Differential gene expression of blood-based ABCA9, CNOT8, SESN1, UCP3, MAP2K1 and DDIT4 in Alzheimer’s disease


  • Ainon Zahariah Samsudin Brain Degeneration & Therapeutic Group, Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia.
  • Kalavathy Ramasamy Collaborative Drug Discovery Research (CDDR) Group, Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia.
  • Siong Meng Lim Collaborative Drug Discovery Research (CDDR) Group, Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia.
  • Ai Vyrn Chin Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia.
  • Maw Pin Tan Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia.
  • Shahrul Bahyah Kamaruzzaman Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia.
  • Baharudin Ibrahim Faculty of Pharmacy, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • Abu Bakar Abdul Majeed Brain Degeneration & Therapeutic Group, Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia.




Alzheimer’s disease, ATP-binding cassette, biomarkers, blood, transcriptomics


This study uncovered differential gene expression in blood to distinguish subjects with probable Alzheimer’s disease (AD) from normal elderly participants (non-demented controls, NDC). The participants were recruited via training (Phase 1) and validation cohorts (Phase 2). The changes of gene expression in blood samples from the training cohort (92 AD vs 92 NDC) were assessed using the microarray technology. The Partial Least Square Discrimination Analysis (PLSDA) was then used to develop a disease classifier algorithm (accuracy = 88.3%). Six differentially expressed genes  were validated through RT-qPCR using blood samples from the validation cohort [(25 AD, 25 NDC, 12 mild cognitive impairment (MCI) and 12 vascular dementia (VaD) subjects] . The PLSDA model indicated a good separation between AD and NDC [area under the receiver operating characteristic curve (ROC AUC) = 0.88]. ABCA9, CNOT8, SESN1, UCP3, MAP2K1 and DDIT4 were found to be differentially expressed between the two groups. Validation of the panel of six genes gave an overall accuracy of 82.0% (AUC=0.86). The ABCA9 mRNA level, which was significantly (p < 0.05) lower in the AD group, correctly classified 90.9% of all subjects (AUC=0.94). This group of  genes may be responsible for dysregulation of pathways related to inflammation, mitochondrial dysfunction, oxidative injury, DNA damage, apoptosis and lipid metabolism. The disease classifier algorithm discriminated probable AD from MCI and VaD at specificity of 83.3% and 75.0%, respectively. These findings warrant further validation of potential blood-based biomarkers in larger samples of clinical AD.


Alzheimer's Disease International. (2021). World Alzheimer Report 2021. Retrieved 2 June 2023, from https://www.alzint.org/resource/world-alzheimer-report-2021/

Amadoro, G., Ciotti, M. T., Costanzi, M., Cestari, V., Calissano, P., & Canu, N. (2006). NMDA receptor mediates tau-induced neurotoxicity by calpain and ERK/MAPK activation. Proceedings of the National Academy of Sciences (PNAS), 103(8), 2892–2897. https://doi.org/10.1073/pnas.0511065103

Bai, Z., Stamova, B., Xu, H., Ander, B. P., Wang, J., Jickling, G. C., Zhan, X., Liu, D., Han, G., Jin, L.-W., DeCarli, C., Lei, H., & Sharp, F. R. (2014). Distinctive RNA expression profiles in blood associated with Alzheimer disease after accounting for white matter hyperintensities. Alzheimer Disease and Associated Disorders, 28(3), 226–233. https://doi.org/10.1097/WAD.0000000000000022

Booij, B. B., Lindahl, T., Wetterberg, P., Skaane, N. V., Sæbø, S., Feten, G., Rye, P. D., Kristiansen, L. I., Hagen, N., Jensen, M., Bårdsen, K., Winblad, B., Sharma, P., & Lönneborg, A. (2011). A gene expression pattern in blood for the early detection of Alzheimer's disease. Journal of Alzheimer's Disease, 23(1), 109–119. https://doi.org/10.3233/JAD-2010-101518

