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Volume 26, Issue 3
  • ISSN: 1389-2029
  • E-ISSN: 1875-5488

Abstract

Background

Lactylation is increasingly recognized to play a crucial role in human health and diseases. However, its involvement in age-related macular degeneration (AMD) remains largely unclear.

Objectives

The aim of this study was to identify and characterize the pivotal lactylation-related genes and explore their underlying mechanism in AMD.

Methods

Gene expression profiles of AMD patients and control individuals were obtained and integrated from the GSE29801 and GSE50195 datasets. Differentially expressed genes (DEGs) were screened and intersected with lactylation-related genes for lactylation-related DEGs. Machine learning algorithms were used to identify hub genes associated with AMD. Subsequently, the selected hub genes were subject to correlation analysis, and reverse transcription quantitative real-time PCR (RT-qPCR) was used to detect the expression of hub genes in AMD patients and healthy control individuals.

Results

A total of 68 lactylation-related DEGs in AMD were identified, and seven genes, including ,, , , , , and were selected as key genes. RT-qPCR analysis validated that all 7 key genes were down-regulated in AMD patients.

Conclusion

We identified seven lactylation-related key genes potentially associated with the progression of AMD, which might deepen our understanding of the underlying mechanisms involved in AMD and provide clues for the targeted therapy.

© 2025 Bentham Science Publishers
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2025-01-02
2025-05-12

