Skip to main content

Advertisement

SpringerLink
Log in

Behavioural Effects and RNA-seq Analysis of Aβ42-Mediated Toxicity in a Drosophila Alzheimer’s Disease Model

  • Published:
Molecular Neurobiology Aims and scope Submit manuscript

Abstract

Alzheimer’s disease (AD) is the most common neurological ailment worldwide. Its process comprises the unique aggregation of extracellular senile plaques composed of amyloid-beta (Aβ) in the brain. Aβ42 is the most neurotoxic and aggressive of the Aβ42 isomers released in the brain. Despite much research on AD, the complete pathophysiology of this disease remains unknown. Technical and ethical constraints place limits on experiments utilizing human subjects. Thus, animal models were used to replicate human diseases. The Drosophila melanogaster is an excellent model for studying both physiological and behavioural aspects of human neurodegenerative illnesses. Here, the negative effects of Aβ42-expression on a Drosophila AD model were investigated through three behavioural assays followed by RNA-seq. The RNA-seq data was verified using qPCR. AD Drosophila expressing human Aβ42 exhibited degenerated eye structures, shortened lifespan, and declined mobility function compared to the wild-type Control. RNA-seq revealed 1496 genes that were differentially expressed from the Aβ42-expressing samples against the control. Among the pathways that were identified from the differentially expressed genes include carbon metabolism, oxidative phosphorylation, antimicrobial peptides, and longevity-regulating pathways. While AD is a complicated neurological condition whose aetiology is influenced by a number of factors, it is hoped that the current data will be sufficient to give a general picture of how Aβ42 influences the disease pathology. The discovery of molecular connections from the current Drosophila AD model offers fresh perspectives on the usage of this Drosophila which could aid in the discovery of new anti-AD medications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

The data that support the findings of this study are openly available in NCBI SRA (BioProject ID: PRJNA921963): https://www.ncbi.nlm.nih.gov/bioproject/PRJNA921963. Ascension numbers for Control (Actin5C-OreR) samples are SRR23047823, SRR23047822 and SRR23047821. Ascension numbers for Aβ42 (Actin5C-Aβ42) samples are SRR23047820, SRR23047819 and SRR23047818.

Abbreviations

AD:

Alzheimer’s disease

Aβ:

Amyloid-beta

APP:

Amyloid precursor protein

AMPs:

Antimicrobial peptides

APLP2:

APP-like protein 2

BP:

Biological processes

CAFE:

CApillary FEeder

CC:

Cellular components

cDNA:

Complementary DNA

CYP:

Cytochrome P450

DEGs:

Differentially expressed genes

GMR-GAL4:

Glass multiple reporter-gal4

GO:

Gene ontology

HSP:

Heat shock protein

KEGG:

Kyoto Encyclopedia of Genes and Genomes

log2FC:

Log2foldchange

MF:

Molecular functions

padj:

Adjusted P values

P-score:

Phenotypic score

qRT-PCR:

Quantitative real-time PCR

RNA-seq:

RNA-sequencing

REP:

Rough eye phenotype

SEM:

Scanning electron microscopy

UPS:

Ubiquitin-proteasome system

References

  1. Halim MA, Tan FHP, Azlan A, Rasyid II, Rosli N, Shamsuddin S, Azzam G (2020) Ageing, Drosophila melanogaster and Epigenetics. Malays J Med Sci 27(3):7. https://doi.org/10.21315/mjms2020.27.3.2

    Article  PubMed  PubMed Central  Google Scholar 

  2. Gibson J, Olivia S (2020) Direct and indirect effects of covid-19 on life expectancy and poverty in Indonesia. Bull Indones Econ Stud 56(3):325–344

    Article  Google Scholar 

  3. Gauthier S, Rosa-Neto P, Morais J, Webster C (2021) World Alzheimer Report 2021: Journey through the diagnosis of dementia. Alzheimer’s Disease International

    Google Scholar 

  4. Rochoy M, Bordet R, Gautier S, Chazard E (2019) Factors associated with the onset of Alzheimer’s disease: data mining in the French nationwide discharge summary database between 2008 and 2014. PloS one 14(7):e0220174. https://doi.org/10.1371/journal.pone.0220174

