Search

Deciphering how the human striatum develops is necessary for understanding the diseases that affect this region. To decode the transcriptional modules that regulate this structure during development, we compiled a catalog of 1116 long intergenic noncoding RNAs (lincRNAs) identified de novo and then profiled 96,789 single cells from the early human fetal striatum. We found that D1 and D2 medium spiny neurons (D1- and D2-MSNs) arise from a common progenitor and that lineage commitment is established during the postmitotic transition, across a pre-MSN phase that exhibits a continuous spectrum of fate determinants. We then uncovered cell type-specific gene regulatory networks that we validated through in silico perturbation. Finally, we identified human-specific lincRNAs that contribute to the phylogenetic divergence of this structure in humans. This work delineates the cellular hierarchies governing MSN lineage commitment.

RNA-Seq has become the de facto standard technique for characterization and quantification of transcriptomes, and a large number of methods and tools have been proposed to model and detect differential gene expression based on the comparison of transcript abundances across different samples. However, state-of-the-art methods for this task are usually designed for pairwise comparisons, that is, can identify significant variation of expression only between two conditions or samples. We describe the use of RNentropy, a methodology based on information theory, devised to overcome this limitation. RNentropy can thus detect significant variations of gene expression in RNA-Seq data across any number of samples and conditions, and can be applied downstream of any analysis pipeline for the quantification of gene expression from raw sequencing data. RNentropy takes as input gene (or transcript) expression values, defined with any measure suitable for the comparison of transcript levels across samples and conditions. The output consists of genes (or transcripts) exhibiting significant variation of expression across the conditions studied, together with the samples in which they result to be over- or underexpressed. RNentropy is implemented as an R package and freely available from the CRAN repository. We provide a detailed guide to the functions and parameters of the package and usage examples to demonstrate the software capabilities, also showing how it can be applied to the analysis of single-cell RNA sequencing data.

The transcription factor SOX2 is important for brain development and for neural stem cells (NSC) maintenance. Sox2-deleted (Sox2-del) NSC from neonatal mouse brain are lost after few passages in culture. Two highly expressed genes, Fos and Socs3, are strongly downregulated in Sox2-del NSC, we previously showed that Fos or Socs3 overexpression by lentiviral transduction fully rescues NSC’s long-term maintenance in culture. Sox2-del NSC are severely defective in neuronal production when induced to differentiate. NSC rescued by Sox2 reintroduction correctly differentiate into neurons. Similarly, Fos transduction rescues normal or even increased numbers of immature neurons expressing beta-tubulinIII, but not more differentiated markers (MAP2). Additionally, many cells with both beta-tubulinIII and GFAP expression appear, indicating that FOS stimulates the initial differentiation of a “mixed” neuronal/glial progenitor. The unexpected rescue by FOS suggested that FOS, a SOX2 transcriptional target, might act on neuronal genes, together with SOX2. CUT&amp, RUN analysis to detect genome-wide binding of SOX2, FOS, and JUN (the AP1 complex) revealed that a high proportion of genes expressed in NSC are bound by both SOX2 and AP1. Downregulated genes in Sox2-del NSC are highly enriched in genes that are also expressed in neurons, and a high proportion of the “neuronal” genes are bound by both SOX2 and AP1.

Various next generation sequencing (NGS) based strategies have been successfully used in the recent past for tracing origins and understanding the evolution of infectious agents, investigating the spread and transmission chains of outbreaks, as well as facilitating the development of effective and rapid molecular diagnostic tests and contributing to the hunt for treatments and vaccines. The ongoing COVID-19 pandemic poses one of the greatest global threats in modern history and has already caused severe social and economic costs. The development of efficient and rapid sequencing methods to reconstruct the genomic sequence of SARS-CoV-2, the etiological agent of COVID-19, has been fundamental for the design of diagnostic molecular tests and to devise effective measures and strategies to mitigate the diffusion of the pandemic.Diverse approaches and sequencing methods can, as testified by the number of available sequences, be applied to SARS-CoV-2 genomes. However, each technology and sequencing approach has its own advantages and limitations. In the current review, we will provide a brief, but hopefully comprehensive, account of currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes. We also present an outline of current repositories and databases that provide access to SARS-CoV-2 genomic data and associated metadata. Finally, we offer general advice and guidelines for the appropriate sharing and deposition of SARS-CoV-2 data and metadata, and suggest that more efficient and standardized integration of current and future SARS-CoV-2-related data would greatly facilitate the struggle against this new pathogen. We hope that our ‘vademecum’ for the production and handling of SARS-CoV-2-related sequencing data, will contribute to this objective.

RNA-Seq has become the de facto standard technique for characterization and quantification of transcriptomes, and a large number of methods and tools have been proposed to model and detect differential gene expression based on the comparison of transcript abundances across different samples. However, state-of-the-art methods for this task are usually designed for pairwise comparisons, that is, can identify significant variation of expression only between two conditions or samples. We describe the use of RNentropy, a methodology based on information theory, devised to overcome this limitation. RNentropy can thus detect significant variations of gene expression in RNA-Seq data across any number of samples and conditions, and can be applied downstream of any analysis pipeline for the quantification of gene expression from raw sequencing data. RNentropy takes as input gene (or transcript) expression values, defined with any measure suitable for the comparison of transcript levels across samples and conditions. The output consists of genes (or transcripts) exhibiting significant variation of expression across the conditions studied, together with the samples in which they result to be over- or underexpressed. RNentropy is implemented as an R package and freely available from the CRAN repository. We provide a detailed guide to the functions and parameters of the package and usage examples to demonstrate the software capabilities, also showing how it can be applied to the analysis of single-cell RNA sequencing data.

NF-Y is a trimeric Transcription Factor -TF- which binds with high selectivity to the conserved CCAAT element. Individual ChIP-seq analysis as well as ENCODE have progressively identified locations shared by other TFs. Here, we have analyzed data introduced by ENCODE over the last five years in K562, HeLa-S3 and GM12878, including several chromatin features, as well RNA-seq profiling of HeLa cells after NF-Y inactivation. We double the number of sequence-specific TFs and co-factors reported. We catalogue them in 4 classes based on co-association criteria, infer target genes categorizations, identify positional bias of binding sites and gene expression changes. Larger and novel co-associations emerge, specifically concerning subunits of repressive complexes as well as RNA-binding proteins. On the one hand, these data better define NF-Y association with single members of major classes of TFs, on the other, they suggest that it might have a wider role in the control of mRNA production., Author summary The ongoing ENCODE consortium represents a useful compendium of locations of TFs, chromatin marks, gene expression data. In previous reports, we identified modules of CCAAT-binding NF-Y with individual TFs. Here, we analyzed all 363 factors currently present: 68 with enrichment of CCAAT in their locations, 38 with overlap of peaks. New sequence-specific TFs, co-activators and co-repressors are reported. Co-association patterns correspond to specific targeted genes categorizations and gene expression changes, as assessed by RNA-seq after NF-Y inactivation. These data widen and better define a coherent model of synergy of NF-Y with selected groups of TFs and co-factors.

