Large-scale Gene Expression Studies Research Articles

SEMbap: Bow-free covariance search and data de-correlation.

Large-scale studies of gene expression are commonly influenced by biological and technical sources of expression variation, including batch effects, sample characteristics, and environmental impacts. Learning the causal relationships between observable variables may be challenging in the presence of unobserved confounders. Furthermore, many high-dimensional regression techniques may perform worse. In fact, controlling for unobserved confounding variables is essential, and many deconfounding methods have been suggested for application in a variety of situations. The main contribution of this article is the development of a two-stage deconfounding procedure based on Bow-free Acyclic Paths (BAP) search developed into the framework of Structural Equation Models (SEM), called SEMbap(). In the first stage, an exhaustive search of missing edges with significant covariance is performed via Shipley d-separation tests; then, in the second stage, a Constrained Gaussian Graphical Model (CGGM) is fitted or a low dimensional representation of bow-free edges structure is obtained via Graph Laplacian Principal Component Analysis (gLPCA). We compare four popular deconfounding methods to BAP search approach with applications on simulated and observed expression data. In the former, different structures of the hidden covariance matrix have been replicated. Compared to existing methods, BAP search algorithm is able to correctly identify hidden confounding whilst controlling false positive rate and achieving good fitting and perturbation metrics.

Information-incorporated gene network construction with FDR control.

Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. R package is freely available for download at https://cran.r-project.org/package=PCGII.

Mirikizumab Regulates Genes Involved in Ulcerative Colitis Disease Activity and Anti-TNF Resistance: Results From a Phase 2 Study.

Mirikizumab, a monoclonal antibody targeting the p19 subunit of interleukin (IL)-23, demonstrated efficacy and was well-tolerated in a phase 2 randomized clinical trial in patients with moderate-to-severe ulcerative colitis (UC) (NCT02589665). We explored gene expression changes in colonic tissue from study patients and their association with clinical outcomes. Patients were randomized to receive intravenous placebo or 3 mirikizumab induction doses. Patient biopsies were collected at baseline and week 12, and differential gene expression was measured using a microarray platform and compared in all treatment groups to determine differential expression values between baseline and week 12. The greatest improvement in clinical outcomes and placebo-adjusted change from baseline in transcripts at week 12 was observed in the 200 mg mirikizumab group. Transcripts significantly modified by mirikizumab correlate with key UC disease activity indices (modified Mayo score, Geboes score, and Robarts Histopathology Index) and include MMP1, MMP3, S100A8, and IL1β. Changes in transcripts associated with increased disease activity were decreased after 12 weeks of mirikizumab treatment. Mirikizumab treatment affected transcripts associated with resistance to current therapies, including IL-1β, OSMR, FCGR3A and FCGR3B, and CXCL6, suggesting that anti-IL23p19 therapy modulates biological pathways involved in resistance to antitumor necrosis factor and Janus kinase inhibitors. This is the first large-scale gene expression study of inflamed mucosa from patients with UC treated with anti-IL23p19 therapy. These results provide molecular evidence for mucosal healing from an extensive survey of changes in transcripts that improve our understanding of the molecular effects of IL-23p19 inhibition in UC.

High-Throughput Plant Gene Expression Analysis by 384-Format Reverse Transcription-Quantitative PCR for Investigating Plant Iron Homeostasis.

Analysis of plant gene expression is important in determining iron (Fe) homeostasis gene functions during plant development or in response to biotic and abiotic factors. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) has many advantages. It is fast, inexpensive, accurate, and reproducible in any lab. Furthermore, RT-qPCR can be scaled up to study several genes of interest in many biological samples from any organism. We hereby provide a straightforward protocol on RT-qPCR analysis using a 384-well format for large-scale gene expression studies on Fe-regulated responses. The protocol highlights in detail, the steps ranging from choice and design of qPCR analysis, collection of plant material and RNA preparation, cDNA synthesis, set up of qPCR and run, thorough analysis of qPCR run data, and display of multiple gene expression data for convenient interpretation.

Transcriptional Basis of Psoriasis from Large Scale Gene Expression Studies: The Importance of Moving towards a Precision Medicine Approach.

