proportions of 0.25, 0.30 and 0.45, which suggests that the sample was heterogeneous. The simulation analyses revealed differences between the referent PRO scores and the PRO scores produced by the “conventional” CAT. The “mixture” CAT produced PRO scores that were nearly equivalent to the referent scores.

Conclusion

Bias in PRO scores based on latent variable models may result when population heterogeneity is ignored. Improved accuracy could be obtained by using CATs that are parameterized using LVMM.     

Genomic aberrations in lung adenocarcinoma in never smokers

Background

Lung cancer in never smokers would rank as the seventh most common cause of cancer death worldwide.

Methods and Findings

We performed high-resolution array comparative genomic hybridization analysis of lung adenocarcinoma in sixty never smokers and identified fourteen new minimal common regions (MCR) of gain or loss, of which five contained a single gene (MOCS2, NSUN3, KHDRBS2, SNTG1 and ST18). One larger MCR of gain contained NSD1. One focal amplification and nine gains contained FUS. NSD1 and FUS are oncogenes hitherto not known to be associated with lung cancer. FISH showed that the amplicon containing FUS was joined to the next telomeric amplicon at 16p11.2. FUS was over-expressed in 10 tumors with gain of 16p11.2 compared to 30 tumors without that gain. Other cancer genes present in aberrations included ARNT, BCL9, CDK4, CDKN2B, EGFR, ERBB2, MDM2, MDM4, MET, MYC and KRAS. Unsupervised hierarchical clustering with adjustment for false-discovery rate revealed clusters differing by the level and pattern of aberrations and displaying particular tumor characteristics. One cluster was strongly associated with gain of MYC. Another cluster was characterized by extensive losses containing tumor suppressor genes of which RB1 and WRN. Tumors in that cluster frequently harbored a central scar-like fibrosis. A third cluster was associated with gains on 7p and 7q, containing ETV1 and BRAF, and displayed the highest rate of EGFR mutations. SNP array analysis validated copy-number aberrations and revealed that RB1 and WRN were altered by recurrent copy-neutral loss of heterozygosity.

Conclusions

The present study has uncovered new aberrations containing cancer genes. The oncogene FUS is a candidate gene in the 16p region that is frequently gained in never smokers. Multiple genetic pathways defined by gains of MYC, deletions of RB1 and WRN or gains on 7p and 7q are involved in lung adenocarcinoma in never smokers.     

Adhesion-dependent interactions between eosinophils and cholinergic nerves

Eosinophils adhere to airway cholinergic nerves and influence nerve cell function by releasing granule proteins onto inhibitory neuronal M(2) muscarinic receptors. This study investigated the mechanism of eosinophil degranulation by cholinergic nerves. Eosinophils were cocultured with IMR32 cholinergic nerve cells, and eosinophil peroxidase (EPO) or leukotriene C(4) (LTC(4)) release was measured. Coculture of eosinophils with nerves significantly increased EPO and LTC(4) release compared with eosinophils alone. IMR32 cells, like parasympathetic nerves, express the adhesion molecules vascular cell adhesion molecule-1 and intercellular adhesion molecule-1 (ICAM-1). Inhibition of these adhesion molecules alone or in combination significantly inhibited eosinophil degranulation. IMR32 cells also significantly augmented the eosinophil degranulation produced by formyl-Met-Leu-Phe. Eosinophil adhesion to IMR32 cells resulted in an ICAM-1-mediated production of reactive oxygen species via a neuronal NADPH oxidase, inhibition of which significantly inhibited eosinophil degranulation. Additionally, eosinophil adhesion increased the release of ACh from IMR32 cells. These neuroinflammatory cell interactions may be relevant in a variety of inflammatory and neurological conditions.     

Genome-wide association and biological pathway analysis for milk-fat composition in Danish Holstein and Danish Jersey cattle

Background

The milk fat profile of the Danish Holstein (DH) and Danish Jersey (DJ) show clear differences. Identification of the genomic regions, genes and biological pathways underlying the milk fat biosynthesis will improve the understanding of the biology underlying bovine milk fat production and may provide new possibilities to change the milk fat composition by selective breeding. In this study a genome wide association scan (GWAS) in the DH and DJ was performed for a detailed milk fatty acid (FA) profile using the HD bovine SNP array and subsequently a biological pathway analysis based on the SNP data was performed.

Results

The GWAS identified in total 1,233 SNPs (FDR < 0.10) spread over 18 chromosomes for nine different FA traits for the DH breed and 1,122 SNPs (FDR < 0.10) spread over 26 chromosomes for 13 different FA traits were detected for the DJ breed. Of these significant SNPs, 108 SNP markers were significant in both DH and DJ (C14-index, BTA26; C16, BTA14; fat percentage (FP), BTA14). This was supported by an enrichment test. The QTL on BTA14 and BTA26 represented the known candidate genes DGAT and SCD. In addition we suggest ACSS3 to be a good candidate gene for the QTL on BTA5 for C10:0 and C15:0. In addition, genetic correlations between the FA traits within breed showed large similarity across breeds. Furthermore, the biological pathway analysis revealed that fat digestion and absorption (KEGG04975) plays a role for the traits FP, C14:1, C16 index and C16:1.

Conclusion

There was a clear similarity between the underlying genetics of FA in the milk between DH and DJ. This was supported by the fact that there was substantial overlap between SNPs for FP, C14 index, C14:1, C16 index and C16:1. In addition genetic correlations between FA showed a similar pattern across DH and DJ. Furthermore the biological pathway analysis suggested that fat digestion and absorption KEGG04975 is important for the traits FP, C14:1, C16 index and C16:1.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1112) contains supplementary material, which is available to authorized users.     

Efficiency of population structures for mapping of Mendelian and imprinted quantitative trait loci in outbred pigs using variance component methods

In a simulation study different designs for a pure line pig population were compared for efficiency of mapping QTL using the variance component method. Phenotypes affected by a Mendelian QTL, a paternally expressed QTL, a maternally expressed QTL or by a QTL without an effect were simulated. In all alternative designs 960 progeny were phenotyped. Given the limited number of animals there is an optimum between the number of families and the family size. Estimation of Mendelian and parentally expressed QTL is more efficient in a design with large family sizes. Too small a number of sires should be avoided to minimize chances of sires to be non-segregating. When a large number of families is used, the number of haplotypes increases which reduces the accuracy of estimating the QTL effect and thereby reduces the power to show a significant QTL and to correctly position the QTL. Dense maps allow for smaller family size due to exploitation of LD-information. Given the different possible modes of inheritance of the QTL using 8 to16 boars, two litters per dam was optimal with respect to determining significance and correct location of the QTL for a data set consisting of 960 progeny. The variance component method combining linkage disequilibrium and linkage analysis seems to be an appropriate choice to analyze data sets which vary in marker density and which contain complex family structures.