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yan Y, Wang C (2006) Aβ42 is more rigid than Aβ40 at the C terminus: implications for Aβ aggregation and toxicity. J Mol Biol 364(5):853–862

    Article  CAS  PubMed  Google Scholar 

  6. Tan FHP, Azzam G (2017) Drosophila melanogaster: deciphering Alzheimer’s disease. Malays J Med Sci 24(2):6. https://doi.org/10.21315/mjms2017.24.2.2

    Article  PubMed  PubMed Central  Google Scholar 

  7. Iqbal K, Alonso AC, Chen S, Chohan MO, El-Akkad E, Gong C-X, Khatoon S, Li B et al (2005) Tau pathology in Alzheimer disease and other tauopathies. Biochim Biophys Acta Mol Basis Dis BBA-Mol Basis Dis 1739(2-3):198–210

    Article  CAS  Google Scholar 

  8. Swomley AM, Butterfield DA (2015) Oxidative stress in Alzheimer disease and mild cognitive impairment: evidence from human data provided by redox proteomics. Arch Toxicol 89:1669–1680

    Article  CAS  PubMed  Google Scholar 

  9. Prüßing K, Voigt A, Schulz JB (2013) Drosophila melanogaster as a model organism for Alzheimer’s disease. Mol Neurodegener 8(1):1–12. https://doi.org/10.1186/1750-1326-8-35

    Article  CAS  Google Scholar 

  10. St Johnston D (2002) The art and design of genetic screens: Drosophila melanogaster. Nat Rev Genet 3(3):176–188. https://doi.org/10.1038/nrg751

    Article  CAS  PubMed  Google Scholar 

  11. McGurk L, Berson A, Bonini NM (2015) Drosophila as an in vivo model for human neurodegenerative disease. Genetics 201(2):377–402. https://doi.org/10.1534/genetics.115.179457

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Tan F, Liu G, Lau S-Y, Jaafar M, Park Y-H, Azzam G, Li Y, Liong M-T (2020) Lactobacillus probiotics improved the gut microbiota profile of a Drosophila melanogaster Alzheimer’s disease model and alleviated neurodegeneration in the eye. Benef Microbes 11(1):79–89. https://doi.org/10.3920/BM2019.0086

    Article  CAS  PubMed  Google Scholar 

  13. Liu G, Tan FP, Lau SY, Jaafar MH, Chung FL, Azzam G, Liong MT, Li Y (2020) Lactic acid bacteria feeding reversed the malformed eye structures and ameliorated gut microbiota profiles of Drosophila melanogaster Alzheimer’s disease model. J Appl Microbiol. https://doi.org/10.1111/jam.14773

  14. Tan FHP, Hadri NAB, Najimudin N, Watanabe N, Azzam G (2021) Ethyl caffeate ameliorated amyloid-beta42 protein-associated toxicity in PC12 cells and Drosophila melanogaster. Geriatr Gerontol Int 21(12):1125–1130. https://doi.org/10.1111/ggi.14296

    Article  PubMed  Google Scholar 

  15. Tan FHP, Ting ACJ, Leow BG, Najimudin N, Watanabe N, Azzam G (2021) Alleviatory effects of Danshen, Salvianolic acid A and Salvianolic acid B on PC12 neuronal cells and Drosophila melanogaster model of Alzheimer’s disease. J Ethnopharmacol 279:114389

    Article  CAS  PubMed  Google Scholar 

  16. Mangleburg CG, Wu T, Yalamanchili HK, Guo C, Hsieh Y-C, Duong DM, Dammer EB, De Jager PL et al (2020) Integrated analysis of the aging brain transcriptome and proteome in tauopathy. Mol Neurodegener 15(1):1–17

    Article  Google Scholar 

  17. Hsieh Y-C, Guo C, Yalamanchili HK, Abreha M, Al-Ouran R, Li Y, Dammer EB, Lah JJ et al (2019) Tau-mediated disruption of the spliceosome triggers cryptic RNA splicing and neurodegeneration in Alzheimer’s disease. Cell Rep 29(2):301–316

