an accurate pipeline for analysis of ngs data of small non coding rna

Motivations. The discovery of various families of small non-coding RNAs (sncRNAs) in recent years revealed the complexity of the regulation of gene expression at both transcriptional and post-transcriptional level. Of the numerous sncRNAs, microRNAs (miRNAs) constitute a large family of 19-23 nucleotides long RNAs that participate in a variety of processes, such as cell development and differentiation, apoptosis and stress responses to carcinogenesis. Computational analysis indicates that a unique miRNA can regulate hundreds of genes, underlining the potential influence of miRNAs in almost every cellular pathway. Deep sequencing technologies provides a powerful strategy to explore miRNA populations (miRNA-Seq) with high sensitivity and specificity. Different computational approaches have been developed to analyze miRNA-Seq data, allowing to identify known and novel miRNAs, perform differential expression analysis and predict putative miRNAs targets. We combined these algorithms into an analysis pipeline and tested it on data obtained from our experiments in cancer cell lines. Methods. The data obtained from the sequencer were filtered following several criteria. Since the sequence of the adapter is known, a Perl script was used to trim, from the raw data, the adaptors. The sequence reads were then filtered for quality and clustered to unique sequences to remove redundancy, retaining their individual read count information. Unique sequences 18 nucleotides or more in length were mapped, allowing up to one mismatch, on miRNA annotation according to miRBase version 18 using miRanalyzer. This detects the reads which correspond to known miRNAs, giving an estimation of expression level. miRBase repository is used because it offers information about mature (the mature sequence of known miRNAs), mature-star (the sequence which pairs with the mature miRNA in the miRNA secondary structure) and precursor miRNA sequences (sequence of the hairpin). miRNAs have been considered as expressed if they are detected at least 5 reads/sample. After detecting those that correspond to known miRNAs, the remaining reads were mapped to databases of transcribed sequences as mRNA and non-coding RNA (RFam). This step has two goals: (i) the number of matches can be viewed as a sample quality parameter (contamination of the RNA sample with degradation products and poly A tails) and (ii) it might be interesting to see which other known sncRNAs are in the sample. To predict novel miRNAs we used a probabilistic algorithm, miRDeep2, based on miRNA biogenesis model, to score compatibility of the position and frequency of sequenced RNA with the secondary structure of the miRNA precursor. This tool aligns sequencing reads to potential hairpin structures in a manner consistent with Dicer processing and assigns log-odds scores to measure the probability that hairpins are true miRNA precursors. To detect novel miRNAs by miRDeep2, a score cutoff corresponding to a prediction signal-to-noise ratio >10 was used. Identification of differentially expressed miRNAs was performed with the Bioconductor DESeq package. Starting from the expression values, the first step was to minimize the effect of the systematic technical variations, and then a negative binomial distribution model was used to test differential expression in deep sequencing datasets. Only miRNAs with a p-values less or equal to 0.05 and fold-change less or equal to -1.5 and greater or equal to 1.5 were considered as differentially expressed. Given the critical roles of miRNAs in regulating gene expression and cellular functions, we predicted their putative targets, intersecting results obtained from two resources, TargetScan and microRNA.org. TargetScan provide computationally predicted miRNA gene targets by searching for the presence of 8 and 7 mer sites that match the seed region of each miRNA, while microRNA.org target prediction incorporates current knowledge on target rules and on the use of a compendium of mammalian miRNAs. A further step of the analysis was to investigate nucleotide variations relative to the reference genome. To this purpose, preliminary steps were to reduce alignment artifacts and compute a more accurate quality estimation, removing biases due to sequencing cycle and preceding nucleotide. Further evidences were used to identify new miRNA variation sites: (i) Sequencing depth of variation sites should be equal to or larger than 5 reads per site, (ii) frequency of Single Nucleotide Variant occurrence >5% and (iii) variants not found in previous SNP annotation databases, like dbSNP. Results. We developed an accurate pipeline for integral analysis of next generation sequencing data of small RNA molecules. Based on solid statistical methods, this allows both detection of known miRNAs and prediction of new miRNAs, integrating steps for differential analysis, sequence analysis and target prediction. Acknowledgements Research support by: Fondazione con il Sud; Italian Association for Cancer Research; Italian Ministry for Education, University and Research; Regione Campania; University of Salerno; Fondazione Veronesi. Giorgio Giurato is a student of PhD School in Experimental and Clinic Medicine / Doctorate in Experimental Physiopathology and Neuroscience, Second University of Naples. Maria Ravo is supported by a 'Vladimir Ashkenazy' fellowship of Italian Association for Cancer Research. Concita Cantarella and Giovanni Nassa are fellows of Fondazione con il Sud

