Schlame, M. & Xu, Y. The function of tafazzin, a mitochondrial phospholipid-lysophospholipid acyltransferase. J. Mol. Biol. 432, 5043–5051 (2020).
Schlame, M. & Ren, M. Barth syndrome, a human disorder of cardiolipin metabolism. FEBS Lett. 580, 5450–5455 (2006).
Hornby, B. et al. Natural history comparison study to assess the efficacy of elamipretide in patients with Barth syndrome. Orphanet J. Rare Dis. 17, 336 (2022).
Kim, A. Y., Vernon, H., Manuel, R., Almuqbil, M. & Hornby, B. Quality of life in Barth syndrome. Ther. Adv. Rare Dis. 3, 26330040221093743 (2022).
Sabbah, H. N., Taylor, C. & Vernon, H. J. Temporal evolution of the heart failure phenotype in Barth syndrome and treatment with elamipretide. Future Cardiol. 19, 211–225 (2023).
Taylor, C. et al. Clinical presentation and natural history of Barth syndrome: an overview. J. Inherit. Metab. Dis. 45, 7–16 (2022).
Wang, S. et al. Genetic modifiers modulate phenotypic expression of tafazzin deficiency in a mouse model of Barth syndrome. Hum. Mol. Genet. 32, 2055–2067 (2023).
He, Q. & Han, X. Cardiolipin remodeling in diabetic heart. Chem. Phys. Lipids 179, 75–81 (2014).
Zhu, S. et al. Cardiolipin remodeling defects impair mitochondrial architecture and function in a murine model of Barth syndrome cardiomyopathy. Circ. Heart Fail. https://doi.org/10.1161/CIRCHEARTFAILURE.121.008289 (2021).
Whited, K., Baile, M. G., Currier, P. & Claypool, S. M. Seven functional classes of Barth syndrome mutation. Hum. Mol. Genet. 22, 483–492 (2013).
Anzmann, A. F. et al. Diverse mitochondrial abnormalities in a new cellular model of TAFFAZZIN deficiency are remediated by cardiolipin-interacting small molecules. J. Biol. Chem. 297, 101005 (2021).
Costanzo, M. et al. Global genetic networks and the genotype-to-phenotype relationship. Cell 177, https://doi.org/10.1016/j.cell.2019.01.033 (2019).
Chen, R. et al. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat. Biotechnol. 34, 531–538 (2016).
Pu, W. T. Experimental models of Barth syndrome. J. Inherit. Metab. Dis. 45, 72–81 (2022).
Aregger, M. et al. Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism. Nat. Metab. 2, https://doi.org/10.1038/s42255-020-0211-z (2020).
Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015).
Mair, B. et al. Essential gene profiles for human pluripotent stem cells identify uncharacterized genes and substrate dependencies. Cell Rep. 27, 599–615 (2019).
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).
Bachovchin, D. A. & Cravatt, B. F. The pharmacological landscape and therapeutic potential of serine hydrolases. Nat. Rev. Drug Discov. 11, 52–68 (2012).
Morgenstern, M. et al. Quantitative high-confidence human mitochondrial proteome and its dynamics in cellular context. Cell Metab. 33, 2464–2483 (2021).
Price, T. R. et al. Lipidomic QTL in Diversity Outbred mice identifies a novel function for α/β hydrolase domain 2 (Abhd2) as an enzyme that metabolizes phosphatidylcholine and cardiolipin. PLoS Genet. 19, e1010713 (2023).
Long, J. Z. & Cravatt, B. F. The metabolic serine hydrolases and their functions in mammalian physiology and disease. Chem. Rev. 111, 6022–6063 (2011).
Brandner, K. et al. Taz1, an outer mitochondrial membrane protein, affects stability and assembly of inner membrane protein complexes: implications for Barth syndrome. Mol. Biol. Cell 16, 5202–5214 (2005).
Le, C. H. et al. Tafazzin deficiency impairs CoA-dependent oxidative metabolism in cardiac mitochondria. J. Biol. Chem. 295, 12485–12497 (2020).
