Designing Effective small interfering RNA for Post-Transcriptional Silencing of Human GREM1: A Comprehensive Bioinformatics Approach

Asiedu Ebenezer

To determine the specificity of the selected siRNA, local alignment search was performed against human genomic and transcript database (see Supplementary Fig. 3) . This was to reduce off-target effects of siRNAs, a major challenge in RNAi-based therapy [16] . The three siRNA candidates shared a maximum of 78% similarity with other transcripts not recognized as gremlin-1. However, RNA transcripts are subjected to processing including splicing of intron segments to form mature mRNA. It is possible that the homologous nucleotides in the other transcripts are not components of an mRNA sequence. Furthermore, the mRNA target for this study was searched and aligned against human transcript and genome database and all the hits were identified as gremlin transcripts (see Supplementary Fig. 3d) The selected siRNA candidates were compared to experimentally functional siRNAs, in order to evaluate the silencing potential of the designed siRNAs in this study. Reference [39] used gremlin-1 siRNAs to silence gremlin-1 mRNA in a study to determine the effect of gremlin-1 on BMP2-induced osteogenic differentiation of human bone marrow-derived mesenchymal stem cells. The siRNA sense strands used are designated as H1, H2, H3 and H4 in Table 2 . The functional siRNAs (H1 and H2) were compared with the selected siRNAs in this study in terms of preferred sequence features for effective silencing. All three selected siRNAs in this study outscored H1 and H2, suggesting that they are effective siRNA candidates to silence human gremlin-1.