1. Cell Biology

Axonal distribution of mitochondria maintains neuronal autophagy during aging via eIF2β

  1. Kanako Shinno
  2. Yuri Miura
  3. Koichi M Iijima
  4. Emiko Suzuki
  5. Kanae Ando  Is a corresponding author
  1. Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Japan
  2. Research Team for Mechanism of Aging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Japan
  3. Department of Neurogenetics, National Center for Geriatrics and Gerontology, Japan
  4. Department of Experimental Gerontology, Graduate School of Pharmaceutical Sciences, Nagoya City University, Japan
  5. Gene Network Laboratory, National Institute of Genetics and Department of Genetics, SOKENDAI, Japan
  6. Department of Biological Sciences, School of Science, Tokyo Metropolitan University, Japan
https://doi.org/10.7554/eLife.95576.5
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  1. Kanako Shinno
  2. Yuri Miura
  3. Koichi M Iijima
  4. Emiko Suzuki
  5. Kanae Ando
(2026)
Axonal distribution of mitochondria maintains neuronal autophagy during aging via eIF2β
eLife 13:RP95576.
https://doi.org/10.7554/eLife.95576.5
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eLife Assessment

In flies defective for axonal transport of mitochondria, the authors report the upregulation of one subunit, the beta subunit, of the heterotrimeric eIF2 complex via mass spectroscopy proteomics. Neuronal overexpression of eIF2β phenocopied aspects of neuronal dysfunction observed when axonal transport of mitochondria was compromised. Conversely, lowering eIF2β expression suppressed aspects of neuronal dysfunction. While these are intriguing and useful observations, technical weaknesses limit the interpretation. On balance, the evidence supporting the current claims is suggestive but incomplete, especially concerning the characterization of the eIF2 heterotrimer and the data regarding translational regulation.

https://doi.org/10.7554/eLife.95576.5.sa0

Abstract

Neuronal aging and neurodegenerative diseases are accompanied by proteostasis collapse, while the cellular factors that trigger it have not been identified. Impaired mitochondrial transport in the axon is another feature of aging and neurodegenerative diseases. Using Drosophila, we found that genetic depletion of axonal mitochondria causes dysregulation of protein degradation. Axons with mitochondrial depletion showed abnormal protein accumulation and autophagic defects. Lowering neuronal ATP levels by blocking glycolysis did not reduce autophagy, suggesting that autophagic defects are associated with mitochondrial distribution. We found that eIF2β was increased by the depletion of axonal mitochondria via proteome analysis. Phosphorylation of eIF2α, another subunit of eIF2, was lowered, and global translation was suppressed. Neuronal overexpression of eIF2β phenocopied the autophagic defects and neuronal dysfunctions, and lowering eIF2β expression rescued those perturbations caused by depletion of axonal mitochondria. These results indicate the mitochondria-eIF2β axis maintains proteostasis in the axon, of which disruption may underlie the onset and progression of age-related neurodegenerative diseases.

Introduction

Neurons have a morphologically complex architecture composed of microcompartments and require tight regulation of the abundance of proteins and organelles spatially and temporally (Yerbury et al., 2016). Such control of protein amounts, or proteostasis, is essential for neuronal functions (Hetz, 2021) and is achieved through the orchestration of protein expression, folding, trafficking, and degradation controlled by intrinsic and environmental signals (Balch et al., 2008). Translation is initiated by the eukaryotic initiation factor 2 (eIF2) complex (Kimball, 1999). eIF2, a heterotrimer of α, β, and γ subunits, transports Met-tRNA to the ribosome in a GTP-dependent manner (Jackson et al., 2010). Under stressed conditions, phosphorylation of eIF2α attenuates global translation and initiates translation of mRNAs related to the integrated stress response (ISR) (Pakos-Zebrucka et al., 2016). As for protein degradation, autophagy and proteasome are major systems that maintain proteostasis (Kroemer et al., 2010). The proteasome degrades unnecessary proteins, followed by regulated ubiquitination processes (Nandi et al., 2006), and autophagy removes damaged or harmful components, including large protein aggregates and organelles, through catabolism (selective autophagy) (Glick et al., 2010). In addition to autophagy induced by acute stressors, a basal level of selective autophagy mediates the global turnover of damaged proteins (Vargas et al., 2023).

Such constitutive autophagy decreases during aging, which may underlie declines in the structural and functional integrity of neurons (Aman et al., 2021). Decreased protein degradation and accumulation of abnormal proteins also contribute to increased risks of neurodegenerative diseases. Age-related neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease are often associated with the accumulation of misfolded proteins such as amyloid-β, tau, and α-synuclein (Ross and Poirier, 2004). Enhancement of autophagy mitigates age-related dysfunctions and neurodegeneration caused by proteotoxic stress (Rubinsztein et al., 2011). However, it is not fully understood how aging disrupts the regulation of this constitutive autophagy.

Neurons are also highly energy-demanding cells. At nerve terminals, action potentials trigger the release of neurotransmitters via exocytosis of synaptic vesicles, which requires a constant supply of ATP and calcium buffering (Vos et al., 2010). Such neuronal activity relies on mitochondrial functions (Cheng et al., 2010), and mitochondria are actively transported from their major sites of biogenesis in soma to axons (Hollenbeck and Saxton, 2005). However, the axonal transport of mitochondria declines during aging (Takihara et al., 2015; Milde et al., 2015; Vagnoni et al., 2016). Reduced axonal transport of mitochondria is thought to contribute to age-related declines in neuronal functions (Takihara et al., 2015, Vagnoni et al., 2016; Morsci et al., 2016; Adalbert and Coleman, 2013). The number of functional mitochondria in synapses is reduced in the brains of patients suffering from age-related neurodegenerative diseases such as Alzheimer’s disease (Duncan and Goldstein, 2006), and mutations in genes involved in mitochondrial dynamics are linked to neurodegenerative diseases (Chen and Chan, 2009). The mislocalization of mitochondria is sufficient to cause age-dependent neurodegeneration in Drosophila and mice (Iijima-Ando et al., 2012; López-Doménech et al., 2016), indicating that the proper distribution of mitochondria is essential to maintain neuronal functions. Thus, depletion of functional mitochondria from axons and proteostasis collapse are common features of aging and neurodegenerative diseases.

Mitochondrial transport is regulated by a series of molecular adaptors that mediate the attachment of mitochondria to molecular motors (Hollenbeck and Saxton, 2005). In Drosophila, mitochondrial transport is mediated by milton and Miro, which attaches mitochondria to microtubules via kinesin heavy chain (Guo et al., 2005; Glater et al., 2006). In the absence of milton or Miro, synaptic terminals and axons lack mitochondria, although mitochondria are numerous in the neuronal cell body (Stowers et al., 2002). We previously reported that RNAi-mediated knockdown of milton or Miro in neurons causes a reduction in axonal mitochondria, age-dependent locomotor defects (Iijima-Ando et al., 2009), and age-dependent neurodegeneration in neuropile area starting around 30 days after eclosion (day-old; Iijima-Ando et al., 2012), and enhances axon degeneration caused by human tau proteins (Iijima-Ando et al., 2012), suggesting that these flies can be used as a model to analyze the effect of depletion of axonal mitochondria during aging. In this study, we investigated a causal relationship between mitochondrial distribution and neuronal proteostasis by using neuronal knockdown of milton. We found that depletion of axonal mitochondria reduced autophagy and increased the accumulation of aggregated proteins in the axon prior to gross neurodegeneration. Proteome analysis and follow-up biochemical analyses revealed that neuronal knockdown of milton increased eIF2β levels and lowered phosphorylation of eIF2α in the axon. In addition, milton knockdown suppressed global translation. Overexpression of eIF2β was sufficient to decrease autophagy and induce neuronal dysfunction, and genetic suppression of eIF2β restored autophagy and improved neuronal function in the milton knockdown background. These findings suggest that loss of axonal mitochondria and elevated levels of eIF2β mediate proteostasis collapse and neuronal dysfunction during aging.

Results

Depletion of axonal mitochondria by knockdown of milton or Miro causes protein accumulation in the axon

In Drosophila, mitochondrial transport is mediated by milton and Miro, which attach mitochondria to microtubules via kinesin heavy chain (Guo et al., 2005; Glater et al., 2006; Figure 1A). It has been reported that expression of milton RNAi in neurons via pan-neuronal elav-GAL4 driver reduced milton protein levels in Drosophila head lysate to 40% and mito-GFP signals in axons to 50% (Iijima-Ando et al., 2012; Iijima-Ando et al., 2009).

Figure 1 with 1 supplement see all
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Knockdown of milton or Miro causes protein accumulation in the axon.

