Insights into the kinetics of siRNA-mediated gene silencing from live-cell and live-animal bioluminescent imaging

Small interfering RNA (siRNA) molecules are potent effectors of post-transcriptional gene silencing. Using noninvasive bioluminescent imaging and a mathematical model of siRNA delivery and function, the effects of target-specific and treatment-specific parameters on siRNA-mediated gene silencing are monitored in cells stably expressing the firefly luciferase protein. In vitro, luciferase protein levels recover to pre-treatment values within <1 week in rapidly dividing cell lines, but take longer than 3 weeks to return to steady-state levels in nondividing fibroblasts. Similar results are observed in vivo, with knockdown lasting ~10 days in subcutaneous tumors in A/J mice and 3–4 weeks in the nondividing hepatocytes of BALB/c mice. These data indicate that dilution due to cell division, and not intracellular siRNA half-life, governs the duration of gene silencing under these conditions. To demonstrate the practical use of the model in treatment design, model calculations are used to predict the dosing schedule required to maintain persistent silencing of target proteins with different half-lives in rapidly dividing or nondividing cells. The approach of bioluminescent imaging combined with mathematical modeling provides useful insights into siRNA function and may help expedite the translation of siRNA into clinically relevant therapeutics for disease treatment and management

Physicochemical and Biological Characterization of Targeted, Nucleic Acid-Containing Nanoparticles

Nucleic acid-based therapeutics have the potential to provide potent and highly specific treatments for a variety of human ailments. However, systemic delivery continues to be a significant hurdle to success. Multifunctional nanoparticles are being investigated as systemic, nonviral delivery systems, and here, we describe the physicochemical and biological characterization of cyclodextrin-containing polycations (CDP) and their nanoparticles formed with nucleic acids including plasmid DNA (pDNA) and small interfering RNA (siRNA). These polycation/nucleic acid complexes can be tuned by formulation conditions to yield particles with sizes ranging from 60 to 150 nm, ζ potentials from 10 to 30 mV, and molecular weights from ∼7 × 10^7 to 1 × 10^9 g mol^(-1) as determined by light scattering techniques. Inclusion complexes formed between adamantane (AD)-containing molecules and the β-cyclodextrin molecules enable the modular attachment of poly(ethylene glycol) (AD−PEG) conjugates for steric stabilization and targeting ligands (AD−PEG−transferrin) for cell-specific targeting. A 70 nm particle can contain ∼10 000 CDP polymer chains, ∼2000 siRNA molecules, ∼4000 AD−PEG_(5000) molecules, and ∼100 AD−PEG_(5000)−Tf molecules; this represents a significant payload of siRNA and a large ratio of siRNA to targeting ligand (20:1). The particles protect the nucleic acid payload from nuclease degradation, do not aggregate at physiological salt concentrations, and cause minimal erythrocyte aggregation and complement fixation at the concentrations typically used for in vivo application. Uptake of the nucleic acid-containing particles by HeLa cells is measured by flow cytometry and visualized by confocal microscopy. Competitive uptake experiments show that the transferrin-targeted particles display enhanced affinity for the transferrin receptor through avidity effects (multiligand binding). Functional efficacy of the delivered pDNA and siRNA is demonstrated through luciferase reporter protein expression and knockdown, respectively. The analysis of the CDP delivery vehicle provides insights that can be applied to the design of targeted nucleic acid delivery vehicles in general

Impact of tumor-specific targeting on the biodistribution and efficacy of siRNA nanoparticles measured by multimodality in vivo imaging

Targeted delivery represents a promising approach for the development of safer and more effective therapeutics for oncology applications. Although macromolecules accumulate nonspecifically in tumors through the enhanced permeability and retention (EPR) effect, previous studies using nanoparticles to deliver chemotherapeutics or siRNA demonstrated that attachment of cell-specific targeting ligands to the surface of nanoparticles leads to enhanced potency relative to nontargeted formulations. Here, we use positron emission tomography (PET) and bioluminescent imaging to quantify the in vivo biodistribution and function of nanoparticles formed with cyclodextrin-containing polycations and siRNA. Conjugation of 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid to the 5' end of the siRNA molecules allows labeling with 64Cu for PET imaging. Bioluminescent imaging of mice bearing luciferase-expressing Neuro2A s.c. tumors before and after PET imaging enables correlation of functional efficacy with biodistribution data. Although both nontargeted and transferrin-targeted siRNA nanoparticles exhibit similar biodistribution and tumor localization by PET, transferrin-targeted siRNA nanoparticles reduce tumor luciferase activity by {approx}50% relative to nontargeted siRNA nanoparticles 1 d after injection. Compartmental modeling is used to show that the primary advantage of targeted nanoparticles is associated with processes involved in cellular uptake in tumor cells rather than overall tumor localization. Optimization of internalization may therefore be key for the development of effective nanoparticle-based targeted therapeutics

Physicochemical and Biological Characterization of Targeted, Nucleic Acid-Containing Nanoparticles

