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International audience; Advances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein. We introduce a range of analytical techniques to interrogate single-molecule RNA fluorescence in situ hybridization (smFISH) data in combination with protein immunolabeling. DypFISH is suited to study patterns of clustering of molecules, the association of mRNA-protein subcellular localization with microtubule organizing center orientation, and interdependence of mRNA-protein spatial distributions. We showcase how our analytical tools can achieve biological insights by utilizing cell micropatterning to constrain cellular architecture, which leads to reduction in subcellular mRNA distribution variation, allowing for the characterization of their localization patterns. Furthermore, we show that our method can be applied to physiological systems such as skeletal muscle fibers.

Summary RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ hybridization-based techniques. Although such imaging data are ideal for characterization of RNA distribution, these techniques remain costly, time-consuming, and technically challenging. Given these limitations, imaging data exist only for a limited number of RNAs. We argue that the field of RNA localization would greatly benefit from complementary techniques able to characterize location of RNA. Here we discuss the importance of RNA localization and the current methodology in the field, followed by an introduction on prediction of location of molecules. We then suggest a machine learning approach based on the integration between imaging localization data and sequence-based data to assist in characterization of RNA localization on a transcriptome level., Graphical Abstract, Cell biology; Transcriptomics; Machine learning

Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

BackgroundRice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci (QTL) discovery and molecular marker development. Comparative sequence analyses across QTL regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.FindingsWe adopted the Galaxy framework to build the federated Rice Galaxy resource, with shared datasets, tools, and analysis workflows relevant to rice research. The shared datasets include high density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from nine published rice genomes. Rice Galaxy includes tools for designing single nucleotide polymorphism (SNP) assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.ConclusionsRice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

// Christiane Margue 1 , Susanne Reinsbach 1,* , Demetra Philippidou 1,* , Nicolas Beaume 1 , Casandra Walters 1 , Jochen G. Schneider 1 , Dorothee Nashan 1,2 , Iris Behrmann 1 , Stephanie Kreis 1 1 Life Sciences Research Unit, University of Luxembourg, Luxembourg 2 Klinikum Dortmund GmbH, Germany * These authors have contributed equally to this work Correspondence to: Stephanie Kreis, email: // Keywords : serum/tissue samples, circulating miRNAs, healthy miRNome, miRNA qPCR arrays, melanoma biomarkers Received : January 21, 2015 Accepted : February 28, 2015 Published : March 26, 2015 Abstract MiRNAs are increasingly recognized as biomarkers for the diagnosis of cancers where they are profiled from tumor tissue (intracellular miRNAs) or serum/plasma samples (extracellular miRNAs). To improve detection of reliable biomarkers from blood samples, we first compiled a healthy reference miRNome and established a well-controlled analysis pipeline allowing for standardized quantification of circulating miRNAs. Using whole miRNome and custom qPCR arrays, miRNA expression profiles were analyzed in 126 serum, whole blood and tissue samples of healthy volunteers and melanoma patients and in primary melanocyte and keratinocyte cell lines. We found characteristic signatures with excellent prognostic scores only in late stage but not in early stage melanoma patients. Upon comparison of melanoma tissue miRNomes with matching serum samples, several miRNAs were identified to be exclusively tissue-derived (miR-30b-5p, miR-374a-5p and others) while others had higher expression levels in serum (miR-3201 and miR-122-5p). Here we have compiled a healthy and widely applicable miRNome from serum samples and we provide strong evidence that levels of cell-free miRNAs only change significantly at later stages of melanoma progression, which has serious implications for miRNA biomarker studies in cancer.

Parkinson’s disease (PD) is histologically described by the deposition of α-synuclein, whose accumulation in Lewy bodies causes dopaminergic neuronal death. Although most of PD cases are sporadic, point mutations of the gene encoding the α-synuclein protein cause inherited forms of PD. There are currently six known point mutations that result in familial PD. Oxidative stress and neuroinflammation have also been described as early events associated with dopaminergic neuronal degeneration in PD. Though it is known that microglia are activated by wild-type α-synuclein, little is known about its mutated forms and the signaling cascades responsible for this microglial activation. The present study was designed to investigate consequences of wild-type and mutant α-synuclein (A53T, A30P and E46K) exposure on microglial reactivity. Interestingly, we described that α-synuclein-induced microglial reactivity appeared to be peptide-dependent. Indeed, the A53T protein activated more strongly microglia than the wild-type α-synuclein and other mutants. This A53T-induced microglial reactivity mechanism was found to depend on phosphorylation mechanisms mediated by MAPKs and on successive NFkB/AP-1/Nrf2 pathways activation. These results suggest that the microgliosis intensity during PD might depend on the type of α-synuclein protein implicated. Indeed, mutated forms are more potent microglial stimulators than wild-type α-synuclein. Based on these data, anti-inflammatory and antioxidant therapeutic strategies may be valid in order to reduce microgliosis but also to subsequently slow down PD progression, especially in familial cases.

