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image of Development of a Novel Mitochondrial Dysfunction-Related Alzheimer's Disease Diagnostic Model Using Bioinformatics and Machine Learning
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Abstract

Introduction

Alzheimer's disease (AD) represents the most common neurodegenerative disorder, characterized by progressive cognitive decline and memory loss. Despite the recognition of mitochondrial dysfunction as a critical factor in the pathogenesis of AD, the specific molecular mechanisms remain largely undefined.

Method

This study aimed to identify novel biomarkers and therapeutic strategies associated with mitochondrial dysfunction in AD by employing bioinformatics combined with machine learning methodologies. We performed Weighted Gene Co-expression Network Analysis (WGCNA) utilizing gene expression data from the NCBI Gene Expression Omnibus (GEO) database and isolated mitochondria-related genes through the MitoCarta3.0 database. By intersecting WGCNA-derived module genes with identified mitochondrial genes, we compiled a list of 60 mitochondrial dysfunction-related genes (MRGs) significantly enriched in pathways pertinent to mitochondrial function, such as the citrate cycle and oxidative phosphorylation.

Results

Employing machine learning techniques, including random forest and LASSO, along with the CytoHubba algorithm, we identified key genes with strong diagnostic potential, such as ACO2, CS, MRPS27, SDHA, SLC25A20, and SYNJ2BP, verified through ROC analysis. Furthermore, an interaction network involving miRNA-MRGs-transcription factors and a protein-drug interaction network revealed potential therapeutic compounds such as Congo red and kynurenic acid that target MRGs.

Conclusion

These findings delineate the intricate role of mitochondrial dysfunction in AD and highlight promising avenues for further exploration of biomarkers and therapeutic interventions in this devastating disease.

© 2024 Bentham Science Publishers
© 2024 The Author(s). Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-01-18

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References

  1. Gibbs D.M. Alzheimer’s dementia or alzheimer’s disease – What’s the difference and why should we care? Ageing Res. Rev. 2022 82 101779 10.1016/j.arr.2022.101779 36332757
    [Google Scholar]
  2. Athanasaki A. Melanis K. Tsantzali I. Stefanou M.I. Ntymenou S. Paraskevas S.G. Kalamatianos T. Boutati E. Lambadiari V. Voumvourakis K.I. Stranjalis G. Giannopoulos S. Tsivgoulis G. Paraskevas G.P. Type 2 diabetes mellitus as a risk factor for alzheimer’s disease: Review and meta-analysis. Biomedicines 2022 10 4 778 10.3390/biomedicines10040778 35453527
    [Google Scholar]
  3. Ashleigh T. Swerdlow R.H. Beal M.F. The role of mitochondrial dysfunction in alzheimer’s disease pathogenesis. Alzheimers Dement. 2023 19 1 333 342 10.1002/alz.12683 35522844
    [Google Scholar]
  4. Perez Ortiz J.M. Swerdlow R.H. Mitochondrial dysfunction in alzheimer’s disease: Role in pathogenesis and novel therapeutic opportunities. Br. J. Pharmacol. 2019 176 18 3489 3507 10.1111/bph.14585 30675901