Nucleic acid-based therapeutics have the potential to provide potent and highly specific treatments for a variety of human ailments. However, systemic delivery continues to be a significant hurdle to success. Multifunctional nanoparticles are being investigated as systemic, nonviral delivery systems, and here, we describe the physicochemical and biological characterization of cyclodextrin-containing polycations (CDP) and their nanoparticles formed with nucleic acids including plasmid DNA (pDNA) and small interfering RNA (siRNA). These polycation/nucleic acid complexes can be tuned by formulation conditions to yield particles with sizes ranging from 60 to 150 nm, ζ potentials from 10 to 30 mV, and molecular weights from ∼7 × 10^7 to 1 × 10^9 g mol^(-1) as determined by light scattering techniques. Inclusion complexes formed between adamantane (AD)-containing molecules and the β-cyclodextrin molecules enable the modular attachment of poly(ethylene glycol) (AD−PEG) conjugates for steric stabilization and targeting ligands (AD−PEG−transferrin) for cell-specific targeting. A 70 nm particle can contain ∼10 000 CDP polymer chains, ∼2000 siRNA molecules, ∼4000 AD−PEG_(5000) molecules, and ∼100 AD−PEG_(5000)−Tf molecules; this represents a significant payload of siRNA and a large ratio of siRNA to targeting ligand (20:1). The particles protect the nucleic acid payload from nuclease degradation, do not aggregate at physiological salt concentrations, and cause minimal erythrocyte aggregation and complement fixation at the concentrations typically used for in vivo application. Uptake of the nucleic acid-containing particles by HeLa cells is measured by flow cytometry and visualized by confocal microscopy. Competitive uptake experiments show that the transferrin-targeted particles display enhanced affinity for the transferrin receptor through avidity effects (multiligand binding). Functional efficacy of the delivered pDNA and siRNA is demonstrated through luciferase reporter protein expression and knockdown, respectively. The analysis of the CDP delivery vehicle provides insights that can be applied to the design of targeted nucleic acid delivery vehicles in general

De novo protein design:How do we expand into the universe of possible protein structures?

Protein scientists are paving the way to a new phase in protein design and engineering. Approaches and methods are being developed that could allow the design of proteins beyond the confines of natural protein structures. This possibility of designing entirely new proteins opens new questions: What do we build? How do we build into protein-structure space where there are few, if any, natural structures to guide us? To what uses can the resulting proteins be put? And, what, if anything, does this pursuit tell us about how natural proteins fold, function and evolve? We describe the origins of this emerging area of fully de novo protein design, how it could be developed, where it might lead, and what challenges lie ahead

Computational design of water-soluble α-helical barrels

The design of protein sequences that fold into prescribed de novo structures is challenging. General solutions to this problem require geometric descriptions of protein folds and methods to fit sequences to these. The α-helical coiled coils present a promising class of protein for this and offer considerable scope for exploring hitherto unseen structures. For α-helical barrels, which have more than four helices and accessible central channels, many of the possible structures remain unobserved. Here, we combine geometrical considerations, knowledge-based scoring, and atomistic modeling to facilitate the design of new channel-containing α-helical barrels. X-ray crystal structures of the resulting designs match predicted in silico models. Furthermore, the observed channels are chemically defined and have diameters related to oligomer state, which present routes to design protein function

Applying graph theory to protein structures:An atlas of coiled coils

Motivation: To understand protein structure, folding and function fully and to design proteins de novo reliably, we must learn from natural protein structures that have been characterised experimentally. The number of protein structures available is large and growing exponentially, which makes this task challenging. Indeed, computational resources are becoming increasingly important for classifying and analysing this resource. Here, we use tools from graph theory to define an atlas classification scheme for automatically categorising certain protein substructures. Results: Focusing on the α-helical coiled coils, which are ubiquitous protein-structure and protein-protein interaction motifs, we present a suite of computational resources designed for analysing these assemblies. iSOCKET enables interactive analysis of side-chain packing within proteins to identify coiled coils automatically and with considerable user control. Applying a graph theory-based atlas classification scheme to structures identified by iSOCKET gives the Atlas of Coiled Coils, a fully automated, updated overview of extant coiled coils. The utility of this approach is illustrated with the first formal classification of an emerging subclass of coiled coils called α-helical barrels. Furthermore, in the Atlas, the known coiled-coil universe is presented alongside a partial enumeration of the ‘dark matter’ of coiled-coil structures; i.e., those coiled-coil architectures that are theoretically possible but have not been observed to date, and thus present defined targets for protein design. Availability: iSOCKET is available as part of the open-source GitHub repository associated with this work (https://github.com/woolfson-group/isocket). This repository also contains all the data generated when classifying the protein graphs. The Atlas of Coiled Coils is available at: http://coiledcoils.chm.bris.ac.uk/atlas/app

ISAMBARD:An open-source computational environment for biomolecular analysis, modelling and design

Motivation: The rational design of biomolecules is becoming a reality. However, further computational tools are needed to facilitate and accelerate this, and to make it accessible to more users. Results: Here we introduce ISAMBARD, a tool for structural analysis, model building and rational design of biomolecules. ISAMBARD is open-source, modular, computationally scalable and intuitive to use. These features allow non-experts to explore biomolecular design in silico. ISAMBARD addresses a standing issue in protein design, namely, how to introduce backbone variability in a controlled manner. This is achieved through the generalization of tools for parametric modelling, describing the overall shape of proteins geometrically, and without input from experimentally determined structures. This will allow backbone conformations for entire folds and assemblies not observed in nature to be generated de novo, that is, to access the ‘dark matter of protein-fold space’. We anticipate that ISAMBARD will find broad applications in biomolecular design, biotechnology and synthetic biology. Availability and implementation: A current stable build can be downloaded from the python package index (https://pypi.python.org/pypi/isambard/) with development builds available on GitHub (https://github.com/woolfson-group/) along with documentation, tutorial material and all the scripts used to generate the data described in this paper. Contact:[email protected] or [email protected] Supplementary information:Supplementary data are available at Bioinformatics online

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo