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Hughes LA, Rackham O, Filipovska A. Illuminating mitochondrial translation through mouse models. Hum Mol Genet 2024; 33:R61-R79. [PMID: 38779771 PMCID: PMC11112386 DOI: 10.1093/hmg/ddae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/22/2024] [Accepted: 01/31/2024] [Indexed: 05/25/2024] Open
Abstract
Mitochondria are hubs of metabolic activity with a major role in ATP conversion by oxidative phosphorylation (OXPHOS). The mammalian mitochondrial genome encodes 11 mRNAs encoding 13 OXPHOS proteins along with 2 rRNAs and 22 tRNAs, that facilitate their translation on mitoribosomes. Maintaining the internal production of core OXPHOS subunits requires modulation of the mitochondrial capacity to match the cellular requirements and correct insertion of particularly hydrophobic proteins into the inner mitochondrial membrane. The mitochondrial translation system is essential for energy production and defects result in severe, phenotypically diverse diseases, including mitochondrial diseases that typically affect postmitotic tissues with high metabolic demands. Understanding the complex mechanisms that underlie the pathologies of diseases involving impaired mitochondrial translation is key to tailoring specific treatments and effectively targeting the affected organs. Disease mutations have provided a fundamental, yet limited, understanding of mitochondrial protein synthesis, since effective modification of the mitochondrial genome has proven challenging. However, advances in next generation sequencing, cryoelectron microscopy, and multi-omic technologies have revealed unexpected and unusual features of the mitochondrial protein synthesis machinery in the last decade. Genome editing tools have generated unique models that have accelerated our mechanistic understanding of mitochondrial translation and its physiological importance. Here we review the most recent mouse models of disease pathogenesis caused by defects in mitochondrial protein synthesis and discuss their value for preclinical research and therapeutic development.
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Affiliation(s)
- Laetitia A Hughes
- Telethon Kids Institute, Northern Entrance, Perth Children’s Hospital, 15 Hospital Avenue, Nedlands, WA 6009, Australia
- Harry Perkins Institute of Medical Research, 6 Verdun Street, Nedlands, WA 6009, Australia
- ARC Centre of Excellence in Synthetic Biology, 35 Stirling Highway, Crawley, WA 6009, The University of Western Australia, Crawley, WA 6009, Australia
| | - Oliver Rackham
- Telethon Kids Institute, Northern Entrance, Perth Children’s Hospital, 15 Hospital Avenue, Nedlands, WA 6009, Australia
- Harry Perkins Institute of Medical Research, 6 Verdun Street, Nedlands, WA 6009, Australia
- ARC Centre of Excellence in Synthetic Biology, 35 Stirling Highway, Crawley, WA 6009, The University of Western Australia, Crawley, WA 6009, Australia
- Curtin Medical School, Curtin University, Kent Street, Bentley, WA 6102, Australia
- Curtin Health Innovation Research Institute, Curtin University, Kent Street, Bentley, WA 6102, Australia
| | - Aleksandra Filipovska
- Telethon Kids Institute, Northern Entrance, Perth Children’s Hospital, 15 Hospital Avenue, Nedlands, WA 6009, Australia
- ARC Centre of Excellence in Synthetic Biology, 35 Stirling Highway, Crawley, WA 6009, The University of Western Australia, Crawley, WA 6009, Australia
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, 19 Innovation Walk, Clayton, Clayton, VIC 3168, Australia
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2
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Vučković A, Freyer C, Wredenberg A, Hillen HS. The molecular machinery for maturation of primary mtDNA transcripts. Hum Mol Genet 2024; 33:R19-R25. [PMID: 38779769 PMCID: PMC11112384 DOI: 10.1093/hmg/ddae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 01/31/2024] [Accepted: 02/08/2024] [Indexed: 05/25/2024] Open
Abstract
Human mitochondria harbour a circular, polyploid genome (mtDNA) encoding 11 messenger RNAs (mRNAs), two ribosomal RNAs (rRNAs) and 22 transfer RNAs (tRNAs). Mitochondrial transcription produces long, polycistronic transcripts that span almost the entire length of the genome, and hence contain all three types of RNAs. The primary transcripts then undergo a number of processing and maturation steps, which constitute key regulatory points of mitochondrial gene expression. The first step of mitochondrial RNA processing consists of the separation of primary transcripts into individual, functional RNA molecules and can occur by two distinct pathways. Both are carried out by dedicated molecular machineries that substantially differ from RNA processing enzymes found elsewhere. As a result, the underlying molecular mechanisms remain poorly understood. Over the last years, genetic, biochemical and structural studies have identified key players involved in both RNA processing pathways and provided the first insights into the underlying mechanisms. Here, we review our current understanding of RNA processing in mammalian mitochondria and provide an outlook on open questions in the field.