Transcriptome profiling techniques, such as microarrays and RNA sequencing (RNA-seq), are valuable tools for deciphering the regulatory network underlying psoriasis and have revealed large number of differentially expressed genes in lesional and non-lesional skin. Such approaches provide a more precise measurement of transcript levels and their isoforms than any other methods. Large cohort transcriptomic analyses have greatly improved our understanding of the physiological and molecular mechanisms underlying disease pathogenesis and progression. Here, we mostly review the findings of some important large scale psoriatic transcriptomic studies, and the benefits of such studies in elucidating potential therapeutic targets and biomarkers for psoriasis treatment. We also emphasised the importance of looking into the alternatively spliced RNA isoforms/transcripts in psoriasis, rather than focussing only on the gene-level annotation. The neutrophil and blood transcriptome signature in psoriasis is also briefly reviewed, as it provides the immune status information of patients and is a less invasive platform. The application of precision medicine in current management of psoriasis, by combining transcriptomic data, improves the clinical response outcome in individual patients. Drugs tailored to individual patient’s genetic profile will greatly improve patient outcome and cost savings for the healthcare system.

The Psoriatic Nonlesional Skin: A Battlefield between Susceptibility and Protective Factors

In the last two decades, large-scale gene-expression studies on psoriatic skin samples revealed that even though nonlesional skin is macroscopically identical to healthy skin, it harbors several molecular differences. Originally, these molecular differences were thought to represent susceptibility factors for plaque formation. However, we review in this paper the several factors of immune regulation and structural alteration that are specific for the nonlesional skin and serve as protective factors by counteracting plaque formation and contributing to the maintenance of the nonlesional phenotype.

The knowledge of chromosomal sex is important for large-scale analysis of gene expression

The aim of the study was to determine the sex of the fetus in gene expression data lacking this information using expression of the Y-linked genes, and to elucidate the difference between sex-chromosomal-linked gene expression between placental samples with XX and XY genotypes during pregnacy. We have detected 27 differentially expressed sex-chromosomes-linked genes. We have shown that, in most cases, the expression of genes from X-chromosomes in pregnancy carrying baby girls is higher than in pregnancy carrying baby boys, but there are exceptions to this pattern, which must be taken into account in large-scale studies of gene expression. The nature of the difference in gene expression during pregnancy carrying baby girls and boys (positive or ne gative difference) persists during pregnancy, but the magnitude of the difference may remain unchanged or decrease from the first to the third trimester. Taking sex dimorphism into account when analyzing large-scale gene expression data between trimesters of pregnancy increases the number of differentially expressed genes, which improves the informative value of the study and is important for elucidating the pathogenesis of pregnancy complications associated with placental dysfunction.

UNIFYING AND GENERALIZING METHODS FOR REMOVING UNWANTED VARIATION BASED ON NEGATIVE CONTROLS.

Unwanted variation, including hidden confounding, is a well-known problem in many fields, but particularly in large-scale gene expression studies. Recent proposals to use control genes, genes assumed to be unassociated with the covariates of interest, have led to new methods to deal with this problem. Several versions of these removing unwanted variation (RUV) methods have been proposed, including RUV1, RUV2, RUV4, RUVinv, RUVrinv, and RUVfun. Here, we introduce a general framework, RUV*, that both unites and generalizes these approaches. This unifying framework helps clarify the connections between existing methods. In particular, we provide conditions under which RUV2 and RUV4 are equivalent. The RUV* framework preserves an advantage of the RUV approaches, namely, their modularity, which facilitates the development of novel methods based on existing matrix imputation algorithms. We illustrate this by implementing RUVB, a version of RUV* based on Bayesian factor analysis. In realistic simulations based on real data, we found RUVB to be competitive with existing methods in terms of both power and calibration. However, providing a consistently reliable calibration among the data sets remains challenging.

Processing and Analysis of RNA-seq Data from Public Resources.

Advances in next generation sequencing (NGS) technologies resulted in a broad array of large-scale gene expression studies and an unprecedented volume of whole messenger RNA (mRNA) sequencing data, or the transcriptome (also known as RNA sequencing, or RNA-seq). These include the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA), among others. Here we cover some of the commonly used datasets, provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources.