In diploid organisms, two copies of each allele are normally inherited from parents. Paternal and maternal alleles can be regulated and expressed unequally, which is referred to as allele-specific expression (ASE). In this work, we present aScan, a novel method for the identification of ASE from the analysis of matched individual genomic and RNA sequencing data. By performing extensive analyses of both real and simulated data, we demonstrate that aScan can correctly identify ASE with high accuracy and sensitivity in different experimental settings. Additionally, by applying our method to a small cohort of individuals that are not included in publicly available databases of human genetic variation, we outline the value of possible applications of ASE analysis in single individuals for deriving a more accurate annotation of "private" low-frequency genetic variants associated with regulatory effects on transcription. All in all, we believe that aScan will represent a beneficial addition to the set of bioinformatics tools for the analysis of ASE. Finally, while our method was initially conceived for the analysis of RNA-seq data, it can in principle be applied to any quantitative NGS assay for which matched genotypic and expression data are available. AVAILABILITY: aScan is currently available in the form of an open source standalone software package at: https://github.com/Federico77z/aScan/. aScan version 1.0.3, available at https://github.com/Federico77z/aScan/releases/tag/1.0.3, has been used for all the analyses included in this manuscript. A Docker image of the tool has also been made available at https://github.com/pmandreoli/aScanDocker.

The Sox2 transcription factor is necessary for the long-term self-renewal of neural stem cells (NSC). Its mechanism of action is still poorly defined. To identify molecules regulated by Sox2, and acting in mouse NSC maintenance, we transduced, into Sox2-deleted NSC, genes whose expression is strongly downregulated following Sox2 loss (Fos, Jun, Egr2), individually or in combination. Fos alone rescued long-term proliferation, as shown by in vitro cell growth and clonal analysis. Further, pharmacological inhibition of the FOS/JUN AP1 complex binding to its targets, decreased cell proliferation and expression of the putative target Suppressor of cytokine signaling 3 (Socs3). Additionally, Fos requirement for efficient long-term proliferation was demonstrated by the reduction of NSC clones capable of long-term expansion following CRISPR/Cas9-mediated Fos inactivation. Previous work showed that the Socs3 gene is strongly downregulated following Sox2 deletion, and its reexpression by lentiviral transduction rescues long-term NSC proliferation. Fos appears to be an upstream regulator of Socs3, possibly together with Jun and Egr2; indeed, Sox2 reexpression in Sox2-deleted NSC progressively activates both Fos and Socs3 expression; in turn, Fos transduction activates Socs3 expression. Based on available SOX2 ChIPseq and ChIA-PET data, we propose a model whereby Sox2 is a direct activator of both Socs3 and Fos, as well as possibly Jun and Egr2; further, we provide direct evidence for FOS and JUN binding onSocs3promoter, suggesting direct transcriptional regulation. These results provide the basis for developing a model of a network of interactions, regulating critical effectors of NSC proliferation and long-term maintenance.Significance statementProliferation and maintenance of NSC are essential during normal brain development, and, postnatally, for the maintenance of hippocampal function and memory until advanced age. Little is known about the molecular mechanisms that maintain the critical aspects of NSC biology (quiescence and proliferation) in postnatal age. Our work provides a methodology, transduction of genes deregulated following Sox2 deletion, that allows to test many candidate genes for their ability to sustain NSC proliferation. In principle, this may have interesting implications for identifying targets for pharmacological manipulations.Abstract Figure

Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-Seq) has opened new avenues of research in the genome-wide characterization of regulatory DNA-protein interactions at the genetic and epigenetic level. As a consequence, it has become the de facto standard for studies on the regulation of transcription, and literally thousands of data sets for transcription factors and cofactors in different conditions and species are now available to the scientific community. However, while pipelines and best practices have been established for the analysis of a single experiment, there is still no consensus on the best way to perform an integrated analysis of multiple datasets in the same condition, in order to identify the most relevant and widespread regulatory modules composed by different transcription factors and cofactors. We present here a computational pipeline for this task, that integrates peak summit colocalization, a novel statistical framework for the evaluation of its significance, and motif enrichment analysis. We show examples of its application to ENCODE data, that led to the identification of relevant regulatory modules composed of different factors, as well as the organization on DNA of the binding motifs responsible for their recruitment.

The study addresses the mechanism through which inhibition of LSD1 sensitizes AML cells to retinoic acid–induced differentiation., The histone demethylase LSD1 is deregulated in several tumors, including leukemias, providing the rationale for the clinical use of LSD1 inhibitors. In acute promyelocytic leukemia (APL), pharmacological doses of retinoic acid (RA) induce differentiation of APL cells, triggering degradation of the PML-RAR oncogene. APL cells are resistant to LSD1 inhibition or knockout, but targeting LSD1 sensitizes them to physiological doses of RA without altering of PML-RAR levels, and extends survival of leukemic mice upon RA treatment. The combination of RA with LSD1 inhibition (or knockout) is also effective in other non-APL, acute myeloid leukemia (AML) cells. Nonenzymatic activities of LSD1 are essential to block differentiation, while RA with targeting of LSD1 releases a differentiation gene expression program, not strictly dependent on changes in histone H3K4 methylation. Integration of proteomic/epigenomic/mutational studies showed that LSD1 inhibitors alter the recruitment of LSD1-containing complexes to chromatin, inhibiting the interaction between LSD1 and the transcription factor GFI1.

