Genes related to maintenance of autophagy and successful aging

Genes relacionados à manutenção da autofagia e envelhecimento bem-sucedido

Carolina Fioroto Chaves Diego Robles Mazzotti Maysa Seabra Cendoroglo Luiz Roberto Ramos Sergio Tufik Vanessa Cavalcante da Silva Vânia D'Almeida About the authors

ABSTRACT

Considering aging as a phenomenon in which there is a decline in essential processes for cell survival, we investigated the autophagic and proteasome pathways in three different groups: young, older and oldest old male adults. The expression profile of autophagic pathway-related genes was carried out in peripheral blood, and the proteasome quantification was performed in plasma. No significant changes were found in plasma proteasome concentrations or in correlations between proteasome concentrations and ages. However, some autophagy- and/or apoptosis-related genes were differentially expressed. In addition, the network and enrichment analysis showed an interaction between four of the five differentially expressed genes and an association of these genes with the transcriptional process. Considering that the oldest old individuals maintained both the expression of genes linked to the autophagic machinery, and the proteasome levels, when compared with the older group, we concluded that these factors could be considered crucial for successful aging.

Keywords:
Autophagy; longevity; gene expression; gene networks; proteasome endopeptidase complex

RESUMO

Considerando o envelhecimento como um fenômeno em que há um declínio nos processos essenciais a sobrevivência celular, investigamos as vias autofágica e proteassômica em três grupos: jovens, idosos e longevos. O perfil de expressão dos genes relacionados à via autofágica foi analisado em sangue periférico, e a quantificação do proteassoma realizada em plasma. Não foram encontradas alterações significativas nas concentrações plasmáticas de proteassoma ou na correlação entre as concentrações de proteassoma e as idades. No entanto, alguns genes relacionados a autofagia e / ou apoptose foram expressos diferencialmente. Além disso, as análises de rede e de enriquecimento mostraram uma interação entre quatro dos cinco genes diferencialmente expressos e a associação desses ao processo transcricional. Considerando que os indivíduos longevos mantiveram tanto a expressão de genes ligados à maquinaria autofágica, quanto os níveis de proteassoma quando comparados aos idosos, concluímos que esses fatores poderiam ser considerados cruciais para o envelhecimento bem-sucedido.

Palavras-chave:
Autofagia; longevidade; expressão gênica; redes regurladoras de genes; complexo de endopeptidases do proteassoma

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.

As the molecular characterization of the autophagic machinery may allow the development of tools for a better physiological and molecular evaluation of successful aging, our objective was to quantify the expression of genes involved in autophagic machinery regulation in young, older and oldest old adult male individuals.

METHODS

Volunteers

The individuals selected for this study were previously recruited by the Department of Preventive Medicine and Discipline of Geriatrics and Gerontology of the Universidade Federal de São Paulo for a different study but the samples were not fully used. All volunteers signed a free and informed consent form. For the current study, they signed an authorization for sample use, following norms determined by the Research Ethics Committee of the Universidade Federal de São Paulo, which approved the study (# 451631/2013). The sample consisted of male volunteers, distributed into three groups: individuals aged between 20 and 30 years (young group, n = 15), individuals aged between 60 and 70 years (older group, n = 13) and individuals between 85 and 105 years old (oldest old group, n = 10). Individuals with neoplasias or severe unmanaged diseases, such as heart diseases, gastrointestinal diseases, type 2 diabetes, or with neurological and psychiatric antecedents were excluded.

Samples previously collected

Peripheral blood was collected in EDTA tubes, centrifuged at 3,000 rpm for 10 minutes and the separated plasma stored at −20°C. In addition, 5 mL of blood was collected in specific tubes (PaxGene RNA collection tubes - PreAnalytiX, Switzerland) for total RNA extraction using the PaxGene kit (PaxGene blood RNA isolation kit - PreAnalytiX, Switzerland). After verification of integrity and purity, total RNA was stored at −80°C.