Carlson, R. V., Boyd, K. M., & Webb, D. J. (2004). The revision of the Declaration of Helsinki: past, present and future. British Journal of Clinical Pharmacology, 57(6), 695–713. https://doi.org/10.1111/j.1365-2125.2004.02103.x

Chen, K.-D., Chang, P.-T., Ping, Y.-H., Lee, H.-C., Yeh, C.-W., & Wang, P.-N. (2011). Gene expression profiling of peripheral blood leukocytes identifies and validates ABCB1 as a novel biomarker for Alzheimer's disease. Neurobiology of Disease, 43(3), 698–705. https://doi.org/10.1016/j.nbd.2011.05.023

Cullen, N. C., Leuzy, A., Palmqvist, S., Janelidze, S., Stomrud, E., Pesini, P., Sarasa, L., Allué, J. A., Proctor, N. K., Zetterberg, H., Dage, J. L., Blennow, K., Mattsson-Carlgren, N., & Hansson, O. (2021). Individualized prognosis of cognitive decline and dementia in mild cognitive impairment based on plasma biomarker combinations. Nature Aging, 1, 114–123. https://doi.org/10.1038/s43587-020-00003-5

Donaghy, P. C., Cockell, S. J., Martin-Ruiz, C., Coxhead, J., Kane, J., Erskine, D., Koss, D., Taylor, J.-P., Morris, C. M., O'Brien, J. T., & Thomas, A. J. (2022). Blood mRNA expression in Alzheimer's disease and dementia with Lewy bodies. The American Journal of Geriatric Psychiatry, 30(9), 964–975. https://doi.org/10.1016/j.jagp.2022.02.003

ElAli, A., & Rivest, S. (2013). The role of ABCB1 and ABCA1 in beta-amyloid clearance at the neurovascular unit in Alzheimer's disease. Frontiers in Physiology, 4, 45. https://doi.org/10.3389/fphys.2013.00045.

Fehlbaum-Beurdeley, P., Jarrige-Le Prado, A. C., Pallares, D., Carrière, J., Guihal, C., Soucaille, C., Rouet, F., Drouin, D., Sol, O., Jordan, H., Wu, D., Lei, L., Einstein, R., Schweighoffer, F., & Bracco, L. (2010). Toward an Alzheimer's disease diagnosis via high-resolution blood gene expression. Alzheimer's & Dementia, 6(1), 25–38. https://doi.org/10.1016/j.jalz.2009.07.001

GBD 2019 Dementia Forecasting Collaborators. (2022). Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health, 7(2), e105–e125. https://doi.org/10.1016/S2468-2667(21)00249-8

Griswold, A. J., Sivasankaran, S. K., Van Booven, D., Gardner, O. K., Rajabli, F., Whitehead, P. L., Hamilton-Nelson, K. L., Adams, L. D., Scott, A. M., Hofmann, N. K., Vance, J. M., Cuccaro, M. L., Bush, W. S., Martin, E. R., Byrd, G. S., Haines, J. L., Pericak-Vance, M. A., & Beecham, G. W. (2020). Immune and inflammatory pathways implicated by whole blood transcriptomic analysis in a diverse ancestry Alzheimer's disease cohort. Journal of Alzheimer's Disease, 76(3), 1047–1060. https://doi.org/10.3233/JAD-190855

Lee, T., & Lee, H. (2020). Prediction of Alzheimer's disease using blood gene expression data. Scientific Reports, 10(1), 3485. https://doi.org/10.1038/s41598-020-60595-1

Li, G., Gu, H.-M., & Zhang, D.-W. (2013). ATP-binding cassette transporters and cholesterol translocation. IUBMB Life, 65(6), 505–512. https://doi.org/10.1002/iub.1165

Lovell, M. A., & Markesbery, W. R. (2007). Oxidative DNA damage in mild cognitive impairment and late-stage Alzheimer's disease. Nucleic Acids Research, 35(22), 7497–7504. https://doi.org/10.1093/nar/gkm821