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhang H, Karisetty BC, Bhatnagar A, Armour EM, Beaver M, Roach TV, Mortazavi S, Mandloi S et al (2020) Tip60 protects against amyloid-β-induced transcriptomic alterations via different modes of action in early versus late stages of neurodegeneration. Mol Cell Neurosci 109:103570

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 18(9):1509–1517. https://doi.org/10.1101/gr.079558.108

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ja WW, Carvalho GB, Mak EM, de la Rosa NN, Fang AY, Liong JC, Brummel T, Benzer S (2007) Prandiology of Drosophila and the CAFE assay. Proc Natl Acad Sci USA 104(20):8253–8256. https://doi.org/10.1073/pnas.0702726104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Rodriguez A, Zhang H, Klaminder J, Brodin T, Andersson PL, Andersson M (2018) ToxTrac: a fast and robust software for tracking organisms. Methods Ecol Evol 9(3):460–464

    Article  Google Scholar 

  22. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635

    Article  CAS  PubMed  Google Scholar 

  23. Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30(7):923–930. https://doi.org/10.1093/bioinformatics/btt656

    Article  CAS  PubMed  Google Scholar 

  24. Love M, Anders S, Huber M (2014) Differential gene expression analysis based on the negative binomial distribution. Genome Biol 15:550

    Article  PubMed  PubMed Central  Google Scholar 

  25. Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R et al (2007) DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucl Acids Res 35(suppl_2):W169–W175. https://doi.org/10.1093/nar/gkm415

    Article  PubMed  PubMed Central  Google Scholar 

  26. Supek F, Bošnjak M, Škunca N, Šmuc T (2011) REVIGO summarizes and visualizes long lists of gene ontology terms. PloS One 6(7):e21800. https://doi.org/10.1371/journal.pone.0021800

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S et al (2007) KEGG for linking genomes to life and the environment. Nucl Acids Res 36(suppl_1):D480–D484. https://doi.org/10.1093/nar/gkm882

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kleino A, Myllymaki H, Kallio J, Vanha-aho L-M, Oksanen K, Ulvila J, Hultmark D, Valanne S et al (2008) Pirk is a negative regulator of the Drosophila Imd pathway. J Immun J 180(8):5413–5422. https://doi.org/10.4049/jimmunol.180.8.5413

    Article  CAS  Google Scholar 

  29. Chen T, Sun H, Lu J, Zhao Y, Tao D, Li X, Huang B (2002) Histone acetylation is involved in hsp70 gene transcription regulation in Drosophila melanogaster. Arch Biochem Biophys 408(2):171–176. https://doi.org/10.1016/s0003-9861(02)00564-7

    Article  CAS  PubMed  Google Scholar 

  30. Fingerhut JM, Yamashita YM (2020) mRNA localization mediates maturation of cytoplasmic cilia in Drosophila spermatogenesis. J Cell Biol 219(9). https://doi.org/10.1083/jcb.202003084

  31. Voolstra O, Kiefer C, Hoehne M, Welsch R, Vogt K, von Lintig J (2006) The Drosophila class B scavenger receptor NinaD-I is a cell surface receptor mediating carotenoid transport for visual chromophore synthesis. Biochemistry 45(45):13429–13437. https://doi.org/10.1021/bi060701u

    Article  CAS  PubMed  Google Scholar 

  32. Ling D, Salvaterra PM (2011) Robust RT-qPCR data normalization: validation and selection of internal reference genes during post-experimental data analysis. PloS One 6(3):e17762. https://doi.org/10.1371/journal.pone.0017762

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Greeve I, Kretzschmar D, Tschäpe J-A, Beyn A, Brellinger C, Schweizer M, Nitsch RM, Reifegerste R (2004) Age-dependent neurodegeneration and Alzheimer-amyloid plaque formation in transgenic Drosophila. J Neurosci 24(16):3899–3906. https://doi.org/10.1523/JNEUROSCI.0283-04.2004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tolea MI, Morris JC, Galvin JE (2016) Trajectory of mobility decline by type of dementia. Alzheimer Dis Assoc Disord 30(1):60. https://doi.org/10.1097/WAD.0000000000000091

