Intra-abdominal fat burden discriminated in vivo using proton magnetic resonance spectroscopy

Objective: To assess proton magnetic resonance spectroscopy (¹H‐MRS) as a means to distinguish among mice with disparate intra‐abdominal body fat compositions, and to measure changes in intra‐abdominal fat burden during weight loss and regain. Research Methods and Procedures: Intra‐abdominal fat burden was analyzed as a ratio of integrated areas under the curves of fat to water ¹H‐MRS signals collected from a region of interest standardized across B6.V‐Lep^ob, C57BL/6, and A‐ZIP/F mice that exhibited various genotypically related body fat compositions, ranging from obese (B6.V‐Lep^ob) to minimal body fat (A‐ZIP/F). ¹H‐MRS analysis of fat burden was compared with intra‐abdominal fat volume and with a single cross‐sectional intra‐abdominal fat area calculated from segmented magnetic resonance images. Similar measurements were made from obese B6.V‐Lep^ob mice before, during, and after they were induced to lose weight by leptin administration. Results: Relative amounts of intra‐abdominal fat analyzed by ¹H‐MRS differed significantly according to body composition and genotype of the three strains of mice (p < 0.05). Intra‐abdominal fat assessed by ¹H‐MRS correlated with both intra‐abdominal fat volume (r = 0.88, p < 0.001) and body weight (r = 0.82, p < 0.001) among, but not within, all three genotypes. During weight loss and regain, there was a significant overall pattern of changes in intra‐abdominal fat quantity that occurred, which was reflected by ¹H‐MRS (p = 0.006). Discussion: Results support the use of localized ¹H‐MRS for assessing differences in intra‐abdominal fat. Refinements in ¹H‐MRS voxel region of interest size and location as well as instrument precision may result in improved correlations within certain body compositions.     

Shared spatial effects on quantitative genetic parameters: accounting for spatial autocorrelation and home range overlap reduces estimates of heritability in wild red deer

Social structure, limited dispersal, and spatial heterogeneity in resources are ubiquitous in wild vertebrate populations. As a result, relatives share environments as well as genes, and environmental and genetic sources of similarity between individuals are potentially confounded. Quantitative genetic studies in the wild therefore typically account for easily captured shared environmental effects (e.g., parent, nest, or region). Fine-scale spatial effects are likely to be just as important in wild vertebrates, but have been largely ignored. We used data from wild red deer to build "animal models" to estimate additive genetic variance and heritability in four female traits (spring and rut home range size, offspring birth weight, and lifetime breeding success). We then, separately, incorporated spatial autocorrelation and a matrix of home range overlap into these models to estimate the effect of location or shared habitat on phenotypic variation. These terms explained a substantial amount of variation in all traits and their inclusion resulted in reductions in heritability estimates, up to an order of magnitude up for home range size. Our results highlight the potential of multiple covariance matrices to dissect environmental, social, and genetic contributions to phenotypic variation, and the importance of considering fine-scale spatial processes in quantitative genetic studies.     

Mouse large-scale phenotyping initiatives: overview of the European Mouse Disease Clinic (EUMODIC) and of the Wellcome Trust Sanger Institute Mouse Genetics Project

Two large-scale phenotyping efforts, the European Mouse Disease Clinic (EUMODIC) and the Wellcome Trust Sanger Institute Mouse Genetics Project (SANGER-MGP), started during the late 2000s with the aim to deliver a comprehensive assessment of phenotypes or to screen for robust indicators of diseases in mouse mutants. They both took advantage of available mouse mutant lines but predominantly of the embryonic stem (ES) cells resources derived from the European Conditional Mouse Mutagenesis programme (EUCOMM) and the Knockout Mouse Project (KOMP) to produce and study 799 mouse models that were systematically analysed with a comprehensive set of physiological and behavioural paradigms. They captured more than 400 variables and an additional panel of metadata describing the conditions of the tests. All the data are now available through EuroPhenome database (www.europhenome.org) and the WTSI mouse portal (http://www.sanger.ac.uk/mouseportal/), and the corresponding mouse lines are available through the European Mouse Mutant Archive (EMMA), the International Knockout Mouse Consortium (IKMC), or the Knockout Mouse Project (KOMP) Repository. Overall conclusions from both studies converged, with at least one phenotype scored in at least 80?% of the mutant lines. In addition, 57?% of the lines were viable, 13?% subviable, 30?% embryonic lethal, and 7?% displayed fertility impairments. These efforts provide an important underpinning for a future global programme that will undertake the complete functional annotation of the mammalian genome in the mouse model.     

Targeting human langerin promotes HIV-1 specific humoral immune responses

The main avenue for the development of an HIV-1 vaccine remains the induction of protective antibodies. A rationale approach is to target antigen to specific receptors on dendritic cells (DC) via fused monoclonal antibodies (mAb). In mouse and non-human primate models, targeting of skin Langerhans cells (LC) with anti-Langerin mAbs fused with HIV-1 Gag antigen drives antigen-specific humoral responses. The development of these immunization strategies in humans requires a better understanding of early immune events driven by human LC. We therefore produced anti-Langerin mAbs fused with the HIV-1 gp140z Envelope (αLC.Env). First, we show that primary skin human LC and in vitro differentiated LC induce differentiation and expansion of naïve CD4⁺ T cells into T follicular helper (Tfh) cells. Second, when human LC are pre-treated with αLC.Env, differentiated Tfh cells significantly promote the production of specific IgG by B cells. Strikingly, HIV-Env-specific Ig are secreted by HIV-specific memory B cells. Consistently, we found that receptors and cytokines involved in Tfh differentiation and B cell functions are upregulated by LC during their maturation and after targeting Langerin. Finally, we show that subcutaneous immunization of mice by αLC.Env induces germinal center (GC) reaction in draining lymph nodes with higher numbers of Tfh cells, Env-specific B cells, as well as specific IgG serum levels compared to mice immunized with the non-targeting Env antigen. Altogether, we provide evidence that human LC properly targeted may be licensed to efficiently induce Tfh cell and B cell responses in GC.     

