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MicroRNA-offset RNAs (moRNAs) are microRNA-like small RNAs generated by microRNA precursors. To date, little is known about moRNAs and bioinformatics tools to inspect their expression are still missing. We developed miR&amp, moRe2, the first bioinformatics method to consistently characterize microRNAs, moRNAs, and their isoforms from small RNA sequencing data. To illustrate miR&amp, moRe2 discovery power, we applied it to several published datasets. MoRNAs identified by miR&amp, moRe2 were in agreement with previous research findings. Moreover, we observed that moRNAs and new microRNAs predicted by miR&amp, moRe2 were downregulated upon the silencing of the microRNA-biogenesis pathway. Further, in a sizeable dataset of human blood cell populations, tens of novel miRNAs and moRNAs were discovered, some of them with significantly varied expression levels among the cell types. Results demonstrate that miR&amp, moRe2 is a valid tool for a comprehensive study of small RNAs generated from microRNA precursors and could help to investigate their biogenesis and function.

Microarray analysis of the multiple myeloma (MM) miRNome has unraveled the differential expression of miRNAs in cytogenetic subgroups, their involvement in the tumor biology and their effectiveness in prognostic models. Herein, the small RNA transcriptional landscape in MM has been investigated exploiting the possibilities offered by small RNA-seq, including accurate quantification of known mature species, discovery and characterization of isomiRs, and miRNA-offset RNAs (moRNAs). Matched small RNA-seq and miRNA GeneChip® microarray expression profiles were obtained in a representative panel of 30 primary MM tumors, fully characterized for genomic aberrations and mutations. RNA-seq and microarray gave concordant estimations of known species. Enhanced analysis of RNA-seq data with the miR&moRe pipeline led to the characterization of 655 known and 17 new mature miRNAs and of 74 moRNAs expressed in the considered cohort, 5 of which (moR-150-3p, moR-24-2-5p, moR-421-5p, moR-21-5p, and moR-6724-5p) at high level. Ectopic expression of miR-135a-3p in t(4;14) patients, upregulation of moR-150-3p and moR-21-5p in t(14;16)/t(14;20) samples, and of moR-6724-1-5p in patients overexpressing CCND1 were uncovered and validated by qRT-PCR. Overall, RNA-seq offered a more complete overview of small non-coding RNA in MM tumors, indicating specific moRNAs that demand further investigations to explore their role in MM biology.

Alternative splicing (AS) is a common mechanism which creates diverse RNA isoforms from a single gene, potentially increasing protein variety. Growing evidence suggests that this mechanism is closely related to cancer progression. In this study, whole transcriptome analysis was performed with GeneChip Human exon 1.0 ST Array from 80 samples comprising 23 normal colon mucosa, 30 primary colorectal cancer and 27 liver metastatic specimens from 46 patients, to identify AS events in colorectal cancer progression. Differentially expressed genes and exons were estimated and AS events were reconstructed by combining exon‐level analyses with AltAnalyze algorithms and transcript‐level estimations (MMBGX probabilistic method). The number of AS genes in the transition from normal colon mucosa to primary tumor was the most abundant, but fell considerably in the next transition to liver metastasis. 206 genes with probable AS events in colon cancer development and progression were identified, that are involved in processes and pathways relevant to tumor biology, as cell–cell and cell‐matrix interactions. Several AS events in VCL, CALD1, B3GNT6 and CTHRC1 genes, differentially expressed during tumor development were validated, at RNA and at protein level. Taken together, these results demonstrate that cancer‐specific AS is common in early phases of colorectal cancer natural history.

