Quirks of dye nomenclature. 5. Rhodamines

Rhodamines were first produced in the late 19^th century, when they constituted a new class of synthetic dyes. These compounds since have been used to color many things including cosmetics, inks, textiles, and in some countries, food products. Certain rhodamine dyes also have been used to stain biological specimens and currently are widely used as fluorescent probes for mitochondria in living cells. The early history and current biological applications are sketched briefly and an account of the ambiguities, complications and confusions concerning dye identification and nomenclature are discussed.     

Quirks of dye nomenclature. 6. Malachite green

Malachite green was discovered independently by two researchers in Germany in the 19^th century and found immediate employment as a dye and a pigment. Subsequently, other uses, such as staining biological specimens, emerged. A much later application was the control of fungal and protozoan infections in fish, for which the dye remains popular, although illegal in many countries owing to a variety of toxicity problems. In solution, malachite green can exist as five different species depending on the pH. The location of the positive charge of the colored cation on a carbon atom or a nitrogen atom is still debated. The original names of this dye, and their origins, are briefly surveyed.     

Dothistroma pini,a forest pathogen,contains homologs of aflatoxin biosynthetic pathway genes

Homologs of aflatoxin biosynthetic genes have been identified in the pine needle pathogen Dothistroma pini. D. pini produces dothistromin, a difuranoanthraquinone toxin with structural similarity to the aflatoxin precursor versicolorin B. Previous studies with purified dothistromin suggest a possible role for this toxin in pathogenicity. By using an aflatoxin gene as a hybridization probe, a genomic D. pini clone was identified that contained four dot genes with similarity to genes in aflatoxin and sterigmatocystin gene clusters with predicted activities of a ketoreductase (dotA), oxidase (dotB), major facilitator superfamily transporter (dotC), and thioesterase (dotD). A D. pini dotA mutant was made by targeted gene replacement and shown to be severely impaired in dothistromin production, confirming that dotA is involved in dothistromin biosynthesis. Accumulation of versicolorin A (a precursor of aflatoxin) by the dotA mutant confirms that the dotA gene product is involved in an aflatoxin-like biosynthetic pathway. Since toxin genes have been found to be clustered in fungi in every case analyzed so far, it is speculated that the four dot genes may comprise part of a dothistromin biosynthetic gene cluster. A fifth gene, ddhA, is not a homolog of aflatoxin genes and could be at one end of the dothistromin cluster. These genes will allow comparative biochemical and genetic studies of the aflatoxin and dothistromin biosynthetic pathways and may also lead to new ways to control Dothistroma needle blight.     

Quantitative variability of 342 plasma proteins in a human twin population

The degree and the origins of quantitative variability of most human plasma proteins are largely unknown. Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2–7 years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors. The data indicate that different proteins show vastly different patterns of abundance variability among humans and that genetic control and longitudinal variation affect protein levels and biological processes to different degrees. The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors. Moreover, we identified 13 cis‐SNPs significantly influencing the level of specific plasma proteins. These results therefore have immediate implications for the effective design of blood‐based biomarker studies.     

Soybean seed lipoxygenase genes: molecular characterization and development of molecular marker assays

Soybean seeds contain three lipoxygenase (Lox) enzymes that are controlled by separate genes, Lox1, Lox2 and Lox3. Lipoxygenases play a role in the development of unpleasant flavors in foods containing soybean by oxidation of polyunsaturated fatty acids. Null alleles for all three enzymes have been identified, lox1, lox2 and lox3, and are known to be inherited as simple recessive alleles. Previous studies determined that a missense mutation rendered Lox2 inactive; however, the genetic cause of either lox1 or lox3 mutation was not known. The objectives of this study were the molecular characterization of both lox1 and lox3 mutant alleles and the development of molecular markers to accelerate breeding for Lox-free soybean varieties. We identified two independent mutant alleles as the genetic causes of the lack of Lox1 in seeds of two lox1 mutant soybean lines. Similarly, a mutant allele that truncates Lox3 in a lox3 mutant soybean line was identified. Molecular markers were designed and confirmed to distinguish mutant, wild type, and heterozygous individuals for Lox1, Lox2 and Lox3 genes. Genotype and Lox phenotype analysis showed a perfect association between the inheritance of homozygous lox mutant alleles and the lack of Lox activity. Molecular characterization of a seed-lipoxygenase-free soybean line led to the discovery that an induced recombination event within the Lox1 gene was responsible for breaking the tight linkage in repulsion phase between mutant alleles at the Lox1 and Lox2 loci. The molecular resources developed in this work should accelerate the inclusion of the lipoxygenase-free trait in soybean varieties.     

Impact of QTL properties on the accuracy of multi-breed genomic prediction

Background

Although simulation studies show that combining multiple breeds in one reference population increases accuracy of genomic prediction, this is not always confirmed in empirical studies. This discrepancy might be due to the assumptions on quantitative trait loci (QTL) properties applied in simulation studies, including number of QTL, spectrum of QTL allele frequencies across breeds, and distribution of allele substitution effects. We investigated the effects of QTL properties and of including a random across- and within-breed animal effect in a genomic best linear unbiased prediction (GBLUP) model on accuracy of multi-breed genomic prediction using genotypes of Holstein-Friesian and Jersey cows.

Methods

Genotypes of three classes of variants obtained from whole-genome sequence data, with moderately low, very low or extremely low average minor allele frequencies (MAF), were imputed in 3000 Holstein-Friesian and 3000 Jersey cows that had real high-density genotypes. Phenotypes of traits controlled by QTL with different properties were simulated by sampling 100 or 1000 QTL from one class of variants and their allele substitution effects either randomly from a gamma distribution, or computed such that each QTL explained the same variance, i.e. rare alleles had a large effect. Genomic breeding values for 1000 selection candidates per breed were estimated using GBLUP modelsincluding a random across- and a within-breed animal effect.

Results

For all three classes of QTL allele frequency spectra, accuracies of genomic prediction were not affected by the addition of 2000 individuals of the other breed to a reference population of the same breed as the selection candidates. Accuracies of both single- and multi-breed genomic prediction decreased as MAF of QTL decreased, especially when rare alleles had a large effect. Accuracies of genomic prediction were similar for the models with and without a random within-breed animal effect, probably because of insufficient power to separate across- and within-breed animal effects.

Conclusions

Accuracy of both single- and multi-breed genomic prediction depends on the properties of the QTL that underlie the trait. As QTL MAF decreased, accuracy decreased, especially when rare alleles had a large effect. This demonstrates that QTL properties are key parameters that determine the accuracy of genomic prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0124-6) contains supplementary material, which is available to authorized users.