Brief report: a comparison of the preference for viewing social and non-social movies in typical and autistic adolescents

The recently proposed Social Motivation theory (Chevallier et al., Trends in cognitive sciences 16(4):231–239, 2012) suggests that social difficulties in Autism Spectrum Condition (ASC) might be caused by a difference in the motivation to engage with other people. Here we compared adolescents with (N = 31) and without (N = 37) ASC on the Choose-a-Movie paradigm that measures the social seeking. The results showed a preference for viewing objects over smiling faces in ASC, which is in line with the theory of low social motivation. However, typical adolescents did not show any stimuli preferences, raising questions about developmental changes in social motivation. Age was found to play a significant role in moderating the choice behaviour of the participants. We discuss the implications of these findings in detail

Atypicalities in Perceptual Adaptation in Autism Do Not Extend to Perceptual Causality

A recent study showed that adaptation to causal events (collisions) in adults caused subsequent events to be less likely perceived as causal. In this study, we examined if a similar negative adaptation effect for perceptual causality occurs in children, both typically developing and with autism. Previous studies have reported diminished adaptation for face identity, facial configuration and gaze direction in children with autism. To test whether diminished adaptive coding extends beyond high-level social stimuli (such as faces) and could be a general property of autistic perception, we developed a child-friendly paradigm for adaptation of perceptual causality. We compared the performance of 22 children with autism with 22 typically developing children, individually matched on age and ability (IQ scores). We found significant and equally robust adaptation aftereffects for perceptual causality in both groups. There were also no differences between the two groups in their attention, as revealed by reaction times and accuracy in a change-detection task. These findings suggest that adaptation to perceptual causality in autism is largely similar to typical development and, further, that diminished adaptive coding might not be a general characteristic of autism at low levels of the perceptual hierarchy, constraining existing theories of adaptation in autism.16 page(s

Plant-symbiotic fungi as chemical engineers: multi-genome analysis of the Clavicipitaceae reveals dynamics of alkaloid Loci

The fungal family Clavicipitaceae includes plant symbionts and parasites that produce several psychoactive and bioprotective alkaloids. The family includes grass symbionts in the epichloae clade (Epichloë and Neotyphodium species), which are extraordinarily diverse both in their host interactions and in their alkaloid profiles. Epichloae produce alkaloids of four distinct classes, all of which deter insects, and some—including the infamous ergot alkaloids—have potent effects on mammals. The exceptional chemotypic diversity of the epichloae may relate to their broad range of host interactions, whereby some are pathogenic and contagious, others are mutualistic and vertically transmitted (seed-borne), and still others vary in pathogenic or mutualistic behavior. We profiled the alkaloids and sequenced the genomes of 10 epichloae, three ergot fungi (Claviceps species), a morning-glory symbiont (Periglandula ipomoeae), and a bamboo pathogen (Aciculosporium take), and compared the gene clusters for four classes of alkaloids. Results indicated a strong tendency for alkaloid loci to have conserved cores that specify the skeleton structures and peripheral genes that determine chemical variations that are known to affect their pharmacological specificities. Generally, gene locations in cluster peripheries positioned them near to transposon-derived, AT-rich repeat blocks, which were probably involved in gene losses, duplications, and neofunctionalizations. The alkaloid loci in the epichloae had unusual structures riddled with large, complex, and dynamic repeat blocks. This feature was not reflective of overall differences in repeat contents in the genomes, nor was it characteristic of most other specialized metabolism loci. The organization and dynamics of alkaloid loci and abundant repeat blocks in the epichloae suggested that these fungi are under selection for alkaloid diversification. We suggest that such selection is related to the variable life histories of the epichloae, their protective roles as symbionts, and their associations with the highly speciose and ecologically diverse cool-season grasses

Immunosuppressive potential of human amnion epithelial cells in the treatment of experimental autoimmune encephalomyelitis

