Search

MotivationAcute Myeloid Leukemia is a highly heterogeneous disease. Although current classifications are well-known and widely adopted, many patients experience drug resistance and disease relapse. New biomarkers are needed to make classifications more reliable and propose personalized treatment.ResultsWe performed tests on a large scale in 3 AML cohorts, 1112 RNAseq samples. The accuracy to distinguish NPM1 mutant and non-mutant patients using machine learning models achieved more than 95% in three different scenarios. Using our approach, we found already described genes associated with NPM1 mutations and new genes to be investigated. Furthermore, we provide a new view to search for signatures/biomarkers and explore diagnosis/prognosis, at the k-mer level.AvailabilityCode available athttps://github.com/railorena/npm1amlandhttps://osf.io/4s9tc/. The cohorts used in this article were authorized for use.Contact*therese.commes@inserm.fr

Background The development of RNA sequencing (RNAseq) and the corresponding emergence of public datasets have created new avenues of transcriptional marker search. The long non-coding RNAs (lncRNAs) constitute an emerging class of transcripts with a potential for high tissue specificity and function. Therefore, we tested the biomarker potential of lncRNAs on Mesenchymal Stem Cells (MSCs), a complex type of adult multipotent stem cells of diverse tissue origins, that is frequently used in clinics but which is lacking extensive characterization. Results We developed a dedicated bioinformatics pipeline for the purpose of building a cell-specific catalogue of unannotated lncRNAs. The pipeline performs ab initio transcript identification, pseudoalignment and uses new methodologies such as a specific k-mer approach for naive quantification of expression in numerous RNAseq data. We next applied it on MSCs, and our pipeline was able to highlight novel lncRNAs with high cell specificity. Furthermore, with original and efficient approaches for functional prediction, we demonstrated that each candidate represents one specific state of MSCs biology. Conclusions We showed that our approach can be employed to harness lncRNAs as cell markers. More specifically, our results suggest different candidates as potential actors in MSCs biology and propose promising directions for future experimental investigations.

The huge body of publicly available RNA-sequencing (RNA-seq) libraries is a treasure of functional information allowing to quantify the expression of known or novel transcripts in tissues. However, transcript quantification commonly relies on alignment methods requiring a lot of computational resources and processing time, which does not scale easily to large datasets. K-mer decomposition constitutes a new way to process RNA-seq data for the identification of transcriptional signatures, as k-mers can be used to quantify accurately gene expression in a less resource-consuming way. We present the Kmerator Suite, a set of three tools designed to extract specific k-mer signatures, quantify these k-mers into RNA-seq datasets and quickly visualize large dataset characteristics. The core tool, Kmerator, produces specific k-mers for 97% of human genes, enabling the measure of gene expression with high accuracy in simulated datasets. KmerExploR, a direct application of Kmerator, uses a set of predictor gene-specific k-mers to infer metadata including library protocol, sample features or contaminations from RNA-seq datasets. KmerExploR results are visualized through a user-friendly interface. Moreover, we demonstrate that the Kmerator Suite can be used for advanced queries targeting known or new biomarkers such as mutations, gene fusions or long non-coding RNAs for human health applications.

The huge body of publicly available RNA-sequencing (RNA-seq) libraries is a treasure of functional information allowing to quantify the expression of known or novel transcripts in tissues. However, transcript quantification commonly relies on alignment methods requiring a lot of computational resources and processing time, which does not scale easily to large datasets. K-mer decomposition constitutes a new way to process RNA-seq data for the identification of transcriptional signatures, as k-mers can be used to quantify accurately gene expression in a less resource-consuming way. We present the Kmerator Suite, a set of three tools designed to extract specific k-mer signatures, quantify these k-mers into RNA-seq datasets and quickly visualize large dataset characteristics. The core tool, Kmerator, produces specific k-mers for 97% of human genes, enabling the measure of gene expression with high accuracy in simulated datasets. KmerExploR, a direct application of Kmerator, uses a set of predictor gene-specific k-mers to infer metadata including library protocol, sample features or contaminations from RNA-seq datasets. KmerExploR results are visualized through a user-friendly interface. Moreover, we demonstrate that the Kmerator Suite can be used for advanced queries targeting known or new biomarkers such as mutations, gene fusions or long non-coding RNAs for human health applications.

The development of RNA sequencing (RNAseq) and corresponding emergence of public datasets have created new avenues of transcriptional marker search. The long non-coding RNAs (lncRNAs) constitute an emerging class of transcripts with a potential for high tissue specificity and function. Using a dedicated bioinformatics pipeline, we propose to construct a cell-specific catalogue of unannotated lncRNAs and to identify the strongest cell markers. This pipeline uses ab initio transcript identification, pseudoalignment and new methodologies such as a specific k-mer approach for naive quantification of expression in numerous RNAseq data.For an application model, we focused on Mesenchymal Stem Cells (MSCs), a type of adult multipotent stem-cells of diverse tissue origins. Frequently used in clinics, these cells lack extensive characterisation. Our pipeline was able to highlight different lncRNAs with high specificity for MSCs. In silico methodologies for functional prediction demonstrated that each candidate represents one specific state of MSCs biology. Together, these results suggest an approach that can be employed to harness lncRNA as cell marker, showing different candidates as potential actors in MSCs biology, while suggesting promising directions for future experimental investigations.

