Novel approaches in cancer treatment: preclinical and clinical development of small non-coding RNA therapeutics

Short or small interfering RNAs (siRNAs) and microRNA (miRNAs) are molecules similar in size and function able to inhibit gene expression based on their complementarity with mRNA sequences, inducing the degradation of the transcript or the inhibition of their translation.siRNAs bind specifically to a single gene location by sequence complementarity and regulate gene expression by specifically targeting transcription units via posttranscriptional gene silencing. miRNAs can regulate the expression of different gene targets through their imperfect base pairing.This process - known as RNA interference (RNAi) - modulates transcription in order to maintain a correct physiological environment, playing a role in almost the totality of the cellular pathways.siRNAs have been evolutionary evolved for the protection of genome integrity in response to exogenous and invasive nucleic acids such as transgenes or transposons. Artificial siRNAs are widely used in molecular biology for transient silencing of genes of interest. This strategy allows to inhibit the expression of any target protein of known sequence and is currently used for the treatment of different human diseases including cancer.Modifications and rearrangements in gene regions encoding for miRNAs have been found in cancer cells, and specific miRNA expression profiles characterize the developmental lineage and the differentiation state of the tumor. miRNAs with different expression patterns in tumors have been reported as oncogenes (oncomirs) or tumor-suppressors (anti-oncomirs). RNA modulation has become important in cancer research not only for development of early and easy diagnosis tools but also as a promising novel therapeutic approach.Despite the emerging discoveries supporting the role of miRNAs in carcinogenesis and their and siRNAs possible use in therapy, a series of concerns regarding their development, delivery and side effects have arisen.In this review we report the biology of miRNAs and siRNAs in relation to cancer summarizing the recent methods described to use them as novel therapeutic drugs and methods to specifically deliver them to cancer cells and overcome the limitations in the use of these molecules.

microRNA-20-1 and miR-101a suppress the NF-κB-mediated inflammation production by targeting TRAF6 in miiuy croaker

Upon recognition of the pathogen components by PRR (pattern recognition receptors), then the cells could be activated to produce inflammatory cytokines and type I interferons. The inflammation is tightly modulated by the host to prevent inappropriate inflammatory responses. MicroRNAs (miRNAs) are non-coding and small RNAs that can inhibit gene expression and participate in various biological functions, including maintaining a balanced immune response in the host. To maintain the balance of the immune response, these pathways are closely regulated by the host to prevent inappropriate reactions of the cells. However, in low vertebrates, the miRNA-mediated inflammatory response regulatory networks remain largely unknown. Here, we report that two miRNAs, miR-20-1 and miR-101a are identified as negative regulators in teleost inflammatory responses. Initially, we find that both miR-20-1 and miR-101a dramatically increased after lipopolysaccharide (LPS) stimulation and Vibrio harveyi infection. Upregulated miR-20-1 and miR-101a inhibit LPS-induced inflammatory cytokines production by targeting TNF receptor-associated factor 6 (TRAF6), thus avoiding excessive inflammation. Moreover, miR-20-1 and miR-101a regulate the inflammatory responses through the TRAF6-mediated nuclear factor kappa (NF-κB) signaling pathways. Collectively, these data indicate that miR-20-1 and miR-101a act as negative regulators through regulating the TRAF6-mediated NF-κB signaling pathway, and participate in the host antibacterial immune responses, which will provide new insight into the intricate networks of the host-pathogen interaction in the lower vertebrates.

Natural Selection of H5N1 Avian Influenza A Viruses with Increased PA-X and NS1 Shutoff Activity

Influenza A viruses (IAV) can infect a broad range of mammalian and avian species. However, the host innate immune system provides defenses that restrict IAV replication and infection. Likewise, IAV have evolved to develop efficient mechanisms to counteract host antiviral responses to efficiently replicate in their hosts. The IAV PA-X and NS1 non-structural proteins are key virulence factors that modulate innate immune responses and virus pathogenicity during infection. To study the determinants of IAV pathogenicity and their functional co-evolution, we evaluated amino acid differences in the PA-X and NS1 proteins of early (1996–1997) and more recent (since 2016) H5N1 IAV. H5N1 IAV have zoonotic and pandemic potential and represent an important challenge both in poultry farming and human health. The results indicate that amino acid changes occurred over time, affecting the ability of these two non-structural H5N1 IAV proteins to inhibit gene expression and affecting virus pathogenicity. These results highlight the importance to monitor the evolution of these two virulence factors of IAV, which could result in enhanced viral replication and virulence.