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References

  1. ThomasC.J. MirzaR.G. GillM.K. Age-related macular degeneration.Med. Clin. North Am.2021105347349110.1016/j.mcna.2021.01.00333926642
     [Google Scholar]
  2. StahlA. The diagnosis and treatment of age-related macular degeneration.Dtsch. Arztebl. Int.202011729-3051352010.3238/arztebl.2020.051333087239
     [Google Scholar]
  3. Al-ZamilW. YassinS. Recent developments in age-related macular degeneration: A review.Clin. Interv. Aging2017121313133010.2147/CIA.S14350828860733
     [Google Scholar]
  4. NashineS. Potential therapeutic candidates for age-related macular degeneration (AMD).Cells2021109248310.3390/cells1009248334572131
     [Google Scholar]
  5. LeeQ. ChanW.C. QuX. SunY. AbdelkarimH. LeJ. SaqibU. SunM.Y. KruseK. BanerjeeA. HitchinsonB. GeyerM. HuangF. GuaiquilV. MutsoA.A. SandersM. RosenblattM.I. Maienschein-ClineM. LawrenceM.S. GaponenkoV. MalikA.B. KomarovaY.A. End binding-3 inhibitor activates regenerative program in age-related macular degeneration.Cell Rep. Med.202341010122310.1016/j.xcrm.2023.10122337794584
     [Google Scholar]
  6. WilliamsonR.C. SelvamA. SantV. PatelM. BollepalliS.C. VupparaboinaK.K. SahelJ.A. ChhablaniJ. Radiomics-based prediction of anti-vegf treatment response in neovascular age-related macular degeneration with pigment epithelial detachment.Transl. Vis. Sci. Technol.20231210310.1167/tvst.12.10.337792693
     [Google Scholar]
  7. RosenfeldP.J. ChengY. ShenM. GregoriG. WangR.K. Unleashing the power of optical attenuation coefficients to facilitate segmentation strategies in OCT imaging of age-related macular degeneration: Perspective.Biomed. Opt. Express20231494947496310.1364/BOE.49608037791280
     [Google Scholar]
  8. HamatiJ. PrashanthiS. NarayananR. SahooN. DasA.V. RaniP.K. BeheraU.C. KhannaR. MurthyG.V.S. Prevalence of age-related macular degeneration and associated factors in Indian cohort in a tertiary care setting.Indian J. Ophthalmol.202371103361336610.4103/IJO.IJO_199_2337787236
     [Google Scholar]
  9. ShawL. KhannaS. HymanM.J. HamS. BlitzerA. ParvarS.P. SooJ. FloresA. HariprasadS. SkondraD. Interactions of Metformin and other medications in reducing the odds of age-related macular degeneration in a diabetic cohort.Retina202310.1097/IAE.0000000000003949
     [Google Scholar]
  10. ZhaoT. LiJ. WangY. GuoX. SunY. Integrative metabolome and lipidome analyses of plasma in neovascular macular degeneration.Heliyon2023910e2032910.1016/j.heliyon.2023.e2032937780745
     [Google Scholar]
  11. HuangZ.W. ZhangX.N. ZhangL. LiuL.L. ZhangJ.W. SunY.X. XuJ.Q. LiuQ. LongZ.J. STAT5 promotes PD-L1 expression by facilitating histone lactylation to drive immunosuppression in acute myeloid leukemia.Signal Transduct. Target. Ther.20238139110.1038/s41392‑023‑01605‑237777506
     [Google Scholar]
  12. ZhaoS. LiuJ. WuQ. ZhouX. Role of histone lactylation interference RNA m6A modification and immune microenvironment homeostasis in pulmonary arterial hypertension.Front. Cell Dev. Biol.202311126864610.3389/fcell.2023.126864637771377
     [Google Scholar]
  13. SuJ. ZhengZ. BianC. ChangS. BaoJ. YuH. XinY. JiangX. Functions and mechanisms of lactylation in carcinogenesis and immunosuppression.Front. Immunol.202314125306410.3389/fimmu.2023.125306437646027
     [Google Scholar]
  14. ChengZ. HuangH. LiM. LiangX. TanY. ChenY. Lactylation-related gene signature effectively predicts prognosis and treatment responsiveness in Hepatocellular Carcinoma.Pharmaceuticals (Basel)202316564410.3390/ph1605064437242427
     [Google Scholar]
  15. WanN. WangN. YuS. ZhangH. TangS. WangD. LuW. LiH. DelafieldD.G. KongY. WangX. ShaoC. LvL. WangG. TanR. WangN. HaoH. YeH. Cyclic immonium ion of lactyllysine reveals widespread lactylation in the human proteome.Nat. Methods202219785486410.1038/s41592‑022‑01523‑135761067
     [Google Scholar]
  16. LiuX. ZhangY. LiW. ZhouX. Lactylation, an emerging hallmark of metabolic reprogramming: Current progress and open challenges.Front. Cell Dev. Biol.20221097202010.3389/fcell.2022.97202036092712
     [Google Scholar]
  17. EngebretsenS. BohlinJ. Statistical predictions with glmnet.Clin. Epigenetics201911112310.1186/s13148‑019‑0730‑131443682
     [Google Scholar]
  18. GargeN.R. BobashevG. EgglestonB. Random forest methodology for model-based recursive partitioning: The mobForest package for R.BMC Bioinformatics201314112510.1186/1471‑2105‑14‑12523577585
     [Google Scholar]
  19. SanzH. ValimC. VegasE. OllerJ.M. ReverterF. SVM-RFE: Selection and visualization of the most relevant features through non-linear kernels.BMC Bioinformatics201819143210.1186/s12859‑018‑2451‑430453885
     [Google Scholar]
  20. UrbańskaK. StępieńP.W. NowakowskaK.N. StefaniakM. OsialN. ChorągiewiczT. ToroM.D. NowomiejskaK. RejdakR. The role of Dysregulated miRNAs in the pathogenesis, diagnosis and treatment of age-related macular degeneration.Int. J. Mol. Sci.20222314776110.3390/ijms2314776135887109
     [Google Scholar]
  21. GemenetziM. LoteryA.J. Epigenetics in age-related macular degeneration: New discoveries and future perspectives.Cell. Mol. Life Sci.202077580781810.1007/s00018‑019‑03421‑w31897542
     [Google Scholar]
  22. AlfaarA.S. StürzbecherL. Diedrichs-MöhringM. LamM. RoubeixC. RitterJ. SchumannK. AnnamalaiB. PompösI.M. RohrerB. SennlaubF. ReichhartN. WildnerG. StraußO. FoxP3 expression by retinal pigment epithelial cells: transcription factor with potential relevance for the pathology of age-related macular degeneration.J. Neuroinflammation202219126010.1186/s12974‑022‑02620‑w36273134
     [Google Scholar]
  23. ChoudharyM. MalekG. Potential therapeutic targets for age-related macular degeneration: The nuclear option.Prog. Retin. Eye Res.20239410113010.1016/j.preteyeres.2022.10113036220751
     [Google Scholar]
  24. LadE.M. FingerR.P. GuymerR. Biomarkers for the progression of intermediate age-related macular degeneration.Ophthalmol. Ther.20231262917294110.1007/s40123‑023‑00807‑937773477
     [Google Scholar]
  25. NamS.W. NohH. YoonJ.M. KongM. HamD.I. Macular neovascularization in eyes with pachydrusenSci Rep20211117495
     [Google Scholar]
  26. InterlenghiM. SborgiaG. VenturiA. SardoneR. PastoreV. BosciaG. LandiniL. ScottiG. NiroA. MoscaraF. BandiL. SalvatoreC. CastiglioniI. A radiomic-based machine learning system to diagnose age-related macular degeneration from ultra-widefield fundus Retinography.Diagnostics (Basel)20231318296510.3390/diagnostics1318296537761333
     [Google Scholar]
  27. KeX. JiangH. LiQ. LuoS. QinY. LiJ. XieQ. ZhengQ. Preclinical evaluation of KH631, a novel rAAV8 gene therapy product for neovascular age-related macular degeneration.Mol. Ther.202331113308332110.1016/j.ymthe.2023.09.01937752703
     [Google Scholar]
  28. Vidal-GilL. Sancho-PelluzJ. ZrennerE. OltraM. SahabogluA. Poly ADP ribosylation and extracellular vesicle activity in rod photoreceptor degeneration.Sci. Rep.201991375810.1038/s41598‑019‑40215‑330842506
     [Google Scholar]
  29. KarthikeyanB. HariniL. KrishnakumarV. KannanV.R. SundarK. KathiresanT. Insights on the involvement of (–)-epigallocatechin gallate in ER stress-mediated apoptosis in age-related macular degeneration.Apoptosis2017221728510.1007/s10495‑016‑1318‑227778132
     [Google Scholar]
  30. KalariyaN.M. RamanaK.V. SrivastavaS.K. van KuijkF.J.G.M. Post-translational protein modification by carotenoid cleavage products.Biofactors201137210411610.1002/biof.15221488133
     [Google Scholar]
  31. LiY. ZhangR. HeiH. Advances in post-translational modifications of proteins and cancer immunotherapy.Front. Immunol.202314122939710.3389/fimmu.2023.122939737675097
     [Google Scholar]
  32. HouJ. WenX. LongP. XiongS. LiuH. CaiL. DengH. ZhangZ. The role of post-translational modifications in driving abnormal cardiovascular complications at high altitude.Front. Cardiovasc. Med.2022988630010.3389/fcvm.2022.88630036186970
     [Google Scholar]
  33. SongY. LiuX. StielowJ.B. de HoogS. LiR. Post-translational changes in Phialophora verrucosa via lysine lactylation during prolonged presence in a patient with a CARD9-related immune disorder.Front. Immunol.20221396645710.3389/fimmu.2022.96645736003392
     [Google Scholar]
  34. RhoH. TerryA.R. ChronisC. HayN. Hexokinase 2-mediated gene expression via histone lactylation is required for hepatic stellate cell activation and liver fibrosis.Cell Metab.202335814061423.e810.1016/j.cmet.2023.06.01337463576
     [Google Scholar]
  35. ZhangN. ZhangY. XuJ. WangP. WuB. LuS. LuX. YouS. HuangX. LiM. ZouY. LiuM. ZhaoY. SunG. WangW. GengD. LiuJ. CaoL. SunY. α-myosin heavy chain lactylation maintains sarcomeric structure and function and alleviates the development of heart failure.Cell Res.202333967969810.1038/s41422‑023‑00844‑w37443257
     [Google Scholar]
  36. ZhangY. SunY. HuY. ZhengS. ShaoH. LinL. PanY. LiC. Porphyromonas gingivalis msRNA P.G_45033 induces amyloid-β production by enhancing glycolysis and histone lactylation in macrophages.Int. Immunopharmacol.202312111046810.1016/j.intimp.2023.11046837320870
     [Google Scholar]
  37. HuangW. SuJ. ChenX. LiY. XingZ. GuoL. LiS. ZhangJ. High-intensity interval training induces protein Lactylation in different tissues of mice with specificity and time dependence.Metabolites202313564710.3390/metabo1305064737233688
     [Google Scholar]
  38. ZhaoS. ZhangL. JiW. ShiY. LaiG. ChiH. HuangW. ChengC. Machine learning-based characterization of cuprotosis-related biomarkers and immune infiltration in parkinson’s disease.Front. Genet.202213101036110.3389/fgene.2022.101036136338988
     [Google Scholar]
  39. YangY. YiX. CaiY. ZhangY. XuZ. Immune-associated gene signatures and subtypes to predict the progression of Atherosclerotic Plaques based on machine learning.Front. Pharmacol.20221386562410.3389/fphar.2022.86562435559253
     [Google Scholar]
  40. BianQ. ChenJ. WuJ. DingF. LiX. MaQ. ZhangL. ZouX. ChenJ. Bioinformatics analysis of a TF-miRNA-lncRNA regulatory network in major depressive disorder.Psychiatry Res.202129911384210.1016/j.psychres.2021.11384233751989
     [Google Scholar]
  41. ZengY. CaoS. LiN. TangJ. LinG. Identification of key lipid metabolism-related genes in alzheimer’s disease.Lipids Health Dis.202322115510.1186/s12944‑023‑01918‑937736681
     [Google Scholar]
  42. WangX. ChenM. LiuL. ZengL. Integrated aqueous humor ceRNA and miRNA–TF–mRNA network analysis reveals potential molecular mechanisms governing primary open-angle glaucoma pathogenesis.Indian J. Ophthalmol.202371255355910.4103/ijo.IJO_1448_2236727359
     [Google Scholar]
  43. WangW. WangQ. WanD. SunY. WangL. ChenH. LiuC. PetersenR.B. LiJ. XueW. ZhengL. HuangK. Histone HIST1H1C/H1.2 regulates autophagy in the development of diabetic retinopathy.Autophagy201713594195410.1080/15548627.2017.129376828409999
     [Google Scholar]
  44. WeiT.T. ZhangM.Y. ZhengX.H. XieT.H. WangW. ZouJ. LiY. LiH.Y. CaiJ. WangX. TanJ. YangX. YaoY. ZhuL. Interferon-γ induces retinal pigment epithelial cell Ferroptosis by a JAK1-2/STAT1/SLC7A11 signaling pathway in age‐related macular degeneration.FEBS J.202228971968198310.1111/febs.1627234741776
     [Google Scholar]
  45. DhirachaikulpanichD. LiX. PorterL.F. ParaoanL. Integrated Microarray and RNAseq transcriptomic analysis of Retinal Pigment Epithelium/Choroid in age-related macular degeneration.Front. Cell Dev. Biol.2020880810.3389/fcell.2020.0080832984320
     [Google Scholar]
  46. SuY. YiY. LiL. ChenC. circRNA-miRNA-mRNA network in age-related macular degeneration: From construction to identification.Exp. Eye Res.202120310842710.1016/j.exer.2020.10842733383027
     [Google Scholar]
  47. ChoiY.A. JeongA. WooC.H. ChaS.C. ParkD.Y. SagongM. Aqueous microRNA profiling in age-related macular degeneration and polypoidal choroidal vasculopathy by next-generation sequencing.Sci. Rep.2023131127410.1038/s41598‑023‑28385‑736690666
     [Google Scholar]
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Bioinformatics Analysis of Lactylation-related Biomarkers and Potential Pathogenesis Mechanisms in Age-related Macular Degeneration
Curr. Genomics 26, 191 (2025); https://doi.org/10.2174/0113892029291661241114055924
/content/journals/cg/10.2174/0113892029291661241114055924
/content/journals/cg/10.2174/0113892029291661241114055924
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  • Article Type:     Research Article
Keyword(s): Age-related macular degeneration; bioinformatics; hub genes; immune infiltration; lactylation; machine learning

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