Abstract
BackgroundRNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive.ResultsIn this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi), which employs a unique combination of group of small interfering RNAs (siRNAs) to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs) and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear), which is ill-posed in general. However, the recently developed compressed sensing (CS) theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially.ConclusionsThis csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which may benefit the biological research with respect to cellular processes and pathways.
Highlights
RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest
From a systems level, the specified one-to-one relationship between small interfering RNAs (siRNAs) and target gene hinders a high-throughput screening (HTS) of functional genes in a genome-wide scope. To overcome this burden for designing a large scale RNAi-based screening of functional genes with better efficiency, we propose this conceptual model, the compressed sensing RNAi, based on the fact that a targetcomplementary siRNA can simultaneously target at other mRNAs containing sequence segments that are partially complementary to it [19]
The upper left panel shows the conventional RNAi screening, i.e., each spot in the microarray has an RNA fragment that serves as a unique identifier of only one target gene; while the upper right panel refers to our proposed compressed sensing RNAi (csRNAi) scheme, in which the same target gene group can be knockdown with an siRNA pool of much smaller size
Summary
RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The RNAi pathway is initiated by the enzyme dicer, which cleaves long double-stranded RNA (dsRNA) molecules (either endogenous or exogenous) into short fragments of ~20 nucleotides [2,3,4] These short doublestranded fragments are called small interfering RNAs (siRNAs), which are sequences comprised by a four-alphabet, {A, U, G, C}. The largest gene knockdown experiments performed so far have used multiple siRNAs per gene [9,17]
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