title: Theoretical design of antisense genes with statistically increased efficacy.
abstract: Endogenous expression of antisense RNA represents one major way of applying antisense nucleic acids. To express antisense RNA intracellularly, recombinant antisense genes have to be designed and introduced into cells where the target RNA is encountered. Efficient annealing between the antisense RNA and the target RNA is crucial for efficacy and is strongly influenced by RNA structure. Here we extend structural rules for the design of in vitro transcribed antisense RNAs to the design of recombinant antisense genes. Intracellularly expressed antisense RNA transcripts contain a central antisense portion and additional flanking vector-derived sequences. A computer algorithm was generated to compose large sets of antisense genes, to calculate secondary structures of the transcribed sequences and to select for favorable structures of antisense RNA in terms of annealing and efficacy. The biological test system to measure efficiency of antisense genes was human immunodeficiency virus type 1 (HIV-1) replication in 293T cells. When considering the lower intracellular steady-state levels of favorably structured endogenous transcripts, an antisense effect against HIV-1 replication was observed that was up to 60-fold stronger than that measured for predicted unfavorable species. The computational selection was successful for antisense portions of 300 nt but not 100 nt in length. This theoretical design of antisense genes supports their improved application under time- and labor-saving conditions.
authors.affiliation: Deutsches Krebsforschungszentrum, Forschungsschwerpunkt Angewandte Tumorvirologie, Heidelberg, Germany.
authors.forename: M J
authors.initials: MJ
authors.lastname: Lehmann
authors.forename: V
authors.initials: V
authors.lastname: Patzel
authors.forename: G
authors.initials: G
authors.lastname: Sczakiel
mesh_terms: D000465:Algorithms; D020029:Base Pairing / Q000235:genetics; D001665:Binding Sites; D002460:Cell Line; D003198:Computer Simulation; D015683:Gene Products, gag / Q000235:genetics; D005818:Genetic Engineering; D015497:HIV-1 / Q000235:genetics / Q000502:physiology; D006801:Humans; D009690:Nucleic Acid Conformation; D018583:Pliability; D020871:RNA Stability; D016372:RNA, Antisense / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D018075:RNA, Complementary / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D012333:RNA, Messenger / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D012367:RNA, Viral / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D020133:Reverse Transcriptase Polymerase Chain Reaction; D013379:Substrate Specificity; D013816:Thermodynamics; D014779:Virus Replication
chemical_list: D015683:Gene Products, gag; D016372:RNA, Antisense; D018075:RNA, Complementary; D012333:RNA, Messenger; D012367:RNA, Viral