Autophagy-related Proteins in Genome Stability: Autophagy-Dependent and Independent Actions

Zhang, Ye; Guo, Ran; Wang, Shanshan; Jiang, Xiao-You; Cui, Hongyan; Guo, Yang; Song, Xiaoyu; Guo, Qiqiang; Cao, Liu

doi:10.7150/ijbs.76134

Cited by 7 publications

(4 citation statements)

References 133 publications

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“…However, the extent to which this is necessary in order to firmly prove causality should not be underestimated. The reason for this is firstly that, as opposed to what was assumed upon their initial identification in the 1990s, most, if not all, ATG proteins have non-autophagic cellular functions ( Yousefi et al, 2006 ; Gan and Guan, 2008 ; Baisamy et al, 2009 ; Hanson et al, 2010 ; DeSelm et al, 2011 ; Chung et al, 2012 ; Lee et al, 2012 ; Velikkakath et al, 2012 ; Maskey et al, 2013 ; Elgendy et al, 2014 ; Rohatgi et al, 2015 ; Chen et al, 2016 ; Joo et al, 2016 ; Joshi et al, 2016 ; Kaizuka and Mizushima, 2016 ; Nunes et al, 2016 ; Yang et al, 2016 ; Guo et al, 2017 ; Ramkumar et al, 2017 ; Wang and Kundu, 2017 ; Cadwell and Debnath, 2018 ; Saleiro et al, 2018 ; Galluzzi and Green, 2019 ; Hu et al, 2020 ; Lindner et al, 2020 ; Wu et al, 2020 ; Fang et al, 2021 ; Li et al, 2021 ; Mailler et al, 2021 ; Nieto-Torres et al, 2021 ; Sun et al, 2021 ; Zhang et al, 2022a ; Hamaoui and Subtil, 2022 ; Rajak et al, 2022 ; Chen et al, 2023 ; Deng et al, 2023 ; Liang et al, 2023 ; Wang et al, 2023 ; Tedesco et al, 2024 ; Tran et al, 2024 ; Yoon et al, 2024 ) that are likely to influence the cellular/phenotypic effects that are observed upon their knockdown, knockout or overexpression, and many of these functions affect pathways that are highly relevant to cancer. Therefore, in order to infer causality, the same cellular/phenotypic effect must be observed upon interference with a number of different ATGs.…”

Section: Discussion—limitations Of Current Studies Main Challenges An...mentioning

confidence: 99%

“…As the names imply, the ATG proteins were identified through their roles in autophagy. Importantly, however, it has become increasingly evident that most, if not all, ATG proteins also have non-autophagic functions ( Velikkakath et al, 2012 ; Elgendy et al, 2014 ; Rohatgi et al, 2015 ; Kaizuka and Mizushima, 2016 ; Nunes et al, 2016 ; Yang et al, 2016 ; Cadwell and Debnath, 2018 ; Galluzzi and Green, 2019 ; Hu et al, 2020 ; Fang et al, 2021 ; Mailler et al, 2021 ; Zhang et al, 2022a ; Hamaoui and Subtil, 2022 ; Tedesco et al, 2024 ; Tran et al, 2024 ). This includes the by far most used autophagic marker LC3B ( Baisamy et al, 2009 ; Hanson et al, 2010 ; Chung et al, 2012 ; Ramkumar et al, 2017 ; Lindner et al, 2020 ; Nieto-Torres et al, 2021 ; Yoon et al, 2024 ), which originally was identified as a microtubule-associated protein in the 1980s ( Vallee and Davis, 1983 ) before its revelation as a mammalian homologue of yeast ATG8 in 2000 ( Kabeya et al, 2000 ).…”

Section: Autophagymentioning

confidence: 99%

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To eat or not to eat: a critical review on the role of autophagy in prostate carcinogenesis and prostate cancer therapeutics

Kurganovs,

Engedal

2024

Front. Pharmacol.

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Around 1 in 7 men will be diagnosed with prostate cancer during their lifetime. Many strides have been made in the understanding and treatment of this malignancy over the years, however, despite this; treatment resistance and disease progression remain major clinical concerns. Recent evidence indicate that autophagy can affect cancer formation, progression, and therapeutic resistance. Autophagy is an evolutionarily conserved process that can remove unnecessary or dysfunctional components of the cell as a response to metabolic or environmental stress. Due to the emerging importance of autophagy in cancer, targeting autophagy should be considered as a potential option in disease management. In this review, along with exploring the advances made on understanding the role of autophagy in prostate carcinogenesis and therapeutics, we will critically consider the conflicting evidence observed in the literature and suggest how to obtain stronger experimental evidence, as the application of current findings in clinical practice is presently not viable.

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Section: Discussion—limitations Of Current Studies Main Challenges An...mentioning

confidence: 99%

Section: Autophagymentioning

confidence: 99%

To eat or not to eat: a critical review on the role of autophagy in prostate carcinogenesis and prostate cancer therapeutics

Kurganovs,

Engedal

2024

Front. Pharmacol.

