Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Novel coenzyme Q6 genetic variant increases susceptibility to pneumococcal disease

Abstract

Acute lower respiratory tract infection (ALRI) remains a major worldwide cause of childhood mortality, compelling innovation in prevention and treatment. Children in Papua New Guinea (PNG) experience profound morbidity from ALRI caused by Streptococcus pneumoniae. As a result of evolutionary divergence, the human PNG population exhibits profound genetic variation and diversity. To address unmet health needs of children in PNG, we tested whether genetic variants increased ALRI morbidity. Whole-exome sequencing of a pilot child cohort identified homozygosity for a novel single-nucleotide variant (SNV) in coenzyme Q6 (COQ6) in cases with ALRI. COQ6 encodes a mitochondrial enzyme essential for biosynthesis of ubiquinone, an electron acceptor in the electron transport chain. A significant association of SNV homozygosity with ALRI was replicated in an independent ALRI cohort (P = 0.036). Mice homozygous for homologous mouse variant Coq6 exhibited increased mortality after pneumococcal lung infection, confirming causality. Bone marrow chimeric mice further revealed that expression of variant Coq6 in recipient (that is, nonhematopoietic) tissues conferred increased mortality. Variant Coq6 maintained ubiquinone biosynthesis, while accelerating metabolic remodeling after pneumococcal challenge. Identification of this COQ6 variant provides a genetic basis for increased pneumonia susceptibility in PNG and establishes a previously unrecognized role for the enzyme COQ6 in regulating inflammatory-mediated metabolic remodeling.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Discovery and replication of a novel SNV associated with ALRI in PNG.
Fig. 2: Mice homozygous for Coq6DY exhibit increased mortality after pneumococcal challenge.
Fig. 3: Chimeric mice that reveal recipient genotype confers increased susceptibility to Sp infection.
Fig. 4: No impairment of acute inflammatory response in WT.1 → DY chimeric mice.
Fig. 5: COQ6DY does not impair ubiquinone production.
Fig. 6: Expression of Coq6DY-accelerated OxPhos downregulation after in vivo Sp infection or in vitro exposure to PLY.
Fig. 7: Increased susceptibility to Pa infection in WT.1 → DY chimeric mice.

Similar content being viewed by others

Data availability

The COQ6DY variant has been deposited to ClinVar, accession no RCV004698891.1, release date 9 September 2024. Publicly available databases used in the present study include GnomAD (formerly known as ExAC; https://gnomad.broadinstitute.org), AlphaFold (https://alphafold.ebi.ac.uk) and UniProt (https://www.uniprot.org). The UniProt unique identifier for human COQ6 is Q9Y2Z9 and for mouse COQ6 Q8R1S0. There are no restrictions on availability of data shown here. Source data are provided with this paper.

Code availability

No customized code was used in this manuscript. Software programs are either commercially or publicly available.

References

  1. Caballero, M. T. et al. Uncovering causes of childhood death using the minimally invasive autopsy at the community level in an urban vulnerable setting of Argentina: a population-based study. Clin. Infect. Dis. 73, S435–S441 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Chidlow, G. R. et al. Respiratory viral pathogens associated with lower respiratory tract disease among young children in the highlands of Papua New Guinea. J. Clin. Virol. 54, 235–239 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Pomat, W. S. et al. Safety and immunogenicity of neonatal pneumococcal conjugate vaccination in Papua New Guinean children: a randomised controlled trial. PLoS ONE 8, e56698 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Bergstrom, A. et al. A Neolithic expansion, but strong genetic structure, in the independent history of New Guinea. Science 357, 1160–1163 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Brucato, N. et al. Papua New Guinean genomes reveal the complex settlement of North Sahul. Mol. Biol. Evol. 38, 5107–5121 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Guerra, R. M. & Pagliarini, D. J. Coenzyme Q biochemistry and biosynthesis. Trends Biochem. Sci. 48, 463–476 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Emmanuele, V. et al. Heterogeneity of coenzyme Q10 deficiency: patient study and literature review. Arch. Neurol. 69, 978–983 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Chen, S. et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024).