Seneviratne, A. K. et al. The mitochondrial transacylase, tafazzin, regulates for AML stemness by modulating intracellular levels of phospholipids. Cell Stem Cell 24, 621–636 (2019).
Beranek, A. et al. Identification of a cardiolipin-specific phospholipase encoded by the gene CLD1 (YGR110W) in yeast. J. Biol. Chem. 284, 11572–11578 (2009).
Huang, Y. et al. Cardiac metabolic pathways affected in the mouse model of Barth syndrome. PLoS ONE 10, e0128561 (2015).
Kutschka, I. et al. Activation of the integrated stress response rewires cardiac metabolism in Barth syndrome. Basic Res. Cardiol. 118, 47 (2023).
Liu, O., Chinni, B. K., Manlhiot, C. & Vernon, H. J. FGF21 and GDF15 are elevated in Barth syndrome and are correlated to important clinical measures. Mol. Genet. Metab. 140, 107676 (2023).
Tung, C. et al. Elamipretide: a review of its structure, mechanism of action, and therapeutic potential. Int. J. Mol. Sci. 26, https://doi.org/10.3390/ijms26030944 (2025).
Bononi, G., Tuccinardi, T., Rizzolio, F. & Granchi, C. α/β-Hydrolase domain (ABHD) inhibitors as new potential therapeutic options against lipid-related diseases. J. Med. Chem. 64, 9759–9785 (2021).
Ben Ali, Y. et al. Use of an inhibitor to identify members of the hormone-sensitive lipase family. Biochemistry 45, 14183–14191 (2006).
Duncan, A. L. Monolysocardiolipin (MLCL) interactions with mitochondrial membrane proteins. Biochem. Soc. Trans. 48, 993–1004 (2020).
Burkhalter, M. D. et al. Imbalanced mitochondrial function provokes heterotaxy via aberrant ciliogenesis. J. Clin. Invest. 129, 2841–2855 (2019).
Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
Lesurf, R. et al. Whole genome sequencing delineates regulatory, copy number, and cryptic splice variants in early onset cardiomyopathy. npj Genom. Med. 7, 18 (2022).
Carney, O. S. et al. Stem cell models of TAFAZZIN deficiency reveal novel tissue-specific pathologies in Barth syndrome. Hum. Mol. Genet. 34, 101–115 (2024).
Ye, C. et al. Deletion of the cardiolipin-specific phospholipase Cld1 rescues growth and life span defects in the tafazzin mutant: implications for Barth syndrome. J. Biol. Chem. 289, 3114–3125 (2014).
Tyurina, Y. Y. et al. Lipidomics characterization of biosynthetic and remodeling pathways of cardiolipins in genetically and nutritionally manipulated yeast cells. ACS Chem. Biol. 12, 265–281 (2017).
Dudek, J. et al. Cardiolipin deficiency affects respiratory chain function and organization in an induced pluripotent stem cell model of Barth syndrome. Stem Cell Res 11, 806–819 (2013).
McKenzie, M., Lazarou, M., Thorburn, D. R. & Ryan, M. T. Mitochondrial respiratory chain supercomplexes are destabilized in Barth syndrome patients. J. Mol. Biol. 361, 462–469 (2006).
Zong, S. et al. Structure of the intact 14-subunit human cytochrome c oxidase. Cell Res 28, 1026–1034 (2018).
Musatov, A. & Robinson, N. C. Bound cardiolipin is essential for cytochrome c oxidase proton translocation. Biochimie 105, https://doi.org/10.1016/j.biochi.2014.07.005 (2014).
Sedlák, E. & Robinson, N. C. Destabilization of the quaternary structure of bovine heart cytochrome c oxidase upon removal of tightly bound cardiolipin. Biochemistry 54, https://doi.org/10.1021/acs.biochem.5b00540 (2015).
Benegiamo, G. et al. COX7A2L genetic variants determine cardiorespiratory fitness in mice and human. Nat. Metab. 4, 1336–1351 (2022).
Pérez-Pérez, R. et al. COX7A2L is a mitochondrial complex III binding protein that stabilizes the III2+IV supercomplex without affecting respirasome formation. Cell Rep. 16, 2387–2398 (2016).
Cogliati, S. et al. Mechanism of super-assembly of respiratory complexes III and IV. Nature 539, 579–582 (2016).
Mair, B., Aregger, M., Tong, A. H. Y., Chan, K. S. K. & Moffat, J. A method to map gene essentiality of human pluripotent stem cells by genome-scale CRISPR screens with inducible Cas9. Methods Mol. Biol. 2377, 1–27 (2022).
Brockmann, M. et al. Genetic wiring maps of single-cell protein states reveal an off-switch for GPCR signalling. Nature 546, 307–311 (2017).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, https://doi.org/10.14806/ej.17.1.200 (2011).
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).
Herzog, K. et al. Lipidomic analysis of fibroblasts from Zellweger spectrum disorder patients identifies disease-specific phospholipid ratios. J. Lipid Res. 57, 1447–1454 (2016).
Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).
Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).
Pinault, M. et al. A 1D high performance thin layer chromatography method validated to quantify phospholipids including cardiolipin and monolysocardiolipin from biological samples. Eur. J. Lipid Sci. Technol. 122, 1900240 (2020).
Plekhanov, A. Y. Rapid staining of lipids on thin-layer chromatograms with amido black 10B and other water-soluble stains. Anal. Biochem. 271, 186–187 (1999).
Jha, P., Wang, X. & Auwerx, J. Analysis of mitochondrial respiratory chain supercomplexes using blue native polyacrylamide gel electrophoresis (BN-PAGE). Curr. Protoc. Mouse Biol. 6, 1–14 (2016).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Aken, B. L. et al. Ensembl 2017. Nucleic Acids Res. 45, D635–D642 (2017).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Fonslow, B. R. et al. Digestion and depletion of abundant proteins improves proteomic coverage. Nat. Methods 10, 54–56 (2013).
Washburn, M. P., Wolters, D. & Yates, J. R. III Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242–247 (2001).
He, L., Diedrich, J., Chu, Y. Y. & Yates, J. R. 3rd Extracting accurate precursor information for tandem mass spectra by RawConverter. Anal. Chem. 87, 11361–11367 (2015).
Xu, T. et al. ProLuCID: an improved SEQUEST-like algorithm with enhanced sensitivity and specificity. J. Proteomics 129, 16–24 (2015).
Tabb, D. L., McDonald, W. H. & Yates, J. R. III DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J. Proteome Res. 1, 21–26 (2002).
Gao, J. et al. CIMAGE2.0: an expanded tool for quantitative analysis of activity-based protein profiling (ABPP) data. J. Proteome Res. 20, 4893–4900 (2021).
Weerapana, E. et al. Quantitative reactivity profiling predicts functional cysteines in proteomes. Nature 468, 790–795 (2010).
Wang, S. et al. AAV gene therapy prevents and reverses heart failure in a murine knockout model of Barth syndrome. Circ. Res. 126, 1024–1039 (2020).
Ren, M. et al. Extramitochondrial cardiolipin suggests a novel function of mitochondria in spermatogenesis. J. Cell Biol. 218, 1491–1502 (2019).
Molenaars, M. et al. Metabolomics and lipidomics in Caenorhabditis elegans using a single-sample preparation. Dis. Model. Mech. 14, https://doi.org/10.1242/dmm.047746 (2021).
Kulik, W. et al. Bloodspot assay using HPLC–tandem mass spectrometry for detection of Barth syndrome. Clin. Chem. 54, 371–378 (2008).
van der Sande, M. et al. Seq2science: an end-to-end workflow for functional genomics analysis. PeerJ 11, e16380 (2023).
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
Frölich, S., van der Sande, M., Schäfers, T. & van Heeringen, S. J. genomepy: genes and genomes at your fingertips. Bioinformatics 39, https://doi.org/10.1093/bioinformatics/btad119 (2023).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).
Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).
Sayols, S., Scherzinger, D. & Klein, H. dupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data. BMC Bioinformatics 17, 428 (2016).
Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).
Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
Gertsenstein, M. & Nutter, L. M. J. Production of knockout mouse lines with Cas9. Methods 191, 32–43 (2021).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. J. Chem. Inf. Model. 61, 3891–3898 (2021).