Deciphering the rules of in silico autophagy methods for expediting

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Deciphering the rules of in silico autophagy methods for expediting medicinal research Yi Chen, Guan Wang, Haoyang Cai, Yang Sun, Liang Ouyang, and Bo Liu J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.8b01673 • Publication Date (Web): 19 Mar 2019 Downloaded from http://pubs.acs.org on March 19, 2019

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Journal of Medicinal Chemistry

Deciphering the rules of in silico autophagy methods for expediting medicinal research Yi Chen1#, Guan Wang1#, Haoyang Cai2#, Yang Sun3, Liang Ouyang1, Bo Liu1* 1State

Key Laboratory of Biotherapy and Cancer Center & Department of Gastrointestinal

Surgery, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, China 2Center

of Growth, Metabolism, and Aging, Key Laboratory of Bio-Resources and Eco-

Environment, College of Life Sciences, Sichuan University, Chengdu 610064, China 3State

Key Laboratory of Pharmaceutical Biotechnology, Department of Biotechnology

and Pharmaceutical Sciences, School of Life Sciences, Nanjing University, Nanjing 210023, China

ABSTRACT Autophagy is a highly evolutionarily conserved cellular catabolic process responsible for degradation of damaged organelles and long-lived proteins. It is not surprising that scientific interest in the connection of autophagy and diseases has been growing. Over the past two decades, many new experimental approaches have been emerging in the field of targeting autophagy for medicinal research. Interestingly, in addition to experimental methods, a number of databases, webservers, mathematics models, omics approaches and systems biology network approaches related to autophagy have become available online, which may be collectively considered to be “in silico autophagy methods” for expediting current medicinal research. Thus, we have summarized a series of relevant in silico autophagy approaches for promoting the most appropriate usage of these resources for potential therapeutic purposes. Key words: autophagy; medicinal research; in silico autophagy method; database; webserver; mathematics model; omics approach; systems-biology network

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INTRODUCTION Autophagy is a physiological degradation process in lysosomes targeting superfluous proteins and damaged organelles.1-3 In human diseases, autophagy is believed to be a physiological mechanism to maintain cell homeostasis and survival temporarily.4-6 On the other hand, continuous and excessively autophagy induced by cellular stress will lead to cell death.7,8 Accumulating experimental evidence has revealed that autophagy plays various pivotal roles by regulating several key mediators as potential targets in medicinal research.9,10 More recently, a number of small-molecule compounds (e.g., LYN-1604, rapamycin and IPI-3063) targeting some key autophagyrelated proteins (e.g., ULK1, mTOR and PI3K) or even the autophagic process have been widely reported to be potential candidate drugs to treat disease, such as cancer, neurodegenerative disease, and immunity disease11,12 (Figure.1). Accordingly, targeting autophagy by “experimental methods” may be an encouraging therapeutic strategy in different model organisms and relevant diseases.13 However, there are still some limitations regarding the use of experimental methods to analyze the complicated process of autophagy. Thus, several “in silico” autophagy methods have recently been utilized to investigate autophagy. To date, there are more than 30,000 articles available when using “autophagy” as a keyword to search PubMed. These studies provide a wealth of information for further collection, classification, analysis and even knowledge-based prediction in the field of autophagy research. To better utilize the available information on autophagy, there are several databases, webservers, mathematics models, omics methods and systems-biology network approaches that can collectively be considered to be key supplements to experimental methods to expedite the current medicinal research. In this review, we focus on summarizing relevant in silico autophagy approaches that will promote the most appropriate usage of the above-mentioned resources for the future therapeutic purposes.

DATABASES AND WEBSERVERS FOR AUTOPHAGY 2


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Hitherto, there have been a number of new databases and webservers related to in silico autophagy methods that provide a wealth of useful information for subsequent medicinal research. In this section, we discuss the categories of databases or webservers according to different types of autophagy-related data they provide, including (i) autophagy-related proteins, (ii) autophagy-related Ribonucleic acids (RNAs), (iii) autophagy-related compounds, (iv.) orthologue analyses, and (v.) predicted autophagic targets (Table 1).

Autophagy-related protein databases The autophagy database, which is known to be an all-inclusive information resource for autophagy, collects data and identifies homologs in 41 eukaryotes.14 By searching the keywords or sequences in this database, corresponding data including functional and structural information and possible functional homologs can be obtained. In addition, the microtubule-associated protein 1 light chain 3 (LC3)-interacting region (iLIR) database, is a web-based resource for LIR motif-containing proteins in eukaryotes. It is used for the identification

of

LC3-interacting

region-containing

proteins

(LIRCPs)

in

various

organisms.15 This database lists all of the putative canonical LIRCPs in the proteomes of 8 model organisms identified in silico and also provides a way to identify novel putative LICRPs in mammals by curated text-mining analysis of the literature. Similarly, the high fidelity Atg8 interacting motif (hfAIM) represents a bioinformatics methodology of in silico genome-wide identification, which is used to identify autophagy-associated AIMs in organisms.16 The hfAIM is an effective tool for screening proteins that are governed by certain types of selective autophagy by performing genome-wide in silico. Human autophagy database (HADb) is another comprehensive database for autophagy-related gene and protein. Over 200 genes and proteins in this database, collected from online resources and literature, provide general, genomic and functional information. HADb can help analyze and search for autophagy-related gene and protein regulation in cellular models and diseases.17

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Autophagy-related RNA databases The Gerontology-Autophagic-MicroRNA Database (GAMDB) is a web-based resource that analyzes microRNAs (miRNAs) and autophagy in gerontology.18 This database contains 56 aging-related diseases, 197 targeted genes/proteins and 836 autophagy-related miRNAs. In addition, autophagy-microRNA database (AutomiRDB) is another web-based resource for microRNAs involved in autophagy in cancer.19 In AutomiRDB, information of experimentally identified microRNAs is integrated and autophagic target genes of these microRNAs in different types of cancer are provided. 18 types of cancer, 90 targeted autophagic genes/proteins and 493 autophagy-related miRNAs are included in AutomiRDB. Moreover, miRDeathDB is another well-known database related to microRNAs and programmed cell death (PCD).20 This database integrates useful information of the miRNAs that identified in experiments and lists their targets in the PCD network. There are 210 records including 86 miRNAs and 95 protein coding genes in five model species in total. Similarly, ncRDeathDB comprehensively deciphered the network organization of cell death system mediated by noncoding RNA (ncRNA).21 More than 4,600 ncRNA-mediated PCD entries, which collected from 12

species, are documented in this database.

