A functional genomic approach, based on systematic data gathering, was used to characterize a family of proteins containing a tripartite motif (TRIM). A total of 37 TRIM genes/proteins were studied, 21 of which were novel. The results demonstrate that TRIM proteins share a common function: by means of homo-multimerization they identify speci®c cell compartments.

The nematode worm Caenorhabditis elegans and its relatives are unique among animals in having operons. Operons are regulated multigene transcription units, in which polycistronic pre-messenger RNA (pre-mRNA coding for multiple peptides) is processed to monocistronic mRNAs. This occurs by 3' end formation and trans-splicing using the specialized SL2 small nuclear ribonucleoprotein particle for downstream mRNAs. Previously, the correlation between downstream location in an operon and SL2 trans-splicing has been strong, but anecdotal. Although only 28 operons have been reported, the complete sequence of the C. elegans genome reveals numerous gene clusters. To determine how many of these clusters represent operons, we probed full-genome microarrays for SL2-containing mRNAs. We found significant enrichment for about 1,200 genes, including most of a group of several hundred genes represented by complementary DNAs that contain SL2 sequence. Analysis of their genomic arrangements indicates that >90% are downstream genes, falling in 790 distinct operons. Our evidence indicates that the genome contains at least 1,000 operons, 2 8 genes long, that contain about 15% of all C. elegans genes. Numerous examples of co-transcription of genes encoding functionally related proteins are evident. Inspection of the operon list should reveal previously unknown functional relationships.

IntroductionPioneering clinical studies have shown that transplantation of genetically modified hematopoietic stem cells may cure severe genetic diseases such as severe combined immunodeficiencies (SCID), 1,2 chronic granulomatous disease (CGD), 3 and lysosomal storage disorders. 4 Unfortunately, some of these studies showed also the limitations of retroviral gene transfer technology, which may cause severe and sometimes fatal adverse effects. In particular, insertional activation of proto-oncogenes by vectors derived from the Moloney murine leukemia virus (MLV) caused T-cell lymphoproliferative disorders in patients with X-linked SCID 5,6 and premalignant expansion of myeloid progenitors in patients with CGD. 3 Preclinical studies showed that HIV-derived lentiviral vectors are less likely to cause insertional gene activation, 7,8 although they can still interfere with normal gene expression at the posttranscriptional level, as observed in a clinical trial of gene therapy for ␤-thalassemia. 9 The molecular bases of vector-induced genotoxicity and the influence of vector design, transduction protocols, and the patient's genetic background in inducing severe adverse effects are still poorly understood. A better understanding of the interactions between retroviral vectors and the human genome may provide new cues to explain these phenomena and a rational basis for predicting genotoxic risks in gene therapy.A large number of studies have focused on the molecular mechanisms by which mammalian retroviruses choose their integration sites in the target cell genome. After entering a cell, retroviral RNA genomes are reverse transcribed into double-stranded DNA and assembled in preintegration complexes (PICs) containing viral and cellular proteins. PICs translocate to the nucleus and associate with the host cell chromatin, where the viral integrase mediates proviral insertion in genomic DNA. Integration is a nonrandom process, whereby PICs of different viruses recognize components or features of the host cell chromatin in a specific fashion. 10 For HIV and other lentiviruses, the LEDGF/p75 protein has been identified as the main factor tethering PICs to active chromatin, 11 whereas mechanisms underlying integration site selection of other retroviruses remain largely unknown. We recently showed that MLV-derived vectors integrate preferentially in hot spots near genes controlling growth and development of hematopoietic cells and flanked by defined subsets of transcription factor binding sites (TFBSs) and suggested that MLV PICs are tethered to active regulatory regions by basal components of the transcriptional machinery. 12,13 The MLV integrase and long terminal repeat enhancer are the main determinants of the selection of TFBS-rich regions of the genome. 13,14 We used ligation-mediated polymerase chain reaction (LM-PCR) and pyrosequencing to build a genomewide, high-definition map of Ͼ 32 000 MLV integration sites in the genome of human CD34 ϩ hematopoietic progenitor cells (HPCs) and used gene expression profiling, chroma...