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“…Recent studies have shown that autophagy is directly involved in DNA damage response (DDR) 47 . p62 (SQSTM1) promotes the proteasomal degradation of FLNA and RAD51 components of the DNA repair machinery, and knockout of p62 increases the DNA repair rate 48 .…”

Section: Discussionmentioning

confidence: 99%

Antiviral therapy reduces hepatocellular carcinoma through suppressing hepatitis B virus replication may improve ER stress, mitochondrial and metabolic dysfunctions and decrease p62 in hybridized mice with single HBV transgene and miR‐122

Liang,

Chiou,

Chiu

et al. 2023

Journal of Medical Virology

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Hepatitis B virus (HBV) hijacks autophagy for its replication. Nucleos(t)ide analogs (NUCs) treatment suppressed HBV replication and reduced hepatocellular carcinoma (HCC) incidence. However, the use of NUCs in chronic hepatitis B (CHB) patients with normal or minimally elevated serum alanine aminotransferase (ALT) levels is still debated. Animal models are crucial for studying the unanswered issue and evaluating new therapies. MicroRNA‐122 (miR‐122), which regulates fatty acid and cholesterol metabolism, is downregulated during hepatitis and HCC progression. The reciprocal inhibition of miR‐122 with HBV highlights its role in HCC development as a tumor suppressor. By crossbreeding HBV‐transgenic mice with miR‐122 knockout mice, we generated a hybrid mouse model with a high incidence of HCC up to 89% and normal ALT levels before HCC. The model exhibited early‐onset hepatic steatosis, progressive liver fibrosis, and impaired late‐phase autophagy. Metabolomics and microarray analysis identified metabolic signatures, including dysregulation of lipid metabolism, inflammation, genomic instability, the Warburg effect, reduced TCA cycle flux, energy deficiency, and impaired free radical scavenging. Antiviral treatment reduced HCC incidence in hybrid mice by approximately 30–35% compared to untreated mice. This effect was linked to the activation of ER stress‐responsive transcription factor ATF4, clearance of autophagosome cargo p62, and suppression of the CHOP‐mediated apoptosis pathway. In summary, this study suggests that despite minimal ALT elevation, HBV replication can lead to liver injury. Endoplasmic reticulum stress, reduced miR‐122 levels, mitochondrial and metabolic dysfunctions, blocking protective autophagy resulting in p62 accumulation, apoptosis, fibrosis, and HCC. Antiviral may improve the above‐mentioned pathogenesis through HBV suppression.

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“…By intersecting DEGs and ARGs, 57 DE-ARGs were obtained. Gene co-expression analysis was performed on 57 DE-ARGs, and the results indicated a possible regulatory network of interaction between DE-ARGs 19,20 . GO and KEGG enrichment analysis of DE-ARGs revealed that DE-ARGs were mainly enriched in cellular autophagy and cellular response to stress 21,22 .…”

Section: Discussionmentioning

confidence: 99%

Dysregulated autophagy-related genes in abdominal aortic aneurysm: Comprehensive bioinformatics analysis and experimental validation

Xie

Shen

Wang

et al. 2022

Preprint

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Background Abdominal aortic aneurysm (AAA) is a serious life-threatening cardiovascular disease that occurs in middle-aged and elderly people. Previous experimental studies have suggested that autophagy may be involved in the pathological process of AAA, but the pathogenesis of autophagy in AAA is unclear. We aim to identify and validate key potential autophagy-related genes involved in AAA through bioinformatics analysis to further elucidate the mechanisms of autophagy dysregulation in AAA. Methods The GSE57691 microarray dataset was downloaded from the Gene Expression Omnibus database (GEO), including 49 AAA samples and 10 normal aortic samples. 232 autophagy-related genes were obtained from the Human Autophagy Database (HADb). The GSE57691 dataset was crossed with the autophagy gene set to screen for differentially expressed autophagy-related genes (DE-ARGs) involved in AAA. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DE-ARGs in AAA using R software. Protein-protein interaction (PPI) networks were constructed using the STRING database, significant gene cluster modules were identified using the MCODE Cytoscape plugin, and hub genes in AAA associated DE-ARGs were screened using the CytoHubba Cytospace plugin. Meanwhile, DE-ARGs were calculated using the least absolute shrinkage selection algorithm (LASSO) algorithm. By crossing the LASSO calculation results and hub genes, the final key genes were identified, whose expression levels were further validated in AAA aortic samples by qRT-PCR. Finally, the transcription factor regulatory networks and target drugs of these key genes were predicted by the JASPAR database and DsigDB database, respectively. Results A total of 57 DE-ARGs were identified in aortic samples from normal controls and AAA. GO and KEGG analysis showed that these 57 DE-ARGs involved in AAA were particularly enriched in macroautophagy, PI3K-Akt signaling pathway, AMPK signaling pathway, and apoptosis. PPI results indicated that the 57 DE-ARGs interacted with each other. A total of 6 key genes (ATG5, ATG12, MTOR, BCL2L1, EIF4EBP1, and RPTOR) were identified using CytoHubba and LASSO algorithms. Detection of clinical samples by qRT-PCR indicated that ATG5, ATG12, BCL2L1, EIF4EBP1, and RPTOR expression was consistent with bioinformatic analysis. A regulatory network containing 6 key genes and 30 transcription factors was constructed through the JASPAR database. Finally, four targeted autophagy regulatory drugs, rapamycin, Temsirolimus, Sorafenib, and NVP-BEZ235, were screened by the DsigDB database. Conclusions Bioinformatics analysis identified 57 autophagy-related genes that may be involved in AAA. ATG5, ATG12, BCL2L1, EIF4EBP1and RPTOR may serve as potential drug targets and biomarkers as they regulate autophagy. These results expand the understanding of autophagy dysfunction in AAA and may contribute to the diagnosis and prognosis of AAA.

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Autophagy-related Proteins in Genome Stability: Autophagy-Dependent and Independent Actions

Cited by 7 publications

References 133 publications

To eat or not to eat: a critical review on the role of autophagy in prostate carcinogenesis and prostate cancer therapeutics

To eat or not to eat: a critical review on the role of autophagy in prostate carcinogenesis and prostate cancer therapeutics

Antiviral therapy reduces hepatocellular carcinoma through suppressing hepatitis B virus replication may improve ER stress, mitochondrial and metabolic dysfunctions and decrease p62 in hybridized mice with single HBV transgene and miR‐122

Dysregulated autophagy-related genes in abdominal aortic aneurysm: Comprehensive bioinformatics analysis and experimental validation

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