Skip to content
2000
image of A Genetic Perspective to Reveal the Impact of Mitochondrial Dysfunction-related Genes on Diabetic Kidney Disease: A Multi-omics Study

Abstract

Objective

This study investigated the causes of Mitochondrial Dysfunction (MD) in Diabetic Kidney Disease (DKD) progression, and identified genes associated with DKD, especially those with significant genetic causal effects, to provide a theoretical basis for DKD treatment.

Methods

Using a large database and single-cell RNA sequencing (scRNA-seq) data, 333 MDRDEGs were discovered. MDRDEGs were linked to AGE-RAGE signaling, RNA processing, protein transport, and energy metabolism using functional enrichment analysis. Seven MDRDEGs with significant genetic causal effects in DKD were discovered using SMR and MR analyses: ACTN1, ALG11, CCNB1, HIVEP2, MANBA, TUBA1A, and WFS1. Co-localization and scRNA-seq analyses examined these genes' DKD connections. Due to the high significance of its prediction model and DKD expression, ACTN1 was studied in depth. PheWAS and molecular dynamics analysis assessed ACTN1's safety and efficacy as a therapeutic target, and its connection with other symptoms. ACTN1 protein expression in DKD tissues was confirmed by immunofluorescence.

Results

Functional enrichment analysis revealed that MDRDEGs were mostly related to AGE-RAGE signaling, RNA processing, protein transport, and energy metabolism. Seven MDRDEGs caused DKD genetically in SMR and MR investigations. Genetic variations in ACTN1, ALG11, MANBA, and TUBA1A were linked to DKD by co-localization studies. scRNA-seq showed a dramatic increase in ACTN1 expression in DKD. Molecular dynamics analysis demonstrated that Dihydroergocristine can safely bind to ACTN1, while the PheWAS investigation found no significant relationships. DKD tissues exhibited higher ACTN1 protein levels immunofluorescence.

Discussion

This study identified MDRDEGs linked to inflammation, cytoskeletal stabilization, and glucose metabolism pathways critical in Diabetic Kidney Disease (DKD) pathogenesis, highlighting their clinical potential as therapeutic targets. Notably, ACTN1 emerged as a causally linked gene overexpressed in DKD, with the prediction of dihydroergocristine as a targeting compound, offering novel avenues for clinical intervention.

Conclusion

This study suggests that ACTN1 may be a therapeutic target for DKD and sheds light on its molecular pathogenesis, clinical prevention, and treatment.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673401081250523061057
2025-06-03
2025-09-10

  • From This Site

    /content/journals/cmc/10.2174/0109298673401081250523061057
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Tuttle K.R. Agarwal R. Alpers C.E. Bakris G.L. Brosius F.C. Kolkhof P. Uribarri J. Molecular mechanisms and therapeutic targets for diabetic kidney disease. Kidney Int. 2022 102 2 248 260 10.1016/j.kint.2022.05.012 35661785
    [Google Scholar]
  2. Johansen K.L. Chertow G.M. Foley R.N. Gilbertson D.T. Herzog C.A. Ishani A. Israni A.K. Ku E. Kurella Tamura M. Li S. Li S. Liu J. Obrador G.T. O’Hare A.M. Peng Y. Powe N.R. Roetker N.S. St Peter W.L. Abbott K.C. Chan K.E. Schulman I.H. Snyder J. Solid C. Weinhandl E.D. Winkelmayer W.C. Wetmore J.B. US renal data system 2020 annual data report: Epidemiology of kidney disease in the united states. Am. J. Kidney Dis. 2021 77 4 A7 A8 10.1053/j.ajkd.2021.01.002 33752804
    [Google Scholar]
  3. Barkoudah E. Skali H. Uno H. Solomon S.D. Pfeffer M.A. Mortality rates in trials of subjects with type 2 diabetes. J. Am. Heart Assoc. 2012 1 111 8 15 10.1161/xJAHA.111.000059 23130114
    [Google Scholar]
  4. Wang Z. Ying Z. Bosy-Westphal A. Zhang J. Schautz B. Later W. Heymsfield S.B. Müller M.J. Specific metabolic rates of major organs and tissues across adulthood: Evaluation by mechanistic model of resting energy expenditure. Am. J. Clin. Nutr. 2010 92 6 1369 1377 10.3945/ajcn.2010.29885 20962155
    [Google Scholar]
  5. Ahmad A.A. Draves S.O. Rosca M. Mitochondria in diabetic kidney disease. Cells 2021 10 11 2945 10.3390/cells10112945 34831168
    [Google Scholar]
  6. Forbes J.M. Thorburn D.R. Mitochondrial dysfunction in diabetic kidney disease. Nat. Rev. Nephrol. 2018 14 5 291 312 10.1038/nrneph.2018.9 29456246
    [Google Scholar]
  7. Yao L. Liang X. Qiao Y. Chen B. Wang P. Liu Z. Mitochondrial dysfunction in diabetic tubulopathy. Metabolism 2022 131 155195 10.1016/j.metabol.2022.155195 35358497
    [Google Scholar]
  8. Bian C. Ren H. Sirtuin family and diabetic kidney disease. Front. Endocrinol. 2022 13 901066 10.3389/fendo.2022.901066 35774140
    [Google Scholar]
  9. Fontecha-Barriuso M. Martin-Sanchez D. Martinez-Moreno J. Monsalve M. Ramos A. Sanchez-Niño M. Ruiz-Ortega M. Ortiz A. Sanz A. The role of PGC-1α and mitochondrial biogenesis in kidney diseases. Biomolecules 2020 10 2 347 10.3390/biom10020347 32102312
    [Google Scholar]
  10. Wu Y. Zeng J. Zhang F. Zhu Z. Qi T. Zheng Z. Lloyd-Jones L.R. Marioni R.E. Martin N.G. Montgomery G.W. Deary I.J. Wray N.R. Visscher P.M. McRae A.F. Yang J. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 2018 9 1 918 10.1038/s41467‑018‑03371‑0 29500431
    [Google Scholar]
  11. Zhu Z. Zhang F. Hu H. Bakshi A. Robinson M.R. Powell J.E. Montgomery G.W. Goddard M.E. Wray N.R. Visscher P.M. Yang J. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 2016 48 5 481 487 10.1038/ng.3538 27019110
    [Google Scholar]
  12. Srivastava S.P. Srivastava R. Chand S. Goodwin J.E. Coronavirus disease (COVID)-19 and diabetic kidney disease. Pharmaceuticals 2021 14 8 751 10.3390/ph14080751 34451848
    [Google Scholar]
  13. Srivastava S.P. Kanasaki K. Goodwin J.E. Editorial: Combating diabetes and diabetic kidney disease. Front. Pharmacol. 2021 12 716029 10.3389/fphar.2021.716029 34305620
    [Google Scholar]
  14. Li H.D. You Y.K. Shao B.Y. Wu W.F. Wang Y.F. Guo J.B. Meng X.M. Chen H. Roles and crosstalks of macrophages in diabetic nephropathy. Front. Immunol. 2022 13 1015142 10.3389/fimmu.2022.1015142 36405700
    [Google Scholar]