Background The execution of many genetic programs, influenced by environmental conditions, is epigenetically controlled. Thus, small molecules of the intermediate metabolism being precursors of most of nutrition-deriving epigenetic modifications, sense the cell surrounding environment. Methods Here we describe histone H4K16 acetylation distribution in S. cerevisiae nhp6ab mutant, using ChIP-seq analysis; its transcription profile by RNA-seq and its metabolic features by studying the metabolome. We then intersected these three -omic approaches to unveil common crosspoints (if any). Results In the nhp6ab mutant, the glucose metabolism is switched to pathways leading to Acetyl-CoA synthesis. These enhanced pathways could lead to histone hyperacetylation altering RNA transcription, particularly of those metabolic genes that maintain high Acetyl-CoA availability. Conclusions Thus, the absence of chromatin regulators like Nhp6 A and B, interferes with a regulative circular mechanism where histone modification, transcription and metabolism influence each other and contribute to clarify the more general phenomenon in which gene regulation feeds metabolic alterations on epigenetic basis. General significance This study allowed us to identify, in these two factors, a common element of regulation in metabolism and chromatin acetylation state that could represent a powerful tool to find out relationships existing between metabolism and gene expression in more complex systems.

Mammalian centromeres are associated with highly repetitive DNA (satellite DNA), which has so far hindered molecular analysis of this chromatin domain. Centromeres are epigenetically specified, and binding of the CENPA protein is their main determinant. In previous work, we described the first example of a natural satellite-free centromere on Equus caballus Chromosome 11. Here, we investigated the satellite-free centromeres of Equus asinus by using ChIP-seq with anti-CENPA antibodies. We identified an extraordinarily high number of centromeres lacking satellite DNA (16 of 31). All of them lay in LINE- and AT-rich regions. A subset of these centromeres is associated with DNA amplification. The location of CENPA binding domains can vary in different individuals, giving rise to epialleles. The analysis of epiallele transmission in hybrids (three mules and one hinny) showed that centromeric domains are inherited as Mendelian traits, but their position can slide in one generation. Conversely, centromere location is stable during mitotic propagation of cultured cells. Our results demonstrate that the presence of more than half of centromeres void of satellite DNA is compatible with genome stability and species survival. The presence of amplified DNA at some centromeres suggests that these arrays may represent an intermediate stage toward satellite DNA formation during evolution. The fact that CENPA binding domains can move within relatively restricted regions (a few hundred kilobases) suggests that the centromeric function is physically limited by epigenetic boundaries.

NF-Y is a trimeric transcription factor that binds on DNA the CCAAT-box motif. In this article we reviewed and complemented with additional bioinformatic analysis existing data on genome-wide NF-Y binding characterization in human, reaching the following main conclusions: (1) about half of NF-Y binding sites are located at promoters, about 60-80 base pairs from transcription start sites; NF-Y binding to distal genomic regions takes place at inactive chromatin loci and/or DNA repetitive elements more often than active enhancers; (2) on almost half of its binding sites, regardless of their genomic localization (promoters or distal regions), NF-Y finds on DNA more than one CCAAT-box, and most of those multiple CCAAT binding loci present precise spacing and organization of the elements composing them; (3) there exists a well defined class of transcription factors that show genome-wide co-localization with NF-Y. Some of them lack their canonical binding site in binding regions overlapping with NF-Y, hence hinting at NF-Y mediated recruitment, while others show a precise positioning on DNA of their binding sites with respect to the CCAAT box bound by NF-Y. This article is part of a Special Issue entitled: Nuclear Factor Y in Development and Disease, edited by Prof. Roberto Mantovani.

During their lifespan, plants respond to a multitude of stressful factors. Dynamic changes in chromatin and concomitant transcriptional variations control stress response and adaptation, with epigenetic memory mechanisms integrating environmental conditions and appropriate developmental programs over the time. Here we analyzed transcriptome and genome-wide histone modifications of maize plants subjected to a mild and prolonged drought stress just before the flowering transition. Stress was followed by a complete recovery period to evaluate drought memory mechanisms. Three categories of stress-memory genes were identified: i) "transcriptional memory" genes, with stable transcriptional changes persisting after the recovery; ii) "epigenetic memory candidate" genes in which stress-induced chromatin changes persist longer than the stimulus, in absence of transcriptional changes; iii) "delayed memory" genes, not immediately affected by the stress, but perceiving and storing stress signal for a delayed response. This last memory mechanism is described for the first time in drought response. In addition, applied drought stress altered floral patterning, possibly by affecting expression and chromatin of flowering regulatory genes. Altogether, we provided a genome-wide map of the coordination between genes and chromatin marks utilized by plants to adapt to a stressful environment, describing how this serves as a backbone for setting stress memory.

Heavy methyl Stable Isotope Labeling with Amino acids in Cell culture (hmSILAC) is a metabolic labeling strategy employed in proteomics to increase the confidence of global identification of methylated peptides by MS. However, to this day, the automatic and robust identification of heavy and light peak doublets from MS-raw data of hmSILAC experiments is a challenging task, for which the choice of computational methods is very limited. Here, hmSEEKER, a software designed to work downstream of a MaxQuant analysis for in-depth search of MS peak pairs that correspond to light and heavy methyl-peptide within MaxQuant-generated tables is described with good sensitivity and specificity. The software is written in Perl, and its code and user manual are freely available at Bitbucket (https://bit.ly/2scCT9u).

Fine regulation of exocytosis and endocytosis plays a basic role in pollen tube growth. Excess plasma membrane secreted during pollen tube elongation is known to be retrieved by endocytosis and partially reused in secretory pathways through the Golgi apparatus. Dissection of endocytosis has enabled distinct degradation pathways to be identified in tobacco pollen tubes and has shown that microtubules influence the transport of plasma membrane internalized in the tip region to vacuoles. Here, we used different drugs affecting the polymerization state of microtubules together with SYP21, a marker of prevacuolar compartments, to characterize trafficking of prevacuolar compartments in Nicotiana tabacum pollen tubes. Ultrastructural and biochemical analysis showed that microtubules bind SYP21-positive microsomes. Transient transformation of pollen tubes with LAT52-YFP-SYP21 revealed that microtubules play a key role in the delivery of prevacuolar compartments to tubular vacuoles.