Autophagy-related compound databases The autophagic compound database (ACDB) is an online resource providing information of autophagy-modulating compounds, their targets and relevant diseases.22 This database covers 357 compounds with their potential targets and 164 corresponding signaling pathways in various human diseases. Valuably, ACDB allows its users to access more than 300 curated small-molecule autophagic compounds. Additionally, TBC2health is another database describing experimentally identified tea bioactive compounds with health-beneficial effects.23 This database currently records 1,338 relationships among 497 tea bioactive compounds and 206 relevant phenotypes or diseases. Recently, several autophagy databases as sources for structural and functional information have been generated. However, a special database covering information of 4


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autophagy modulator (microRNAs, chemicals and proteins)-related targets, signaling pathways and diseases has not been developed yet. Therefore, Human Autophagy Modulator Database (HAMdb) is developed by our research group, which can be searched in the web set of http://hamdb.scbdd.com. It provides information of related pathways and disease to researchers. HAMdb contains 132 microRNAs, 841 chemicals and 796 proteins. We manually collected and compiled these entities information of biological, physicochemical, disease, and their specific effects on autophagy. HAMdb is a user-friendly interface for users to search, browse and query autophagy modulators and to obtain comprehensive relevant information. It provides detailed information about autophagy related diseases and promotes the understanding of autophagy process. Additionally, HAMdb provides hints to the users to identify new diagnostic and therapeutic targets and discover new autophagy modulators. Therefore, it is reasonable to believe that HAMdb can be a potential platform to facilitate autophagy research.24 Moreover, Autophagy Small Molecule Database (AutophagySMDB) has been reported to be a curated database containing about 1,0000 small molecules that regulate 71 protein targets in autophagy25. AutophagySMDB may help the discovery of more potential smallmolecule drug candidates for autophagy in human diseases.

Orthologue analysis database The Autophagy Necrosis, ApopTosis OrchestratorS (THANATOS) is an orthologue analysis database resource for proteins in regulating autophagy and post-translational modifications. This database contains 4,237 experimentally identified proteins that are involved in the process of autophagy and cell death.26 Several potential orthologues of known proteins have been identified and stored in THANATOS database. THANATOS contains 191,543 proteins in which show potential to regulate autophagy and cell death process in 164 eukaryotes. Predicted autophagic target webservers Autophagic Compound-Target Prediction (ACTP) is a webserver providing a basis for rapid predicting potential autophagic targets and pathways of possible autophagy5



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inhibiting or autophagy-activating small-molecule compounds/candidate drugs.27 ACTP sheds light on identifying future therapeutic implications of novel autophagy-inhibiting or autophagy-activating compounds. Moreover, Protein Structure Selection (ProSelection) is a novel algorithm for in silico drug target identification to screen appropriate protein structure subsets.28 ProSelection is another helpful algorithm of docking preferred-protein selection and can be used to generate appropriate structure subset(s), including 249 protein structures and 14 autophagic targets. Furtherly, the performance of ProSelection was verified using 43 small-molecule antineoplastic drugs approved by Food and Drug Administration (FDA), which predicted the potential off-targets of drugs successfully. In addition, ANCHOR is a webserver to predict binding regions of protein in disordered proteins.29 The minimum input of ANCHOR is a single amino acid sequence. Besides, ANCHOR can predict disordered protein binding regions in isolation. Taken together, the aforementioned autophagy-related databases and webservers can help us to take advantage of web-based resources to achieve a better understanding of in silico autophagy methods for potential medicinal applications.

MATHEMATICAL MODELS OF AUTOPHAGY With the rapid progress of autophagy research, an increasing number of mathematical models have been introduced in the field of in silico autophagy research. In this section, we mainly focus on highlighting some interesting mathematical models for autophagy research, including autophagy/autophagic mechanisms and autophagyrelated diseases (Table. 2).

In silico models for autophagy/autophagic mechanisms In silico comparative analysis of autophagy proteins has been reported in ciliates, demonstrating that autophagy-related (Atg) proteins are partially conserved and thus provide a better understanding of the autophagic destruction of parental macronucleus.30 Additionally, Salmonella xenophagy as a key defense mechanism in epithelial cells has 6


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been investigated using the Petri net model.31 The performance of in silico knockout analyses displayed a consistent result with the published experimental data, which provided guidance to researchers for the future study of Salmonella xenophagy pathway. Additionally, a recent study has carried out an extensive in silico mutational analysis of Atg proteins, including SCD1, BECN1, LC3A and LC3B, and indicated that high-risk missense mutations damaged the structure and function of Atg proteins. This study provided an insight into pathological missense single-nucleotide polymorphisms (SNPs).32 Moreover, a recent study has shown an in silico model that has been used in a mammalian system to describe autophagic vesicle dynamics. This accurate model is proposed to be a useful approach for quantitative characterization of autophagy.33 Agentbased model (ABM) of autophagy has been reported to reveal the emergent regulation of spatio-temporal autophagy dynamics. In ABM, components of autophagy pathways, metabolic feedbacks with the cellular environment and subcellular vesicle dynamics can be represented individually. ABM shed new light on elucidation of spatio-temporal autophagy regulation and dynamic behavior.34

In silico models for autophagy-related diseases In silico simulation approaches have been reported to investigate the mitochondrial dynamics and dysfunction linked to mitochondrial autophagy (mitophagy) in degenerative aging. In this case, a variety of genetic and pharmacological schemes is applied to virtually perturbed the in silico system. Then, the alterations specific mechanistic targets in the transient alterations is quantitated, the cytoprotective agents and molecular determinants of aging is supported to improve muscular or neurological health.35 More recently, dynamic modeling of the interaction has been reported between autophagy and apoptosis in cells of mammal.36 Since altered level of autophagy is observed in many diseases, including cancer, it is necessary to have a comprehensive understanding of the crucial process from autophagy to apoptosis. As mentioned above, these mathematical models have been widely used to solve a variety of autophagy-related scientific problems, some of which can be also utilized in future medicinal research. However, these models 7



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are still in their infancy and require further in-depth investigations.

OMICS-BASED ANALYSES OF AUTOPHAGY To date, researchers have performed numerous studies of “omics” technology covering genomics, transcriptomics, proteomics, and so on. Virtually, omics-based analyses have been validated to be powerful tools for understanding the underlying mechanisms of autophagy, especially in medicinal research (Table.3).