(A) Schematic representation of the mitochondrial transport machinery. Knockdown of milton, an adapter protein for mitochondrial transport, depletes mitochondria in the axon. (B, C) Ubiquitinated proteins in brains with neuronal knockdown of milton or Miro. Brains dissected at 14-day-old (B) or 30-day-old (C) were immunostained with an antibody against ubiquitin. Firefly luciferase RNAi was used as a control. Representative images (left) and quantitation of the number of ubiquitin-positive puncta (right) are shown. Scale bars of hemibrains, 100 µm, Scale bars of high magnifications, 10 µm. Means ± SE, n=8. N.S., p>0.05; ***p<0.005 (one-way analysis of variance (ANOVA) followed by Tukey’s honestly significant difference (HSD) post hoc test). (D) Cross-sections in the lamina and in the retina were used to analyze the ultrastructure of synapses and cell bodies, respectively. milton RNAi was expressed in the retina and neurons via a combination of GAL4 drivers, a pan-retinal gmr-GAL4 and pan-neuronal elav-GAL4. (E) Quantitation of the number of mitochondria in a presynaptic terminal from transmission electron micrographs. 180 presynaptic terminals from cross-sections of the lamina from three brains were analyzed. ***p<0.005 (Chi-square test). (F, G) Presynaptic terminals of photoreceptor neurons of control and milton knockdown flies. Photoreceptor neurons are highlighted in blue. Swollen presynaptic terminals (asterisks in F), characterized by the enlargement and higher circularity, were found more frequently in milton knockdown neurons. Arrowheads indicate presynaptic terminals with dense materials. Scale bars, 2 µm. Representative images (Left) and quantitation (Right) are shown. 918–1118 from three heads were quantified for the percentage of swollen presynaptic terminals, and 180 presynaptic terminals from three heads were quantified for the size of presynaptic terminals. Mean ± SE, **p<0.01, ***p<0.005 (Student’s t-test). (G) Dense materials (arrowheads in G) in the presynaptic terminals of milton knockdown neurons. Scale bars, 2 µm. The ratio of presynaptic terminals containing dense materials was quantified from 918 to 1118 presynaptic terminals from three heads. Mean ± SE, ***p<0.005 (Student’s t-test). (H) Cell bodies of photoreceptor neurons of control and milton knockdown flies. Scale bars, 2 µm. Flies were 27-day-old.

To test how loss of axonal mitochondria affects proteostasis in neurons, we first examined the accumulation of ubiquitinated proteins. At 14 days old, more ubiquitinated proteins were deposited in the brains of milton knockdown flies than in those of age-matched control flies (Figure 1B, p<0.005 between control RNAi and milton RNAi). There was no significant increase in ubiquitinated proteins in milton knockdown flies at 1 day old, suggesting that the accumulation of ubiquitinated proteins caused by milton knockdown is age-dependent (Figure 1—figure supplement 1). We also analyzed the effect of the neuronal knockdown of Miro, a partner of milton, on the accumulation of ubiquitin-positive proteins. Since severe knockdown of Miro in neurons causes lethality, we used UAS-Miro RNAi strain with low knockdown efficiency, whose expression driven by elav-GAL4 caused 30% reduction of Miro mRNA in head extract (Iijima-Ando et al., 2012). Although there was a tendency for increased ubiquitin-positive puncta in Miro knockdown brains, the difference was not significant (Figure 1B, p>0.05 between control RNAi and Miro RNAi). These data suggest that the depletion of axonal mitochondria induced by milton knockdown leads to the accumulation of ubiquitinated proteins before neurodegeneration occurs.

It has been reported that ubiquitinated proteins accumulate with aging (Tonoki et al., 2009); thus, we analyzed the accumulation of ubiquitinated proteins in aged brains (30-day-old) with milton knockdown. The number of puncta of ubiquitinated proteins did not significantly differ between control and milton knockdown flies or between control and Miro knockdown flies (Figure 1C, p>0.05). These results suggest that depletion of axonal mitochondria may have more impact on proteostasis in young neurons than in old neurons.

We examined the ultrastructure of presynaptic terminals and cell bodies in photoreceptor neurons with milton knockdown by transmission electron microscopy in 27-day-old flies (Figure 1D). As previously reported (Iijima-Ando et al., 2012), the number of mitochondria in presynaptic terminals decreased in milton knockdown (Figure 1E). The swelling of presynaptic terminals, characterized by the enlargement and roundness, was not reported at 3-day-old (Iijima-Ando et al., 2012) but observed at this age with about 4% of total presynaptic terminals (Figure 1F, asterisks).

Some presynaptic terminals of milton knockdown neurons contained dense materials (Figure 1F and G, arrowheads). Dense materials are rarely found in age-matched control neurons, indicating that milton knockdown induces abnormal protein accumulation in the presynaptic terminals (Figure 1G and H). In milton knockdown neurons, dense materials are found in swollen presynaptic terminals more often than in presynaptic terminals without swelling, suggesting a positive correlation between the disruption of proteostasis and axonal damage (Figure 1G). In contrast, dense materials were not observed in cell bodies in the milton knockdown retina (Figure 1H). These results indicate that the depletion of axonal mitochondria induces protein accumulation in the axon.

Depletion of axonal mitochondria impairs protein degradation pathways

Since abnormal proteins were accumulated in milton knockdown brains, we next examined if protein degradation pathways were suppressed. We analyzed autophagy via western blotting of the autophagy markers LC3 and p62 (Klionsky et al., 2021). During autophagy progression, LC3 is conjugated with phosphatidylethanolamine to form LC3-II, which localizes to isolation membranes and autophagosomes. LC3-I accumulation occurs when autophagosome formation is impaired, and LC3-II accumulation is associated with lysosomal defects (Klionsky et al., 2021; Bartlett et al., 2011). p62 is an autophagy substrate, and its accumulation suggests autophagic defects (Klionsky et al., 2021; Bartlett et al., 2011). We found that milton knockdown increased LC3-I, and the LC3-II/LC3-I ratio was lower in milton knockdown flies than in control flies at 14-day-old (Figure 2A). We also analyzed p62 levels in head lysates sequentially extracted using detergents with different stringencies (1% Triton X-100 and 2% SDS). Western blotting revealed that p62 levels were increased in the brains of 14-day-old milton knockdown flies (Figure 2B). The increase in the p62 level was significant in the Triton X-100-soluble fraction but not in the SDS-soluble fraction (Figure 2B), suggesting that depletion of axonal mitochondria impairs the degradation of less-aggregated proteins. Proteasome activity was also significantly decreased in brains with neuronal knockdown of milton (Figure 2C, p<0.005).

Figure 2
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milton knockdown impairs protein degradation pathways.

(A, B) Western blotting of head extracts of control and milton knockdown flies with antibodies against LC3 (A) and Ref2P, the fly homolog of mammalian p62 (B). For the analyses of p62 levels, heads were extracted with 1% Triton X-100 or 2% SDS (B). Flies were 14-day-old. Representative blots (left) and quantitation (right) are shown. Actin was used as a loading control. Means ± SE, n=6 (LC3), n=3 (p62). (C) Proteasome activity in head extracts of control and milton knockdown flies was measured by hydrolysis of Suc-LLVY-AMC at 14-day-old. Means ± SE, n=3. (D, E) Western blotting of head extracts of 30-day-old control and milton knockdown flies. Blotting was performed with anti-LC3 (D) and anti-p62 (E) antibodies. Representative blots (left) and quantitation (right) are shown. Actin was used as a loading control. Means ± SE, n=6 (LC3), n=3 (p62). (F) Proteasome activity in head extracts of 30-day-old control and milton knockdown flies. Means ± SE, n=3. N.S., p>0.05; *p<0.05; **p<0.01; ***p<0.005 (Student’s t-test).

Figure 2—source data 1

PDF file containing original western blots for Figure 2, indicating the relevant bands.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig2-data1-v1.zip
Figure 2—source data 2

Original files for western blot analysis displayed in Figure 2.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig2-data2-v1.zip

At 30 days old, LC3-I was still higher, and the LC3-II/LC3-I ratio was lower, in milton knockdown compared to the control (Figure 2D). At this age, milton knockdown increased p62 significantly in 1% Triton X-100 fraction and 2% SDS fraction (Figure 2E). Proteasome activities were also decreased in milton knockdown flies at 30-day-old (Figure 2F). These results indicate that depletion of axonal mitochondria impairs protein degradation pathways.

ATP deprivation does not impair autophagy

milton knockdown downregulates ATP in the axon (Oka et al., 2021). To examine whether the disruption of protein degradation pathways by milton knockdown is due to ATP deprivation, we investigated the effects of knocking down phosphofructokinase (Pfk), a rate-limiting enzyme in glycolysis, on protein degradation pathways. Neuronal knockdown of Pfk was reported to lower ATP levels in brain neurons (Oka et al., 2021). Pfk knockdown and milton knockdown decreased ATP to similar levels (Figure 3A–C). However, in contrast with milton knockdown, Pfk knockdown did not affect the levels of LC3-I, LC3-II, or the LC3-II/LC3-I ratio (Figure 3D). Pfk knockdown decreased p62 level (Figure 3E), suggesting that autophagy is promoted. On the other hand, proteasome activity was decreased by Pfk knockdown (Figure 3F). These results suggest that the downregulation of axonal ATP upon depletion of axonal mitochondria decreases proteasome activity, but not autophagy.

Figure 3
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ATP deprivation does not impair autophagy.

(A–C) ATP levels in brain extracts of control and milton knockdown flies (A) and control and Pfk knockdown flies (B) and comparison of the effects of milton knockdown and Pfk knockdown on ATP levels (C). Flies were 14-day-old. Means ± SE, n=3. (D, E) Western blotting of head extracts of flies with neuronal expression of control or Pfk RNAi. Blotting was performed with anti-LC3 (D) and anti-p62 (E) antibodies. For analyses of p62 levels, heads were extracted with 1% Triton X-100 or 2% SDS. Representative blots (left) and quantitation (right) are shown. Actin was used as a loading control. Means ± SE, n=6 (LC3), n=3 (p62). (F) Proteasome activity in head lysates of flies with neuronal expression of control or Pfk RNAi was measured by hydrolysis of Suc-LLVY-AMC. Means ± SE, n=3. N.S., p>0.05; *p<0.05; **p<0.01; ***p<0.005 (Student’s t-test). Flies were 14 days old.

