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Ani, A.

Publications and source records attributed to Ani, A..

2 recordsLinked to original sources

Controlled and orthogonal partitioning of large particles into biomolecular condensates

Biomolecular condensates arising from liquid-liquid phase separation contribute to diverse cellular processes, such as gene expression. Partitioning of client molecules into condensates is critical to regulating the composition and function of condensates. Previous studies suggest that client size limits partitioning, with dextrans >5 nm excluded from condensates. Here, we asked whether larger particles, such as macromolecular complexes, can partition into condensates based on particle-condensate interactions. We sought to discover the biophysical principles that govern particle inclusion in or exclusion from condensates using polymer nanoparticles with tailored surface chemistries as models of macromolecular complexes. Particles coated with polyethylene glycol (PEG) did not partition into condensates. We next leveraged the PEGylated particles as an inert platform to which we conjugated specific adhesive moieties. Particles functionalized with biotin partitioned into condensates containing streptavidin, driven by high-affinity biotin-streptavidin binding. Oligonucleotide-decorated particles exhibited varying degrees of partitioning into condensates, depending on condensate composition. Partitioning of oligonucleotide-coated particles was tuned by altering salt concentration, oligonucleotide length, and oligonucleotide surface density. Remarkably, beads with distinct surface chemistries partitioned orthogonally into immiscible condensates. Based on our experiments, we conclude that arbitrarily large particles can controllably partition into biomolecular condensates given sufficiently strong condensate-particle interactions, a conclusion also supported by our coarse-grained molecular dynamics simulations and theory. These findings may provide insights into how various cellular processes are achieved based on partitioning of large clients into biomolecular condensates, as well as offer design principles for the development of drug delivery systems that selectively target disease-related biomolecular condensates. Significance StatementBiomolecular condensates are subcellular compartments that selectively recruit or exclude client molecules, even though condensates lack an enclosing membrane. Many biochemical reconstitution experiments have investigated mechanisms by which membraneless organelles control partitioning, modeling how cells spatiotemporally recruit components into condensates to regulate cellular functions. One outstanding question is whether partitioning is strictly limited by client size. In this work, we engineered nanoparticles with various sizes and surface functionalities and measured how these variables determine partitioning. We observed controlled and orthogonal partitioning of large particles into several condensate types, driven by strong particle-condensate interactions. Molecular simulations recapitulated key results. Our work advances understanding of how condensate composition is regulated, and our nanoparticle toolbox may also inspire a platform for drug delivery.

biophysics↗

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries

We develop a new method, SBayesRC, that integrates GWAS summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyse 28 traits in the UK Biobank using [~]7 million common SNPs and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and by up to 33% in trans-ancestry prediction, compared to the baseline method SBayesR which does not use annotations, and outperforms state-of-the-art methods LDpred-funct, PolyPred-S and PRS-CSx by 12-15%. Investigation of factors affecting prediction accuracy identified a significant interaction between SNP density and annotation information, encouraging future use of whole-genome sequence variants for prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from non-synonymous SNPs.

genetics↗