Genomics analyses A recent report has performed a gene network study in chronic myelogenous leukemia to explore the interplay between apoptosis and autophagy. To elucidate the crosstalk between apoptosis and autophagy, genomics analyses were applied to investigate the underlying mechanisms and explore the roles of transcription factors and microRNAs.37 In addition, according to analyses of the cancer genome atlas (TCGA) and tissue microarray (TMA), a remarkable downregulated level of Unc-51-like kinase 1 (ULK1) in breast cancer tissue samples, especially in triple negative breast cancer (TNBC) is observed. These results revealed that ULK1 activation is a new strategy for future TNBC therapy.38 In another study, the relationship between genetic variants in core Atgs with prognosis of breast cancer is investigated. As a result, the Atg7 variant rs8154 is validated to be a prognostic marker for patients with breast cancer.39

Transcriptomics analyses In silico research has been applied on transcripts encoding Atg proteins within various tissue transcriptomes of Macrobrachium rosenbergii. This method offers a platform for autophagy investigation in crustaceans, which led to a better understanding of reproduction- and stress-related autophagy, and may promote more efficient aquaculture practices.40 In another study, it has shown that the “division of labor” among histone modifications could facilitate the independent regulation of the autophagic gene 8


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expression level and noise. This “histone code” hypothesis can be extended to include expression noise. These results provide insight into the optimization of transcriptomes in evolution.41

Proteomics analyses Proteomics-based discovery and integrative bioinformatics have been utilized to identify an eukaryotic elongation factor 2 kinase (eEF2K) inhibitor, called cefatrizine, which induces endoplasmic reticulum (ER) stress in breast cancer cells.42 Phosphoproteomebased kinase activity profiling has been reported to reveal that MAP2K2 and PLK1 play pivotal roles in neuronal autophagy.43 Moreover, the autophagy interaction network has been investigated with a proteomic analysis in human cells under basal autophagy. As a result, a network of 751 interactions was obtained, among 409 candidate interacting proteins with extensive connectivity among subnetworks.44 Recently, a global quantitative proteomics method has been used for verifying that phosphorylation RAB7A by TBK1 triggers mitophagy through PINK-PARKIN pathway45. The above-mentioned genomics, transcriptomics and proteomics analyses of autophagy shed new light on further elucidating autophagic mechanisms and relevant diseases for potential medicinal implications.

SYSTEMS

BIOLOGY

NETWORK

APPROACHES

OF

AUTOPHAGY In autophagy research, a new method with therapeutic implications lies in discovering candidate drugs, which acting on autophagy-related networks and the key signaling pathways rather than their individual genes or proteins. In this section, several systems-biology network approaches are summarized to solve the problems of in silico autophagy research, such as autophagy/autophagic mechanisms and relevant human diseases (Table 4).

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Network approaches for autophagy/autophagic mechanisms A novel network analysis strategy of hybrid yeast-human has been performed and revealed that FNBP1L is essential in antibacterial autophagy. To derive functional biological information by applying network-based biology, integrated genomics has been utilized for novel molecule discovery in autophagy.46 In addition, taking regulatory network of autophagy as a systems-level bioinformatics resource has been reported for exploring the regulation and mechanism of autophagy. The known and predicted regulators

may

play

important

role

in

pharmacological

attempts

against

neurodegenerative diseases and cancer.47 Furthermore, protein-protein interactions within the programmed cell death network has been discovered by protein-fragment complementation screening.48 The Gaussia luciferase protein fragment complementation assay (GLuc PCA) has been utilized to measure the global profile of these interactions. The binding status between proteins fused to the complementary fragments of a luciferase reporter was monitored. Thus, the GLuc PCA platform can be a useful approach for the discovery of biochemical pathways of the cell death network. Moreover, the disease-gene-drug network is a significant strategy for the application of natural products in disease therapy. A disease-gene-drug network was constructed based on human protein reference database and the Gene Expression Omnibus database. Then, 30 herbal monomers were screened that could regulate 7 autophagic genes simultaneously, indicating their potential role in autophagy.49

Network approaches for autophagy-related diseases Recently, the microRNA-regulated autophagic pathways have been identified in plant lectin-induced cancer cell death. 9 hub proteins and 13 relevant targeted miRNAs were identified

in

human

breast

carcinoma

MCF-7

cells.50

Additionally,

a

novel

caspase/autophagy-related gene switch to determine the fate of breast cancer cells has been identified.51 Three molecular switches, including mitogen-activated protein kinase-3 (MAPK3), serine/threonine-protein kinase PAK-1 (PAK1) and the androgen receptor, were identified between Atgs and certain caspases in MCF-7 cells. In addition, ULK1 has 10


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been identified as a potential biomarker that is involved in miR-595 and miR-4487 regulation in SH-SY5Y cells, which provides a light to explore ULK1 and its target miRNAs in future PD therapy.52 Recently, a potential Atg4B agonist (termed as Flubendazole), which could induce autophagy in breast cancer, has been discovered based on systems biology technology,.53 Moreover, a bioinformatic analysis of Cacybpassociated proteins by using human glioma databases has been reported in a recent study. It identifies 121 differentially expressed genes, including their biological processes, protein–protein interaction (PPI) network and molecular functions.54 Taken together, systems-biology network methods can help explore complicated autophagic mechanisms for further drug discovery but still require additional experimental supports.

CONCLUSIONS Currently, despite the great advances that have been achieved by numerous experimental methods, many complicated problems in the mechanisms of autophagy remain unresolved. In addition to the aforementioned experimental approaches, a series of databases and webservers, mathematical models, “omics” techniques, systemsbiology networks, as well as other approaches related to autophagy have become available online, which may be collectively termed as in silico autophagy methods that are key supplements to experimental methods in medicinal research (Figure.2). However, compared with a wealth of experimental methods, the development of in silico autophagy approaches still remain in its infancy. Therefore, with the rapid progress of autophagy research, we believe that more new emerging in silico methods will be developed to solve the important problems of autophagy, which may be combined with experimental methods to decipher the rules of autophagy for the future pharmaceutical applications and therapeutics.

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AUTHOR INFORMATION Corresponding Authors *B.L.

E-mail address: [email protected]. Phone/Fax: (+86)28-85164063.

Author Contributions #These

authors contributed equally.

Notes The authors declare no competing ﬁnancial interest.

Biographies Yi Chen is an associate professor at State Key Laboratory of Biotherapy and Cancer Center of West China Hospital in Sichuan University. His research interest focuses on target identification and structure-based drug design. Guan Wang is an assistant professor at State Key Laboratory of Biotherapy and Cancer Center of West China Hospital in Sichuan University. His research interest focuses on targeted drug design and discovery. Haoyang Cai is a professor at School of Life Science in Sichuan University. His research interest focuses on cancer genomics, bioinformatics and drug target identification. Yang Sun is a professor at State Key Laboratory of Pharmaceutical Biotechnology in Nanjing University. His research interest focuses on cancer immunity and molecular pharmacology. Liang Ouyang received his Ph.D. degree in medicinal chemistry from West China School of Pharmacy, Sichuan University in 2010. He began his academic career in 2012 at the State Key Lab of Biotherapy of West China Hospital in Sichuan University as a professor. His research interests include identification of novel drug targets in cell death, and structure-based discovery of small-molecule and peptide drugs. Bo Liu received his Ph.D. degree in Bioinformatics (Drug design) from School of Life 12


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Sciences, Sichuan University in 2010. In 2012, he joined the faculty of State Key Lab of Biotherapy of West China Hospital in Sichuan University as a professor. His research interests include autophagic target identification and structure-based drug design.