Figure 3—source data 1

PDF file containing original western blots for Figure 3 indicating the relevant bands.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig3-data1-v1.zip
Figure 3—source data 2

Original files for western blot analysis displayed in Figure 3.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig3-data2-v1.zip

Proteome analysis suggests that depletion of axonal mitochondria causes disruption of autophagy and premature aging

To identify the pathways that mediate the decrease in autophagy in milton knockdown brains, we performed proteome analysis to systematically detect differentially expressed proteins upon neuronal knockdown of milton. We analyzed flies at 7- and 21-day-old, the age before autophagic defects are detected and the age just before the onset of neurodegeneration, respectively (Figure 4A). 1039 proteins were detected by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Expression of 36 proteins was significantly increased (22 proteins) or decreased (14 proteins) by milton knockdown at 7-day-old (Figure 4B, Table 1 and Supplementary file 1). At 21 days old, the expression of 41 proteins (31 upregulated and ten downregulated proteins) was significantly altered in milton knockdown flies compared with control flies (Figure 4C, Table 1 and Supplementary file 1).

Figure 4
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milton knockdown upregulates eIF2β in young flies.

(A) Timing of proteome analysis and phenotypes observed in milton knockdown flies. (B) and (C) Volcano plots of the log2 abundance ratio (x-axis) against the -log10 p-value (y-axis) of proteins at 7 days old (B) and 21 days old (C). (D) eIF2 subunit protein levels from proteome analysis of milton knockdown flies compared to those of control flies. (E) Western blotting of head extracts of flies expressing control or milton RNAi in neurons with an anti-eIF2β antibody. Flies were 14-day-old. Representative blots (left) and quantitation (right) are shown. Tubulin was used as a loading control. Means ± SE, n=6. (F) eIF2β mRNA levels quantified by qRT-PCR. Means ± SE, n=4. (G) Western blotting of head extracts of wild-type flies with an anti-eIF2β antibody. Flies were 7-, 21-, 35-, 49-, and 63-day-old. Representative blots (left) and quantitation (right) are shown. Tubulin was used as a loading control. Means ± SE, n=3, *p<0.05 (one-way analysis of variance (ANOVA) followed by Dunnett’s multiple comparison test).

Figure 4—source data 1

PDF file containing original western blots for Figure 4, indicating the relevant bands.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig4-data1-v1.zip
Figure 4—source data 2

Original files for western blot analysis displayed in Figure 4.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig4-data2-v1.zip
Table 1
Differentially expressed proteins in milton RNAi fly brains compared to control at 7 day-old detected by proteome analysis.
7-day-old
Accession*NameAbundance ratio:(7 days, milton KD)/ (7 days, control)Abundance ratio p-value: (7 days, milton KD) / (7 days, control)
Q9V751Attacin-B1001E-17
Q04448Bifunctional methylenetetrahydrofolate dehydrogenase/cyclohydrolase, mitochondrial1001E-17
P22700Calcium-transporting ATPase sarcoplasmic/endoplasmic reticulum type1001E-17
Q9V558Cytochrome P450 4p11001E-17
P10552FMRFamide-related peptides1001E-17
P05661-19Isoform F of Myosin heavy chain, muscle1001E-17
Q9VE01Probable cytochrome P450 12a5, mitochondrial1001E-17
Q7KIN0Toll-like receptor 71001E-17
Q8MKN0Ubiquinone biosynthesis protein COQ9, mitochondrial1001E-17
Q9VJG0Xaa-Pro aminopeptidase ApepP1001E-17
Q9V8F5Bomanin Bicipital 14.9081E-17
P07701Salivary glue protein Sgs-52.8431.99252E-09
O76902Pleckstrin homology domain-containing family F member 1 homolog2.8362.22045E-16
P81641Alpha-amylase B2.6847.44847E-09
P19351-4Isoform 4 of Troponin T, skeletal muscle2.666.65563E-11
Q9VTJ8Mitochondrial import inner membrane translocase subunit TIM142.613.54205E-07
P41375Eukaryotic translation initiation factor 2 subunit 22.4653.38486E-09
Q9VYB0Selenoprotein BthD2.4622.25579E-08
B7Z0W9Proton channel OtopLc2.3821.71741E-06
Q9VLR5RNA polymerase II transcriptional coactivator2.2456.70245E-09
Q8IN44Protein Turandot A2.1278.38662E-13
P27779Pupal cuticle protein Edg-78E2.1131.00215E-08
Q9W1X8Probable GDP-L-fucose synthase0.4962.12601E-08
P5503526 S proteasome non-ATPase regulatory subunit 40.4875.50158E-11
Q9VHN639 S ribosomal protein L19, mitochondrial0.4870.000551337
Q9VPD2Cytosolic Fe-S cluster assembly factor NUBP2 homolog0.466.34514E-05
Q9VHD3Probable maleylacetoacetate isomerase 10.4324.50956E-06
Q94529Probable pseudouridine-5'-phosphatase0.4161.08802E-14
Q27606Cytochrome P450 4e20.3984.13128E-10
Q24388Larval serum protein 20.3781.33227E-14
Q9VKH6Lysosomal thioesterase PPT2 homolog0.3692.05225E-06
Q24114Division abnormally delayed protein0.3072.24406E-09
Q95NH6Attacin-C0.011E-17
P29993Inositol 1,4,5-trisphosphate receptor0.011E-17
Q94526Open rectifier potassium channel protein 10.011E-17
Q9Y115UNC93-like protein0.011E-17
21-day-old
Accession*NameAbundance ratio:(21 days, milton KD) /(21 days, control)Abundance ratio p-value:(21 days, milton KD) /(21 days, control)
Q10714Angiotensin-converting enzyme1001E-17
C0HKQ8Cecropin-A21001E-17
Q9V558Cytochrome P450 4p11001E-17
P51592E3 ubiquitin-protein ligase hyd1001E-17
Q9VMJ7Lysine-specific demethylase lid1001E-17
Q9VXP4Platelet-activating factor acetylhydrolase IB subunit beta homolog1001E-17
Q9VY28Probable 28 S ribosomal protein S25, mitochondrial1001E-17
Q9W391Probable phosphorylase b kinase regulatory subunit alpha1001E-17
Q9VUQ5Protein argonaute-21001E-17
P54359Septin-21001E-17
P24492Diptericin A15.7161E-17
Q9VVY3Glycogen-binding subunit 76 A8.9861E-17
Q70PY2Peptidoglycan-recognition protein SB16.6691E-17
Q9W0M1Centrosomal protein cep2906.5261E-17
P81641Alpha-amylase B5.7221E-17
P45884Attacin-A4.9971E-17
C0HL66Histone H3.3A4.7781E-17
P26675Protein son of sevenless4.6961E-17
P02515Heat shock protein 224.691E-17
Q95NH6Attacin-C4.351E-17
P17971-1Isoform A of Potassium voltage-gated channel protein Shal4.1951E-17
Q7K1U0Activity-regulated cytoskeleton associated protein 13.2711E-17
P14199Protein ref(2)P3.0141E-17
Q9VU02Probable small nuclear ribonucleoprotein Sm D12.434.91607E-13
Q9VD44Poly(A) RNA polymerase gld-2 homolog A2.2682.6084E-11
Q9V8F5Bomanin Bicipital 12.245.16671E-11
P22979Heat shock protein 67B32.2237.90048E-11
P27779Pupal cuticle protein Edg-78E2.1921.65944E-10
Q9NBK5Serine/threonine-protein kinase tricornered2.0594.15071E-09
Q8MLZ7Chitinase-like protein Idgf32.0554.61182E-09
Q9V751Attacin-B2.0386.79416E-09
Q9V8M5Probable 3-hydroxyisobutyrate dehydrogenase, mitochondrial0.4927.42952E-09
P84345ATP synthase protein 80.4211.809E-12
P33438Glutactin0.4146.13731E-13
Q8IN44Protein Turandot A0.2181E-17
Q8IN43Protein Turandot C0.1951E-17
Q9VFI9cGMP-specific 3',5'-cyclic phosphodiesterase0.011E-17
Q94526Open rectifier potassium channel protein 10.011E-17
Q9VHD3Probable maleylacetoacetate isomerase 10.011E-17
Q9W0A0Protein draper0.011E-17
A1Z7T0Serine/threonine-protein kinase N0.011E-17
  1. *

    UniProt accession number.

The ‘Interaction search’ algorithm using KeyMolnet showed that proteins whose expression was significantly altered in the brains of milton knockdown flies at both 7- and 21-day-old were closely associated with the autophagic pathway (Table 2). Proteins involved in pathways characteristics of aging, such as the immune response (transcriptional regulation by STAT), cancer (transcriptional regulation by SMAD, transcriptional regulation by myc), longevity (transcriptional regulation by FOXO, Sirtuin signaling pathway), and stress responses (HSP90 signaling pathway, MAPK signaling pathway; Zia et al., 2021; Haigis and Yankner, 2010), were enriched in the proteome profiles of milton knockdown flies compared with those of control flies at 7-day-old (Table 2), suggesting that depletion of axonal mitochondria accelerates aging in the brain.