BACKGROUND: Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system (CNS). In recent years, it has been found that cells such as human amnion epithelial cells (hAECs) have the ability to modulate immune responses in vitro and in vivo and can differentiate into multiple cell lineages. Accordingly, we investigated the immunoregulatory effects of hAECs as a potential therapy in an MS-like disease, EAE (experimental autoimmune encephalomyelitis), in mice. METHODS: Using flow cytometry, the phenotypic profile of hAECs from different donors was assessed. The immunomodulatory properties of hAECs were examined in vitro using antigen-specific and one-way mixed lymphocyte proliferation assays. The therapeutic efficacy of hAECs was examined using a relapsing-remitting model of EAE in NOD/Lt mice. T cell responsiveness, cytokine secretion, T regulatory, and T helper cell phenotype were determined in the peripheral lymphoid organs and CNS of these animals. RESULTS: In vitro, hAECs suppressed both specific and non-specific T cell proliferation, decreased pro-inflammatory cytokine production, and inhibited the activation of stimulated T cells. Furthermore, T cells retained their naïve phenotype when co-cultured with hAECs. In vivo studies revealed that hAECs not only suppressed the development of EAE but also prevented disease relapse in these mice. T cell responses and production of the pro-inflammatory cytokine interleukin (IL)-17A were reduced in hAEC-treated mice, and this was coupled with a significant increase in the number of peripheral T regulatory cells and naïve CD4+ T cells. Furthermore, increased proportions of Th2 cells in the peripheral lymphoid organs and within the CNS were observed. CONCLUSION: The therapeutic effect of hAECs is in part mediated by inducing an anti-inflammatory response within the CNS, demonstrating that hAECs hold promise for the treatment of autoimmune diseases like MS

Accounting for Redundancy when Integrating Gene Interaction Databases

During the last years gene interaction networks are increasingly being used for the assessment and interpretation of biological measurements. Knowledge of the interaction partners of an unknown protein allows scientists to understand the complex relationships between genetic products, helps to reveal unknown biological functions and pathways, and get a more detailed picture of an organism's complexity. Being able to measure all protein interactions under all relevant conditions is virtually impossible. Hence, computational methods integrating different datasets for predicting gene interactions are needed. However, when integrating different sources one has to account for the fact that some parts of the information may be redundant, which may lead to an overestimation of the true likelihood of an interaction. Our method integrates information derived from three different databases (Bioverse, HiMAP and STRING) for predicting human gene interactions. A Bayesian approach was implemented in order to integrate the different data sources on a common quantitative scale. An important assumption of the Bayesian integration is independence of the input data (features). Our study shows that the conditional dependency cannot be ignored when combining gene interaction databases that rely on partially overlapping input data. In addition, we show how the correlation structure between the databases can be detected and we propose a linear model to correct for this bias. Benchmarking the results against two independent reference data sets shows that the integrated model outperforms the individual datasets. Our method provides an intuitive strategy for weighting the different features while accounting for their conditional dependencies

Evaluating the association of common PBX1 variants with type 2 diabetes

<p>Abstract</p> <p>Background</p> <p><it>PBX1 </it>is a biological candidate gene for type 2 diabetes at the 1q21-q24 susceptibility locus. The aim of this study was to evaluate the association of common <it>PBX1 </it>variants with type 2 diabetes in French Caucasian subjects.</p> <p>Methods</p> <p>Employing a case-control design, we genotyped 39 SNPs spanning the <it>PBX1 </it>locus in 3,093 subjects to test for association with type 2 diabetes.</p> <p>Results</p> <p>Several <it>PBX1 </it>SNPs, including the G21S coding SNP rs2275558, were nominally associated with type 2 diabetes but the strongest result was obtained with the intron 2 SNP rs2792248 (P = 0.004, OR 1.20 [95% CI 1.06–1.37]). The SNPSpD multiple testing correction method gave a significance threshold of P = 0.002 for the 39 SNPs genotyped, indicating that the rs2792248 association did not survive multiple testing adjustment. SNP rs2792248 did not show evidence of association with the French 1q linkage signal (P = 0.31; weighted NPL score 2.16). None of the <it>PBX1 </it>SNPs nominally associated with type 2 diabetes were associated with a range of quantitative metabolic traits in the normoglycemic control subjects</p> <p>Conclusion</p> <p>The available data does not support a major influence of common <it>PBX1 </it>variants on type 2 diabetes susceptibility or quantitative metabolic traits. In order to make progress in identifying the elusive susceptibility variants in the 1q region it will be necessary to carry out further large association studies, meta-analyses of existing data from individual studies, and deep resequencing of the 1q region.</p

