Long non-coding RNA exploration for mesenchymal stem cell characterisation.

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.

New insights into diagnosis and therapeutic options for proliferative hepatoblastoma.

Surgery and cisplatin-based treatment of hepatoblastoma (HB) currently guarantee the survival of 70%-80% of patients. However, some important challenges remain in diagnosing high-risk tumors and identifying relevant targetable pathways offering new therapeutic avenues. Previously, two molecular subclasses of HB tumors have been described, C1 and C2, with C2 being the subgroup with the poorest prognosis, a more advanced tumor stage, and the worst overall survival rate. An associated 16-gene signature to discriminate the two tumoral subgroups was proposed, but it has not been transferred into clinical routine. To address these issues, we performed RNA sequencing of 25 tumors and matched normal liver samples from patients. The transcript profiling separated HB into three distinct subgroups named C1, C2A, and C2B, identifiable by a concise four-gene signature: hydroxysteroid 17-beta dehydrogenase 6, integrin alpha 6, topoisomerase 2-alpha, and vimentin, with topoisomerase 2-alpha being characteristic for the proliferative C2A tumors. Differential expression of these genes was confirmed by quantitative RT-PCR on an expanded cohort and by immunohistochemistry. We also revealed significant overexpression of genes involved in the Fanconi anemia (FA) pathway in the C2A subgroup. We then investigated the ability of several described FA inhibitors to block growth of HB cells in vitro and in vivo. We demonstrated that bortezomib, a Food and Drug Administration-approved proteasome inhibitor, strongly impairs the proliferation and survival of HB cell lines in vitro, blocks FA pathway-associated double-strand DNA repair, and significantly impedes HB growth in vivo. CONCLUSION: The highly proliferating C2A subtype is characterized by topoisomerase 2-alpha gene up-regulation and FA pathway activation, and the HB therapeutic arsenal could include bortezomib for the treatment of patients with the most aggressive tumors. (Hepatology 2018;68:89-102).

SimBA: A methodology and tools for evaluating the performance of RNA-Seq bioinformatic pipelines.

BACKGROUND: The evolution of next-generation sequencing (NGS) technologies has led to increased focus on RNA-Seq. Many bioinformatic tools have been developed for RNA-Seq analysis, each with unique performance characteristics and configuration parameters. Users face an increasingly complex task in understanding which bioinformatic tools are best for their specific needs and how they should be configured. In order to provide some answers to these questions, we investigate the performance of leading bioinformatic tools designed for RNA-Seq analysis and propose a methodology for systematic evaluation and comparison of performance to help users make well informed choices. RESULTS: To evaluate RNA-Seq pipelines, we developed a suite of two benchmarking tools. SimCT generates simulated datasets that get as close as possible to specific real biological conditions accompanied by the list of genomic incidents and mutations that have been inserted. BenchCT then compares the output of any bioinformatics pipeline that has been run against a SimCT dataset with the simulated genomic and transcriptional variations it contains to give an accurate performance evaluation in addressing specific biological question. We used these tools to simulate a real-world genomic medicine question s involving the comparison of healthy and cancerous cells. Results revealed that performance in addressing a particular biological context varied significantly depending on the choice of tools and settings used. We also found that by combining the output of certain pipelines, substantial performance improvements could be achieved. CONCLUSION: Our research emphasizes the importance of selecting and configuring bioinformatic tools for the specific biological question being investigated to obtain optimal results. Pipeline designers, developers and users should include benchmarking in the context of their biological question as part of their design and quality control process. Our SimBA suite of benchmarking tools provides a reliable basis for comparing the performance of RNA-Seq bioinformatics pipelines in addressing a specific biological question. We would like to see the creation of a reference corpus of data-sets that would allow accurate comparison between benchmarks performed by different groups and the publication of more benchmarks based on this public corpus. SimBA software and data-set are available at http://cractools.gforge.inria.fr/softwares/simba/ .

Combining DGE and RNA-sequencing data to identify new polyA+ non-coding transcripts in the human genome.

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.

New prognostic markers, determined using gene expression analyses, reveal two distinct subtypes of chronic myelomonocytic leukaemia patients.

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.

Querying large read collections in main memory: a versatile data structure.

