Targeted delivery of antisense oligonucleotides by molecular conjugates

Antisense oligonucleotides efficiently inhibit gene expression in vitro; however, the successful therapeutic application of this technology in vivo will require the development of improved delivery systems. In this report we describe a technique that efficiently delivers antisense oligonucleotides into cells using molecular conjugates. This technique, which was initially developed for the delivery of eukaryotic genes, is based on the construction of DNA-protein complexes that are recognized by the liver-specific asialoglycoprotein receptor. Binding of poly( l -lysine)-asialoorosomucoid (AsOR) protein conjugates with phosphorothioate antisense oligonucleotides to chloramphenicol acetyltransferase (CAT) led to the formation of 50- to 150-nm toroids. Exposure of the antisense molecular complexes (3 µM oligonucleotide) to NIH 3T3 cells genetically modified to express both the AsOR receptor and CAT, inhibited CAT expression by 54%, which was completely blocked by excess AsOR. Equivalent inhibition of CAT activity with purified oligonucleotide alone was observed at a 30 µM concentration. Furthermore, examination of the cells using indirect immunofluorescence for the presence of CAT protein showed 28% of cells exposed to the molecular conjugates lacked any detectable CAT enzyme. Cells exposed to oligonucleotide alone showed a highly variable staining pattern, and only a few of the cells were completely void of CAT protein. Together these data demonstrate that molecular conjugates provide a highly specific and efficient system for the delivery of antisense oligonucleotides.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45543/1/11188_2005_Article_BF01232652.pd

Star–galaxy classification in the Dark Energy Survey Y1 data set

International audienceWe perform a comparison of different approaches to star–galaxy classification using the broad-band photometric data from Year 1 of the Dark Energy Survey. This is done by performing a wide range of tests with and without external ‘truth’ information, which can be ported to other similar data sets. We make a broad evaluation of the performance of the classifiers in two science cases with DES data that are most affected by this systematic effect: large-scale structure and Milky Way studies. In general, even though the default morphological classifiers used for DES Y1 cosmology studies are sufficient to maintain a low level of systematic contamination from stellar misclassification, contamination can be reduced to the O(1 per cent) level by using multi-epoch and infrared information from external data sets. For Milky Way studies, the stellar sample can be augmented by |${\sim }20{{\ \rm per\ cent}}$| for a given flux limit. Reference catalogues used in this work are available at http://des.ncsa.illinois.edu/releases/y1a1