Functional features defining the efficacy of cholesterol-conjugated, self-deliverable, chemically modified siRNAs.

Progress in oligonucleotide chemistry has produced a shift in the nature of siRNA used, from formulated, minimally modified siRNAs, to unformulated, heavily modified siRNA conjugates. The introduction of extensive chemical modifications is essential for conjugate-mediated delivery. Modifications have a significant impact on siRNA efficacy through interference with recognition and processing by RNAi enzymatic machinery, severely restricting the sequence space available for siRNA design. Many algorithms available publicly can successfully predict the activity of non-modified siRNAs, but the efficiency of the algorithms for designing heavily modified siRNAs has never been systematically evaluated experimentally. Here we screened 356 cholesterol-conjugated siRNAs with extensive modifications and developed a linear regression-based algorithm that effectively predicts siRNA activity using two independent datasets. We further demonstrate that predictive determinants for modified and non-modified siRNAs differ substantially. The algorithm developed from the non-modified siRNAs dataset has no predictive power for modified siRNAs and vice versa. In the context of heavily modified siRNAs, the introduction of chemical asymmetry fully eliminates the requirement for thermodynamic bias, the major determinant for non-modified siRNA efficacy. Finally, we demonstrate that in addition to the sequence of the target site, the accessibility of the neighboring 3' region significantly contributes to siRNA efficacy.

Type 3 Secretion Translocators Spontaneously Assemble a Hexadecameric Transmembrane Complex.

A type 3 secretion system is used by many bacterial pathogens to inject proteins into eukaryotic cells. Pathogens insert a translocon complex into the target eukaryotic membrane by secreting two proteins known as translocators. How these translocators form a translocon in the lipid bilayer and why both proteins are required remains elusive. Pseudomonas aeruginosa translocators PopB and PopD insert pores into membranes forming homo- or hetero-complexes of undetermined stoichiometry. Single-molecule fluorescence photobleaching experiments revealed that PopD formed mostly hexameric structures in membranes, whereas PopB displayed a bi-modal distribution with 6 and 12 subunits peaks. However, individually the proteins are not functional for effector translocation. We have found that when added together, the translocators formed distinct hetero-complexes containing 8 PopB and 8 PopD molecules. Thus, the interaction between PopB and PopD guide the assembly of a unique hetero-oligomer in membranes.

Multispecies-targeting siRNAs for the modulation of JAK1 in the skin.

Identifying therapeutic oligonucleotides that are cross-reactive to experimental animal species can dramatically accelerate the process of preclinical development and clinical translation. Here, we identify fully chemically-modified small interfering RNAs (siRNAs) that are cross-reactive to Janus kinase 1 (JAK1) in humans and a large variety of other species. We validated the identified siRNAs in silencing JAK1 in cell lines and skin tissues of multiple species. JAK1 is one of the four members of the JAK family of tyrosine kinases that mediate the signaling transduction of many inflammatory cytokine pathways. Dysregulation of these pathways is often involved in the pathogenesis of various immune disorders, and modulation of JAK family enzymes is an effective strategy in the clinic. Thus, this work may open up unprecedented opportunities for evaluating the modulation of JAK1 in many animal models of human inflammatory skin diseases. Further chemical engineering of the optimized JAK1 siRNAs may expand the utility of these compounds for treating immune disorders in additional tissues.

Asymmetric trichotomous partitioning overcomes dataset limitations in building machine learning models for predicting siRNA efficacy.

Chemically modified small interfering RNAs (siRNAs) are promising therapeutics guiding sequence-specific silencing of disease genes. Identifying chemically modified siRNA sequences that effectively silence target genes remains challenging. Such determinations necessitate computational algorithms. Machine learning is a powerful predictive approach for tackling biological problems but typically requires datasets significantly larger than most available siRNA datasets. Here, we describe a framework applying machine learning to a small dataset (356 modified sequences) for siRNA efficacy prediction. To overcome noise and biological limitations in siRNA datasets, we apply a trichotomous, two-threshold, partitioning approach, producing several combinations of classification threshold pairs. We then test the effects of different thresholds on random forest machine learning model performance using a novel evaluation metric accounting for class imbalances. We identify thresholds yielding a model with high predictive power, outperforming a linear model generated from the same data, that was predictive upon experimental evaluation. Using a novel model feature extraction method, we observe target site base importances and base preferences consistent with our current understanding of the siRNA-mediated silencing mechanism, with the random forest providing higher resolution than the linear model. This framework applies to any classification challenge involving small biological datasets, providing an opportunity to develop high-performing design algorithms for oligonucleotide therapies.

Rational design of a JAK1-selective siRNA inhibitor for the modulation of autoimmunity in the skin.

Inhibition of Janus kinase (JAK) family enzymes is a popular strategy for treating inflammatory and autoimmune skin diseases. In the clinic, small molecule JAK inhibitors show distinct efficacy and safety profiles, likely reflecting variable selectivity for JAK subtypes. Absolute JAK subtype selectivity has not yet been achieved. Here, we rationally design small interfering RNAs (siRNAs) that offer sequence-specific gene silencing of JAK1, narrowing the spectrum of action on JAK-dependent cytokine signaling to maintain efficacy and improve safety. Our fully chemically modified siRNA supports efficient silencing of JAK1 expression in human skin explant and modulation of JAK1-dependent inflammatory signaling. A single injection into mouse skin enables five weeks of duration of effect. In a mouse model of vitiligo, local administration of the JAK1 siRNA significantly reduces skin infiltration of autoreactive CD8+ T cells and prevents epidermal depigmentation. This work establishes a path toward siRNA treatments as a new class of therapeutic modality for inflammatory and autoimmune skin diseases.