Exogenous retroelement integration in sperm and embryos affects preimplantation development.

Retroelement transcripts are present in male and female gametes, where they are typically regulated by methylation, noncoding RNAs and transcription factors. Such transcripts are required for occurrence of retrotransposition events, while failure of retrotransposition control may exert negative effects on cellular function and proliferation. In order to investigate the occurrence of retrotransposition events in mouse epididymal spermatozoa and to address the impact of uncontrolled retroelement RNA expression in early preimplantation embryos, we performed in vitro fertilization experiments using spermatozoa preincubated with plasmid vectors containing the human retroelements LINE-1, HERVK-10 or the mouse retroelement VL30, tagged with an enhanced green fluorescence (EGFP) gene-based cassette. Retrotransposition events in mouse spermatozoa and embryos were detected using PCR, FACS analysis and confocal microscopy. Our findings show that: (i) sperm cell incorporates exogenous retroelements and favors retrotransposition events, (ii) the inhibition of spermatozoa reverse transcriptase can decrease the retrotransposition frequency in sperm cells, (iii) spermatozoa can transfer exogenous human or mouse retroelements to the oocyte during fertilization and (iv) retroelement RNA overexpression affects embryo morphology and impairs preimplantation development. These findings suggest that the integration of exogenous retroelements in the sperm genome, as well as their transfer into the mouse oocyte, could give rise to new retrotransposition events and genetic alterations in mouse spermatozoa and embryos.

Enhancement of the classification of multichannel chromosome images using support vector machines.

Color chromosome classification (karyotyping) allows simultaneous analysis of numerical and structural chromosome abnormalities. The success of the technique largely depends on the accuracy of pixel classification. In this paper we present a method for multichannel chromosome image classification based on support vector machines. First, the image is segmented using a multichannel watershed segmentation method. Classification of the pixels of the segmented regions using support vector machines is then employed. The method has been tested on images from normal cells, showing the improvement in classification accuracy by 10.16% when compared to a Bayesian classifier. The increased classification improves the reliability of the M-FISH imaging technique in identifying subtle and cryptic chromosomal abnormalities for cancer diagnosis and genetic disorders research.