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Cited by 26Mitochondrial Function and PathologyGenetic and phenotypic traits in livestockGenomics and Phylogenetic Studies

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Expression of mitochondrial protein genes encoded by nuclear and mitochondrial genomes correlate with energy metabolism in dairy cattle

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Dorji, Jigme, Vander Jagt, Christy J., Garner, Josie B., Marett, Leah C., Mason, Brett A., Reich, Coralie M., Xiang, Ruidong, Clark, Emily L., Cocks, Benjamin G., Chamberlain, Amanda J., MacLeod, Iona M., Daetwyler, Hans D.·2020·BMC Genomics
DOI 10.1186/s12864-020-07018-7PMC PMC7574280

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Abstract

Background: Mutations in the mitochondrial genome have been implicated in mitochondrial disease, often characterized by impaired cellular energy metabolism. Cellular energy metabolism in mitochondria involves mitochondrial proteins (MP) from both the nuclear ( Nu MP) and mitochondrial ( Mt MP) genomes. The expression of MP genes in tissues may be tissue specific to meet varying specific energy demands across the tissues. Currently, the characteristics of MP gene expression in tissues of dairy cattle are not well understood. In this study, we profile the expression of MP genes in 29 adult and six foetal tissues in dairy cattle using RNA sequencing and gene expression analyses: particularly differential gene expression and co-expression network analyses. Results: MP genes were differentially expressed (DE; over-expressed or under-expressed) across tissues in cattle. All 29 tissues showed DE Nu MP genes in varying proportions of over-expression and under-expression. On the other hand, DE of Mt MP genes was observed in < 50% of tissues and notably Mt MP genes within a tissue was either all over-expressed or all under-expressed. A high proportion of Nu MP (up to 60%) and Mt MP (up to 100%) genes were over-expressed in tissues with expected high metabolic demand; heart, skeletal muscles and tongue, and under-expressed (up to 45% of Nu MP, 77% of Mt MP genes) in tissues with expected low metabolic rates; leukocytes, thymus, and lymph nodes. These tissues also invariably had the expression of all Mt MP genes in the direction of dominant Nu MP genes expression. The Nu MP and Mt MP genes were highly co-expressed across tissues and co-expression of genes in a cluster were non-random and functionally enriched for energy generation pathway. The differential gene expression and co-expression patterns were validated in independent cow and sheep datasets. Conclusions: The results of this study support the concept that there are biological interaction of MP genes from the mitochondrial and nuclear genomes given their over-expression in tissues with high energy demand and co-expression in tissues. This highlights the importance of considering MP genes from both genomes in future studies related to mitochondrial functions and traits related to energy metabolism.

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Fig. 1 Percentage of differentially expressed genes by gene categories for 29 tissues in the Main Cows dataset. m. = muscle, LN = lymph node; Gene category: All = All protein coding genes from nuclear and mitochondrial genomes, Nu = Mitochondrial protein coding genes from the nuclear genome (NuMP), Mt = Mitochondrial protein coding genes from the mitochondrial genome (MtMP)
Fig. 1 Percentage of differentially expressed genes by gene categories for 29 tissues in the Main Cows dataset. m. = muscle, LN = lymph node; Gene category: All = All protein coding genes from nuclear and mitochondrial genomes, Nu = Mitochondrial protein coding genes from the nuclear genome (NuMP), Mt = Mitochondrial protein coding genes from the mitochondrial genome (MtMP)CC BY · PubMed Central
Fig. 2 Heatmap of nuclear genome encoded mitochondrial protein (NuMP) gene expression the Main Cows dataset. 6819 and 2181 are Cow No. 6819 and Cow No. 2181 respectively
Fig. 2 Heatmap of nuclear genome encoded mitochondrial protein (NuMP) gene expression the Main Cows dataset. 6819 and 2181 are Cow No. 6819 and Cow No. 2181 respectivelyCC BY · PubMed Central
Fig. 3 Heatmap of mitochondrial genome encoded mitochondrial protein (MtMP) gene expression in the Main Cows dataset. Based on the log2 counts per million of MtMP genes across 29 tissues from cows 6819 and 2181
Fig. 3 Heatmap of mitochondrial genome encoded mitochondrial protein (MtMP) gene expression in the Main Cows dataset. Based on the log2 counts per million of MtMP genes across 29 tissues from cows 6819 and 2181CC BY · PubMed Central
Fig. 4 Gene co-expression network clusters across tissues in the Main Cows based on similarity matrix computed using Pearson correlations > |0.95|
Fig. 4 Gene co-expression network clusters across tissues in the Main Cows based on similarity matrix computed using Pearson correlations > |0.95|CC BY · PubMed Central
Fig. 5 Scatterplot of log2 fold change values of differentially expressed mitochondrial protein genes from nuclear genome in the Main Cows against the Validation Cow
Fig. 5 Scatterplot of log2 fold change values of differentially expressed mitochondrial protein genes from nuclear genome in the Main Cows against the Validation CowCC BY · PubMed Central
Fig. 6 Venn diagram showing the number of genes in common among the NuMP-MtMP co-expression clusters. a. Mitochondrial protein genes encoded by the nuclear genome (NuMP genes), b. Mitochondrial protein genes encoded by the mitochondrial genome (MtMP genes) and c. non-mitochondrial protein genes from nuclear genomes in common between the Main Cows and Validation Cow
Fig. 6 Venn diagram showing the number of genes in common among the NuMP-MtMP co-expression clusters. a. Mitochondrial protein genes encoded by the nuclear genome (NuMP genes), b. Mitochondrial protein genes encoded by the mitochondrial genome (MtMP genes) and c. non-mitochondrial protein genes from nuclear genomes in common between the Main Cows and Validation CowCC BY · PubMed Central

