Identification of dilated cardiomyopathy signature genes through gene expression and network data integration

AbstractDilated cardiomyopathy (DCM) is a leading cause of heart failure (HF) and cardiac transplantations in Western countries. Single-source gene expression analysis studies have identified potential disease biomarkers and drug targets. However, because of the diversity of experimental settings and relative lack of data, concerns have been raised about the robustness and reproducibility of the predictions. This study presents the identification of robust and reproducible DCM signature genes based on the integration of several independent data sets and functional network information. Gene expression profiles from three public data sets containing DCM and non-DCM samples were integrated and analyzed, which allowed the implementation of clinical diagnostic models. Differentially expressed genes were evaluated in the context of a global protein–protein interaction network, constructed as part of this study. Potential associations with HF were identified by searching the scientific literature. From these analyses, classification models were built and their effectiveness in differentiating between DCM and non-DCM samples was estimated. The main outcome was a set of integrated, potentially novel DCM signature genes, which may be used as reliable disease biomarkers. An empirical demonstration of the power of the integrative classification models against single-source models is also given

Latin America:A development pole for phenomics

Latin America and the Caribbean (LAC) has long been associated with the production and export of a diverse range of agricultural commodities. Due to its strategic geographic location, which encompasses a wide range of climates, it is possible to produce almost any crop. The climate diversity in LAC is a major factor in its agricultural potential but this also means climate change represents a real threat to the region. Therefore, LAC farming must prepare and quickly adapt to a climate that is likely to feature long periods of drought, excessive rainfall and extreme temperatures. With the aim of moving towards a more resilient agriculture, LAC scientists have created the Latin American Plant Phenomics Network (LatPPN) which focuses on LAC’s economically important crops. LatPPN’s key strategies to achieve its main goal are: 1) training of LAC members on plant phenomics and phenotyping, 2) establish international and multidisciplinary collaborations, 3) develop standards for data exchange and research protocols, 4) share equipment and infrastructure, 5) disseminate data and research results, 6) identify funding opportunities and 7) develop strategies to guarantee LatPPN’s relevance and sustainability across time. Despite the challenges ahead, LatPPN represents a big step forward towards the consolidation of a common mind-set in the field of plant phenotyping and phenomics in LAC

Objective Definition of Rosette Shape Variation Using a Combined Computer Vision and Data Mining Approach

Computer-vision based measurements of phenotypic variation have implications for crop improvement and food security because they are intrinsically objective. It should be possible therefore to use such approaches to select robust genotypes. However, plants are morphologically complex and identification of meaningful traits from automatically acquired image data is not straightforward. Bespoke algorithms can be designed to capture and/or quantitate specific features but this approach is inflexible and is not generally applicable to a wide range of traits. In this paper, we have used industry-standard computer vision techniques to extract a wide range of features from images of genetically diverse Arabidopsis rosettes growing under non-stimulated conditions, and then used statistical analysis to identify those features that provide good discrimination between ecotypes. This analysis indicates that almost all the observed shape variation can be described by 5 principal components. We describe an easily implemented pipeline including image segmentation, feature extraction and statistical analysis. This pipeline provides a cost-effective and inherently scalable method to parameterise and analyse variation in rosette shape. The acquisition of images does not require any specialised equipment and the computer routines for image processing and data analysis have been implemented using open source software. Source code for data analysis is written using the R package. The equations to calculate image descriptors have been also provided

Editorial:Plant Phenotyping and Phenomics for Plant Breeding

publishersversionPeer reviewe

Reprogramming of the wheat transcriptome in response to infection with Claviceps purpurea, the causal agent of ergot.

BACKGROUND: Ergot, caused by the fungal pathogen Claviceps purpurea, infects the female flowers of a range of cereal crops, including wheat. To understand the interaction between C. purpurea and hexaploid wheat we undertook an extensive examination of the reprogramming of the wheat transcriptome in response to C. purpurea infection through floral tissues (i.e. the stigma, transmitting and base ovule tissues of the ovary) and over time. RESULTS: C. purpurea hyphae were observed to have grown into and down the stigma at 24 h (H) after inoculation. By 48H hyphae had grown through the transmitting tissue into the base, while by 72H hyphae had surrounded the ovule. By 5 days (D) the ovule had been replaced by fungal tissue. Differential gene expression was first observed at 1H in the stigma tissue. Many of the wheat genes differentially transcribed in response to C. purpurea infection were associated with plant hormones and included the ethylene (ET), auxin, cytokinin, gibberellic acid (GA), salicylic acid and jasmonic acid (JA) biosynthetic and signaling pathways. Hormone-associated genes were first detected in the stigma and base tissues at 24H, but not in the transmitting tissue. Genes associated with GA and JA pathways were seen in the stigma at 24H, while JA and ET-associated genes were identified in the base at 24H. In addition, several defence-related genes were differential expressed in response to C. purpurea infection, including antifungal proteins, endocytosis/exocytosis-related proteins, NBS-LRR class proteins, genes involved in programmed cell death, receptor protein kinases and transcription factors. Of particular interest was the identification of differential expression of wheat genes in the base tissue well before the appearance of fungal hyphae, suggesting that a mobile signal, either pathogen or plant-derived, is delivered to the base prior to colonisation. CONCLUSIONS: Multiple host hormone biosynthesis and signalling pathways were significantly perturbed from an early stage in the wheat - C. purpurea interaction. Differential gene expression at the base of the ovary, ahead of arrival of the pathogen, indicated the potential presence of a long-distance signal modifying host gene expression

Determining phenological patterns associated with the onset of senescence in a wheat MAGIC mapping population

The appropriate timing of developmental transitions is critical for adapting many crops to their local climatic conditions. Therefore, understanding the genetic basis of different aspects of phenology could be useful in highlighting mechanisms underpinning adaptation, with implications in breeding for climate change. For bread wheat (Triticum aestivum), the transition from vegetative to reproductive growth, the start and rate of leaf senescence and the relative timing of different stages of flowering and grain filling all contribute to plant performance. In this study we screened under Smart house conditions a large, multi-founder “NIAB elite MAGIC” wheat population, to evaluate the genetic elements that influence the timing of developmental stages in European elite varieties. This panel of recombinant inbred lines was derived from eight parents that are or recently have been grown commercially in the UK and Northern Europe. We undertook a detailed temporal phenotypic analysis under Smart house conditions of the population and its parents, to try to identify known or novel Quantitative Trait Loci associated with variation in the timing of key phenological stages in senescence. This analysis resulted in the detection of QTL interactions with novel traits such the time between “half of ear emergence above flag leaf ligule” and the onset of senescence at the flag leaf as well as traits associated with plant morphology such as stem height. In addition, strong correlations between several traits and the onset of senescence of the flag leaf were identified. This work establishes the value of systematically phenotyping genetically unstructured populations to reveal the genetic architecture underlying morphological variation in commercial wheat

Functional mapping of quantitative trait loci (QTLs) associated with plant performance in a wheat MAGIC mapping population

In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Trait data from single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding

Colombia's cyberinfrastructure for biodiversity: Building data infrastructure in emerging countries to foster socioeconomic growth

Science and innovation are not a luxury but a prerequisite for social and economic development (Annan, 2003)