Eukaryotic cells contain a population of mitochondria, variable in number and shape, which in turn contain multiple copies of a tiny compact genome (mtDNA) whose expression and function is strictly coordinated with the nuclear one. mtDNA copy number varies between different cell or tissues types, both in response to overall metabolic and bioenergetics demands and as a consequence or cause of specific pathological conditions. Here we present a novel and reliable methodology to assess the effective mtDNA copy number per diploid genome by investigating off-target reads obtained by whole-exome sequencing (WES) experiments. We also investigate whether and how mtDNA copy number correlates with mitochondrial mass, respiratory activity and expression levels. Analyzing six different tissues from three age- and sex-matched human individuals, we found a highly significant linear correlation between mtDNA copy number estimated by qPCR and the frequency of mtDNA off target WES reads. Furthermore, mtDNA copy number showed highly significant correlation with mitochondrial gene expression levels as measured by RNA-Seq as well as with mitochondrial mass and respiratory activity. Our methodology makes thus feasible, at a large scale, the investigation of mtDNA copy number in diverse cell-types, tissues and pathological conditions or in response to specific treatments., This work was supported by Ministero dell'Istruzione, Università e Ricerca (projects PRIN-2009, Micromap [PON01_02589], Virtualab [PON01_01297]) and by Consiglio Nazionale delle Ricerche (progetto strategico “Medicina personalizzata”, progetto strategico “Invecchiamento”, progetto bandiera “Epigen”).

Scientific Reports 7: Article number: 44797; published online: 20 March 2017; updated: 16 June 2017. In the HTML version of this Article, Figure 6 was incorrect. The correct Figure 6 appears below as Figure 1. This error has been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.

SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called “fight-club hubs”, characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called “switch genes”, appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer.

The first step in the definition of transcriptional regulatory networks is to establish correct relationships between transcription factors (TFs) and their target genes, together with the effect of their regulatory activity (activator or repressor). Fundamental advances in this direction have been made possible by the introduction of experimental techniques such as Chromatin Immunoprecipitation, which, coupled with next-generation sequencing technologies (ChIP-Seq), permit the genome-wide identification of TF binding sites. This chapter provides a survey on how data of this kind are to be processed and integrated with expression and other types of data to infer transcriptional regulatory rules and codes.

The integrated activity of cis-regulatory elements fine-tunes transcriptional programs of mammalian cells by recruiting cell type–specific as well as ubiquitous transcription factors (TFs). Despite their key role in modulating transcription, enhancers are still poorly characterized at the molecular level, and their limited DNA sequence conservation in evolution and variable distance from target genes make their unbiased identification challenging. The coexistence of high mono-methylation and low tri-methylation levels of lysine 4 of histone H3 is considered a signature of enhancers, but a comprehensive view of histone modifications associated to enhancers is still lacking. By combining chromatin immunoprecipitation (ChIP) with mass spectrometry, we investigated cis-regulatory regions in macrophages to comprehensively identify histone marks specifically associated with enhancers, and to profile their dynamics after transcriptional activation elicited by an inflammatory stimulation. The intersection of the proteomics data with ChIP-seq and RNA-seq analyses revealed the existence of novel subpopulations of enhancers, marked by specific histone modification signatures: specifically, H3K4me1/K36me2 marks transcribed enhancers, while H3K4me1/K36me3 and H3K4me1/K79me2 combinations mark distinct classes of intronic enhancers. Thus, our MS analysis of functionally distinct genomic regions revealed the combinatorial code of histone modifications, highlighting the potential of proteomics in addressing fundamental questions in epigenetics.

MYC deregulation is common in human cancer and has a role in sustaining the aggressive cancer stem cell populations. MYC mediates a broad transcriptional response controlling normal biological programmes, but its activity is not clearly understood. We address MYC function in cancer stem cells through the inducible expression of Omomyc—a MYC‐derived polypeptide interfering with MYC activity—taking as model the most lethal brain tumour, glioblastoma. Omomyc bridles the key cancer stemlike cell features and affects the tumour microenvironment, inhibiting angiogenesis. This occurs because Omomyc interferes with proper MYC localization and itself associates with the genome, with a preference for sites occupied by MYC. This is accompanied by selective repression of master transcription factors for glioblastoma stemlike cell identity such as OLIG2, POU3F2, SOX2, upregulation of effectors of tumour suppression and differentiation such as ID4, MIAT, PTEN, and modulation of the expression of microRNAs that target molecules implicated in glioblastoma growth and invasion such as EGFR and ZEB1. Data support a novel view of MYC as a network stabilizer that strengthens the regulatory nodes of gene expression networks controlling cell phenotype and highlight Omomyc as model molecule for targeting cancer stem cells.

We recently investigated the prognostic impact of a Chronic Care Model (CCM)-based healthcare program applied in primary care in Tuscany Region mainly run by multidisciplinary teams composed of general practitioners (GPs) and nurses. The project included proactively planned follow-up visits for each patient, individualized counselling to optimize lifestyle modifications and adherence to appropriate diagnostic and therapeutic pathways. 1761 patients with Chronic heart failure (CHF) directly enrolled by the GPs were matched with 3522 CHF controls not involved in the project. Over a 4-year follow-up in the CCM group a higher CHF hospitalization rate was found (12.1 vs 10.3 events/100 patient-years; incidence rate ratio [IRR] 1.15, p=0.0030), whereas mortality was lower (10.8 vs 12.6 events/100 patient-years; IRR 0.82, p0.0001). The CCM status was independently associated with a 34% increase in the risk of CHF hospitalization and a 18% reduction in the risk of death (p0.0001 for both). The CCM status was associated with a 50% increase in the rate of planned Heart failure (HF) hospitalizations whereas the rate of 1-month CHF readmissions showed no differences. Such a divergent trend could be explained by the direct involvement of GPs in the CCM program, leading them to a better awareness of patients' clinical status, and then to a more frequent use of clinical pathways and facilities, including hospitalization. It is reasonable to argue that not all hospitalizations must necessarily be considered as a poor outcome, as they often provide additional opportunities to improve therapies, optimize patient education, or define follow-up strategies. The evidence of a divergent trend between mortality and hospitalization in our population might support the clinical importance of a multidisciplinary approach for the management of patients with HF.

The first step in the definition of transcriptional regulatory networks is to establish correct relationships between transcription factors (TFs) and their target genes, together with the effect of their regulatory activity (activator or repressor). Fundamental advances in this direction have been made possible by the introduction of experimental techniques such as Chromatin Immunoprecipitation, which, coupled with next-generation sequencing technologies (ChIP-Seq), permit the genome-wide identification of TF binding sites. This chapter provides a survey on how data of this kind are to be processed and integrated with expression and other types of data to infer transcriptional regulatory rules and codes.