    Article  PubMed  PubMed Central  Google Scholar 

  35. Gargano JW, Martin I, Bhandari P, Grotewiel MS (2005) Rapid iterative negative geotaxis (RING): a new method for assessing age-related locomotor decline in Drosophila. Exp Gerontol 40(5):386–395. https://doi.org/10.1016/j.exger.2005.02.005

    Article  PubMed  Google Scholar 

  36. Yang Y, Geldmacher DS, Herrup K (2001) DNA replication precedes neuronal cell death in Alzheimer’s disease. J Neurosci 21(8):2661–2668. https://doi.org/10.1523/JNEUROSCI.21-08-02661.2001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Cacabelos R, Fernández-Novoa L, Lombardi V, Kubota Y, Takeda M (2005) Molecular genetics of Alzheimer’s disease and aging. Methods Find Exp Clin Pharmacol 27:1–573

    PubMed  Google Scholar 

  38. Gillette-Guyonnet S, Van Kan GA, Alix E, Andrieu S (2007) IANA (International Academy on Nutrition and Aging) Expert Group: weight loss and Alzheimer’s disease. J Nutr 11(1):38

    CAS  Google Scholar 

  39. Zhang X, Alshakhshir N, Zhao L (2021) Glycolytic metabolism, brain resilience, and Alzheimer’s disease. Front Neurol 476. https://doi.org/10.3389/fnins.2021.662242

  40. Bigl M, Brückner M, Arendt T, Bigl V, Eschrich K (1999) Activities of key glycolytic enzymes in the brains of patients with Alzheimer’s disease. J Neural Transm 106(5):499–511. https://doi.org/10.1007/s007020050174

    Article  CAS  PubMed  Google Scholar 

  41. Jayasena T, Poljak A, Braidy N, Smythe G, Raftery M, Hill M, Brodaty H, Trollor J et al (2015) Upregulation of glycolytic enzymes, mitochondrial dysfunction and increased cytotoxicity in glial cells treated with Alzheimer’s disease plasma. PLoS One 10(3):e0116092. https://doi.org/10.1371/journal.pone.0116092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wang W, Zhao F, Ma X, Perry G, Zhu X (2020) Mitochondria dysfunction in the pathogenesis of Alzheimer’s disease: recent advances. Mol Neurodegener 15(1):1–22. https://doi.org/10.1186/s13024-020-00376-6

    Article  Google Scholar 

  43. Miao J, Shi R, Li L, Chen F, Zhou Y, Tung YC, Hu W, Gong C-X et al (2019) Pathological tau from Alzheimer’s brain induces site-specific hyperphosphorylation and SDS-and reducing agent-resistant aggregation of tau in vivo. Front Aging Neurosci 11:34. https://doi.org/10.3389/fnagi.2019.00034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Inaba K, Mizuno K (2016) Sperm dysfunction and ciliopathy. Reprod Med Biol 15(2):77–94. https://doi.org/10.1007/s12522-015-0225-5

    Article  CAS  PubMed  Google Scholar 

  45. Fardilha M, Vieira SI, Barros A, Sousa M, Da Cruz e Silva OA, Da Cruz e Silva EF (2007) Differential distribution of Alzheimer’s amyloid precursor protein family variants in human sperm. Ann N Y Acad Sci 1096(1):196–206. https://doi.org/10.1196/annals.1397.086

    Article  CAS  PubMed  Google Scholar 

  46. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40(D1):D109–D114. https://doi.org/10.1093/nar/gkr988

    Article  CAS  PubMed  Google Scholar 

  47. Lu T, Pan Y, Kao S-Y, Li C, Kohane I, Chan J, Yankner BA (2004) Gene regulation and DNA damage in the ageing human brain. Nature 429(6994):883–891. https://doi.org/10.1038/nature02661