    [Google Scholar]
  5. Singh M. Agarwal V. Pancham P. Jindal D. Agarwal S. Rai S. Singh S. Gupta V. A comprehensive review and androgen deprivation therapy and its impact on alzheimer’s disease risk in older men with prostate cancer. Degener. Neurol. Neuromuscul. Dis. 2024 14 33 46 10.2147/DNND.S445130 38774717
    [Google Scholar]
  6. Cummings J. Lee G. Ritter A. Sabbagh M. Zhong K. Alzheimer’s disease drug development pipeline: 2019. Alzheimers Dement. (N. Y.) 2019 5 1 272 293 10.1016/j.trci.2019.05.008 31334330
    [Google Scholar]
  7. Egan M.F. Kost J. Voss T. Mukai Y. Aisen P.S. Cummings J.L. Tariot P.N. Vellas B. van Dyck C.H. Boada M. Zhang Y. Li W. Furtek C. Mahoney E. Harper Mozley L. Mo Y. Sur C. Michelson D. Randomized trial of verubecestat for prodromal alzheimer’s disease. N. Engl. J. Med. 2019 380 15 1408 1420 10.1056/NEJMoa1812840 30970186
    [Google Scholar]
  8. Honig L.S. Vellas B. Woodward M. Boada M. Bullock R. Borrie M. Hager K. Andreasen N. Scarpini E. Liu-Seifert H. Case M. Dean R.A. Hake A. Sundell K. Poole Hoffmann V. Carlson C. Khanna R. Mintun M. DeMattos R. Selzler K.J. Siemers E. Trial of Solanezumab for mild dementia due to alzheimer’s disease. N. Engl. J. Med. 2018 378 4 321 330 10.1056/NEJMoa1705971 29365294
    [Google Scholar]
  9. Tripathi P.N. Srivastava P. Sharma P. Tripathi M.K. Seth A. Tripathi A. Rai S.N. Singh S.P. Shrivastava S.K. Biphenyl-3-oxo-1,2,4-triazine linked piperazine derivatives as potential cholinesterase inhibitors with anti-oxidant property to improve the learning and memory. Bioorg. Chem. 2019 85 82 96 10.1016/j.bioorg.2018.12.017 30605887
    [Google Scholar]
  10. Srivastava P. Tripathi P.N. Sharma P. Rai S.N. Singh S.P. Srivastava R.K. Shankar S. Shrivastava S.K. Design and development of some phenyl benzoxazole derivatives as a potent acetylcholinesterase inhibitor with antioxidant property to enhance learning and memory. Eur. J. Med. Chem. 2019 163 116 135 10.1016/j.ejmech.2018.11.049 30503937
    [Google Scholar]
  11. Ramakrishna K. Karuturi P. Siakabinga Q. G. T.A. Krishnamurthy S. Singh S. Kumari S. Kumar G.S. Sobhia M.E. Rai S.N. Indole-3 carbinol and Diindolylmethane Mitigated β-Amyloid-induced Neurotoxicity and Acetylcholinesterase enzyme activity: In silico, in vitro, and network pharmacology study. Diseases 2024 12 184
    [Google Scholar]
  12. Golpich M. Amini E. Mohamed Z. Azman Ali R. Mohamed Ibrahim N. Ahmadiani A. Mitochondrial dysfunction and biogenesis in neurodegenerative diseases: Pathogenesis and treatment. CNS Neurosci. Ther. 2017 23 1 5 22 10.1111/cns.12655 27873462
    [Google Scholar]
  13. Sharma C. Kim S. Nam Y. Jung U.J. Kim S.R. Mitochondrial dysfunction as a driver of cognitive impairment in alzheimer’s disease. Int. J. Mol. Sci. 2021 22 9 4850 10.3390/ijms22094850 34063708
    [Google Scholar]
  14. Rai S.N. Singh C. Singh A. Singh M.P. Singh B.K. Mitochondrial dysfunction: A potential therapeutic target to treat alzheimer’s disease. Mol. Neurobiol. 2020 57 7 3075 3088 10.1007/s12035‑020‑01945‑y 32462551
    [Google Scholar]
