Next maSigPro: updating maSigPro Bioconductor package for RNA-seq time series

[EN] Motivation: The widespread adoption of RNA-seq to quantitatively measure gene expression has increased the scope of sequencing experimental designs to include time-course experiments. maSigPro is an R package specifically suited for the analysis of time-course gene expression data, which was developed originally for microarrays and hence was limited in its application to count data. Results: We have updated maSigPro to support RNA-seq time series analysis by introducing generalized linear models in the algorithm to support the modeling of count data while maintaining the traditional functionalities of the package. We show a good performance of the maSigPro-GLM method in several simulated time-course scenarios and in a real experimental dataset. Availability and implementation: The package is freely available under the LGPL license from the Bioconductor Web site (http:// bioconductor.org)This work has been funded by the FP7 STATegra [GA-30600] project, EU FP7 [30600] and the Spanish MINECO [BIO2012-40244].Nueda, MJ.; Tarazona Campos, S.; Conesa, A. (2014). Next maSigPro: updating maSigPro Bioconductor package for RNA-seq time series. Bioinformatics. 30(18):2598-2602. https://doi.org/10.1093/bioinformatics/btu333S259826023018Anders, S., & Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biology, 11(10). doi:10.1186/gb-2010-11-10-r106Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010). Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics, 11(1). doi:10.1186/1471-2105-11-94Conesa, A., Nueda, M. J., Ferrer, A., & Talon, M. (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics, 22(9), 1096-1102. doi:10.1093/bioinformatics/btl056Hacquard, S., Kracher, B., Maekawa, T., Vernaldi, S., Schulze-Lefert, P., & Ver Loren van Themaat, E. (2013). Mosaic genome structure of the barley powdery mildew pathogen and conservation of transcriptional programs in divergent hosts. Proceedings of the National Academy of Sciences, 110(24), E2219-E2228. doi:10.1073/pnas.1306807110Hoogerwerf, W. A., Sinha, M., Conesa, A., Luxon, B. A., Shahinian, V. B., Cornélissen, G., … Cassone, V. M. (2008). Transcriptional Profiling of mRNA Expression in the Mouse Distal Colon. Gastroenterology, 135(6), 2019-2029. doi:10.1053/j.gastro.2008.08.048Levin, A. M., de Vries, R. P., Conesa, A., de Bekker, C., Talon, M., Menke, H. H., … Wösten, H. A. B. (2007). Spatial Differentiation in the Vegetative Mycelium ofAspergillus niger. Eukaryotic Cell, 6(12), 2311-2322. doi:10.1128/ec.00244-07Liu, Y., Zhou, J., & White, K. P. (2013). RNA-seq differential expression studies: more sequence or more replication? Bioinformatics, 30(3), 301-304. doi:10.1093/bioinformatics/btt688Maekawa, T., Kracher, B., Vernaldi, S., Ver Loren van Themaat, E., & Schulze-Lefert, P. (2012). Conservation of NLR-triggered immunity across plant lineages. Proceedings of the National Academy of Sciences, 109(49), 20119-20123. doi:10.1073/pnas.1218059109Medina, I., Carbonell, J., Pulido, L., Madeira, S. C., Goetz, S., Conesa, A., … Dopazo, J. (2010). Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Research, 38(suppl_2), W210-W213. doi:10.1093/nar/gkq388Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods, 5(7), 621-628. doi:10.1038/nmeth.1226Nueda, M. j., Ferrer, A., & Conesa, A. (2011). ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics, 13(3), 553-566. doi:10.1093/biostatistics/kxr042Risso, D., Schwartz, K., Sherlock, G., & Dudoit, S. (2011). GC-Content Normalization for RNA-Seq Data. BMC Bioinformatics, 12(1), 480. doi:10.1186/1471-2105-12-480Roberts, A., & Pachter, L. (2012). Streaming fragment assignment for real-time analysis of sequencing experiments. Nature Methods, 10(1), 71-73. doi:10.1038/nmeth.2251Robinson, M. D., & Oshlack, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology, 11(3), R25. doi:10.1186/gb-2010-11-3-r25Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2009). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140. doi:10.1093/bioinformatics/btp616Sims, D., Sudbery, I., Ilott, N. E., Heger, A., & Ponting, C. P. (2014). Sequencing depth and coverage: key considerations in genomic analyses. Nature Reviews Genetics, 15(2), 121-132. doi:10.1038/nrg3642Tarazona, S., Garcia-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A. (2011). Differential expression in RNA-seq: A matter of depth. Genome Research, 21(12), 2213-2223. doi:10.1101/gr.124321.111Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D. R., … Pachter, L. (2012). Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols, 7(3), 562-578. doi:10.1038/nprot.2012.016Terol, J., Conesa, A., Colmenero, J. M., Cercos, M., Tadeo, F., Agustí, J., … Talon, M. (2007). BMC Genomics, 8(1), 31. doi:10.1186/1471-2164-8-3

