A genome-wide association study for shared risk across major psychiatric disorders in a nation-wide birth cohort implicates fetal neurodevelopment as a key mediator, bioRxiv, 2017-12-30

AbstractThere is mounting evidence that seemingly diverse psychiatric disorders share genetic etiology, but the biological substrates mediating this overlap are not well characterized. Here, we leverage the unique iPSYCH study, a nationally representative cohort ascertained through clinical psychiatric diagnoses indicated in Danish national health registers. We confirm previous reports of individual and cross-disorder SNP-heritability for major psychiatric disorders and perform a cross-disorder genome-wide association study. We identify four novel genome-wide significant loci encompassing variants predicted to regulate genes expressed in radial glia and interneurons in the developing neocortex during midgestation. This epoch is supported by partitioning cross-disorder SNP-heritability which is enriched at regulatory chromatin active during fetal neurodevelopment. These findings indicate that dysregulation of genes that direct neurodevelopment by common genetic variants results in general liability for many later psychiatric outcomes.

The network structure of cancer ecosystems, bioRxiv, 2017-12-30

Ever since Paget’s seed-and-soil and Ewing’s connectivity hypotheses to explain tumor metastasis (1,2), it has become clear that cancer progression can be envisaged as an ecological phenomenon. This connection has flourished during the past two decades (3–7), giving rise to important insights into the ecology and evolution of cancer progression, with therapeutic implications (8–10). Here, we take a metapopulation view of metastasis (i.e. the migration to and colonization of, habitat patches) and represent it as a bipartite network, distinguishing source patches, or organs that host a primary tumor, and acceptor patches, or organs colonized ultimately from the source through metastasis. Using 20,326, biomedical records obtained from literature, we show that (i) the network structure of cancer ecosystems is non-random, exhibiting a nested subset pattern as has been found both in the distribution of species across islands and island-like habitats (11–13), and in the distribution of among species interactions across different ecological networks (14–16); (ii) similar to ecological networks, there is a heterogeneous distribution of degree (i.e., number of connections associated with a source or acceptor organ); (iii) there is a significant correlation between metastatic incidence (or the frequency with which tumor cells from a source organ colonize an acceptor one) and arterial blood supply, suggesting that more irrigated organs have a higher probability of developing metastasis or being invaded; (iv) there is a positive correlation between metastatic incidence and acceptor organ degree (or number of different tumor-bearing source organs that generate metastasis in a given acceptor organ), and a negative one between acceptor organ degree and number of stem cell divisions, implying that there are preferred sink organs for metastasis and that this could be related to average acceptor organ cell longevity; (v) there is a negative association between organ cell turnover and source organ degree, implying that organs with rapid cell turnovers tend to generate more metastasis, a process akin to the phenomenon of propagule pressure in ecology (17); and (vi) the cancer ecosystem network exhibits a modular structure in both source and acceptor patches, suggesting that some of them share more connections among themselves than with the rest of the network. We show that both niche-related processes occurring at the organ level as well as spatial connectivity and propagule pressure contribute to metastaticspread and result in a non-random cancer network, which exhibits a truncated power law degree distribution, clustering and a nested subset structure. The similarity between the cancer network and ecological networks highlights the importance of ecological approaches in increasing our understanding of patterns in cancer incidence and dynamics, which may lead to new strategies to control tumor spread within the human ecosystem.

Reproducible Bioinformatics Project A community for reproducible bioinformatics analysis pipelines, bioRxiv, 2017-12-27

AbstractBackgroundReproducibility of a research is a key element in the modern science and it is mandatory for any industrial application. It represents the ability of replicating an experiment independently by the location and the operator. Therefore, a study can be considered reproducible only if all used data are available and the exploited computational analysis workflow is clearly described. However, today for reproducing a complex bioinformatics analysis, the raw data and a list of tools used in the workflow could be not enough to guarantee the reproducibility of the results obtained. Indeed, different releases of the same tools andor of the system libraries (exploited by such tools) might lead to sneaky reproducibility issues.ResultsTo address this challenge, we established the Reproducible Bioinformatics Project (RBP), which is a non-profit and open-source project, whose aim is to provide a schema and an infrastructure, based on docker images and R package, to provide reproducible results in Bioinformatics. One or more Docker images are then defined for a workflow (typically one for each task), while the workflow implementation is handled via R-functions embedded in a package available at github repository. Thus, a bioinformatician participating to the project has firstly to integrate herhis workflow modules into Docker image(s) exploiting an Ubuntu docker image developed ad hoc by RPB to make easier this task. Secondly, the workflow implementation must be realized in R according to an R-skeleton function made available by RPB to guarantee homogeneity and reusability among different RPB functions. Moreover shehe has to provide the R vignette explaining the package functionality together with an example dataset which can be used to improve the user confidence in the workflow utilization.ConclusionsReproducible Bioinformatics Project provides a general schema and an infrastructure to distribute robust and reproducible workflows. Thus, it guarantees to final users the ability to repeat consistently any analysis independently by the used UNIX-like architecture.

An ancient integration in a plant NLR is maintained as a trans-species polymorphism, bioRxiv, 2017-12-26

Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing, bioRxiv, 2017-12-26

Transcriptional profiling of complex tissues by RNA-sequencing of single nuclei presents some advantages over whole cell analysis. It enables unbiased cellular coverage, lack of cell isolation-based transcriptional effects, and application to archived frozen specimens. Using a well-matched pair of single-nucleus RNA-seq (snRNA-seq) and single-cell RNA-seq (scRNA-seq) SMART-Seq v4 datasets from mouse visual cortex, we demonstrate that similarly high-resolution clustering of closely related neuronal types can be achieved with both methods if intronic sequences are included in nuclear RNA-seq analysis. More transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), but the majority of genes have similar detection across cells and nuclei. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

MAPLE a Modular Automated Platform for Large-scale Experiments, a low-cost robot for integrated animal-handling and phenotyping, bioRxiv, 2017-12-25

AbstractGenetic model system animals have significant scientific value in part because of large-scale experiments like screens, but performing such experiments over long time periods by hand is arduous and risks errors. Thus the field is poised to benefit from automation, just as molecular biology did from liquid-handling robots. We developed a Modular Automated Platform for Large-scale Experiments (MAPLE), a Drosophila-handling robot capable of conducting lab tasks and experiments. We demonstrate MAPLE’s ability to accelerate the collection of virgin female flies (a pervasive experimental chore in fly genetics) and assist high-throughput phenotyping assays. Using MAPLE to autonomously run a novel social interaction experiment, we found that 1) pairs of flies exhibit persistent idiosyncrasies in affiliative behavior, 2) these dyad-specific interactions require olfactory and visual cues, and 3) social interaction network structure is topologically stable over time. These diverse examples demonstrate MAPLE’s versatility as a general platform for conducting fly science automatically.