ACKNOWLEDGEMENTS This work was supported by grants from National Key R&D Program of China (Grant No. 2017YFC0909301 and Grant No. 2017YFC0909302) and National Natural Science Foundation of China (Grant No. 91853109, Grant No. 81673290, Grant No. 81602130 and Grant No. 81673455).

ABBREVIATIONS USED ABM, agent-based model; ACDB, autophagic compound database; ACTP, autophagic compound-target prediction; Atg, autophagy-related; AutomiRDB, autophagy-microRNA database; AutopahgySMDB, autophagy small molecule database; eEF2K, eukaryotic elongation factor 2 kinase; ER, endoplasmic reticulum; FDA, food and drug administration; GAMDB, gerontology-autophagic-microRNA database; GLuc PCA, gaussia luciferase protein fragment complementation assay; HADb, human autophagy database; HAMdb, human autophagy modulator database; hfAIM, high fidelity Atg8 interacting motif; miRNA, microRNA; LC3, microtubule-associated protein 1 light

chain 3; iLIR, LC3-interacting region database; LIRCPs, LC3-interacting regioncontaining proteins; MAPK3, mitogen-activated protein kinase-3; ncRNA, noncoding RNA; PAK1, serine/threonine-protein kinase PAK-1; PCD, programmed cell death; PPI, protein–protein interaction; ProSelection, protein structure selection; RNA, ribonucleic acids; SNPs, single-nucleotide polymorphisms; TCGA, the cancer genome atlas; THANATOS, the autophagy necrosis, apoptosis orchestrators; TMA, tissue microarray; TNBC, triple negative breast cancer; ULK1, unc-51-like kinase 1.

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REFERENCES (1) Klionsky, D. J.; Emr, S. D. Autophagy as a regulated pathway of cellular degradation. Science 2000, 290, 1717-1721. (2) Klionsky, D. J. Autophagy: from phenomenology to molecular understanding in less than a decade. Nat. Rev. Mol. Cell Biol. 2007, 8, 931-937. (3) Lamb, C. A.; Yoshimori, T.; Tooze, S. A. The autophagosome: origins unknown, biogenesis complex. Nat. Rev. Mol. Cell Biol. 2013, 14(12), 759-774. (4) Levine, B.; Kroemer, G. Autophagy in the pathogenesis of disease. Cell 2008, 132, 27-42. (5) Wang, S. Y.; Yu, Q. J.; Zhang, R. D.; Liu, B. Core signaling pathways of survival/death in autophagy-related cancer networks. Int. J. Biochem. Cell Biol. 2011, 43, 1263-1266. (6) Liu, B.; Wen, X.; Cheng, Y. Survival or death: disequilibrating the oncogenic and tumor suppressive autophagy in cancer. Cell Death Dis. 2013, 4, e892. (7) Ylä-Anttila, P.; Vihinen, H.; Jokitalo, E.; Eskelinen, E. L. Monitoring autophagy by electron microscopy in Mammalian cells. Methods Enzymol 2009, 452, 143-164. (8) Wen, X.; Wu, J. M.; Wang, F. T.; Liu, B.; Huang, C.; Wei, Y. Deconvoluting the role of reactive oxygen species and autophagy in human diseases. Free Radical Bio. Med. 2013, 65(6), 402-410. (9) Rabinowitz, J. D.; White, E. Autophagy and metabolism. Science 2010, 330, 13441348. (10) Yang, Z.; Klionsky, D. J. Eaten alive: a history of macroautophagy. Nat. Cell Biol. 2010, 12, 814-822. (11) Levine, B.; Kroemer, G. Biological functions of autophagy genes: a disease perspective. Cell 2019, 176, 11-42. (12) Zhang, J.; Wang, G.; Zhou, Y.; Chen, Y.; Ouyang, L.; Liu, B. Mechanisms of autophagy and relevant small-molecule compounds for targeted cancer therapy. Cell Mol. 14


Page 15 of 34 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Life Sci. 2018, 75(10), 1803-1826. (13) Choi, A. M.; Ryter, S. W.; Levine, B. Autophagy in human health and disease. N. Engl. J. Med. 2013, 368(7), 651-662. (14) Homma, K.; Suzuki, K.; Suqawara, H. The autophagy database: an all-inclusive information resource on autophagy that provides nourishment for research. Nucleic Acids Res. 2011, 39, D986-D990. (15) Jacomin, A. C.; Samavedam, S.; Promponas, V.; Nezis, I. P. iLIR database: a web resource for LIR motif-containing proteins in eukaryotes. Autophagy 2016, 12(10), 19451953. (16) Xie, Q.; Tzfadia, O.; Levy, M.; Weithorn, E.; Peled-Zehavi, H.; Van Parys, T.; Van de Peer, Y.; Galili, G. hfAIM: a reliable bioinformatics approach for in silico genome-wide identification of autophagy-associated Atg8-interacting motifs in vatious organisms. Autophagy 2016, 12(5), 876-887. (17) Moussay, E.; Kaoma, T.; Baginska, J.; Muller, A.; Van Moer, K.; Nicot, N.; Nazarov, P. V.; Vallar, L.; Chouaib, S.; Berchem, G.; Janji, B. The acquisition of resistance to TNFα in breast cancer cells is associated with constitutive activation of autophagy as revealed by a transcriptome analysis using a custom microarray. Autophagy 2011, 7(7), 760-770. (18) Zhang, L.; Xie, T.; Tian, M.; Li, J.; Song, S.; Ouyang, L.; Liu, B.; Cai, H. GAMDB: a web resource to connect microRNAs with autophagy in gerontology. Cell Prolif. 2016, 49(2), 246-251. (19) Chen, Y.; Huang, J.; Liu, B. AutomiRDB: a web resource connecting microRNAs and autophagy in cancer. Apoptosis 2015, 20(7), 1016-1017. (20) Xu, J.; Li, Y. H. miRDearhDB: a database bridging microRNAs and the programmed cell death. Cell Death Differ. 2012, 19(9), 1571. (21) Wu, D.; Huang, Y.; Kang, J.; Li, K.; Bi, X.; Zhang, T.; Jin, N.; Hu, Y.; Tan, P.; Zhang, L.; Yi, Y.; Shen, W.; Huang, J.; Li, X.; Li, X.; Xu, J.; Wang, D. ncRDeathDB: a comprehensive bioinformatics resource for deciphering network organization of the ncRNA-mediated cell death system. Autophagy 2015, 11(10), 1917-1926. 15