DOP65 Mirikizumab regulates genes involved in anti-TNF resistance and ulcerative colitis disease activity

Abstract Background Mirikizumab (miri), a p19-directed IL-23 antibody, demonstrated efficacy and was well-tolerated in a phase 2 randomised clinical trial in patients with moderate-to-severe UC (NCT02589665). This abstract explores gene expression changes in colonic tissue from study patients and their association with clinical outcomes. Methods Patients were randomised 1:1:1:1 to receive intravenous placebo (PBO, N = 63), miri 50 mg (N = 63) or 200 mg (N = 62) with the possibility of exposure-based dose increases, or fixed miri 600 mg (N = 61) every 4 weeks for 12 weeks. Patient biopsies were collected at baseline (BL) and Week 12, and differential gene expression was measured using an Affymetrix HTA2.0 exon-format microarray workflow. Genes were represented by their largest groups of highly correlated exons. Weeks 0 and 12 data were compared in all treatment groups to produce differential expression values (DEVs). Mean fold changes in DEVs between PBO and each dose group were calculated in a mixed-effect model. A threshold of false discovery rate-adjusted p-value ≤ 0.05 was applied to the significance of the fold change values, and a filter of an absolute value for the fold changes of ≥0.5 log2 units was applied. Results The greatest improvement in clinical outcomes at Week 12 was observed in the 200 mg miri group1; likewise, the greatest PBO-adjusted change from BL in transcripts was observed in this group. Transcripts correlating with key UC disease activity indices at BL, including modified Mayo score (MMS), ulcerative colitis Endoscopic Index of Severity (UCEIS), Geboes score, and Robarts Histopathology Index (RHI), included MMP1, MMP3, S100A8, IL1B, and UGT2A3, with the highest correlations occurring with the histopathologic indices (Figure 1). Miri treatment modulated the expression of transcriptional modules predicted to be enriched in cell profiles identified as key drivers of UC2 (Table 1, columns 1–2) as well as genes determined to be associated with UC by genome-wide association studies (GWAS; Table 1, column 3). Moreover, miri treatment affected transcripts involved in resistance to anti-TNF treatments (Table 1, column 4). A number of the genes in these categories were among those most affected by miri treatment (Table 1, columns 5–6). Conclusion This is the first large-scale gene expression study of diseased tissue from UC patients treated with anti-IL23p19 therapy. It is the first study to show how anti-IL23p19 therapy modulates biological pathways involved in resistance to anti-TNFs. These results are consistent with the demonstrated efficacy of miri in patients in whom TNF antagonists have failed. References

A Novel View to Varicose Veins Pathogenesis: Proteomic Analysis

Introduction - The advent of proteomics techniques allows large-scale studies of gene expression at protein level. Although morphological and anatomical studies indicate that venous wall weakening and subendothelial fibrosis characterize varicose veins, the pathogenesis of varicose veins remains poorly understood. The aim this study is to obtain protein expression profiles in patients with varicose veins. Finally, the identification of possible biomarkers may open possibilities for pharmacological inhibition of disease progression. Methods - Varicose saphenous veins removed during phlebectomy and normal saphenous veins obtained during vascular surgery were collected for proteomics analysis. The same layers of venous wall from varicose and non-varicose veins were incubated, and the proteins released were analyzed by ion mobility spectrometry (IMS-MS) with Synapt G2.All differentially expressed proteins and their pathways, coexpression and physical interactions were analyzed in GeneMANIA and AmiGO databases. Results - Proteomic analysis of the human vein revealed totally 1387 proteins. 200 proteins demonstrated significant differences in their quantity (more than 1.5 fold) between the two types of venous tissue (p<0.05). Among the most differentially expressed proteins 10 were found significantly decreased in the varicose vein tissue, and only two- increased. CXXC-type zinc finger protein was more permanent (38- fold down regulated). This protein is known as receptor for vascular endothelial growth factor. Most prominent proteins were proved with Western Blotting analysis. Conclusion - This study provides novel insights into the biochemical mechanisms of this disease and a basis for further studies. Our proteomics discovery approach suggests that extracellular matrix degradation play a pivotal role in the pathogenesis of venous varicose. The identified proteins suggest that the varicose venous wall responds to a stressful condition and that proteolytic degradation of the cytoskeleton, inflammation and apoptosis of smooth muscle cells may be part of the response. Larger studies are required to confirm the potential and clinical role of the identified proteins.

Bioinformatics analysis of differentially expressed genes involved in human developmental chondrogenesis.