Effects of Oestrogen on MicroRNA Expression in Hormone-Responsive Breast Cancer Cells

Oestrogen receptor alpha (ERα) is a ligand-dependent transcription factor that mediates oestrogen effects in hormone-responsive cells. Following oestrogenic activation, ERα directly regulates the transcription of target genes via DNA binding. MicroRNAs (miRNAs) represent a class of small noncoding RNAs that function as negative regulators of protein-coding gene expression. They are found aberrantly expressed or mutated in cancer, suggesting their crucial role as either oncogenes or tumour suppressor genes. Here, we analysed changes in miRNA expression in response to oestrogen in hormone-responsive breast cancer MCF-7 and ZR-75.1 cells by microarray-mediated expression profiling. This led to the identification of 172 miRNAs up- or down-regulated by ERα in response to 17β-oestradiol, of which 52 are similarly regulated by the hormone in the two cell models investigated. To identify mechanisms by which ERα exerts its effects on oestrogen-responsive miRNA genes, the oestrogen-dependent miRNA expression profiles were integrated with global in vivo ERα binding site mapping in the genome by ChIP-Seq. In addition, data from miRNA and messenger RNA (mRNA) expression profiles obtained under identical experimental conditions were compared to identify relevant miRNA target transcripts. Results show that miRNAs modulated by ERα represent a novel genomic pathway to impact oestrogen-dependent processes that affect hormone-responsive breast cancer cell behaviour. MiRNome analysis in tumour tissues from breast cancer patients confirmed a strong association between expression of these small RNAs and clinical outcome of the disease, although this appears to involve only marginally the oestrogen-regulated miRNAs identified in this study

Direct regulation of microRNA biogenesis and expression by estrogen receptor beta in hormone-responsive breast cancer.

Estrogen effects on mammary epithelial and breast cancer (BC) cells are mediated by the nuclear receptors ERα and ERβ, transcription factors that display functional antagonism with each other, with ERβ acting as oncosuppressor and interfering with the effects of ERα on cell proliferation, tumor promotion and progression. Indeed, hormone-responsive, ERα+ BC cells often lack ERβ, which when present associates with a less aggressive clinical phenotype of the disease. Recent evidences point to a significant role of microRNAs (miRNAs) in BC, where specific miRNA expression profiles associate with distinct clinical and biological phenotypes of the lesion. Considering the possibility that ERβ might influence BC cell behavior via miRNAs, we compared miRNome expression in ERβ+ vs ERβ- hormone-responsive BC cells and found a widespread effect of this ER subtype on the expression pattern of these non-coding RNAs. More importantly, the expression pattern of 67 miRNAs, including 10 regulated by ERβ in BC cells, clearly distinguishes ERβ+, node-negative, from ERβ-, metastatic, mammary tumors. Molecular dissection of miRNA biogenesis revealed multiple mechanisms for direct regulation of this process by ERβ+ in BC cell nuclei. In particular, ERβ downregulates miR-30a by binding to two specific sites proximal to the gene and thereby inhibiting pri-miR synthesis. On the other hand, the receptor promotes miR-23b, -27b and 24-1 accumulation in the cell by binding in close proximity of the corresponding gene cluster and preventing in situ the inhibitory effects of ERα on pri-miR maturation by the p68/DDX5-Drosha microprocessor complex. These results indicate that cell autonomous regulation of miRNA expression is part of the mechanism of action of ERβ in BC cells and could contribute to establishment or maintenance of a less aggressive tumor phenotype mediated by this nuclear receptor

The Calcitonin and Glucocorticoids Combination: Mechanistic Insights into Their Class-Effect Synergy in Experimental Arthritis

PMCID: PMC3564948This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Site Fidelity in Space Use by Spider Monkeys (Ateles geoffroyi) in the Yucatan Peninsula, Mexico

