The mutation causing the black-and-tan pigmentation phenotype of Mangalitza pigs maps to the porcine ASIP locus but does not affect its coding sequence

The gene for agouti signaling protein (ASIP) is centrally involved in the expression of coat color traits in animals. The Mangalitza pig breed is characterized by a black-and-tan phenotype with black dorsal pigmentation and yellow or white ventral pigmentation. We investigated a Mangalitza x Piétrain cross and observed a coat color segregation pattern in the F2 generation that can be explained by virtue of two alleles at the MC1R locus and two alleles at the ASIP locus. Complete linkage of the black-and-tan phenotype to microsatellite alleles at the ASIP locus on SSC 17q21 was observed. Corroborated by the knowledge of similar mouse coat color mutants, it seems therefore conceivable that the black-and-tan pigmentation of Mangalitza pigs is caused by an ASIP allele a(t), which is recessive to the wild-type allele A. Toward positional cloning of the a(t) mutation, a 200-kb genomic BAC/PAC contig of this chromosomal region has been constructed and subsequently sequenced. Full-length ASIP cDNAs obtained by RACE differed in their 5' untranslated regions, whereas they shared a common open reading frame. Comparative sequencing of all ASIP exons and ASIP cDNAs between Mangalitza and Piétrain pigs did not reveal any differences associated with the coat color phenotype. Relative qRT-PCR analyses showed different dorsoventral skin expression intensities of the five ASIP transcripts in black-and-tan Mangalitza. The a(t) mutation is therefore probably a regulatory ASIP mutation that alters its dorsoventral expression pattern.

The locus for bovine dilated cardiomyopathy maps to chromosome 18

Bovine dilated cardiomyopathy (BDCMP) is a severe and terminal disease of the heart muscle observed in Holstein-Friesian cattle over the last 30 years. There is strong evidence for an autosomal recessive mode of inheritance for BDCMP. The objective of this study was to genetically map BDCMP, with the ultimate goal of identifying the causative mutation. A whole-genome scan using 199 microsatellite markers and one SNP revealed an assignment of BDCMP to BTA18. Fine-mapping on BTA18 refined the candidate region to the MSBDCMP06-BMS2785 interval. The interval containing the BDCMP locus was confirmed by multipoint linkage analysis using the software loki. The interval is about 6.7 Mb on the bovine genome sequence (Btau 3.1). The corresponding region of HSA19 is very gene-rich and contains roughly 200 genes. Although telomeric of the marker interval, TNNI3 is a possible positional and a functional candidate for BDCMP given its involvement in a human form of dilated cardiomyopathy. Sequence analysis of TNNI3 in cattle revealed no mutation in the coding sequence, but there was a G-to-A transition in intron 6 (AJ842179:c.378+315G>A). The analysis of this SNP using the study's BDCMP pedigree did not conclusively exclude TNNI3 as a candidate gene for BDCMP. Considering the high density of genes on the homologous region of HSA19, further refinement of the interval on BTA18 containing the BDCMP locus is needed.

Genetic maps of the proximal half of chromosome arm 2L of Drosophila melanogaster

Drosophila mutants have played an important role in elucidating the physiologic function of genes. Large-scale projects have succeeded in producing mutations in a large proportion of Drosophila genes. Many mutant fly lines have also been produced through the efforts of individual laboratories over the past century. In an effort to make some of these mutants more useful to the research community, we systematically mapped a large number of mutations affecting genes in the proximal half of chromosome arm 2L to more precisely defined regions, defined by deficiency intervals, and, when possible, by individual complementation groups. To further analyze regions 36 and 39-40, we produced 11 new deficiencies with gamma irradiation, and we constructed 6 new deficiencies in region 30-33, using the DrosDel system. trans-heterozygous combinations of deficiencies revealed 5 additional functions, essential for viability or fertility.

UPLC-ESI-TOFMS-based metabolomics and gene expression dynamics inspector self-organizing metabolomic maps as tools for understanding the cellular response to ionizing radiation