  15. Narongkiatikhun P. Choi Y.J. Hampson H. Gotzamanis J. Zhang G. van Raalte D.H. de Boer I.H. Nelson R.G. Tommerdahl K.L. McCown P.J. Kanter J. Sharma K. Bjornstad P. Saulnier P.J. Unraveling diabetic kidney disease: The roles of mitochondrial dysfunction and immunometabolism. Kidney Int. Rep. 2024 9 12 3386 3402 10.1016/j.ekir.2024.09.019 39698345
    [Google Scholar]
  16. Basso P.J. Andrade-Oliveira V. Câmara N.O.S. Targeting immune cell metabolism in kidney diseases. Nat. Rev. Nephrol. 2021 17 7 465 480 10.1038/s41581‑021‑00413‑7 33828286
    [Google Scholar]
  17. Hamill K.J. Hiroyasu S. Colburn Z.T. Ventrella R.V. Hopkinson S.B. Skalli O. Jones J.C.R. Alpha actinin-1 regulates cell-matrix adhesion organization in keratinocytes: Consequences for skin cell motility. J. Invest. Dermatol. 2015 135 4 1043 1052 10.1038/jid.2014.505 25431851
    [Google Scholar]
  18. Marx D. Dupuis A. Eckly A. Molitor A. Olagne J. Touchard G. Kaaki S. Ory C. Faller A.L. Gérard B. Cotter M. Westerberg L. Keszei M. Moulin B. Gachet C. Caillard S. Bahram S. Carapito R. A gain-of-function variant in the Wiskott-Aldrich syndrome gene is associated with a MYH9-related disease-like syndrome. Blood Adv. 2022 6 18 5279 5284 10.1182/bloodadvances.2021006789 35404999
    [Google Scholar]
  19. Wang N. Li S.T. Xiang M.H. Gao X.D. Alg mannosyltransferases: From functional and structural analyses to the lipid-linked oligosaccharide pathway reconstitution. Biochim. Biophys. Acta, Gen. Subj. 2022 1866 5 130112 10.1016/j.bbagen.2022.130112 35217128
    [Google Scholar]
  20. Gu X. Yang H. Sheng X. Ko Y.A. Qiu C. Park J. Huang S. Kember R. Judy R.L. Park J. Damrauer S.M. Nadkarni G. Loos R.J.F. My V.T.H. Chaudhary K. Bottinger E.P. Paranjpe I. Saha A. Brown C. Akilesh S. Hung A.M. Palmer M. Baras A. Overton J.D. Reid J. Ritchie M. Rader D.J. Susztak K. Kidney disease genetic risk variants alter lysosomal beta-mannosidase ( MANBA ) expression and disease severity. Sci. Transl. Med. 2021 13 576 eaaz1458 10.1126/scitranslmed.aaz1458 33441424
    [Google Scholar]
  21. Rachman A. Kellmann L. Krieglstein J. Effect of dihydroergocristine on energy metabolism studied in the isolated perfused rat brain affected by ischemia and in neuroblastoma cells deprived of oxygen and glucose. J. Cereb. Blood Flow Metab. 1984 4 4 610 614 10.1038/jcbfm.1984.86 6438125
    [Google Scholar]
  22. Sharma K. Mitochondrial hormesis and diabetic complications. Diabetes 2015 64 3 663 672 10.2337/db14‑0874 25713188
    [Google Scholar]
  23. Czajka A. Malik A.N. Hyperglycemia induced damage to mitochondrial respiration in renal mesangial and tubular cells: Implications for diabetic nephropathy. Redox Biol. 2016 10 100 107 10.1016/j.redox.2016.09.007 27710853
    [Google Scholar]
  24. Reidy K. Kang H.M. Hostetter T. Susztak K. Molecular mechanisms of diabetic kidney disease. J. Clin. Invest. 2014 124 6 2333 2340 10.1172/JCI72271 24892707
    [Google Scholar]
  25. Wu X.Q. Zhang D.D. Wang Y.N. Tan Y.Q. Yu X.Y. Zhao Y.Y. AGE/RAGE in diabetic kidney disease and ageing kidney. Free Radic. Biol. Med. 2021 171 260 271 10.1016/j.freeradbiomed.2021.05.025 34019934
    [Google Scholar]
  26. Li T. Bao Y. Xia Y. Meng H. Zhou C. Huang L. Wang X. Lai E.Y. Jiang P. Mao J. Loss of MTX2 causes mitochondrial dysfunction, podocyte injury, nephrotic proteinuria and glomerulopathy in mice and patients. Int. J. Biol. Sci. 2024 20 3 937 952 10.7150/ijbs.89916 38250156
    [Google Scholar]
  27. Ye B. Chen B. Guo C. Xiong N. Huang Y. Li M. Lai Y. Li J. Zhou M. Wang S. Wang S. Yang N. Zhang H. C5a-C5aR1 axis controls mitochondrial fission to promote podocyte injury in lupus nephritis. Mol. Ther. 2024 32 5 1540 1560 10.1016/j.ymthe.2024.03.003 38449312
    [Google Scholar]
  28. Fan X. Yang M. Lang Y. Lu S. Kong Z. Gao Y. Shen N. Zhang D. Lv Z. Mitochondrial metabolic reprogramming in diabetic kidney disease. Cell Death Dis. 2024 15 6 442 10.1038/s41419‑024‑06833‑0 38910210
    [Google Scholar]
  29. Ducasa G.M. Mitrofanova A. Mallela S.K. Liu X. Molina J. Sloan A. Pedigo C.E. Ge M. Santos J.V. Hernandez Y. Kim J.J. Maugeais C. Mendez A.J. Nair V. Kretzler M. Burke G.W. Nelson R.G. Ishimoto Y. Inagi R. Banerjee S. Liu S. Szeto H.H. Merscher S. Fontanesi F. Fornoni A. ATP-binding cassette A1 deficiency causes cardiolipin-driven mitochondrial dysfunction in podocytes. J. Clin. Invest. 2019 129 8 3387 3400 10.1172/JCI125316 31329164
    [Google Scholar]
  30. Sen S. Dong M. Kumar S. Isoform-specific contributions of alpha-actinin to glioma cell mechanobiology. PLoS One 2009 4 12 8427 10.1371/journal.pone.0008427 20037648
    [Google Scholar]
  31. Chen Q. Zhou X.W. Zhang A.J. He K. ACTN1 supports tumor growth by inhibiting Hippo signaling in hepatocellular carcinoma. J. Exp. Clin. Cancer Res. 2021 40 1 23 10.1186/s13046‑020‑01821‑6 33413564
    [Google Scholar]
  32. Cui L. Lu Y. Zheng J. Guo B. Zhao X. ACTN1 promotes HNSCC tumorigenesis and cisplatin resistance by enhancing MYH9-dependent degradation of GSK-3β and integrin β1-mediated phosphorylation of FAK. J. Exp. Clin. Cancer Res. 2023 42 1 335 10.1186/s13046‑023‑02904‑w 38057867
    [Google Scholar]
  33. Kirita Y. Wu H. Uchimura K. Wilson P.C. Humphreys B.D. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proc. Natl. Acad. Sci. USA 2020 117 27 15874 15883 10.1073/pnas.2005477117 32571916
    [Google Scholar]
  34. Le S. Hu X. Yao M. Chen H. Yu M. Xu X. Nakazawa N. Margadant F.M. Sheetz M.P. Yan J. Mechanotransmission and mechanosensing of human alpha-actinin 1. Cell Rep. 2017 21 10 2714 2723 10.1016/j.celrep.2017.11.040 29212020
    [Google Scholar]
  35. Huang R. Southall N. Wang Y. Yasgar A. Shinn P. Jadhav A. Nguyen D.T. Austin C.P. The NCGC pharmaceutical collection: A comprehensive resource of clinically approved drugs enabling repurposing and chemical genomics. Sci. Transl. Med. 2011 3 80 80ps16 10.1126/scitranslmed.3001862 21525397
    [Google Scholar]
  36. Chong C.R. Sullivan D.J. Jr. New uses for old drugs. Nature 2007 448 7154 645 646 10.1038/448645a 17687303
    [Google Scholar]
  37. Młynarska E. Buławska D. Czarnik W. Hajdys J. Majchrowicz G. Prusinowski F. Stabrawa M. Rysz J. Franczyk B. Novel insights into diabetic kidney disease. Int. J. Mol. Sci. 2024 25 18 10222 10.3390/ijms251810222 39337706
    [Google Scholar]
  38. Danesh N. Sedighi Z.N. Beigoli S. Sharifi-Rad A. Saberi M.R. Chamani J. Determining the binding site and binding affinity of estradiol to human serum albumin and holo-transferrin: Fluorescence spectroscopic, isothermal titration calorimetry and molecular modeling approaches. J. Biomol. Struct. Dyn. 2018 36 7 1747 1763 10.1080/07391102.2017.1333460 28573922
    [Google Scholar]
/content/journals/cmc/10.2174/0109298673401081250523061057
Loading
A Genetic Perspective to Reveal the Impact of Mitochondrial Dysfunction-related Genes on Diabetic Kidney Disease: A Multi-omics Study
Bentham Science Publishers ; https://doi.org/10.2174/0109298673401081250523061057
/content/journals/cmc/10.2174/0109298673401081250523061057
/content/journals/cmc/10.2174/0109298673401081250523061057
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Research Article
Keywords: mendelian randomization ; machine learning ; Diabetic kidney disease ; multi-omics ; ACTN1 ; mitochondrial dysfunction

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test