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(22) Deng, Y.; Zhu, L.; Cai, H.; Wang, G.; Liu, B. Autophagic compound database: A resource connecting autophagy-modulating compounds, their potential targets and relevant diseases. Cell Prolif. 2018, 51(3), e12403. (23) Zhang, S.; Xuan, H.; Zhang, L.; Fu, S.; Wang, Y.; Yang, H.; Tai, Y.; Song, Y.; Zhang, J.; Ho, C. T.; Li, S.; Wan, X. TBC2health: a database of experimentally validated healthbeneficial effects of tea bioactive compounds. Brief Bioinform. 2017, 18(5), 830-836. (24) Wang, N.; Dong, J.; Zhang, L.; Ouyang, D.; Cheng, Y.; Chen, A. F.; Lu, A. P.; Cao, D. S. HAMdb: a database of human autophagy modulators with specific pathway and disease information. J. Cheminform. 2018, 10(1), 34. (25) Nanduri, R.; Kalra, R.; Bhagyaraj, E.; Chacko, A. P.; Ahuja, N.; Tiwari, D.; Kumar, S.; Jain, M.; Parkesh, R.; Gupta, P. AutoppahgySMDB: a curated database of small molecules that modulate protein targets regulating autophagy. Autophagy 2019, 22, 1-16. (26) Deng, W.; Ma, L.; Zhang, Y.; Zhou, J.; Wang, Y.; Liu, Z.; Xue, Y. THANATOS: an integrative data resource of proteins and post-translational modifications in the regulation of autophagy. Autophagy 2018, 14(2), 296-310. (27) Xie, T.; Zhang, L.; Zhang, S.; Ouyang, L.; Cai, H.; Liu, B. ACTP: a webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds. Oncotarget 2016, 7(9), 10015-10022. (28) Wang, N.; Wang, L.; Xie, X. Q. ProSelection: A novel algorithm to select proper protein structure subsets for in silico target identification and drug discovery research. J. Chem. Ing. Model 2017, 57(11), 2686-2898. (29) Dosztányi, Z.; Mészáros, B.; Simon, I. ANCHOR: web server for predicting protein binding regions in disordered proteins. Bioinformatics 2009, 25(20), 2745-2746. (30) Aslan, E.; Küçükoğlu, N.; Arslanyolu, M. A comparative in-silico analysis of autophagy proteins in ciliates. PeerJ. 2017, 5, e2878. (31) Scheidel, J.; Amstein, L.; Ackermann, J.; Dikic, I.; Koch, I. In silico knockout studies of xenopahgic capturing of salmonella. PLoS Comput. Biol. 2016, 12(12), e1005200. (32) Awan, F. M.; Obaid, A.; Ikarm, A.; Janjua, H. A. Mutation-Structure-Function relationship based integrated strategy reveals the potential impact of deleterious 16


Page 17 of 34 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


missense mutations in autophagy related proteins on hepatocellular carcinoma (HCC): a comprehensive informatics approach. Int. J. Mol. Sci. 2017, 18(1), E139. (33) Martin, K. R.; Barua, D.; Kauffman, A. L.; Westrate, L. M.; Posner, R. G.; Hlavacek, W. S.; Mackeigan, J. P. Computational model for autophagic vesicle dynamics in single cells. Autophagy 2013, 9(1), 74-92. (34) Börlin, C. S.; Lang, V.; Hamacher-Brady, A.; Brady, N. R. Agent-based modeling of autophagy reveals emergent regulatory behavior of spatio-temporal autophagy dynamics. Cell Commun. Signal. 2014, 12, 56. (35) Hoffman, T. E.; Barnett, K. J.; Wallis, L.; Hanneman, W. H. A multimethod computational simulation approach for investigating mitochondrial dynamics and dysfunction in degenerative aging. Aging Cell 2017, 16(6), 1244-1255. (36) Tavassoly, I.; Parmar, J.; Shajahan-Haq, A. N.; Clarke, R.; Baumann, W. T.; Tyson, J. J. Dynamic modeling of the interaction between autophagy and apoptosis in mammalian cells. CPT Pharmacometrics Syst. Pharmacol. 2015, 4(4), 263-272. (37) Wang, F.; Cho, W. C.; Chan, L. W.; Wong, S. C.; Tsui, N. B.; Siu, P. M.; Yip, S. P.; Yung, B. Y. Gene network exploration of crosstalk between apoptosis and autophagy in chronic myelogenous leukemia. Biomed. Res. Int. 2015, 2015, 459840. (38) Zhang, L.; Fu, L.; Zhang, S.; Zhang, J.; Zhao, Y.; Zheng, Y.; He, G.; Yang, S.; Ouyang, L.; Liu, B. Discovery of a small molecule targeting ULK1-modulated cell death of triple negative breast cancer in vitro and in vivo. Chem. Sci. 2017, 8(4), 2687-2701. (39) Zhou, J.; Hang, D.; Jiang, Y.; Chen, J.; Han, J.; Zhou, W.; Jin, G.; Ma, H.; Dai, J. Evaluation of genetic vatiants in autophagy pathway genes as prognostic biomarkers for breast cancer. Gene 2017, 627, 549-555. (40) Suwansa-Ard, S.; Kankuan, W.; Thongbuakaew, T.; Saetan, J.; Kornthong, N.; Kruangkum, T.; Khornchatri, K.; Cummins, S. F.; Isidoro, C.; Sobhon, P. Transcriptomic analysis of the autophagy machinery in crustaceans. BMC Genomics 2016, 17, 587. (41) Wu, S.; Li, K.; Li, Y.; Zhao, T.; Li, T.; Yang, Y. F.; Qian, W. Independent regulation of gene expression level and noise by histone modifications. PLoS Comput. Biol. 2017, 13(6), e1005585. 17