  15. Arnst N. Redolfi N. Lia A. Bedetta M. Greotti E. Pizzo P. Mitochondrial Ca2+ signaling and bioenergetics in alzheimer’s disease. Biomedicines 2022 10 12 3025 10.3390/biomedicines10123025 36551781
    [Google Scholar]
  16. Jurcău M.C. Andronie-Cioara F.L. Jurcău A. Marcu F. Ţiț D.M. Pașcalău N. Nistor-Cseppentö D.C. The link between oxidative stress, mitochondrial dysfunction and neuroinflammation in the pathophysiology of alzheimer’s disease: Therapeutic implications and future perspectives. Antioxidants 2022 11 11 2167 10.3390/antiox11112167 36358538
    [Google Scholar]
  17. Swerdlow R.H. Mitochondria and mitochondrial cascades in alzheimer’s disease. J. Alzheimers Dis. 2018 62 3 1403 1416 10.3233/JAD‑170585 29036828
    [Google Scholar]
  18. Gao J. Wang L. Liu J. Xie F. Su B. Wang X. Abnormalities of mitochondrial dynamics in neurodegenerative diseases. Antioxidants 2017 6 2 25 10.3390/antiox6020025 28379197
    [Google Scholar]
  19. Lin M.T. Beal M.F. Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature 2006 443 7113 787 795 10.1038/nature05292 17051205
    [Google Scholar]
  20. Weidling I. Swerdlow R.H. Mitochondrial dysfunction and stress responses in alzheimer’s disease. Biology (Basel) 2019 8 2 39 10.3390/biology8020039 31083585
    [Google Scholar]
  21. Piras I.S. Krate J. Delvaux E. Nolz J. Mastroeni D.F. Persico A.M. Jepsen W.M. Beach T.G. Huentelman M.J. Coleman P.D. Transcriptome changes in the alzheimer’s disease middle Temporal Gyrus: Importance of RNA metabolism and mitochondria-associated membrane genes. J. Alzheimers Dis. 2019 70 3 691 713 10.3233/JAD‑181113 31256118
    [Google Scholar]
  22. Liang W.S. Dunckley T. Beach T.G. Grover A. Mastroeni D. Walker D.G. Caselli R.J. Kukull W.A. McKeel D. Morris J.C. Hulette C. Schmechel D. Alexander G.E. Reiman E.M. Rogers J. Stephan D.A. Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol. Genomics 2007 28 3 311 322 10.1152/physiolgenomics.00208.2006 17077275
    [Google Scholar]
  23. Readhead B. Haure-Mirande J.V. Funk C.C. Richards M.A. Shannon P. Haroutunian V. Sano M. Liang W.S. Beckmann N.D. Price N.D. Reiman E.M. Schadt E.E. Ehrlich M.E. Gandy S. Dudley J.T. Multiscale analysis of independent alzheimer’s cohorts finds disruption of molecular, genetic, and clinical networks by human Herpesvirus. Neuron 2018 99 1 64 82.e7 10.1016/j.neuron.2018.05.023 29937276
    [Google Scholar]
  24. Wang B. Fu C. Wei Y. Xu B. Yang R. Li C. Qiu M. Yin Y. Qin D. Ferroptosis-related biomarkers for alzheimer’s disease: Identification by bioinformatic analysis in hippocampus. Front. Cell. Neurosci. 2022 16 1023947 10.3389/fncel.2022.1023947 36467613
    [Google Scholar]
  25. Hamarsha A. Balachandran K. Sailan A.T. Nasruddin N.S. Predicting key genes and therapeutic molecular modelling to explain the association between Porphyromonas gingivalis (P. gingivalis) and alzheimer’s disease (AD). Int. J. Mol. Sci. 2023 24 6 5432 10.3390/ijms24065432 36982508
    [Google Scholar]
  26. Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 43 7 e47 e47 10.1093/nar/gkv007 25605792
    [Google Scholar]
