Retroviral integration into the host genome is not entirely random, and integration site preferences vary among different retroviruses. Human immunodeficiency virus (HIV) prefers to integrate within active genes, whereas murine leukemia virus (MLV) prefers to integrate near transcription start sites and CpG islands. On the other hand, integration of avian sarcoma-leukosis virus (ASLV) shows little preference either for genes, transcription start sites, or CpG islands. While host cellular factors play important roles in target site selection, the viral integrase is probably the major viral determinant. It is reasonable to hypothesize that retroviruses with similar integrases have similar preferences for target site selection. Although integration profiles are well defined for members of the lentivirus, spumaretrovirus, alpharetrovirus, and gammaretrovirus genera, no members of the deltaretroviruses, for example, human T-cell leukemia virus type 1 (HTLV-1), have been evaluated. We have mapped 541 HTLV-1 integration sites in human HeLa cells and show that HTLV-1, like ASLV, does not specifically target transcription units and transcription start sites. Comparing the integration sites of HTLV-1 with those of ASLV, HIV, simian immunodeficiency virus, MLV, and foamy virus, we show that global and local integration site preferences correlate with the sequence/structure of virus-encoded integrases, supporting the idea that integrase is the major determinant of retroviral integration site selection. Our results suggest that the global integration profiles of other retroviruses could be predicted from phylogenetic comparisons of the integrase proteins. Our results show that retroviruses that engender different insertional mutagenesis risks can have similar integration profiles.

Dense genetic maps of mammalian genomes facilitate a variety of biological studies including the mapping of polygenic traits, positional cloning of monogenic traits, mapping of quantitative or qualitative trait loci, marker association, allelic imbalance, speed congenic construction, and evolutionary or phylogenetic comparison. In particular, single nucleotide polymorphisms (SNPs) have proved useful because of their abundance and compatibility with multiple high-throughput technology platforms. SNP genotyping is especially suited for the genetic analysis of model organisms such as the mouse because biallelic markers remain fully informative when used to characterize crosses between inbred strains. Here we report the mapping and genotyping of 673 SNPs (including 519 novel SNPs) in 55 of the most commonly used mouse strains. These data have allowed us to construct a phylogenetic tree that correlates and expands known genealogical relationships and clarifies the origin of strains previously having an uncertain ancestry. All 55 inbred strains are distinguishable genetically using this SNP panel. Our data reveal an uneven SNP distribution consistent with a mosaic pattern of inheritance and provide some insight into the changing dynamics of the physical architecture of the genome. Furthermore, these data represent a valuable resource for the selection of markers and the design of experiments that require the genetic distinction of any pair of mouse inbred strains such as the generation of congenic mice, positional cloning, and the mapping of quantitative or qualitative trait loci.

Microarray technology was evaluated for usefulness in assessing relationships between serum corticosterone and hepatic gene expression. Nine pairs of female Swiss mice were chosen to provide a wide range of serum corticosterone ratios; cDNA microarray analysis (∼8000 genes) was performed on their livers. A statistical method based on calculation of 99% confidence intervals discovered 32 genes which varied significantly among the livers. Five of these ratios correlated significantly with serum corticosterone ratio, including tyrosine aminotransferase, stress-induced protein, pleiotropic regulator 1 and insulin-like growth factor-binding protein-1; the latter has a potential role in cancer development. Secondly, linear regression of gene expression vs corticosterone ratios was screened for those with r ≥ 0·8 (P<0·01), yielding 141 genes, including some known to be corticosterone regulated and others of interest as possible glucocorticoid targets. Half of these significant correlations involved data sets where no microarray ratio exceeded±1·5. These results showed that microarray may be used to survey tissues for changes in gene expression related to serum hormones, and that even small changes in expression can be of statistical significance in a study with adequate numbers of replicate samples.

Issues implicit in a multicenter microarray study are protocol standardization and monitoring center adherence to established protocols. This study explored the effects of submitting center and sample preservation method on the quality of isolated RNA. In addition, the effects of sample preservation method and laboratory on microarray quality were also examined. Herein we evaluated the contribution of specific technical factors [center, laboratory, and preservation method (frozen/RNAlater)] on quality of isolated RNA, cRNA synthesis products, and reproducibility of gene expression microarray data for independent biologic samples collected in a multicenter microarray study. The Kruskal-Wallis test was used to test for differences owing to submitting center on isolated RNA quality. Mixed effects analysis of variance was used in assessing the impact of laboratory and preservation method on gene expression values for the 12 samples hybridized at 2 independent laboratories (24 GeneChips). One center was found to be in violation of the tissue handling protocol. No significant effect was noted owing to preservation method, which ensured that our tissue handling protocols are working properly. There was a significant laboratory effect with respect to cRNA yield, though this effect did not impact sample quality. We conclude that use of consistent protocols for sample collection, RNA extraction, cDNA/cRNA synthesis, labeling, hybridization, platform, image acquisition, normalization, and expression summaries can yield consistent expression values. Moreover, evaluation of sample quality at various steps in the data acquisition process is an important component of a multicenter study to ensure all participating centers adhere to established protocols.