    Article  PubMed  CAS  Google Scholar 

  9. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Licitra, F. & Puccio, H. An overview of current mouse models recapitulating coenzyme q10 deficiency syndrome. Mol. Syndromol. 5, 180–186 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Ben-Meir, A. et al. Coenzyme Q10 restores oocyte mitochondrial function and fertility during reproductive aging. Aging Cell 14, 887–895 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Wang, Y., Oxer, D. & Hekimi, S. Mitochondrial function and lifespan of mice with controlled ubiquinone biosynthesis. Nat. Commun. 6, 6393 (2015).

    Article  PubMed  CAS  Google Scholar 

  13. Fazakerley, D. J. et al. Mitochondrial CoQ deficiency is a common driver of mitochondrial oxidants and insulin resistance. eLife 7, e32111 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bewley, M. A. et al. Impaired mitochondrial microbicidal responses in chronic obstructive pulmonary disease macrophages. Am. J. Respir. Crit. Care Med. 196, 845–855 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Heeringa, S. F. et al. COQ6 mutations in human patients produce nephrotic syndrome with sensorineural deafness. J. Clin. Invest. 121, 2013–2024 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Doimo, M. et al. Effect of vanillic acid on COQ6 mutants identified in patients with coenzyme Q10 deficiency. Biochim. Biophys. Acta 1842, 1–6 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Park, E. et al. COQ6 mutations in children with steroid-resistant focal segmental glomerulosclerosis and sensorineural hearing loss. Am. J. Kidney Dis. 70, 139–144 (2017).

    Article  PubMed  CAS  Google Scholar 

  18. Liu, J., Zhou, G., Wang, X. & Liu, D. Metabolic reprogramming consequences of sepsis: adaptations and contradictions. Cell. Mol. Life Sci. 79, 456 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Vandewalle, J. & Libert, C. Sepsis: a failing starvation response. Trends Endocrinol. Metab. 33, 292–304 (2022).

    Article  PubMed  CAS  Google Scholar 

  20. Long, Q., Huang, L., Huang, K. & Yang, Q. Assessing mitochondrial bioenergetics in isolated mitochondria from mouse heart tissues using Oroboros 2k-Oxygraph. Methods Mol. Biol. 1966, 237–246 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Wang, Z. et al. Specific metabolic rates of major organs and tissues across adulthood: evaluation by mechanistic model of resting energy expenditure. Am. J. Clin. Nutr. 92, 1369–1377 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Shenoy, A. T. et al. Severity and properties of cardiac damage caused by Streptococcus pneumoniae are strain dependent. PLoS ONE 13, e0204032 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Beno, S. M. et al. Inhibition of necroptosis to prevent long-term cardiac damage during pneumococcal pneumonia and invasive disease. J. Infect. Dis. 222, 1882–1893 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Kruckow, K. L., Zhao, K., Bowdish, D. M. E. & Orihuela, C. J. Acute organ injury and long-term sequelae of severe pneumococcal infections. Pneumonia 15, 5 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Platt, M. P., Lin, Y. H., Wiscovitch-Russo, R., Yu, Y. & Gonzalez-Juarbe, N. Pandemic Influenza infection promotes Streptococcus pneumoniae infiltration, necrotic damage, and proteomic remodeling in the heart. mBio 13, e0325721 (2022).

    Article  PubMed  Google Scholar 

  26. Viasus, D. et al. Risk stratification and prognosis of acute cardiac events in hospitalized adults with community-acquired pneumonia. J. Infect. 66, 27–33 (2013).

    Article  PubMed  Google Scholar 

  27. Musher, D. M., Rueda, A. M., Kaka, A. S. & Mapara, S. M. The association between pneumococcal pneumonia and acute cardiac events. Clin. Infect. Dis. 45, 158–165 (2007).

    Article  PubMed  Google Scholar 

  28. Perry, T. W. et al. Incidence of cardiovascular events after hospital admission for pneumonia. Am. J. Med. 124, 244–251 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bazaz, R., Marriott, H. M., Francis, S. E. & Dockrell, D. H. Mechanistic links between acute respiratory tract infections and acute coronary syndromes. J. Infect. 66, 1–17 (2013).

