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.      

Using genetic algorithms for the construction of phylogenetic trees: application to G-protein coupled receptor sequences

Many different phylogenetic clustering techniques are used currently. One approach is to first determine the topology with a common clustering method and then calculate the branch lengths of the tree. If the resulting tree is not optimal exchanging tree branches can make some local changes in the tree topology. The whole process can be iterated until a satisfactory result has been obtained. The efficiency of this method fully depends on the initially generated tree. Although local changes are made, the optimal tree will never be found if the initial tree is poorly chosen. In this article, genetic algorithms are applied such that the optimal tree can be found even with a bad initial tree topology. This tree generating method is tested by comparing its results with the results of the FITCH program in the PHYLIP software package. Two simulated data sets and a real data set are used.     

Integrated statistical analysis of cDNA microarray and NIR spectroscopic data applied to a hemp dataset

Both cDNA microarray and spectroscopic data provide indirect information about the chemical compounds present in the biological tissue under consideration. In this paper simple univariate and bivariate measures are used to investigate correlations between both types of high dimensional analyses. A large dataset of 42 hemp samples on which 3456 cDNA clones and 351 NIR wavelengths have been measured, was analyzed using graphical representations. For this purpose we propose clustered correlation and clustered discrimination images. Large, tissue-related differences are seen to dominate the cDNA-NIR correlation structure but smaller, more difficult to detect, variety-related differences can be found at specific cDNA clone/NIR wavelength combinations.     

Structure-activity relationships of 1,3,5-triazine-2,4,6-triones as human gonadotropin-releasing hormone receptor antagonists

SAR studies of 1,3,5-triazine-2,4,6-triones as human gonadotropin-releasing hormone receptor antagonists resulted in potent compounds. The best compound from the series had a binding affinity of 2 nM.     

Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study)

Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5?years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24?h urine samples were obtained by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0291-6) contains supplementary material, which is available to authorized users.     

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.     

Influence of clotting time on the protein composition of serum samples based on LC-MS data

Many large, disease-related biobanks of serum samples have been established prior to the widespread use of proteomics in biomarker research. These biobanks may contain relevant information about the disease process, response to therapy or patient classifications especially with respect to long-term follow-up that is otherwise very difficult to obtain based on newly initiated studies, particularly in the case of slowly developing diseases. An important parameter that may influence the composition of serum but that is often not exactly known is clotting time. We therefore investigated the influence of clotting time on the protein and peptide composition of serum by label-free and stable-isotope labeling techniques. The label-free analysis of trypsin-digested serum showed that the overall pattern of LC-MS data is not affected by clotting times varying from 2 to 8h. However, univariate and multivariate statistical analyses revealed that proteins that are directly involved in blood clot formation, such as the clotting-derived fibrinopeptides, change significantly. This is most easily detected in the supernatant of acid-precipitated, immunodepleted serum. Stable-isotope labeling techniques show that truncated or phosphorylated forms of fibrinopeptides A and B increase or decrease depending on clotting time. These patterns can be easily recognized and should be taken into consideration when analyzing LC-MS data using serum sample collections of which the clotting time is not known. Next to the fibrinopeptides, leucine-rich alpha-2-glycoprotein (P02750) was shown to be consistently decreased in samples with clotting times of more than 1h. For prospective studies, we recommend to let blood clot for at least 2h at room temperature using glass tubes with a separation gel and micronized silica to accelerate blood clotting.     

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.      

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.