The effect of pyramiding Phytophthora infestans resistance genes RPi-mcd1 and RPi-ber in potato

Despite efforts to control late blight in potatoes by introducing Rpi-genes from wild species into cultivated potato, there are still concerns regarding the durability and level of resistance. Pyramiding Rpi-genes can be a solution to increase both durability and level of resistance. In this study, two resistance genes, RPi-mcd1 and RPi-ber, introgressed from the wild tuber-bearing potato species Solanum microdontum and S. berthaultii were combined in a diploid S. tuberosum population. Individual genotypes from this population were classified after four groups, carrying no Rpi-gene, with only RPi-mcd1, with only RPi-ber, and a group with the pyramided RPi-mcd1 and RPi-ber by means of tightly linked molecular markers. The levels of resistance between the groups were compared in a field experiment in 2007. The group with RPi-mcd1 showed a significant delay to reach 50% infection of the leaf area of 3 days. The group with RPi-ber showed a delay of 3 weeks. The resistance level in the pyramid group suggested an additive effect of RPi-mcd1 with RPi-ber. This suggests that potato breeding can benefit from combining individual Rpi-genes, irrespective of the weak effect of RPi-mcd1 or the strong effect of RPi-ber

Bio::Homology::InterologWalk - A Perl module to build putative protein-protein interaction networks through interolog mapping

<p>Abstract</p> <p>Background</p> <p>Protein-protein interaction (PPI) data are widely used to generate network models that aim to describe the relationships between proteins in biological systems. The fidelity and completeness of such networks is primarily limited by the paucity of protein interaction information and by the restriction of most of these data to just a few widely studied experimental organisms. In order to extend the utility of existing PPIs, computational methods can be used that exploit functional conservation between orthologous proteins across taxa to predict putative PPIs or 'interologs'. To date most interolog prediction efforts have been restricted to specific biological domains with fixed underlying data sources and there are no software tools available that provide a generalised framework for 'on-the-fly' interolog prediction.</p> <p>Results</p> <p>We introduce <monospace>Bio::Homology::InterologWalk</monospace>, a Perl module to retrieve, prioritise and visualise putative protein-protein interactions through an orthology-walk method. The module uses orthology and experimental interaction data to generate putative PPIs and optionally collates meta-data into an Interaction Prioritisation Index that can be used to help prioritise interologs for further analysis. We show the application of our interolog prediction method to the genomic interactome of the fruit fly, <it>Drosophila melanogaster</it>. We analyse the resulting interaction networks and show that the method proposes new interactome members and interactions that are candidates for future experimental investigation.</p> <p>Conclusions</p> <p>Our interolog prediction tool employs the Ensembl Perl API and PSICQUIC enabled protein interaction data sources to generate up to date interologs 'on-the-fly'. This represents a significant advance on previous methods for interolog prediction as it allows the use of the latest orthology and protein interaction data for all of the genomes in Ensembl. The module outputs simple text files, making it easy to customise the results by post-processing, allowing the putative PPI datasets to be easily integrated into existing analysis workflows. The <monospace>Bio::Homology::InterologWalk</monospace> module, sample scripts and full documentation are freely available from the Comprehensive Perl Archive Network (CPAN) under the GNU Public license.</p