A comprehensive knowledge of all the factors involved in splicing, both proteins and RNAs, and of their interaction network is crucial for reaching a better understanding of this process and its functions. A large part of relevant information is buried in the literature or collected in various different databases. By hand-curated screenings of literature and databases, we retrieved experimentally validated data on 71 human RNA-binding splicing regulatory proteins and organized them into a database called ‘SpliceAid-F’ (http://www.caspur.it/SpliceAidF/). For each splicing factor (SF), the database reports its functional domains, its protein and chemical interactors and its expression data. Furthermore, we collected experimentally validated RNA–SF interactions, including relevant information on the RNA-binding sites, such as the genes where these sites lie, their genomic coordinates, the splicing effects, the experimental procedures used, as well as the corresponding bibliographic references. We also collected information from experiments showing no RNA–SF binding, at least in the assayed conditions. In total, SpliceAid-F contains 4227 interactions, 2590 RNA-binding sites and 1141 ‘no-binding’ sites, including information on cellular contexts and conditions where binding was tested. The data collected in SpliceAid-F can provide significant information to explain an observed splicing pattern as well as the effect of mutations in functional regulatory elements.

The 5' and 3' untranslated regions of eukaryotic mRNAs (UTRs) play crucial roles in the post-trans-criptional regulation of gene expression through the modulation of nucleo-cytoplasmic mRNA transport, translation efficiency, subcellular localization and message stability. UTRdb is a curated database of 5' and 3' untranslated sequences of eukaryotic mRNAs, derived from several sources of primary data. Experimentally validated functional motifs are annotated and also collated as the UTRsite database where more specific information on the functional motifs and cross-links to interacting regulatory protein are provided. In the current update, the UTR entries have been organized in a gene-centric structure to better visualize and retrieve 5' and 3' UTR variants generated by alter-native initiation and termination of transcription and alternative splicing. Experimentally validated miRNA targets and conserved sequence elements are also annotated. The integration of UTRdb with genomic data has allowed the implementation of an efficient annotation system and a powerful retrieval resource for the selection and extraction of specific UTR subsets. All internet resources implemented for retrieval and functional analysis of 5' and 3' untranslated regions of eukaryotic mRNAs are accessible at http://utrdb.ba.itb.cnr.it/.

The analysis of non-coding DNA regulatory regions is one of the most challenging open problems in computational biology. In this paper we investigate whether we can predict functional information about genes by using information extracted from their sequences together with expression data. We formalize this problem as a classification problem, and we apply Support Vector Machines (SVMs) with non-linear kernels to predict classes of co-expressed genes obtained from clustering procedures. SVMs are trained using information about selected motifs extracted from DNA regulatory regions through combinatorial and statistical methods. In our experiments, we show that functional classes of genes can be predicted from biological sequence data in Saccharomices cerevisiae, achieving results competitive with those recently presented in the literature.

The first step in gene expression, transcription, is modulated by the interaction of transcription factors with their corresponding binding sites on the DNA sequence. Pscan is a software tool that scans a set of sequences (e.g. promoters) from co-regulated or co-expressed genes with motifs describing the binding specificity of known transcription factors and assesses which motifs are significantly over- or under-represented, providing thus hints on which transcription factors could be common regulators of the genes studied, together with the location of their candidate binding sites in the sequences. Pscan does not resort to comparisons with orthologous sequences and experimental results show that it compares favorably to other tools for the same task in terms of false positive predictions and computation time. The website is free and open to all users and there is no login requirement. Address: http://www.beaconlab.it/pscan.

Objective: In the last few years several complete genome sequences have been made available to the research community. The annotation of their complete inventory of protein coding genes, however, has been so far an elusive goal. Classical ab initio gene prediction methods have been of great support for this task, but show notable weakness in the prediction of genes with unusual structural features. On the other hand, annotation on the basis of similarity to already known genes in other species does not permit the detection of genuinely novel genes and also introduces a potential source of classification error when based on similarity to sequences erroneously annotated as protein coding. Finally, several methods for the functional classification and assessment of evolutionarily conserved regions have been proposed, but, to our knowledge, signal processing techniques have not been applied yet to this problem, despite their proven usefulness at the single genome level. Results: In this article we introduce the use of signal processing in comparative genomics and we propose a simple test able to evaluate the coding potential of a pairwise genomic sequence alignment according to the pattern and periodicity with which substitutions and gaps appear in the alignment. We assess the feasibility of our approach on an annotated set of human-mouse genomic alignments. Conclusion: Results show that the application of signal processing techniques to sequence alignments can be a useful tool for the identification of evolutionarily conserved protein-coding regions.

The plasma membrane of Drosophila ( Sophophora ) melanogaster spermatozoa contains an α- l -fucosidase that might be involved in fertilization by interacting with α- l -fucose residues on the micropyle of the eggshell. D. ( S. ) melanogaster has a single gene called CG6128 or Fuca encoding for a putative α- l -fucosidase. Two transcripts have been annotated, RA of 3514 bp, and RB of 1673 bp. While both transcripts encode an α- l -fucosidase, RA contains an upstream open reading frame, translated into a polypeptide containing a predicted BTB/POZ domain. We demonstrate that Fuca is expressed in male and female germ lines. RT-PCR analysis indicated a broader tissue expression. Homologous genes are expressed in the same tissues in several drosophilid flies belonging to the genera Drosophila and Scaptodrosophila . However, the long transcript is restricted to species belonging to the subgenus Sophophora . The presence of two transcripts in species of the subgenus Sophophora and only one in species belonging to the subgenus Drosophila might be related to the phylogenetic relationships of these subgenera. Immunofluorescence demonstrated that the gene product, localized to the sperm plasma membrane, is absent from Scaptodrosophila lebanonensis spermatozoa. These findings support the hypothesis that the enzyme is involved in the molecular events of primary gamete interactions that are conserved among drosophilids belonging to Drosophila genus.

