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Tools & Techniques: Abasic Substitutions in siRNAs; Lipid Nanoparticle Evaluation; and More

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As part of an effort to develop RNAi-based therapies for conditions caused by CAG repeat expansions, researchers from Alnylam Pharmaceuticals and the University of Texas Southwestern Medical Center have reported on the use of abasic substitutions to create siRNAs with tailored activity.

Conditions such as Huntington’s disease and spinocerebellar ataxia 3 are caused by expansion of CAG repeats within one allele of the mRNA, making allele selectivity key for gene-silencing treatments, the investigators wrote in Nucleic Acids Research.

They previously used mismatched RNA to modulate RNAi activity to achieve allele selectivity, finding that mismatched duplexes allow for discrimination between the wild-type and mutant alleles of the gene that causes Huntington’s disease.

Looking for alternative ways of optimizing their RNAi agents, the researchers turned to abasic substitutions, which remove the potential for normal base pairing but, unlike mismatched bases, eliminate stacking interactions and the potential for formation of suboptimal or wobble base pairs.

With siRNAs containing the modified residues, the team observed recruitment of Argonaute 2, but no cleavage of target mRNA. They also found that RNA duplexes with one or two abasic substitutions inhibited the expression of the genes responsible for Huntington’s disease and spinocerebellar ataxia 3 with “selectivity similar to analogous mismatch-containing duplexes” in cell-based assays, according to the paper.

Notably, 19-mer duplexes with three or four abasic substitutions in the central region of the guide strand showed a “clear reduction in potency” compared with mismatched duplexes, although this could be compensated for when the length of the parent duplex was increased to 22 base pairs.

Overall, the findings point to abasic duplexes as a “novel starting point” for allele-selective inhibition of the genes that cause CAG-repeat disorders, the researchers concluded. “In combination with other chemical modifications or strategically placed mismatches, the inclusion of abasic sites might lead to development of duplexes with optimized potency and selectivity. It is also possible that abasic substitutions may have subtle influence on biodistribution, and this impact may be favorable.”


With the selection of efficient delivery vehicles vital to the success of RNAi-based drugs, a research team from the Massachusetts Institute of Technology has published the details of a multiparametric approach for the evaluation of lipid nanoparticles as siRNA carriers.

“Challenges to efficient delivery include nanoparticle dissociation via serum proteins, cellular uptake, endosomal escape, and appropriate intracellular disassembly,” they wrote in Proceedings of the National Academy of Sciences.

Single-parameter studies evaluating the effect of chemical structure on a single biological property or on delivery performance are routinely carried out, while high-throughput synthetic methods have been used to accelerate the discovery of potent lipid nanoparticles and evaluate structure activity relationships, they noted. Still, “the relationships between physicochemical properties of nanoparticles and biological barriers, and that between biological barriers and gene-silencing activity remain unclear.”

Meanwhile, limited resources lead many to rely on in vitro predictability for in vivo activity.

To overcome these issues, the MIT group reported an approach for the systematic evaluation of multiple parameters associated with both the physicochemical properties and biological barriers to delivery for a group of lipid nanoparticles.

The method involves mapping out the “entire delivery pathway and evaluating the correlation between each property or delivery barrier and gene silencing,” according to the PNAS paper.

“The correlation of multiple physicochemical properties and biological barriers for a large set of [nanoparticles] with gene silencing allows for identification of relevant relationships between structure, biological function, and biological activity,” the researchers wrote. Meanwhile, multiparametric evaluation could allow for the identification of parameters that can “complement” in vitro gene knockdown as a prescreening tool for the selection of lipid nanoparticles for in vivo use.

By evaluating multiple parameters such as siRNA entrapment, pharmacokinetics, nanoparticle stability, and cell uptake, the group was able to identify lipid nanoparticle pKa as “one of the key determinants” for nanoparticle function and activity both in vitro and in vivo.

“This type of analysis can aid in the identification of meaningful structure-function-activity relationships, improve the in vitro screening process of nanoparticles before in vivo use, and facilitate the future design of potent nanocarriers,” the scientists stated.


Despite the potential of microarray technology to quantify the expression of multiple microRNAs in a single experiment, systematic variation can greatly impact measured probe expression levels, making data normalization necessary.

To that end, collaborators from Merck and Infinity Pharmaceuticals have developed a hierarchical Bayesian approach for miRNA microarray data normalization.

Currently, most normalization methods used in miRNA studies were developed for mRNA data, which are very different from miRNA data mostly because of the possibly larger proportion of differentially expressed miRNA probes and the “much larger percentage of left-censored miRNA probes below detection limit,” the researchers wrote in BMC Genomics.

The newly developed method integrates normalization, missing data imputation, and feature selection in the same model, and out-performed existing normalization strategies both in a simulation study and in the analysis of a real dataset, they stated.

And although the method was designed to handle normalization issues in miRNA data, it may also prove useful with other high-dimensional profiling technology where there are a significant proportion of left-censored data points such as proteomics, they added.

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