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      Human cell surface-AAV interactomes identify LRP6 as blood-brain barrier transcytosis receptor and immune cytokine IL3 as AAV9 binder

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          Abstract

          Adeno-associated viruses (AAVs) are foundational gene delivery tools for basic science and clinical therapeutics. However, lack of mechanistic insight, especially for engineered vectors created by directed evolution, can hamper their application. Here, we adapt an unbiased human cell microarray platform to determine the extracellular and cell surface interactomes of natural and engineered AAVs. We identify a naturally-evolved and serotype-specific interaction between the AAV9 capsid and human interleukin 3 (IL3), with possible roles in host immune modulation, as well as lab-evolved low-density lipoprotein receptor-related protein 6 (LRP6) interactions specific to engineered capsids with enhanced blood-brain barrier crossing in non-human primates after intravenous administration. The unbiased cell microarray screening approach also allows us to identify off-target tissue binding interactions of engineered brain-enriched AAV capsids that may inform vectors’ peripheral organ tropism and side effects. Our cryo-electron tomography and AlphaFold modeling of capsid-interactor complexes reveal LRP6 and IL3 binding sites. These results allow confident application of engineered AAVs in diverse organisms and unlock future target-informed engineering of improved viral and non-viral vectors for non-invasive therapeutic delivery to the brain.

          Abstract

          Engineered adeno-associated viruses (AAVs) aim to improve safety and potency for use in gene therapy but mechanisms underlying these features are poorly understood. Here, authors use unbiased screens to identify an interaction with the human immune system and a determinant of enhanced brain potency.

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          Proteomics. Tissue-based map of the human proteome.

          Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
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            ColabFold: making protein folding accessible to all

            ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.
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              The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

              The PRoteomics IDEntifications (PRIDE) database ( https://www.ebi.ac.uk/pride/ ) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.
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                Author and article information

                Contributors
                tshay@caltech.edu
                viviana@caltech.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                8 September 2024
                8 September 2024
                2024
                : 15
                : 7853
                Affiliations
                [1 ]Division of Biology & Biological Engineering, California Institute of Technology, ( https://ror.org/05dxps055) Pasadena, CA 91125 USA
                [2 ]Charles River Laboratories, ( https://ror.org/00q2mch05) High Peak Business Park, Buxton Road, Chinley, SK23 6FJ UK
                Author information
                http://orcid.org/0000-0001-6591-3271
                http://orcid.org/0000-0002-9822-6790
                http://orcid.org/0000-0001-6411-999X
                http://orcid.org/0000-0003-3201-9868
                http://orcid.org/0000-0003-2410-2186
                http://orcid.org/0000-0001-5868-348X
                Article
                52149
                10.1038/s41467-024-52149-0
                11381518
                39245720
                0511dbaa-bc5c-4aed-ae74-859533e84d5a
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 June 2024
                : 27 August 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000025, U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH);
                Award ID: UF1MH128336
                Award ID: UF1MH128336
                Award Recipient :
                Funded by: Beckman Institute CLOVER Center
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: DP1NS111369
                Award Recipient :
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                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                blood-brain barrier,genetic vectors,high-throughput screening,cryoelectron tomography

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