Table 2
Molecule networks based on “Interaction search” of KeyMolnet.
7-day-old
RankNameScoreScore (p)*Score (v)†Score (c)‡
1Autophagy-related protein signaling pathway50.3946.76E-160.1590.11
2Calcium signaling pathway47.5834.75E-150.1460.117
3Transcriptional regulation by SMAD44.0125.64E-140.1460.095
4GABA signaling pathway40.7065.58E-130.1220.123
5estrogen signaling pathway37.5075.12E-120.110.13
6Sirtuin signaling pathway36.877.96E-120.1220.095
7Transcriptional regulation by AP-134.8743.18E-110.110.107
8Arrestin signaling pathway32.841.30E-100.110.092
9G protein (Gq/11) signaling pathway30.8895.03E-100.0850.149
10Kainate receptor signaling pathway30.0499.00E-100.0730.214
11Transcriptional regulation by C/EBP29.51.32E-090.0980.093
12Calpain signaling pathway28.5972.46E-090.110.066
13Phospholipase D signaling pathway28.3442.94E-090.0980.084
14HSP90 signaling pathway27.1886.54E-090.0850.104
14CYP family27.1886.54E-090.0850.104
16Kir3 channel signaling pathway26.4951.06E-080.0610.25
17Estrogen biosynthesis26.1071.38E-080.0610.238
18CaSR signaling pathway25.392.27E-080.0610.217
19PI3K signaling pathway24.9273.14E-080.0730.122
20PAF receptor signaling pathway24.5554.06E-080.0490.4
21Transcriptional regulation by PPARa24.3984.52E-080.0730.115
21BTK signaling pathway24.3984.52E-080.0730.115
23Transcriptional regulation by STAT24.0765.66E-080.0850.077
24G protein (Gi/o) signaling pathway24.0635.71E-080.0730.111
25PARP signaling pathway23.7427.13E-080.0730.107
25mGluR signaling pathway23.7427.13E-080.0730.107
27Free fatty acid signaling pathway23.4338.83E-080.0730.103
28Kir channel signaling pathway23.3389.43E-080.0610.167
29Oxytocin signaling pathway23.3279.51E-080.0490.333
30Transcriptional regulation by MEF222.991.20E-070.0730.098
31S100 family signaling pathway22.4341.77E-070.0730.092
32Transcriptional regulation by FOXO22.3011.94E-070.0730.091
33P2Y signaling pathway22.1722.12E-070.0610.143
34Transcriptional regulation by SRF21.1744.23E-070.0610.125
34ATF4/ATF6/IRE1 signaling pathway21.1744.23E-070.0610.125
36Chemerin signaling pathway21.0824.50E-070.0490.235
36Vasopressin signaling pathway21.0824.50E-070.0490.235
38Serotonin signaling pathway20.8545.28E-070.0730.077
39Transcriptional regulation by HIF20.8345.35E-070.0980.043
40Leukotriene receptor signaling pathway20.7245.78E-070.0490.222
40CART signaling pathway20.7245.78E-070.0490.222
42MAPK signaling pathway20.6935.90E-070.0850.055
43Transcriptional regulation by RB/E2F20.5436.55E-070.0980.042
44NAD metabolism20.4686.89E-070.0610.114
45ERK signaling pathway20.4257.11E-070.0730.073
46Adenylyl Cyclase signaling pathway20.3037.73E-070.0610.111
47Bile acid signaling pathway20.1418.65E-070.0610.109
21-day-old
RankNameScoreScore (p)*Score (v)†Score (c)‡
1Histone demethylation84.1984.51E-260.1020.425
2CDK inhibitor signaling pathway56.4979.83E-180.0780.295
3Transcriptional regulation by RB/E2F46.5989.39E-150.1080.095
4Mst(Hippo) signaling pathway46.3431.12E-140.090.133
5Transcriptional regulation by androgen receptor46.0781.35E-140.0780.178
6p160 SRC signaling pathway45.8091.62E-140.0780.176
7Transcriptional regulation by SMAD43.9615.84E-140.090.119
8Autophagy-related protein signaling pathway41.0634.35E-130.0840.119
9Transcriptional regulation by HIF39.5271.26E-120.0960.086
10Nucleophosmin signaling pathway38.4172.72E-120.0540.273
11HSP90 signaling pathway37.8873.93E-120.0660.164
12PAF metabolism37.5624.93E-120.0420.5
13Transcriptional regulation by STAT37.2766.01E-120.0720.132
14Bcl-2 family signaling pathway36.1571.31E-110.0720.124
15Sirtuin signaling pathway34.7823.39E-110.0720.114
16Transcriptional regulation by C/EBP33.8196.60E-110.0660.128
17PIN1 signaling pathway33.1721.03E-100.060.149
18RSK signaling pathway30.5666.29E-100.060.125
19Transcriptional regulation by High mobility group protein29.8731.02E-090.0540.148
20BET family signaling pathway29.6561.18E-090.0540.145
21Transcriptional regulation by Myc28.8382.08E-090.0660.093
22Transcriptional regulation by FOXO28.8272.10E-090.0540.136
23PSD-95 family signaling pathway26.1541.34E-080.0480.14
24AKT signaling pathway25.1692.65E-080.0480.129
25Arginine methylation24.7993.43E-080.0480.125
26gp130 signaling pathway24.255.01E-080.0540.096
27Transcriptional regulation by CREB23.8586.58E-080.0660.067
28Gene regulation by microRNAs (metastasis)23.8526.60E-080.0540.093
29HDAC signaling pathway23.5368.22E-080.0360.207
30Calpain signaling pathway23.0921.12E-070.060.074
31Transcriptional regulation by IRF22.7381.43E-070.0540.085
322-Oxoglutarate signaling pathway22.6731.50E-070.0480.104
3214-3-3 signaling pathway22.6731.50E-070.0480.104
34Transcriptional regulation by POU domain factor22.6011.57E-070.060.071
35Transcriptional regulation by BLIMP-122.4741.72E-070.0420.132
36Gene regulation by microRNAs (metabolism)22.391.82E-070.0540.083
37Fatty acid beta oxidation22.0962.23E-070.0420.127
38Transcriptional regulation by RXR22.082.26E-070.0360.176
39ERK signaling pathway21.962.45E-070.0480.098
40PARP signaling pathway21.9132.53E-070.0420.125
41Transcriptional regulation by VDR21.6183.11E-070.0540.078
42Transcriptional regulation by p5321.1684.24E-070.0720.05
43Acetylcholine metabolism21.1524.29E-070.0240.444
44Gene regulation by microRNAs (embryonic stem cells)21.084.51E-070.0360.158
45mTOR signaling pathway21.0484.61E-070.0420.115
46Gene regulation by microRNAs (cancer)21.044.64E-070.0480.09
47Transcriptional regulation by Ets-1/220.7245.77E-070.0420.111
48MAPK signaling pathway20.4117.18E-070.0540.07
49Gene regulation by microRNAs (cell cycle)20.4047.21E-070.0360.146
50Transcriptional regulation by p7320.2597.97E-070.0420.106
  1. *

    Score(p) indicates p-value of the pathway.

  2. †

    Score(v) indicates the ratio of ‘Count’ to total molecules associated with the loaded list.

  3. ‡

    Score(c) indicates the ratio of ‘Count’ to total molecules contained in the pathway.

Depletion of axonal mitochondria upregulates eIF2β and decreases phosphorylation of eIF2α

Differentially expressed proteins at 7-day-old flies may reflect alterations that are causal for autophagic defects. We noticed that the expression level of eIF2β was 2.465-fold higher in the brains of milton knockdown flies than in those of control flies (Figure 4B and D). Upregulation of eIF2β in the brains of milton knockdown flies was confirmed by western blotting. milton knockdown increased eIF2β protein levels more than twice (Figure 4E), but did not change the level of eIF2β mRNA (Figure 4F).

We also investigated age-dependent changes in eIF2β by western blotting of control flies at 7-, 21-, 35-, 49-, and 63-day-old. eIF2β levels increased during aging until 49-day-old (Figure 4G). These results suggest that upregulation of eIF2β in milton knockdown fly brain reflects early an onset of age-dependent increase of eIF2β levels.

eIF2β is a subunit of the eukaryotic initiation factor 2 (eIF2) complex, which is critical for translation initiation and the integrated stress response (ISR; Kimball, 1999). eIF2 is a heterotrimer of α, β, and γ subunits, and eIF2α is phosphorylated during the ISR (Pakos-Zebrucka et al., 2016). As for the other subunits of the eIF2 complex, proteome analysis did not detect a significant difference in the protein levels of eIF2α and eIF2γ between milton knockdown and control flies at 7- and 21-day-old (Figure 4D). Western blotting of brain lysates showed that milton knockdown reduced eIF2α levels (Figure 5A), while p-eIF2α levels were not significantly affected (Figure 5B).

Figure 5
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milton knockdown decreases phosphorylation of eIF2α.

(A, B) Western blotting of head extracts with anti-eIF2α (A) and anti-p-eIF2α (B) antibodies. Flies were 14-day-old. Representative blots (left) and quantitation (right) are shown. Tubulin was used as a loading control. Means ± SE, n=6. (C) A schematic representation of the axon (Lobe tips), the cell body region (Kenyon cells), and dendritic region (Calyxes) in the fly brain. Scale bars, 100 µm. (D, E) Immunostaining with anti-eIF2α and anti-p-eIF2α antibodies. The mushroom body was identified by expression of mito-GFP. Scale bars, 20 µm. The signal intensities of eIF2α and p-eIF2α in axons, dendrites, and cell bodies were quantified and are shown as ratios relative to the control. Means ± SE, n =12. N.S., p>0.05; *p<0.05; **p<0.01; ***p<0.005 (Student’s t-test).

Figure 5—source data 1

PDF file containing original western blots for Figure 5 indicating the relevant bands.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig5-data1-v1.zip
Figure 5—source data 2

Original files for western blot analysis displayed in Figure 5.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig5-data2-v1.zip

To analyze local changes of eIF2α and p-eIF2α, we carried out immunostaining. We focused on the mushroom body, where axons, dendrites, and cell bodies can be easily identified (Figure 5C). Both eIF2α and p-eIF2α were downregulated in the cell body (Kenyon cells) and dendritic (Calyxes) regions of the brains of milton knockdown flies (Figure 5D). In axons (lobe tips), milton knockdown did not affect eIF2α (Figure 5E, p=0.271) but significantly downregulated p-eIF2α (Figure 5E). The ratio of p-eIF2α to eIF2α was lower in the axon but not in the soma or dendritic region. These results suggest that axonal distribution of mitochondria regulates the level of overall eIF2α protein and local p-eIF2α.