We have studied the presence of four sperm glycosidases, α-mannosidase, α-l- fucosidase and two β-hexosaminidase isoforms, in 11 species of the genus Drosophila spanning approximately an evolutionary 60 MY period, and in Scaptodrosophila lebanonensis, belonging to the ancestor genus Scaptodrosophila. These enzymes had been previously identified in Drosophila melanogaster as putative receptors for glycoconjugates of the egg surface. Alpha-mannosidase and β-hexosaminidases are intrinsic proteins of the sperm plasma membrane in species closely related to D. melanogaster as well as in the divergent species D. willistoni,D. hydei, D. virilis, and S. lebanonensis. Alpha-l-fucosidase is restricted to the species of the genus Drosophila. Alpha-mannosidase and β-hexosaminidases have been purified and characterized in all species. Their molecular masses and optimal pHs are similar in all the species, whereas interspecific differences in enzyme activities were detected. Cross-species comparison of kinetic parameters indicated a relationship between enzyme efficiency and phylogenetic relatedness. Beta-hexosaminidases were the most efficient enzymes. Lectin cytochemistry suggested the presence of carbohydrate residues complementary to the glycosidases on the eggshell at the site of sperm entry in all species. Bioinformatic analysis of the coding sequences of β-hexosaminases and α-l-fucosidase and of their predicted products showed no evidence of positive selection of the genes coding for these enzymes and a high degree of sequence identity of the predicted polypeptides among the species of the genus Drosophila. Collectively, our findings indicate that the Drosophila sperm glycosidases are structurally and functionally conserved and strengthen the hypothesis of their involvement in the interactions with the egg surface. Mol. Reprod. Dev. 76: 85–100, 2009. © 2008 Wiley-Liss, Inc.

Fucosylated glycoconjugates play a role in a wide variety of biological processes, including immune responses, signal transduction, ontogenic events and pathogenesis of several human diseases. Alpha-L-fucosidases, which are responsible for their processing, have been demonstrated to be involved in lysosomal storage disease, inflammation, cystic fibrosis, cancer development and in the interactions between gametes in vertebrates as well as invertebrates. The sequence and comparative genomic analysis of these glycosyl hydrolases and the study of their evolutionary relationships appear therefore to be of considerable interest. In this work we carried out extensive similarity searches and comparative analyses to identify sequences encoding alpha-L-fucosidases. We have identified novel alpha-L-fucosidase coding sequences in worms, insects, sea urchin, ascidians, fish, chicken, amphibians, mammals and various bacteria resulting in a total of 39 alpha-L-fucosidase sequences. Two alpha-L-fucosidases that are present in all vertebrates likely reflect a distinct biological role for paralogous genes. Comparative sequence analysis of all metazoan alpha-L-fucosidases reveals a broad conservation of features, including the aspartate residue that constitutes the catalytic nucleophile. However, a cysteine which is thought to be part of the active site is also conserved in metazoa but not in arthropods, where it is replaced by an alanine. Phylogenetic analysis suggests a gene duplication event very early in metazoan evolution with the subsequent differential loss of isoforms in various metazoan lineages.

The synergism between c-MYC and miR-17-19b, a truncated version of the miR-17-92 cluster, is well-documented during tumor initiation. However, little is known about miR-17-19b function in established cancers. Here we investigate the role of miR-17-19b in c-MYC-driven lymphomas by integrating SILAC-based quantitative proteomics, transcriptomics and 3′ untranslated region (UTR) analysis upon miR-17-19b overexpression. We identify over one hundred miR-17-19b targets, of which 40% are co-regulated by c-MYC. Downregulation of a new miR-17/20 target, checkpoint kinase 2 (Chek2), increases the recruitment of HuR to c-MYC transcripts, resulting in the inhibition of c-MYC translation and thus interfering with in vivo tumor growth. Hence, in established lymphomas, miR-17-19b fine-tunes c-MYC activity through a tight control of its function and expression, ultimately ensuring cancer cell homeostasis. Our data highlight the plasticity of miRNA function, reflecting changes in the mRNA landscape and 3′ UTR shortening at different stages of tumorigenesis., The synergism between c-MYC and miR-17-19b plays an important role in lymphoma initiation. In this study, the authors identify a panel of targets co-regulated by miR-17-19b and in MYC-driven lymphoma and unravel the molecular mechanism through which miR-17-19b inhibits MYC translation.

In order to study the role played by cellular RNA pools produced by homologous genomic loci in defining the transcriptional state of a silenced gene, we tested the effect of non-functional alleles of the white gene in the presence of a functional copy of white, silenced by heterochromatin. We found that non-functional alleles of white, unable to produce a coding transcript, could reactivate in trans the expression of a wild type copy of the same gene silenced by heterochromatin. This new epigenetic phenomenon of transcriptional trans-reactivation is heritable, relies on the presence of homologous RNA’s and is affected by mutations in genes involved in post-transcriptional gene silencing. Our data suggest a general new unexpected level of gene expression control mediated by homologous RNA molecules in the context of heterochromatic genes., Author Summary We discovered a new epigenetic phenomenon we called trans-reactivation. We found that genes, unable to produce a functional coding transcript, but with the potential of transcribing other RNA’s within their gene body, strongly reactivate the transcription of a wildtype copy of the same gene silenced by heterochomatin. This new epigenetic phenomenon is heritable, relies on the presence of diffusible RNAs able to carry and transfer epigenetic information and is affected by mutations in genes involved in Post-Transcriptional Gene Silencing. Our data strongly suggest that homologous non-coding RNA can reactivate the expression of genes silenced by heterochromatin, thus defining a new unpredicted level of gene expression control in the context of heterochromatic genes.

RNA secondary structure plays critical roles in several biological processes. For example, many trans-acting noncoding RNA genes and cis-acting RNA regulatory elements present functional motifs, conserved both in structure and sequence, that can be hardly detected by primary sequence analysis alone. We describe here how conserved secondary structure motifs shared by functionally related RNA sequences can be detected through the software tool RNAProfile. RNAProfile takes as input a set of unaligned RNA sequences expected to share a common motif, and outputs the regions that are most conserved throughout the sequences, according to a similarity measure that takes into account both the sequence of the regions and the secondary structure they can form according to base-pairing and thermodynamic rules. The method is split into two parts. First, it identifies candidate regions within the input sequences, and associates with each region a locally optimal secondary structure. Then, it compares candidate regions to one another, both at sequence and structure level, and builds motifs exploring the search space through a greedy heuristic. We provide a detailed guide to the different parameters that can be employed, and usage examples showing the different software capabilities.

Next-generation sequencing (NGS) technologies have opened new avenues of unprecedented power for research in molecular biology and genetics. In particular, their application to the study of RNA-binding proteins (RBPs), extracted through immunoprecipitation (RIP), permits to sequence and characterize all RNAs that were found to be bound in vivo by a given RBP (RIP-Seq). On the other hand, NGS-based experiments, including RIP-Seq, produce millions of short sequence fragments that have to be processed with suitable bioinformatic tools and methods to recover and/or quantify the original sequence sample. In this chapter we provide a survey of different approaches that can be taken for the analysis of RIP-Seq data and the identification of the RNAs bound by a given RBP.