BACKGROUND: High Throughput Sequencing (HTS) is now heavily exploited for genome (re-) sequencing, metagenomics, epigenomics, and transcriptomics and requires different, but computer intensive bioinformatic analyses. When a reference genome is available, mapping reads on it is the first step of this analysis. Read mapping programs owe their efficiency to the use of involved genome indexing data structures, like the Burrows-Wheeler transform. Recent solutions index both the genome, and the k-mers of the reads using hash-tables to further increase efficiency and accuracy. In various contexts (e.g. assembly or transcriptome analysis), read processing requires to determine the sub-collection of reads that are related to a given sequence, which is done by searching for some k-mers in the reads. Currently, many developments have focused on genome indexing structures for read mapping, but the question of read indexing remains broadly unexplored. However, the increase in sequence throughput urges for new algorithmic solutions to query large read collections efficiently. RESULTS: Here, we present a solution, named Gk arrays, to index large collections of reads, an algorithm to build the structure, and procedures to query it. Once constructed, the index structure is kept in main memory and is repeatedly accessed to answer queries like "given a k-mer, get the reads containing this k-mer (once/at least once)". We compared our structure to other solutions that adapt uncompressed indexing structures designed for long texts and show that it processes queries fast, while requiring much less memory. Our structure can thus handle larger read collections. We provide examples where such queries are adapted to different types of read analysis (SNP detection, assembly, RNA-Seq). CONCLUSIONS: Gk arrays constitute a versatile data structure that enables fast and more accurate read analysis in various contexts. The Gk arrays provide a flexible brick to design innovative programs that mine efficiently genomics, epigenomics, metagenomics, or transcriptomics reads. The Gk arrays library is available under Cecill (GPL compliant) license from http://www.atgc-montpellier.fr/ngs/.

Using reads to annotate the genome: influence of length, background distribution, and sequence errors on prediction capacity.

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.

Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-seq datasets.

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.

Gene Expression Signature Associated with Clinical Outcome in ALK-Positive Anaplastic Large Cell Lymphoma.

Anaplastic large cell lymphomas associated with ALK translocation have a good outcome after CHOP treatment; however, the 2-year relapse rate remains at 30%. Microarray gene-expression profiling of 48 samples obtained at diagnosis was used to identify 47 genes that were differentially expressed between patients with early relapse/progression and no relapse. In the relapsing group, the most significant overrepresented genes were related to the regulation of the immune response and T-cell activation while those in the non-relapsing group were involved in the extracellular matrix. Fluidigm technology gave concordant results for 29 genes, of which FN1, FAM179A, and SLC40A1 had the strongest predictive power after logistic regression and two classification algorithms. In parallel with 39 samples, we used a Kallisto/Sleuth pipeline to analyze RNA sequencing data and identified 20 genes common to the 28 genes validated by Fluidigm technology-notably, the FAM179A and FN1 genes. Interestingly, FN1 also belongs to the gene signature predicting longer survival in diffuse large B-cell lymphomas treated with CHOP. Thus, our molecular signatures indicate that the FN1 gene, a matrix key regulator, might also be involved in the prognosis and the therapeutic response in anaplastic lymphomas.

Recurrent Genetic Abnormalities in Human Pluripotent Stem Cells: Definition and Routine Detection in Culture Supernatant by Targeted Droplet Digital PCR.

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.

Serial analysis of gene expression adapted for downsized extracts (SAGE/SADE) analysis in reticulocytes.

Reticulocytes are the last stage of erythropoiesis before red blood cells (RBC). Although most of the RBC proteins have been characterized, little is known about expression profile of their mRNA during differentiation. Our goal was to initiate a genomic global solution to provide a transcriptional data base on which it will be possible to follow the erythroid differentiation and study RBC disorders. We used a modified protocol of serial analysis of gene expression (SAGE), the serial analysis of downsized extracts (SADE) which allows identification of genes from small amounts of mRNA and is thus an appropriate method starting from reticulocyte. SAGE does not depend on previous knowledge of genes. The method produces a set of cellular transcribed gene signatures (tags) and provides a comprehensive view of cells phenotypes by computational analyses.

Transcriptome annotation using tandem SAGE tags.

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.

Simultaneous gene expression profiling in human macrophages infected with Leishmania major parasites using SAGE.