A remarkable number of guanine-rich sequences with potential to adopt non-canonical secondary structures called G-quadruplexes (or G4 DNA) are found within gene promoters. Despite growing interest, regulatory role of quadruplex DNA motifs in intrinsic cellular function remains poorly understood. Herein, we asked whether occurrence of potential G4 (PG4) DNA in promoters is associated with specific function(s) in bacteria. Using a normalized promoter-PG4-content (PG4(P)) index we analysed60,000 promoters in 19 well-annotated species for (a) function class(es) and (b) gene(s) with enriched PG4(P). Unexpectedly, PG4-associated functional classes were organism specific, suggesting that PG4 motifs may impart specific function to organisms. As a case study, we analysed radioresistance. Interestingly, unsupervised clustering using PG4(P) of 21 genes, crucial for radioresistance, grouped three radioresistant microorganisms including Deinococcus radiodurans. Based on these predictions we tested and found that in presence of nanomolar amounts of the intracellular quadruplex-binding ligand N-methyl mesoporphyrin (NMM), radioresistance of D. radiodurans was attenuated by ~60%. In addition, important components of the RecF recombinational repair pathway recA, recF, recO, recR and recQ genes were found to harbour promoter-PG4 motifs and were also down-regulated in presence of NMM. Together these results provide first evidence that radioresistance may involve G4 DNA-mediated regulation and support the rationale that promoter-PG4s influence selective functions.

Despite progress made during the past decades, colorectal cancer (CRC) is still one of the most frequent and deadly cancers world-wide in both women and men. The prevention of this disease is, therefore, a significant public health issue. The primary cause of death is the development of distant metastases in organs such as liver and lungs; ∼40–50% of patients who undergo curative surgery relapse and die of metastatic disease (Johnston, 2005). Strikingly, diagnosed in time, CRC can be cured in 9 out of 10 cases. Thus, it is highly important to identify more sensitive and specific CRC markers to strengthen the efficiency of early diagnosis as well as to improve therapeutic strategies. Aberrant cytokine signalling has been associated with many diseases, including several cancers, disorders in haematopoiesis and autoimmune diseases. Cytokine responses have to be stringently controlled by a number of key regulatory proteins, such as the suppressors of cytokine signalling (SOCSs) family members. SOCS proteins are rapidly induced upon JAK/STAT signalling by activated signal transducer and activator of transcription factors (STAT) to negatively regulate cytokine signalling via a classical feedback loop (Inagaki-Ohara et al, 2013). Taking into consideration that ∼20% of all malignancies are initiated or exacerbated by inflammation, it is not surprising that SOCS proteins are regarded as tumour suppressor-like proteins (Elliott et al, 2008; Culig, 2013). The SOCS family consists of eight proteins, namely SOCS1–SOCS7 and cytokine-inducible SH2-containing protein (CIS). Each of these proteins has a central SH2 domain, an amino-terminal domain of variable length and sequence, and a carboxy-terminal 40 amino-acid region called SOCS box (Inagaki-Ohara et al, 2013). The SOCSs can interact with a series of signalling intermediates through the binding of their SH2 domain to phosphorylated tyrosine residues, particularly those on cytokine receptors and JAKs, leading to the blockade of the signal (Inagaki-Ohara et al, 2013). Among SOCS family members, SOCS1 and SOCS3 have been most widely studied. SOCS1 and SOCS3 have been shown to suppress cell growth and their expression is frequently downregulated in human cancers (Trengove and Ward, 2013). Low SOCS2 gene expression has been associated with hepatocellular, breast, pulmonary and ovarian cancers (Wikman et al, 2002; Sutherland et al, 2004; Farabegoli et al, 2005; Haffner et al, 2007; Qiu et al, 2013). SOCS4 shows a reduced expression in gastric cancer and is associated with a better clinical outcome in breast cancer patients (Sasi et al, 2010; Kobayashi et al, 2012) whereas loss of SOCS5 expression is correlated with poor prognosis in liver cancer (Yoon et al, 2012). SOCS6 has been reported to be downregulated in carcinomas of prostate, stomach, liver and colon (Lai et al, 2009, 2010; Qiu et al, 2013; Zhu et al, 2013). SOCS7 has a favourable prognostic value in breast cancer (Sasi et al, 2010). Altogether, these findings suggest the involvement of SOCSs in cancer. In the present study, we examined the expression as well as the methylation status of SOCS proteins in primary colon tumours, its precursor lesions and distant normal tissue. We used laser microdissection (LMD) to select for a highly pure population of epithelial cells in primary CRC tumours as well as in normal colon tissue. Our results point towards the involvement of SOCS2 and SOCS6 in the carcinogenesis of CRC and demonstrate their potential use as biomarkers.