Osteoarthritis (OA), also known as degenerative arthritis, affects millions of people all over the world. OA occurs when the cartilage wears down over time, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental chondrogenesis as well as to explore potential molecular mechanisms.The expression profiles of GSE51812 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 9 samples, including 6-week pre-chondrocytes (PC, 6 independent specimens) and 17-week fetal periarticular resting chondrocytes (RC, 3 independent specimens). The raw data were integrated to obtain differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. The Gene Ontology (GO) and pathway enrichment of DEGs were conducted via Database for Annotation, Visualization, and Integrated Discovery (DAVID). The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the search tool for the retrieval of interacting genes (STRING) database. An intersection figure was provided to show the relationship between the DEGs identified in this study and genes from any existed related studies.A total of 9486 DEGs, including 4821 upregulated genes and 4665 downregulated genes were observed. The top 30 developmental chondrogenesis associated genes were identified, including matrix metalloproteinase (MMP)1, MMP3, MMP13, prostaglandin-endoperoxide synthase 2 (PTGS2), and so on. The majority of DEGs, including PTGS2, CCL20, CHI3L1, LIF, CXCL8, and CXCL12 were intensively enriched in immune-associated biological process terms, including inflammatory, and immune responses. Additionally, the majority of DEGs were mainly enriched in NF-kappa β (NF-kβ) signaling pathway and tumor necrosis factor (TNF) signaling pathway. The hub genes identified in STRING and Cytoscape databases included MMP1, MMP3, MMP13, PTGS2 and so on. Among the top 30 upregulated and downregulated DEGs, there were 15 genes have been reported to be associated with OA or developmental chondrogenesis.This large scale gene expression study observed genes associated with human developmental chondrogenesis and their relative GO function, which may offer opportunities for the research for cartilage tissue engineering and novel insights into the prevention of OA in the near future.

SA55 - NEURAL CELL ADHESION MOLECULE 1 (NCAM-1) IS ASSOCIATED TO LONG-TERM AVERSIVE MEMORY IN NEMATODES AND HUMANS

Background The Neural Cell Adhesion Molecule 1 (NCAM-1) has been implicated in several brain-related biological processes, including neuronal migration, axonal branching, fasciculation, and synaptogenesis, with a pivotal role in synaptic plasticity. Here, using an interdisciplinary approach we investigated the evolutionary conserved role of Neural Cell Adhesion Molecule 1 (NCAM-1) in learning and memory. Methods First, we examined the temporal transcriptome profiles during C. elegans aversive olfactory long-term memory in a large-scale gene expression study (up to 24 hours upon conditioning). We observed sustained changes in NCAM-1 expression and thus we further investigated its association with aversive memory by using molecular genetics methods. Following, we examined the link between molecular signatures of the NCAM-1 gene and aversive memory in two independent human samples of European descent, by using genome-wide expression and methylation profiling. Finally, in a population of genocide survivors of African descent we investigated the link between NCAM-1 promoter DNA methylation and PTSD. Results NCAM-1 expression showed sustained expression changes up to 24 hours upon aversive olfactory conditioning. Loss of NCAM-1 function selectively impaired long-term memory in a negative but not in positive olfactory associative memory paradigm, without causing acquisition, sensory, motor or short-term memory deficits. Finally, both C. elegans and human NCAM-1 expression in nematodes rescued loss of C. elegans NCAM-1 gene function. Considering the conserved role of NCAM-1 in higher complex organisms, we next expanded our C. elegans findings in human. In two samples of European descent, we showed a negative correlation between DNA methylation of the NCAM-1 promoter and aversive memory (i.e., recognition of negative pictures) as well as a strong positive correlation between NCAM-1 expression and the same recognition performance. In the second sample, we could also show a strong correlation between NCAM-1 expression and long-delayed recall of negative pictures. Finally, in a population of genocide survivors DNA methylation at NCAM-1 gene promoter was inversely correlated with intrusive memories symptoms and Posttraumatic Stress Disorder (PTSD) risk. Discussion Taken together, our results support a conserved role of NCAM-1 in negative associative memory from nematodes to humans and might, ultimately, prove to be helpful in elucidating diagnostic markers and suggest novel therapy targets for memory-related disorders, like PTSD.