ARSyN: a method for the identification and removal of systematic noise in multifactorial time-course microarray experiments

Transcriptomic profiling experiments that aim to the identification of responsive genes in specific biological conditions are commonly set up under defined experimental designs that try to assess the effects of factors and their interactions on gene expression. Data from these controlled experiments, however, may also contain sources of unwanted noise that can distort the signal under study, affect the residuals of applied statistical models, and hamper data analysis. Commonly, normalization methods are applied to transcriptomics data to remove technical artifacts, but these are normally based on general assumptions of transcript distribution and greatly ignore both the characteristics of the experiment under consideration and the coordinative nature of gene expression. In this paper, we propose a novel methodology, ARSyN, for the preprocessing of microarray data that takes into account these 2 last aspects. By combining analysis of variance (ANOVA) modeling of gene expression values and multivariate analysis of estimated effects, the method identifies the nonstructured part of the signal associated to the experimental factors (the noise within the signal) and the structured variation of the ANOVA errors (the signal of the noise). By removing these noise fractions from the original data, we create a filtered data set that is rich in the information of interest and includes only the random noise required for inferential analysis. In this work, we focus on multifactorial time course microarray (MTCM) experiments with 2 factors: one quantitative such as time or dosage and the other qualitative, as tissue, strain, or treatment. However, the method can be used in other situations such as experiments with only one factor or more complex designs with more than 2 factors. The filtered data obtained after applying ARSyN can be further analyzed with the appropriate statistical technique to obtain the biological information required. To evaluate the performance of the filtering strategy, we have applied different statistical approaches for MTCM analysis to several real and simulateddata sets, studying also the efficiency of these techniques. By comparing the results obtained with the original and ARSyN filtered data and also with other filtering techniques, we can conclude that the proposed method increases the statistical power to detect biological signals, especially in cases where there are high levels of structural noise. Software for ARSyN is freely available at http://www.ua.es/personal/mj.nuedaSpanish MICINN Project (BIO2008-04368-E and DPI2008-06880-C03-03/DPI).Nueda, MJ.; Ferrer Riquelme, AJ.; Conesa, A. (2011). ARSyN: a method for the identification and removal of systematic noise in multifactorial time-course microarray experiments. Biostatistics. 13(3):553-566. doi:10.1093/biostatistics/kxr042S553566133Al-Shahrour, F., Minguez, P., Tárraga, J., Medina, I., Alloza, E., Montaner, D., & Dopazo, J. (2007). FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Research, 35(suppl_2), W91-W96. doi:10.1093/nar/gkm260Alter, O., Brown, P. O., & Botstein, D. (2000). Singular value decomposition for genome-wide expression data processing and modeling. Proceedings of the National Academy of Sciences, 97(18), 10101-10106. doi:10.1073/pnas.97.18.10101Benito, M., Parker, J., Du, Q., Wu, J., Xiang, D., Perou, C. M., & Marron, J. S. (2003). Adjustment of systematic microarray data biases. Bioinformatics, 20(1), 105-114. doi:10.1093/bioinformatics/btg385Brumós, J., Colmenero-Flores, J. M., Conesa, A., Izquierdo, P., Sánchez, G., Iglesias, D. J., … Talón, M. (2009). Membrane transporters and carbon metabolism implicated in chloride homeostasis differentiate salt stress responses in tolerant and sensitive Citrus rootstocks. Functional & Integrative Genomics, 9(3), 293-309. doi:10.1007/s10142-008-0107-6Conesa, A., Nueda, M. J., Ferrer, A., & Talon, M. (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics, 22(9), 1096-1102. doi:10.1093/bioinformatics/btl056Heijne, W. H. ., Stierum, R. H., Slijper, M., van Bladeren, P. J., & van Ommen, B. (2003). Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach. Biochemical Pharmacology, 65(5), 857-875. doi:10.1016/s0006-2952(02)01613-1Jansen, J. J., Hoefsloot, H. C. J., van der Greef, J., Timmerman, M. E., Westerhuis, J. A., & Smilde, A. K. (2005). ASCA: analysis of multivariate data obtained from an experimental design. Journal of Chemometrics, 19(9), 469-481. doi:10.1002/cem.952Johnson, W. E., Li, C., & Rabinovic, A. (2006). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1), 118-127. doi:10.1093/biostatistics/kxj037Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., … Irizarry, R. A. (2010). Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics, 11(10), 733-739. doi:10.1038/nrg2825Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., … Zhang, J. (2010). A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data. The Pharmacogenomics Journal, 10(4), 278-291. doi:10.1038/tpj.2010.57(2010). The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature Biotechnology, 28(8), 827-838. doi:10.1038/nbt.1665Morán, J. M., Ortiz-Ortiz, M. A., Ruiz-Mesa, L. M., & Fuentes, J. M. (2010). Nitric oxide in paraquat-mediated toxicity: A review. Journal of Biochemical and Molecular Toxicology, 24(6), 402-409. doi:10.1002/jbt.20348Nueda, M. J., Conesa, A., Westerhuis, J. A., Hoefsloot, H. C. J., Smilde, A. K., Talón, M., & Ferrer, A. (2007). Discovering gene expression patterns in time course microarray experiments by ANOVA–SCA. Bioinformatics, 23(14), 1792-1800. doi:10.1093/bioinformatics/btm251Rensink, W. A., Iobst, S., Hart, A., Stegalkina, S., Liu, J., & Buell, C. R. (2005). Gene expression profiling of potato responses to cold, heat, and salt stress. Functional & Integrative Genomics, 5(4), 201-207. doi:10.1007/s10142-005-0141-6Smilde, A. K., Jansen, J. J., Hoefsloot, H. C. J., Lamers, R.-J. A. N., van der Greef, J., & Timmerman, M. E. (2005). ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics, 21(13), 3043-3048. doi:10.1093/bioinformatics/bti476Storey, J. D., Xiao, W., Leek, J. T., Tompkins, R. G., & Davis, R. W. (2005). Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences, 102(36), 12837-12842. doi:10.1073/pnas.0504609102Svendsen, C., Owen, J., Kille, P., Wren, J., Jonker, M. J., Headley, B. A., … Spurgeon, D. J. (2008). Comparative Transcriptomic Responses to Chronic Cadmium, Fluoranthene, and Atrazine Exposure in Lumbricus rubellus. Environmental Science & Technology, 42(11), 4208-4214. doi:10.1021/es702745dTai, Y. C., & Speed, T. P. (2006). A multivariate empirical Bayes statistic for replicated microarray time course data. The Annals of Statistics, 34(5), 2387-2412. doi:10.1214/009053606000000759Chuan Tai, Y., & Speed, T. P. (2008). On Gene Ranking Using Replicated Microarray Time Course Data. 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Expression of DLK1 and MEG3 genes in porcine tissues during postnatal development