Page 18 of 34

(42) Yao, Z.; Li, J.; Liu, Z.; Zheng, L.; Fan, N.; Zhang, Y.; Jia, N.; Lv, J.; Liu, N.; Zhu, X.; Du, J.; Lv, C.; Xie, F.; Liu, Y.; Wang, X.; Fei, Z.; Gao, C. Integrative bioinformatics and proteomics-based discovery of an eEF2K inhibitor (cefatrizine) with ER stress modulation in breast cancer cells. Mol. Biosyst. 2016, 12(3), 729-736. (43) Chen, L. L.; Wang, Y. B.; Song, J. X.; Deng, W. K.; Lu, J. H.; Ma, L. L.; Yang, C. B.; Li, M.; Xue, Y. Phosphoproteome-based kinase activity profiling reveals the critical role of MAP2K2 and PLK1 in neuronal autophagy. Autophagy 2017, 13(11), 1969-1980. (44) Behrends, C.; Sowa, M. E.; Gygi, S. P.; Harper, J. W. Network organization of the human autophagy system. Nature 2010, 466(7302), 68-76. (45) Heo, J. M.; Ordureau, A.; Swarup, S.; Paulo, J. A.; Shen, K.; Sabatini, D. M.; Harper, J. W. RAB7A phosphorylation by TBK1 promotes mitophagy bia the PINK-PARKIN pathway. Sci. Adv. 2018, 4(11), eaav0443. (46) Huett, A.; Ng, A.; Cao, Z.; Kuballa, P.; Komatsu, M.; Daly, M. J.; Podolsky, D. K.; Xavier, R. J. A novel hybrid yeast-human network analysis reveals an essential role for FNBP1L in antibacterial autophagy. J. Immunol. 2009, 182(8), 4917-4930. (47) Türei, D.; Földvári-Nagy, L.; Fazekas, D.; Módos, D.; Kubisch, J.; Kadlecsik, T.; Demeter, A.; Lenti, K.; Csermely, P.; Vellar, T.; Korcsmáros T. Autophagy regulatory network-a systems-level bioinformatics resource for studying the mechanism and regulation of autophagy. Autophagy 2015, 11(1), 155-165. (48) Gilad, Y.; Shiloh, R.; Ber, Y.; Bialik, S.; Kimchi, A. Discovering protein-protein interactions within the programmed cell death network using a protein-fragment complementation screen. Cell Rep. 2014, 8(3), 909-921. (49) Hao, C.; Yang, Z.; Gao, B.; Lu, M.; Meng, X., Qiao, X.; Xue, D.; Zhang, W. Database screening of herbal monomers regulating autophagy by constructing a "disease-genedrug" network. BMC Complement. Altern. Med. 2014, 14, 466. (50) Fu, L. L.; Zhao, X.; Xu, H. L.; Wen, X.; Wang, S. Y.; Liu, B.; Bao, J. K.; Wei, Y. Q. Identification of microRNA-regulated autophagic pathways in plant lectin-induced ccancer cell death. Cell Prolif. 2012, 45(5), 477-485. (51) Fu, L. L.; Yang, Y.; Xu, H. L.; Cheng, Y.; Wen, X.; Ouyang, L.; Bao, J. K.; Wei, Y. Q.; 18


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Liu, B. Identification of novel caspase/autophagy-related gene switch to cell fate decisions in breast cancers. Cell Prolif. 2013, 46(1), 67-75. (52) Chen, Y.; Wang, S.; Zhang, L.; Xie, T.; Song, S.; Huang, J.; Zhang, Y.; Ouyang, L.; Liu, B. Identification of ULK1 as a novel biomarker involved in miR-4487 and miR-595 regulation. Sci. Rep. 2015, 5, 11035. (53) Zhang, L.; Guo, M.; Li, J.; Zheng, Y.; Zhang, S.; Xie, T.; Liu, B. Systems biologybased discovery of a potential Atg4B agonist (Flubendazole) that induces autophagy in breast cancer. Mol. Biosyst. 2015, 11(11), 2860-2866. (54) Xuan, C.; Gao, Y.; Jin, M.; Xu, S.; Wang, L.; Wang, Y.; Han, R.; Shi, K.; Chen, X.; An, Q. Bioinformatic analysis of cacybp-associated proteins using human glioma databases. IUBMB Life 2019, doi: 10.1002/liub.1999.

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Table of Contents graphic

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Figure Legends

Figure 1. A schematic model of the representative small-molecule compounds targeting autophagic proteins or autophagic process in different types of diseases.

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Figure 2. A schematic model of in silico autophagy methods for expediting the current medicinal research A variety of in silico autophagy approaches for expediting medicinal research are classified as follows: (A) databases and webservers, (B) mathematics models, (C) omics approaches and (D) systems-biology network approaches.

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Table.1 Databases and webservers related to autophagy Name

ACDB

ACTP

ANCHOR

AutomiRD B

Autophag y database

Autophag ySMDB

GAMDB

Data resource 1. It includes agreat deal of text-mining, information of autophagyPubmed, Autophag related compounds; 2. For Scifinder y-related users to access to more than Scholar, comound 300 curated compounds ChemcalBook correlated to autopahgy , Reaxys Protein Data Bank 1. ACTP can predict database, Uniprot Predict autophagic targets and autophagi relevant pathways; 2. Flexible database, c target docking and backend reverse DAVID docking can be provided database, PrePPI database 1. It can Provide the predicting protein binding regions in disordered Predict proteins; 2. Incorporates the autophagi result of a general disorder c target prediction method, IUPred and can carry out simple motif searches text-mining, Gene Ontology, 1. It includes human miRNAs, Autophag OMIM, autophagic target y-related miRBase, genes/proteins, differnt types RNA miTarBase, of cancer TargetScan, MiRanda, PicTar 1. It collected autophagyrelated proteins are identified Autophag in each species;2. Users can text-mining, y-related search for proteins to obtain mainly from 3 protein functional, structureal data literatures and find possible functional nomologs of proteins 1. It collected curated information on the konwn Autophag autophagy targets and related Text-mining, y-related molecules; 2. Users can PubChem, compoun search by text, structure and Protein data d advanced content; 3. Various bank (PDB) computational tools were provided 1. The corresponding textAutophag information about the mining,Gene y-related relations between miRNAs Ontology, RNA and autophagy were collected OMIM, Type

Feature

Data statistics

Web link

Latest Ref. update

357 compounds; 96 pathways; 68 potential targets; 443 cell types

http://w Septem ww.acd ber 30, 22 bliulab. 2017 com/

199 reviewed proteins, 231 unreviewed proteins, 24035 human protein accession numbers

http://a Januar ctp.liuy 13, 26 lab.co 2016 m/

6 samples were provided in the web server

http://a nchor.e August 28 nzim.h 7, 2009 u

http://w 493 autopahgy-related ww.che miRNAs; 90 targeted May 1, nautophagic 19 lab.co 2015 genes/proteins; 18 types m/inde of cancer x.php 41 eukaryotes; 2,163 proteins that are either known to be involved in autophagy or are homologous to autophagy-related proteins

http://w ww.tan paku.or Octobe g/autop r 6, 14 hagy/o 2010 vervie w.html

71 targets, ~10,000 small molecule modulators

http://w ww.aut Januar ophagy y 14, 25 smdb.o 2019 rg/

836 autophagy-related miRNAs; 197 targeted genes/proteins; 56 aging-related diseases