  27. Gautier L. Cope L. Bolstad B.M. Irizarry R.A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004 20 3 307 315 10.1093/bioinformatics/btg405 14960456
    [Google Scholar]
  28. Wu T. Hu E. Xu S. Chen M. Guo P. Dai Z. Feng T. Zhou L. Tang W. Zhan L. Fu X. Liu S. Bo X. Yu G. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb.) 2021 2 3 100141 10.1016/j.xinn.2021.100141 34557778
    [Google Scholar]
  29. Chen H. Boutros P.C. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 2011 12 1 35 10.1186/1471‑2105‑12‑35 21269502
    [Google Scholar]
  30. Shannon P. Markiel A. Ozier O. Baliga N.S. Wang J.T. Ramage D. Amin N. Schwikowski B. Ideker T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 13 11 2498 2504 10.1101/gr.1239303 14597658
    [Google Scholar]
  31. Chin C.H. Chen S.H. Wu H.H. Ho C.W. Ko M.T. Lin C.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014 8 S4 Suppl. 4 S11 10.1186/1752‑0509‑8‑S4‑S11 25521941
    [Google Scholar]
  32. Sing T. Sander O. Beerenwinkel N. Lengauer T. ROCR: Visualizing classifier performance in R. Bioinformatics 2005 21 20 3940 3941 10.1093/bioinformatics/bti623 16096348
    [Google Scholar]
  33. Newman A.M. Liu C.L. Green M.R. Gentles A.J. Feng W. Xu Y. Hoang C.D. Diehn M. Alizadeh A.A. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 2015 12 5 453 457 10.1038/nmeth.3337 25822800
    [Google Scholar]
  34. Yoo M. Shin J. Kim J. Ryall K.A. Lee K. Lee S. Jeon M. Kang J. Tan A.C. DSigDB: Drug signatures database for gene set analysis. Bioinformatics 2015 31 18 3069 3071 10.1093/bioinformatics/btv313 25990557
    [Google Scholar]
  35. Li Y. Xia X. Wang Y. Zheng J.C. Mitochondrial dysfunction in microglia: A novel perspective for pathogenesis of alzheimer’s disease. J. Neuroinflammation 2022 19 1 248 10.1186/s12974‑022‑02613‑9 36203194
    [Google Scholar]
  36. Rudler D.L. Hughes L.A. Viola H.M. Hool L.C. Rackham O. Filipovska A. Fidelity and coordination of mitochondrial protein synthesis in health and disease. J. Physiol. 2021 599 14 3449 3462 10.1113/JP280359 32710561
    [Google Scholar]
  37. Priesnitz C. Becker T. Pathways to balance mitochondrial translation and protein import. Genes Dev. 2018 32 19-20 1285 1296 10.1101/gad.316547.118 30275044
    [Google Scholar]
  38. Larsson N.G. Wang J. Wilhelmsson H. Oldfors A. Rustin P. Lewandoski M. Barsh G.S. Clayton D.A. Mitochondrial transcription factor A is necessary for mtDNA maintance and embryogenesis in mice. Nat. Genet. 1998 18 3 231 236 10.1038/ng0398‑231 9500544
    [Google Scholar]
  39. Yamada E.S. Respondek G. Müssner S. de Andrade A. Höllerhage M. Depienne C. Rastetter A. Tarze A. Friguet B. Salama M. Champy P. Oertel W.H. Höglinger G.U. Annonacin, a natural lipophilic mitochondrial complex I inhibitor, increases phosphorylation of tau in the brain of FTDP-17 transgenic mice. Exp. Neurol. 2014 253 113 125 10.1016/j.expneurol.2013.12.017 24389273
    [Google Scholar]
  40. Schirrmacher V. Mitochondria at work: New insights into regulation and dysregulation of cellular energy supply and metabolism. Biomedicines 2020 8 11 526 10.3390/biomedicines8110526 33266387
    [Google Scholar]