Depletion of axonal mitochondria suppressed global translation

Phosphorylation of eIF2α induces conformational changes in the eIF2 complex and inhibits global translation (Wek, 2018). To analyze the effects of milton knockdown on translation, we performed polysome gradient centrifugation to examine the level of ribosome binding to mRNA. Since p-eIF2α was downregulated, we hypothesized that milton knockdown would enhance translation. However, unexpectedly, we found that milton knockdown significantly reduced the level of mRNAs associated with polysomes (Figure 6A and B). We also compared the level of translation between the brains of control and milton knockdown flies by assessing the incorporation of puromycin (Figure 6C). Puromycin incorporation was lower in the brains of milton knockdown flies than in those of control flies, while it was not statistically significant (Figure 6C, indicated by a bracket). These data suggest that the depletion of axonal mitochondria suppresses global translation.

Figure 6
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milton knockdown suppressed global translation.

(A) Representative polysome traces of head lysates of control and milton knockdown flies. (B) Quantitation of polysome fraction. The relative ratio of area under the curve (AUC) of polysome fractions (sedimentation 28–50%). Means ± SE, n=3. ***p<0.005 (Student’s t-test) (C) Western blotting of head lysates of control and milton knockdown flies fed puromycin alone or puromycin and cycloheximide (CHX) with an anti-puromycin antibody. Flies were 14-day-old. Actin was used as a loading control. Representative blots (left) and quantitation (right) are shown. Means ± SE, n=3. Student’s t-test.

Figure 6—source data 1

PDF file containing original western blots for Figure 6, indicating the relevant bands.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig6-data1-v1.zip
Figure 6—source data 2

Original files for western blot analysis displayed in Figure 6.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig6-data2-v1.zip

eIF2β upregulation reduces the level of p-eIF2α, impairs autophagy, and decreases locomotor function

We were motivated to ask if eIF2β upregulation mediates autophagic defects caused by milton knockdown. If so, neuronal overexpression of eIF2β would also induce autophagy impairment. Neuronal overexpression of eIF2β increased LC3-II, while the LC3-II/LC3-I ratio was not significantly different (Figure 7A and B). Overexpression of eIF2β significantly increased the p62 level in the Triton X-100-soluble fraction (Figure 7C, fourfold vs. control, p<0.005 [1% Triton X-100]) but not in the SDS-soluble fraction (Figure 7C, twofold vs. control, p=0.062 [2% SDS]), as observed in brains of milton knockdown flies (Figure 2B). These data suggest that neuronal overexpression of eIF2β accumulates autophagic substrates.

Figure 7 with 1 supplement see all
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eIF2β upregulation impairs autophagy and decreases locomotor function.

(A) eIF2β mRNA levels in head extracts of flies with UAS-eIF2β driven by elav-Gal4 (eIF2β OE) or UAS-GFP driven by elav-Gal4 (control) were quantified by qRT-PCR. Flies were 2-day-old. Means ± SE, n=4. (B, C) Western blotting of head extracts with anti-LC3 (B) and anti-p62 (C) antibodies. Flies were 14-day-old. Representative blots (left) and quantitation (right) are shown. Tubulin and actin were used as loading controls. Means ± SE, n=3 (p62), n=5 (LC3). (D, E) Western blotting of head extracts with anti-eIF2α (D) and anti-p-eIF2α (E) antibodies. Flies were 14-day-old. Representative blots (left) and quantitation (right) are shown. Tubulin was used as a loading control. Means ± SE, n=6. (F) Climbing assay revealed early-onset of age-dependent locomotor defects in eIF2β-overexpressing flies. Means ± SE, n=5. N.S., p>0.05; ***p<0.005 (Student’s t-test).

Figure 7—source data 1

PDF file containing original western blots for Figure 7 indicating the relevant bands.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig7-data1-v1.zip
Figure 7—source data 2

Original files for western blot analysis displayed in Figure 7.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig7-data2-v1.zip

Since the milton knockdown reduced the p-eIF2α level (Figure 5E), we asked whether an increase in eIF2β affects p-eIF2α. Neuronal overexpression of eIF2β did not affect the eIF2α level but significantly decreased the p-eIF2α level (Figure 7D and E).

Depletion of axonal mitochondria causes age-dependent decline in locomotor function (Iijima-Ando et al., 2012). We found that neuronal overexpression of eIF2β also caused locomotor dysfunction (Figure 7F). Locomotor functions were significantly impaired in those flies at 20 days old and worsened further during aging (Figure 7F, compare 4-, 20-, and 30-day-old). We asked if eIF2β overexpression causes neurodegeneration, as depletion of axonal mitochondria in the photoreceptor neurons causes axon degeneration in an age-dependent manner (Iijima-Ando et al., 2012). eIF2β overexpression in photoreceptor neurons tends to increase neurodegeneration in aged flies, while it was not statistically significant (p>0.05, Figure 7—figure supplement 1).

These data indicate that an increase of eIF2β in neurons phenocopies depletion of axonal mitochondria, including suppression of autophagy and age-dependent locomotor dysfunction, and suggest that increase of eIF2β mediates these phenotypes downstream of loss of axonal mitochondria.

Lowering eIF2β rescues autophagic impairment and locomotor dysfunction induced by milton knockdown

Finally, we investigated whether suppression of eIF2β rescues autophagy impairment and locomotor dysfunction caused by neuronal knockdown of milton. Null mutants and flies with RNAi-mediated knockdown of eIF2β in neurons did not survive. Flies lacking one copy of the eIF2β gene survived without any gross abnormality, and the level of eIF2β mRNA in these flies was about 80% of that in control flies (Figure 8A). eIF2β heterozygosity did not affect the eIF2α and p-eIF2α levels (Figure 8—figure supplement 1A and B).

Figure 8 with 1 supplement see all
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Lowering eIF2β rescues autophagic impairment and locomotor dysfunction induced by milton knockdown.

(A) eIF2β mRNA levels with one disrupted copy of the eIF2β gene (eIF2βSAstopDsRed/+ [eIF2β -/+]). Head extracts of flies 2–3 day-old were analyzed by qRT-PCR. Means ± SE, n=3. (B, C) Western blotting of head extracts of flies with neuronal expression of milton RNAi with or without eIF2β heterozygosity with anti-LC3 (B) and anti-p62 (C) antibodies. Flies were 14-day-old. Representative blots (left) and quantitation (right) are shown. Actin was used as a loading control. Means ± SE, n=5 (LC3), n=3 (p62). (D) The climbing ability of 20-day-old flies expressing milton RNAi with or without eIF2β heterozygosity. Means ± SE, n=15. N.S., p>0.05; *p<0.05; ***p<0.005 (Student’s t-test).

Figure 8—source data 1

PDF file containing original western blots for Figure 8, indicating the relevant bands.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig8-data1-v1.zip
Figure 8—source data 2

Original files for western blot analysis displayed in Figure 8.

https://cdn.elifesciences.org/articles/95576/elife-95576-fig8-data2-v1.zip

Neuronal knockdown of milton causes accumulation of autophagic substrate p62 in the Triton X-100-soluble fraction (Figure 2B), and we tested if lowering eIF2β ameliorates it. We found that eIF2β heterozygosity caused a mild increase in LC3-I levels and decreases in LC3-II levels, resulting in a significantly lower LC3-II/LC3-I ratio in milton knockdown flies (Figure 8B). eIF2β heterozygosity decreased the p62 level in the Triton X-100-soluble fraction in the brains of milton knockdown flies (Figure 8C). The p62 level in the SDS-soluble fraction, which is not sensitive to milton knockdown (Figure 2B), was not affected (Figure 8C). These results suggest that suppression of eIF2β ameliorates the impairment of autophagy caused by milton knockdown.

eIF2β heterozygosity also rescued locomotor dysfunction induced by milton knockdown. milton knockdown flies with eIF2β heterozygosity exhibited better locomotor function than milton knockdown alone (Figure 8D). The milton mRNA level was not increased in these flies, indicating that the rescue effect in the eIF2β heterozygous background was not mediated by an increase in the milton mRNA level (Figure 8—figure supplement 1). These data suggest that eIF2β upregulation mediates autophagy impairment and locomotor dysfunction caused by the depletion of axonal mitochondria.

Discussion

The depletion of axonal mitochondria and accumulation of abnormal proteins are both characteristics of aged brains (Currais et al., 2017; Grimm and Eckert, 2017). Proteostasis perturbations trigger the formation of pathological aggregates and increase the risks of neurodegenerative diseases during aging. By using neuronal milton knockdown to deplete mitochondria from the axon, we provide evidence that loss of axonal mitochondria drives age-related proteostasis collapse via eIF2β (Figure 9). We observed declines in autophagy-mediated degradation of less-aggregated proteins and proteasome activity in milton knockdown flies (Figure 2). Accumulation of ubiquitinated proteins and changes in age-related pathways started prematurely in milton knockdown flies (Figure 1 and Table 2). milton knockdown increased eIF2β and lowered eIF2α phosphorylation in young fly brain (Figures 4 and 5). Overexpression of eIF2β phenocopied the effects of milton knockdown, including reduced autophagy and accelerated age-related locomotor defects (Figure 7). Furthermore, lowering eIF2β levels suppressed the impairment of autophagy and locomotor dysfunction induced by milton knockdown (Figure 8). From these results, we propose that upregulation of eIF2β downstream of depletion of axonal mitochondria drives age-dependent collapse of proteostasis (Figure 9). Our results suggest that mitochondrial distribution and eIF2β are part of the mechanisms constituting proteostasis.

Figure 9
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The mitochondria-eIF2β axis in the axon maintains neuronal proteostasis during aging.