Summary Genomic integrity of human pluripotent stem cells (hPSCs) is essential for research and clinical applications. However, genetic abnormalities can accumulate during hPSC generation and routine culture and following gene editing. Their occurrence should be regularly monitored, but the current assays to assess hPSC genomic integrity are not fully suitable for such regular screening. To address this issue, we first carried out a large meta-analysis of all hPSC genetic abnormalities reported in more than 100 publications and identified 738 recurrent genetic abnormalities (i.e., overlapping abnormalities found in at least five distinct scientific publications). We then developed a test based on the droplet digital PCR technology that can potentially detect more than 90% of these hPSC recurrent genetic abnormalities in DNA extracted from culture supernatant samples. This test can be used to routinely screen genomic integrity in hPSCs., Graphical Abstract, Highlights • A meta-analysis was carried out to list genetic abnormalities detected in hPSCs • Recurrent genetic abnormalities in hPSCs were precisely defined • ddPCR is a robust and sensitive approach to screen these recurrent abnormalities • In culture supernatant digital test can be used to rapidly screen iPSC clones, In this article, De Vos and colleagues performed a comprehensive meta-analysis of genetic abnormalities detected in hPSC lines. They then used this information to devise a ddPCR test targeting the majority of recurrent abnormalities and show that this test can be applied to the cell culture supernatant. Finally, they illustrate the interest of this test for routine hPSC screening.

Background & Aim Genetic integrity of human pluripotent stem cells (hPSCs) is essential for the accuracy of the disease models and for the security of cell therapies that are use those cells. Because genetic abnormalities can accumulate during the generation of hPSC, their long term maintenance or during genome editing, it is mandatory to regularly check the genome of hPSCs. However, the current methods to assess genomic integrity of hPSC are not fully suitable for such regular screening. These approaches can be limited in term of resolution, flexibility, cost and time to process. Our aim is improving genetic screening of hPSC. Methods, Results & Conclusion Based on a large meta-analysis of all hPSC genetic abnormalities reported in more than 100 publications, we were able to exhaustively identify recurrent genetic abnormalities accumulating in hPSCs. We then developed a test (iCS-digital test) based on the droplet digital PCR technology that can detect more than 90% of these hPSC recurrent genetic abnormalities in DNA extracted from culture supernatant samples. This test was successfully used to detect copy number variations that arose during cell culture or gene editing. Hence, the iCS-digital test can be used to routinely screen genomic integrity in hPSCs, contribute to secure genome editing workflow, and reduce time and financial losses induced by the use of an abnormal hPSC lines.

Background: High-throughput next generation sequencing (NGS) technologies enable the detection of biomarkers used for tumor classification, disease monitoring and cancer therapy. Whole-transcriptome analysis using RNA-seq is important, not only as a means of understanding the mechanisms responsible for complex diseases but also to efficiently identify novel genes/exons, splice isoforms, RNA editing, allele-specific mutations, differential gene expression and fusion-transcripts or chimeric RNA (chRNA). Methods: We used Crac, a tool that uses genomic locations and local coverage to classify biological events and directly infer splice and chimeric junctions within a single read. Crac’s algorithm extracts transcriptional chimeric events irrespective of annotation with a high sensitivity, and CracTools was used to aggregate, annotate and filter the chRNA reads. The selected chRNA candidates were validated by real time PCR and sequencing. In order to check the tumor specific expression of chRNA, we analyzed a publicly available dataset using a new tag search approach. Results: We present data related to acute myeloid leukemia (AML) RNA-seq analysis. We highlight novel biological cases of chRNA, in addition to previously well characterized leukemia chRNA. We have identified and validated 17 chRNAs among 3 AML patients: 10 from an AML patient with a translocation between chromosomes 15 and 17 (AML-t(15;17), 4 from patient with normal karyotype (AML-NK) 3 from a patient with chromosomal 16 inversion (AML-inv16). The new fusion transcripts can be classified into four groups according to the exon organization. Conclusions: All groups suggest complex but distinct synthesis mechanisms involving either collinear exons of different genes, non-collinear exons, or exons of different chromosomes. Finally, we check tumor-specific expression in a larger RNA-seq AML cohort and identify new AML biomarkers that could improve diagnosis and prognosis of AML.

Background: High-throughput next generation sequencing (NGS) technologies enable the detection of biomarkers used for tumor classification, disease monitoring and cancer therapy. Whole-transcriptome analysis using RNA-seq is important, not only as a means of understanding the mechanisms responsible for complex diseases but also to efficiently identify novel genes/exons, splice isoforms, RNA editing, allele-specific mutations, differential gene expression and fusion-transcripts or chimeric RNA (chRNA). Methods: We used Crac, a tool that uses genomic locations and local coverage to classify biological events and directly infer splice and chimeric junctions within a single read. Crac’s algorithm extracts transcriptional chimeric events irrespective of annotation with a high sensitivity, and CracTools was used to aggregate, annotate and filter the chRNA reads. The selected chRNA candidates were validated by real time PCR and sequencing. In order to check the tumor specific expression of chRNA, we analyzed a publicly available dataset using a new tag search approach. Results: We present data related to acute myeloid leukemia (AML) RNA-seq analysis. We highlight novel biological cases of chRNA, in addition to previously well characterized leukemia chRNA. We have identified and validated 17 chRNAs among 3 AML patients: 10 from an AML patient with a translocation between chromosomes 15 and 17 (AML-t(15;17), 4 from patient with normal karyotype (AML-NK) 3 from a patient with chromosomal 16 inversion (AML-inv16). The new fusion transcripts can be classified into four groups according to the exon organization. Conclusions: All groups suggest complex but distinct synthesis mechanisms involving either collinear exons of different genes, non-collinear exons, or exons of different chromosomes. Finally, we check tumor-specific expression in a larger RNA-seq AML cohort and identify new AML biomarkers that could improve diagnosis and prognosis of AML.

