Dataset Information

Ligand-Guided Selection of Target-Specific Aptamers: A Screening Technology for Identifying Specific Aptamers Against Cell-Surface Proteins.

ABSTRACT: We report on a new strategy for identifying highly specific aptamers against a predetermined epitope of a target. Termed "ligand-guided selection" (LIGS), this method uniquely exploits the selection step, the core of SELEX (Systematic Evolution Exponential enrichment). LIGS uses a naturally occurring stronger and highly specific bivalent binder, an antibody (Ab) interacting with its cognate antigen to outcompete specific aptamers from a partially enriched SELEX pool, as a strategy. We demonstrate the hypothesis of LIGS by utilizing an Ab binding to membrane-bound Immunoglobulin M (mIgM) to selectively elute aptamers that are specific for mIgM from a SELEX pool that is partially enriched toward mIgM expressing Ramos cells. The selected aptamers show specificity toward Ramos cells. We identified three aptamer candidates utilizing LIGS that could be outcompeted by mIgM Ab, demonstrating that LIGS can be successfully applied to select aptamers from a partially evolved cell-SELEX library, against predetermined receptor proteins using a cognate ligand. This proof-of-concept study introduces a new biochemical-screening platform that exploits the binding of a secondary stronger molecular entity to its target as a partition step, to identify highly specific artificial nucleic acid ligands.

SUBMITTER: Zumrut HE

PROVIDER: S-EPMC4900185 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Publications

Ligand-Guided Selection of Target-Specific Aptamers: A Screening Technology for Identifying Specific Aptamers Against Cell-Surface Proteins.

Zumrut Hasan E HE Ara Mst Naznin MN Fraile Maria M Maio George G Mallikaratchy Prabodhika P

Nucleic acid therapeutics 20160505 3

We report on a new strategy for identifying highly specific aptamers against a predetermined epitope of a target. Termed "ligand-guided selection" (LIGS), this method uniquely exploits the selection step, the core of SELEX (Systematic Evolution Exponential enrichment). LIGS uses a naturally occurring stronger and highly specific bivalent binder, an antibody (Ab) interacting with its cognate antigen to outcompete specific aptamers from a partially enriched SELEX pool, as a strategy. We demonstrat ...[more]

PMID: 27148897

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Project description:Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.

Dataset Information

Ligand-Guided Selection of Target-Specific Aptamers: A Screening Technology for Identifying Specific Aptamers Against Cell-Surface Proteins.

Publications

Ligand-Guided Selection of Target-Specific Aptamers: A Screening Technology for Identifying Specific Aptamers Against Cell-Surface Proteins.

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