  41. Sang C. Philbert S.A. Hartland D. Unwin R.D. Dowsey A.W. Xu J. Cooper G.J.S. Coenzyme A-dependent Tricarboxylic acid cycle enzymes are decreased in alzheimer’s disease consistent with Cerebral Pantothenate deficiency. Front. Aging Neurosci. 2022 14 893159 10.3389/fnagi.2022.893159 35754968
    [Google Scholar]
  42. Bell S.M. Barnes K. De Marco M. Shaw P.J. Ferraiuolo L. Blackburn D.J. Venneri A. Mortiboys H. Mitochondrial dysfunction in alzheimer’s disease: A biomarker of the future? Biomedicines 2021 9 1 63 10.3390/biomedicines9010063 33440662
    [Google Scholar]
  43. Li J. Zhang Y. Lu T. Liang R. Wu Z. Liu M. Qin L. Chen H. Yan X. Deng S. Zheng J. Liu Q. Identification of diagnostic genes for both alzheimer’s disease and metabolic syndrome by the machine learning algorithm. Front. Immunol. 2022 13 1037318 10.3389/fimmu.2022.1037318 36405716
    [Google Scholar]
  44. Lai Y. Lin P. Lin F. Chen M. Lin C. Lin X. Wu L. Zheng M. Chen J. Identification of immune microenvironment subtypes and signature genes for alzheimer’s disease diagnosis and risk prediction based on explainable machine learning. Front. Immunol. 2022 13 1046410 10.3389/fimmu.2022.1046410 36569892
    [Google Scholar]
  45. Davies S.M.K. Lopez Sanchez M.I.G. Narsai R. Shearwood A.M.J. Razif M.F.M. Small I.D. Whelan J. Rackham O. Filipovska A. MRPS27 is a pentatricopeptide repeat domain protein required for the translation of mitochondrially encoded proteins. FEBS Lett. 2012 586 20 3555 3561 10.1016/j.febslet.2012.07.043 22841715
    [Google Scholar]
  46. Renkema G.H. Wortmann S.B. Smeets R.J. Venselaar H. Antoine M. Visser G. Ben-Omran T. van den Heuvel L.P. Timmers H.J.L.M. Smeitink J.A. Rodenburg R.J.T. SDHA mutations causing a multisystem mitochondrial disease: Novel mutations and genetic overlap with hereditary tumors. Eur. J. Hum. Genet. 2015 23 2 202 209 10.1038/ejhg.2014.80 24781757
    [Google Scholar]
  47. Dröse S. Differential effects of complex II on mitochondrial ROS production and their relation to cardioprotective pre- and postconditioning. Biochim. Biophys. Acta Bioenerg. 2013 1827 5 578 587 10.1016/j.bbabio.2013.01.004 23333272
    [Google Scholar]
  48. Ramon De Lucas J. Ramon De Lucas J. Indiveri C. Ramon De Lucas J. Indiveri C. Tonazzi A. Perez P. Giangregorio N. Iacobazzi V. Palmieri F. Functional characterization of residues within the carnitine/acylcarnitine translocase RX 2 PANAAXF distinct motif. Mol. Membr. Biol. 2008 25 2 152 163 10.1080/09687680701697476 18307102
    [Google Scholar]
  49. Rui L. Energy metabolism in the liver. Compr. Physiol. 2014 4 1 177 197 10.1002/cphy.c130024 24692138
    [Google Scholar]
  50. Castellano C.A. Nugent S. Paquet N. Tremblay S. Bocti C. Lacombe G. Imbeault H. Turcotte É. Fulop T. Cunnane S.C. Lower brain 18F-fluorodeoxyglucose uptake but normal 11C-acetoacetate metabolism in mild alzheimer’s disease dementia. J. Alzheimers Dis. 2014 43 4 1343 1353 10.3233/JAD‑141074 25147107
    [Google Scholar]
  51. Pourshafie N. Masati E. Lopez A. Bunker E. Snyder A. Edwards N.A. Winkelsas A.M. Fischbeck K.H. Grunseich C. Altered SYNJ2BP-mediated mitochondrial-ER contacts in motor neuron disease. Neurobiol. Dis. 2022 172 105832 10.1016/j.nbd.2022.105832 35907632
    [Google Scholar]
  52. Leal N.S. Dentoni G. Schreiner B. Naia L. Piras A. Graff C. Cattaneo A. Meli G. Hamasaki M. Nilsson P. Ankarcrona M. Amyloid β-peptide increases mitochondria-endoplasmic reticulum contact altering mitochondrial function and autophagosome formation in alzheimer’s disease-related models. Cells 2020 9 12 2552 10.3390/cells9122552 33260715
    [Google Scholar]
  53. Wang W. Zhao F. Ma X. Perry G. Zhu X. Mitochondria dysfunction in the pathogenesis of alzheimer’s disease: Recent advances. Mol. Neurodegener. 2020 15 1 30 10.1186/s13024‑020‑00376‑6 32471464
    [Google Scholar]
  54. Sadat R. Barca E. Masand R. Donti T.R. Naini A. De Vivo D.C. DiMauro S. Hanchard N.A. Graham B.H. Functional cellular analyses reveal energy metabolism defect and mitochondrial DNA depletion in a case of mitochondrial aconitase deficiency. Mol. Genet. Metab. 2016 118 1 28 34 10.1016/j.ymgme.2016.03.004 26992325
    [Google Scholar]
  55. Ge Y.D. Hou S.L. Jiang L.L. Su F.Z. Wang P. Expression and characterization of a thermostable citrate synthase from Microcystis aeruginosa PCC7806. FEMS Microbiol. Lett. 2019 366 19 fnz236 10.1093/femsle/fnz236 31755935
    [Google Scholar]
  56. Huang J. Song Z. Wei B. Li Q. Lin P. Li H. Dong K. Immunological evaluation of patients with alzheimer’s disease based on mitogen-stimulated cytokine productions and mitochondrial DNA indicators. BMC Psychiatry 2023 23 1 145 10.1186/s12888‑023‑04634‑x 36890488
    [Google Scholar]
  57. Onyango I.G. Jauregui G.V. Čarná M. Bennett J.P. Jr Stokin G.B. Neuroinflammation in alzheimer’s disease. Biomedicines 2021 9 5 524 10.3390/biomedicines9050524 34067173
    [Google Scholar]
  58. Zhang B. Gaiteri C. Bodea L.G. Wang Z. McElwee J. Podtelezhnikov A.A. Zhang C. Xie T. Tran L. Dobrin R. Fluder E. Clurman B. Melquist S. Narayanan M. Suver C. Shah H. Mahajan M. Gillis T. Mysore J. MacDonald M.E. Lamb J.R. Bennett D.A. Molony C. Stone D.J. Gudnason V. Myers A.J. Schadt E.E. Neumann H. Zhu J. Emilsson V. Integrated systems approach identifies genetic nodes and networks in late-onset alzheimer’s disease. Cell 2013 153 3 707 720 10.1016/j.cell.2013.03.030 23622250
    [Google Scholar]
  59. Zhang Y. Miao Y. Tan J. Chen F. Lei P. Zhang Q. Identification of mitochondrial related signature associated with immune microenvironment in alzheimer’s disease. J. Transl. Med. 2023 21 1 458 10.1186/s12967‑023‑04254‑9 37434203
    [Google Scholar]
  60. Demir S.A. Timur Z.K. Ateş N. Martínez L.A. Seyrantepe V. GM2 ganglioside accumulation causes neuroinflammation and behavioral alterations in a mouse model of early onset Tay-Sachs disease. J. Neuroinflammation 2020 17 1 277 10.1186/s12974‑020‑01947‑6 32951593
    [Google Scholar]
  61. Zenaro E. Pietronigro E. Bianca V.D. Piacentino G. Marongiu L. Budui S. Turano E. Rossi B. Angiari S. Dusi S. Montresor A. Carlucci T. Nanì S. Tosadori G. Calciano L. Catalucci D. Berton G. Bonetti B. Constantin G. Neutrophils promote alzheimer’s disease–like pathology and cognitive decline via LFA-1 integrin. Nat. Med. 2015 21 8 880 886 10.1038/nm.3913 26214837