    Article  PubMed  Google Scholar 

  30. Busch, C. J., Favret, J., Geirsdóttir, L., Molawi, K. & Sieweke, M. H. Isolation and long-term cultivation of mouse alveolar macrophages. Bio. Protoc. 9, e3302 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Recio, R. et al. Predictors of mortality in bloodstream infections caused by pseudomonas aeruginosa and impact of antimicrobial resistance and bacterial virulence. Antimicrob. Agents Chemother. 64, e01759–19 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Papadimitriou-Olivgeris, M., Senn, L., Jacot, D. & Guery, B. Predictors of mortality of Pseudomonas aeruginosa bacteraemia and the role of infectious diseases consultation and source control; a retrospective cohort study. Infection https://doi.org/10.1007/s15010-024-02326-6 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zhang, Y. et al. Risk factors for mortality of inpatients with Pseudomonas aeruginosa bacteremia in China: impact of resistance profile in the mortality. Infect. Drug Resist. 13, 4115–4123 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Chambers, H. F. et al. The antibacterial resistance leadership group: scientific advancements and future directions. Clin. Infect. Dis. 77, S279–S287 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Gibbs, K. D., Schott, B. H. & Ko, D. C. The awesome power of human genetics of infectious disease. Annu. Rev. Genet. 56, 41–62 (2022).

    Article  PubMed  CAS  Google Scholar 

  36. Mellor, K. C. et al. Distinct Streptococcus pneumoniae cause invasive disease in Papua New Guinea. Microb. Genom. 8, mgen000835 (2022).

    PubMed  PubMed Central  Google Scholar 

  37. Aithal, V., Gupta, A. C. & Vele, D. Hearing loss in Papua New Guinea: a study of outpatients attending Port Moresby General Hospital. P. N. G. Med. J. 38, 36–44 (1995).

    PubMed  CAS  Google Scholar 

  38. Weil, E. J. & Nelson, R. G. Kidney disease among the indigenous peoples of Oceania. Ethn. Dis. 16, S2-24–S2-30 (2006).

    Google Scholar 

  39. González-Mariscal, I. et al. Balanced CoQ6 biosynthesis is required for lifespan and mitophagy in yeast. Microb. Cell 4, 38–51 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Buck, M. D., Sowell, R. T., Kaech, S. M. & Pearce, E. L. Metabolic instruction of immunity. Cell 169, 570–586 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Rambold, A. S. & Pearce, E. L. Mitochondrial dynamics at the interface of immune cell metabolism and function. Trends Immunol. 39, 6–18 (2018).

    Article  PubMed  CAS  Google Scholar 

  42. Diskin, C., Ryan, T. A. J. & O’Neill, L. A. J. Modification of proteins by metabolites in immunity. Immunity 54, 19–31 (2021).

    Article  PubMed  CAS  Google Scholar 

  43. Wasyluk, W., Wasyluk, M. & Zwolak, A. Sepsis as a pan-endocrine illness—endocrine disorders in septic patients. J. Clin. Med. 10, 2075 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Singer, M. et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315, 801–810 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Vespasiani, D. M. et al. Denisovan introgression has shaped the immune system of present-day Papuans. PLoS Genet. 18, e1010470 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Jacobs, G. S. et al. Multiple deeply divergent denisovan ancestries in Papuans. Cell 177, 1010–1021.e1032 (2019).

    Article  PubMed  CAS  Google Scholar 

  47. Davidson, M. et al. The epidemiology of invasive pneumococcal disease in Alaska, 1986-1990—ethnic differences and opportunities for prevention. J. Infect. Dis. 170, 368–376 (1994).

    Article  PubMed  CAS  Google Scholar 

  48. Said, M. A. et al. The epidemiologic evidence underlying recommendations for use of pneumococcal polysaccharide vaccine among American Indian and Alaska Native populations. Vaccine 29, 5355–5362 (2011).

    Article  PubMed  CAS  Google Scholar 

  49. Segal, N., Greenberg, D., Dagan, R. & Ben-Shimol, S. Disparities in PCV impact between different ethnic populations cohabiting in the same region: a systematic review of the literature. Vaccine 34, 4371–4377 (2016).

    Article  PubMed  Google Scholar 

  50. LeMeur, J. B., Lefebvre, B., Proulx, J. F. & De Wals, P. Limited impact of pneumococcal vaccines on invasive pneumococcal disease in Nunavik (Quebec). Can. J. Public Health 110, 36–43 (2019).

    Article  PubMed  Google Scholar 

  51. Blyth, C. C. et al. Childhood pneumonia and meningitis in the Eastern Highlands Province, Papua New Guinea in the era of conjugate vaccines: study methods and challenges. Pneumonia 9, 5 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Hindson, B. J. et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 83, 8604–8610 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. UniProt Consortium et al. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 51, D523–D531 (2023).

    Article  Google Scholar 

  54. Simossis, V. A. & Heringa, J. PRALINE: a multiple sequence alignment toolbox that integrates homology-extended and secondary structure information. Nucleic Acids Res. 33, W289–W294 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Madeira, F. et al. The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024. Nucleic Acids Res. 52, W521–W525 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2021).

    Article  PubMed Central  Google Scholar 

  57. Deady, L. E. et al. L-Plastin is essential for alveolar macrophage production and control of pulmonary pneumococcal infection. Infect. Immun. 82, 1982–1993 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Smith, P., Jeffers, L. A. & Koval, M. Measurement of lung vessel and epithelial permeability in vivo with Evans Blue. Methods Mol. Biol. 2367, 137–148 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Amberg, D. C., Burke, D., & Strathern, J. N. Methods in Yeast Genetics: A Cold Spring Harbor Laboratory course Manual (Cold Spring Harbor Laboratory Press, 2005).

  60. Bittel, D. C., Bittel, A. J., Varadhachary, A. S., Pietka, T. & Sinacore, D. R. Deficits in the skeletal muscle transcriptome and mitochondrial coupling in progressive diabetes-induced CKD relate to functional decline. Diabetes 70, 1130–1144 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Gauderman, W. J. Sample size requirements for association studies of gene–gene interaction. Am. J. Epidemiol. 155, 478–484 (2002).

    Article  PubMed  Google Scholar 

  62. Kane S. P. Sample Size Calculator. ClinCalc https://clincalc.com/Stats/SampleSize.aspx

  63. Todd, E. M. et al. Alveolar macrophage development in mice requires L-plastin for cellular localization in alveoli. Blood 128, 2785–2796 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Todd, E. M., Ramani, R., Szasz, T. P. & Morley, S. C. Inhaled GM-CSF in neonatal mice provides durable protection against bacterial pneumonia. Sci. Adv. 5, eaax3387 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Anaya, E. P., Lin, X., Todd, E. M., Szasz, T. P. & Morley, S. C. Novel mouse model reveals that serine phosphorylation of L-plastin is essential for effective splenic clearance of pneumococcus. J. Immunol. 206, 2135–2145 (2021).

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

Funding was provided by the National Institutes of Health (NIH)/National Institute of Allergy and Infectious Diseases (NIAID) (grant nos. R01 AI139540 and R21 AI142723), NIH/National Heart, Lung, and Blood Institute (grant no. R01 HL177453) and the Children’s Discovery Institute (grant nos. PD-II-2013-295, PD-II-2014-366 and PD-II-2018-742) (all to S.C.M.). E.C.W. was supported by the NIH (grant no. T32 AI007172). Funding was provided by the NIH/NIAID (grant no. R01 AI036478) to J.W.K. The GTAC is partially supported by the National Cancer Institute Cancer Center (support grant no. P30 CA91842 to the Siteman Cancer Center) and by the Institute for Clinical and Translational Science/Clinical and Translational Science Awards (grant no. UL1TR000448 from the National Center for Research Resources (NCRR)), a component of the NIH and NIH Roadmap for Medical Research. The Diabetes Research Center at WUSM is supported by the NIH (grant no. P30 DK020579). This publication is solely the responsibility of the authors and does not necessarily represent the official view of the NCRR or the NIH. Our deepest thanks go to all participants who consented to take part in our studies and to the many team members who contributed. We thank D. Lehmann for her support of our partnership with PNGIMR and for her critical review of the manuscript. We also thank D. Kreamalmeyer for technical support of our animal program. Our eternal and most profound gratitude go to P. Tarr for his superb mentorship, which encompasses the ideal mix of infectious enthusiasm, sage advice and insistent goading, without which this manuscript might not have been written. We thank the donors to the St. Louis Children’s Hospital Foundation and the Children’s Discovery Institute. We thank the GTAC in the Department of Genetics at WUSM for help with genomic analysis. This manuscript presents research conducted by an equal partnership between researchers at PNGIMR and researchers at WUSM throughout the research process, including study design, study implementation, data ownership, intellectual property and authorship. The equal partnership is shown by inclusion of two co-senior authors (W.S.P. and S.C.M.). As determined in collaboration with researchers in PNG, the research is highly relevant to PNG. Roles and responsibilities were agreed among collaborators ahead of the conduct of the research. Capacity building included an exchange of research staff, with E.C.W. traveling to PNGIMR for 2 weeks and S.J. to WUSM for a month during the conduct of the research. The human studies performed in PNG were approved in advance by the PNGIMR IRB and the PNG MRAC. Potential risks to study participants (potential privacy violation owing to acquisition of genetic material) were disclosed in study consent forms and risks ameliorated by provision of deidentified samples only to WUSM researchers (WUSM researchers retain genetic information and PNGIMR researchers retain personally identifying information, such that no researcher can ‘pair’ potentially personally identifying information with the genetic information). Potential benefits derived from this research will be shared among coauthors. Biological materials obtained in PNG were divided, with half of each sample being sent to WUSM for analysis and half retained by PNGIMR researchers for their own use.

Author information

Authors and Affiliations

Authors

Contributions

E.C.W., W.S.P. and S.C.M. conceived the project. E.C.W., M.B., E.T., X.L. and S.C.M. curated the data. E.C.W., M.B. and S.C.M. did the formal analysis. S.C.M. acquired the funding. E.C.W., S.J., E.M.T., J.P.M., M.B., R.R., X.L., P.C., E.K., L.G., L.W., S.B., F.Y., J.N., M.J.G., R.C., C.M.H., B.J.D., T.P.G., E.P.A., J.Y.Z., K.A.A., R.S.T. and L.Y. carried out the investigations. E.C.W., E.M.T., R.R., P.C., E.K., L.G., L.W., S.B., F.Y., J.N., M.J.G., R.C., C.M.H., B.J.D., J.A.P., C.C.B. and T.E.D. provided the methodology. J.A.P., C.C.B., T.E.D., J.W.K., W.S.P. and S.C.M. administered the project. J.W.K., W.S.P. and S.C.M. provided the resources. J.A.P., M.J.G., R.C., T.E.D., J.W.K., W.S.P. and S.C.M. supervised the project. S.C.M. validated the project. E.C.W., R.C. and S.C.M. visualized the project. E.C.W. and S.C.M. wrote the original manuscript. All authors reviewed and edited the manuscript. E.C.W. and S.C.M. wrote the major revisions to the manuscript.

Corresponding author

Correspondence to Sharon Celeste Morley.

Ethics declarations

Competing interests

T.E.D. had no financial conflicts of interest during the years in which he contributed to the project (2018 and earlier). He is currently employed by Mission Bio, Inc. and serves as a scientific advisor for RhoDx, Inc. The other authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Navdeep Chandel, Ruben Martinez-Barricarte and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Mouse and human COQ6 are highly conserved.

a. Complete protein sequences of human and mouse COQ6 aligned using Clustal O. ‘*’ indicates identical amino acid residues, ‘:’ indicates conservation between amino acids with strongly similar properties. b. The predicted 3D structure of mouse COQ6 indicates that D316 resides in a similar location in an α-helix as human D308 and forms a hydrogen bond with nearby H311. Graphic is a screenshot obtained from AlphaFold, provided as freely available for both academic and commercial use under Creative Commons Attribution 4.0 (CC-BY 4.0) license terms.

Extended Data Fig. 2 Equivalent immune cell populations in naive WT (gray) and DY (blue) mice.

Flow cytometry data from a. BAL fluid, b. lung, c. blood, d. spleen, e. inguinal lymph nodes, and f. thymus, quantifying major leukocyte populations. We also quantify total number of cells per tissue. Data from 2-4 independent experiments, each symbol represents value from one animal, line at median, (n) given underneath x-axis labels. P-value determined by Mann-Whitney (two-tailed).

Source data

Extended Data Fig. 3 Equivalent recruitment of inflammatory cells in WT and DY mice after Sp challenge, with mild intracellular Sp killing defect.

a. Percentage of AMs and PMNs identified by flow cytometry in BAL fluid of matched WT and DY mice 4 dpi. b. Equivalent concentrations of TNFα, IL-1α, IFNγ and IL-10 in BAL fluid of Sp-challenged WT and DY mice 4 dpi. (a,b) Each symbol represents value from one animal, line at median, 95% CI shown, p-values determined by Mann-Whitney(two-tailed), data combined from two independent experiments, (n) given below x-axis labels. c. Percentage of intracellular bacteria killed in one hour by BMDMs derived from WT (solid) or DY (blue) mice. Each symbol represents the average of triplicate technical samples derived from one experiment. Data are derived from 4 independent experiments (biological replicates), line at median, P-value determined by Mann-Whitney (two-tailed). d. CellRox fluorescence in BMDMs from WT (grey) or DY (blue) mice 3 h after Sp infection in vitro. Total cellular ROS production was measured by CellRox fluorescence, with contribution of mtROS to total ROS defined by mitoTEMPO inhibition. BMDMs incubated with CellRox with MitoTEMPO (squares; 10 µM), NAC (triangles;10 mM) or without either inhibitor (circles). Maximum ROS signal was determined by incubating BMDMs with tBHP (dark grey); background fluorescence measured on BMDMs with no CellRox added (light grey). MFI acquired by microscopy. Whiskers show 1–99% confidence interval, with boxes showing 25–75% confidence intervals and lines at median. Outliers shown as individual symbols, each representing value from one cell, n given below x-axis. P-values determined by Kruskal-Wallis (p < 0.0001) followed by selected pairwise comparisons with Dunn’s adjustment for multiple comparisons. e. WT or DY mice received MitoSox via i.t. instillation 30 m prior to i.t. instillation of DDAO-labeled Sp. BAL lavage was performed on animals 30 min after Sp challenge and MitoSox MFI of AMs measured by flow cytometry. Data shown as ratio of MFI (infected AMs):MFI (uninfected AMs). Each symbol represents value from one mouse, data from 3 independent experiments, p-value determined by Mann-Whitney (two-tailed). In two experiments, one control mouse and two infected animals were used.

Source data

Extended Data Fig. 4 Complete reconstitution of chimeric recipients with innate immune cells derived from bone marrow donor.

a. Flow cytometric analysis of BAL fluid and whole lung homogenates obtained from WTWT.1 and DYWT.1 chimeric mice 72 hpi revealed >98% of all hematopoeitic (CD45+) cells were marked as CD45.2 (donor), with equivalent or increased AM, PMN, or monocyte populations derived from DY donors. b. Schematic of reconstitution of CD45.2+ WT or DY mice with bone marrow cells derived from CD45.1+ WT (WT.1) mice. Mice were challenged with Sp eight weeks after reconstitution. c. Flow cytometric analysis revealed >98% of CD45+ cells in BAL fluid of infected chimeric mice were donor-derived (CD45.1+), with proportionally fewer recipient hematopoeitic cells (CD45.2+) remaining in WT.1DY chimeric mice. Similar numbers of CD45.1+ cells were observed in BAL fluid from WT.1WT and WT.1DY mice. d. Of the recipient CD45.2+ cells remaining in BAL fluid from infected chimeras, virtually none were AMs or PMNs; most were CD3+. e. Total cell numbers of circulating CD45.2+ cells in blood from WT.1WT and WT.1DY chimeric mice, revealing that the majority of remaining recipient CD45.2+ cells were CD3+. f. Wet:dry weight ratio of and Evans Blue infiltration into BAL fluid of female chimeric WT.1WT or WT.1DY mice 72 hpi. (a,c,d,e,f) Each symbol represents value from one animal, line at median, 95% CI shown, p-values determined by Mann-Whitney (two-tailed), data combined from two (b,c,d,f) or seven (e) independent experiments, (n) given below x-axis labels.

Source data

Extended Data Fig. 5 Equivalent expression of COQ6 in mitochondria isolated from hearts of WT, heterozygous WT/DY, or DY mice.

Immunoblot of mitochondria (normalized to total protein; 40 µg each sample) from hearts of indicated mice, illuminated using the LiCOR Odyssey system. Representative of three independent immunoblots. The entire immunoblot is shown.

Source data

Extended Data Fig. 6 Equivalent number and morphology of mitochondria in AMs obtained from WT or DY mice.

a. Representative images of transmission electron micrographs of fixed AMs harvested from WT or DY mice. AMs were either uninfected or infected in vitro with Sp prior to fixation. Mitochondria are indicated in boxes. Scale bars = 500 nm. b. Quantification of mitochondrial numbers per cell, area of each mitochondria, and circularity of each mitochondria (perfect circle = 1, line = 0). Each symbol represents values for a cell (number) or for a mitochondrion (area and circularity). Median with 95% CI shown, (n) given below x-axis labels, p-value determined by Kruskal-Wallis followed by adjustment for multiple comparisons for pairwise analyses.

Source data

Extended Data Fig. 7 COQ6DY does not impair calcium flux in AMs or BMDMs.

Cytosolic calcium was measured by confocal microscopy in Fluo-4 loaded BMDMs (a,b) and AMs (c,d) treated with pneumolysin. a. Peak amplitude of calcium signals from WT (grey) and DY (blue) BMDMs from a representative experiment of three independent experiments. b. Average peak calcium signal from n = 3 experiments. c. Peak amplitude of calcium signals from WT (grey) and DY (blue) AMs from three independent experiments (each experiment shown). d. Average peak calcium signal from n = 3 experiments. e. Representative kinetic calcium tracing from BMDMs stimulated with ionomycin (400nM). f. Peak amplitude of calcium signals from > 100 WT and DY BMDMs stimulated with ionomycin. g,h,i. Calcium flux measured by flow cytometry in Indo-1 loaded AMs. g. Representative kinetic calcium tracing from AMs stimulated with ionomycin (100nM) first in calcium-free media (arrow) to induce ER store release. 1mM CaCl2 was added at 120s as indicated by the darker blue bar to allow extracellular store-operated calcium entry (SOCE). h. Area under the curve (AUC) of ER store release (30–120s) and i. SOCE (150–450s) segments from n = 7 experiments. (a,c,f) Each symbol represents value from one cell, line at median, 95% CI shown. N=number of cells, given below each x-axis. (b,d,h,i) Each symbol represents average value for all cells analyzed in each experiment; bars show mean ± SD. N=number of experiments, given below x-axis. e) Each symbol shows mean ± SEM of all WT (gray) or DY (blue) cells analyzed in one representative experiment. Data were analyzed using unpaired Mann-Whitney (two-tailed) (a, c, h-i) or paired Wilcoxon rank test (two-tailed) (b, d).

Source data

Extended Data Fig. 8 Single mouse cardiomyocytes treated with pneumolysin show no changes in the calcium transient.