Proteasome

To perform proteasome quantification in plasma, we used enzyme-linked immunosorbent assay - Proteasome ELISA Kit (Enzo Life Sciences, BML-PW0575, EUA), which employs specific antibodies for the 20S proteasome subunit. The product absorbance was detected using the SpectraMax M2 apparatus (Molecular Devices, USA).

Gene expression

Total RNA was quantified using the NanoDrop 8000 (Thermo Scientific, USA). For complementary DNA synthesis, we used the RT2 First Strand Kit (QIAGEN, Germany) plus 625 ng of RNA. Cycling parameters comprised a holding stage at 42°C for 15 minutes, followed by inactivation at 95°C for 15 minutes. The expression profile of 84 autophagic pathway-related genes was analyzed in peripheral blood RNA samples using the Superarray-RT2 Profiler” PCR Array System (QIAGEN, Germany - PAHS-084ZD-24) in the 7500 PCR Real-Time System (Applied Biosystems, USA). In addition, ACTB, B2M, GAPDH, HPRT1 and RPLP0 genes were evaluated as an endogenous control. Thermal cycling conditions comprised an initial denaturation at 95°C for 15 seconds and annealing and extension at 60°C for one minute (Table 1).

Table 1
Genes from autophagic machinery investigated by the Superarray - RT2 Profiler ™ PCR Array System (PAHS-084Z).

Gene expression quantification was obtained using ΔCT calculation, and the endogenous control CT was obtained from the arithmetic mean of two endogenous controls that showed a lower variation between groups (standard deviation > 0.1). Then, the relative gene expression was calculated by CT comparative method (ΔΔCT) using the following formula: FC=2ΔΔCT=2(ΔCT interest groupΔCT reference group). For better visualization of variation, data were presented by fold regulation (FR) (if FC was greater than 1, FR = FC, if the HR was less than 1, the FR = -(1/FC)), which represents the number of times a gene is expressed in one group in relation to the other. Both the older and oldest old groups were compared with the young group (reference group).

Network and enrichment analysis

To investigate interactions and pathways shared by differentially expressed genes, two online software applications were used: GeneMANIA2020. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010 Jul 1;38 Suppl:W214-20. https://doi.org/10.1093/nar/gkq537.
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(www.genemania.org]) and Enrichr2121. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013 Apr;14(1):128. https://doi.org/10.1186/1471-2105-14-128
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(http://amp.pharm.mssm.edu/Enrichr). GeneMANIA allows genes with shared or functionally similar properties to be identified. These analyses may be performed with genes of interest or with 20, 50 or 100 other genes in the interaction. In the present study, we used 20 genes. Enrichr enables enrichment analysis, where genes of interest are searched and compared in databases to verify possible pathways and over-represented cellular processes in which they may participate.

Statistical analysis

The normality of data was analyzed with Shapiro-Wilk's test and, when necessary, normalized using the Z-score. Data was compared using one-way analysis of variance (ANOVA). We used the Pearson’s correlation test to compare age and proteasome levels in each age group. Data are presented as mean ± standard error. The level of significance was set at p ≤ 0.05. However, the p value was corrected by the Benjamini-Hochberg method (pBH) for gene expression analyses. Fold regulation (FR) values greater than 1.50 (genes with increased expression) or less than −1.50 (genes with decreased expression) were used to select differentially expressed genes, and to exclude those potentially subject to methodological noise. Thus, the differentially expressed genes were those included in one of the following conditions: 1) pBH ≤ 0.05, independent of the FR value or 2) p ≤ 0.05 and FR ≥ 1.50 or FR ≤ −1.50.