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MESH Headings
- Humans
- DNA, Mitochondrial/genetics
- RNA Processing, Post-Transcriptional
- Mitochondria/genetics
- Mitochondria/metabolism
- RNA, Mitochondrial/genetics
- RNA, Mitochondrial/metabolism
- RNA, Messenger/genetics
- RNA, Messenger/metabolism
- Animals
- Transcription, Genetic
- RNA, Ribosomal/genetics
- RNA, Ribosomal/metabolism
- RNA, Transfer/genetics
- RNA, Transfer/metabolism
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Affiliation(s)
- Ana Vučković
- Department of Cellular Biochemistry, University Medical Center Göttingen, Humboldtallee 23, 37073 Göttingen, Germany
- Research Group Structure and Function of Molecular Machines, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Christoph Freyer
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solnavägen 9, 171 65 Solna, Sweden
- Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Anna Steckséns gata 47, 171 64 Solna, Sweden
| | - Anna Wredenberg
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solnavägen 9, 171 65 Solna, Sweden
- Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Anna Steckséns gata 47, 171 64 Solna, Sweden
| | - Hauke S Hillen
- Department of Cellular Biochemistry, University Medical Center Göttingen, Humboldtallee 23, 37073 Göttingen, Germany
- Research Group Structure and Function of Molecular Machines, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
- Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Robert-Koch-Straße 40, 37073 Göttingen, Germany
- Research Group Structure and Function of Molecular Machines, Goettingen Center for Molecular Biosciences (GZMB), University of Goettingen, Justus-von-Liebig-Weg 11, Goettingen 37077, Germany
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3
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Zhang E, Sun Q, Zhang C, Ma H, Zhang J, Ding Y, Wang G, Jin C, Jin C, Fu Y, Yan C, Zhu M, Wang C, Dai J, Jin G, Hu Z, Shen H, Ma H. Comprehensive functional interrogation of susceptibility loci in GWASs identified KIAA0391 as a novel oncogenic driver via regulating pyroptosis in NSCLC. Cancer Lett 2024; 585:216646. [PMID: 38262497 DOI: 10.1016/j.canlet.2024.216646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/23/2023] [Accepted: 01/05/2024] [Indexed: 01/25/2024]
Abstract
Approximately 51 non-small-cell lung cancer (NSCLC) risk loci have been identified by genome-wide association studies (GWASs). We conducted a high throughput RNA-interference (RNAi) screening to identify the candidate causal genes in NSCLC risk loci. KIAA0391 at 14q13.1 had the highest score and could promote proliferation and metastasis of NSCLC in vitro and in vivo. We next prioritized rs3783313 as a causal variant at 14q13.1, by integrating a large-scale population study consisting of 27,120 lung cancer cases and 27,355 controls, functional annotation, and expression quantitative trait locus (eQTL) analysis. Then we found that rs3783313 could facilitate a promoter-enhancer interaction to upregulate KIAA0391 expression by affecting the affinity of transcription factor NFYA. Mechanistically, KIAA0391 knockdown dramatically influenced pyroptosis-related pathways and increased the expression of CASP1. And KIAA0391 transcriptionally repressed CASP1 by binding to SMAD2 and induced an anti-pyroptosis phenotype, promoting tumorigenesis of NSCLC, which provides new insights and potential target for NSCLC.
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Affiliation(s)
- Erbao Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Qi Sun
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chang Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou Second People's Hospital, Changzhou Medical Center, Nanjing Medical University, Nanjing 211166, China
| | - Huimin Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Jing Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Yue Ding
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Guoqing Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chen Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chenying Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Yating Fu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Caiwang Yan
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Cheng Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100142, China.
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100142, China.