http://g Januar amdb.li y 29, 18 u2016 lab.co 23



DianaTools, miRBase, UniProt, DIANATarBase text-mining, 1. The features and NCBI RefSeq, sequences of autophagy GenBank, genes, tracscripts, exons and Gene proteins were described; 2. Autophag Ontology, HADb integrates autophgy234 autophagy-related y-related UniProt, HADb related genes and proteins in genes protein Ingenuity research reports thereby Pathway constituting the first Analysis, exhaustive database DAVID, dedicated to autophagy FatiGO text-mining, search by 1. 796 proteins, 841 databases chemicals and 132 such as microRNAs from 871 HADb, literatures and 286 cell lines autophagy were collected; 2. A lot of database, Autophag external links were available 796 autophagy-related autophagy y-related HAMdb for more information; 3. The proteins; 841 chemicals; regulatory compoun user-friendly website allows 132 microRNAs network d readers without computational database, backgroud to search and MedChem download; 4. It can give hints express, for discovery of new Selleck, modulators and targets APExBIO, ncRDeathDB 1. To reliably identify Atg8 interacting motifs in proteins in various organisms; 2. 9 peroxisomal PEX proteins 36 verified functional Autophag were identified; 3. AtPEX6 text-mining, AIMs; 26 verfied hfAIM y-related and AtPEX10 could interact PeroxisomeD Arabidopsis Atg8protein with Atg8 in planta; 4. B interacting proteins Mutations occurring within or nearby hfAIMs in PEX1, PEX6 and PEX10 caused defects in various organisms 1. Listed all the putative 8 model organisms: canonical LIRCPs identified in Arabidopsis thaliana, silico in the proteomes of 8 Caenorhabditis elegans, Autophag model organisms; 2. The UniProt Danio rerio, Gallus iLIR y-related results were combined with database, GO gallus, Homo sapiens, protein the Gene Ontology term database Mus musculus, Rattus analysis; 3. Users can identify norvegicus and novel putative LICRPs in Saccharomyces mammals cerevisiae 1. integrates information for Autophag miRDeath experimentally identified 92 miRNAs; 106 y-related text-mining DB miRNAs and their targets in proteins; 233 entries RNA PCD network; 2. Users can

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m/inde x.php

www.a March utopha 14, 17 gy.lu 2011

http://h amdb.s July 31, 24 cbdd.c 2018 om

http://bi oinform Februa atics.p ry 2, 16 sb.uge 2016 nt.be/hf AIM/

https://i Februa lir.war ry 24, 15 wick.ac 2016 .uk

http://r Octobe nar 1, 20 world.o 2012 rg/mird 24


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search the PCD-associated miRNAs and their targets; 3. Users can retrieve plenty of genomic, proteomic and disease-associated data 1. Users can query, browse and manipulate ncRNAassociated cell death Autophag ncRDeath interactions; 2. The database y-related DB can help to visualize and RNA navigate knowledge of the ncRNA of cell death and autophagy

ProSelecti on

TBC2heal th database

THANAT OS

4615 ncRNA-mediated PCD entries in 12 test-mining, species, 2403 1495 apoptosis-associated references, 12 entries, 2205 species autophagy-associated entries, 7 necrosisassociated entries collect from 1. A computational and literature and statistical algorithm for picking public the proper protein structure databases, subsets; 2. It could accelerate including Predict 249 protein structures of the computational work in PubMed, autophagi 14 autophagy-related protein structure selection; 3. PubChem, c target targetss a useful tool for molecular DrugBank, docking, target prediction, and SciFinder, protein-chemical database UniProt, establishment research ChEMBL, PDB 1. Provided compound, disease or phenotype, evidence and reference information; 2. It provides 1338 relationships test-mining, bipartite network and Autophag between 497 tea over 300 topological analyses; 3. A y-related bioactive compounds submission page and useful published comound and 206 disease or articles tools are provided; 4. phenotypes TBC2health can serve for the exploration of beneficial effects of tea on human health Proteins from PubMed, ARN, DeathBase, the autophagy 4,237 known proteins; 1. The collection, curation and census; 191,543 potential integration of regulators and proteome sets proteins in 164 post-translational eukaryotes; 93,222 from modifications will be help for Ensembl, PPIs; 65,015 known Ortholog understanding the molecular Ensembl sites of 11 types of postanalysis mechanisms of autophagy at translational Metazoa, a systems-level, and provide modifications; 559 wellEnsembl highly useful information for Fungi; PPI curated cancer genes; further experimental pairs from the 2,247 human drug consideration targets I2D, IntAct, MINT; PTM sites from previous studies and

eathdb/

http://w ww.rna May society 26, .org/nc 2015 rdeath db

http://w ww.cbli Octobe gand.o r 10, 28 rg/auto 2010 phagy

http://c amellia .ahau.e July 5, 23 du.cn/T 2016 BC2he alth

http://th anatos. May 7, 26 biocuc 2017 koo.org

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CPLM; cancer genes from COSMIC; drug targets from DrugBank

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Table.2 Mathematical models of autophagy Title

Purpose

Method

Feature Application Ref. 1. Atg proteins were partially A comparative inIntegrated The role of conserved in ciliates; 2. Mechanism databases silico analysis of autophagy Provided a better understanding 30 autophagy proteins in s and proteins in for the autophagic destruction ciliates resources ciliates of the parental macronucleus 1. Petri net model of xenopahgy In silico knockout Based on in epithelial cells were Mechanism of studies of xenophagic Mechanism models and presented; 2.The in silico xenophagic 31 knockout analyses provided a capturing of capturing of analysis s basis for investigations of the Salmonella methods Salmonella Salmonella xenopahgy pathway Mutation-structurefunction relationship based integrated 1. LC3B Y113C, BECN1 I403T, strategy reveals the SCD1 R126S and SCD1 Y218C Deleterious potential impact of Integrated were highly deleterious HCC- missense deleterious missense mutations in Mechanism databases associated mutations; 2. mutations in 32 and s Provided a valuable resource autophagy autophagy related related resources for mechanistic insight into proteins on proteins investigations of pathological hepatocellular missense SNPs carcinoma (HCC): A comprehensive informatics approach Computational model 1. The computational model Autophagic for autophagic vesicle Mechanism Based on describing autophagic vesicle vesicle 33 dynamics in single s algorithms dynamics in a mammalian dynamics in cells system were developed single cells 1. An agent-based model were Agent-based The emergent developed to represent modeling of regulatory individual components of the autophagy reveals behavior of autophagy pathway; 2. The Mechanism Based on emergent regulatory model could reproduce short- spatio34 s algorithms behavior of spatioterm autophagic flux temporal temporal autophagy measurements and to measure autophagy dynamics the degree of cell-to-cell dynamics variability 1. A multilevel hybrid-modeling A multimethod paradigm were developed to computational Mitochondrial simulate aging mitochondrial simulation approach dynamics and phenotypes; 2. The model Based on for investigating Diseases supported insights into dysfunction in 35 mitochondrial softwares molecular determinants of aging degenerative dynamics and as well as cytoprotective agents aging dysfunction in that may improve neurological degenerative aging or muscular healthspan 1. The mathematical model of Dynamic modeling of cell fate decisions mediated by The interaction the interaction Based on autophagy was established; 2. between between autophagy Diseases 36 algorithms The model was consistent with autophagy and and apoptosis in apoptosis existing quantitative mammalian cells measurements of autophagy 27



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and apoptosis in rat kidney proximal tubular cells responding to cisplatin-induced stress

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Table. 3 Omics-based analyses of autophagy Method and resource

Feature

Gene network exploration of crosstalk Data between apoptosis and analysis autophagy in chronic myelogenous leukemia

Integrated data from GEO database

1. Exploring the functional linkages and the potential coordinate regulations; 2. Mcl1, Chronic Bcl2, Atg, Beclin-1, Bax, BAK, myeloid E2F,cMYC, PI3K, AKT, BAD leukemia and LC3 were extracted for the crosstalk between apoptosis and autophagy

Discovery of a small molecule targeting ULK1-modulated cell Gene death of triple negative microarray breast cancer in vitro and in vivo

The samples were obtained from cells

1. ULK1 was remarkably downregulated in breast Triple negative 38 cancer; 2. a novel ULK1 agonist breast cancer was designed

Title

Type

Application

Ref.

