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      Transmembrane proteins with unknown function (TMEMs) as ion channels: electrophysiological properties, structure, and pathophysiological roles

      review-article
      ,
      Experimental & Molecular Medicine
      Nature Publishing Group UK
      Physiology, Diseases

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          Abstract

          A transmembrane (TMEM) protein with an unknown function is a type of membrane-spanning protein expressed in the plasma membrane or the membranes of intracellular organelles. Recently, several TMEM proteins have been identified as functional ion channels. The structures and functions of these proteins have been extensively studied over the last two decades, starting with TMEM16A (ANO1). In this review, we provide a summary of the electrophysiological properties of known TMEM proteins that function as ion channels, such as TMEM175 (K EL), TMEM206 (PAC), TMEM38 (TRIC), TMEM87A (GolpHCat), TMEM120A (TACAN), TMEM63 (OSCA), TMEM150C (Tentonin3), and TMEM43 (Gapjinc). Additionally, we examine the unique structural features of these channels compared to those of other well-known ion channels. Furthermore, we discuss the diverse physiological roles of these proteins in lysosomal/endosomal/Golgi pH regulation, intracellular Ca 2+ regulation, spatial memory, cell migration, adipocyte differentiation, and mechanical pain, as well as their pathophysiological roles in Parkinson’s disease, cancer, osteogenesis imperfecta, infantile hypomyelination, cardiomyopathy, and auditory neuropathy spectrum disorder. This review highlights the potential for the discovery of novel ion channels within the TMEM protein family and the development of new therapeutic targets for related channelopathies.

          TMEM channels: potential therapeutic targets for various disorders

          Transmembrane proteins (proteins that stretch across the cell membrane) play a role in many cell functions. Yet, we don’t fully understand the structure and roles of many of these proteins. Recent research has found that some transmembrane proteins, like TMEM16A, TMEM87A, TMEM150C, TMEM63, TMEM43, and TMEM175, act as ion channels (pathways for charged particles) crucial for various bodily functions. These proteins participate in normal and abnormal bodily processes, and when they don’t work properly, they can cause diseases like Parkinson’s disease, cancer, brittle bone disease, and hearing loss. By understanding these proteins better, we could develop new treatments for these diseases. Future studies should aim to understand exactly how these proteins work and their potential as targets for treatment. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

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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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            An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex.

            The major cell classes of the brain differ in their developmental processes, metabolism, signaling, and function. To better understand the functions and interactions of the cell types that comprise these classes, we acutely purified representative populations of neurons, astrocytes, oligodendrocyte precursor cells, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia, endothelial cells, and pericytes from mouse cerebral cortex. We generated a transcriptome database for these eight cell types by RNA sequencing and used a sensitive algorithm to detect alternative splicing events in each cell type. Bioinformatic analyses identified thousands of new cell type-enriched genes and splicing isoforms that will provide novel markers for cell identification, tools for genetic manipulation, and insights into the biology of the brain. For example, our data provide clues as to how neurons and astrocytes differ in their ability to dynamically regulate glycolytic flux and lactate generation attributable to unique splicing of PKM2, the gene encoding the glycolytic enzyme pyruvate kinase. This dataset will provide a powerful new resource for understanding the development and function of the brain. To ensure the widespread distribution of these datasets, we have created a user-friendly website (http://web.stanford.edu/group/barres_lab/brain_rnaseq.html) that provides a platform for analyzing and comparing transciption and alternative splicing profiles for various cell classes in the brain. Copyright © 2014 the authors 0270-6474/14/3411929-19$15.00/0.
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              Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse.

              The functional and molecular similarities and distinctions between human and murine astrocytes are poorly understood. Here, we report the development of an immunopanning method to acutely purify astrocytes from fetal, juvenile, and adult human brains and to maintain these cells in serum-free cultures. We found that human astrocytes have abilities similar to those of murine astrocytes in promoting neuronal survival, inducing functional synapse formation, and engulfing synaptosomes. In contrast to existing observations in mice, we found that mature human astrocytes respond robustly to glutamate. Next, we performed RNA sequencing of healthy human astrocytes along with astrocytes from epileptic and tumor foci and compared these to human neurons, oligodendrocytes, microglia, and endothelial cells (available at http://www.brainrnaseq.org). With these profiles, we identified novel human-specific astrocyte genes and discovered a transcriptome-wide transformation between astrocyte precursor cells and mature post-mitotic astrocytes. These data represent some of the first cell-type-specific molecular profiles of the healthy and diseased human brain.
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                Author and article information

                Contributors
                cjl@ibs.re.kr
                Journal
                Exp Mol Med
                Exp Mol Med
                Experimental & Molecular Medicine
                Nature Publishing Group UK (London )
                1226-3613
                2092-6413
                1 April 2024
                1 April 2024
                April 2024
                : 56
                : 4
                : 850-860
                Affiliations
                Center for Cognition and Sociality, Life Science Cluster, Institute for Basic Science (IBS), ( https://ror.org/00y0zf565) 55 Expo-ro, Yuseong-gu, Daejeon, 34126 Republic of Korea
                Author information
                http://orcid.org/0000-0002-3555-0980
                Article
                1206
                10.1038/s12276-024-01206-1
                11059273
                38556553
                d8e45cf8-f924-4b89-a792-0e2f27c69d1c
                © 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 July 2023
                : 27 December 2023
                : 19 January 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100010446, Institute for Basic Science (IBS);
                Award ID: IBS-R001-D2
                Award Recipient :
                Categories
                Review Article
                Custom metadata
                © Korean Society for Biochemical and Molecular Biology 2024

                Molecular medicine
                physiology,diseases
                Molecular medicine
                physiology, diseases

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