// Enrica Calura 1,* , Andrea Bisognin 2,* , Martina Manzoni 3 , Katia Todoerti 4 , Elisa Taiana 3 , Gabriele Sales 1 , Gareth J. Morgan 5 , Giovanni Tonon 6 , Nicola Amodio 7 , Pierfrancesco Tassone 7 , Antonino Neri 3 , Luca Agnelli 3,** , Chiara Romualdi 1,** and Stefania Bortoluzzi 2,** 1 Department of Biology, University of Padua, Padua, Italy 2 Department of Molecular Medicine, University of Padua, Padua, Italy 3 Department of Clinical Sciences and Community Health, University of Milan, and Hematology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy 4 Laboratory of Pre-Clinical and Translational Research, IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero in Vulture, Italy 5 Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA 6 Functional Genomics of Cancer Unit, Division of Experimental Oncology, San Raffaele Scientific Institute, Milan, Italy 7 Department of Experimental and Clinical Medicine, University of Study Magna Graecia, Catanzaro, Italy * The first two authors equally contributed to this work ** A substantial contribution to the conception, design, execution and writing of this work was equally provided by these three groups. LA, CR, SB have therefore to be considered co-last authors Correspondence to: Luca Agnelli, email: // Keywords : multiple myeloma, transciptional regulatory network, t(4;14) translocation, microRNA, expression profiling Received : August 05, 2015 Accepted : September 30, 2015 Published : October 19, 2015 Abstract The identification of overexpressed miRNAs in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. miRNA and gene expression profiles of two large representative MM datasets, available from retrospective and prospective series and encompassing a total of 249 patients at diagnosis, were analyzed by means of i n silico integrative genomics methods, based on MAGIA 2 and Micrographite computational procedures. We first identified relevant miRNA/transcription factors/target gene regulation circuits in the disease and linked them to biological processes. Members of the miR-99b/let-7e/miR-125a cluster, or of its paralog, upregulated in t(4;14), were connected with the specific transcription factors PBX1 and CEBPA and several target genes. These results were validated in two additional independent plasma cell tumor datasets. Then, we reconstructed a non-redundant miRNA-gene regulatory network in MM, linking miRNAs, such as let-7g , miR-19a , mirR-20a , mir-21 , miR-29 family, miR-34 family, miR-125b , miR-155 , miR-221 to pathways associated with MM subtypes, in particular the ErbB, the Hippo, and the Acute myeloid leukemia associated pathways.

MAGIA(2) (http://gencomp.bio.unipd.it/magia2) is an update, extension and evolution of the MAGIA web tool. It is dedicated to the integrated analysis of in silico target prediction, microRNA (miRNA) and gene expression data for the reconstruction of post-transcriptional regulatory networks. miRNAs are fundamental post-transcriptional regulators of several key biological and pathological processes. As miRNAs act prevalently through target degradation, their expression profiles are expected to be inversely correlated to those of the target genes. Low specificity of target prediction algorithms makes integration approaches an interesting solution for target prediction refinement. MAGIA(2) performs this integrative approach supporting different association measures, multiple organisms and almost all target predictions algorithms. Nevertheless, miRNAs activity should be viewed as part of a more complex scenario where regulatory elements and their interactors generate a highly connected network and where gene expression profiles are the result of different levels of regulation. The updated MAGIA(2) tries to dissect this complexity by reconstructing mixed regulatory circuits involving either miRNA or transcription factor (TF) as regulators. Two types of circuits are identified: (i) a TF that regulates both a miRNA and its target and (ii) a miRNA that regulates both a TF and its target.

To gain insights into a possible role of microRNAs in myeloproliferative neoplasms, we performed short RNA massive sequencing and extensive bioinformatic analysis in the JAK2V617F-mutated SET2 cell line. Overall, 652 known mature miRNAs were detected, of which 21 were highly expressed, thus being responsible of most of miRNA-mediated gene repression. microRNA putative targets were enriched in specific signaling pathways, providing information about cell activities under massive posttranscriptional regulation. The majority of miRNAs were mixtures of sequence variants, called isomiRs, mainly because of alternative, noncanonical processing of hairpin precursors. We also identified 78 novel miRNAs (miRNA*) derived from known hairpin precursors. Both major and minor (*) forms of miRNAs were expressed concurrently from half of expressed hairpins, highlighting the relevance of miRNA* and the complexity of strand selection bias regulation. Finally, we discovered that SET2 cells express a number of miRNA-offset RNAs (moRNAs), short RNAs derived from genomic regions flanking mature miRNAs. We provide novel data about the possible origin of moRNAs, although their functional role remains to be elucidated. Overall, this study shed light on the complexity of microRNA-mediated gene regulation in SET2 cells and represents the basis for future studies in JAK2V617F-mutated cellular models.

