Loss of huntingtin function slows synaptic vesicle endocytosis in striatal neurons from the htt(Q140/Q140) mouse model of Huntington\u27s disease

Huntington\u27s disease (HD) is caused by CAG repeat expansion within the HTT gene, with the dysfunction and eventual loss of striatal medium spiny neurons a notable feature. Since medium spiny neurons receive high amounts of synaptic input, we hypothesised that this vulnerability originates from an inability to sustain presynaptic performance during intense neuronal activity. To test this hypothesis, primary cultures of either hippocampal or striatal neurons were prepared from either wild-type mice or a knock-in HD mouse model which contains 140 poly-glutamine repeats in the huntingtin protein (htt(Q140/Q140)). We identified a striatum-specific defect in synaptic vesicle (SV) endocytosis in htt(Q140/Q140) neurons that was only revealed during high frequency stimulation. This dysfunction was also present in neurons that were heterozygous for the mutant HTT allele. Depletion of endogenous huntingtin using hydrophobically-modified siRNA recapitulated this activity-dependent defect in wild-type neurons, whereas depletion of mutant huntingtin did not rescue the effect in htt(Q140/Q140) neurons. Importantly, this SV endocytosis defect was corrected by overexpression of wild-type huntingtin in homozygous htt(Q140/Q140) neurons. Therefore, we have identified an activity-dependent and striatum-specific signature of presynaptic dysfunction in neurons derived from pre-symptomatic HD mice, which is due to loss of wild-type huntingtin function. This presynaptic defect may render this specific neuronal subtype unable to operate efficiently during high frequency activity patterns, potentially resulting in dysfunctional neurotransmission, synapse failure and ultimately degeneration

Structural characterization of cationic lipid–tRNA complexes

Despite considerable interest and investigations on cationic lipid–DNA complexes, reports on lipid–RNA interaction are very limited. In contrast to lipid–DNA complexes where lipid binding induces partial B to A and B to C conformational changes, lipid–tRNA complexation preserves tRNA folded state. This study is the first attempt to investigate the binding of cationic lipid with transfer RNA and the effect of lipid complexation on tRNA aggregation and condensation. We examine the interaction of tRNA with cholesterol (Chol), 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), dioctadecyldimethylammoniumbromide (DDAB) and dioleoylphosphatidylethanolamine (DOPE), at physiological condition, using constant tRNA concentration and various lipid contents. FTIR, UV-visible, CD spectroscopic methods and atomic force microscopy (AFM) were used to analyze lipid binding site, the binding constant and the effects of lipid interaction on tRNA stability, conformation and condensation. Structural analysis showed lipid–tRNA interactions with G–C and A–U base pairs as well as the backbone phosphate group with overall binding constants of KChol = 5.94 (± 0.8) × 104 M–1, KDDAB = 8.33 (± 0.90) × 105 M–1, KDOTAP = 1.05 (± 0.30) × 105 M–1 and KDOPE = 2.75 (± 0.50) × 104 M–1. The order of stability of lipid–tRNA complexation is DDAB > DOTAP > Chol > DOPE. Hydrophobic interactions between lipid aliphatic tails and tRNA were observed. RNA remains in A-family structure, while biopolymer aggregation and condensation occurred at high lipid concentrations

