Integrazioni alla flora vascolare dell’Isola di Capri (Campania, sud Italia)

In the present work new data of vascular plant species (Atriplex prostrata Boucher ex DC., Bidens subalternans DC., Cyrtomium falcatum (L.f.) C.Presl, Lantana camara L. subsp. glandulosissima (Hayek) R.W.Sanders, Oloptum thomasii (Duby) Banfi & Galasso) and confirms (Anacamptis morio (L.) R.M.Bateman, Pridgeon & M.W.Chase, Neotinea maculata (Desf.) Stearn, Salvia clandestina L., Schoenus nigricans L., Viola riviniana Rchb.) are reported for Island of Capri

Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells

<p>Abstract</p> <p>Background</p> <p>Microarray experiments enable simultaneous measurement of the expression levels of virtually all transcripts present in cells, thereby providing a ‘molecular picture’ of the cell state. On the other hand, the genomic responses to a pharmacological or hormonal stimulus are dynamic molecular processes, where time influences gene activity and expression. The potential use of the statistical analysis of microarray data in time series has not been fully exploited so far, due to the fact that only few methods are available which take into proper account temporal relationships between samples.</p> <p>Results</p> <p>We compared here four different methods to analyze data derived from a time course mRNA expression profiling experiment which consisted in the study of the effects of estrogen on hormone-responsive human breast cancer cells. Gene expression was monitored with the innovative Illumina BeadArray platform, which includes an average of 30-40 replicates for each probe sequence randomly distributed on the chip surface. We present and discuss the results obtained by applying to these datasets different statistical methods for serial gene expression analysis. The influence of the normalization algorithm applied on data and of different parameter or threshold choices for the selection of differentially expressed transcripts has also been evaluated. In most cases, the selection was found fairly robust with respect to changes in parameters and type of normalization. We then identified which genes showed an expression profile significantly affected by the hormonal treatment over time. The final list of differentially expressed genes underwent cluster analysis of functional type, to identify groups of genes with similar regulation dynamics.</p> <p>Conclusions</p> <p>Several methods for processing time series gene expression data are presented, including evaluation of benefits and drawbacks of the different methods applied. The resulting protocol for data analysis was applied to characterization of the gene expression changes induced by estrogen in human breast cancer ZR-75.1 cells over an entire cell cycle.</p

Quantitative expression profiling of highly degraded RNA from formalin-fixed, paraffin-embedded breast tumor biopsies by oligonucleotide microarrays.

Microarray-based gene expression profiling is well suited for parallel quantitative analysis of large numbers of RNAs, but its application to cancer biopsies, particularly formalin-fixed, paraffin-embedded (FFPE) archived tissues, is limited by the poor quality of the RNA recovered. This represents a serious drawback, as FFPE tumor tissue banks are available with clinical and prognostic annotations, which could be exploited for molecular profiling studies, provided that reliable analytical technologies are found. We applied and evaluated here a microarray-based cDNA-mediated annealing, selection, extension and ligation (DASL) assay for analysis of 502 mRNAs in highly degraded total RNA extracted from cultured cells or FFPE breast cancer (MT) biopsies. The study included quantitative and qualitative comparison of data obtained by analysis of the same RNAs with genome-wide oligonucleotide microarrays vs DASL arrays and, by DASL, before and after extensive in vitro RNA fragmentation. The DASL-based expression profiling assay applied to RNA extracted from MCF-7 cells, before or after 24 h stimulation with a mitogenic dose of 17b-estradiol, consistently allowed to detect hormone-induced gene expression changes following extensive RNA degradation in vitro. Comparable results where obtained with tumor RNA extracted from FFPE MT biopsies (6 to 19 years old). The method proved itself sensitive, reproducible and accurate, when compared to results obtained by microarray analysis of RNA extracted from snap-frozen tissue of the same tumor

Comparative analysis of nuclear estrogen receptor alpha and beta interactomes in breast cancer cells.