Estimating a common covariance matrix for network meta-analysis of gene expression datasets in diffuse large B-cell lymphoma

The estimation of covariance matrices of gene expressions has many applications in cancer systems biology. Many gene expression studies, however, are hampered by low sample size and it has therefore become popular to increase sample size by collecting gene expression data across studies. Motivated by the traditional meta-analysis using random effects models, we present a hierarchical random covariance model and use it for the meta-analysis of gene correlation networks across 11 large-scale gene expression studies of diffuse large B-cell lymphoma (DLBCL). We suggest to use a maximum likelihood estimator for the underlying common covariance matrix and introduce an EM algorithm for estimation. By simulation experiments comparing the estimated covariance matrices by cophenetic correlation and Kullback–Leibler divergence the suggested estimator showed to perform better or not worse than a simple pooled estimator. In a posthoc analysis of the estimated common covariance matrix for the DLBCL data we were able to identify novel biologically meaningful gene correlation networks with eigengenes of prognostic value. In conclusion, the method seems to provide a generally applicable framework for meta-analysis, when multiple features are measured and believed to share a common covariance matrix obscured by study dependent noise.

Characterization of twenty Camelina spp. accessions using single nucleotide polymorphism genotyping

Sequencing the complete genome of Camelina sativa will facilitate studies to improve this oilseed crop. We analysed 20 accessions of Camelina spp. using genotyping-by-sequence technology. After stringent screening, 35,783 single nucleotide polymorphisms (SNPs) were generated, and basic genetic studies were performed to check the diversity of these SNPs. STRUCTURE and phylogenetic analyses revealed five subgroups in these 20 Camelina accessions. Winter-types may have diverged from summer-types. Some genomic regions were negatively selected, and most of these were gene-rich regions. As expected, the most ancient subgroup was less affected by negative selection. Marker–trait associations with plant height, leaf length, and pod size generated 154 SNPs, and 72 adjacent genes were significantly associated with these phenotypes. Further large-scale analysis and gene expression studies with these SNPs and genes are needed to develop potentially valuable resources for improving C. sativa.

Genetic Approaches to Study Meiosis and Meiosis-Specific Gene Expression in Saccharomyces cerevisiae.

The budding yeast Saccharomyces cerevisiae has a long history as a model organism for studies of meiosis and the cell cycle. The popularity of this yeast as a model is in large part due to the variety of genetic and cytological approaches that can be effectively performed with the cells. Cultures of the cells can be induced to synchronously progress through meiosis and sporulation allowing large-scale gene expression and biochemical studies to be performed. Additionally, the spore tetrads resulting from meiosis make it possible to characterize the haploid products of meiosis allowing investigation of meiotic recombination and chromosome segregation. Here we describe genetic methods for analysis progression of S. cerevisiae through meiosis and sporulation with an emphasis on strategies for the genetic analysis of regulators of meiosis-specific genes.

VariancePartition: interpreting drivers of variation in complex gene expression studies.

BackgroundAs large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics.ResultsWe describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation based on a genome-wide summary, and identify genes that deviate from the genome-wide trend. Using a linear mixed model, variancePartition quantifies variation in each expression trait attributable to differences in disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large-scale transcriptome profiling datasets illustrates that variancePartition recovers striking patterns of biological and technical variation that are reproducible across multiple datasets.ConclusionsOur open source software, variancePartition, enables rapid interpretation of complex gene expression studies as well as other high-throughput genomics assays. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1323-z) contains supplementary material, which is available to authorized users.

Construction of an Ostrea edulis database from genomic and expressed sequence tags (ESTs) obtained from Bonamia ostreae infected haemocytes: Development of an immune-enriched oligo-microarray