    Article  CAS  PubMed  Google Scholar 

  48. Lee C-K, Weindruch R, Prolla TA (2000) Gene-expression profile of the ageing brain in mice. Nat Genet 25(3):294–297. https://doi.org/10.1038/77046

    Article  CAS  PubMed  Google Scholar 

  49. Mayer M, Bukau B (2005) Hsp70 chaperones: cellular functions and molecular mechanism. Cell Mol Life Sci 62(6):670–684. https://doi.org/10.1007/s00018-004-4464-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hoshino T, Murao N, Namba T, Takehara M, Adachi H, Katsuno M, Sobue G, Matsushima T et al (2011) Suppression of Alzheimer’s disease-related phenotypes by expression of heat shock protein 70 in mice. J Neurosci 31(14):5225–5234. https://doi.org/10.1523/JNEUROSCI.5478-10.2011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Jucker M, Walker LC (2011) Pathogenic protein seeding in Alzheimer disease and other neurodegenerative disorders. Ann Neurol 70(4):532–540. https://doi.org/10.1002/ana.22615

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Leak RK (2014) Heat shock proteins in neurodegenerative disorders and aging. J Cell Commun Signaling 8(4):293–310. https://doi.org/10.1007/s12079-014-0243-9

    Article  Google Scholar 

  53. Ponnappan S, Ponnappan U (2011) Aging and immune function: molecular mechanisms to interventions. Antioxid Redox Signal 14(8):1551–1585. https://doi.org/10.1089/ars.2010.3228

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Wang M, Peng I-F, Li S, Hu X (2020) Dysregulation of antimicrobial peptide expression distinguishes Alzheimer’s disease from normal aging. Aging 12(1):690. https://doi.org/10.18632/aging.102650

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Badinloo M, Nguyen E, Suh W, Alzahrani F, Castellanos J, Klichko VI, Orr WC, Radyuk SN (2018) Overexpression of antimicrobial peptides contributes to aging through cytotoxic effects in Drosophila tissues. Arch Insect Biochem 98(4):e21464. https://doi.org/10.1002/arch.21464

    Article  CAS  Google Scholar 

  56. Dutheil F, Beaune P, Loriot M-A (2008) Xenobiotic metabolizing enzymes in the central nervous system: contribution of cytochrome P450 enzymes in normal and pathological human brain. Biochimie 90(3):426–436. https://doi.org/10.1016/j.biochi.2007.10.007

    Article  CAS  PubMed  Google Scholar 

  57. Webers A, Heneka MT, Gleeson PA (2020) The role of innate immune responses and neuroinflammation in amyloid accumulation and progression of Alzheimer’s disease. Immunol Cell Biol 98(1):28–41. https://doi.org/10.1111/imcb.12301

    Article  PubMed  Google Scholar 

  58. Yanev S (2014) Immune system-drug metabolism interactions: toxicological insight. Adipobiology 6:30–36

    Article  Google Scholar 

  59. Djelti F, Braudeau J, Hudry E, Dhenain M, Varin J, Bieche I, Marquer C, Chali F et al (2015) CYP46A1 inhibition, brain cholesterol accumulation and neurodegeneration pave the way for Alzheimer’s disease. Brain 138(8):2383–2398. https://doi.org/10.1093/brain/awv166

    Article  PubMed  Google Scholar 

  60. Medala VK, Gollapelli B, Dewanjee S, Ogunmokun G, Kandimalla R, Vallamkondu J (2021) Mitochondrial dysfunction, mitophagy, and role of dynamin-related protein 1 in Alzheimer’s disease. J Neurosci Res 99(4):1120–1135. https://doi.org/10.1002/jnr.24781

    Article  CAS  PubMed  Google Scholar 

  61. Keck S, Nitsch R, Grune T, Ullrich O (2003) Proteasome inhibition by paired helical filament-tau in brains of patients with Alzheimer’s disease. J Neurochem 85(1):115–122. https://doi.org/10.1046/j.1471-4159.2003.01642.x

    Article  CAS  PubMed  Google Scholar 

  62. Lv H, Wei G-Y, Guo C-S, Deng Y-F, Jiang Y-M, Gao C, Jian C-D (2020) 20S proteasome and glyoxalase 1 activities decrease in erythrocytes derived from Alzheimer’s disease patients. Neural Regen Res 15(1):178. https://doi.org/10.4103/1673-5374.264473