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Novo LC, Poindexter MB, Rezende FM, Santos JEP, Nelson CD, Hernandez LL, Kirkpatrick BW, Peñagaricano F. Identification of genetic variants and individual genes associated with postpartum hypocalcemia in Holstein cows. Sci Rep 2023; 13:21900. [PMID: 38082150 PMCID: PMC10713536 DOI: 10.1038/s41598-023-49496-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023] Open
Abstract
Periparturient hypocalcemia is a complex metabolic disorder that occurs at the onset of lactation because of a sudden irreversible loss of Ca incorporated into colostrum and milk. Some cows are unable to quickly adapt to this demand and succumb to clinical hypocalcemia, commonly known as milk fever, whereas a larger proportion of cows develop subclinical hypocalcemia. The main goal of this study was to identify causative mutations and candidate genes affecting postpartum blood calcium concentration in Holstein cows. Data consisted of blood calcium concentration measured in 2513 Holstein cows on the first three days after parturition. All cows had genotypic information for 79 k SNP markers. Two consecutive rounds of imputation were performed: first, the 2513 Holstein cows were imputed from 79 k to 312 k SNP markers. This imputation was performed using a reference set of 17,131 proven Holstein bulls with 312 k SNP markers. Then, the 2513 Holstein cows were imputed from 312 k markers to whole-genome sequence data. This second round of imputation used 179 Holstein animals from the 1000 Bulls Genome Project as a reference set. Three alternative phenotypes were evaluated: (1) total calcium concentration in the first 24 h postpartum, (2) total calcium concentration in the first 72 h postpartum calculated as the area under the curve; and (3) the recovery of total calcium concentration calculated as the difference in total calcium concentration between 72 and 24 h. The identification of genetic variants associated with these traits was performed using a two-step mixed model-based approach implemented in the R package MixABEL. The most significant variants were located within or near genes involved in calcium homeostasis and vitamin D transport (GC), calcium and potassium channels (JPH3 and KCNK13), energy and lipid metabolism (CA5A, PRORP, and SREBP1), and immune response (IL12RB2 and CXCL8), among other functions. This work provides the foundation for the development of novel breeding and management tools for reducing the incidence of periparturient hypocalcemia in dairy cattle.
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Affiliation(s)
- Larissa C Novo
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, 53706, USA
| | - Michael B Poindexter
- Department of Animal Sciences, University of Florida, Gainesville, FL, 32611, USA
| | - Fernanda M Rezende
- Department of Animal Sciences, University of Florida, Gainesville, FL, 32611, USA
| | - José E P Santos
- Department of Animal Sciences, University of Florida, Gainesville, FL, 32611, USA
| | - Corwin D Nelson
- Department of Animal Sciences, University of Florida, Gainesville, FL, 32611, USA
| | - Laura L Hernandez
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, 53706, USA
| | - Brian W Kirkpatrick
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, 53706, USA
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, 53706, USA.
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Maity D, Guha Ray P, Buchmann P, Mansouri M, Fussenegger M. Blood-Glucose-Powered Metabolic Fuel Cell for Self-Sufficient Bioelectronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2300890. [PMID: 36893359 DOI: 10.1002/adma.202300890] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/28/2023] [Indexed: 05/26/2023]
Abstract
Currently available bioelectronic devices consume too much power to be continuously operated on rechargeable batteries, and are often powered wirelessly, with attendant issues regarding reliability, convenience, and mobility. Thus, the availability of a robust, self-sufficient, implantable electrical power generator that works under physiological conditions would be transformative for many applications, from driving bioelectronic implants and prostheses to programing cellular behavior and patients' metabolism. Here, capitalizing on a new copper-containing, conductively tuned 3D carbon nanotube composite, an implantable blood-glucose-powered metabolic fuel cell is designed that continuously monitors blood-glucose levels, converts excess glucose into electrical power during hyperglycemia, and produces sufficient energy (0.7 mW cm-2 , 0.9 V, 50 mm glucose) to drive opto- and electro-genetic regulation of vesicular insulin release from engineered beta cells. It is shown that this integration of blood-glucose monitoring with elimination of excessive blood glucose by combined electro-metabolic conversion and insulin-release-mediated cellular consumption enables the metabolic fuel cell to restore blood-glucose homeostasis in an automatic, self-sufficient, and closed-loop manner in an experimental model of type-1 diabetes.
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Affiliation(s)
- Debasis Maity
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, CH-4058, Switzerland
| | - Preetam Guha Ray
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, CH-4058, Switzerland
| | - Peter Buchmann
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, CH-4058, Switzerland
| | - Maysam Mansouri
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, CH-4058, Switzerland
| | - Martin Fussenegger
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, CH-4058, Switzerland
- Faculty of Science, University of Basel, Mattenstrasse 26, Basel, CH-4058, Switzerland
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