BACKGROUND: Leishmania (L) are intracellular protozoan parasites that are able to survive and replicate within the harsh and potentially hostile phagolysosomal environment of mammalian mononuclear phagocytes. A complex interplay then takes place between the macrophage (MPhi) striving to eliminate the pathogen and the parasite struggling for its own survival. To investigate this host-parasite conflict at the transcriptional level, in the context of monocyte-derived human MPhis (MDM) infection by L. major metacyclic promastigotes, the quantitative technique of serial analysis of gene expression (SAGE) was used. RESULTS: After extracting mRNA from resting human MPhis, Leishmania-infected human MPhis and L. major parasites, three SAGE libraries were constructed and sequenced generating up to 28,173; 57,514 and 33,906 tags respectively (corresponding to 12,946; 23,442 and 9,530 unique tags). Using computational data analysis and direct comparison to 357,888 publicly available experimental human tags, the parasite and the host cell transcriptomes were then simultaneously characterized from the mixed cellular extract, confidently discriminating host from parasite transcripts. This procedure led us to reliably assign 3,814 tags to MPhis' and 3,666 tags to L. major parasites transcripts. We focused on these, showing significant changes in their expression that are likely to be relevant to the pathogenesis of parasite infection: (i) human MPhis genes, belonging to key immune response proteins (e.g., IFNgamma pathway, S100 and chemokine families) and (ii) a group of Leishmania genes showing a preferential expression at the parasite's intra-cellular developing stage. CONCLUSION: Dual SAGE transcriptome analysis provided a useful, powerful and accurate approach to discriminating genes of human or parasitic origin in Leishmania-infected human MPhis. The findings presented in this work suggest that the Leishmania parasite modulates key transcripts in human MPhis that may be beneficial for its establishment and survival. Furthermore, these results provide an overview of gene expression at two developmental stages of the parasite, namely metacyclic promastigotes and intracellular amastigotes and indicate a broad difference between their transcriptomic profiles. Finally, our reported set of expressed genes will be useful in future rounds of data mining and gene annotation.

The tumor metastasis suppressor gene Drg-1 down-regulates the expression of activating transcription factor 3 in prostate cancer.

The tumor metastasis suppressor gene Drg-1 has been shown to suppress metastasis without affecting tumorigenicity in immunodeficient mouse models of prostate and colon cancer. Expression of Drg-1 has also been found to have a significant inverse correlation with metastasis or invasiveness in various types of human cancer. However, how Drg-1 exerts its metastasis suppressor function remains unknown. In the present study, to elucidate the mechanism of action of the Drg-1 gene, we did a microarray analysis and found that induction of Drg-1 significantly inhibited the expression of activating transcription factor (ATF) 3, a member of the ATF/cyclic AMP-responsive element binding protein family of transcription factors. We also showed that Drg-1 attenuated the endogenous level of ATF3 mRNA and protein in prostate cancer cells, whereas Drg-1 small interfering RNA up-regulated the ATF3 expression. Furthermore, Drg-1 suppressed the promoter activity of the ATF3 gene, indicating that Drg-1 regulates ATF3 expression at the transcriptional level. Our immunohistochemical analysis on prostate cancer specimens revealed that nuclear expression of ATF3 was inversely correlated to Drg-1 expression and positively correlated to metastases. Consistently, we have found that ATF3 overexpression promoted invasiveness of prostate tumor cells in vitro, whereas Drg-1 suppressed the invasive ability of these cells. More importantly, overexpression of ATF3 in prostate cancer cells significantly enhanced spontaneous lung metastasis of these cells without affecting primary tumorigenicity in a severe combined immunodeficient mouse model. Taken together, our results strongly suggest that Drg-1 suppresses metastasis of prostate tumor cells, at least in part, by inhibiting the invasive ability of the cells via down-regulation of the expression of the ATF3 gene.

RNA-Seq Analysis to Detect Abnormal Fusion Transcripts Linked to Chromothripsis.

RNA-Seq approach enables the detection and characterization of fusion or chimeric transcript associated to complex genome rearrangement. Until now, these events are classically identified at DNA level.Here we describe a complete procedure including a novel way of analyzing reads that combines genomic locations and local coverage to directly infer chimeric junctions with a high sensitivity and specificity, allowing identification of different classes of chimeric RNA events. We also recommend the best practices for the bioinformatics analysis and describe the experimental process for RNA validation using real-time PCR and sequencing.

Differential long non-coding RNA expression profiles in human oocytes and cumulus cells.

Progress in assisted reproductive technologies strongly relies on understanding the regulation of the dialogue between oocyte and cumulus cells (CCs). Little is known about the role of long non-coding RNAs (lncRNAs) in the human cumulus-oocyte complex (COC). To this aim, publicly available RNA-sequencing data were analyzed to identify lncRNAs that were abundant in metaphase II (MII) oocytes (BCAR4, C3orf56, TUNAR, OOEP-AS1, CASC18, and LINC01118) and CCs (NEAT1, MALAT1, ANXA2P2, MEG3, IL6STP1, and VIM-AS1). These data were validated by RT-qPCR analysis using independent oocytes and CC samples. The functions of the identified lncRNAs were then predicted by constructing lncRNA-mRNA co-expression networks. This analysis suggested that MII oocyte lncRNAs could be involved in chromatin remodeling, cell pluripotency and in driving early embryonic development. CC lncRNAs were co-expressed with genes involved in apoptosis and extracellular matrix-related functions. A bioinformatic analysis of RNA-sequencing data to identify CC lncRNAs that are affected by maternal age showed that lncRNAs with age-related altered expression in CCs are essential for oocyte growth. This comprehensive analysis of lncRNAs expressed in human MII oocytes and CCs could provide biomarkers of oocyte quality for the development of non-invasive tests to identify embryos with high developmental potential.