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Research works printed but not linked: 72

  1. Taylor RW, Turnbull DM. Mitochondrial DNA mutations in human disease. Nat Rev Genet. 2005;6(5):389–402. · DOI
  2. Gorman GS, Chinnery PF, DiMauro S, Hirano M, Koga Y, McFarland R, et al. Mitochondrial diseases. Nat Rev Dis Prim. 2016;2:16080. · DOI
  3. Wallace DC. Mitochondrial diseases in man and mouse. Science. 1999;283(5407):1482–8. · DOI · Paper page
  4. Elia M, Livesey G. Energy expenditure and fuel selection in biological systems: the theory and practice of calculations based on indirect calorimetry and tracer methods. World Rev Nutr Diet. 1992;70:68–131. · DOI
  5. Wang C, Youle RJ. The role of mitochondria in apoptosis*. Annu Rev Genet. 2009;43:95–118. · DOI · Paper page
  6. Contreras L, Drago I, Zampese E, Pozzan T. Mitochondria: the calcium connection. Biochim Biophys Acta. 2010;1797(6):607–18. · DOI
  7. Fox TD. Mitochondrial protein synthesis, import, and assembly. Genetics. 2012;192(4):1203–34. · DOI
  8. Lopez MF, Kristal BS, Chernokalskaya E, Lazarev A, Shestopalov AI, Bogdanova A, et al. High-throughput profiling of the mitochondrial proteome using affinity fractionation and automation. Electrophoresis. 2000;21(16):3427–40. · DOI
  9. Calvo SE, Clauser KR, Mootha VK. MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins. Nucleic Acids Res. 2016;44(Database issue):D1251–D7. · DOI · Paper page
  10. Dudek J, Rehling P, van der Laan M. Mitochondrial protein import: common principles and physiological networks. Biochim Biophys Acta. 2013;1833(2):274–85. · DOI
  11. Chacinska A, Koehler CM, Milenkovic D, Lithgow T, Pfanner N. Importing mitochondrial proteins: machineries and mechanisms. Cell. 2009;138(4):628–44. · DOI
  12. Margulis L. Origin of eukaryotic cells : evidence and research implications for a theory of the origin and evolution of microbial, plant, and animal cells on the Precambrian earth. New Haven: Yale University Press; 1970.
  13. Giles RE, Blanc H, Cann HM, Wallace DC. Maternal inheritance of human mitochondrial DNA. Proc Natl Acad Sci. 1980;77(11):6715–5719. · DOI · Paper page
  14. Hutchison CA, Newbold JE, Potter SS, Edgell MH. Maternal inheritance of mammalian mitochondrial DNA. Nature. 1974;251(5475):536–8. · DOI
  15. Anderson S, de Bruijn MHL, Coulson AR, Eperon IC, Sanger F, Young IG. Complete sequence of bovine mitochondrial DNA conserved features of the mammalian mitochondrial genome. J Mol Biol. 1982;156(4):683–717. · DOI · Paper page
  16. Sutarno CJM, Greeff J, Lymbery AJ. Mitochondrial DNA polymorphisms and fertility in beef cattle. Theriogenology. 2002;57(6):1603–10. · DOI
  17. Holyoake AJ, McHugh P, Wu M, O'Carroll S, Benny P, Sin IL, et al. High incidence of single nucleotide substitutions in the mitochondrial genome is associated with poor semen parameters in men. Int J Androl. 2001;24(3):175–82. · DOI
  18. Chan CC, Liu VW, Lau EY, Yeung WS, Ng EH, Ho PC. Mitochondrial DNA content and 4977 bp deletion in unfertilized oocytes. Mol Hum Reprod. 2005;11(12):843–6. · DOI
  19. Schutz MM, Freeman AE, Lindberg GL, Koehler CM, Beitz DC. The effect of mitochondrial DNA on milk production and health of dairy cattle. Livest Prod Sci. 1994;37(3):283–95. · DOI
  20. Wang J, Shi Y, Elzo MA, Dang S, Jia X, Lai S. Genetic diversity of ATP8 and ATP6 genes is associated with high-altitude adaptation in yak. Mitochondrial DNA Part A. 2018;29(3):385–93. · DOI
  21. Shi Y, Hu Y, Wang J, Elzo MA, Yang X, Lai S. Genetic diversities of MT-ND1 and MT-ND2 genes are associated with high-altitude adaptation in yak. Mitochondrial DNA Part A. 2018;29(3):485–94. · DOI
  22. Robin ED, Wong R. Mitochondrial DNA molecules and virtual number of mitochondria per cell in mammalian cells. J Cell Physiol. 1988;136(3):507–13. · DOI
  23. Goffart S, Wiesner RJ. Regulation and co-ordination of nuclear gene expression during mitochondrial biogenesis. Exp Physiol. 2003;88(1):33–40. · DOI