RESULTS

Autophagic pathway gene expression and proteasome levels were evaluated in the individuals from three different age groups (mean ± standard deviation): young, 24.3 ± 2.2 years: older, 65.5 ± 3.0 years; and oldest old, 91.9 ± 6.1 years (F (2.35) = 999.95; p < 0.001). The mean and standard deviations of body mass index were 24.04 ± 2.74 in the young group, 25.87 ± 3.56 in the older group, and 24.94 ± 3.55 in oldest old group (F (2.35) = 1.10; p = 0.333). No difference was observed in proteasome levels between the three age groups (ANOVA; F (2.34) = 0.619 and p = 0.545; Figure 1). Additionally, plasma proteasome levels were not related to the individuals' ages in each group (Figure 2). However, when the oldest individual (105 years) was excluded from the oldest old group analysis, a statistically significant correlation was observed (Figure 2D).

Figure 1
Proteasome levels in young, older and oldest old groups.

ANOVA. F (2.34) = 0.619 and p = 0.545. Data are presented as the mean ± standard error.


Figure 2
Pearson's correlation between proteasome levels and age in the young, older and oldest old groups.

Young: n = 15, r = −0.240, p = 0.389; Older: n = 12, r = −0.097, p = 0.765; Oldest old: n = 9, r = −0.052, p = 0.894.


Regarding gene expression, from the 84 genes linked to autophagic machinery, only five were differentially expressed according to the adopted criteria: ATG4C, BCL2L1, EIF2AK3, EIF4G1 and TP53 (Table 2). The ATG4C gene was significantly less expressed in the oldest old group when compared with the young group (1.91-fold decrease); in addition, there was also a difference in the older group when compared with the oldest old (1.47-fold increase; p = 0.031). The BCL2L1 gene was significantly more expressed in the oldest old when compared with the young group (increase of 1.91 times). The EIF2AK3 gene was significantly less expressed in the older group (1.46-fold decrease), as well as in oldest old individuals when compared with the young group (1.44-fold decrease). The EIF4G1 gene was significantly less expressed in the older and oldest old when compared with the young group (decrease of 1.47 and 1.32 times, respectively). The TP53 gene was significantly less expressed in the older and oldest old when compared with the young group (decrease of 1.57 and 1.66-fold, respectively).

Table 2
Differentially expressed genes in older and oldest old in relation the young group.

In the network analysis, we observed that from the five differentially expressed genes, only two showed evidence of some interaction — TP53 and BCL2L1 (Figure 3). When the other 20 genes were added, we observed that four of the five genes showed some type of interaction, the exception being ATG4C (Figure 4). The enrichment analysis was divided into two stages: the first was done with the five differentially expressed genes, and the second with the differentially expressed genes plus the genes that showed the most frequent pathways in the network analysis (HSPA5, SIN3A and EIF2S1). In the first step, the following databases were used: TRANSFAC and JASPAR PWMs, ENCODE TF ChIP-seq 2015, ESCAPE, ENCODE TF ChIP-seq and GO Biological Process 2013. The databases used in the second stage were: ChEA, TRANSFAC and JASPAR PWMs, ENCODE TF ChIP-seq 2015, transcription factor PPIs, ESCAPE, ENCODE TF ChIP-seq. All databases used in the first and second stages showed direct or indirect linkage of the genes analyzed with the transcription process.

Figure 3
Interactions between the five differentially expressed genes among the young, older and oldest old groups.

Blue line: interaction by pathways; pink line: physical interaction; orange line: prediction of protein-protein interaction.


Figure 4
Interaction of five differentially expressed genes in the young, older and oldest old groups after inclusion of 20 genes in the network analysis.

Light blue line: interaction by pathways; pink line: physical interaction; purple line: co-expression; green line: genetic interaction; dark blue line: co-localization; brown line: shared protein domains; orange line: protein-protein interaction prediction.