Aging is associated with a reduction in axonal transport of mitochondria. Our results suggest that the loss of axonal mitochondria leads to an increase in eIF2β, while the upregulation of eIF2β decreases autophagy-mediated protein degradation and promotes aging.

milton knockdown causes loss of mitochondria in the axon and accumulation of mitochondria in the soma. Thus, the detrimental effects may be mediated by the accumulation of mitochondria. However, degeneration induced by milton knockdown is prominent in the axon and not detected in the cell body (Iijima-Ando et al., 2012). Furthermore, abnormal protein accumulation was observed in the axon (Figure 1), and p-eIF2α/eIF2α was decreased in the neurites but not in the soma (Figure 5), suggesting that proteostasis defects studied in this work are caused by depletion of mitochondria rather than accumulation of mitochondria. Further analyses to dissect the effects of milton knockdown on proteostasis and translation in the cell body and axon by experiments with spatial resolution would be needed.

Our results suggest that the loss of axonal mitochondria is an event upstream of proteostasis collapse during aging. The number of puncta of ubiquitinated proteins was higher in milton knockdown at 14-day-old, but there was no significant difference at 30-day-old (Figure 1). Proteome analyses also showed that age-related pathways, such as immune responses, are enhanced in young flies with milton knockdown (Table 2). We also found that eIF2β protein levels increase in an age-dependent manner until 49-day-old and reduce after that (Figure 4G). In the brains with neuronal knockdown of milton, eIF2β levels were higher at 7 days old than those in control and lower at the 21 days old (Figure 4D and Supplementary file 1). These results suggest that milton knockdown is likely accelerating age-dependent changes rather than increasing their magnitude. Disruption of proteostasis is expected to contribute to neurodegeneration (Grimm and Eckert, 2017), and it would be interesting to analyze the sequence of protein accumulation and axonal degeneration in milton knockdown (Iijima-Ando et al., 2012; Iijima-Ando et al., 2009 and Figure 1) in detail with higher time resolution.

Our results revealed that eIF2β regulates autophagy and maintains proteostasis during aging. eIF2β is a component of eIF2, which mediates translational regulation and ISR initiation. When ISR is activated, phosphorylated eIF2α suppresses global translation and induces translation of ATF4, which mediates transcription of autophagy-related genes (Bond et al., 2020; B’chir et al., 2013). Since ISR can positively regulate autophagy, we suspected that suppression of ISR underlies a reduction in autophagic protein degradation. We found neuronal knockdown of milton reduced phosphorylated eIF2α, suggesting that ISR is reduced (Figure 5). However, we also found that global translation was reduced (Figure 6). Increased levels of eIF2β might disrupt the eIF2 complex or alter its functions. The stoichiometric mismatch caused by an imbalance of eIF2 components may inhibit ISR induction. Supporting this model, we found that eIF2β upregulation reduced the levels of p-eIF2α (Figure 7). It is also possible that eIF2β mediates autophagy defects via mechanisms independent of ISR since eIF2β has functions independent of eIF2 (Salton et al., 2017; Lee et al., 2007). For example, suppression of eIF2β has been reported to slow down cancer cell growth (Salton et al., 2017). In developing neurons, eIF2β can directly interact with the translational repressor Kra to regulate midline axon guidance (Lee et al., 2007). Our results also suggest that milton knockdown and overexpression of eIF2β affect autophagy via increased LC3-I abundance (Figures 2 and 7), suggesting an unconventional mechanism of autophagy suppression. To our knowledge, the roles of eIF2β in aging and autophagy independent of ISR have not been reported. Our results revealed a novel function of eIF2β to maintain proteostasis during aging, while further investigation is required to elucidate underlying mechanisms.

How depletion of axonal mitochondria upregulates eIF2β is currently under investigation. A major mitochondrial function is ATP production, and depletion of axonal mitochondria downregulates ATP in axons (Oka et al., 2021). However, we found that ATP deprivation did not always suppress autophagy (Figure 3), suggesting it is unlikely to be involved in the mechanisms that induce eIF2β upregulation. Mitochondria also serve as signaling hubs for translation and protein degradation. Mitochondrial proteins are regulated by co-translational protein quality control, and mitochondrial damage induces translational stalling of mitochondrial outer membrane-associated complex-I 30 kD subunit (C-I30) mRNA (Wu et al., 2019). Additionally, the mitochondrial outer membrane ubiquitin ligase MITOL (also known as MARCHF5) ubiquitinates and regulates not only mitochondrial proteins such as Mfn2 (Sugiura et al., 2013) but also microtubule-associated (Yonashiro et al., 2012) and endoplasmic reticulum (Takeda et al., 2019) proteins. These findings indicate that mitochondria serve as local signaling centers for proteostasis maintenance, and eIF2β levels may also be regulated by mechanisms related to mitochondria.

In conclusion, our results suggest that axonal mitochondria and eIF2β form an axis to maintain constitutive autophagy. Suppression of eIF2β rescued autophagic defects and neuronal dysfunction upon loss of axonal mitochondria. Since eIF2β is conserved across many species, including Drosophila and humans, our results suggest that eIF2β may be a possible therapeutic target for aging and diseases associated with mitochondrial mislocalization.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Drosophila)UAS-milton RNAiVienna Drosophila Resource Center (VDRC)VDRC:v41508, FLYB:FBst0464139
Strain, strain background (Drosophila)UAS-Miro RNAiIijima-Ando et al., 2012
Strain, strain background (Drosophila)UAS-luciferase RNAiIijima-Ando et al., 2012
Strain, strain background (Drosophila)UAS-Pfk RNAiBloomington Drosophila Stock CenterBDSC:36782, FLYB:FBti0146432
Strain, strain background (Drosophila)UAS-luciferase RNAiBloomington Drosophila Stock CenterBDSC:31603, FLYB:FBti0130444
strain, strain background (Drosophila)UAS-eIF2βBloomington Drosophila Stock CenterBDSC:17425, FLYB:FBti0038792
Strain, strain background (Drosophila)UAS-GFPBloomington Drosophila Stock CenterBDSC:1521, FLYB:FBti0003040
Strain, strain background (Drosophila)eIF2β[PBac{SAstopDsRed} LL07719]KYOTO Drosophila Stock Center (DGRC)DGRC:142114, FLYB:FBgn0004926
Strain, strain background (Drosophila)w1118Vienna Drosophila Resource Center (VDRC)VDRC:60000
Strain, strain background (Drosophila)UAS-mitoGFPM. Saxton, University of California, Santa Cruz
Strain, strain background (Drosophila)elav-GAL4Bloomington Drosophila Stock CenterBDSC:458,
FLYB:FBti0002575
Strain, strain background (Drosophila)GMR-gal4Bloomington Drosophila Stock CenterBDSC:1104,
FLYB:FBti0002994
Antibodyanti-ubiquitin antibody
Ubi-1		Thermo FisherCat#:13–1600,
RRID:AB_2533002		IHC:1:50
Antibodyanti-LC3 antibody Atg8Merck MilliporeCat#:ABC974,
RRID:AB_2939040		WB:1:1000
Antibodyanti-p62 antibody Ref2PAbcamCat#:ab178440,
RRID:AB_2938801		WB:1:750
Antibodyanti-eIF2αAbcamCat#:ab26197,
RRID:AB_2096478		IHC:1:50
Antibodyanti-p-eIF2αCell signalingCat#:3398 S,
RRID:AB_2096481		IHC:1:50
Antibodyanti-Drosophila eIF2βThis paperWB:1:1500
Antibodyanti-puromycinEnzoCat#:CAC-CAC-PEN-MA001, RRID:AB_2620162WB:1:1000
Antibodyanti-actinSigmaCat#:A2066, RRID:AB_476693WB:1:3000
Antibodyanti-β tubulinSigmaCat#:T9026, RRID:AB_477593WB:1:10000
Antibodyperoxidase-conjugated goat anti-mouse IgG antibodyDakoCat#:P0447, RRID:AB_2617137WB:1:2000
Antibodyperoxidase-conjugated
pig anti-rabbit IgG antibody		DakoCat#:P0399, RRID:AB_2617141WB:1:2000
Commercial Assay
or Kit		20 S Proteasome Substrate (SUC-LLVY-AMC)CaymanCat#:10011095
Commercial Assay
or Kit		ATP Determination KitInvitrogenCat#:A22066

Fly stocks and husbandry

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Flies were maintained in standard cornmeal medium (10% glucose, 0.7% agar, 9% cornmeal, 4% yeast extract, 0.3% propionic acid, and 0.1% n-butyl p-hydroxybenzoate) at 25 °C under light–dark cycles of 12:12 hr. The flies were transferred to fresh food vials for every 2–3 days. UAS-milton RNAi (v41508) was from VDRC and outcrossed to [w1118] for five generations in our laboratory. Transgenic fly lines carrying UAS-Miro RNAi and UAS-luciferase RNAi (control for milton RNAi) were reported previously (Iijima-Ando et al., 2012). GMR-gal4, Elav-gal4, UAS-Pfk RNAi (Bloomington stock center #36782), UAS-luciferase RNAi (Bloomington stock center #31603) (control for Pfk RNAi), UAS-GFP (used for control for UAS-eIF2β), and UAS-eIF2β (eIF2βEY08063, Bloomington stock center #17425) were from the Bloomington stock center. eIF2β loss-of-function strain (PBac{SAstopDsRed} LL07719, DGRC#142114) was from KYOTO Drosophila Stock Center. UAS-mitoGFP was a kind gift from Dr. W. M. Saxton (University of California, Santa Cruz). Fly genotypes used in this study are listed in Supplementary file 2.