Next-generation sequencing (NGS) technologies have opened new avenues of unprecedented power for research in molecular biology and genetics. In particular, their application to the study of RNA-binding proteins (RBPs), extracted through immunoprecipitation (RIP), permits to sequence and characterize all RNAs that were found to be bound in vivo by a given RBP (RIP-Seq). On the other hand, NGS-based experiments, including RIP-Seq, produce millions of short sequence fragments that have to be processed with suitable bioinformatic tools and methods to recover and/or quantify the original sequence sample. In this chapter we provide a survey of different approaches that can be taken for the analysis of RIP-Seq data and the identification of the RNAs bound by a given RBP.

RNA secondary structure plays critical roles in several biological processes. For example, many trans-acting noncoding RNA genes and cis-acting RNA regulatory elements present functional motifs, conserved both in structure and sequence, that can be hardly detected by primary sequence analysis alone. We describe here how conserved secondary structure motifs shared by functionally related RNA sequences can be detected through the software tool RNAProfile. RNAProfile takes as input a set of unaligned RNA sequences expected to share a common motif, and outputs the regions that are most conserved throughout the sequences, according to a similarity measure that takes into account both the sequence of the regions and the secondary structure they can form according to base-pairing and thermodynamic rules. The method is split into two parts. First, it identifies candidate regions within the input sequences, and associates with each region a locally optimal secondary structure. Then, it compares candidate regions to one another, both at sequence and structure level, and builds motifs exploring the search space through a greedy heuristic. We provide a detailed guide to the different parameters that can be employed, and usage examples showing the different software capabilities.

Many different index structures, providing efficient solutions to problems related to pattern matching, have been introduced so far. Examples of these structures are suffix trees and directed acyclic word graphs (DAWGs), which can be efficiently constructed in linear time and space. Compact directed acyclic word graphs (CDAWGs) are an index structure preserving some features of both suffix trees and DAWGs, and require less space than both of them. An algorithm which directly constructs CDAWGs in linear time and space was first introduced by Crochemore and Vérin, based on McCreight's algorithm for constructing suffix trees. In this work, we present a novel on-line linear-time algorithm that builds the CDAWG for a single string as well as for a set of strings, inspired by Ukkonen's on-line algorithm for constructing suffix trees.

Understanding the complex mechanisms governing basic biological processes requires the characterisation of regulatory motifs modulating gene expression at transcriptional and post-transcriptional level. In particular, extent, chronology and cell-specificity of transcription are modulated by the interaction of transcription factors with their corresponding binding sites, mostly located near (or sometimes quite far away from) the transcription start site of the gene. The constantly growing amount of genomic data, complemented by other sources of information such as expression data derived from microarray experiments, has opened new opportunities to researchers in this field. Many different methods have been proposed for the identification of transcription factor binding sites in the regulatory regions of co-expressed genes: unfortunately this is a very challenging problem both from the computational and the biological viewpoint. This paper provides a survey of existing methods proposed for the problem, focusing both on the ideas underlying them and their availability to the scientific community.

Software has become as essential to molecular biologists as the Bunsen burner was a few decades ago. Biological data come mainly in the form of DNA or protein sequences, i.e., strings over alphabets of four or 20 symbols, respectively. The main challenge now is to develop efficient and powerful algorithms to extract as much meaning as possible from the huge amount of data generated in the last few years. In this paper we present a parallel pattern discovery algorithm that given a set of functionally related sequences finds the substrings that occur in all (or most of) the sequences of the set. The occurrences of the substrings can be approximate, that is, can differ up to a maximum number of mismatches that depends on the length of the substrings.

One of the greatest challenges facing modern molecular biology is understanding the complex mechanisms regulating gene expression. A fundamental step in this process requires the characterization of sequence motifs involved in the regulation of gene expression at transcriptional and post-transcriptional levels. In particular, transcription is modulated by the interaction of transcription factors (TFs) with their corresponding binding sites. Weeder, Pscan, and PscanChIP are software tools freely available for noncommercial users as a stand-alone or Web-based applications for the automatic discovery of conserved motifs in a set of DNA sequences likely to be bound by the same TFs. Input for the tools can be promoter sequences from co-expressed or co-regulated genes (for which Weeder and Pscan are suitable), or regions identified through genome wide ChIP-seq or similar experiments (Weeder and PscanChIP). The motifs are either found by a de novo approach (Weeder) or by using descriptors of the binding specificity of TFs (Pscan and PscanChIP).

Although Rett syndrome (RTT) represents one of the most frequent forms of severe intellectual disability in females worldwide, we still have an inadequate knowledge of the many roles played by MeCP2 (whose mutations are responsible for most cases of RTT) and their relevance for RTT pathobiology. Several studies support a role of MeCP2 in the regulation of synaptic plasticity and homeostasis. At the molecular level, MeCP2 is described as a repressor capable of inhibiting gene transcription through chromatin compaction. Indeed, it interacts with several chromatin remodeling factors, such as HDAC-containing complexes and ATRX. Other studies have inferred that MeCP2 functions also as an activator; a role in regulating mRNA splicing and in modulating protein synthesis has also been proposed. Further, MeCP2 avidly binds both 5-methyl- and 5-hydroxymethyl-cytosine. Recent evidence suggests that it is the highly disorganized structure of MeCP2, together with its post-translational modifications (PTMs) that generate and regulate this functional versatility. Indeed, several reports have demonstrated that differential phosphorylation of MeCP2 is a key mechanism by which the methyl binding protein modulates its affinity for its partners, gene expression and cellular adaptations to stimuli and neuronal plasticity. As logic consequence, generation of phospho-defective Mecp2 knock-in mice has permitted associating alterations in neuronal morphology, circuit formation, and mouse behavioral phenotypes with specific phosphorylation events. MeCP2 undergoes various other PTMs, including acetylation, ubiquitination and sumoylation, whose functional roles remain largely unexplored. These results, together with the genome-wide distribution of MeCP2 and its capability to substitute histone H1, recall the complex regulation of histones and suggest the relevance of quickly gaining a deeper comprehension of MeCP2 PTMs, the respective writers and readers and the consequent functional outcomes.