BACKGROUND: Human long non-coding RNAs (lncRNAs) are an emerging category of transcripts with increasingly documented functional roles during development. LncRNAs and roles during human early embryo development have recently begun to be unravelled. OBJECTIVE AND RATIONALE: This review summarizes the most recent knowledge on lncRNAs and focuses on their expression patterns and role during early human embryo development and in pluripotent stem cells (PSCs). Public mRNA sequencing (mRNA-seq) data were used to illustrate these expression signatures. SEARCH METHODS: The PubMed and EMBASE databases were first interrogated using specific terms, such as 'lncRNAs', to get an extensive overview on lncRNAs up to February 2016, and then using 'human lncRNAs' and 'embryo', 'development', or 'PSCs' to focus on lncRNAs involved in human embryo development or in PSC.Recently published RNA-seq data from human oocytes and pre-implantation embryos (including single-cell data), PSC and a panel of normal and malignant adult tissues were used to describe the specific expression patterns of some lncRNAs in early human embryos. OUTCOMES: The existence and the crucial role of lncRNAs in many important biological phenomena in each branch of the life tree are now well documented. The number of identified lncRNAs is rapidly increasing and has already outnumbered that of protein-coding genes. Unlike small non-coding RNAs, a variety of mechanisms of action have been proposed for lncRNAs. The functional role of lncRNAs has been demonstrated in many biological and developmental processes, including cell pluripotency induction, X-inactivation or gene imprinting. Analysis of RNA-seq data highlights that lncRNA abundance changes significantly during human early embryonic development. This suggests that lncRNAs could represent candidate biomarkers for developing non-invasive tests for oocyte or embryo quality. Finally, some of these lncRNAs are also expressed in human cancer tissues, suggesting that reactivation of an embryonic lncRNA program may contribute to human malignancies. WIDER IMPLICATIONS: LncRNAs are emerging potential key players in gene expression regulation. Analysis of RNA-seq data from human pre-implantation embryos identified lncRNA signatures that are specific to this critical step. We anticipate that further studies will show that these new transcripts are major regulators of embryo development. These findings might also be used to develop new tests/treatments for improving the pregnancy success rate in IVF procedures or for regenerative medicine applications involving PSC.

Background High-throughput sequencing technology and bioinformatics have identified chimeric RNAs (chRNAs), raising the possibility of chRNAs expressing particularly in diseases can be used as potential biomarkers in both diagnosis and prognosis. Results The task of discriminating true chRNAs from the false ones poses an interesting Machine Learning (ML) challenge. First of all, the sequencing data may contain false reads due to technical artifacts and during the analysis process, bioinformatics tools may generate false positives due to methodological biases. Moreover, if we succeed to have a proper set of observations (enough sequencing data) about true chRNAs, chances are that the devised model can not be able to generalize beyond it. Like any other machine learning problem, the first big issue is finding the good data to build models. As far as we were concerned, there is no common benchmark data available for chRNAs detection. The definition of a classification baseline is lacking in the related literature too. In this work we are moving towards benchmark data and an evaluation of the fidelity of supervised classifiers in the prediction of chRNAs. Conclusions We proposed a modelization strategy that can be used to increase the tools performances in context of chRNA classification based on a simulated data generator, that permit to continuously integrate new complex chimeric events. The pipeline incorporated a genome mutation process and simulated RNA-seq data. The reads within distinct depth were aligned and analysed by CRAC that integrates genomic location and local coverage, allowing biological predictions at the read scale. Additionally, these reads were functionally annotated and aggregated to form chRNAs events, making it possible to evaluate ML methods (classifiers) performance in both levels of reads and events. Ensemble learning strategies demonstrated to be more robust to this classification problem, providing an average AUC performance of 95 % (ACC=94 %, Kappa=0.87 %). The resulting classification models were also tested on real RNA-seq data from a set of twenty-seven patients with acute myeloid leukemia (AML). Electronic supplementary material The online version of this article (doi:10.1186/s13040-016-0112-6) contains supplementary material, which is available to authorized users.

Summary Chronic myelomonocytic leukaemia (CMML) is a heterogeneous haematopoietic disorder characterized by myeloproliferative or myelodysplastic features. At present, the pathogenesis of this malignancy is not completely understood. In this study, we sought to analyse gene expression profiles of CMML in order to characterize new molecular outcome predictors. A learning set of 32 untreated CMML patients at diagnosis was available for TaqMan low-density array gene expression analysis. From 93 selected genes related to cancer and cell cycle, we built a five-gene prognostic index after multiplicity correction. Using this index, we characterized two categories of patients with distinct overall survival (94% vs. 19% for good and poor overall survival, respectively; P = 0·007) and we successfully validated its strength on an independent cohort of 21 CMML patients with Affymetrix gene expression data. We found no specific patterns of association with traditional prognostic stratification parameters in the learning cohort. However, the poor survival group strongly correlated with high-risk treated patients and transformation to acute myeloid leukaemia. We report here a new multigene prognostic index for CMML, independent of the gene expression measurement method, which could be used as a powerful tool to predict clinical outcome and help physicians to evaluate criteria for treatments.