tRFTars: predicting the targets of tRNA-derived fragments

Background tRNA-derived fragments (tRFs) are 14–40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene expression in the same manner as miRNAs. However, there are currently no tools for accurately predicting tRF target genes. Methods We used tRF-mRNA pairs identified by crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous AGO-bound RNAs (CLEAR)-CLIP to assess features that may participate in tRF targeting, including the sequence context of each site and tRF-mRNA interactions. We applied genetic algorithm (GA) to select key features and support vector machine (SVM) to construct tRF prediction models. Results We first identified features that globally influenced tRF targeting. Among these features, the most significant were the minimum free folding energy (MFE), position 8 match, number of bases paired in the tRF-mRNA duplex, and length of the tRF, which were consistent with previous findings. Our constructed model yielded an area under the receiver operating characteristic (ROC) curve (AUC) = 0.980 (0.977–0.983) in the training process and an AUC = 0.847 (0.83–0.861) in the test process. The model was applied to all the sites with perfect Watson–Crick complementarity to the seed in the 3′ untranslated region (3′-UTR) of the human genome. Seven of nine target/nontarget genes of tRFs confirmed by reporter assay were predicted. We also validated the predictions via quantitative real-time PCR (qRT-PCR). Thirteen potential target genes from the top of the predictions were significantly down-regulated at the mRNA levels by overexpression of the tRFs (tRF-3001a, tRF-3003a or tRF-3009a). Conclusions Predictions can be obtained online, tRFTars, freely available at http://trftars.cmuzhenninglab.org:3838/tar/, which is the first tool to predict targets of tRFs in humans with a user-friendly interface.

tRFTars: predicting the targets of tRNA-derived fragments

Background: tRNA-derived fragments (tRFs) are 14–40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene expression in the same manner as miRNAs. However, there are currently no tools for accurately predicting tRF target genes.Methods: We used tRF-mRNA pairs identified by crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous AGO-bound RNAs (CLEAR)-CLIP to assess features that may participate in tRF targeting, including the sequence context of each site and tRF-mRNA interactions. We applied genetic algorithm (GA) to select key features and support vector machine (SVM) to construct tRF prediction models.Results: We first identified features that globally influenced tRF targeting. Among these features, the most significant were the minimum free folding energy (MFE), position 8 match, number of bases paired in the tRF-mRNA duplex, and length of the tRF, which were consistent with previous findings. Our constructed model yielded an area under the receiver operating characteristic (ROC) curve (AUC) = 0.980 (0.977-0.983) in the training process and an AUC = 0.847 (0.83-0.861) in the test process. The model was applied to all the sites with perfect Watson-Crick complementarity to the seed in the 3' untranslated region (3'-UTR) of the human genome. Seven of nine target/nontarget genes of tRFs confirmed by reporter assay were predicted. We also validated the predictions via quantitative real-time PCR (qRT-PCR). Thirteen potential target genes from the top of the predictions were significantly down-regulated at the mRNA levels by overexpression of the tRFs (tRF-3001a, tRF-3003a or tRF-3009a).Conclusions: Predictions can be obtained online, tRFTars, freely available at http://trftars.cmuzhenninglab.org:3838/tar/, which is the first tool to predict targets of tRFs in humans with a user-friendly interface.

Regulation of oligonucleotide adsorption by a thermo and pH dual-responsive copolymer layer

Oligonucleotides hold great promise as therapeutic agents to specifically and selectively inhibit gene expression. In order to achieve better targeting efficiency and treatment efficacy, nanocarriers that are dual-responsive to both...

tRFTar: predicting the targets of tRNA-derived fragments

Background: tRNA-derived fragments (tRFs) are 14–40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene expression in the same manner as miRNAs. However, there are currently no tools for accurately predicting tRF target genes. Methods: We used tRF-mRNA pairs identified by crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous AGO-bound RNAs (CLEAR)-CLIP to assess features that may participate in tRF targeting, including the sequence context of each site and tRF-mRNA interactions. We applied genetic algorithm (GA) to select key features and support vector machine (SVM) to construct tRF prediction models. Results: We first identified features that globally influenced tRF targeting. Among these features, the most significant were the minimum free folding energy (MFE), position 8 match, number of bases paired in the tRF-mRNA duplex, and length of the tRF, which were consistent with previous findings. Our constructed model yielded an area under the receiver operating characteristic (ROC) curve (AUC) = 0.980 (0.977-0.983) in the training process and an AUC = 0.847 (0.83-0.861) in the test process. The model was applied to all the sites with perfect Watson-Crick complementarity to the seed in the 3' untranslated region (3'-UTR) of the human genome. Seven of nine target/nontarget genes of tRFs confirmed by reporter assay were predicted. Conclusions: Predictions can be obtained online, tRFTar, freely available at http://trftar.cmuzhenninglab.org:3838/tar/, which is the first tool to predict targets of tRFs in humans with a user-friendly interface.