We present a model based on cellular automata for the simulation of reaction–diffusion processes that has been applied to the percolation of pesticides in the soil. The main contribution of our approach consists of a model where chemical reactions and the movement of fluid particles in a porous medium can be explicitly described and simulated. In this paper we show how the model has been used to reproduce the process that causes release of pesticides contained in the soil after their application to crops, into water flowing through the soil and to be carried to the groundwater layer, polluting it. A parallel version of the model has been successfully implemented on Cray T3E and SGI Origin 2000 parallel computers.

Sequencing technologies, that permit to determine the exact sequence of base pairs forming a DNA molecule, have been an historical breakthrough for all life sciences. Their large scale application to the sequencing of entire genomes, like the human genome, has on the other hand revealed some unexpected features of the organization of genes, as well as of the regulation of their expression. This article briefly summarizes how the post-genomic era, in which the whole genome could be studied, instead of the single parts composing it, has brought to the revision and the update of some of the most basic assumptions of classic genetics, as well as the discovery of the fundamental role of the epigenetic regulation of gene expression.

Gene expression is a very complex process, which is finely regulated and modulated at different levels. The first step of gene expression, the transcription of DNA into mRNA, is in turn regulated both at the genetic and epigenetic level. In particular, the latter, which involves the structure formed by DNA wrapped around histones (chromatin), has been recently shown to be a key factor, with post-translational modifications of histones acting combinatorially to activate or block transcription. In this work we addressed the problem of predicting the level of expression of genes starting from genome-wide maps of chromatin structure, that is, of the localization of several different histone modifications, which have been recently made available through the introduction of technologies like ChIP-Seq. We formalized the problem as a multi-class bipartite ranking problem, in which for each class a gene can be under-or over-expressed with respect to a given reference expression value. In order to deal with this problem, we exploit and extend a semi-supervised method (COSNet) based on a family of Hopfield neural networks. Benchmark genome-wide tests performed on six different human cell lines yielded satisfactory results, with clear improvements over the alternative approach most commonly adopted in the literature.

NF-Y, a trimeric transcription factor (TF) composed of two histone-like subunits (NF-YB and NF-YC) and a sequence-specific subunit (NF-YA), binds to the CCAAT motif, a common promoter element. Genome-wide mapping reveals 5000–15,000 NF-Y binding sites depending on the cell type, with the NF-YA and NF-YB subunits binding asymmetrically with respect to the CCAAT motif. Despite being characterized as a proximal promoter TF, only 25% of NF-Y sites map to promoters. A comparable number of NF-Y sites are located at enhancers, many of which are tissue specific, and nearly half of the NF-Y sites are in select subclasses of HERV LTR repeats. Unlike most TFs, NF-Y can access its target DNA motif in inactive (nonmodified) or polycomb-repressed chromatin domains. Unexpectedly, NF-Y extensively colocalizes with FOS in all genomic contexts, and this often occurs in the absence of JUN and the AP-1 motif. NF-Y also coassociates with a select cluster of growth-controlling and oncogenic TFs, consistent with the abundance of CCAAT motifs in the promoters of genes overexpressed in cancer. Interestingly, NF-Y and several growth-controlling TFs bind in a stereo-specific manner, suggesting a mechanism for cooperative action at promoters and enhancers. Our results indicate that NF-Y is not merely a commonly used proximal promoter TF, but rather performs a more diverse set of biological functions, many of which are likely to involve coassociation with FOS.

Background MADS-domain transcription factors play important roles during plant development. The Arabidopsis MADS-box gene SHORT VEGETATIVE PHASE (SVP) is a key regulator of two developmental phases. It functions as a repressor of the floral transition during the vegetative phase and later it contributes to the specification of floral meristems. How these distinct activities are conferred by a single transcription factor is unclear, but interactions with other MADS domain proteins which specify binding to different genomic regions is likely one mechanism. Results To compare the genome-wide DNA binding profile of SVP during vegetative and reproductive development we performed ChIP-seq analyses. These ChIP-seq data were combined with tiling array expression analysis, induction experiments and qRT-PCR to identify biologically relevant binding sites. In addition, we compared genome-wide target genes of SVP with those published for the MADS domain transcription factors FLC and AP1, which interact with SVP during the vegetative and reproductive phases, respectively. Conclusions Our analyses resulted in the identification of pathways that are regulated by SVP including those controlling meristem development during vegetative growth and flower development whereas floral transition pathways and hormonal signaling were regulated predominantly during the vegetative phase. Thus, SVP regulates many developmental pathways, some of which are common to both of its developmental roles whereas others are specific to only one of them.

GeneSyn is a software tool that allows automatic detection of conserved gene order from annotated genomes. The increasing amount of available genome data makes it possible now to shed light on the molecular dynamics shaping their evolution. Genome evolution not only involves changes at the sequence level with base substitutions and/or insertion/deletion events but also rearrangements of the gene order along the genome. Comparative analysis of gene order in multiple genomes may help in investigating general or lineage-specific genome plasticity as well as in the inference of phylogenetic relationships. In particular, investigation of gene contiguity on a chromosome (sometimes improperly referred to as conserved gene synteny), even if sometimes interrupted by intervening genes, suggests a functional coupling. We present here an algorithm and software to detect gene strings conserved in at least a given fraction of input genomes and present a simple example application in animal mitochondrial gene order

Renalase is a flavoprotein recently discovered in humans, preferentially expressed in the proximal tubules of the kidney and secreted in blood and urine. It is highly conserved in vertebrates, with homologs identified in eukaryotic and prokaryotic organisms. Several genetic, epidemiological, clinical and experimental studies show that renalase plays a role in the modulation of the functions of the cardiovascular system, being particularly active in decreasing the catecholaminergic tone, in lowering blood pressure and in exerting a protective action against myocardial ischemic damage. Deficient renalase synthesis might be the cause of the high occurrence of hypertension and adverse cardiac events in kidney disease patients. Very recently, recombinant human renalase has been structurally and functionally characterized in vitro. Results show that it belongs to the p-hydroxybenzoate hydroxylase structural family of flavoenzymes, contains non-covalently bound FAD with redox features suggestive of a dehydrogenase activity, and is not a catecholamine-degrading enzyme, either through oxidase or NAD(P)H-dependent monooxygenase reactions. The biochemical data now available will hopefully provide the basis for a systematic and rational quest toward the identification of the reaction catalyzed by renalase and of the molecular mechanism of its physiological action, which in turn are expected to favor the development of novel therapeutic tools for the treatment of kidney and cardiovascular diseases.