DISCUSSION

The accumulation of macromolecules and damaged organelles is one of the most predominant alterations found in aged cells, and the main cause is related to a deficient autophagic process2222. Terman A, Brunk UT. Myocyte aging and mitochondrial turnover. Exp Gerontol. 2004 May;39(5):701-5. https://doi.org/10.1016Zj.exger.2004.01.005
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. To evaluate the contribution of the autophagic machinery in successful aging, we quantified the expression of 84 genes related to the autophagic pathway in young, older and oldest old individuals; five presented with differential expression between the studied groups: ATG4C, BCL2L1, TP53, EIF2AK3 and EIF4G1.

The ATG4C encodes a protein with protease activity involved in autophagic vacuole formation. However, studies suggest that this protein is not essential to generate the basal level of autophagy required, since knockout mice for the ATG4C gene exhibit normal development2424. Kuma A, Hatano M, Matsui M, Yamamoto A, Nakaya H, Yoshimori T et al. The role of autophagy during the early neonatal starvation period. Nature. 2004 Dec;432(7020):1032-6. https://doi.org/10.1038/nature03029
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. The lower expression of ATG4C observed in the oldest old people group does not suggest lower autophagic activity per se, but may contribute to a higher risk of these individuals developing tumors, a condition that could be related to aging. On the other hand, the increased expression of BCL2L1 observed in the older and oldest old groups indicates that autophagy levels decrease during aging2727. Christensen KE, Wu Q, Wang X, Deng L, Caudill MA, Rozen R. Steatosis in mice is associated with gender, folate intake, and expression of genes of one-carbon metabolism. J Nutr. 2010 0ct;140(10):1736-41. https://doi.org/10.3945/jn.110.124917
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. The BCL2L1 is a co-regulator of autophagy and apoptosis and proteins from the BCL-2 family may also interact with p53 in the induction of autophagy. P53 exhibits tumor suppressor activity and the ability to control autophagic processes and cellular senescence2828. Demidenko ZN, Zubova SG, Bukreeva EI, Pospelov VA, Pospelova TV, Blagosklonny MV. Rapamycin decelerates cellular senescence. Cell Cycle. 2009 Jun;8(12):1888-95. https://doi.org/10.4161/cc.812.8606
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. In the current study, there was decreased TP53 expression in both the oldest old and older groups in relation to the young group, suggesting that the autophagic process decreases with increased age. In addition, decreased expression of EIF2AK3 and EIF4G1 in both the oldest old and older individuals reflects the body's declining ability to maintain reticulum homeostasis and cellular processes with increasing age1919. Ramírez-Valle F, Braunstein S, Zavadil J, Formenti SC, Schneider RJ. eIF4GI links nutrient sensing by mTOR to cell proliferation and inhibition of autophagy. J Cell Biol. 2008 Apr;181(2):293-307. https://doi.org/10.1083/jcb.200710215.
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. In the current study, we evaluated, for the first time, the plasmatic proteasome levels in the young, older and oldest old groups and we did not observe a significant difference between them. Although there is no evidence that plasmatic proteasome concentrations reflect the intracellular proteasome activity, we hypothesized that the similarity of plasma proteasome concentrations between the groups found in our samples could be one of the factors contributing to the longevity in the oldest old group. In fact, we previously observed that these same oldest old individuals had a more favorable lipid profile compared with the other groups3232. Mazzotti DR, Guindalini C, Moraes WA, Andersen ML, Cendoroglo MS, Ramos LR et al. Human longevity is associated with regular sleep patterns, maintenance of slow wave sleep, and favorable lipid profile. Front Aging Neurosci. 2014 Jun;6:134. https://doi.org/10.3389/fnagi.2014.00134
