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Tools & Techniques: Reconstructing miRNA Interaction Networks; Condensing siRNA Nanoparticles; and More

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While adeno-associated viral vectors are effective tools for shRNA-mediated RNAi, precise quantification of a vector is required to achieve maximal effects with minimal potency.

A number of titration methods exist to quantify recombinant AAV genomes, but lack of standardization remains a key challenge, according to researchers from the Berlin Institute of Technology.

"Thus, it is hardly possible to use data reported in the literature as a guide for initial dosing in cell culture or in vivo experiments," they wrote in a paper appearing in Human Gene Therapy Methods.

To address this, the scientists developed a new method for the exact quantification of self-complementary AAV, or scAAV, vectors by quantitative PCR using purified genomic vector DNA as a standard.

"The standard can easily be produced for a specific rAAV and allows a simple, sensitive, and reliable quantification of scAAV vector genomes," the research team noted.

The team compared scAAV vector-titration experiments using their new method to those using either a coiled or a linearized plasmid standard and four different primer sets, and found that "consistent and reproducible data" were only obtainable by using purified scAAV vector genomes as a standard.

In additional experiments, they used their approach to assess the vector genome numbers and transduction units of two preparations of scAAV vectors expressing an shRNA targeting human cyclophilin B.

The researchers analyzed target knockdown as a function of either vector genome numbers or transduction units, and found that only transduction units gave reproducible results when comparing different vector preparations.

Overall, the new method allows for a "precise characterization of an AAV vector preparation, which could facilitate the comparison of functional data that will be published for AAV vector applications in the future," the investigators concluded.


As microRNA profiling becomes increasingly routine in the study of biological processes and disease states, there is a growing need to accurately identify targets of the small, non-coding RNAs.

Computational target-prediction methods exist, but these often yield false positives and false negatives. Meanwhile, methods that incorporate expression data have been developed, they do not address the temporal dynamics of miRNA-regulated networks, according to a group of researchers from Carnegie Mellon University.

"Similar to other causal events, shifts in miRNA and mRNA profiles in a specific developmental stage or disease state can happen in a sequential manner," the group wrote in the Proceedings of the National Academy of Sciences. "Static, correlation-based analysis may miss key regulatory miRNAs when these change at an earlier stage and so their global expression levels do not correlate with their targets."

With this problem in mind, the researchers developed what they call MIRna Dynamic Regulatory Events Miner, or miRDREM, a modeling method that reconstructs dynamic regulatory networks that model the effects of transcription factors and miRNAs on their targets over time.

Such models have been used before to study transcription factor activity, but they have only used the transcriptional factor data as static information — for example, whether a transcriptional factor can bind a gene or not, according to the PNAS paper.

Transcription factor activity is often posttranscriptionally regulated, but "miRNA expression levels are an excellent indicator of their activity level and so can be used to determine if a specific miRNA is actively regulating genes," the scientists wrote. "We have developed computational methods for using dynamic activity information and for restricting the assignments of miRNAs to targets based on their expected inhibitory effects."

To test the tool, the researchers generated expression data for lung development in mice, and found that miRDREM was able to identify several miRNAs that were controlling "major developmental stages," several of which were experimentally validated.


Protamine has proven to be an effective condensing agent for incorporating siRNAs into liposome-based nanoparticles, helping form a stable core that supports an oppositely charged lipid bylayer.

However, it does not readily release its payload, lowering the bioavailability of the nucleic acid cargo. As such, there is a need for alternate condensing agents that don't suffer from this limitation.

In a study published in the Journal of Controlled Release, a team from the University of North Carolina at Chapel Hill reported on the replacement of protamine with a novel recombinant protein that degrades under certain cellular conditions in order to release siRNAs contained within the nanoparticle.

According to the researchers, the protein is composed of four tandem repeats of the histone H2A N-terminal sequence, intervened by the cathepsin D cleavage site. "The repeating H2A sequence enhances the binding affinity to anionic nucleic acids, forming more stable condensates," they wrote.

The protein and an siRNA payload can be condensed into a stable complex capable of supporting a cationic lipid coating, as well as "a high degree of pegylation," which together form a lipid nanoparticle.

In testing the nanoparticles, the distal end of the PEG chain was conjugated with anisamide to target cancer cells that overexpress sigma receptor. Once the nanoparticle was taken up by human lung cancer cells through receptor-mediated endocytosis, the cathepsin D cleavage sites in the recombinant protein were subject to enzymatic digestion in the endosome compartment as pH decreased.

The investigators showed that siRNAs delivered in the nanoparticles triggered a "higher silencing efficiency of target genes" than those in nanoparticles assembled with protamine as their nucleic acid condensing agent, both in vitro and in mouse cancer models.

Further, minimal immunostimulation and systemic toxicity was observed, even after repeated administration.

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