The cytoplasmic tyrosine kinase Src has been implicated in signal transduction induced by growth factors and integrins. Src also shows oncogenic activity when deregulated. Accumulating evidence indicates that the tyrosine kinase Abl is an important substrate for Src signalling in normal cells. Here we show that Abl is also required for Src-induced transformation of mouse fibroblasts. Abl does not mediate tyrosine phosphorylation of Stat3 and Shc, two important regulators of Src oncogenic activity. In contrast, Abl controls the activation of the small GTPase Rac for oncogenic signalling and active Rac partly rescued Src transformation in cells with inactive Abl. Moreover, Abl mediates Src-induced extracellular regulated kinase 5 (ERK5) activation to drive cell transformation. Finally, we find that Abl/Rac and Abl/ERK5 pathways also operate in human MCF7 and BT549 breast cancer cells, where neoplastic transformation depends on Src-like activities. Therefore, Abl is an important regulator of Src oncogenic activity both in mouse fibroblasts and in human cancer cells. Targeting these Abl-dependent signalling cascades may be of therapeutic value in breast cancers where Src-like function is important.Oncogene (2007) 26, 7313-7323; doi:10.1038/sj.onc.1210543; published online 28 May 2007.

High-throughput sequencing technology and bioinformatics have identified chimeric RNAs (chRNAs), raising the possibility of chRNAs expressing particularly in diseases can be used as potential biomarkers in both diagnosis and prognosis. The task of discriminating true chRNA from the false ones poses an interesting Machine Learning (ML) challenge. First of all, the sequencing data may contain false reads due to technical artefacts and during the analysis process, bioinformatics tools may generate false positives due to methodological biases. Thus predicting the real signal from the noise can be a hard task. Furthermore, even if we succeed to have a proper set of observations (enough sequencing data) about true chRNAs, chances are that the devised model can not be able to generalize beyond it. Like any other machine learning problem, the first big issue is finding the good data, observations, to build the prediction model. Unfortunately, as far as we were concerned, there is no common benchmark data available for chRNAs. And, the definition of a classification baseline is lacking in the related literature. In this work we are moving towards a benchmark data and a fair comparison analysis unraveling the role of ML techniques in finding chRNAs. We have developed a benchmark pipeline incorporating a mutated genome process and simulated RNA-seq data by Flux Simulator. These sequencing reads were aligned and annotated by CRAC. CRAC offers a new way to analyze the RNA-seq data by integrating genomic location and local coverage, allowing biological predictions in one step. The resulting data were used as a benchmark for our comparison analysis. We have observed that the no free lunch theorem do not hold for ensemble classifiers. Ensemble learning strategies demonstrated to be more robust to this classification problem, providing an average AUC performance of 95% (ACC=94%, Kappa=0.87%).

International audience; GTPases of the Rho family are molecular switches that play important roles in converting and amplifying external signals into cellular effects. Originally de onstrated to control the dynamics of the F-actin cytoskeleton, Rho GTPases have been implicated in many basic cellular processes that influence cell proliferation, differentiation, motility, adhesion, survival, or secretion. To elucidate the evolutionary history of the Rho family, we have analyzed over 20 species covering major eukaryotic clades from unicellular organisms to mammals, including platypus and opossum, and have reconstructed the ontogeny and the chronology of emergence of the different subfamilies. Our data establish that the 20 mammalian Rho members are structured into 8 subfamilies, among which Rac is the founder of the whole family. Rho, Cdc42, RhoUV, and RhoBTB subfamilies appeared before Coelomates and RhoJQ, Cdc42 isoforms, RhoDF, and Rnd emerged in chordates. In vertebrates, gene duplications and retrotranspositions increased the size of each chordate Rho subfamily, whereas RhoH, the last subfamily, arose probably by horizontal gene transfer. Rac1b, a Rac1 isoform generated by alternative splicing, emerged in amniotes, and RhoD, only in therians. Analysis of Rho mRNA expression patterns in mouse tissues shows that recent subfamilies have tissue-specific and low-level expression that supports their implication only in narrow time windows or in differentiated metabolic functions. These findings give a comprehensive view of the evolutionary canvas of the Rho family and provide guides for future structure and evolution studies of other components of Rho signaling pathways, in particular regulators of the RhoGEF family.

The mechanism by which the Src family of protein-tyrosine kinases (SFKs) regulate mitogenesis and morphological changes induced by platelet-derived growth factor (PDGF) is not well known. The cholesterol-enriched membrane microdomains, caveolae, regulate PDGF receptor signalling in fibroblasts and we examined their role in SFK functions. Here we show that caveolae disruption by membrane cholesterol depletion or expression of the dominant-negative caveolin-3 DGV mutant impaired Src mitogenic signalling including kinase activation, Myc gene induction and DNA synthesis. The impact of caveolae on SFK function was underscored by the capacity of Myc to overcome mitogenic inhibition as a result of caveolae disruption. Using biochemical fractionation we show that caveolae-enriched subcellular membranes regulate the formation of PDGF-receptor-SFK complexes. An additional pool of PDGF-activated SFKs that was insensitive to membrane cholesterol depletion was characterised in non-caveolae fractions. SFK activation outside caveolae was linked to the capacity of PDGF to induce F-actin rearrangements leading to dorsal ruffle formation. Inhibition of phospholipase C γ (PLCγ), sphingosine kinase and heterotrimeric Gi proteins implicates a PLC γ–sphingosine-1-phosphate–Gi pathway for PDGF-induced SFK activation outside caveolae and actin assembly. In addition, the cytoplasmic tyrosine kinase Abl was identified as an important effector of this signalling cascade. We conclude that PDGF may stimulate two spatially distinct pools of SFKs leading to two different biological outcomes: DNA synthesis and dorsal ruffle formation.