37

Tissue 1. Atg7 variant rs8154 samples represents a novel prognostic was marker; 2. discovered the effect Breast cancer 39 recruited of variants in core Atgs on from breast cancer survival hospitals Tissue 1. Five autophagy markers samples Transcriptomic (Beclin-1, Vps34, MAP1LC3B, were Transcripto analysis of the SQSTM1 and Lamp1) were obtained In crustaceans 40 mic autophagy machinery identified that conserved in from analysis in crustaceans Macrobrachi crustaceans camparable in mammals um rosenbergii 1. The “division of labor” among Integrated histone modifications facilitates Independent regulation data from the independent regulation of Human of gene expression Data database expression level and noise; 2. embryonic 41 level and noise by analysis and Expression noise had increased cells histone modifications reported when the H3K79 methylation articles was knocked out in yeast. Integrative bioinformatics and Performed 1. Eukaryotic elongation factorproteomics-based by iTRAQ- 2 kinase (eEF2K) could be discovery of an eEF2K Proteomics Breast cancer 42 based regarded as a promising inhibitor (cefatrizine) analysis proteomics therapeutic taret; 2. A novel with ER stress analysis eEF2K inhibitor was discovered modultation in breast cancer cells 1. Developed a powerful PhosphoproteomePerformed method for the identification of based kinase activity by important regulators; 2. profiling reveals the Proteomics quantitative Neuronal 43 MAP2K2 and PLK1 play the autophagy critical role of MAP2K2 analysis phosphoprot important role in neuronal and PLK1 in neuronal eomic autophagy by autophagy profiling phosphoproteomic analysis Evalution of genetic variants in autophagy Gene pathway genes as microarray prognostic biomarkers for breast cancer

29



Network organization of the human autophagy system

1. Revealed a network of 751 interactions among 409 candidate interacting proteins with extensive connectivity Performed among subnetworks; 2. Proteomics by Provided a global view of the analysis proteomics mammalian autophagy analysis interaction landscape; 3. Provided a resource for mechanistic analysis of this pathway

RAB7A phosphorylation by TBK1 promotes mitophagy via the PINK-PARKIN pathway

Performed by Proteomics Phosphopro analysis teomics analysis

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Human autophagy system

44

1. TBK1 could promot downstream steps in capture of Part damaged mitochondria for mechnism of mitopagy; 2. Reveal novel mitopahgy functions of TBK1 in mitopahgy

30


45

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Table. 4 Systems-biology network approaches to autophagy Title

Purpose

A novel hybrid yeasthuman network analysis reveals an Mechanism essential role for s FNBP1L in antivacterial autophagy Autophagy regulatory network-a systemslevel bioinformatics resource for studying the mechanism and regulation of autophagy

Mechanism s

Discovering proteinprotein interactions within the programmed cell death network Mechanism using a proteins fragment complementation screen Database screening of herbal monomers regulating autophagy Mechanism by constructing a s "disease-gene-drug" network Identification of microRNA-regulated autophagic pathways Diseases in plant lectin-induced cancer cell death Identification of novel caspase/autophagyrelated gene switch to Diseases cell fate decisions in breast cancers Identification of ULK1 as a novel biomarker involved in miR-4487 Diseases and miR-595 regulation in neuroblastoma SHSY5Y cell autophagy Systems biology-based Diseases discovery of a potential

Method

Feature 1. Fourteen novel mammalian genes involoved in autophagy were revealed; 2. FNBP1L Integrated appears dispensable for the interolog starvation- or rapamycinnetwork induced autophagy; 3. FNBP1L is a defferntially used molecule in specific autophagic contexts Collected 1. 1485 proteins with 4013 the interactions; 2. 413 transcription literature factors and 386 miRNAs which and could regulate autophagy integrated components or their protein external regulators; 3. established an resources user-friendly website 1. Forty-six novel interactions were identified; 2. The interaction of 14-3-3τ and Modeling DAPK2 could inhibit DAPK2 the PCD dimerization and activity; 3. modules Supported the power of the and their connectivity Gluc PCA platform for the discovery of biochemical pathways 1. 544 differentially expressed Modeling a genes, 375 pairs of differentially compoundexpressed genes, 7 gene gene modeles were screened; 2. 30 regulatory herbal monomers can regulate network 7 genes were identified 1. Nine autophagic hub proteins Modeling were identified; 2. 13 relevant the global oncogenic and tumour proteinsuppressive miTNAs were protein identified; Plant lectins could interaction block the sugar-containing network receptor EGFR-mediated survival pathways Modeling the global 1. Three genes (Androgen proteinreceptor, PAK1, MAPK3) were protein identified as the potential interaction targets, especially MAPK3 network

Application

Ref.

The role for genes in antibacterial autophagy

46

1. MiR-4487 and miR-595 could Analysis of target ULK1 to regulate the RNAautophagy; 2. A nobel ULK1mediated p7056K autophagic pathway network was identified

Identification of targets and pathways in 52 Parkinson’s disease

Modeling the global

Identification of targets in

1. Atg4B was identified as a novel target

The mechanism 47 and regulation of autophagy

The interaction of apoptosis 48 and autophagy

The application of herbal 49 medicines in cancer therapy

Identification of targets and 50 pathways in breast cancer

Identification of targets in 51 breast cancer

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53


Atg4B agonist (Flubendazole) that induces autophagy in breast cancer

proteinprotein interaction network

Bioinformatic analysis of cacybp-associated Diseases proteins using human glioma databases

Modeling the protinproteininteraction network

Page 32 of 34

triple-negative breast cancer 1. Cacybp expressions in different grades of gliomas are no significant difference; 2. Cacybp expressions are no significant association with the prognosis of low-grade glioma and glioblastoma

Prognosis of low-grade glioma and glioblastoma

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1354x1298mm (96 x 96 DPI)




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Deciphering the rules of in silico autophagy methods for expediting

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