The flat oyster, Ostrea edulis, is one of the main farmed oysters, not only in Europe but also in the United States and Canada. Bonamiosis due to the parasite Bonamia ostreae has been associated with high mortality episodes in this species. This parasite is an intracellular protozoan that infects haemocytes, the main cells involved in oyster defence. Due to the economical and ecological importance of flat oyster, genomic data are badly needed for genetic improvement of the species, but they are still very scarce. The objective of this study is to develop a sequence database, OedulisDB, with new genomic and transcriptomic resources, providing new data and convenient tools to improve our knowledge of the oyster's immune mechanisms. Transcriptomic and genomic sequences were obtained using 454 pyrosequencing and compiled into an O. edulis database, OedulisDB, consisting of two sets of 10,318 and 7159 unique sequences that represent the oyster's genome (WG) and de novo haemocyte transcriptome (HT), respectively. The flat oyster transcriptome was obtained from two strains (naïve and tolerant) challenged with B. ostreae, and from their corresponding non-challenged controls. Approximately 78.5% of 5619 HT unique sequences were successfully annotated by Blast search using public databases. A total of 984 sequences were identified as being related to immune response and several key immune genes were identified for the first time in flat oyster. Additionally, transcriptome information was used to design and validate the first oligo-microarray in flat oyster enriched with immune sequences from haemocytes. Our transcriptomic and genomic sequencing and subsequent annotation have largely increased the scarce resources available for this economically important species and have enabled us to develop an OedulisDB database and accompanying tools for gene expression analysis. This study represents the first attempt to characterize in depth the O. edulis haemocyte transcriptome in response to B. ostreae through massively sequencing and has aided to improve our knowledge of the immune mechanisms of flat oyster. The validated oligo-microarray and the establishment of a reference transcriptome will be useful for large-scale gene expression studies in this species.

Predicting delayed kidney graft function with gene expression in preimplantation biopsies and first-day posttransplant blood

The purpose of this study was to investigate possible markers for predicting delayed graft function (DGF). To this end we analyzed, in pre-implantation biopsies (PIB) and in first-day post-Tx peripheral blood mononuclear cells (PBMC), the expression of five genes (ACSL4, CUBN, DEFB1, FABP3, GK) through real-time TaqMan PCR assays. These genes were selected from a large scale gene expression study in PIB. DEFB1, FABP3 and GK expression levels in PIB were lower in cases with DGF and, in a multivariate analysis which included these genes and clinical variables, only FABP3 expression remained independently associated with DGF. FABP3 expression lower than −1.32 units of relative expression conferred an odds ratio for DGF of 41.1. Compared to the PBMC of recipients without DGF, recipients with prolonged DGF (pDGF) had lower ACSL4 and higher DEFB1 expression levels. In a multivariate analysis, including PBMC gene expression levels of ACSL4, DEFB1 and TLR4 (data from a previous study with the same patients) and clinical variables, only TLR4 remained independently associated with pDGF. In summary, this study revealed FABP3 expression in PIB as a marker for DGF and disclosed new genes possibly involved in the pathogenesis of DGF.

Large-scale gene expression study in the ophiuroid Amphiura filiformis provides insights into evolution of gene regulatory networks.

BackgroundThe evolutionary mechanisms involved in shaping complex gene regulatory networks (GRN) that encode for morphologically similar structures in distantly related animals remain elusive. In this context, echinoderm larval skeletons found in brittle stars and sea urchins provide an ideal system. Here, we characterize for the first time the development of the larval skeleton in the ophiuroid Amphiura filiformis and compare it systematically with its counterpart in sea urchin.ResultsWe show that ophiuroids and euechinoids, that split at least 480 Million years ago (Mya), have remarkable similarities in tempo and mode of skeletal development. Despite morphological and ontological similarities, our high-resolution study of the dynamics of genetic regulatory states in A. filiformis highlights numerous differences in the architecture of their underlying GRNs. Importantly, the A.filiformispplx, the closest gene to the sea urchin double negative gate (DNG) repressor pmar1, fails to drive the skeletogenic program in sea urchin, showing important evolutionary differences in protein function. hesC, the second repressor of the DNG, is co-expressed with most of the genes that are repressed in sea urchin, indicating the absence of direct repression of tbr, ets1/2, and delta in A. filiformis. Furthermore, the absence of expression in later stages of brittle star skeleton development of key regulatory genes, such as foxb and dri, shows significantly different regulatory states.ConclusionOur data fill up an important gap in the picture of larval mesoderm in echinoderms and allows us to explore the evolutionary implications relative to the recently established phylogeny of echinoderm classes. In light of recent studies on other echinoderms, our data highlight a high evolutionary plasticity of the same nodes throughout evolution of echinoderm skeletogenesis. Finally, gene duplication, protein function diversification, and cis-regulatory element evolution all contributed to shape the regulatory program for larval skeletogenesis in different branches of echinoderms.Electronic supplementary materialThe online version of this article (doi:10.1186/s13227-015-0039-x) contains supplementary material, which is available to authorized users.