MAGIA (miRNA and genes integrated analysis) is a novel web tool for the integrative analysis of target predictions, miRNA and gene expression data. MAGIA is divided into two parts: the query section allows the user to retrieve and browse updated miRNA target predictions computed with a number of different algorithms (PITA, miRanda and Target Scan) and Boolean combinations thereof. The analysis section comprises a multistep procedure for (i) direct integration through different functional measures (parametric and non-parametric correlation indexes, a variational Bayesian model, mutual information and a meta-analysis approach based on P-value combination) of mRNA and miRNA expression data, (ii) construction of bipartite regulatory network of the best miRNA and mRNA putative interactions and (iii) retrieval of information available in several public databases of genes, miRNAs and diseases and via scientific literature text-mining. MAGIA is freely available for Academic users at http://gencomp.bio.unipd.it/magia.

The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor mediating the toxicity and tumor-promoting properties of dioxin. AHR has been reported to be overexpressed and constitutively active in a variety of solid tumors, but few data are currently available concerning its role in thyroid cancer. In this study we quantitatively explored a series of 51 paired-normal and papillary thyroid carcinoma (PTC) tissues for AHR-related genes. We identified an increased AHR expression/activity in PTC, independently from its nuclear dimerization partner and repressor but strictly related to a constitutive active MAPK/ERK pathway. The AHR up-regulation followed by an increased expression of AHR target genes was confirmed by a meta-analysis of published microarray data, suggesting a ligand-independent active AHR pathway in PTC. In-vitro studies using a PTC-derived cell line (BCPAP) and HEK293 cells showed that BRAFV600E may directly modulate AHR localization, induce AHR expression and activity in an exogenous ligand-independent manner. The AHR pathway might represent a potential novel therapeutic target for PTC in the clinical practice.

Transcriptional profiles of an alveolar rhabdomyosarcoma (RMS) and of a RMS cell line were reconstructed by a computational and statistical approach. Expression data of 29,963 genes in 11 adult human healthy tissues and in 37 tumour tissues were analysed for comparison. We identified 202 genes differentially expressed in at least one RMS sample, as compared with normal skeletal muscle. Among them, 107 resulted specifically overexpressed in RMS, but in no tumour affecting other tissues. Cluster analysis applied to expression data detected a series of genes presumably co-expressed with genes encoding known tumour markers and/or reportedly involved in genesis or development of rhabdomyosarcoma. This study succeeded in identifying a number of genes, which become candidates for in vitro study, thus facilitating discovery of novel tumour markers or targets for drug therapy.

In a previous study, we showed that the silencing of the heavy subunit (FHC) offerritin, the central iron storage molecule in the cell, is accompanied by a modification in global gene expression. In this work, we explored whether different FHC amounts might modulate miRNA expression levels in K562 cells and studied the impact of miRNAs in gene expression profile modifications. To this aim, we performed a miRNA-mRNA integrative analysis in K562 silenced for FHC (K562shFHC) comparing it with K562 transduced with scrambled RNA (K562shRNA). Four miRNAs, namely hsa-let-7g, hsa-let-7f, hsa-let-7i and hsa-miR-125b, were significantly up-regulated in silenced cells. The remarkable down-regulation of these miRNAs, following FHC expression rescue, supports a specific relation between FHC silencing and miRNA-modulation. The integration of target predictions with miRNA and gene expression profiles led to the identification of a regulatory network which includes the miRNAs up-regulated by FHC silencing, as well as91 down-regulated putative target genes. These genes were further classified in 9 networks; the highest scoring network, “Cell Death and Survival, Hematological System Development and Function, Hematopoiesis”, is composed by 18 focus molecules including RAF1 and ERK1/2. We confirmed that, following FHC silencing, ERK1/2 phosphorylation is severely impaired and that RAF1 mRNA is significantly down-regulated. Taken all together, our data indicate that, in our experimental model, FHC silencing may affect RAF1/pERK1/2 levels through the modulation of a specific set of miRNAs and add new insights in to the relationship among iron homeostasis and miRNAs.