Estimation of genetic associations between reproduction and production traits based on a sire and dam line with common ancestry

Genetic parameters for survival, reproduction and production traits were estimated for a sire and dam line, originating from one Large White breed separated more than 25 years ago. The change in parameters due to different selection pressure on reproduction and production traits in both lines was also examined. Data collected between 1990 and 2007 were available for the analysis of reproduction traits in 4713 litters (sire line) and 14836 litters (dam line) and for the production traits in 58329 pigs (sire line) and 108912 pigs (dam line). Genetic parameters were estimated using a Bayesian approach. Average phenotypic differences between lines were substantial with 1.5 more piglets born in the dam line and 1.7 mm less backfat thickness (BF) in the sire line. Based on a multiple trait analysis which included both reproduction and production traits, heritabilities for survival and litter size traits in the sire (or dam) line were estimated at 0.03 ± 0.01 (0.06 ± 0.01) for percentage of stillborn piglets (SB), 0.10 ± 0.03 (0.11 ± 0.01) for total number of piglets born (NBT) and 0.09 ± 0.03 (0.09 ± 0.01) for number of piglets born alive. Heritabilities for production traits were estimated at 0.29 ± 0.01 (0.29 ± 0.01) for average daily gain, 0.50 ± 0.01 (0.42 ± 0.01) for BF and 0.41 ± 0.01 for muscle depth. Selection pressure on litter size in the dam line resulted in a slightly unfavourable correlation for SB-NBT (0.21 ± 0.11), which was only marginally unfavourable in the sire line (0.06 ± 0.24). Selection pressure on BF in the sire line may have resulted in the moderately undesirable correlation with SB (-0.46 ± 0.15), which was not significant in the dam line (-0.08 ± 0.06). Changing the base population in the dam line to animals born since the year 2000 indicated that selection pressure on different traits has altered the heritabilities and correlations of the traits within the line. The undesirable correlations between survival at birth and reproduction traits or production traits were low so that simultaneous improvement of all traits can be achieved. Heritabilities for survival at birth and reproduction traits were low, but genetic variation was substantial and extensive pedigree information can be used to improve the accuracy of breeding values, so that genetic improvement is expected to be efficient.     

An ecologist's guide to the animal model

1. Efforts to understand the links between evolutionary and ecological dynamics hinge on our ability to measure and understand how genes influence phenotypes, fitness and population dynamics. Quantitative genetics provides a range of theoretical and empirical tools with which to achieve this when the relatedness between individuals within a population is known.
2. A number of recent studies have used a type of mixed-effects model, known as the animal model, to estimate the genetic component of phenotypic variation using data collected in the field. Here, we provide a practical guide for ecologists interested in exploring the potential to apply this quantitative genetic method in their research.
3. We begin by outlining, in simple terms, key concepts in quantitative genetics and how an animal model estimates relevant quantitative genetic parameters, such as heritabilities or genetic correlations.
4. We then provide three detailed example tutorials, for implementation in a variety of software packages, for some basic applications of the animal model. We discuss several important statistical issues relating to best practice when fitting different kinds of mixed models.
5. We conclude by briefly summarizing more complex applications of the animal model, and by highlighting key pitfalls and dangers for the researcher wanting to begin using quantitative genetic tools to address ecological and evolutionary questions.     

Nitration and nitrosation of N-acetyl-L-tryptophan and tryptophan residues in proteins by various reactive nitrogen species

Proteins are targets of reactive nitrogen species such as peroxynitrite and nitrogen dioxide. Among the various amino acids in proteins, tryptophan residues are especially susceptible to attack by reactive nitrogen species. We carried out experiments on the reactions of peroxynitrite and other reactive nitrogen species with N-acetyl-L-tryptophan under various conditions. Four major products were identified as 1-nitroso-N-acetyl-L-tryptophan, 1-nitro-N-acetyl-L-tryptophan, 6-nitro-N-acetyl-L-tryptophan, and N-acetyl-N'-formyl-L-kynurenine on the basis of their mass and UV spectra. The reactions with SIN-1 (a peroxynitrite generator), Angeli's salt (a nitroxyl donor), and spermine NONOate (a nitric oxide donor) generated the nitroso derivative but not the nitro derivatives. A myeloperoxidase-H(2)O(2)-NO(2)(-) system generated the nitro derivatives but not the nitroso derivative. Under physiological conditions 6-nitro-N-acetyl-L-tryptophan was stable, whereas the 1-nitroso and 1-nitro derivatives decomposed with half-lives of 1.5 and 18 h, respectively. After treatment with various reactive nitrogen species, bovine serum albumin was enzymatically hydrolyzed and analyzed for 6-nitro-L-tryptophan and 3-nitro-L-tyrosine by HPLC with electrochemical detection. Levels of 6-nitro-L-tryptophan and 3-nitro-L-tyrosine were similar in the nitrated protein. 6-Nitro-L-tryptophan in proteins can be measured as an additional biomarker of protein nitration.