  24. Clay Montier LL, Deng JJ, Bai Y. Number matters: control of mammalian mitochondrial DNA copy number. J Genet Genomics. 2009;36(3):125–31. · DOI
  25. San Segundo-Val I, Sanz-Lozano CS. Introduction to the gene expression analysis. In: Isidoro García M, editor. Molecular genetics of asthma. New York, NY: Springer New York; 2016. p. 29–43. · DOI
  26. Chamberlain AJ, Vander Jagt CJ, Hayes BJ, Khansefid M, Marett LC, Millen CA, et al. Extensive variation between tissues in allele specific expression in an outbred mammal. BMC Genomics. 2015;16:993. · DOI
  27. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998;95(25):14863–8. · DOI · Paper page
  28. Michalak P. Coexpression, coregulation, and cofunctionality of neighboring genes in eukaryotic genomes. Genomics. 2008;91(3):243–8. · DOI
  29. Hurst LD, Pal C, Lercher MJ. The evolutionary dynamics of eukaryotic gene order. Nat Rev Genet. 2004;5(4):299–310. · DOI
  30. Nora EP, Lajoie BR, Schulz EG, Giorgetti L, Okamoto I, Servant N, et al. Spatial partitioning of the regulatory landscape of the X-inactivation Centre. Nature. 2012;485(7398):381–5. · DOI · Paper page
  31. Harhay GP, Smith TPL, Alexander LJ, Haudenschild CD, Keele JW, Matukumalli LK, et al. An atlas of bovine gene expression reveals novel distinctive tissue characteristics and evidence for improving genome annotation. Genome Biol. 2010;11(10):R102 R. · DOI
  32. Wang ZM, O'Connor TP, Heshka S, Heymsfield SB. The reconstruction of Kleiber's law at the organ-tissue level. J Nutr. 2001;131:2967–70. · DOI
  33. Berg JM, Tymoczko JL, Stryer L. Biochemistry. In: Tymoczko JL, Stryer L, editors. National Center for biotechnology I. 5th ed. New York: Basingstoke: W. H. Freeman; 2002.
  34. Wang Z, Zhang J, Ying Z, Heymsfield SB. Organ-tissue level model of resting energy expenditure across mammals: new insights into Kleiber's law. ISRN Zoology. 2012;2012:9. · DOI
  35. Clark EL, Bush SJ, McCulloch MEB, Farquhar IL, Young R, Lefevre L, et al. A high resolution atlas of gene expression in the domestic sheep (Ovis aries). PLoS Genet. 2017;13(9):e1006997. · DOI
  36. Stanley WC, Chandler MP. Energy metabolism in the Normal and failing heart: potential for therapeutic interventions. Heart Fail Rev. 2002;7(2):115–30. · DOI
  37. Herbers E, Kekäläinen NJ, Hangas A, Pohjoismäki JL, Goffart S. Tissue specific differences in mitochondrial DNA maintenance and expression. Mitochondrion. 2019;44:85–92. · DOI · Paper page
  38. Glaister M. Multiple sprint work: physiological responses, mechanisms of fatigue and the influence of aerobic fitness. Sports Med (Auckland, NZ). 2005;35(9):757–77. · DOI
  39. Spriet LL. Anaerobic metabolism in human skeletal muscle during short-term, intense activity. Can J Physiol Pharmacol. 1992;70(1):157–65. · DOI
  40. Hargreaves M. Skeletal muscle metabolism during exercise in humans. Clin Exp Pharmacol Physiol. 2000;27(3):225–8. · DOI
  41. Forbes JM. Mitochondria–power players in kidney function? Trends Endocrinol Metab. 2016;27(7):441–2. · DOI
  42. Bhargava P, Schnellmann RG. Mitochondrial energetics in the kidney. Nat Rev Nephrol. 2017;13:629. · DOI
  43. Kramer PA, Ravi S, Chacko B, Johnson MS, Darley-Usmar VM. A review of the mitochondrial and glycolytic metabolism in human platelets and leukocytes: implications for their use as bioenergetic biomarkers. Redox Biol. 2014;2:206–10. · DOI
  44. Yan X, Zhu M-J, Dodson MV, Du M. Developmental programming of fetal skeletal muscle and adipose tissue development. J Genomics. 2013;1:29–38. · DOI
  45. Strang LB. The lungs at birth. Arch Dis Child. 1965;40:575. · DOI
  46. Edfors F, Danielsson F, Hallström BM, Käll L, Lundberg E, Pontén F, et al. Gene-specific correlation of RNA and protein levels in human cells and tissues. Mol Syst Biol. 2016;12(10):883. · DOI · Paper page
  47. Miller FJ, Rosenfeldt FL, Zhang C, Linnane AW, Nagley P. Precise determination of mitochondrial DNA copy number in human skeletal and cardiac muscle by a PCR-based assay: lack of change of copy number with age. Nucleic Acids Res. 2003;31(11):e61–e. · DOI
  48. Lindinger A, Peterli R, Peters T, Kern B, von Flue M, Calame M, et al. Mitochondrial DNA content in human omental adipose tissue. Obes Surg. 2010;20(1):84–92. · DOI
  49. Svendsen AJ, Tan Q, Jakobsen MA, Thyagarajan B, Nygaard M, Christiansen L, et al. White blood cell mitochondrial DNA copy number is decreased in rheumatoid arthritis and linked with risk factors. A twin study. J Autoimmun. 2019;96:142–6. · DOI
  50. Williams RS. Mitochondrial gene expression in mammalian striated muscle. Evidence that variation in gene dosage is the major regulatory event. J Biol Chem. 1986;261(26):12390–4. · DOI
  51. Aloni Y, Attardi G. Expression of the mitochondrial genome in HeLa cells. II. Evidence for complete transcription of mitochondrial DNA. J Mol Biol. 1971;55(2):251–67. · DOI
  52. Gelfand R, Attardi G. Synthesis and turnover of mitochondrial ribonucleic acid in HeLa cells: the mature ribosomal and messenger ribonucleic acid species are metabolically unstable. Mol Cell Biol. 1981;1(6):497–511.
  53. Shokolenko IN, Alexeyev MF. Mitochondrial transcription in mammalian cells. Front Biosci (Landmark edition). 2017;22:835–53. · DOI
  54. Lee JM, Sonnhammer EL. Genomic gene clustering analysis of pathways in eukaryotes. Genome Res. 2003;13(5):875–82. · DOI
  55. Mercer Tim R, Neph S, Dinger Marcel E, Crawford J, Smith Martin A, Shearwood A-Marie J, et al. The human mitochondrial Transcriptome. Cell. 2011;146(4):645–58. · DOI · Paper page
  56. Soler-Oliva ME, Guerrero-Martínez JA, Bachetti V, Reyes JC. Analysis of the relationship between coexpression domains and chromatin 3D organization. PLoS Comput Biol. 2017;13(9):e1005708. · DOI
  57. Fantz CR, Dagogo-Jack S, Ladenson JH, Gronowski AM. Thyroid function during pregnancy. Clin Chem. 1999;45(12):2250. · DOI
  58. Bush SJ, Hume DA, Clark EL. Unnormalised raw count estimates for the sheep gene expression atlas,[dataset]: University of Edinburgh; 2019. https://doi.org/10.7488/ds/2616. · DOI
  59. Zimin AV, Delcher AL, Florea L, Kelley DR, Schatz MC, Puiu D, et al. A whole-genome assembly of the domestic cow, Bos taurus. Genome Biol. 2009;10(4):R42. · DOI
  60. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. · DOI · Paper page
  61. Okonechnikov K, Conesa A, García-Alcalde F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics. 2016;32(2):292–4. · DOI · Paper page
  62. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30. · DOI · Paper page
  63. da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. · DOI · Paper page
  64. da Huang W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1–13. · DOI · Paper page
  65. Brand MD, Orr AL, Perevoshchikova IV, Quinlan CL. The role of mitochondrial function and cellular bioenergetics in ageing and disease. Br J Dermatol. 2013;169(Suppl 2(0 2)):1–8. · DOI
  66. Chinnery PF, Schon EA. Mitochondria. J Neurol Neurosurg Psychiatry. 2003;74(9):1188–99. · DOI
  67. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40. · DOI · Paper page
  68. Kolde R. Pheatmap: pretty heatmaps. R Package Version 61 ed; 2012.
  69. Hui S, Sander C, Potylitsine E, Whitaker W, Bader G, Morrison L, et al. ExpressionCorrelation. Makes a similarity network where nodes are genes, and edges denote highly correlated genes. Version 1.1.0 ed 2015.
  70. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. · DOI · Paper page
  71. Wang M, Hancock TP, Chamberlain AJ, Vander Jagt CJ, Pryce JE, Cocks BG, et al. Putative bovine topological association domains and CTCF binding motifs can reduce the search space for causative regulatory variants of complex traits. BMC Genomics. 2018;19(1):395. · DOI
  72. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. · DOI · Paper page

Metadata

Authors
Dorji, J., Vander Jagt, C. J., Garner, J. B., Marett, L. C., Mason, B. A., Reich, C. M., Xiang, R., Clark, E. L., Cocks, B. G., Chamberlain, A. J., MacLeod, I. M., & Daetwyler, H. D.
Affiliations
  • La Trobe University (AU)
  • Agriculture Victoria (AU)
  • The University of Melbourne (AU)
  • Roslin Institute (GB)
Corresponding author
Dorji, Jigme
ORCID
Venue
BMC Genomics
Publisher
Springer Science and Business Media LLC
Published
2020-10-19
Volume
21
Issue
1
Pages
720-720
ISSN
1471-2164 (ISSN-L)
DOI
10.1186/s12864-020-07018-7
PMC
PMC7574280
OpenAlex
W3092917020
Cited by
26
References
7
Citations by year
2026: 3 · 2025: 2 · 2024: 7 · 2023: 8 · 2022: 1 · 2021: 4 · 2020: 1
FWCI
1.13
Type
journal article
Language
en
Update notices
correction (2022-04-20)
Open access
Licence
Locations
  • BMC Genomics (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders
Agriculture Victoria & Dairy Australia (DairyBio)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Mitochondrial Function and Pathology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
mitochondrial proteins, mitochondrial genome, energy metabolism, dairy cattle, tissue-specific gene expression, differential gene expression, co-expression network, mitochondrial disease, fetal tissue
MeSH terms
Animals, Cattle, Energy Metabolism, Female, Sheep, Gene Expression, Gene Expression Profiling, Mitochondrial Proteins, Genome, Mitochondrial

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