Immunohistochemistry and image acquisition

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Fly brains were dissected in PBS and fixed for 45 min in formaldehyde (4% v/v in PBS) at room temperature. After incubation in PBST containing 0.1% Triton X-100 for 10 min three times, samples were incubated for 1 hr at room temperature in PBST containing 1% normal goat serum (Wako, #143–06561) and then incubated overnight with the primary antibody (anti-ubiquitin antibody Ubi-1 (Thermo Fisher #13–1600) (1:50), anti-eIF2α (abcam #ab26197) (1:50) and anti-p-eIF2α (Cell signaling #3398 S) (1:50)) diluted in 1% NGS/PBST at 4 °C. Samples were then washed for 10 min with PBST including 0.1% Triton X-100 three times and incubated with the secondary antibody overnight at 4 ° C. Brains were mounted in Vectashield (Vectorlab Cat#H-1100) and analyzed under a confocal microscope (Nikon). Quantitative analysis was performed using ImageJ (National Institutes of Health) with maximum projection images derived from Z-stack images acquired with same settings. Puncta were identified with mean intensity and area using ImageJ. For eIF2α and p-eIF2α immunostaining, the mushroom body was detected by mitoGFP expression.

Electron microscopy

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Proboscis was removed from decapitated heads, which were then incubated in primary fixative solution (2.5% glutaraldehyde and 2% paraformaldehyde in 0.1 M sodium cacodylate buffer) at R.T. for 2 hr. After washing heads with 3% sucrose in 0.1 M sodium cacodylate buffer, fly heads were post-fixed for 1 hr in secondary fixation (1% osmium tetroxide in 0.1 M sodium cacodylate buffer) on ice. After washing with H2O, heads were dehydrated with ethanol and infiltrated with propylene oxide and Epon mixture (TAAB and Nissin EM) for 3 hr. After infiltration, specimens were embedded with an Epon mixture at 70 °C for 2–3 days. Thin sections (70 nm) of laminas were collected on copper grids. The sections were stained with 5% uranyl acetate in 50% ethanol and Reynolds' lead citrate solution. Electron micrographs were obtained with a CCD camera mounted on a JEM-1400 plus electron microscope (Jeol Ltd.). Quantitation was performed using ImageJ (National Institutes of Health).

SDS–PAGE and immunoblotting

Western blotting was performed as reported previously (Iijima-Ando et al., 2012). Briefly, heads of 10–20 Drosophila were homogenized with SDS-Tris-Glycine sample buffer (0.312 M Tris, 5% SDS, 8% glycerol, 0.0625% BPB, 10% β-mercaptoethanol, 10 μg/mL leupeptin, 0.4 μM Pefabloc, 10 mM β-glycerophosphate, 10 mM NaF) and after boiling at 95 °C for 2 min, it was centrifuged at 13,200 rpm, and the supernatant was used as a sample. For p62 western blot, fly heads were homogenized with 1% PBST and after centrifugation at 13,200 rpm, the supernatant was mixed 1:1 SDS-Tris-Glycine sample buffer, and boiled at 95 °C for 2 min. The pellet was dissolved with 2% SDS in PBS, then centrifuged again at 13,200 rpm. The supernatant was mixed 1:1 SDS-Tris-Glycine sample buffer and then boiled at 95 °C for 2 min. SDS–PAGE for western blotting was performed using 15%(w/v) (LC3), 7.5%(w/v) (p62), 10% (w/v) (eIF2α, β, and p-eIF2α) polyacrylamide gels. After electrophoresis, they were transferred to PVDF membrane (Merck Millipore) using a transfer device (BIO-RAD). After transfer, the membrane was blocked with 5% skim milk/TBST (50 mM Tris (pH 7.5), 0.15 M NaCl, 0.05% Tween20) for 1 hr and incubated with primary antibody listed below overnight at 4 °C. Membranes were rinsed twice with TBST containing 0.65 M NaCl and once with TBST containing 0.15 M NaCl. After incubation with the secondary antibody at room temperature for 1 hr, membranes were rinsed twice with TBST containing 0.65 M NaCl and once with TBST containing 0.15 M NaCl. After incubation with Immobilon Western Chemiluminescent HRP Substrate (Merck Millipore), chemiluminescent signals were detected with Fusion FX (Vilber). Experiments were repeated at least three times with independent cohorts of flies.

Primary antibodies

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anti-LC3 antibody Atg8 (Merck Millipore #ABC974) (1:1000), anti-p62 antibody Ref2P (Abcam #ab178440) (1:750), anti-eIF2β antibody (1:1500), anti-eIF2α antibody (Abcam #ab26197) (1:1000), anti-p-eIF2α antibody (Cell signaling #3398 S) (1:2000), anti-actin antibody (Sigma #A2066) (1:3000), and anti-β tubulin antibody (Sigma #T9026) (1:100,000). Polyclonal anti-eIF2β antibody was raised against a synthetic peptide (CGLEDDTKKEDPQDEA) corresponding to the C-terminal residues 29–43 of Drosophila eIF2β (1:1500).

Secondary antibodies

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Peroxidase-conjugated goat anti-mouse IgG antibody (Dako #P0447) (1:2000), peroxidase-conjugated pig anti-rabbit IgG antibody (Dako #P0399) (1:2000).

Proteasome assay

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Heads from ten flies were homogenized in 150 µl of buffer B (25 mM Tris-HCl [pH 7.5], 2 mM ATP, 5 mM MgCl2, and 1 mM dithiothreitol). Proteasome peptidase activity in the lysates was measured with a synthetic peptide substrate, succinyl-Leu-Leu-Val-Tyr-7-amino-4-methyl-coumarin (Suc-LLVY-AMC; Cayman). Luminescence was measured on a multimode plate reader 2300 Enspire (PerkinElmer). Experiments were repeated at least three times with independent cohorts of flies.

ATP assay

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Heads from the 10 flies were homogenized in 50  μl of 6  M guanidine-HCl in extraction buffer (100  mM Tris and 4  mM EDTA, pH 7.8) to inhibit ATPases. Samples were boiled for 5  min and centrifuged. The supernatant was diluted 4% with extraction buffer and mixed with a reaction solution (ATP Determination kit, Invitrogen). Luminescence was measured on a multimode plate reader 2300 Enspire (PerkinElmer). The relative ATP levels were calculated by dividing the luminescence by the total protein concentration, which was determined by the Bradford method. Experiments were repeated at least three times with independent cohorts of flies.

Proteomic assay and pathway analysis

Sample preparation

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Heads from the 35 flies were homogenized in 110 µl of extraction buffer (0.25% RapiGest SF, 50 mM ammonium bicarbonate, 10 mM dithiothreitol, 10 μg/mL leupeptin, 0.4 μM Pefabloc, 10 mM β-glycerophosphate, 10 mM NaF). Homogenized samples were centrifuged and boiled for 5  min. After quantification of the protein concentration using a Pierce 660 nm Protein Assay (Thermo Fisher Scientific), 10 µg proteins from each sample were reduced using 5 mM tris (2-carboxyethyl) phosphine hydrochloride (TCEP-HCl; Thermo Fisher Scientific) at 60 °C for 1 hr, alkylated using 15 mM iodoacetamide (Fujifilm Wako Pure Chemical, Osaka, Japan) at room temperature for 30 min, and then digested using 1.5 µg Trypsin Gold (Mass Spectrometry Grade; Promega, Madison, WI, USA) at 37 °C for 17 hr. The digests were acidified by the addition of trifluoroacetic acid (TFA), incubated at 37 °C for 30 min, and then centrifuged at 17,000×g for 10 min to remove the RapiGest SF. The supernatants were collected and desalted using MonoSpin C18 (GL Sciences, Tokyo, Japan). The resulting eluates were concentrated in vacuo, dissolved in 2% MeCN containing 0.1% formic acid (FA), and subjected to LC-MS/MS analysis.

LC-MS/MS analysis and database search

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LC-MS/MS analyses were performed on an Ultimate 3000 RSLCnano system (Thermo Fisher Scientific) coupled to a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific) equipped with a nano electron spray ionization (ESI) source. The LC system was equipped with a trap column (C18 PepMap 100, 0.3×5 mm, 5 µm, Thermo Fisher Scientific) and an analytical column (NTCC-360/75-3-125, Nikkyo Technos, Tokyo, Japan). Peptide separation was performed using a 90 min gradient of water/0.1% FA (mobile phase A) and MeCN/0.1% FA (mobile phase B) at a flow rate of 300 nL/min. Elution was performed as follows: 0–3 min, 2% B; 3–93 min, 2–40% B; 93–95 min, 40–95% B; 95–105 min, 95% B; 105–107 min, 95–2% B; and 107–120 min, 2% B. The mass spectrometer was operated in data-dependent acquisition mode. The MS parameters were as follows: spray voltage, 2.0 kV; capillary temperature, 275 °C; S-lens RF level, 50; scan type, full MS; scan range, m/z 350–1500; resolution, 70,000; polarity, positive; automatic gain control target, 3×106; and maximum injection time, 100 ms. The MS/MS parameters were as follows: resolution, 17,500; automatic gain control target, 1×105; maximum injection time, 60 msec; normalized collision energy (NCE), 27; dynamic exclusion, 15 s; loop count, 10; isolation window, 1.6 m/z; charge exclusion: unassigned, 1 and ≥8; and injection volume, 1 µL (containing 0.5 µg protein). Measurements were made in duplicate for each sample.