Ontogenesis of T cells in the thymus is

Cardiac hypertrophy, initially an adaptive response of the myocardium to stress, can progress to heart failure. The epigenetic signature underlying this phenomenon is poorly understood. Here, we report on the genome-wide distribution of seven histone modifications in adult mouse cardiomyocytes subjected to a prohypertrophy stimulus in vivo. We found a set of promoters with an epigenetic pattern that distinguishes specific functional classes of genes regulated in hypertrophy and identified 9,207 candidate active enhancers whose activity was modulated. We also analyzed the transcriptional network within which these genetic elements act to orchestrate hypertrophy gene expression, finding a role for myocyte enhancer factor (MEF)2C and MEF2A in regulating enhancers. We propose that the epigenetic landscape is a key determinant of gene expression reprogramming in cardiac hypertrophy and provide a basis for understanding the role of chromatin in regulating this phenomenon.epigenetic regulation | histone acetylation | histone methylation

Background: The cancer transcriptome is difficult to explore due to the heterogeneity of quantitative and qualitative changes in gene expression linked to the disease status. An increasing number of "unconventional" transcripts, such as novel isoforms, non-coding RNAs, somatic gene fusions and deletions have been associated with the tumoral state. Massively parallel sequencing techniques provide a framework for exploring the transcriptional complexity inherent to cancer with a limited laboratory and financial effort. We developed a deep sequencing and bioinformatics analysis protocol to investigate the molecular composition of a breast cancer poly(A) + transcriptome. This method utilizes a cDNA library normalization step to diminish the representation of highly expressed transcripts and biology-oriented bioinformatic analyses to facilitate detection of rare and novel transcripts.

The present study employed mass sequencing of small RNA libraries to identify the repertoire of small noncoding RNAs expressed in normal CD4؉ T cells compared to cells transformed with human T-cell leukemia virus type 1 (HTLV-1), the causative agent of adult T-cell leukemia/lymphoma (ATLL). The results revealed distinct patterns of microRNA expression in HTLV-1-infected CD4؉ T-cell lines with respect to their normal counterparts. In addition, a search for virus-encoded microRNAs yielded 2 sequences that originated from the plus strand of the HTLV-1 genome. Several sequences derived from tRNAs were expressed at substantial levels in both uninfected and infected cells. One of the most abundant tRNA fragments (tRF-3019) was derived from the 3= end of tRNA-proline. tRF-3019 exhibited perfect sequence complementarity to the primer binding site of HTLV-1. The results of an in vitro reverse transcriptase assay verified that tRF-3019 was capable of priming HTLV-1 reverse transcriptase. Both tRNA-proline and tRF-3019 were detected in virus particles isolated from HTLV-1-infected cells. These findings suggest that tRF-3019 may play an important role in priming HTLV-1 reverse transcription and could thus represent a novel target to control HTLV-1 infection. IMPORTANCE Small noncoding RNAs, a growing family of regulatory RNAs that includes microRNAs

The continuously prolonged human lifespan is accompanied by increase in neurodegenerative diseases incidence, calling for the development of inexpensive blood-based diagnostics. Analyzing blood cell transcripts by RNA-Seq is a robust means to identify novel biomarkers that rapidly becomes a commonplace. However, there is lack of tools to discover novel exons, junctions and splicing events and to precisely and sensitively assess differential splicing through RNA-Seq data analysis and across RNA-Seq platforms. Here, we present a new and comprehensive computational workflow for whole-transcriptome RNA-Seq analysis, using an updated version of the software AltAnalyze, to identify both known and novel high-confidence alternative splicing events, and to integrate them with both protein-domains and microRNA binding annotations. We applied the novel workflow on RNA-Seq data from Parkinson's disease (PD) patients' leukocytes pre- and post- Deep Brain Stimulation (DBS) treatment and compared to healthy controls. Disease-mediated changes included decreased usage of alternative promoters and N-termini, 5′-end variations and mutually-exclusive exons. The PD regulated FUS and HNRNP A/B included prion-like domains regulated regions. We also present here a workflow to identify and analyze long non-coding RNAs (lncRNAs) via RNA-Seq data. We identified reduced lncRNA expression and selective PD-induced changes in 13 of over 6,000 detected leukocyte lncRNAs, four of which were inversely altered post-DBS. These included the U1 spliceosomal lncRNA and RP11-462G22.1, each entailing sequence complementarity to numerous microRNAs. Analysis of RNA-Seq from PD and unaffected controls brains revealed over 7,000 brain-expressed lncRNAs, of which 3,495 were co-expressed in the leukocytes including U1, which showed both leukocyte and brain increases. Furthermore, qRT-PCR validations confirmed these co-increases in PD leukocytes and two brain regions, the amygdala and substantia-nigra, compared to controls. This novel workflow allows deep multi-level inspection of RNA-Seq datasets and provides a comprehensive new resource for understanding disease transcriptome modifications in PD and other neurodegenerative diseases.