Background The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for biomarker discovery using microarray data often provide results with limited overlap. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to improve list stability is to integrate biological information from genomic databases in the learning process; however, a comprehensive assessment based on different types of biological information is still lacking in the literature. In this work we have compared the effect of using different biological information in the learning process like functional annotations, protein-protein interactions and expression correlation among genes. Results Biological knowledge has been codified by means of gene similarity matrices and expression data linearly transformed in such a way that the more similar two features are, the more closely they are mapped. Two semantic similarity matrices, based on Biological Process and Molecular Function Gene Ontology annotation, and geodesic distance applied on protein-protein interaction networks, are the best performers in improving list stability maintaining almost equal prediction accuracy. Conclusions The performed analysis supports the idea that when some features are strongly correlated to each other, for example because are close in the protein-protein interaction network, then they might have similar importance and are equally relevant for the task at hand. Obtained results can be a starting point for additional experiments on combining similarity matrices in order to obtain even more stable lists of biomarkers. The implementation of the classification algorithm is available at the link: http://www.math.unipd.it/~dasan/biomarkers.html.

Recent studies have exponentially increased the number of known noncoding RNA categories, including microRNA (miRNA), small interfering RNA (siRNA), PIWI element-interacting RNA and various classes of long noncoding RNA (ncRNA), that fulfill key roles as transcriptional and post-transcriptional regulators and guides of chromatin-modifying complexes [1]. Among these short RNAs, miRNAs are post-transcriptional regulators of gene expression in a wide range of biological processes and diseases [2]. miRNAs are considered as prominent tumour markers, relevant targets for therapy and therapeutic agents [3].

Asthma is a multifactorial disease influenced by genetic and environmental factors. In the past decade, several loci and100 genes have been found to be associated with the disease in at least one population. Among these loci, region 12q13-24 has been implicated in asthma etiology in multiple populations, suggesting that it harbors one or more asthma susceptibility genes. We performed linkage and association analyses by transmission/disequilibrium test and case-control analysis in the candidate region 12q13-24, using the Sardinian founder population, in which limited heterogeneity of pathogenetic alleles for monogenic and complex disorders as well as of environmental conditions should facilitate the study of multifactorial traits. We analyzed our cohort, using a cutoff age of 13 years at asthma onset, and detected significant linkage to a portion of 12q13-24. We identified IRAK-M as the gene contributing to the linkage and showed that it is associated with early-onset persistent asthma. We defined protective and predisposing SNP haplotypes and replicated associations in an outbred Italian population. Sequence analysis in patients found mutations, including inactivating lesions, in the IRAK-M coding region. Immunohistochemistry of lung biopsies showed that IRAK-M is highly expressed in epithelial cells. We report that IRAK-M is involved in the pathogenesis of early-onset persistent asthma. IRAK-M, a negative regulator of the Toll-like receptor/IL-1R pathways, is a master regulator of NF- kappa B and inflammation. Our data suggest a mechanistic link between hyperactivation of the innate immune system and chronic airway inflammation and indicate IRAK-M as a potential target for therapeutic intervention against asthma.

