Feline leukemia virus integrase and capsid packaging functions do not change the insertion profile of standard Moloney retroviral vectors.

Adverse events linked to perturbations of cellular genes by vector insertion reported in gene therapy trials and animal models have prompted attempts to better understand the mechanisms directing viral vector integration. The integration profiles of vectors based on MLV, ASLV, SIV and HIV have all been shown to be non-random, and novel vectors with a safer integration pattern have been sought. Recently, we developed a producer cell line called CatPac that packages standard MoMLV vectors with feline leukemia virus (FeLV) gag, pol and env gene products. We now report the integration profile of this vector, asking if the FeLV integrase and capsid proteins could modify the MoMLV integration profile, potentially resulting in a less genotoxic pattern. We transduced rhesus macaque CD34+ hematopoietic progenitor cells with CatPac or standard MoMLV vectors, and determined their integration profile by LAM-PCR. We obtained 184 and 175 unique integration sites (ISs) respectively for CatPac and standard MoMLV vectors, and these were compared with 10 000 in silico-generated random IS. The integration profile for CatPac vector was similar to MoMLV and equally non-random, with a propensity for integration near transcription start sites and in highly dense gene regions. We found an IS for CatPac vector localized 715 nucleotides upstream of LMO-2, the gene involved in the acute lymphoblastic leukemia developed by X-SCID patients treated by gene therapy using MoMLV vectors. In conclusion, we found that replacement of MoMLV env, gag and pol gene products with FeLV did not alter the basic integration profile. Thus, there appears to be no safety advantage for this packaging system. However, considering the stability and efficacy of CatPac vectors, further development is warranted, using potentially safer vector backbones, for instance those with a SIN configuration.

DNA methylation profiles in diffuse large B-cell lymphoma and their relationship to gene expression status.

In an initial epigenetic characterization of diffuse large B-cell lymphoma (DLBCL), we evaluated the DNA methylation levels of over 500 CpG islands. Twelve CpG islands (AR, CDKN1C, DLC1, DRD2, GATA4, GDNF, GRIN2B, MTHFR, MYOD1, NEUROD1, ONECUT2 and TFAP2A) showed significant methylation in over 85% of tumors. Interestingly, the methylation levels of a CpG island proximal to FLJ21062 differed between the activated B-cell-like (ABC-DLBCL) and germinal center B-cell-like (GCB-DLBCL) subtypes. In addition, we compared the methylation and expression status of 67 genes proximal (within 500 bp) to the methylation assays. We frequently observed that hypermethylated CpG islands are proximal to genes that are expressed at low or undetectable levels in tumors. However, many of these same genes were also poorly expressed in DLBCL tumors where their cognate CpG islands were hypomethylated. Nevertheless, the proportional reductions in BNIP3, MGMT, RBP1, GATA4, IGSF4, CRABP1 and FLJ21062 expression with increasing methylation suggest that epigenetic processes strongly influence these genes. Lastly, the moderate expression of several genes proximal to hypermethylated CpG tracts suggests that DNA methylation assays are not always accurate predictors of gene silencing. Overall, further investigation of the highlighted CpG islands as potential clinical biomarkers is warranted.

Web alert. Genetics of disease.

A selection of World Wide Web sites relevant to papers published in this issue of Current Opinion in Genetics & Development.

Guide to the draft human genome.

There are a number of ways to investigate the structure, function and evolution of the human genome. These include examining the morphology of normal and abnormal chromosomes, constructing maps of genomic landmarks, following the genetic transmission of phenotypes and DNA sequence variations, and characterizing thousands of individual genes. To this list we can now add the elucidation of the genomic DNA sequence, albeit at 'working draft' accuracy. The current challenge is to weave together these disparate types of data to produce the information infrastructure needed to support the next generation of biomedical research. Here we provide an overview of the different sources of information about the human genome and how modern information technology, in particular the internet, allows us to link them together.

Identification of motifs in protein sequences.

This brief appendix serves as a guide for the analysis of functional motifs in proteins. Several database search engines that can be accessed via the World Wide Web are described. Such computerized searches have become the preferred method to scan large sequence and motif databases, as the searches are efficient and the databases are updated frequently. A short list of sorting signals is also included, since these motifs often cannot be predicted reliably by a computer search.

Sequence similarity searching using the BLAST family of programs.

Database sequence similarity searching is carried out thousands of times each day by researchers worldwide and has become a very valuable tool. Over the years, a number of algorithms have been implemented to facilitate database searching. The BLAST (Basic Local Alignment Research Tool) family of sequence similarity search programs allows searches to be done quickly and easily, but with sensitive, yet rigorous statistical expectations. In this unit, which is a completely new version of its predecessor of the same title, the user learns how to access the databases, determine the correct searching strategies, and apply examples of BLAST searches to his or her own data.

Sequence similarity searching using the BLAST family of programs.

The BLAST (Basic Local Alignment Search Tool) family of sequence similarity search programs allows users to input either a nucleotide or amino acid query sequence, and search a nucleotide or amino acid sequence database. The program returns a list of the sequence "hits", alignments to the query sequence, and statistical values. This unit describes how to choose an appropriate BLAST program and database, perform the search, and interpret the results.

Candidate regulatory sequence elements for cell cycle-dependent transcription in Saccharomyces cerevisiae.

Recent developments in genome-wide transcript monitoring have led to a rapid accumulation of data from gene expression studies. Such projects highlight the need for methods to predict the molecular basis of transcriptional coregulation. A microarray project identified the 420 yeast transcripts whose synthesis displays cell cycle-dependent periodicity. We present here a statistical technique we developed to identify the sequence elements that may be responsible for this cell cycle regulation. Because most gene regulatory sites contain a short string of highly conserved nucleotides, any such strings that are involved in gene regulation will occur frequently in the upstream regions of the genes that they regulate, and rarely in the upstream regions of other genes. Our strategy therefore utilizes statistical procedures to identify short oligomers, five or six nucleotides in length, that are over-represented in upstream regions of genes whose expression peaks at the same phase of the cell cycle. We report, with a high level of confidence, that 9 hexamers and 12 pentamers are over-represented in the upstream regions of genes whose expression peaks at the early G(1), late G(1), S, G(2), or M phase of the cell cycle. Some of these sequence elements show a preference for a particular orientation, and others, through a separate statistical test, for a particular position upstream of the ATG start codon. The finding that the majority of the statistically significant sequence elements are located in late G(1) upstream regions correlates with other experiments that identified the late G(1)/early S boundary as a vital cell cycle control point. Our results highlight the importance of MCB, an element implicated previously in late G(1)/early S gene regulation, as most of the late G(1) oligomers contain the MCB sequence or variations thereof. It is striking that most MCB-like sequences localize to a specific region upstream of the ATG start codon. Additional sequences that we have identified may be important for regulation at other phases of the cell cycle.

A genome-wide transcriptional analysis of the mitotic cell cycle.

Progression through the eukaryotic cell cycle is known to be both regulated and accompanied by periodic fluctuation in the expression levels of numerous genes. We report here the genome-wide characterization of mRNA transcript levels during the cell cycle of the budding yeast S. cerevisiae. Cell cycle-dependent periodicity was found for 416 of the 6220 monitored transcripts. More than 25% of the 416 genes were found directly adjacent to other genes in the genome that displayed induction in the same cell cycle phase, suggesting a mechanism for local chromosomal organization in global mRNA regulation. More than 60% of the characterized genes that displayed mRNA fluctuation have already been implicated in cell cycle period-specific biological roles. Because more than 20% of human proteins display significant homology to yeast proteins, these results also link a range of human genes to cell cycle period-specific biological functions.