Federico M. Giorgi

Oxygen sensing in plants is mediated by an N-end rule pathway for protein destabilization

The majority of eukaryotic organisms rely on molecular oxygen for respiratory energy production. When the supply of oxygen is compromised, a variety of acclimation responses are activated to reduce the detrimental effects of energy depletion. Various oxygen-sensing mechanisms have been described that are thought to trigger these responses, but they each seem to be kingdom specific and no sensing mechanism has been identified in plants until now. Here we show that one branch of the ubiquitin-dependent N-end rule pathway for protein degradation, which is active in both mammals and plants, functions as an oxygen-sensing mechanism in Arabidopsis thaliana. We identified a conserved amino-terminal amino acid sequence of the ethylene response factor (ERF)-transcription factor RAP2.12 to be dedicated to an oxygen-dependent sequence of post-translational modifications, which ultimately lead to degradation of RAP2.12 under aerobic conditions. When the oxygen concentration is low—as during flooding—RAP2.12 is released from the plasma membrane and accumulates in the nucleus to activate gene expression for hypoxia acclimation. Our discovery of an oxygen-sensing mechanism opens up new possibilities for improving flooding tolerance in crops.

PlaNet: Combined Sequence and Expression Comparisons across Plant Networks Derived from Seven Species

Genes that are similarly expressed, or coexpressed, are often involved in related biological processes. Such coexpressed relationships also appear to be conserved across species. The PlaNet platform enables comparative analysis of genome-wide coexpression networks across seven plant species, thus enabling prediction of gene function and elucidation of the identity of functional homologs. The model organism Arabidopsis thaliana is readily used in basic research due to resource availability and relative speed of data acquisition. A major goal is to transfer acquired knowledge from Arabidopsis to crop species. However, the identification of functional equivalents of well-characterized Arabidopsis genes in other plants is a nontrivial task. It is well documented that transcriptionally coordinated genes tend to be functionally related and that such relationships may be conserved across different species and even kingdoms. To exploit such relationships, we constructed whole-genome coexpression networks for Arabidopsis and six important plant crop species. The interactive networks, clustered using the HCCA algorithm, are provided under the banner PlaNet (http://aranet.mpimp-golm.mpg.de). We implemented a comparative network algorithm that estimates similarities between network structures. Thus, the platform can be used to swiftly infer similar coexpressed network vicinities within and across species and can predict the identity of functional homologs. We exemplify this using the PSA-D and chalcone synthase-related gene networks. Finally, we assessed how ontology terms are transcriptionally connected in the seven species and provide the corresponding MapMan term coexpression networks. The data support the contention that this platform will considerably improve transfer of knowledge generated in Arabidopsis to valuable crop species.

Genomic and transcriptomic analysis of the AP2/ERF superfamily in Vitis vinifera

BackgroundThe AP2/ERF protein family contains transcription factors that play a crucial role in plant growth and development and in response to biotic and abiotic stress conditions in plants. Grapevine (Vitis vinifera) is the only woody crop whose genome has been fully sequenced. So far, no detailed expression profile of AP2/ERF-like genes is available for grapevine.ResultsAn exhaustive search for AP2/ERF genes was carried out on the Vitis vinifera genome and their expression profile was analyzed by Real-Time quantitative PCR (qRT-PCR) in different vegetative and reproductive tissues and under two different ripening stages.One hundred and forty nine sequences, containing at least one ERF domain, were identified. Specific clusters within the AP2 and ERF families showed conserved expression patterns reminiscent of other species and grapevine specific trends related to berry ripening. Moreover, putative targets of group IX ERFs were identified by co-expression and protein similarity comparisons.ConclusionsThe grapevine genome contains an amount of AP2/ERF genes comparable to that of other dicot species analyzed so far. We observed an increase in the size of specific groups within the ERF family, probably due to recent duplication events. Expression analyses in different aerial tissues display common features previously described in other plant systems and introduce possible new roles for members of some ERF groups during fruit ripening. The presented analysis of AP2/ERF genes in grapevine provides the bases for studying the molecular regulation of berry development and the ripening process.

An Extensive Evaluation of Read Trimming Effects on Illumina NGS Data Analysis

Next Generation Sequencing is having an extremely strong impact in biological and medical research and diagnostics, with applications ranging from gene expression quantification to genotyping and genome reconstruction. Sequencing data is often provided as raw reads which are processed prior to analysis 1 of the most used preprocessing procedures is read trimming, which aims at removing low quality portions while preserving the longest high quality part of a NGS read. In the current work, we evaluate nine different trimming algorithms in four datasets and three common NGS-based applications (RNA-Seq, SNP calling and genome assembly). Trimming is shown to increase the quality and reliability of the analysis, with concurrent gains in terms of execution time and computational resources needed.

Robin: An Intuitive Wizard Application for R-Based Expression Microarray Quality Assessment and Analysis

The wide application of high-throughput transcriptomics using microarrays has generated a plethora of technical platforms, data repositories, and sophisticated statistical analysis methods, leaving the individual scientist with the problem of choosing the appropriate approach to address a biological question. Several software applications that provide a rich environment for microarray analysis and data storage are available (e.g. GeneSpring, EMMA2), but these are mostly commercial or require an advanced informatics infrastructure. There is a need for a noncommercial, easy-to-use graphical application that aids the lab researcher to find the proper method to analyze microarray data, without this requiring expert understanding of the complex underlying statistics, or programming skills. We have developed Robin, a Java-based graphical wizard application that harnesses the advanced statistical analysis functions of the R/BioConductor project. Robin implements streamlined workflows that guide the user through all steps of two-color, single-color, or Affymetrix microarray analysis. It provides functions for thorough quality assessment of the data and automatically generates warnings to notify the user of potential outliers, low-quality chips, or low statistical power. The results are generated in a standard format that allows ready use with both specialized analysis tools like MapMan and PageMan and generic spreadsheet applications. To further improve user friendliness, Robin includes both integrated help and comprehensive external documentation. To demonstrate the statistical power and ease of use of the workflows in Robin, we present a case study in which we apply Robin to analyze a two-color microarray experiment comparing gene expression in tomato (Solanum lycopersicum) leaves, flowers, and roots.