Myeloproliferative neoplasms (MPN) are chronic myeloid cancers thought to arise at the level of CD34+ hematopoietic stem/progenitor cells. They include essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF). All can progress to acute leukemia, but PMF carries the worst prognosis. Increasing evidences indicate that deregulation of microRNAs (miRNAs) might plays an important role in hematologic malignancies, including MPN. To attain deeper knowledge of short RNAs (sRNAs) expression pattern in CD34+ cells and of their possible role in mediating post-transcriptional regulation in PMF, we sequenced with Illumina HiSeq2000 technology CD34+ cells from healthy subjects and PMF patients. We detected the expression of 784 known miRNAs, with a prevalence of miRNA up-regulation in PMF samples, and discovered 34 new miRNAs and 99 new miRNA-offset RNAs (moRNAs), in CD34+ cells. Thirty-seven small RNAs were differentially expressed in PMF patients compared with healthy subjects, according to microRNA sequencing data. Five miRNAs (miR-10b-5p, miR-19b-3p, miR-29a-3p, miR-379-5p, and miR-543) were deregulated also in PMF granulocytes. Moreover, 3'-moR-128-2 resulted consistently downregulated in PMF according to RNA-seq and qRT-PCR data both in CD34+ cells and granulocytes. Target predictions of these validated small RNAs de-regulated in PMF and functional enrichment analyses highlighted many interesting pathways involved in tumor development and progression, such as signaling by FGFR and DAP12 and Oncogene Induced Senescence. As a whole, data obtained in this study deepened the knowledge of miRNAs and moRNAs altered expression in PMF CD34+ cells and allowed to identify and validate a specific small RNA profile that distinguishes PMF granulocytes from those of normal subjects. We thus provided new information regarding the possible role of miRNAs and, specifically, of new moRNAs in this disease.

The problem of detecting DNA motifs with functional relevance in real biological sequences is difficult due to a number of biological, statistical and computational issues and also because of the lack of knowledge about the structure of searched patterns. Many algorithms are implemented in fully automated processes, which are often based upon a guess of input parameters from the user at the very first step. In this paper, we present a novel method for the detection of seeded DNA motifs, composed by regions with a different extent of variability. The method is based on a multi-step approach, which was implemented in a motif searching web tool (MOST). Overrepresented exact patterns are extracted from input sequences and clustered to produce motifs core regions, which are then extended and scored to generate seeded motifs. The combination of automated pattern discovery algorithms and different display tools for the evaluation and selection of results at several analysis steps can potentially lead to much more meaningful results than complete automation can produce. Experimental results on different yeast and human real datasets proved the methodology to be a promising solution for finding seeded motifs. MOST web tool is freely available at http://telethon.bio.unipd.it/bioinfo/MOST.

Background Rhabdomyosarcoma is a relatively common tumour of the soft tissue, probably due to regulatory disruption of growth and differentiation of skeletal muscle stem cells. Identification of genes differentially expressed in normal skeletal muscle and in rhabdomyosarcoma may help in understanding mechanisms of tumour development, in discovering diagnostic and prognostic markers and in identifying novel targets for drug therapy. Results A Perl-code web client was developed to automatically obtain genome map positions of large sets of genes. The software, based on automatic search on Human Genome Browser by sequence alignment, only requires availability of a single transcribed sequence for each gene. In this way, we obtained tissue-specific chromosomal maps of genes expressed in rhabdomyosarcoma or skeletal muscle. Subsequently, Perl software was developed to calculate gene density along chromosomes, by using a sliding window. Thirty-three chromosomal regions harbouring genes mostly expressed in rhabdomyosarcoma were identified. Similarly, 48 chromosomal regions were detected including genes possibly related to function of differentiated skeletal muscle, but silenced in rhabdomyosarcoma. Conclusion In this study we developed a method and the associated software for the comparative analysis of genomic expression in tissues and we identified chromosomal segments showing differential gene expression in human skeletal muscle and in alveolar rhabdomyosarcoma, appearing as candidate regions for harbouring genes involved in origin of alveolar rhabdomyosarcoma representing possible targets for drug treatment and/or development of tumor markers.