The Drosophila-like homolog 1 (DLK1), a transmembrane signal protein similar to other members of the Notch/Delta/Serrate family, regulates the differentiation process in many types of mammalian cells. Callipyge sheep and DLK1 knockout mice are excellent examples of a fundamental role of the gene encoding DLK1 in muscle growth and fat deposition. DLK1 is located within co-regulated imprinted clusters (the DLK1/DIO3 domain), along with other imprinted genes. Some of these, e.g. the RNA coding MEG3 gene, presumedly interfere with DLK1 transcription. The aim of our study was to analyze DLK1 and MEG3 gene expression in porcine tissues (muscle, liver, kidney, heart, brain stem) during postnatal development. The highest expression of both DLK1 and MEG3 variant 1 (MEG3 var.1) was observed in the brain-stem and muscles, whereas that of MEG3 variant 2 (MEG3var.2) was the most abundant in muscles and the heart. During development (between 60 and 210 days of age) expression of analyzed genes was down-regulated in all the tissues. An exception was the brain- stem, where there was no significant change in MEG3 (both variants) mRNA level, and relatively little decline (2-fold) in that of DLK1 transcription. This may indicate a distinct function of the DLK1 gene in the brain-stem, when compared with other tissues

Dynamic metabolomic data analysis: a tutorial review

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a ‘dynamic’ method. Some of the methods are illustrated with real-life metabolomics examples

Fortunella margarita Transcriptional Reprogramming Triggered by Xanthomonas citri subsp. citri

<p>Abstract</p> <p>Background</p> <p>Citrus canker disease caused by the bacterial pathogen <it>Xanthomonas citri </it>subsp. <it>citri (</it>Xcc) <it>has </it>become endemic in areas where high temperature, rain, humidity, and windy conditions provide a favourable environment for the dissemination of the bacterium. Xcc is pathogenic on many commercial citrus varieties but appears to elicit an incompatible reaction on the citrus relative <it>Fortunella margarita </it>Swing (kumquat), in the form of a very distinct delayed necrotic response. We have developed subtractive libraries enriched in sequences expressed in kumquat leaves during both early and late stages of the disease. The isolated differentially expressed transcripts were subsequently sequenced. Our results demonstrate how the use of microarray expression profiling can help assign roles to previously uncharacterized genes and elucidate plant pathogenesis-response related mechanisms. This can be considered to be a case study in a citrus relative where high throughput technologies were utilized to understand defence mechanisms in <it>Fortunella </it>and citrus at the molecular level.</p> <p>Results</p> <p><b>cDNAs from sequenced kumquat libraries (ESTs) made from subtracted RNA populations, healthy vs. infected, were used to make this microarray</b>. Of 2054 selected genes on a customized array, 317 were differentially expressed (P < 0.05) in Xcc challenged kumquat plants compared to mock-inoculated ones. This study identified components of the incompatible interaction such as reactive oxygen species (ROS) and programmed cell death (PCD). Common defence mechanisms and a number of resistance genes were also identified. In addition, there were a considerable number of differentially regulated genes that had no homologues in the databases. This could be an indication of either a specialized set of genes employed by kumquat in response to canker disease or new defence mechanisms in citrus.</p> <p>Conclusion</p> <p>Functional categorization of kumquat Xcc-responsive genes revealed an enhanced defence-related metabolism as well as a number of resistant response-specific genes in the kumquat transcriptome in response to Xcc inoculation. Gene expression profile(s) were analyzed to assemble a comprehensive and inclusive image of the molecular interaction in the kumquat/Xcc system. This was done in order to elucidate molecular mechanisms associated with the development of the hypersensitive response phenotype in kumquat leaves. These data will be used to perform comparisons among citrus species to evaluate means to enhance the host immune responses against bacterial diseases.</p

DLK1 Is a Somato-Dendritic Protein Expressed in Hypothalamic Arginine-Vasopressin and Oxytocin Neurons