Structural characterization of cationic lipid–tRNA complexes

Despite considerable interest and investigations on cationic lipid–DNA complexes, reports on lipid–RNA interaction are very limited. In contrast to lipid–DNA complexes where lipid binding induces partial B to A and B to C conformational changes, lipid–tRNA complexation preserves tRNA folded state. This study is the first attempt to investigate the binding of cationic lipid with transfer RNA and the effect of lipid complexation on tRNA aggregation and condensation. We examine the interaction of tRNA with cholesterol (Chol), 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), dioctadecyldimethylammoniumbromide (DDAB) and dioleoylphosphatidylethanolamine (DOPE), at physiological condition, using constant tRNA concentration and various lipid contents. FTIR, UV-visible, CD spectroscopic methods and atomic force microscopy (AFM) were used to analyze lipid binding site, the binding constant and the effects of lipid interaction on tRNA stability, conformation and condensation. Structural analysis showed lipid–tRNA interactions with G–C and A–U base pairs as well as the backbone phosphate group with overall binding constants of KChol = 5.94 (± 0.8) × 104 M–1, KDDAB = 8.33 (± 0.90) × 105 M–1, KDOTAP = 1.05 (± 0.30) × 105 M–1 and KDOPE = 2.75 (± 0.50) × 104 M–1. The order of stability of lipid–tRNA complexation is DDAB > DOTAP > Chol > DOPE. Hydrophobic interactions between lipid aliphatic tails and tRNA were observed. RNA remains in A-family structure, while biopolymer aggregation and condensation occurred at high lipid concentrations

Serum Deprivation of Mesenchymal Stem Cells Improves Exosome Activity and Alters Lipid and Protein Composition

Exosomes can serve as delivery vehicles for advanced therapeutics. The components necessary and sufficient to support exosomal delivery have not been established. Here we connect biochemical composition and activity of exosomes to optimize exosome-mediated delivery of small interfering RNAs (siRNAs). This information is used to create effective artificial exosomes. We show that serum-deprived mesenchymal stem cells produce exosomes up to 22-fold more effective at delivering siRNAs to neurons than exosomes derived from control cells. Proteinase treatment of exosomes stops siRNA transfer, indicating that surface proteins on exosomes are involved in trafficking. Proteomic and lipidomic analyses show that exosomes derived in serum-deprived conditions are enriched in six protein pathways and one lipid class, dilysocardiolipin. Inspired by these findings, we engineer an artificial exosome, in which the incorporation of one lipid (dilysocardiolipin) and three proteins (Rab7, Desmoplakin, and AHSG) into conventional neutral liposomes produces vesicles that mimic cargo delivering activity of natural exosomes

Comparative route of administration studies using therapeutic siRNAs show widespread gene modulation in Dorset sheep

siRNAs comprise a class of drugs that can be programmed to silence any target gene. Chemical engineering efforts resulted in development of divalent siRNAs (di-siRNAs), which support robust and long-term efficacy in rodent and nonhuman primate brains upon direct cerebrospinal fluid (CSF) administration. Oligonucleotide distribution in the CNS is nonuniform, limiting clinical applications. The contribution of CSF infusion placement and dosing regimen on relative accumulation, specifically in the context of large animals, is not well characterized. To our knowledge, we report the first systemic, comparative study investigating the effects of 3 routes of administration - intrastriatal (i.s.), i.c.v., and intrathecal catheter to the cisterna magna (ITC) - and 2 dosing regimens - single and repetitive via an implanted reservoir device - on di-siRNA distribution and accumulation in the CNS of Dorset sheep. CSF injections (i.c.v. and ITC) resulted in similar distribution and accumulation across brain regions. Repeated dosing increased homogeneity, with greater relative deep brain accumulation. Conversely, i.s. administration supported region-specific delivery. These results suggest that dosing regimen, not CSF infusion placement, may equalize siRNA accumulation and efficacy throughout the brain. These findings inform the planning and execution of preclinical and clinical studies using siRNA therapeutics in the CNS