Global transcriptomic and proteomic profiling platforms have yielded important insights into the complex response to ionizing radiation (IR). Nonetheless, little is known about the ways in which small cellular metabolite concentrations change in response to IR. Here, a metabolomics approach using ultraperformance liquid chromatography coupled with electrospray time-of-flight mass spectrometry was used to profile, over time, the hydrophilic metabolome of TK6 cells exposed to IR doses ranging from 0.5 to 8.0 Gy. Multivariate data analysis of the positive ions revealed dose- and time-dependent clustering of the irradiated cells and identified certain constituents of the water-soluble metabolome as being significantly depleted as early as 1 h after IR. Tandem mass spectrometry was used to confirm metabolite identity. Many of the depleted metabolites are associated with oxidative stress and DNA repair pathways. Included are reduced glutathione, adenosine monophosphate, nicotinamide adenine dinucleotide, and spermine. Similar measurements were performed with a transformed fibroblast cell line, BJ, and it was found that a subset of the identified TK6 metabolites were effective in IR dose discrimination. The GEDI (Gene Expression Dynamics Inspector) algorithm, which is based on self-organizing maps, was used to visualize dynamic global changes in the TK6 metabolome that resulted from IR. It revealed dose-dependent clustering of ions sharing the same trends in concentration change across radiation doses. "Radiation metabolomics," the application of metabolomic analysis to the field of radiobiology, promises to increase our understanding of cellular responses to stressors such as radiation.

Scalp field maps and their characterization

The present chapter gives a comprehensive introduction into the display and quantitative characterization of scalp field data. After introducing the construction of scalp field maps, different interpolation methods, the effect of the recording reference and the computation of spatial derivatives are discussed. The arguments raised in this first part have important implications for resolving a potential ambiguity in the interpretation of differences of scalp field data. In the second part of the chapter different approaches for comparing scalp field data are described. All of these comparisons can be interpreted in terms of differences of intracerebral sources either in strength, or in location and orientation in a nonambiguous way. In the present chapter we only refer to scalp field potentials, but mapping also can be used to display other features, such as power or statistical values. However, the rules for comparing and interpreting scalp field potentials might not apply to such data. Generic form of scalp field data Electroencephalogram (EEG) and event-related potential (ERP) recordings consist of one value for each sample in time and for each electrode. The recorded EEG and ERP data thus represent a two-dimensional array, with one dimension corresponding to the variable “time” and the other dimension corresponding to the variable “space” or electrode. Table 2.1 shows ERP measurements over a brief time period. The ERP data (averaged over a group of healthy subjects) were recorded with 19 electrodes during a visual paradigm. The parietal midline Pz electrode has been used as the reference electrode.

Consistent Layout for Thematic Software Maps

Software visualizations can provide a concise overview of a complex software system. Unfortunately, since software has no physical shape, there is no “natural“ mapping of software to a two-dimensional space. As a consequence most visualizations tend to use a layout in which position and distance have no meaning, and consequently layout typical diverges from one visualization to another. We propose a consistent layout for software maps in which the position of a software artifact reflects its \emph{vocabulary}, and distance corresponds to similarity of vocabulary. We use Latent Semantic Indexing (LSI) to map software artifacts to a vector space, and then use Multidimensional Scaling (MDS) to map this vector space down to two dimensions. The resulting consistent layout allows us to develop a variety of thematic software maps that express very different aspects of software while making it easy to compare them. The approach is especially suitable for comparing views of evolving software, since the vocabulary of software artifacts tends to be stable over time.

The bovine dilated cardiomyopathy locus maps to a 1.0-Mb interval on chromosome 18

Cardiomyopathies are myocardial diseases that lead to cardiac dysfunction, heart failure, arrhythmia, and sudden death. In human medicine, cardiomyopathies frequently warrant heart transplantation in children and adults. Bovine dilated cardiomyopathy (BDCMP) is a heart muscle disorder that has been observed during the last 30 years in cattle of Holstein-Friesian origin. In Switzerland BDCMP affects Swiss Fleckvieh and Red Holstein breeds. BDCMP is characterized by a cardiac enlargement with ventricular remodeling and chamber dilatation. The common symptoms in affected animals are subacute subcutaneous edema, congestion of the jugular veins, and tachycardia with gallop rhythm. A cardiomegaly with dilatation and hypertrophy of all heart chambers, myocardial degeneration, and fibrosis are typical postmortem findings. It was shown that all BDCMP cases reported worldwide traced back to a red factor-carrying Holstein-Friesian bull, ABC Reflection Sovereign. An autosomal recessive mode of inheritance was proposed for BDCMP. Recently, the disease locus was mapped to a 6.7-Mb interval MSBDCMP06-BMS2785 on bovine Chr 18 (BTA18). In the present study the BDCMP locus was fine mapped by using a combined strategy of homozygosity mapping and association study. A BAC contig of 2.9 Mb encompassing the crucial interval was constructed to establish the correct marker order on BTA18. We show that the disease locus is located in a gene-rich interval of 1.0 Mb and is flanked by the microsatellite markers DIK3006 and MSBDCMP51.