Letter | Published:

The microRNA miR-34 modulates ageing and neurodegeneration in Drosophila

Nature volume 482, pages 519523 (23 February 2012) | Download Citation


Human neurodegenerative diseases have the temporal hallmark of afflicting the elderly population. Ageing is one of the most prominent factors to influence disease onset and progression1, yet little is known about the molecular pathways that connect these processes. To understand this connection it is necessary to identify the pathways that functionally integrate ageing, chronic maintenance of the brain and modulation of neurodegenerative disease. MicroRNAs (miRNA) are emerging as critical factors in gene regulation during development; however, their role in adult-onset, age-associated processes is only beginning to be revealed. Here we report that the conserved miRNA miR-34 regulates age-associated events and long-term brain integrity in Drosophila, providing a molecular link between ageing and neurodegeneration. Fly mir-34 expression exhibits adult-onset, brain-enriched and age-modulated characteristics. Whereas mir-34 loss triggers a gene profile of accelerated brain ageing, late-onset brain degeneration and a catastrophic decline in survival, mir-34 upregulation extends median lifespan and mitigates neurodegeneration induced by human pathogenic polyglutamine disease protein. Some of the age-associated effects of miR-34 require adult-onset translational repression of Eip74EF, an essential ETS domain transcription factor involved in steroid hormone pathways. Our studies indicate that miRNA-dependent pathways may have an impact on adult-onset, age-associated events by silencing developmental genes that later have a deleterious influence on adult life cycle and disease, and highlight fly miR-34 as a key miRNA with a role in this process.

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Gene Expression Omnibus

Data deposits

The microarray data can be found in the Gene Expression Omnibus (GEO) of NCBI through accession number GSE25009.


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We thank C. Thummel, T. Jongens and A. Bashirullah for reagents. We are grateful to A. Cashmore, A. Burguete, J. Kim, S. Cherry, B. Gregory, A. Gitler and the Bonini laboratory for discussion and critical reading of the manuscript. We thank X. Teng for assistance with fly paraffin section. This work was funded by the NINDS (R01-NS043578) and the Ellison Foundation (to N.M.B.). L.-S.W. and K.C. are supported by a pilot grant from Penn Genome Frontiers Institute. L.-S.W. is supported by NIA (U01-AG-032984-02 and RC2-AG036528-01) and a Penn Institute on Aging pilot grant (AG010124). N.M.B. is an Investigator of the Howard Hughes Medical Institute. J.R.K. received support from NIH T32 AG00255.

Author information

Author notes

    • Nan Liu
    •  & Michael Landreh

    Present addresses: Divison of Biological Sciences, Section of Neurobiology, Howard Hughes Medical Institute, University of California, San Diego, California 92093, USA (N.L.); Division of Physiological Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-17177 Stockholm, Sweden (M.L.).


  1. Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Nan Liu
    • , Michael Landreh
    • , Masashi Abe
    • , Gert-Jan Hendriks
    • , Jason R. Kennerdell
    • , Yongqing Zhu
    •  & Nancy M. Bonini
  2. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Kajia Cao
    •  & Li-San Wang
  3. Cell and Developmental Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Kajia Cao
  4. Institute on Aging, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Li-San Wang
  5. Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Li-San Wang
  6. Howard Hughes Medical Institute, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Nancy M. Bonini


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N.L. and N.M.B. conceived and designed the project. N.L., M.L., M.A., G.-J.H., J.R.K. and Y.Z. planned, executed and analysed experiments. K.C. and L.S.-W. performed aging computational modelling. N.L. and N.M.B. wrote the manuscript with input from all authors.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Nancy M. Bonini.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Figures 1-8 with legends, Supplementary Table 1 and additional references.

Excel files

  1. 1.

    Supplementary Table 2

    This table contains the age-correlated probesets. The dd*dir value describes if a particular gene is upregulated (positive value) or downregulated (negative value) as well as the slope of the change compared to the diagonal. In miR-34 mutants, a large number of probesets have a positive dd*dir value, meaning they show higher expression and change faster compared to age-matched controls.

  2. 2.

    Supplementary Table 3

    This table contains the DAVID functional analysis of probesets positively and negatively correlated with age. DAVID functional analysis of the probesets positively and negatively correlated with age (See Supplementary Table S2) extracted from microarray analysis of control animal brains. These two sets were enriched for different functional terms. Terms with significance p<0.001 are listed.

  3. 3.

    Supplementary Table 4

    This table contains a summary of lifespan results. For lifespan analysis, flies were generated in the same uniform homogeneous genetic background, 5905. Log-rank test was used for statistics analysis.

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