Functional characterization of somatic mutations in cancer using network-based inference of protein activity

Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, virtual inference of protein activity by enriched regulon analysis (VIPER), for accurate assessment of protein activity from gene expression data. We used VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all samples in The Cancer Genome Atlas (TCGA). In addition to accurately infer aberrant protein activity induced by established mutations, we also identified a fraction of tumors with aberrant activity of druggable oncoproteins despite a lack of mutations, and vice versa. In vitro assays confirmed that VIPER-inferred protein activity outperformed mutational analysis in predicting sensitivity to targeted inhibitors.Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, virtual inference of protein activity by enriched regulon analysis (VIPER), for accurate assessment of protein activity from gene expression data. We used VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all samples in The Cancer Genome Atlas (TCGA). In addition to accurately infer aberrant protein activity induced by established mutations, we also identified a fraction of tumors with aberrant activity of druggable oncoproteins despite a lack of mutations, and vice versa. In vitro assays confirmed that VIPER-inferred protein activity outperformed mutational analysis in predicting sensitivity to targeted inhibitors.

Comparative study of RNA-seq-and Microarray-derived coexpression networks in Arabidopsis thaliana

MOTIVATION Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. They have been used for hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. So far, the main platform for expression data has been DNA microarrays; however, the recent development of RNA-seq allows for higher accuracy and coverage of transcript populations. It is therefore important to assess the potential for biological investigation of coexpression networks derived from this novel technique in a condition-independent dataset. RESULTS We collected 65 publicly available Illumina RNA-seq high quality Arabidopsis thaliana samples and generated Pearson correlation coexpression networks. These networks were then compared with those derived from analogous microarray data. We show how Variance-Stabilizing Transformed (VST) RNA-seq data samples are the most similar to microarray ones, with respect to inter-sample variation, correlation coefficient distribution and network topological architecture. Microarray networks show a slightly higher score in biology-derived quality assessments such as overlap with the known protein-protein interaction network and edge ontological agreement. Different coexpression network centralities are investigated; in particular, we show how betweenness centrality is generally a positive marker for essential genes in A.thaliana, regardless of the platform originating the data. In the end, we focus on a specific gene network case, showing that although microarray data seem more suited for gene network reverse engineering, RNA-seq offers the great advantage of extending coexpression analyses to the entire transcriptome.

Algorithm-driven Artifacts in median polish summarization of Microarray data

BackgroundHigh-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade. Different preprocessing techniques exist to convert the raw signal intensities measured by these chips into gene expression estimates. Although these techniques have been widely benchmarked in the context of differential gene expression analysis, there are only few examples where their performance has been assessed in respect to coexpression-based studies such as sample classification.ResultsIn the present paper we benchmark the three most used normalization procedures (MAS5, RMA and GCRMA) in the context of inter-array correlation analysis, confirming and extending the finding that RMA and GCRMA consistently overestimate sample similarity upon normalization. We determine that median polish summarization is responsible for generating a large proportion of these over-similarity artifacts. Furthermore, we show that most affected probesets show also internal signal disagreement, and tend to be composed by individual probes hitting different gene transcripts. We finally provide a correction to the RMA/GCRMA summarization procedure that massively reduces inter-array correlation artifacts, without affecting the detection of differentially expressed genes.ConclusionsWe propose tRMA as a modification of RMA to normalize microarray experiments for correlation-based analysis.

Transcriptional Wiring of Cell Wall-Related Genes in Arabidopsis

Transcriptional coordination, or co-expression, of genes may signify functional relatedness of the corresponding proteins. For example, several genes involved in secondary cell wall cellulose biosynthesis are co-expressed with genes engaged in the synthesis of xylan, which is a major component of the secondary cell wall. To extend these types of analyses, we investigated the co-expression relationships of all Carbohydrate-Active enZYmes (CAZy)-related genes for Arabidopsis thaliana. Thus, the intention was to transcriptionally link different cell wall-related processes to each other, and also to other biological functions. To facilitate easy manual inspection, we have displayed these interactions as networks and matrices, and created a web-based interface (http://aranet.mpimp-golm.mpg.de/corecarb) containing downloadable files for all the transcriptional associations.

ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information

Summary: The accurate reconstruction of gene regulatory networks from large scale molecular profile datasets represents one of the grand challenges of Systems Biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective tools to accomplish this goal. However, the initial Fixed Bandwidth (FB) implementation is both inefficient and unable to deal with sample sets providing largely uneven coverage of the probability density space. Here, we present a completely new implementation of the algorithm, based on an Adaptive Partitioning strategy (AP) for estimating the Mutual Information. The new AP implementation (ARACNe-AP) achieves a dramatic improvement in computational performance (200× on average) over the previous methodology, while preserving the Mutual Information estimator and the Network inference accuracy of the original algorithm. Given that the previous version of ARACNe is extremely demanding, the new version of the algorithm will allow even researchers with modest computational resources to build complex regulatory networks from hundreds of gene expression profiles. Availability and Implementation: A JAVA cross-platform command line executable of ARACNe, together with all source code and a detailed usage guide are freely available on Sourceforge (http://sourceforge.net/projects/aracne-ap). JAVA version 8 or higher is required. Contact: califano@c2b2.columbia.edu Supplementary information: Supplementary data are available at Bioinformatics online.