Background Qualitative alterations or abnormal expression of microRNAs (miRNAs) in colon cancer have mainly been demonstrated in primary tumors. Poorly overlapping sets of oncomiRs, tumor suppressor miRNAs and metastamiRs have been linked with distinct stages in the progression of colorectal cancer. To identify changes in both miRNA and gene expression levels among normal colon mucosa, primary tumor and liver metastasis samples, and to classify miRNAs into functional networks, in this work miRNA and gene expression profiles in 158 samples from 46 patients were analysed. Results Most changes in miRNA and gene expression levels had already manifested in the primary tumors while these levels were almost stably maintained in the subsequent primary tumor-to-metastasis transition. In addition, comparing normal tissue, tumor and metastasis, we did not observe general impairment or any rise in miRNA biogenesis. While only few mRNAs were found to be differentially expressed between primary colorectal carcinoma and liver metastases, miRNA expression profiles can classify primary tumors and metastases well, including differential expression of miR-10b, miR-210 and miR-708. Of 82 miRNAs that were modulated during tumor progression, 22 were involved in EMT. qRT-PCR confirmed the down-regulation of miR-150 and miR-10b in both primary tumor and metastasis compared to normal mucosa and of miR-146a in metastases compared to primary tumor. The upregulation of miR-201 in metastasis compared both with normal and primary tumour was also confirmed. A preliminary survival analysis considering differentially expressed miRNAs suggested a possible link between miR-10b expression in metastasis and patient survival. By integrating miRNA and target gene expression data, we identified a combination of interconnected miRNAs, which are organized into sub-networks, including several regulatory relationships with differentially expressed genes. Key regulatory interactions were validated experimentally. Specific mixed circuits involving miRNAs and transcription factors were identified and deserve further investigation. The suppressor activity of miR-182 on ENTPD5 gene was identified for the first time and confirmed in an independent set of samples. Conclusions Using a large dataset of CRC miRNA and gene expression profiles, we describe the interplay of miRNA groups in regulating gene expression, which in turn affects modulated pathways that are important for tumor development.

Dysregulation of miRNAs expression plays a critical role in the pathogenesis of genetic, multifactorial disorders and in human cancers. We exploited sequence, genomic and expression information to investigate two main aspects of post-transcriptional regulation in miRNA biogenesis, namely strand selection regulation and expression relationships between intragenic miRNAs and host genes. We considered miRNAs expression profiles, measured in five sizeable microarray datasets, including samples from different normal cell types and tissues, as well as different tumours and disease states. First, the study of expression profiles of "sister" miRNA pairs (miRNA/miRNA*, 5' and 3' strands of the same hairpin precursor) showed that the strand selection is highly regulated since it shows tissue-/cell-/condition-specific modulation. We used information about the direction and the strength of the strand selection bias to perform an unsupervised cluster analysis for the sample classification evidencing that is able to distinguish among different tissues, and sometimes between normal and malignant cells. Then, considering a minimum expression threshold, in few miRNA pairs only one mature miRNA is always present in all considered cell types, whereas the majority of pairs were concurrently expressed in some cell types and alternatively in others. In a significant fraction of concurrently expressed pairs, the major and the minor forms found at comparable levels may contribute to post-transcriptional gene silencing, possibly in a coordinate way. In the second part of the study, the behaved tendency to co-expression of intragenic miRNAs and their "host" mRNA genes was confuted by expression profiles examination, suggesting that the expression profile of a given host gene can hardly be a good estimator of co-transcribed miRNA(s) for post-transcriptional regulatory networks inference. Our results point out the regulatory importance of post-transcriptional phases of miRNAs biogenesis, reinforcing the role of such layer of miRNA biogenesis in miRNA-based regulation of cell activities.

By a computational approach we reconstructed genomic transcriptional profiles of 19 different adult human tissues, based on information on activity of 27,924 genes obtained from unbiased UniGene cDNA libraries. In each considered tissue, a small number of genes resulted highly expressed or “tissue specific.” Distribution of gene expression levels in a tissue appears to follow a power law, thus suggesting a correspondence between transcriptional profile and “scale-free” topology of protein networks. The expression of 737 genes involved in Mendelian diseases was analyzed, compared with a large reference set of known human genes. Disease genes resulted significantly more expressed than expected. The possible correspondence of their products to important nodes of intracellular protein network is suggested. Auto-organization of the protein network, its stability in time in the differentiated state, and relationships with the degree of genetic variability at genome level are discussed.