Purifying Selection against Pathogenic Mitochondrial DNA in Human T Cells

41 ReferencesRelated ArticlesSummary Many mitochondrial diseases are caused by mutations in mitochondrial DNA (mtDNA). Patients’ cells contain a mixture of mutant and nonmutant mtDNA (a phenomenon called heteroplasmy). The proportion of mutant mtDNA varies across patients and among tissues within a patient. We simultaneously assayed single-cell heteroplasmy and cell state in thousands of blood cells obtained from three unrelated patients who had A3243G-associated mitochondrial encephalomyopathy, lactic acidosis, and strokelike episodes. We observed a broad range of heteroplasmy across all cell types but also found markedly reduced heteroplasmy in T cells, a finding consistent with purifying selection within this lineage. We observed this pattern in six additional patients who had heteroplasmic A3243G without strokelike episodes. (Funded by the Marriott Foundation and others.) IntroductionSome of the most challenging mitochondrial disorders arise from mutations in mitochondrial DNA (mtDNA), a high-copy-number genome that is maternally inherited. These disorders manifest with marked clinical heterogeneity, in part because tissues generally contain a mixture of both nonmutant and mutant mtDNA — a phenomenon called heteroplasmy. Heteroplasmy varies dramatically across family members, tissues, and time and is hypothesized to be shaped by a combination of random drift and selection. The molecular mechanisms governing the dynamics of mtDNA segregation remain unclear, but heritability studies involving humans1 and model systems2 indicate the presence of tissue-specific genetic influences.The mitochondrial A3243G mutation, which disrupts the mitochondrial leucyl-tRNA gene, MT-TL1, is the most common heteroplasmic pathogenic mtDNA mutation3,4 and is classically associated with mitochondrial encephalomyopathy with lactic acidosis and strokelike episodes (MELAS).5,6 The phenotypic spectrum of heteroplasmic A3243G mutations is broad and can be associated with a variety of symptoms, including diabetes, deafness, gastrointestinal dysmotility, epilepsy, and strokelike episodes.7 Severity can range from comparatively mild disease in persons with maternally inherited diabetes and deafness to devastating multisystemic disease in persons with MELAS.1 A3243G heteroplasmy is known to vary widely among siblings and across tissues,1,8-10 thereby complicating clinical management and genetic counseling. At present, no therapies have been approved by the Food and Drug Administration (FDA), although emerging reproductive technologies to prevent transmission of the mutation are now in trials.11Investigating the segregation of pathogenic mtDNA mutations such as A3243G by means of bulk analysis of heteroplasmy in human tissues is challenging, because tissues consist of many cell types, with distinct developmental origins and proportions. Single-cell analysis of heteroplasmy in defined cell types has been limited to at most a few dozen cells, primarily in the germline.12-15Here, we report the application of a single-cell genomics technology,16,17 mtDNA single-cell ATAC (assay for transposase-accessible chromatin) sequencing, to determine mtDNA heteroplasmy and cell type simultaneously in many thousands of peripheral-blood mononuclear cells (PBMCs) that were obtained from three unrelated patients with MELAS. PBMCs consist of multiple cell types originating from a common stem and progenitor pool; we sought to use mtDNA single-cell ATAC sequencing to assess the segregation dynamics of pathogenic mutations in each blood-cell lineage.Case Reports Patient 21 was a 35-year-old man with MELAS that was characterized by strokelike episodes, failure to thrive, and steatohepatitis. In this patient, clinical molecular testing identified the A3243G mutation and heteroplasmy was not quantified (Table S1 in the Supplementary Appendix, available with the full text of this article at NEJM.org). Patient 9 was a 29-year-old man with MELAS that was characterized by sensorineural hearing loss, migraine, epilepsy, ptosis, and strokelike episodes. This patient had A3243G heteroplasmy of 39% in whole blood, as assessed with clinical long-range polymerase chain reaction (PCR) and next-generation sequencing. Patient 30 was a 60-year-old man with MELAS and associated sensorineural hearing loss, ptosis, strokelike episodes, diabetes mellitus, skeletal myopathy with ragged red fibers, and cardiomyopathy. This patient had 77% A3243G heteroplasmy in skeletal muscle, as assessed with clinical long-range PCR and next-generation sequencing. Methods Single-Cell Accessible Chromatin and Mitochondrial Genotyping This study was approved by the institutional review board of Massachusetts General Hospital. We obtained samples of venous blood at clinical baseline and purified PBMCs from the patients. We stained cells for viability and applied antihuman CD45 antibodies before fixation and performed fluorescence-activated cell sorting (FACS) to exclude dead and nonleukocyte cells (CD45−). The mtDNA single-cell ATAC sequencing libraries were generated by a 10× Chromium Controller and a modified Chromium Single Cell ATAC Library and Gel Bead Kit protocol, which was followed by paired-end sequencing with the use of an Illumina NextSeq 500 platform (2× 72-bp reads). Data Analysis We demultiplexed and aligned raw sequencing reads to the hg19 reference genome using CellRanger-ATAC software, version 1.0 (10× Genomics). We identified cells as bar codes that met the following criteria: at least 1000 unique fragments mapping to the nuclear genome, at least 40% of nuclear fragments overlapping a previously established chromatin accessibility peak set in the hematopoietic system,18 and a mean mtDNA coverage of at least 20×. From the output of the CellRanger-ATAC call, we quantified mtDNA using the mgatk software package, version 0.5.0.17 We computationally identified cell types on the basis of chromatin accessibility. In brief, we reprocessed cells from a healthy person19 to define axes of variation using latent semantic indexing and uniform manifold approximation and projection (UMAP). Next, we projected patient-derived cells onto this reduced-dimension space using the latent semantic indexing and UMAP loadings, as previously described.20 We used k nearest neighbors (where k=20) to generate 12 data-driven clusters by means of Louvain community detection, which we mapped onto five major expected cell types in PBMCs (monocytes, dendritic cells, T cells, B cells, and natural killer [NK] cells). The clustering was robust to the choice of k (see the Data Analysis section in the Supplementary Appendix). We classified all cell types in the samples from patients by latent sematic indexing projection and minimum distance to cluster medoids. For visualization, we produced two-dimensional representations of patients’ PBMC data by projecting the 25 latent semantic indexing dimensions onto the pretrained UMAP model, as previously reported.20 For heteroplasmy analyses, we excluded all the cells with less than 20× coverage at position m.3243. Outliers with m.3243 coverage of more than 1.5 times the upper boundary of the interquartile range were also excluded to avoid the inclusion of artifactual sequencing multiplets. We calculated the fraction of total read fragments aligning to the mitochondrial genome in each cell as a proxy for the mtDNA copy number. To compare the distribution of heteroplasmy in T cells with that of all PBMCs within an individual patient, we used the Kolmogorov–Smirnov two-sample test statistic, D, which is defined as the maximum difference between cumulative distributions at any given point; the D-statistic is expected to approach 0 for identical distributions and to be as high as 1 when two distributions are distinct. We evaluated significance analytically or empirically using permutation testing. We used the R base and stats software package, version 3.5.1, and base software, version 3.5.1, to perform these computations. Data analyses and visualization were also conducted with the use of R software. Bulk Heteroplasmy Analysis We stained PBMCs with antibodies against antihuman CD45 and antihuman CD56 and used FACS to purify T-cell and T cell–depleted PBMC populations from which DNA was extracted. Small amplicons centered on m.3243 were generated by means of PCR and sequenced on an Illumina MiSeq platform. We aligned reads using the Burrows–Wheeler alignment tool21 and analyzed them with Samtools.22 We additionally purified T cells using magnetic-bead negative selection kits. DNA from purified T cells and total PBMCs was extracted and used for the generation of m.3243 region PCR amplicons for Sanger sequencing. Full details are provided in the Methods section in the Supplementary Appendix. Results Chromatin Accessibility and Heteroplasmy in Single Cells Using mtDNA single-cell ATAC sequencing, we generated high-quality sequencing libraries to simultaneously evaluate cell type and heteroplasmy in thousands of individual cells per patient. From Patient 21, we sequenced 7176 cells (median, 8146 nuclear fragments per cell) that passed quality control; from Patient 9, we sequenced 6003 cells (median, 6672 nuclear fragments per cell) that passed quality control; and from Patient 30, we sequenced 6007 cells (median, 6507 nuclear fragments per cell) that passed quality control. Figure 1. Figure 1. T-Cell–Specific Reduction in A3243G Heteroplasmy in Peripheral-Blood Mononuclear Cells (PBMCs) from Patients with MELAS. The uniform manifold approximation and projection (UMAP) of mitochondrial DNA (mtDNA) single-cell ATAC sequencing data from Patients 21, 9, and 30 show the distribution of indicated major PBMC cell types (left-most graphs) in a single sample of blood obtained from each of these patients with MELAS (mitochondrial encephalomyopathy, lactic acidosis, and strokelike episodes). Histograms show the A3243G heteroplasmy fraction according to indicated cell types for each of the three patients, with the cell number (N) per population shown (center graphs). Box plots are shown for per-cell mtDNA coverage at m.3243 (second graphs from the right) and for a proxy of mtDNA copy number (i.e., the percentage of per-cell reads aligning to mtDNA) (right-most graphs). Our analyses excluded cells with a coverage at m.3243 of less than 20× or of more than 1.5 times the upper boundary of the interquartile range. In the right-most graphs, the horizontal line in the box indicates the median, the edges of the box the interquartile range, and the bars 1.5 times the interquartile range. DC denotes dendritic cell, and NK natural killer.Using accessible chromatin signatures derived from nuclear genomic reads, we defined cell states using a latent semantic indexing projection of each patient’s data set onto a single-cell reference map of healthy-donor PBMCs that had been generated by a similar single-cell ATAC sequencing protocol.18 The clusters generated by each analysis were remarkably similar and had accessible chromatin profiles that were characteristic of canonical PBMC cell types (Figure 1). The overall distributions of the PBMC types identified by this protocol in our patients were similar to those in previously reported healthy-donor PBMC data sets.23 Furthermore, all the patients had normal representation of blood-cell types on clinical complete blood counts (Table S2). Together, these results indicated no major perturbation in lineage frequencies in these patients. Cell Type–Specific Heteroplasmy We next examined heteroplasmy across PBMC cell types, restricting our analyses to those cells with at least 20× coverage at position m.3243. All the cell types showed a broad spectrum of heteroplasmy, ranging from no A3243G alleles detected to exclusively A3243G mutations detected within each lineage, even in patients with low (G mitochondrial disease: the role of nuclear factors. Ann Clin Transl Neurol 2018;5:333-345.2. Jenuth JP, Peterson AC, Shoubridge EA. Tissue-specific selection for different mtDNA genotypes in heteroplasmic mice. Nat Genet 1997;16:93-95.3. 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Mol Genet Metab 2005;84:289-292.15. Brown DT, Samuels DC, Michael EM, Turnbull DM, Chinnery PF. Random genetic drift determines the level of mutant mtDNA in human primary oocytes. Am J Hum Genet 2001;68:533-536.16. Ludwig LS, Lareau CA, Ulirsch JC, et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 2019;176(6):1325-1339.e22.17. Lareau CA, Ludwig LS, Muus C, et al. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat Biotechnol (in press).18. Ulirsch JC, Lareau CA, Bao EL, et al. Interrogation of human hematopoiesis at single-cell and single-variant resolution. Nat Genet 2019;51:683-693.19. Satpathy AT, Granja JM, Yost KE, et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat Biotechnol 2019;37:925-936.20. Granja JM, Klemm S, McGinnis LM, et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat Biotechnol 2019;37:1458-1465.21. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754-1760.22. Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009;25:2078-2079.23. Ludwig LS, Lareau CA, Bao EL, et al. Transcriptional states and chromatin accessibility underlying human erythropoiesis. Cell Rep 2019;27(11):3228-3240.e7.24. Ron-Harel N, Santos D, Ghergurovich JM, et al. Mitochondrial biogenesis and proteome remodeling promote one-carbon metabolism for T cell activation. Cell Metab 2016;24:104-117.25. Filograna R, Koolmeister C, Upadhyay M, et al. Modulation of mtDNA copy number ameliorates the pathological consequences of a heteroplasmic mtDNA mutation in the mouse. Sci Adv 2019;5(4):eaav9824-eaav9824.26. Haroon S, Li A, Weinert JL, et al. Multiple molecular mechanisms rescue mtDNA disease in C. elegans. Cell Rep 2018;22:3115-3125.27. Fayssoil A, Laforêt P, Bougouin W, et al. Prediction of long-term prognosis by heteroplasmy levels of the m.3243A>G mutation in patients with the mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes syndrome. Eur J Neurol 2017;24:255-261.28. Rahman S, Poulton J, Marchington D, Suomalainen A. Decrease of 3243 A→G mtDNA mutation from blood in MELAS syndrome: a longitudinal study. Am J Hum Genet 2001;68:238-240.29. Pyle A, Taylor RW, Durham SE, et al. Depletion of mitochondrial DNA in leucocytes harbouring the 3243A->G mtDNA mutation. J Med Genet 2007;44:69-74.30. Mehrazin M, Shanske S, Kaufmann P, et al. Longitudinal changes of mtDNA A3243G mutation load and level of functioning in MELAS. Am J Med Genet A 2009;149A:584-587.31. Jokinen R, Marttinen P, Sandell HK, et al. Gimap3 regulates tissue-specific mitochondrial DNA segregation. PLoS Genet 2010;6(10):e1001161-e1001161.32. Lynn S, Borthwick GM, Charnley RM, Walker M, Turnbull DM. Heteroplasmic ratio of the A3243G mitochondrial DNA mutation in single pancreatic beta cells. Diabetologia 2003;46:296-299.33. Nishizawa M, Tanaka K, Shinozawa K, et al. A mitochondrial encephalomyopathy with cardiomyopathy: a case revealing a defect of complex I in the respiratory chain. J Neurol Sci 1987;78:189-201.34. Tanaka M, Nishikimi M, Suzuki H, et al. Deficiency of subunits of complex I or IV in mitochondrial myopathies: immunochemical and immunohistochemical study. J Inherit Metab Dis 1987;10:284-288.35. Cabon L, Bertaux A, Brunelle-Navas MN, et al. AIF loss deregulates hematopoiesis and reveals different adaptive metabolic responses in bone marrow cells and thymocytes. Cell Death Differ 2018;25:983-1001.36. Ramstead AG, Wallace JA, Lee SH, et al. Mitochondrial pyruvate carrier 1 promotes peripheral T cell homeostasis through metabolic regulation of thymic development. Cell Rep 2020;30(9):2889-2899.e6.37. Simula L, Pacella I, Colamatteo A, et al. Drp1 controls effective T cell immune-surveillance by regulating T cell migration, proliferation, and cMyc-dependent metabolic reprogramming. Cell Rep 2018;25(11):3059-3073.e10.38. Tarasenko TN, Pacheco SE, Koenig MK, et al. Cytochrome c oxidase activity is a metabolic checkpoint that regulates cell fate decisions during T cell activation and differentiation. Cell Metab 2017;25(6):1254-1268.e7.39. Loveland B, Wang CR, Yonekawa H, Hermel E, Lindahl KF. Maternally transmitted histocompatibility antigen of mice: a hydrophobic peptide of a mitochondrially encoded protein. Cell 1990;60:971-980.40. Desdín-Micó G, Soto-Heredero G, Aranda JF, et al. T cells with dysfunctional mitochondria induce multimorbidity and premature senescence. Science 2020;368:1371-1376.41. Regev A, Teichmann SA, Lander ES, et al. The human cell atlas. Elife 2017;6:e27041-e27041.Table 1. Validation of Reduced A3243G Heteroplasmy in T Cells by Bulk Sequencing.*Patient No.AgeSexBulk Heteroplasmy MeasurementsTotalPBMCsT-Cell–Depleted PBMCsT Cellsyrpercent929Male28.89.93060Male9.69.50.73147Female4.91.03365Female6.25.72.73653Female16.35.93719Female42.124.83833Male46.132.24035Male7.93.2
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