Estrogen Receptor alpha and beta (ER-a and -b) are members of the nuclear receptor family of transcriptional regulators with distinct roles in mediating estrogen dependent breast cancer cell growth and differentiation. Following activation by the hormone, these proteins undergo conformation changes and accumulate in the nucleus, where they bind to chromatin at regulatory sites as homo- and/or heterodimers and assemble in large multiprotein complexes. Although the two ERs share a conserved structure, they exert specific and distinct functional roles in normal and transformed mammary epithelial cells and other cell types. To investigate the molecular bases of such differences, we performed a comparative computational analysis of the nuclear interactomes of the two ER subtypes, exploiting two datasets of receptor interacting proteins identified in breast cancer cell nuclei by Tandem Affinity Purification for their ability to associate in vivo with ligand- activated ER-a and/or ER-b. These datasets comprise 498 proteins, of which only 70 are common to both ERs, suggesting that differences in the nature of the two ER interactomes are likely to sustain the distinct roles of the two receptor subtypes. Functional characterization of the two interactomes and their topological analysis, considering node degree and closeness of the networks, confirmed this possibility. Indeed, clustering and network dissection highlighted the presence of distinct and ER subtype-specific subnetworks endowed with defined functions. Altogether, these data provide new insights on the protein–protein interaction networks controlled by ER-a and -b that mediate their ability to transduce estrogen signaling in breast cancer cells

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

Idiopathic benign retroperitoneal cyst: a case report

<p>Abstract</p> <p>Introduction</p> <p>Retroperitoneal cysts are uncommon, with an estimated incidence of 1/5750 to 1/250,000.</p> <p>Case presentation</p> <p>A male patient was admitted with an abdominal pain, jaundice and fever. Clinical examination and investigations confirmed an idiopathic benign retroperitoneal cyst. He underwent surgery and was discharged after making good recovery.</p> <p>Conclusion</p> <p>Retroperitoneal cysts are very rare, and most of the time they are discovered incidentally. Patients may be asymptomatic or present with abdominal pain, referred pain to the legs or weight loss. Imaging may help diagnose these lesions, but surgery is the keystone in confirming the diagnosis. This case is very rare and very educational as it highlights an unusual presentation of a benign retroperitoneal cyst. In our patient, the course of the disease was unique as the patient presented with jaundice.</p

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

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

C/EBPd gene targets in human keratinocytes

C/EBPs are a family of B-Zip transcription factors -TFs- involved in the regulation of differentiation in several tissues. The two most studied members -C/EBP\u3b1 and C/EBP\u3b2- play important roles in skin homeostasis and their ablation reveals cells with stem cells signatures. Much less is known about C/EBP\u3b4 which is highly expressed in the granular layer of interfollicular epidermis and is a direct target of p63, the master regular of multilayered epithelia. We identified C/EBP\u3b4 target genes in human primary keratinocytes by ChIP on chip and profiling of cells functionally inactivated with siRNA. Categorization suggests a role in differentiation and control of cell-cycle, particularly of G2/M genes. Among positively controlled targets are numerous genes involved in barrier function. Functional inactivation of C/EBP\u3b4 as well as overexpressions of two TF targets -MafB and SOX2- affect expression of markers of keratinocyte differentiation. We performed IHC on skin tumor tissue arrays: expression of C/EBP\u3b4 is lost in Basal Cell Carcinomas, but a majority of Squamous Cell Carcinomas showed elevated levels of the protein. Our data indicate that C/EBP\u3b4 plays a role in late stages of keratinocyte differentiation

New national and regional Annex I Habitat records: from #60 to #82

New Italian data on the distribution of the Annex I Habitats are reported in this contribution. Specifically, 8 new occurrences in Natura 2000 sites are presented and 49 new cells are added in the EEA 10 km × 10 km reference grid. The new data refer to the Italian administrative regions of Campania, Calabria, Marche, Piedmont, Sardinia, Sicily, Tuscany and Umbria. Relevés and figures are provided as Supplementary material respectively 1 and 2. Copyright Antonio Morabito et al