DNA-condensation, redissolution and mesocrystals induced by tetravalent counterions

The distance-resolved effective interaction potential between two parallel DNA molecules is calculated by computer simulations with explicit tetravalent counterions and monovalent salt. Adding counterions first yields an attractive minimum in the potential at short distances which then disappears in favor of a shallower minimum at larger separations. The resulting phase diagram includes a DNA-condensation and redissolution transition and a stable mesocrystal with an intermediate lattice constant for high counterion concentration.Comment: 4 pages, 4 figure

DNA condensation and redissolution: Interaction between overcharged DNA molecules

The effective DNA-DNA interaction force is calculated by computer simulations with explicit tetravalent counterions and monovalent salt. For overcharged DNA molecules, the interaction force shows a double-minimum structure. The positions and depths of these minima are regulated by the counterion density in the bulk. Using two-dimensional lattice sum and free energy perturbation theories, the coexisting phases for DNA bundles are calculated. A DNA-condensation and redissolution transition and a stable mesocrystal with an intermediate lattice constant for high counterion concentration are obtained.Comment: 26 pages, 10 figure

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Structure-based programming of lymph-node targeting in molecular vaccines

In cancer patients, visual identification of sentinel lymph nodes (LNs) is achieved by the injection of dyes that bind avidly to endogenous albumin, targeting these compounds to LNs, where they are efficiently filtered by resident phagocytes1, 2. Here we translate this ‘albumin hitchhiking’ approach to molecular vaccines, through the synthesis of amphiphiles (amph-vaccines) comprising an antigen or adjuvant cargo linked to a lipophilic albumin-binding tail by a solubility-promoting polar polymer chain. Administration of structurally optimized CpG-DNA/peptide amph-vaccines in mice resulted in marked increases in LN accumulation and decreased systemic dissemination relative to their parent compounds, leading to 30-fold increases in T-cell priming and enhanced anti-tumour efficacy while greatly reducing systemic toxicity. Amph-vaccines provide a simple, broadly applicable strategy to simultaneously increase the potency and safety of subunit vaccines.David H. Koch Institute for Integrative Cancer Research at MIT (Koch Institute Support (core) Grant P30-CA14051)National Cancer Institute (U.S.)National Institutes of Health (U.S.) (grant AI091693)National Institutes of Health (U.S.) (grant AI104715)National Institutes of Health (U.S.) (AI095109)United States. Dept. of Defense (contract W911NF-13-D-0001)United States. Dept. of Defense (contract W911NF-07-D-0004)Ragon Institute of MGH, MIT, and Harvar

Layer-by-Layer Assembled Antisense DNA Microsponge Particles for Efficient Delivery of Cancer Therapeutics

Antisense oligonucleotides can be employed as a potential approach to effectively treat cancer. However, the inherent instability and inefficient systemic delivery methods for antisense therapeutics remain major challenges to their clinical application. Here, we present a polymerized oligonucleotides (ODNs) that self-assemble during their formation through an enzymatic elongation method (rolling circle replication) to generate a composite nucleic acid/magnesium pyrophosphate sponge-like microstructure, or DNA microsponge, yielding high molecular weight nucleic acid product. In addition, this densely packed ODN microsponge structure can be further condensed to generate polyelectrolyte complexes with a favorable size for cellular uptake by displacing magnesium pyrophosphate crystals from the microsponge structure. Additional layers are applied to generate a blood-stable and multifunctional nanoparticle via the layer-by-layer (LbL) assembly technique. By taking advantage of DNA nanotechnology and LbL assembly, functionalized DNA nanostructures were utilized to provide extremely high numbers of repeated ODN copies for efficient antisense therapy. Moreover, we show that this formulation significantly improves nucleic acid drug/carrier stability during in vivo biodistribution. These polymeric ODN systems can be designed to serve as a potent means of delivering stable and large quantities of ODN therapeutics systemically for cancer treatment to tumor cells at significantly lower toxicity than traditional synthetic vectors, thus enabling a therapeutic window suitable for clinical translation.United States. Dept. of Defense. Ovarian Cancer Research Program (Teal Innovator Award Grant OC120504)Natural Sciences and Engineering Research Council of Canada (Postdoctoral Fellowship)National Institutes of Health (U.S.) (Ruth L. Kirschstein National Research Service Award 1F32EB017614-01)National Science Foundation (U.S.). Graduate Research Fellowshi

PEG−peptide conjugates

The remarkable diversity of the self-assembly behavior of PEG−peptides is reviewed, including self-assemblies formed by PEG−peptides with β-sheet and α-helical (coiled-coil) peptide sequences. The modes of self-assembly in solution and in the solid state are discussed. Additionally, applications in bionanotechnology and synthetic materials science are summarized

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P &lt; 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.</p

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)