The identification of proteins and label-free quantification (LFQ) of the detected peptides was performed using Proteome Discoverer software ver. 2.4 (Thermo Fisher Scientific). The analytical parameters used for the database search were as follows: parent mass error tolerance, 10.0 ppm; fragment mass error tolerance, 0.02 Da; search engine, sequest HT; protein database, Drosophila melanogaster (Fruit fly: SwissProt Tax ID = 7227); enzyme name, trypsin (full); maximum number of missed cleavages, 2; dynamic modification, oxidation (methionine), phosphorylation (serine, threonine, tyrosine), acetyl (lysine), GG (lysine); N-terminal modification, Met-loss (methionine), and Met-loss+acetyl (methionine); static modification, carbamidomethylation (cysteine) and FDR confidence, High <0.01, 0.01 ≤ Medium < 0.05, 0.05 ≤ Low. The parameters for LFQ were as follows: precursor abundance, based on area; and normalization mode, total peptide amount.

The abundance ratio of milton RNAi to control RNAi at 7- or 21-day-old was calculated. We considered proteins with an abundance ratio of ≥2.0 or≤0.5 and an ANOVA p-value of <0.05 based on volcano plots to be differentially expressed of milton RNAi. To extract molecular networks biologically relevant to the proteins that are differentially expressed in milton RNAi, pathway analysis was performed using KeyMolnet (KM Data Inc, Tokyo, Japan).

RNA extraction and quantitative real-time PCR analysis

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Heads from more than 25 flies were mechanically isolated, and total RNA was extracted using ISOGEN (NipponGene) followed by reverse-transcription using PrimeScript RT reagent kit (Takara). The resulting cDNA was used as a template for PCR with THUNDERBIRD SYBR qPCR mix (TOYOBO) on a Thermal Cycler Dice real-time system TP800 (Takara). Expression of genes of interest was standardized relative to rp49. Relative expression values were determined by the ∆∆CT method. Experiments were repeated three times, and a representative result was shown.

Primers were designed using DRSC FlyPrimerBank (Harvard Medical School). Primer sequences are shown below:

  • eIF2β for 5′-GGACGACGACAAGAGCGAAG-3′

  • eIF2β rev 5′-CGGTCGCATCACGAACTTTG-3′

  • milton for 5′-GGCTTCAGGGCCAGGTATCT-3′

  • milton rev 5′-GCCGAACTTGGCTGACTTTG-3′

  • Actin for 5′-TGCACCGCAAGTGCTTCTAA-3′

  • Actin rev 5′-TGCTGCACTCCAAACTTCCA-3′

  • rp49 for 5′-GCTAAGCTGTCGCACAAATG-3′

  • rp49 rev 5′- GTTCGATCCGTAACCGATGT-3′

Polysome gradient centrifugation

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30 heads were homogenized in 150 µl of lysis buffer (25 mM Tris pH 7.5, 50 mM MgCl2, 250 mM NaCl, 1 mM DTT, 0.5 mg/ml cycloheximide, 0.1 mg/ml heparin). The lysates were centrifuged at 13,200 rpm at 4 °C for 5 min, and the supernatant was collected. The samples containing 38 µg of RNA were layered gently on top of a 10–50% w/w sucrose gradient (50 mM Tris pH 7.5, 50 mM MgCl2, 250 mM NaCl, 0.1 mg/ml heparin, 0.5 mg/ml cycloheximide in 5 ml polyallomer tube) and centrifuged at 37,000 rpm at 4 °C for 150 min in a himac CP-NX ultracentrifuge using a P50AT rotor. Samples were fractionated from top to bottom, and absorbance at OD260 nm was analyzed by a Plate reader (EnSpire). Experiments were repeated at least three times with independent cohorts of flies.

Puromycin analysis

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13-day-old flies were starved for 6 hr and fed 600 μM puromycin (Sigma) or 600 μM puromycin/35 mM cycloheximide (Sigma) in 5% sucrose solution for 20 hr. Incorporated puromycin was quantified by western blot with anti-puromycin antibody (Enzo # CAC-CAC-PEN-MA001) and normalized with actin. Experiments were repeated at least three times with independent cohorts of flies.

Histological analysis

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Fly heads were fixed in Bouin’s fixative solution for 48 hr at room temperature, incubated for 24 hr in 50 mM Tris/150 mM NaCl, and embedded in paraffin. Serial sections (7 μm thickness) through the entire heads were stained with hematoxylin and eosin and examined by bright-field microscopy. Images of the sections that include the lamina were captured with Keyence microscope BZ-X700 (Keyence), and the vacuole area was measured using ImageJ (National Institutes of Health).

Climbing assay

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The climbing assay was performed as previously described (Iijima-Ando et al., 2012). Flies were placed in an empty plastic vial (2.5 cm in diameter ×10 cm in length). The vial was gently tapped to knock the flies to the bottom, and the number of flies that reached the top, middle, and bottom areas of the vials in 10 s was counted. Experiments were repeated 10 times, and the mean percentage of flies in each area and standard deviations were calculated. Experiments were repeated with independent cohorts more than three times, and a representative result was shown.

Statistics

The number of replicates, what n represents, precision measurements, and the meaning of error bars are indicated in Figure Legends. Data are shown as means ± SEM. For pairwise comparisons, Student’s t-test was performed with Microsoft Excel (Microsoft). For multiple comparisons, data were analyzed using one-way ANOVA with Tukey’s HSD multiple-comparisons test in the GraphPad Prism 6.0 software (GraphPad Software, Inc, La Jolla, CA). Results with a p-value of less than 0.05 were considered to be statistically significant.

Data availability

The datasets used and/or analyzed in the current study are available in jPOST (https://rep-demo.jpostdb.org/) with jPOST ID: JPDM000120.

The following data sets were generated
    1. Shinno K
    (2025) JPOST
    ID JPDM000120. Axonal distribution of mitochondria maintains neuronal autophagy during aging.
    https://rep-demo.jpostdb.org/entry/JPDM000120

References

    1. Kimball SR
    (1999) Eukaryotic initiation factor eIF2
    The International Journal of Biochemistry & Cell Biology 31:25–29.
    https://doi.org/10.1016/s1357-2725(98)00128-9

Article and author information

Author details

  1. Kanako Shinno

    Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Hachioji, Japan
    Present address
    Department of Neuroscience and Pathobiology, Research Institute of Environmental Medicine, Nagoya University, Nagoya, Japan
    Contribution
    Conceptualization, Resources, Formal analysis, Funding acquisition, Investigation, Visualization, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0002-7114-7686

  2. Yuri Miura

    Research Team for Mechanism of Aging, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Itabashi, Japan
    Contribution
    Resources, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1239-3780

  3. Koichi M Iijima

    1. Department of Neurogenetics, National Center for Geriatrics and Gerontology, Obu, Japan
    2. Department of Experimental Gerontology, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya, Japan
    Contribution
    Resources
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4794-1863

  4. Emiko Suzuki

    1. Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Hachioji, Japan
    2. Gene Network Laboratory, National Institute of Genetics and Department of Genetics, SOKENDAI, Mishima, Japan
    Contribution
    Resources, Formal analysis, Supervision, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4005-0542

  5. Kanae Ando

    1. Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University, Hachioji, Japan
    2. Department of Biological Sciences, School of Science, Tokyo Metropolitan University, Hachioji, Japan
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    k_ando@tmu.ac.jp
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3956-276X

Funding

Japan Science Society (Sasakawa Scientific Research Grant (2021-4087))

  • Kanako Shinno

Takeda Science Foundation

  • Kanae Ando

Hoansha Foundation

  • Kanae Ando

Japan Foundation for Aging and Health

  • Kanae Ando

NOVARTIS Foundation

  • Kanae Ando

Japan Society for the Promotion of Science (JP19K21593)

  • Kanae Ando

Japan Society for the Promotion of Science (JP24K02860)

  • Kanae Ando

National Institute of Genetics (NIG-JOINT 71A2018)

  • Kanae Ando

National Institute of Genetics (NIG-Joint 25A2019)

  • Kanae Ando

Tokyo Metropolitan University (TMU strategic research fund for social engagement)

  • Kanae Ando

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank the Bloomington stock center; TRiP at Harvard Medical School (NIH/NIGMS R01-GM084947); the Kyoto Drosophila Stock Center and the Vienna Drosophila RNAi Center for fly stocks. The authors thank Dr. Masayuki Miura from Department of Pharmaceutical Science, University of Tokyo, for proteasome activity assay protocol; Dr. Shin-ichi Hisanaga from the Department of Biological Sciences, Tokyo Metropolitan University, for critical comments; Dr. Taro Saito, Dr. Akiko Asada from the Department of Biological Sciences, Tokyo Metropolitan University, Dr. Michiko Sekiya from Department of Alzheimer's Disease Research, National Center for Geriatrics and Gerontology, and Dr. Seiji Watanabe, Dr. Koji Yamanaka from Department of Neuroscience and Pathobiology, Research Institute of Environmental Medicine, Nagoya University for technical supports. This work was supported by the Sasakawa Scientific Research Grant (2021-4087) (to KS), the Takeda Science Foundation (to KA), Hoansha foundation grant (to KA), a research award from the Japan Foundation for Aging and Health (to KA), the Novartis Foundation (Japan) for the promotion of Science (to KA), JSPS KAKENHI Grant-in-Aid for Scientific Research on Challenging Research (Exploratory) JP19K21593 (to KA), JSPS KAKENHI Grant-in-Aid for Scientific Research(B) JP24K02860 (to KA), NIG-JOINT (National Institute of Genetics, 71A2018, 25A2019) (to KA) and TMU strategic research fund for social engagement (to KA).

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You can cite all versions using the DOI https://doi.org/10.7554/eLife.95576. This DOI represents all versions, and will always resolve to the latest one.

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© 2024, Shinno et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Kanako Shinno
  2. Yuri Miura
  3. Koichi M Iijima
  4. Emiko Suzuki
  5. Kanae Ando
(2026)
Axonal distribution of mitochondria maintains neuronal autophagy during aging via eIF2β
eLife 13:RP95576.
https://doi.org/10.7554/eLife.95576.5

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