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. An increase in SIRT2 expression in the oldest old people was also observed when compared with the young group (unpublished data). The increase in SIRT2 seems to contribute to the promotion of longevity by increasing levels of autophagy3333. Wang F, Nguyen M, Qin FX, Tong Q. SIRT2 deacetylates FOXO3a in response to oxidative stress and caloric restriction. Aging Cell. 2007 Aug;6(4):505-14. https://doi.org/10.1111/j.1474-9726.2007.00304.x
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. In the network analysis of differentially expressed genes, we identified interactions between the TP53 and BCL2L1 genes, which was expected, as several studies have shown the promotion of autophagy by the interaction of TP53 with the Bcl-2 family proteins3535. Mihara M, Erster S, Zaika A, Petrenko O, Chittenden T, Pancoska P et al. p53 has a direct apoptogenic role at the mitochondria. Mol Cell. 2003 Mar;11(3):577-90. https://doi.org/10.1016/S1097-2765(03)00050-9
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-3737. Tomita Y, Marchenko N, Erster S, Nemajerova A, Dehner A, Klein C et al. WT p53, but not tumor-derived mutants, bind to Bcl2 via the DNA binding domain and induce mitochondrial permeabilization. J Biol Chem. 2006 Mar;281(13):8600-6. https://doi.org/10.1074/jbc.M507611200
https://doi.org/10.1074/jbc.M507611200...
. However, when we added 20 other genes to this network, four of the five differentially expressed genes had some type of interaction, with ATG4C being the exception (Figure 4). The interaction between the four genes is related to the regulation of transcription, an extremely important process for cell functioning3838. Alberts B, J.A., Lewis J, Raff M, Roberts K, Walter P, Biologia molecular da célula. 5a ed. Porto Alegre: Artmed; 2010.. During the aging process, some genes have increased expression, such as those related to cell adhesion and immune response3939. Hong SE, Heo HS, Kim DH, Kim MS, Kim CH, Lee J et al. Revealing system-level correlations between aging and calorie restriction using a mouse transcriptome. Age (Dordr). 2010 Mar;32(1): 15-30. https://doi.org/10.1007/s11357-009-9106-3
https://doi.org/10.1007/s11357-009-9106-...
, while others have decreased expression, such as genes that participate in lipid metabolism3939. Hong SE, Heo HS, Kim DH, Kim MS, Kim CH, Lee J et al. Revealing system-level correlations between aging and calorie restriction using a mouse transcriptome. Age (Dordr). 2010 Mar;32(1): 15-30. https://doi.org/10.1007/s11357-009-9106-3
https://doi.org/10.1007/s11357-009-9106-...
and those involved in the electron transport chain4040. Zahn JM, Sonu R, Vogel H, Crane E, Mazan-Mamczarz K, Rabkin R et al. Transcriptional profiling of aging in human muscle reveals a common aging signature. PLoS Genet. 2006 Jul;2(7):e115. https://doi.org/10.1371/journal.pgen.0020115
https://doi.org/10.1371/journal.pgen.002...
,4141. McCarroll SA, Murphy CT, Zou S, Pletcher SD, Chin CS, Jan YN et al. Comparing genomic expression patterns across species identifies shared transcriptional profile in aging. Nat Genet. 2004 Feb;36(2):197-204. https://doi.org/10.1038/ng1291
https://doi.org/10.1038/ng1291...
.

In conclusion, the ATG4C, BCL2L1, TP53, EIF2AK3 and EIF4G1 genes differed preferentially when comparing the oldest old and older with the young group, suggesting that autophagy and some processes like maintenance of metabolism and control of gene expression are impaired when the individual ages. On the other hand, the similarity in the expression pattern observed between the older and oldest old suggests that the maintenance of these pathways related to homeostasis plays an important role in increasing life expectancy. In general, these findings, together with the maintenance of proteasome levels observed in the oldest old individuals, point to the maintenance of autophagy as a crucial factor for longevity.

  • Support: CNPq, CAPES, AFIP and FAPESP.CFC was a recipient of a CNPq scholarship. VCS is a recipient of a CAPES scholarship. VD'A is a recipient of a CNPq fellowship.

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Publication Dates

  • Publication in this collection
    Dec 2018

History

  • Received
    02 July 2018
  • Reviewed
    13 Aug 2018
  • Accepted
    25 Sept 2018
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