    Article  PubMed  Google Scholar 

  63. Bence NF, Sampat RM, Kopito RR (2001) Impairment of the ubiquitin-proteasome system by protein aggregation. Science 292(5521):1552–1555. https://doi.org/10.1126/science.292.5521.1552

    Article  CAS  PubMed  Google Scholar 

  64. Chaudhari KS, Tiwari NR, Tiwari RR, Sharma RS (2017) Neurocognitive effect of nootropic drug Brahmi (Bacopa monnieri) in Alzheimer’s disease. Ann Neurosci 24(2):111–122

    Article  PubMed  PubMed Central  Google Scholar 

  65. Andrew MK, Tierney MC (2018) The puzzle of sex, gender and Alzheimer’s disease: why are women more often affected than men? Women’s Health 14:1745506518817995

    CAS  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank all our collaborators and colleagues for the discussion and the work conducted in this lab.

Funding

This work was supported by the Ministry of Higher Education Malaysia for Transdisciplinary Research Grant Scheme (TRGS) for the project titled “Elucidating the molecular pathway of THICAPA and POET using Drosophila melanogaster Alzheimer’s disease models” (TRGS/1/2020/USM/02/3/1).

Author information

Authors and Affiliations

  1. School of Health Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia

    Florence Hui Ping Tan & Shaharum Shamsuddin

  2. USM-RIKEN Interdisciplinary Centre for Advanced Sciences (URICAS), Universiti Sains Malaysia, 11800, Penang, Malaysia

    Florence Hui Ping Tan, Ghows Azzam & Shaharum Shamsuddin

  3. School of Biological Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia

    Ghows Azzam & Nazalan Najimudin

  4. Malaysia Genome and Vaccine Institute (MGVI), National Institutes of Biotechnology Malaysia (NIBM), Jalan Bangi, 43000, Kajang, Selangor, Malaysia

    Ghows Azzam

  5. School of Pharmaceutical Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia

    Nazalan Najimudin

  6. Nanobiotech Research Initiative, Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, 11800, Penang, Malaysia

    Shaharum Shamsuddin

  7. Department of Chemical Pathology, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia

    Azalina Zainuddin

Authors
  1. Florence Hui Ping Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghows Azzam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nazalan Najimudin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaharum Shamsuddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Azalina Zainuddin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Florence Hui Ping Tan contributed to the conceptualization, methodology, formal analysis, investigation, writing (original draft), writing (review and editing), and visualization of the manuscript. Nazalan Najimudin contributed to the conceptualization, methodology, resources, writing (review & editing), supervision, project administration, and funding acquisition of the research. Shaharum Shamsuddin contributed to the supervision, project administration, and funding acquisition of the research. Azalina Zainuddin contributed to the supervision, project administration, and funding acquisition of the research. Ghows Azzam contributed to the conceptualization, methodology, resources, writing (review and editing), supervision, project administration, and funding acquisition of the research.

Corresponding authors

Correspondence to Florence Hui Ping Tan or Ghows Azzam.

Ethics declarations

Ethics Approval and Consent to Participate

Not applicable

Consent for Publication

Not applicable

Competing Interests

The authors declare no competing interestss.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Highlights

• Aβ42 expression in the Drosophila eye resulted in severe rough eye phenotype.

• Drosophila melanogaster–expressing Aβ42 exhibited shortened lifespan.

• Aβ42-expressing Drosophila melanogaster had impaired climbing abilities.

• From the RNA-seq analysis, AD samples showed 1496 DEGs compared to control.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tan, F.H.P., Azzam, G., Najimudin, N. et al. Behavioural Effects and RNA-seq Analysis of Aβ42-Mediated Toxicity in a Drosophila Alzheimer’s Disease Model. Mol Neurobiol 60, 4716–4730 (2023). https://doi.org/10.1007/s12035-023-03368-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12035-023-03368-x

Keywords

Search

Navigation