Ventricular cardiomyocytes were isolated from WT and DY mice. Individual cells were loaded with the calcium fluorescent indicator, FLUOFORTE. Cells were paced at 1 Hz and fluorescence was recorded at 100 Hz by epifluorescence microscopy. We observed no statistically significant differences (P > 0.05; see raw data file for exact P values) in a. the maximal fluorescence, proportional to the amount of calcium released, b. the time to rise to 50% of the peak fluorescence during a calcium transient, or c. the time to decay by 50% of the peak fluorescence during a calcium transient. Data were analyzed using a 2-way ANOVA followed by a post-hoc Tukey’s multiple comparison test for each condition. N = 55 WT – PLY, 50 WT + PLY, 59 DY – PLY, and 54 DY + PLY cardiomyocytes collected over 2 days from two mice.

Source data

Extended Data Fig. 9 Gating strategy for BAL fluid obtained from infected chimeric mice.

Side scatter (SSC) x forward scatter (FSC) used to exclude debris. SSC-A x SSC-H used to gate on singlets/exclude doublets. CD45.1 x CD45.2 used to identify all CD45+ cells (two examples shown; one from CD45.1+ recipient and one from CD45.2+ recipient) and exclude non-hematopoietic cells. SSC x CD45.1+ (or CD45.2+, not shown) used to separate donor and recipient cells. Gating on live/singlets/all CD45/CD45.1+, CD11b x Ly6G is then used to identify PMNs. Gating on live/singlets/all CD45/CD45.1+, CD11c x CD64 is used to identify macrophages, followed by CD11c x SiglecF to identify AMs.

Supplementary information

Supplementary Information

Supplementary Results, Discussion, Methods, References and Figs. 1–3. Source data for CRISPR generation of DY mouse.

Reporting Summary

Peer Review File

Supplementary Video 1

Calcium flux in WT BMDMs induced by PLY. Confocal imaging of calcium responses in Fluo-4-loaded WT BMDMs stimulated with PLY (added at 4 min). Supporting video for Extended Data Fig. 7a,b. Scale bar, 50 mm.

Supplementary Video 2

Calcium flux in DY BMDMs induced by PLY. Confocal imaging of calcium responses in Fluo-4-loaded DY BMDMs stimulated with PLY (added at 4 min). Supporting video for Extended Data Fig. 7a,b. Scale bar, 50 mm.

Supplementary Video 3

Calcium flux in WT AMs induced by PLY. Confocal imaging of calcium responses in Fluo-4-loaded WT AMs stimulated with PLY (added at 4 min). Supporting video for Extended Data Fig. 7c,d. Scale bar, 50 mm.

Supplementary Video 4

Calcium flux in DY AMs induced by PLY. Confocal imaging of calcium responses in Fluo-4-loaded DY AMs stimulated with PLY (added at 4 min). Supporting video for Extended Data Fig. 7c,d. Scale bar, 50 mm.

Supplementary Data

Source data for Supplementary Fig. 1.

Supplementary Data

Source data for Supplementary Fig. 3.

Source data

Source Data Fig. 1

Sanger sequencing results, results from replication cohort including age and biological sex of individuals, and ddPCR results from deidentified samples.

Source Data Fig. 2

Original microscope images.

Source Data Fig. 3

Original microscope images.

Source Data Fig. 4

Excel spreadsheet of all numerical data.

Source Data Fig. 5

Original images obtained of plate.

Source Data Fig. 6

Excel spreadsheet of all numerical data.

Source Data Fig. 7

Excel spreadsheet of all numerical data.

Source Data Extended Data Fig. 2

Excel spreadsheet of all numerical data.

Source Data Extended Data Fig. 3

Excel spreadsheet of all numerical data.

Source Data Extended Data Fig. 4

Excel spreadsheet of all numerical data.

Source Data Extended Data Fig. 5

PNG file of original image obtained from LI-COR ODYSSEY system.

Source Data Extended Data Fig. 6

Original images obtained from TEM.

Source Data Extended Data Fig. 7

Excel spreadsheet of all numerical data.

Source Data Extended Data Fig. 8

Excel spreadsheet of all numerical data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Walker, E.C., Javati, S., Todd, E.M. et al. Novel coenzyme Q6 genetic variant increases susceptibility to pneumococcal disease. Nat Immunol 25, 2247–2258 (2024). https://doi.org/10.1038/s41590-024-01998-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41590-024-01998-4

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing