Molecular basis for clinical diversity between autoantibody subsets in diffuse cutaneous systemic sclerosis.

OBJECTIVES: Clinical heterogeneity is a cardinal feature of systemic sclerosis (SSc). Hallmark SSc autoantibodies are central to diagnosis and associate with distinct patterns of skin-based and organ-based complications. Understanding molecular differences between patients will benefit clinical practice and research and give insight into pathogenesis of the disease. We aimed to improve understanding of the molecular differences between key diffuse cutaneous SSc subgroups as defined by their SSc-specific autoantibodies METHODS: We have used high-dimensional transcriptional and proteomic analysis of blood and the skin in a well-characterised cohort of SSc (n=52) and healthy controls (n=16) to understand the molecular basis of clinical diversity in SSc and explore differences between the hallmark antinuclear autoantibody (ANA) reactivities. RESULTS: Our data define a molecular spectrum of SSc based on skin gene expression and serum protein analysis, reflecting recognised clinical subgroups. Moreover, we show that antitopoisomerase-1 antibodies and anti-RNA polymerase III antibodies specificities associate with remarkably different longitudinal change in serum protein markers of fibrosis and divergent gene expression profiles. Overlapping and distinct disease processes are defined using individual patient pathway analysis. CONCLUSIONS: Our findings provide insight into clinical diversity and imply pathogenetic differences between ANA-based subgroups. This supports stratification of SSc cases by ANA antibody subtype in clinical trials and may explain different outcomes across ANA subgroups in trials targeting specific pathogenic mechanisms

Integrating personality research and animal contest theory: aggressiveness in the green swordtail <i>Xiphophorus helleri</i>

&lt;p&gt;Aggression occurs when individuals compete over limiting resources. While theoretical studies have long placed a strong emphasis on context-specificity of aggression, there is increasing recognition that consistent behavioural differences exist among individuals, and that aggressiveness may be an important component of individual personality. Though empirical studies tend to focus on one aspect or the other, we suggest there is merit in modelling both within-and among-individual variation in agonistic behaviour simultaneously. Here, we demonstrate how this can be achieved using multivariate linear mixed effect models. Using data from repeated mirror trials and dyadic interactions of male green swordtails, &lt;i&gt;Xiphophorus helleri&lt;/i&gt;, we show repeatable components of (co)variation in a suite of agonistic behaviour that is broadly consistent with a major axis of variation in aggressiveness. We also show that observed focal behaviour is dependent on opponent effects, which can themselves be repeatable but were more generally found to be context specific. In particular, our models show that within-individual variation in agonistic behaviour is explained, at least in part, by the relative size of a live opponent as predicted by contest theory. Finally, we suggest several additional applications of the multivariate models demonstrated here. These include testing the recently queried functional equivalence of alternative experimental approaches, (e. g., mirror trials, dyadic interaction tests) for assaying individual aggressiveness.&lt;/p&gt

Normalization and Statistical Analysis of Multiplexed Bead-Based Immunoassay Data Using Mixed-Effects Modeling

Multiplexed bead-based flow cytometric immunoassays are a powerful experimental tool for investigating cellular communication networks, yet their widespread adoption is limited in part by challenges in robust quantitative analysis of the measurements. Here we report our application of mixed-effects modeling for the normalization and statistical analysis of bead-based immunoassay data. Our data set consisted of bead-based immunoassay measurements of 16 phospho-proteins in lysates of HepG2 cells treated with ligands that regulate acute-phase protein secretion. Mixed-effects modeling provided estimates for the effects of both the technical and biological sources of variance, and normalization was achieved by subtracting the technical effects from the measured values. This approach allowed us to detect ligand effects on signaling with greater precision and sensitivity and to more accurately characterize the HepG2 cell signaling network using constrained fuzzy logic. Mixed-effects modeling analysis of our data was vital for ascertaining that IL-1α and TGF-α treatment increased the activities of more pathways than IL-6 and TNF-α and that TGF-α and TNF-α increased p38 MAPK and c-Jun N-terminal kinase (JNK) phospho-protein levels in a synergistic manner. Moreover, we used mixed-effects modeling-based technical effect estimates to reveal the substantial variance contributed by batch effects along with the absence of loading order and assay plate position effects. We conclude that mixed-effects modeling enabled additional insights to be gained from our data than would otherwise be possible and we discuss how this methodology can play an important role in enhancing the value of experiments employing multiplexed bead-based immunoassays.United States. Army Research Office (Contract W911NF-09-D-0001)National Institutes of Health (U.S.) (NIH P50-GM68762

Discovering joint associations between disease and gene pairs with a novel similarity test

Genes in a functional pathway can have complex interactions. A gene might activate or suppress another gene, so it is of interest to test joint associations of gene pairs. To simultaneously detect the joint association between disease and two genes (or two chromosomal regions), we propose a new test with the use of genomic similarities. Our test is designed to detect epistasis in the absence of main effects, main effects in the absence of epistasis, or the presence of both main effects and epistasis. Results: The simulation results show that our similarity test with the matching measure is more powerful than the Pearson's chi(2) test when the disease mutants were introduced at common haplotypes, but is less powerful when the disease mutants were introduced at rare haplotypes. Our similarity tests with the counting measures are more sensitive to marker informativity and linkage disequilibrium patterns, and thus are often inferior to the similarity test with the matching measure and the Pearson 's chi(2) test. Conclusions: In detecting joint associations between disease and gene pairs, our similarity test is a complementary method to the Pearson's chi(2) test

Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis

Multiple sclerosis (MS), a chronic disorder of the central nervous system and common cause of neurological disability in young adults, is characterized by moderate but complex risk heritability. Here we report the results of a genome-wide association study performed in a 1000 prospective case series of well-characterized individuals with MS and group-matched controls using the Sentrix® HumanHap550 BeadChip platform from Illumina. After stringent quality control data filtering, we compared allele frequencies for 551 642 SNPs in 978 cases and 883 controls and assessed genotypic influences on susceptibility, age of onset, disease severity, as well as brain lesion load and normalized brain volume from magnetic resonance imaging exams. A multi-analytical strategy identified 242 susceptibility SNPs exceeding established thresholds of significance, including 65 within the MHC locus in chromosome 6p21.3. Independent replication confirms a role for GPC5, a heparan sulfate proteoglycan, in disease risk. Gene ontology-based analysis shows a functional dichotomy between genes involved in the susceptibility pathway and those affecting the clinical phenotyp

Gene-Based Tests of Association

Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal. Permutation tests provide p-values that correct for the number of independent tests genome-wide and within each genetic locus. When applied to a dataset comprising 2.5 million SNPs in up to 8,000 individuals measured for various electrocardiography (ECG) parameters, this method identifies more validated associations than conventional GWAS approaches. The method also provides, for the first time, systematic assessments of the number of independent effects within a gene and the fraction of disease-associated genes housing multiple independent effects, observed at 35%–50% of loci in our study. This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis

Pastoral Herding Strategies and Governmental Management Objectives: Predation Compensation as a Risk Buffering Strategy in the Saami Reindeer Husbandry

Previously it has been found that an important risk buffering strategy in the Saami reindeer husbandry in Norway is the accumulation of large herds of reindeer as this increases long-term household viability. Nevertheless, few studies have investigated how official policies, such as economic compensation for livestock losses, can influence pastoral strategies. This study investigated the effect of received predation compensation on individual husbandry units’ future herd size. The main finding in this study is that predation compensation had a positive effect on husbandry units’ future herd size. The effect of predation compensation, however, was nonlinear in some years, indicating that predation compensation had a positive effect on future herd size only up to a certain threshold whereby adding additional predation compensation had little effect on future herd size. More importantly, the effect of predation compensation was positive after controlling for reindeer density, indicating that for a given reindeer density husbandry units receiving more predation compensation performed better (measured as the size of future herds) compared to husbandry units receiving less compensation

Juvenile Greylag Geese (Anser anser) Discriminate between Individual Siblings

Social species that maintain individualised relationships with certain others despite continuous changes in age, reproductive status and dominance rank between group members ought to be capable of individual recognition. Tests of “true” individual recognition, where an individual recognises unique features of another, are rare, however. Often kinship and/or familiarity suffice to explain dyadic interactions. The complex relationships within a greylag goose flock suggest that they should be able to recognise individuals irrespective of familiarity or kinship. We tested whether six-week-old hand-raised greylags can discriminate between two of their siblings. We developed a new experimental protocol, in which geese were trained to associate social siblings with geometrical symbols. Subsequently, focals were presented with two geometrical symbols in the presence of a sibling associated with one of the symbols. Significant choice of the geometrical symbol associated with the target present indicated that focals were able to distinguish between individual targets. Greylag goslings successfully learned this association-discrimination task, regardless of genetic relatedness or sex of the sibling targets. Social relationships within a goose flock thus may indeed be based on recognition of unique features of individual conspecifics