Animal home ranges may vary little in their size and location in the short term but nevertheless show more variability in the long term. We evaluated the degree of site fidelity of two groups of spider monkeys (Ateles geoffroyi) over a 10- and 13-year period, respectively, in the northeastern Yucatan peninsula, Mexico. We used the Local Convex Hull method to estimate yearly home ranges and core areas (defined as the 60% probability contour) for the two groups. Home ranges varied from 7.7 to 49.6 ha and core areas varied from 3.1 to 9.2 ha. We evaluated the degree of site fidelity by quantifying the number of years in which different areas were used as either home ranges or core areas. Large tracts were used only as home ranges and only for a few years, whereas small areas were used as either core area or home range for the duration of the study. The sum of the yearly core areas coincided partially with the yearly home ranges, indicating that home ranges contain areas used intermittently. Home ranges, and especially core areas, contained a higher proportion of mature forest than the larger study site as a whole. Across years and only in one group, the size of core areas was positively correlated with the proportion of adult males in the group, while the size of home ranges was positively correlated with both the proportion of males and the number of tree species included in the diet. Our findings suggest that spider monkey home ranges are the result of a combination of long-term site fidelity and year-to-year use variation to enable exploration of new resources

Pyrosequencing as a tool for better understanding of human microbiomes

Next-generation sequencing technologies have revolutionized the analysis of microbial communities in diverse environments, including the human body. This article reviews several aspects of one of these technologies, the pyrosequencing technique, including its principles, applications, and significant contribution to the study of the human microbiome, with especial emphasis on the oral microbiome. The results brought about by pyrosequencing studies have significantly contributed to refining and augmenting the knowledge of the community membership and structure in and on the human body in healthy and diseased conditions. Because most oral infectious diseases are currently regarded as biofilm-related polymicrobial infections, high-throughput sequencing technologies have the potential to disclose specific patterns related to health or disease. Further advances in technology hold the perspective to have important implications in terms of accurate diagnosis and more effective preventive and therapeutic measures for common oral diseases

Antibiotic resistance determinants in the interplay between food and gut microbiota

A complex and heterogeneous microflora performs sugar and lactic acid fermentations in food products. Depending on the fermentable food matrix (dairy, meat, vegetable etc.) as well as on the species composition of the microbiota, specific combinations of molecules are produced that confer unique flavor, texture, and taste to each product. Bacterial populations within such “fermented food microbiota” are often of environmental origin, they persist alive in foods ready for consumption, eventually reaching the gastro-intestinal tract where they can interact with the resident gut microbiota of the host. Although this interaction is mostly of transient nature, it can greatly contribute to human health, as several species within the food microbiota also display probiotic properties. Such an interplay between food and gut microbiota underlines the importance of the microbiological quality of fermented foods, as the crowded environment of the gut is also an ideal site for genetic exchanges among bacteria. Selection and spreading of antibiotic resistance genes in foodborne bacteria has gained increasing interest in the past decade, especially in light of the potential transferability of antibiotic resistance determinants to opportunistic pathogens, natural inhabitants of the human gut but capable of acquiring virulence in immunocompromised individuals. This review aims at describing major findings and future prospects in the field, especially after the use of antibiotics as growth promoters was totally banned in Europe, with special emphasis on the application of genomic technologies to improve quality and safety of fermented foods

The evolution of the plastid chromosome in land plants: gene content, gene order, gene function

This review bridges functional and evolutionary aspects of plastid chromosome architecture in land plants and their putative ancestors. We provide an overview on the structure and composition of the plastid genome of land plants as well as the functions of its genes in an explicit phylogenetic and evolutionary context. We will discuss the architecture of land plant plastid chromosomes, including gene content and synteny across land plants. Moreover, we will explore the functions and roles of plastid encoded genes in metabolism and their evolutionary importance regarding gene retention and conservation. We suggest that the slow mode at which the plastome typically evolves is likely to be influenced by a combination of different molecular mechanisms. These include the organization of plastid genes in operons, the usually uniparental mode of plastid inheritance, the activity of highly effective repair mechanisms as well as the rarity of plastid fusion. Nevertheless, structurally rearranged plastomes can be found in several unrelated lineages (e.g. ferns, Pinaceae, multiple angiosperm families). Rearrangements and gene losses seem to correlate with an unusual mode of plastid transmission, abundance of repeats, or a heterotrophic lifestyle (parasites or myco-heterotrophs). While only a few functional gene gains and more frequent gene losses have been inferred for land plants, the plastid Ndh complex is one example of multiple independent gene losses and will be discussed in detail. Patterns of ndh-gene loss and functional analyses indicate that these losses are usually found in plant groups with a certain degree of heterotrophy, might rendering plastid encoded Ndh1 subunits dispensable