    [Google Scholar]
  62. Kolaczkowska E. Kubes P. Neutrophil recruitment and function in health and inflammation. Nat. Rev. Immunol. 2013 13 3 159 175 10.1038/nri3399 23435331
    [Google Scholar]
  63. Prinz M. Priller J. The role of peripheral immune cells in the CNS in steady state and disease. Nat. Neurosci. 2017 20 2 136 144 10.1038/nn.4475 28092660
    [Google Scholar]
  64. Hao J. Campagnolo D. Liu R. Piao W. Shi S. Hu B. Xiang R. Zhou Q. Vollmer T. Van Kaer L. La Cava A. Shi F.D. Interleukin‐2/interleukin‐2 antibody therapy induces target organ natural killer cells that inhibit central nervous system inflammation. Ann. Neurol. 2011 69 4 721 734 10.1002/ana.22339 21425186
    [Google Scholar]
  65. Liu Z. Li H. Pan S. Discovery and validation of key biomarkers based on immune infiltrates in alzheimer’s disease. Front. Genet. 2021 12 658323 10.3389/fgene.2021.658323 34276768
    [Google Scholar]
  66. Zhang Y. Fung I.T.H. Sankar P. Chen X. Robison L.S. Ye L. D’Souza S.S. Salinero A.E. Kuentzel M.L. Chittur S.V. Zhang W. Zuloaga K.L. Yang Q. Depletion of NK cells improves cognitive function in the alzheimer disease mouse model. J. Immunol. 2020 205 2 502 510 10.4049/jimmunol.2000037 32503894
    [Google Scholar]
  67. You X. Tian J. Zhang H. Guo Y. Yang J. Zhu C. Song M. Wang P. Liu Z. Cancilla J. Lu W. Glorieux C. Wen S. Du H. Huang P. Hu Y. Loss of mitochondrial aconitase promotes colorectal cancer progression via SCD1-mediated lipid remodeling. Mol. Metab. 2021 48 101203 10.1016/j.molmet.2021.101203 33676027
    [Google Scholar]
  68. Lyu C. Meng Y. Zhang X. Yang J. Shen B. Two enzymes contribute to citrate production in the mitochondrion of Toxoplasma gondii. J. Biol. Chem. 2024 300 8 107565 10.1016/j.jbc.2024.107565 39002675
    [Google Scholar]
  69. Palmieri E.M. Gonzalez-Cotto M. Baseler W.A. Davies L.C. Ghesquière B. Maio N. Rice C.M. Rouault T.A. Cassel T. Higashi R.M. Lane A.N. Fan T.W.M. Wink D.A. McVicar D.W. Nitric oxide orchestrates metabolic rewiring in M1 macrophages by targeting aconitase 2 and pyruvate dehydrogenase. Nat. Commun. 2020 11 1 698 10.1038/s41467‑020‑14433‑7 32019928
    [Google Scholar]
  70. Jornayvaz F.R. Shulman G.I. Regulation of mitochondrial biogenesis. Essays Biochem. 2010 47 69 84 10.1042/bse0470069 20533901
    [Google Scholar]
  71. Yang Z.F. Drumea K. Mott S. Wang J. Rosmarin A.G. GABP transcription factor (nuclear respiratory factor 2) is required for mitochondrial biogenesis. Mol. Cell. Biol. 2014 34 17 3194 3201 10.1128/MCB.00492‑12 24958105
    [Google Scholar]
  72. Galbraith L.C.A. Mui E. Nixon C. Hedley A. Strachan D. MacKay G. Sumpton D. Sansom O.J. Leung H.Y. Ahmad I. PPAR-gamma induced AKT3 expression increases levels of mitochondrial biogenesis driving prostate cancer. Oncogene 2021 40 13 2355 2366 10.1038/s41388‑021‑01707‑7 33654198
    [Google Scholar]
  73. Schrag M. Mueller C. Zabel M. Crofton A. Kirsch W.M. Ghribi O. Squitti R. Perry G. Oxidative stress in blood in alzheimer’s disease and mild cognitive impairment: A meta-analysis. Neurobiol. Dis. 2013 59 100 110 10.1016/j.nbd.2013.07.005 23867235
    [Google Scholar]