The cytoplasmic tyrosine kinase Abl is a Src substrate required for platelet-derived growth factor (PDGF) receptor signaling leading to Myc expression and DNA synthesis. Abl targets are, however, ill defined. Here we report that the small GTPase Rac is an important effector of its mitogenic function. PDGF-induced Rac activation was impaired in cells with inactive Abl and active Rac overcame the mitogenic defects found in these cells. Rac function required both a Jun N-terminal kinase (JNK) and a NADPH oxidase (Nox) pathway. Furthermore, co-activation of JNK and Nox were sufficient to mimic the Rac mitogenic rescue. Abl also regulated PDGF-induced JNK and Nox activation. Finally, we found that Myc is an important target of this signaling cascade: Myc induction was sensitive to small inhibitors of JNK and Nox activities and forced expression of Myc overcame the G1 block induced by dominant interfering mutants of mitogen-activated protein kinase kinase 4 (MKK4) and Nox2 activating subunit. We concluded that cytoplasmic Abl operates on a Rac/JNK and a Rac/Nox pathway for PDGF-induced Myc induction and DNA synthesis.

Recent sequencing technologies that allow massive parallel production of short reads are the method of choice for transcriptome analysis. Particularly, digital gene expression (DGE) technologies produce a large dynamic range of expression data by generating short tag signatures for each cell transcript. These tags can be mapped back to a reference genome to identify new transcribed regions that can be further covered by RNA-sequencing (RNA-Seq) reads. Here, we applied an integrated bioinformatics approach that combines DGE tags, RNA-Seq, tiling array expression data and species-comparison to explore new transcriptional regions and their specific biological features, particularly tissue expression or conservation. We analysed tags from a large DGE data set (designated as ‘TranscriRef’). We then annotated 750 000 tags that were uniquely mapped to the human genome according to Ensembl. We retained transcripts originating from both DNA strands and categorized tags corresponding to protein-coding genes, antisense, intronic- or intergenic-transcribed regions and computed their overlap with annotated non-coding transcripts. Using this bioinformatics approach, we identified ∼34 000 novel transcribed regions located outside the boundaries of known protein-coding genes. As demonstrated using sequencing data from human pluripotent stem cells for biological validation, the method could be easily applied for the selection of tissue-specific candidate transcripts. DigitagCT is available at http://cractools.gforge.inria.fr/softwares/digitagct.

Objet La medecine regenerative a pour ambition de regenerer des tissus endommages par la maladie ou la vieillesse. L’utilisation de cellules souches pluripotentes humaines (CSP) manipulees ex-vivo est une solution technique prometteuse pour une medecine regenerative. Cependant, la culture des CSP peut generer des anomalies genetiques telles que des anomalies chromosomiques ou infra-caryotypiques [1] , [2] . Notre travail vise a comprendre la nature et la cause de ces anomalies genetiques de maniere a pouvoir les limiter et ouvrir la voie a de futures applications therapeutiques reposant sur l’utilisation des CSP. Methodes Pour repertorier les anomalies genetiques publiees dans les CSP et identifier en particulier les anomalies hyper-recurrentes, nous avons cree une base de donnees « Stem cell gEnetic Abnormalities database » (SEAdb). Resultats Nous avons associe la base SEAdb a un navigateur genomique permettant de visualiser directement les anomalies genetiques sur le genome humain. Nous avons repertorie plus de 404 000 anomalies et variants d’ADN dans les CSP, issus de pres de 100 publications internationales. En integrant des filtres robustes permettant d’exclure les polymorphismes, nous avons pu determiner les anomalies les plus recurrentes observees dans les CSP. Conclusion SEAdb presente un interet majeur dans la description des anomalies genetiques dans les CSP et la comprehension des causes de leur survenue. SEAdb est une base de donnee qui sera librement accessible la fin de l’annee 2016.

Poster abstract in a workshop: COST Action BM 1006 (SeqAhead): MC BUSINESS MEETING AND SCIENTIFIC MEETING; International audience; Recently developed sequencing technologies offer massively parallel production of short reads and become the technology of choice for a variety of sequencing-based assays, including gene expression. Among them, digital gene expression analysis (DGE), which combines generation of short tag signatures for cellular transcripts with massively parallel sequencing, offers a large dynamic range to detect transcripts and is limited only by sequencing depth. As recently described (Philippe et al, 2009), tag signatures can easily be mapped to a reference genome and used to perform gene discovery. This procedure distinguishes between transcripts originating from both DNA strands and categorizes tags corresponding to protein coding gene (CDS and 3'UTR), antisense, intronic or intergenic transcribed regions. Here, we have applied an integrated bioinformatics approach to investigate tags' properties, including cross-species conservation, and the ability to reveal novel tran- scripts located outside the boundaries of known protein or RNA coding genes. We mapped the tags from a human DGE library obtained with Solexa sequencing, against the human, chimpanzee, and mouse genomic sequences. We considered the subset of uniquely mapped tags in the human genome, and given their genomic location, determined according to Ensembl if they fall within a region annotated by a gene (CDS, UTR and intron) or an intergenic region. We found that 76.4 % of the tags located in human also matched to the chimpanzee genome. The level of conservation between human and chimpanzee varied among annotation categories: 85 % of conserved tags in the CDS, 81 % in the UTR, 76% and 73% respectively in intron and intergenic regions. With the same procedure applied to human and mouse, we obtained 11% of conserved tags in the CDS, 7% in UTR, 1% and 3% respectively in intronic and intergenic regions. We analysed in depth the common CDS and UTR tags in human and mouse for their functional relevance: 90% of them correspond to orthologous genes with a common HUGO. We used DAVID database to extract biological features, the gene clustering revealed specific molecular functions belonging to transcription cofactor and regulator activity, nucleotide binding, ligase and protein kinase, hormone receptor, histone meth- yltransferase or GTPase activity, and also important signaling pathways like WNT pathway. Indeed, intergenic transcription includes mainly new, non protein-coding RNAs (npcRNAs), which could rep- resent an important class of regulatory molecules. By integrating also SAGE genie and RNA-seq ex- pression data, we selected intergenic tags conserved across species and assayed experimentally the npcRNA transcriptome with Q-PCR. We validated 80% of the 32 tested biological cases. These results demonstrate that considering tag conservation helps to identify conserved genes and functions, which is of great relevance when investigating expressed tags located in intergenic regions.

Small RNAs of the piRNA (Piwi-associated RNA) class have various functions in the germline — repressing transposable elements, maintaining germline stem cells and promoting genome stability. Rouget et al. have now uncovered a function for piRNAs outside the germline, in the fruit fly embryo. Specifically, piRNAs that are complementary to a sequence in the 3′-untranslated region of an mRNA for the embryonic posterior morphogen Nanos facilitate adenylation of the mRNA and its subsequent decay. Without piRNAs, Nanos accumulates and developmental defects result. Piwi-associated RNAs (piRNAs) are small RNAs with several functions in the germline, such as repressing transposable elements and helping to maintain germline stem cells. Now, a function for piRNAs has been discovered outside the germline, in the fruitfly embryo. Specifically, piRNAs are required for the decay of the messenger RNA encoding the posterior morphogen Nanos. When piRNA-induced regulation is impaired, this mRNA is stabilized and developmental defects ensue. Piwi-associated RNAs (piRNAs), a specific class of 24- to 30-nucleotide-long RNAs produced by the Piwi-type of Argonaute proteins, have a specific germline function in repressing transposable elements. This repression is thought to involve heterochromatin formation and transcriptional and post-transcriptional silencing1,2,3,4,5,6. The piRNA pathway has other essential functions in germline stem cell maintenance7 and in maintaining germline DNA integrity8,9,10. Here we uncover an unexpected function of the piRNA pathway in the decay of maternal messenger RNAs and in translational repression in the early embryo. A subset of maternal mRNAs is degraded in the embryo at the maternal-to-zygotic transition. In Drosophila, maternal mRNA degradation depends on the RNA-binding protein Smaug and the deadenylase CCR411,12,13, as well as the zygotic expression of a microRNA cluster14. Using mRNA encoding the embryonic posterior morphogen Nanos (Nos) as a paradigm to study maternal mRNA decay, we found that CCR4-mediated deadenylation of nos depends on components of the piRNA pathway including piRNAs complementary to a specific region in the nos 3′ untranslated region. Reduced deadenylation when piRNA-induced regulation is impaired correlates with nos mRNA stabilization and translational derepression in the embryo, resulting in head development defects. Aubergine, one of the Argonaute proteins in the piRNA pathway, is present in a complex with Smaug, CCR4, nos mRNA and piRNAs that target the nos 3′ untranslated region, in the bulk of the embryo. We propose that piRNAs and their associated proteins act together with Smaug to recruit the CCR4 deadenylation complex to specific mRNAs, thus promoting their decay. Because the piRNAs involved in this regulation are produced from transposable elements, this identifies a direct developmental function for transposable elements in the regulation of gene expression.

International audience; Ultra high-throughput sequencing is used to analyse the transcriptome or interactome at unprecedented depth on a genome-wide scale. These techniques yield short sequence reads that are then mapped on a genome sequence to predict putatively transcribed or protein-interacting regions. We argue that factors such as background distribution, sequence errors, and read length impact on the prediction capacity of sequence census experiments. Here we suggest a computational approach to measure these factors and analyse their influence on both transcriptomic and epigenomic assays. This investigation provides new clues on both methodological and biological issues. For instance, by analysing chromatin immunoprecipitation read sets, we estimate that 4.6% of reads are affected by SNPs. We show that, although the nucleotide error probability is low, it significantly increases with the position in the sequence. Choosing a read length above 19 bp practically eliminates the risk of finding irrelevant positions, while above 20 bp the number of uniquely mapped reads decreases. With our procedure, we obtain 0.6% false positives among genomic locations. Hence, even rare signatures should identify biologically relevant regions, if they are mapped on the genome. This indicates that digital transcriptomics may help to characterize the wealth of yet undiscovered, low-abundance transcripts.