Lunnon, K., Ibrahim, Z., Proitsi, P., Lourdusamy, A., Newhouse, S., Sattlecker, M., Furney, S., Saleem, M., Soininen, H., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Coppola, G., Geschwind, D., Simmons, A., Lovestone, S., Dobson, R., Hodges, A., & AddNeuroMed Consortium. (2012). Mitochondrial dysfunction and immune activation are detectable in early Alzheimer's disease blood. Journal of Alzheimer's Disease, 30(3), 685–710. https://doi.org/10.3233/jad-2012-111592

Lunnon, K., Sattlecker, M., Furney, S. J., Coppola, G., Simmons, A., Proitsi, P., Lupton, M. K., Lourdusamy, A., Johnston, C., Soininen, H., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Geschwind, D., Lovestone, S., Dobson, R., Hodges, A., & AddNeuroMed Consortium. (2013). A blood gene expression marker of early Alzheimer's disease. Journal of Alzheimer's Disease, 33(3), 737–753. https://doi.org/10.3233/JAD-2012-121363

Maes, O. C., Xu, S., Yu, B., Chertkow, H. M., Wang, E., & Schipper, H. M. (2007). Transcriptional profiling of Alzheimer blood mononuclear cells by microarray. Neurobiology of Aging, 28(12), 1795–1809. https://doi.org/10.1016/j.neurobiolaging.2006.08.004

Meyer, J. S., Xu, G., Thornby, J., Chowdhury, M. H., & Quach, M. (2002). Is mild cognitive impairment prodromal for vascular dementia like Alzheimer's disease? Stroke, 33(8), 1981–1985. https://doi.org/10.1161/01.STR.0000024432.34557.10

Mohd Hasni, D. S., Lim, S. M., Chin, A. V., Tan, M. P., Poi, P. J. H., Kamaruzzaman, S. B., Abdul Majeed, A. B., & Ramasamy, K. (2017). Peripheral cytokines, C-X-C motif ligand10 and interleukin-13, are associated with Malaysian Alzheimer's disease. Geriatrics & Gerontology International, 17(5), 839–846. https://doi.org/10.1111/ggi.12783

Nho, K., Nudelman, K., Allen, M., Hodges, A., Kim, S., Risacher, S. L., Apostolova, L. G., Lin, K., Lunnon, K., Wang, X., Burgess, J. D., Ertekin-Taner, N., Petersen, R. C., Wang, L., Qi, Z., He, A., Neuhaus, I., Patel, V., Foroud, T., Faber, K., Lovestone, M. S., Simmons, A., Weiner, M. W., & Saykin, A. J. (2020). Genome-wide transcriptome analysis identifies novel dysregulated genes implicated in Alzheimer's pathology. Alzheimer's & Dementia, 16(9), 1213–1223. https://doi.org/10.1002/alz.12092

Niculescu, A. B., Le-Niculescu, H., Roseberry, K., Wang, S., Hart, J., Kaur, A., Robertson, H., Jones, T., Strasburger, A., Williams, A., Kurian, S. M., Lamb, B., Shekhar, A., Lahiri, D. K., & Saykin, A. J. (2020). Blood biomarkers for memory: toward early detection of risk for Alzheimer disease, pharmacogenomics, and repurposed drugs. Molecular Psychiatry, 25, 1651–1672. https://doi.org/10.1038/s41380-019-0602-2

Ou, Y.-N., Yang, Y.-X., Deng, Y.-T., Zhang, C., Hu, H., Wu, B.-S., Liu, Y., Wang, Y.-J., Zhu, Y., Suckling, J., Tan, L., & Yu, J.-T. (2021). Identification of novel drug targets for Alzheimer’s disease by integrating genetics and proteomes from brain and blood. Molecular Psychiatry, 26, 6065–6073. https://doi.org/10.1038/s41380-021-01251-6

Panitch, R., Hu, J., Xia, W., Bennett, D. A., Stein, T. D., Farrer, L. A., & Jun, G. R. (2022). Blood and brain transcriptome analysis reveals APOE genotype-mediated and immune-related pathways involved in Alzheimer disease. Alzheimer's Research & Therapy, 14(1), 30. https://doi.org/10.1186/s13195-022-00975-z