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques

Design Principles for Ligand-Sensing, Conformation-Switching Ribozymes

Nucleic acid sensor elements are proving increasingly useful in biotechnology and biomedical applications. A number of ligand-sensing, conformational-switching ribozymes (also known as allosteric ribozymes or aptazymes) have been generated by some combination of directed evolution or rational design. Such sensor elements typically fuse a molecular recognition domain (aptamer) with a catalytic signal generator (ribozyme). Although the rational design of aptazymes has begun to be explored, the relationships between the thermodynamics of aptazyme conformational changes and aptazyme performance in vitro and in vivo have not been examined in a quantitative framework. We have therefore developed a quantitative and predictive model for aptazymes as biosensors in vitro and as riboswitches in vivo. In the process, we have identified key relationships (or dimensionless parameters) that dictate aptazyme performance, and in consequence, established equations for precisely engineering aptazyme function. In particular, our analysis quantifies the intrinsic trade-off between ligand sensitivity and the dynamic range of activity. We were also able to determine how in vivo parameters, such as mRNA degradation rates, impact the design and function of aptazymes when used as riboswitches. Using this theoretical framework we were able to achieve quantitative agreement between our models and published data. In consequence, we are able to suggest experimental guidelines for quantitatively predicting the performance of aptazyme-based riboswitches. By identifying factors that limit the performance of previously published systems we were able to generate immediately testable hypotheses for their improvement. The robust theoretical framework and identified optimization parameters should now enable the precision design of aptazymes for biotechnological and clinical applications

The Fate of miRNA* Strand through Evolutionary Analysis: Implication for Degradation As Merely Carrier Strand or Potential Regulatory Molecule?

BACKGROUND: During typical microRNA (miRNA) biogenesis, one strand of a approximately 22 nt RNA duplex is preferentially selected for entry into a silencing complex, whereas the other strand, known as the passenger strand or miRNA* strand, is degraded. Recently, some miRNA* sequences were reported as guide miRNAs with abundant expression. Here, we intended to discover evolutionary implication of the fate of miRNA* strand by analyzing miRNA/miRNA* sequences across vertebrates. PRINCIPAL FINDINGS: Mature miRNAs based on gene families were well conserved especially for their seed sequences across vertebrates, while their passenger strands always showed various divergence patterns. The divergence mainly resulted from divergence of different animal species, homologous miRNA genes and multicopy miRNA hairpin precursors. Some miRNA* sequences were phylogenetically conserved in seed and anchor sequences similar to mature miRNAs, while others revealed high levels of nucleotide divergence despite some of their partners were highly conserved. Most of those miRNA precursors that could generate abundant miRNAs from both strands always were well conserved in sequences of miR-#-5p and miR-#-3p, especially for their seed sequences. CONCLUSIONS: The final fate of miRNA* strand, either degraded as merely carrier strand or expressed abundantly as potential functional guide miRNA, may be destined across evolution. Well-conserved miRNA* strands, particularly conservation in seed sequences, maybe afford potential opportunities for contributing to regulation network. The study will broaden our understanding of potential functional miRNA* species

Reconsideration of In-Silico siRNA Design Based on Feature Selection: A Cross-Platform Data Integration Perspective

RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/materials/qiliu/siRNA.html

Genome-wide microRNA screening in Nile tilapia reveals pervasive isomiRs’ transcription, sex-biased arm switching and increasing complexity of expression throughout development

MicroRNAs (miRNAs) are key regulators of gene expression in multicellular organisms. The elucidation of miRNA function and evolution depends on the identification and characterization of miRNA repertoire of strategic organisms, as the fast-evolving cichlid fishes. Using RNA-seq and comparative genomics we carried out an in-depth report of miRNAs in Nile tilapia (Oreochromis niloticus), an emergent model organism to investigate evo-devo mechanisms. Five hundred known miRNAs and almost one hundred putative novel vertebrate miRNAs have been identified, many of which seem to be teleost-specific, cichlid-specific or tilapia-specific. Abundant miRNA isoforms (isomiRs) were identified with modifications in both 5p and 3p miRNA transcripts. Changes in arm usage (arm switching) of nine miRNAs were detected in early development, adult stage and even between male and female samples. We found an increasing complexity of miRNA expression during ontogenetic development, revealing a remarkable synchronism between the rate of new miRNAs recruitment and morphological changes. Overall, our results enlarge vertebrate miRNA collection and reveal a notable differential ratio of miRNA arms and isoforms influenced by sex and developmental life stage, providing a better picture of the evolutionary and spatiotemporal dynamics of miRNAs