  74. Lorenzo A. Yankner B.A. Amyloid fibril toxicity in alzheimer’s disease and diabetes. Ann. N. Y. Acad. Sci. 1996 777 1 89 95 10.1111/j.1749‑6632.1996.tb34406.x 8624132
    [Google Scholar]
  75. Hirakura Y. Yiu W.W. Yamamoto A. Kagan B.L. Amyloid peptide channels: Blockade by zinc and inhibition by Congo red (amyloid channel block). Amyloid 2000 7 3 194 199 10.3109/13506120009146834 11019860
    [Google Scholar]
  76. Schwarcz R. Bruno J.P. Muchowski P.J. Wu H.Q. Kynurenines in the mammalian brain: When physiology meets pathology. Nat. Rev. Neurosci. 2012 13 7 465 477 10.1038/nrn3257 22678511
    [Google Scholar]
  77. Kozak R. Campbell B.M. Strick C.A. Horner W. Hoffmann W.E. Kiss T. Chapin D.S. McGinnis D. Abbott A.L. Roberts B.M. Fonseca K. Guanowsky V. Young D.A. Seymour P.A. Dounay A. Hajos M. Williams G.V. Castner S.A. Reduction of brain kynurenic acid improves cognitive function. J. Neurosci. 2014 34 32 10592 10602 10.1523/JNEUROSCI.1107‑14.2014 25100593
    [Google Scholar]
  78. Costa B. Vale N. Understanding Lamotrigine’s role in the CNS and possible future evolution. Int. J. Mol. Sci. 2023 24 7 6050 10.3390/ijms24076050 37047022
    [Google Scholar]
  79. Wang K. Fernandez-Escobar A. Han S. Zhu P. Wang J.H. Sun Y. Lamotrigine reduces inflammatory response and ameliorates executive function deterioration in an alzheimer’s-like mouse model. BioMed Res. Int. 2016 2016 1 9 10.1155/2016/7810196 28042572
    [Google Scholar]
  80. Sogawa R. Mochinaga S. Inaba T. Matsushima J. Mizoguchi Y. Kawashima T. Fujito H. Monji A. Safety of long-term use of lamotrigine for the treatment of psychiatric disorders. Clin. Neuropharmacol. 2016 39 6 295 298 10.1097/WNF.0000000000000174 27438184
    [Google Scholar]
  81. Liu J.L. Fan Y.G. Yang Z.S. Wang Z.Y. Guo C. Iron and alzheimer’s disease: From pathogenesis to therapeutic implications. Front. Neurosci. 2018 12 632 10.3389/fnins.2018.00632 30250423
    [Google Scholar]
  82. Volani C. Doerrier C. Demetz E. Haschka D. Paglia G. Lavdas A.A. Gnaiger E. Weiss G. Dietary iron loading negatively affects liver mitochondrial function. Metallomics 2017 9 11 1634 1644 10.1039/C7MT00177K 29026901
    [Google Scholar]
  83. Hall E.C. II Lee S.Y. Mairuae N. Simmons Z. Connor J.R. Expression of the HFE allelic variant H63D in SH-SY5Y cells affects tau phosphorylation at serine residues. Neurobiol. Aging 2011 32 8 1409 1419 10.1016/j.neurobiolaging.2009.08.012 19775775
    [Google Scholar]
/content/journals/car/10.2174/0115672050353736241218054012
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Development of a Novel Mitochondrial Dysfunction-Related Alzheimer's Disease Diagnostic Model Using Bioinformatics and Machine Learning
Bentham Science Publishers ; https://doi.org/10.2174/0115672050353736241218054012
/content/journals/car/10.2174/0115672050353736241218054012
/content/journals/car/10.2174/0115672050353736241218054012
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  • Article Type:     Research Article
Keywords: CytoHubba algorithm ; Alzheimer's disease ; devastating disease ; Machine learning

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