Park, Y. H., Hodges, A., Simmons, A., Lovestone, S., Weiner, M. W., Kim, S., Saykin, A. J. & Nho, K. (2020). Association of blood-based transcriptional risk scores with biomarkers for Alzheimer disease. Neurology Genetics, 6(6), e517. https://doi.org/10.1212/NXG.0000000000000517

Park, Y. H., Pyun, J.-M., Hodges, A., Jang, J.-W., Bice, P. J., Kim, S., Saykin, A. J. & Nho, K. (2021). Dysregulated expression levels of APH1B in peripheral blood are associated with brain atrophy and amyloid-β deposition in Alzheimer's disease. Alzheimer's Research & Therapy, 13(1), 183. https://doi.org/10.1186/s13195-021-00919-z

Patel, H., Dobson, R. J. B., & Newhouse, S. J. (2019). A meta-analysis of Alzheimer's disease brain transcriptomic data. Journal of Alzheimer's Disease, 68(4), 1635–1656. https://doi.org/10.3233/jad-181085

Patel, H., Iniesta, R., Stahl, D., Dobson, R. J. B., & Newhouse, S. J. (2020). Working towards a blood-derived gene expression biomarker specific for Alzheimer's disease. Journal of Alzheimer's Disease, 74(2), 545–561. https://doi.org/10.3233/JAD-191163

Pereira, C. D., Martins, F., Wiltfang, J., Silva, O. A. B. d. C. E., & Rebelo, S. (2018). ABC transporters are key players in Alzheimer's disease. Journal of Alzheimer's Disease, 61(2), 463–485. https://doi.org/10.3233/JAD-170639

Perneczky, R., Wagenpfeil, S., Komossa, K., Grimmer, T., Diehl, J., & Kurz, A. (2006). Mapping scores onto stages: mini-mental state examination and clinical dementia rating. The American Journal of Geriatric Psychiatry, 14(2), 139–144. https://doi.org/10.1097/01.JGP.0000192478.82189.a8

Pfaffl, M. W. (2001). A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Research, 29(9), e45. https://doi.org/10.1093/nar/29.9.e45

Piehler, A., Kaminski, W. E., Wenzel, J. J., Langmann, T., & Schmitz, G. (2002). Molecular structure of a novel cholesterol-responsive A subclass ABC transporter, ABCA9. Biochemical and Biophysical Research Communications, 295(2), 408–416. https://doi.org/10.1016/S0006-291X(02)00659-9

Piehler, A. P., Ozcürümez, M., & Kaminski, W. E. (2012). A-subclass ATP-binding cassette proteins in brain lipid homeostasis and neurodegeneration. Frontiers in Psychiatry, 3, 17. https://doi.org/10.3389/fpsyt.2012.00017.

Rehiman, S. H., Lim, S. M., Lim, F. T., Chin, A.-V., Tan, M. P., Kamaruzzaman, S. B., Ramasamy, K., & Abdul Majeed, A. B. (2022). Fibrinogen isoforms as potential blood-based biomarkers of Alzheimer's disease using a proteomics approach. International Journal of Neuroscience, 132(10), 1014–1025. https://doi.org/10.1080/00207454.2020.1860038

Román, G. C., Tatemichi, T. K., Erkinjuntti, T., Cummings, J. L., Masdeu, J. C., Garcia, J. H., Amaducci, L., Orgogozo, J.-M., Brun, A., Hofman, A., Moody, D. M., O'Brien, M. D., Yamaguchi, T., Grafman, J., Drayer, B. P., Bennett, D. A., Fisher, M., Ogata, J., Kokmen, E., Bermejo, F., Wolf, P. A., Gorelick, P. B., Bick, K. L., Pajeau, A. K., Bell, M. A., DeCarli, C., Culebras, A., Korczyn, A. D., Bogousslavsky, J., Hartmann, A., & Scheinberg, P. (1993). Vascular dementia. Diagnostic criteria for research studies: Report of the NINDS-AIREN International Workshop. Neurology, 43(2), 250–260. https://doi.org/10.1212/WNL.43.2.250

Schindler, S. E., & Bateman, R. J. (2021). Combining blood-based biomarkers to predict risk for Alzheimer’s disease dementia. Nature Aging, 1, 26–28. https://doi.org/10.1038/s43587-020-00008-0

Sun, X.-Y., Tuo, Q.-Z., Liuyang, Z.-Y., Xie, A.-J., Feng, X.-L., Yan, X., Qiu, M., Li, S., Wang, X.-L., Cao, F.-Y., Wang, X.-C., Wang, J.-Z., & Liu, R. (2016). Extrasynaptic NMDA receptor-induced tau overexpression mediates neuronal death through suppressing survival signaling ERK phosphorylation. Cell Death & Disease, 7(11), e2449. https://doi.org/10.1038/cddis.2016.329

Teunissen, C. E., Verberk, I. M. W., Thijssen, E. H., Vermunt, L., Hansson, O., Zetterberg, H., van der Flier, W. M., Mielke, M. M., & Del Campo, M. (2022). Blood-based biomarkers for Alzheimer's disease: towards clinical implementation. Lancet Neurology, 21(1), 66–77. https://doi.org/10.1016/S1474-4422(21)00361-6

Thanan, R., Oikawa, S., Hiraku, Y., Ohnishi, S., Ma, N., Pinlaor, S., Yongvanit, P., Kawanishi, S., & Murata, M. (2015). Oxidative stress and its significant roles in neurodegenerative diseases and cancer. International Journal of Molecular Sciences, 16(1), 193–217. https://doi.org/10.3390/ijms16010193

Voyle, N., Keohane, A., Newhouse, S., Lunnon, K., Johnston, C., Soininen, H., Kloszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Lovestone, S., Hodges, A., Kiddle, S., & Dobson, R. J. (2016). A pathway based classification method for analyzing gene expression for Alzheimer's disease diagnosis. Journal of Alzheimer's Disease, 49(3), 659–669. https://doi.org/10.3233/JAD-150440

Wan, X.-Z., Li, B., Li, Y.-C., Yang, X.-L., Zhang, W., Zhong, L., & Tang, S.-J. (2012). Activation of NMDA receptors upregulates a disintegrin and metalloproteinase 10 via a Wnt/MAPK signaling pathway. Journal of Neuroscience, 32(11), 3910–3916. https://doi.org/10.1523/JNEUROSCI.3916-11.2012

Wang, S., Zhang, C., Sheng, X., Zhang, X., Wang, B., & Zhang, G. (2014). Peripheral expression of MAPK pathways in Alzheimer's and Parkinson's diseases. Journal of Clinical Neuroscience, 21(5), 810–814. https://doi.org/10.1016/j.jocn.2013.08.017

Wong, M. W., Braidy, N., Poljak, A., Pickford, R., Thambisetty, M., & Sachdev, P. S. (2017). Dysregulation of lipids in Alzheimer's disease and their role as potential biomarkers. Alzheimer's & Dementia, 13(7), 810–827. https://doi.org/10.1016/j.jalz.2017.01.008

Xia, J., Broadhurst, D. I., Wilson, M., & Wishart, D. S. (2013). Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics, 9(2), 280–299. https://doi.org/10.1007/s11306-012-0482-9




How to Cite

Samsudin, A. Z., Ramasamy, K., Lim, S. M., Chin, A. V., Tan, M. P., Kamaruzzaman, S. B., Ibrahim, B. and Abdul Majeed, A. B. (2023) “Differential gene expression of blood-based ABCA9, CNOT8, SESN1, UCP3, MAP2K1 and DDIT4 in Alzheimer’s disease”, Neuroscience Research Notes, 6(4), pp. 262.1–262.14. doi: 10.31117/neuroscirn.v6i4.262.