The Src-like adaptor protein (SLAP) belongs to the subfamily of adapter proteins that negatively regulate cellular signalling initiated by tyrosine kinases. SLAP has a unique, myristylated N-terminus, followed by SH3 and SH2 domains with high homology to Src family tyrosine kinases (SFK) and a unique C-terminal tail, which is important for c-Cbl binding. We have previously shown that SLAP negatively regulates platelet-derived growth factor (PDGF)-induced mitogenesis in fibroblasts and we now report that it regulates F-actin assembly for dorsal ruffles formation. c-Cbl mediated SLAP inhibition towards actin remodelling. Moreover, SLAP enhanced PDGF-induced c-Cbl phosphorylation by SFK. In contrast, SLAP mitogenic inhibition was not mediated by c-Cbl, but it rather involved a competitive mechanism with SFK for PDGF-receptor (PDGFR) association and mitogenic signalling. Accordingly, phosphorylation of the Src mitogenic substrates Stat3 and Shc were reduced by SLAP. Thus, we concluded that SLAP regulates PDGFR signalling by two independent mechanisms: a competitive mechanism for PDGF-induced Src mitogenic signalling and a non-competitive mechanism for dorsal ruffles formation mediated by c-Cbl.

Program available at http://www.lirmm.fr/~rivals/GENOMICS; International audience; Analysis of several million expressed gene signatures (tags) revealed an increasing number of different sequences, largely exceeding that of annotated genes in mammalian genomes. Serial analysis of gene expression (SAGE) can reveal new Poly(A) RNAs transcribed from previously unrecognized chromosomal regions. However, conventional SAGE tags are too short to identify unambiguously unique sites in large genomes. Here, we design a novel strategy with tags anchored on two different restrictions sites of cDNAs. New transcripts are then tentatively defined by the two SAGE tags in tandem and by the spanning sequence read on the genome between these tagged sites. Having developed a new algorithm to locate these tag-delimited genomic sequences (TDGS), we first validated its capacity to recognize known genes and its ability to reveal new transcripts with two SAGE libraries built in parallel from a single RNA sample. Our algorithm proves fast enough to experiment this strategy at a large scale. We then collected and processed the complete sets of human SAGE tags to predict yet unknown transcripts. A cross-validation with tiling arrays data shows that 47% of these TDGS overlap transcriptional active regions. Our method provides a new and complementary approach for complex transcriptome annotation.

Wolbachia strains are maternally inherited endosymbiotic bacteria that infect many arthropod species and have evolved several different ways of manipulating their hosts, the most frequent way being cytoplasmic incompatibility (CI). CI leads to embryo death in crosses between infected males and uninfected females as well as in crosses between individuals infected by incompatible Wolbachia strains. The mosquito Culex pipiens exhibits the highest crossing type variability reported so far. Our crossing data support the notion that CI might be driven by at least two distinct genetic units that control the CI functions independently in males and females. Although the molecular basis of CI remains unknown, proteins with ankyrin (ANK) domains represent promising candidates since they might interact with a wide range of host proteins. Here we searched for sequence variability in the 58 ANK genes carried in the genomes of Wolbachia variants infecting Culex pipiens . Only five ANK genes were polymorphic in the genomes of incompatible Wolbachia variants, and none correlated with the CI pattern obtained with 15 mosquito strains (representing 14 Wolbachia variants). Further analysis of ANK gene expression evidenced host- and sex-dependent variations, which did not improve the correlation. Taken together, these data do not support the direct implication of ANK genes in CI determinism.

The involvement of the cytokine signaling pathway in oncogenesis has long been postulated. Recently, rearrangements of the gene encoding the tyrosine Janus kinase 2 (JAK2) have been reported in human leukemias indicating a direct JAK-signal transduction and activator of transcription (STAT)-mediated leukemic process. The leukemia-associated TEL-JAK2 fusion protein is formed by the oligomerization domain of the translocated ets leukemia (TEL) protein fused to the catalytic domain of JAK2. TEL-mediated oligomerization results in a constitutive tyrosine kinase activity that, in turn, is able to confer growth factor independence to the murine hematopoietic interleukin-3 (IL-3)-dependent Ba/F3 cell line. Results of the present study indicate that fusion proteins containing the oligomerization domain of TEL and the tyrosine kinase domains of Jak1, Jak2, JAK3, or TYK2 share similar properties and are able to efficiently substitute for the survival and mitogenic signals controlled by IL-3, without concomitant activation of the IL-3 receptor. Electrophoretic mobility shift assays demonstrated Stat5 as the only activated Stat factor in TEL-Jak2- and TEL-Jak1-expressing cells, whereas other Stats, namely Stat1 and Stat3, could be detected in TEL-JAK3-, TEL-TYK2-, and also in TEL-ABL-expressing Ba/F3 cells. High levels of expression of the Stat5-target genes pim-1, osm, and Cis were observed in all the cytokine-independent cell lines. Furthermore, the expression of a dominant negative form of Stat5A markedly interfered with the growth factor independence process mediated by TEL-Jak2 in Ba/F3 cells. Because the BCR-ABL and TEL-PDGFβR oncoproteins also activate Stat5, activation of this factor should be a crucial step in activated tyrosine kinase-mediated leukemogenesis.

The Janus family of tyrosine kinases (JAK) plays an essential role in development and in coupling cytokine receptors to downstream intracellular signaling events. A t(9;12)(p24;p13) chromosomal translocation in a T cell childhood acute lymphoblastic leukemia patient was characterized and shown to fuse the 3′ portion of JAK2 to the 5′ region of TEL , a gene encoding a member of the ETS transcription factor family. The TEL-JAK2 fusion protein includes the catalytic domain of JAK2 and the TEL-specific oligomerization domain. TEL-induced oligomerization of TEL-JAK2 resulted in the constitutive activation of its tyrosine kinase activity and conferred cytokine-independent proliferation to the interleukin-3–dependent Ba/F3 hematopoietic cell line.

The identification of the v-ets oncogene of avian leukemia virus E26 (Leprince et al, 1983; Nunn et al, 1983) has set the stage for the definition of a large protein family of over 30 members characterized by the presence of a conserved domain of about 85 amino acids, the ETS domain (Figure 2.1). For several years and despite the molecular cloning of the cDNAs of several ETS family members including c-ets-1, the cellular homolog of v-ets (Duterque-Coquillaud et al, 1988; Watson et al, 1988 a; Leprince et al, 1988), c-ets-2 (Boulukos et al, 1988; Watson et al, 1988 a), erg (ets related gene) (Reddy et al, 1987) and elk (ets like gene) (Rao et al, 1989), the analysis of the deduced amino acid sequence of the corresponding proteins failed to give any clue as to the nature of their biochemical function. The description of ETS-1 as a nuclear, chromatin-associated protein endowed with general DNA binding activity (Pognonec et al, 1989) and the fact that these properties all depended upon the integrity of the ETS domain (E domain) (Boulukos et a1, 1989) were the first indications for a possible nuclear function for ETS proteins. Definitive experimental evidence supporting this view was provided by a series of independent observations which identified ETS-1 as a sequence-specific DNA binding protein and transcriptional activator of viral and cellular promoters (Bosselut et al, 1990; Gunther et al, 1990; Ho et al, 1990; Wasylyk et al, 1990) and the concomitant identification of the SV40 PU-box transcription factor as an ETS protein (Klemsz et al, 1990).

International audience; TEL is a novel member of the ETS family of transcriptional regulators which is frequently involved in human leukemias as the result of specific chromosomal translocations. We show here by co-immunoprecipitation and GST chromatography analyses that TEL and TEL-derived fusion proteins form homotypic oligomers in vitro and in vivo. Deletion mutagenesis identifies the TEL oligomerization domain as a 65 amino acid region which is conserved in a subset of the ETS proteins including ETS-1, ETS-2, FLI-1, ERG-2 and GABP alpha in vertebrates and PNTP2, YAN and ELG in Drosophila. TEL-induced oligomerization is shown to be essential for the constitutive activation of the protein kinase activity and mitogenic properties of TEL-platelet derived growth factor receptor beta (PDGFR beta), a fusion oncoprotein characteristic of the leukemic cells of chronic myelomonocytic leukemia harboring a t(5;12) chromosomal translocation. Swapping experiments in which the TEL oligomerization domain was exchanged by the homologous domains of representative vertebrate ETS proteins including ETS-1, ERG-2 and GABP alpha show that oligomerization is a specific property of the TEL amino-terminal conserved domain. These results indicate that the amino-terminal domain conserved in a subset of the ETS proteins has evolved to generate a specialized protein-protein interaction interface which is likely to be an important determinant of their specificity as transcriptional regulators.

The cytoplasmic tyrosine kinases of the Src family (SFK) play important roles in cell responses induced by growth factors, including cell growth, survival and migration. Here, we review how SFK participate in PDGF (platelet-derived growth factor) receptor signalling leading to DNA synthesis and actin assembly. Furthermore, evidence for a spatial compartmentalization of SFK signalling is also discussed.

The t(5;12) translocation, associated with chronic myelomonocytic leukemia, generates a novel gene encoding a protein, TEL-PDGF beta R, composed of the 154 amino-terminal amino acids of the transcription factor TEL and the transmembrane and intracellular part of the PDGF beta-receptor (PDGF beta R). TEL also occurs as a tumor-associated fusion partner for the tyrosine kinases c-ABL, JAK2 and TRK-C. Previous studies have demonstrated growth promoting activity of TEL-PDGF beta R and also indicated that the TEL moiety activates the tyrosine kinase of the PDGF beta R through the formation of TEL-PDGF beta R oligomers. We demonstrate that tyrosine phosphorylation of the fusion protein can be attenuated through overexpression of the TEL part of TEL-PDGF beta R, suggesting a strategy for antagonizing the signaling of TEL-PDGF beta R, and other TEL-fusion proteins containing tyrosine kinase domains. Comparison of BaF/3 cell lines expressing TEL-PDGF beta R and ligand-stimulated PDGF beta R revealed that only TEL-PDGF beta R expression conferred IL-3-independent growth, suggesting differences in signaling capacity of the two proteins. Finally, tyrosine residues 17 and 27 in TEL-PDGF beta R was identified as autophosphorylation sites in TEL-PDGF beta R.

International audience; Brucella microti was isolated a decade ago from wildlife and soil in Europe. Compared to the classical Brucella species, it exhibits atypical virulence properties such as increased growth in human and murine macrophages and lethality in experimentally infected mice. A spontaneous rough (R) mutant strain, derived from the smooth reference strain CCM4915T, showed increased macrophage colonization and was non-lethal in murine infections. Whole-genome sequencing and construction of an isogenic mutant of B. microti and Brucella suis 1330 revealed that the R-phenotype was due to a deletion in a single gene, namely wbkE (BMI_I539), encoding a putative glycosyltransferase involved in lipopolysaccharide (LPS) O-polysaccharide biosynthesis. Complementation of the R-strains with the wbkE gene restored the smooth phenotype and the ability of B. microti to kill infected mice. LPS with an intact O-polysaccharide is therefore essential for lethal B. microti infections in the murine model, demonstrating its importance in pathogenesis.