Identification of Key Small Non‐Coding MicroRNAs Controlling Pacemaker Mechanisms in the Human Sinus Node

Background The sinus node (SN) is the primary pacemaker of the heart. SN myocytes possess distinctive action potential morphology with spontaneous diastolic depolarization because of a unique expression of ion channels and Ca 2+ ‐handling proteins. MicroRNAs (miRs) inhibit gene expression. The role of miRs in controlling the expression of genes responsible for human SN pacemaking and conduction has not been explored. The aim of this study was to determine miR expression profile of the human SN as compared with that of non‐pacemaker atrial muscle. Methods and Results SN and atrial muscle biopsies were obtained from donor or post‐mortem hearts (n=10), histology/immunolabeling were used to characterize the tissues, TaqMan Human MicroRNA Arrays were used to measure 754 miRs, Ingenuity Pathway Analysis was used to identify miRs controlling SN pacemaker gene expression. Eighteen miRs were significantly more and 48 significantly less abundant in the SN than atrial muscle. The most interesting miR was miR‐486‐3p predicted to inhibit expression of pacemaking channels: HCN1 (hyperpolarization‐activated cyclic nucleotide‐gated 1), HCN4, voltage‐gated calcium channel (Ca v )1.3, and Ca v 3.1. A luciferase reporter gene assay confirmed that miR‐486‐3p can control HCN4 expression via its 3′ untranslated region. In ex vivo SN preparations, transfection with miR‐486‐3p reduced the beating rate by ≈35±5% ( P <0.05) and HCN4 expression ( P <0.05). Conclusions The human SN possesses a unique pattern of expression of miRs predicted to target functionally important genes. miR‐486‐3p has an important role in SN pacemaker activity by targeting HCN4, making it a potential target for therapeutic treatment of SN disease such as sinus tachycardia.

Selective inhibition of human translation termination by a drug-like compound

Methods to directly inhibit gene expression using small molecules hold promise for the development of new therapeutics targeting proteins that have evaded previous attempts at drug discovery. Among these, small molecules including the drug-like compound PF-06446846 (PF846) selectively inhibit the synthesis of specific proteins, by stalling translation elongation. These molecules also inhibit translation termination by an unknown mechanism. Using cryo-electron microscopy (cryo-EM) and biochemical approaches, we show that PF846 inhibits translation termination by arresting the nascent chain (NC) in the ribosome exit tunnel. The arrested NC adopts a compact α-helical conformation that induces 28 S rRNA nucleotide rearrangements that suppress the peptidyl transferase center (PTC) catalytic activity stimulated by eukaryotic release factor 1 (eRF1). These data support a mechanism of action for a small molecule targeting translation that suppresses peptidyl-tRNA hydrolysis promoted by eRF1, revealing principles of eukaryotic translation termination and laying the foundation for new therapeutic strategies.

Mechanisms by Which Membrane and Nuclear ER Alpha Inhibit Adipogenesis in Cells Isolated From Female Mice

Mesenchymal stem cells can differentiate into mature chondrocytes, osteoblasts, and adipocytes. Excessive and dysfunctional visceral adipocytes increase upon menopause and importantly contribute to altered metabolism in postmenopausal women. We previously showed both plasma membrane and nuclear estrogen receptors alpha (ERα) with endogenous estrogen are required to suppress adipogenesis in vivo. Here we determined mechanisms by which these liganded ER pools collaborate to inhibit the peroxisome proliferator-activated gamma (PPARγ) gene and subsequent progenitor differentiation. In 3T3-L1 pre-adipocytes and adipose-derived stem cells (ADSC), membrane ERα signaled through phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT) to enhance ERα nuclear localization, importantly at the PPARγ gene promoter. AKT also increased overall abundance and recruitment of co-repressors GATA3, β-catenin, and TCF4 to the PPARγ promoter. Membrane ERα signaling additionally enhanced wingless-integrated (Wnt)1 and 10b expression. The components of the repressor complex were required for estrogen to inhibit rosiglitazone-induced differentiation of ADSC and 3T3-L1 cells to mature adipocytes. These mechanisms whereby ER cellular pools collaborate to inhibit gene expression limit progenitor differentiation to mature adipocytes.