Modulating the Crosstalk between the Tumor and the Microenvironment Using SiRNA: A Flexible Strategy for Breast Cancer Treatment

Tumorigenesis is a complex and multistep process in which sequential mutations in oncogenes and tumor-suppressor genes result in enhanced proliferation and apoptosis escape. Over the past decades, several studies have provided evidence that tumors are more than merely a mass of malignant cancer cells, with the tumor microenvironment (TME) also contributing to cancer progression. For this reason, the focus of cancer research in recent years has shifted from the malignant cancer cell itself to the TME and its interactions. Since the TME actively participates in tumor progression, therapeutic strategies targeting it have created great interest. In this context, much attention has been paid to the potential application of small interfering RNA (siRNA), a class of non-coding RNA that has the ability to downregulate the expression of target genes in a sequence-specific way. This is paving the way for a novel therapeutic approach for the treatment of several diseases, including cancer. In this review, we describe recent efforts in developing siRNA therapeutics for the treatment of breast cancer, with particular emphasis on TME regulation. We focus on studies that adapt siRNA design to reprogram/re-educate the TME and eradicate the interplay between cancer cells and TME.

siRNA Design to Silence the 3′UTR Region of Zika Virus

The disease caused by the Zika virus (ZIKV) has positioned itself as one of the main public health problems in Mexico. One of the main reasons is it causes microcephaly and other birth defects. The transmission of ZIKV is through Aedes aegypti and Ae. albopictus mosquitoes, which are found in a larger space of the national territory. In addition, it can also be transmitted via blood transfusion, sexual relations, and maternal-fetal route. So far, there are no vaccines or specific treatments to deal with this infection. Currently, some new therapeutics such as small interfering RNAs (siRNAs) are able to regulate or suppress transcription in viruses. Therefore, in this project, an in silico siRNA was designed for the 3′UTR region of ZIKV via bioinformatics tools. The designed siRNA was synthesized and transfected into the C6/36 cell line, previously infected with ZIKV in order to assess the ability of the siRNA to inhibit viral replication. The designed siRNA was able to inhibit significantly (p<0.05) ZIKV replication; this siRNA could be considered a potential therapeutic towards the disease that causes ZIKV and the medical problems generated.

Designing Effective small interfering RNA for Post-Transcriptional Silencing of Human GREM1: A Comprehensive Bioinformatics Approach

ABSTRACTHuman gremlin-1 is a physiologically versatile signaling molecule that has been associated with several human diseases including cancer. The ability of gremlin-1 to induce fibrosis in organs and transduce angiogenesis makes it a target for cancer therapy. RNAi-based therapy has proven to be very efficient and specific in tumor growth inhibition. The efficacy and specificity of siRNA-mediated gene silencing depends on the designing approaches. Here, empirical guidelines for siRNA design and comprehensive target site availability analysis were used to select effective siRNA from a plethora of potential candidates designed using several computation algorithms. Then, the selected siRNA candidates were subjected to stringent similarity searches in order to obtain siRNA candidates with reduced off-target effects (high specificity). The best candidates were compared to experimentally successful gremlin-1 siRNAs in order to predict the silencing potency of the selected siRNAs. siRNA-6 (sense strand: 5’-CCAAGAAAUUCACUACCAU-3’), siRNA-7 (sense strand: 5’-CCAUGAUGGUCACACUCAA-3’) and siRNA-47 (sense strand: 5’-GGCCCAGCACAAUGACUCA-3’) were predicted to be highly effective siRNA candidates for gremlin-1 silencing. These siRNAs can be considered for RNAi-based therapy because off-target effects are predicted to be minimal.

New opportunities for designing effective small interfering RNAs

Small interfering RNAs (siRNAs) that silence genes of infectious diseases are potentially potent drugs. A continuing obstacle for siRNA-based drugs is how to improve their efficacy for adequate dosage. To overcome this obstacle, the interactions of antiviral siRNAs, tested in vivo, were computationally examined within the RNA-induced silencing complex (RISC). Thermodynamics data show that a persistent RISC cofactor is significantly more exothermic for effective antiviral siRNAs than their ineffective counterparts. Detailed inspection of viral RNA secondary structures reveals that effective antiviral siRNAs target hairpin or pseudoknot loops. These structures are critical for initial RISC interactions since they partially lack intramolecular complementary base pairing. Importing two temporary RISC cofactors from magnesium-rich hairpins and/or pseudoknots then kickstarts full RNA hybridization and hydrolysis. Current siRNA design guidelines are based on RNA primary sequence data. Herein, the thermodynamics of RISC cofactors and targeting magnesium-rich RNA secondary structures provide additional guidelines for improving siRNA design.

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.