Delta-Like 1 Homolog, Dlk1, is a paternally imprinted gene encoding a transmembrane protein involved in the differentiation of several cell types. After birth, Dlk1 expression decreases substantially in all tissues except endocrine glands. Dlk1 deletion in mice results in pre-natal and post-natal growth deficiency, mild obesity, facial abnormalities, and abnormal skeletal development, suggesting involvement of Dlk1 in perinatal survival, normal growth and homeostasis of fat deposition. A neuroendocrine function has also been suggested for DLK1 but never characterised. To evaluate the neuroendocrine function of DLK1, we first characterised Dlk1 expression in mouse hypothalamus and then studied post-natal variations of the hypothalamic expression. Western Blot analysis of adult mouse hypothalamus protein extracts showed that Dlk1 was expressed almost exclusively as a soluble protein produced by cleavage of the extracellular domain. Immunohistochemistry showed neuronal DLK1 expression in the suprachiasmatic (SCN), supraoptic (SON), paraventricular (PVN), arcuate (ARC), dorsomedial (DMN) and lateral hypothalamic (LH) nuclei. DLK1 was expressed in the dendrites and perikarya of arginine-vasopressin neurons in PVN, SCN and SON and in oxytocin neurons in PVN and SON. These findings suggest a role for DLK1 in the post-natal development of hypothalamic functions, most notably those regulated by the arginine-vasopressin and oxytocin systems

Dlk1 Is Necessary for Proper Skeletal Muscle Development and Regeneration

Delta-like 1homolog (Dlk1) is an imprinted gene encoding a transmembrane protein whose increased expression has been associated with muscle hypertrophy in animal models. However, the mechanisms by which Dlk1 regulates skeletal muscle plasticity remain unknown. Here we combine conditional gene knockout and over-expression analyses to investigate the role of Dlk1 in mouse muscle development, regeneration and myogenic stem cells (satellite cells). Genetic ablation of Dlk1 in the myogenic lineage resulted in reduced body weight and skeletal muscle mass due to reductions in myofiber numbers and myosin heavy chain IIB gene expression. In addition, muscle-specific Dlk1 ablation led to postnatal growth retardation and impaired muscle regeneration, associated with augmented myogenic inhibitory signaling mediated by NF-κB and inflammatory cytokines. To examine the role of Dlk1 in satellite cells, we analyzed the proliferation, self-renewal and differentiation of satellite cells cultured on their native host myofibers. We showed that ablation of Dlk1 inhibits the expression of the myogenic regulatory transcription factor MyoD, and facilitated the self-renewal of activated satellite cells. Conversely, Dlk1 over-expression inhibited the proliferation and enhanced differentiation of cultured myoblasts. As Dlk1 is expressed at low levels in satellite cells but its expression rapidly increases upon myogenic differentiation in vitro and in regenerating muscles in vivo, our results suggest a model in which Dlk1 expressed by nascent or regenerating myofibers non-cell autonomously promotes the differentiation of their neighbor satellite cells and therefore leads to muscle hypertrophy

Circadian clock mechanism driving mammalian photoperiodism.

The annual photoperiod cycle provides the critical environmental cue synchronizing rhythms of life in seasonal habitats. In 1936, Bünning proposed a circadian-basis for photoperiodic synchronization. Here, light-dark cycles entrain a circadian rhythm of photosensitivity, and the expression of summer or winter biology depends on whether light coincides with the phase of high photosensitivity. Formal studies support the universality of this so-called coincidence timer, but we lack understanding of the mechanisms involved. Here we show in mammals that coincidence timing takes place in the pars tuberalis of the pituitary, through a melatonin-dependent flip-flop switch between circadian transcriptional activation and repression. Long photoperiods produce short night-time melatonin signals, leading to induction of the circadian transcription factor BMAL2, in turn triggering summer biology through the eyes absent / thyrotrophin (EYA3 / TSH) pathway. Conversely, short photoperiods produce long melatonin signals, inducing circadian repressors including DEC1, in turn suppressing BMAL2 and the EYA3/TSH pathway, triggering winter biology. These actions are associated with progressive genome-wide changes in chromatin state, elaborating the effect of the circadian coincidence timer. Hence, circadian clock interactions with pituitary epigenetic pathways form the basis of the mammalian coincidence timer mechanism. Our results constitute a blueprint for circadian-based seasonal timekeeping in vertebrates

Gene expression profiling of breast cancer in Lebanese women

MPP grants MPP320046 and MPP320061

Transit amplifying cells coordinate mouse incisor mesenchymal stem cell activation

10.1038/s41467-019-11611-0Nature Communications101359