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      A SURF4-to-proteoglycan relay mechanism that mediates the sorting and secretion of a tagged variant of sonic hedgehog

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          Significance

          Sonic Hedgehog (Shh) is a key signaling molecule that plays important roles in embryonic patterning, cell differentiation, and organ development. Although fundamentally important, the molecular mechanisms that regulate secretion of newly synthesized Shh are still unclear. Our study reveals a role for the cargo receptor, SURF4, in facilitating export of Shh from the endoplasmic reticulum (ER) via a ER export signal. In addition, our study provides evidence suggesting that proteoglycans promote the dissociation of SURF4 from Shh at the Golgi, suggesting a SURF4-to-proteoglycan relay mechanism. These analyses provide insight into an important question in cell biology: how do cargo receptors capture their clients in one compartment, then disengage at their destination?

          Abstract

          Sonic Hedgehog (Shh) is a key signaling molecule that plays important roles in various developmental processes in mammals. Although the signal transduction pathway activated by Shh is well understood, the regulation of its secretion remains unclear. Newly synthesized Shh is imported into the endoplasmic reticulum (ER), where it undergoes a series of posttranslational modifications to produce the mature lipid-modified amino-terminal fragment. Here, we have analyzed the molecular mechanisms that mediate secretion of the N-terminal fragment of Shh (ShhN). We found that the Cardin–Weintraub (CW) motif in Shh is necessary and sufficient for ER-to-Golgi transport of ShhN. Mechanistic analyses revealed that a cargo receptor, Surfeit locus protein 4 (SURF4), interacts directly with the CW motif of ShhN to regulate packaging of ShhN into COPII vesicles. ShhN and SURF4 interact with each other at the ER and separate from each other after entering the Golgi. The CW motif is known to interact with proteoglycans (PGs) that are predominantly synthesized at the Golgi. Interestingly, we found that PGs compete with SURF4 to bind ShhN and that inhibiting synthesis of PGs causes defects in export of ShhN from the trans Golgi network (TGN). SURF4 and PG maturation are also important for intracellular traffic of full length Shh in mammalian cells. Our study suggests a SURF4-to-PG relay mechanism that mediates the sorting and secretion of Shh, providing insight into the biosynthetic trafficking of Shh.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

            The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
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              The extracellular matrix: A dynamic niche in cancer progression

              The local microenvironment, or niche, of a cancer cell plays important roles in cancer development. A major component of the niche is the extracellular matrix (ECM), a complex network of macromolecules with distinctive physical, biochemical, and biomechanical properties. Although tightly controlled during embryonic development and organ homeostasis, the ECM is commonly deregulated and becomes disorganized in diseases such as cancer. Abnormal ECM affects cancer progression by directly promoting cellular transformation and metastasis. Importantly, however, ECM anomalies also deregulate behavior of stromal cells, facilitate tumor-associated angiogenesis and inflammation, and thus lead to generation of a tumorigenic microenvironment. Understanding how ECM composition and topography are maintained and how their deregulation influences cancer progression may help develop new therapeutic interventions by targeting the tumor niche.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                10 March 2022
                15 March 2022
                10 March 2022
                : 119
                : 11
                : e2113991119
                Affiliations
                [1] aDivision of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology , Hong Kong, China;
                [2] bDepartment of Chemical and Biological Engineering, The Hong Kong University of Science and Technology , Hong Kong, China;
                [3] cDepartment of Chemistry, The Hong Kong University of Science and Technology , Clear Water Bay, Hong Kong, China;
                [4] dDepartment of Biomedical Sciences, The City University of Hong Kong , Hong Kong, China;
                [5] eCell Biology Division, Medical Research Council Laboratory of Molecular Biology , Cambridge CB2 0QH, United Kingdom;
                [6] fNational Laboratory of Biomacromolecules, Chinese Academy of Sciences Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences , Beijing 100101, China;
                [7] gDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology , Hong Kong, China;
                [8] hShenzhen Research Institute, Hong Kong University of Science and Technology , Shenzhen 518057, China;
                [9] iSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou) , Guangzhou 511458, China
                Author notes
                1To whom correspondence may be addressed. Email: guoyusong@ 123456ust.hk .

                Edited by David Ginsburg, University of Michigan–Ann Arbor, Ann Arbor, MI; received August 3, 2021; accepted January 25, 2022

                Author contributions: X.T. and Y.G. designed research; X.T., R.C., V.S.D.M., T.W., Y.W., X.F., and Y.G. performed research; K.P., H.M., J. Hu, E.A.M., and Y.G. contributed new reagents/analytic tools; X.T., R.C., V.S.D.M., J. Hu, L.Z., J. Huang, S.Y., E.A.M., and Y.G. analyzed data; and X.T., E.A.M., and Y.G. wrote the paper.

                Author information
                https://orcid.org/0000-0003-4665-8046
                https://orcid.org/0000-0002-9985-3569
                https://orcid.org/0000-0001-8390-8521
                https://orcid.org/0000-0003-4712-2243
                https://orcid.org/0000-0001-6865-8528
                https://orcid.org/0000-0001-7059-4092
                https://orcid.org/0000-0002-1033-8369
                https://orcid.org/0000-0002-5539-599X
                Article
                202113991
                10.1073/pnas.2113991119
                8931250
                35271396
                a9de48bd-0af8-4fe6-84a9-83523cc05b53
                Copyright © 2022 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                : 25 January 2022
                Page count
                Pages: 12
                Funding
                Funded by: Hong Kong RESEARCH GRANT COUNCIL
                Award ID: 16102921
                Award ID: 26100315
                Award ID: 16101116
                Award ID: 16102218
                Award ID: 16103319
                Award ID: 16104020
                Award Recipient : Jinqing Huang Award Recipient : Shuhuai Yao Award Recipient : Elizabeth A Miller Award Recipient : Yusong Guo
                Funded by: UK Medical Research Grant Council
                Award ID: MRC_UP_1201/10
                Award Recipient : Jinqing Huang Award Recipient : Shuhuai Yao Award Recipient : Elizabeth A Miller Award Recipient : Yusong Guo
                Funded by: National Natural Science Foundation of China (NSFC) 501100001809
                Award ID: NSFC31871421 and NSFC32070699
                Award Recipient : Yusong Guo
                Categories
                409
                Biological Sciences
                Cell Biology

                copii,cargo receptor,cargo sorting,surf4,er
                copii, cargo receptor, cargo sorting, surf4, er

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