Correlation of N-myc downstream-regulated gene 1 expression with clinical outcomes of colorectal cancer patients of different race/ethnicity.

AIM: To evaluate the role of N-myc downstream-regulated gene 1 (NDRG1) expression in prognosis and survival of colorectal cancer patients with different ethnic backgrounds. METHODS: Because NDRG1 is a downstream target of p53 and hypoxia inducible factor-1 alpha (HIF-1 alpha), we examined NDRG1 expression together with p53 and HIF-1 alpha by immunohistochemistry. A total of 157 colorectal cancer specimens including 80 from Japanese patients and 77 from US patients were examined. The correlation between protein expression with clinicopathological features and survival after surgery was analyzed. RESULTS: NDRG1 protein was significantly increased in colorectal tumor compared with normal epithelium in both Japanese and US patient groups. Expression of NDRG1 protein was significantly correlated with lymphatic invasion, venous invasion, depth of invasion, histopathological type, and Dukes' stage in Japanese colorectal cancer patients. NDRG1 expression was correlated to histopathological type, Dukes' stage and HIF-1 alpha expression in US-Caucasian patients but not in US-African American patients. Interestingly, Kaplan-Meier survival analysis demonstrated that NDRG1 expression correlated significantly with poorer survival in US-African American patients but not in other patient groups. However, in p53-positive US cases, NDRG1 positivity correlated significantly with better survival. In addition, NDRG1 expression also correlated significantly with improved survival in US patients with stages III and IV tumors without chemotherapy. In Japanese patients with stages II and III tumors, strong NDRG1 staining in p53-positive tumors correlated significantly with improved survival but negatively in patients without chemotherapy. CONCLUSION: NDRG1 expression was correlated with various clinicopathological features and clinical outcomes in colorectal cancer depending on the race/ethnicity of the patients. NDRG1 may serve as a biological basis for the disparity of clinical outcomes of colorectal cancer patients with different ethnic backgrounds.

DE-kupl: exhaustive capture of biological variation in RNA-seq data through k-mer decomposition.

We introduce a k-mer-based computational protocol, DE-kupl, for capturing local RNA variation in a set of RNA-seq libraries, independently of a reference genome or transcriptome. DE-kupl extracts all k-mers with differential abundance directly from the raw data files. This enables the retrieval of virtually all variation present in an RNA-seq data set. This variation is subsequently assigned to biological events or entities such as differential long non-coding RNAs, splice and polyadenylation variants, introns, repeats, editing or mutation events, and exogenous RNA. Applying DE-kupl to human RNA-seq data sets identified multiple types of novel events, reproducibly across independent RNA-seq experiments.

New chimeric RNAs in acute myeloid leukemia.

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.

Pituitary hormone mRNA expression in European sea bass Dicentrarchus labrax in seawater and following acclimation to fresh water.

The mRNA expression of pituitary prolactin (prl), growth hormone (gh), somatolactin (sl), proopiomelanocortin (pomc), and gonadotropins (gthI and gthII) was quantified by real-time PCR, in sea bass, Dicentrarchus labrax, adapted for 1 month to seawater (SW) or freshwater (FW). In addition, IGF-I (igfI) mRNA expression in liver and branchial Na+/K+ -ATPase activity were determined. L17 ribosomal protein (rpL17) and elongation factor 1alpha (ef1alpha) were validated as reference genes in real-time PCR in the experimental context. The real-time PCR assays were validated for the different hormone genes considered. Expression of pituitary pomc, gthI, gthII, gh, and liver igfI was not significantly different between FW and SW fish. Pituitary prlwas 4.5-foldhigher in FWthan in SW, whereas pituitary sl was 1.8-fold higher in SW- compared with FW-adapted fish. Gill Na+/K+ -ATPase specific activity was 2.3-fold higher in FW sea bass compared with SW fish. Plasma cortisol levels were 6.5-fold lower in SW- than in FW-adapted specimens. The results are discussed in relation to the osmoregulatory strategy of this euryhaline SW species, which displays features that do not fit present models based on salmonids and FWeuryhaline teleosts.