- Open Access
HAMdb: a database of human autophagy modulators with specific pathway and disease information
- Ning-Ning Wang†1,
- Jie Dong†1, 2,
- Lin Zhang2,
- Defang Ouyang3,
- Yan Cheng1,
- Alex F. Chen4,
- Ai-Ping Lu5 and
- Dong-Sheng Cao1, 4, 5Email authorView ORCID ID profile
© The Author(s) 2018
- Received: 5 April 2018
- Accepted: 17 July 2018
- Published: 31 July 2018
Autophagy is an important homeostatic cellular recycling mechanism responsible for degrading unnecessary or dysfunctional cellular organelles and proteins in all living cells. In addition to its vital homeostatic role, this degradation pathway also involves in various human disorders, including metabolic conditions, neurodegenerative diseases, cancers and infectious diseases. Therefore, the comprehensive understanding of autophagy process, autophagy-related modulators and corresponding pathway and disease information will be of great help for identifying the new autophagy modulators, potential drug candidates, new diagnostic and therapeutic targets. In recent years, some autophagy databases providing structural and functional information were developed, but the specific databases covering autophagy modulator (proteins, chemicals and microRNAs)-related target, pathway and disease information do not exist. Hence, we developed an online resource, Human Autophagy Modulator Database (HAMdb, http://hamdb.scbdd.com), to provide researchers related pathway and disease information as many as possible. HAMdb contains 796 proteins, 841 chemicals and 132 microRNAs. Their specific effects on autophagy, physicochemical information, biological information and disease information were manually collected and compiled. Additionally, lots of external links were available for more information covering extensive biomedical knowledge. HAMdb provides a user-friendly interface to query, search, browse autophagy modulators and their comprehensive related information. HAMdb will help researchers understand the whole autophagy process and provide detailed information about related diseases. Furthermore, it can give hints for the identification of new diagnostic and therapeutic targets and the discovery of new autophagy modulators. In a word, we hope that HAMdb has the potential to promote the autophagy research in pharmacological and pathophysiological area.
- Autophagy modulator
Autophagy is a highly evolved and highly conservative process in eukaryotic organisms for degradation and recycling of biomolecules and damaged organelles. Commonly, there are three discrete types of autophagy: microautophagy, chaperone-mediated autophagy (CMA), and macroautophagy. For microautophagy, the lysosome itself engulfs small components of the cytoplasm for degradation by inward invagination of the lysosomal membrane . CMA is a specific degradation pathway for cytosolic proteins that contain a KFERQ-like pentapeptide. In this process, chaperone protein Hsc70 (heat shock cognate 70) and cochaperones specifically recognize these special proteins and their complex can be delivered into the lysosomal lumen for degradation through a transmembrane protein Lamp-2A . Macroautophagy, as the major type of autophagy, has been studied most extensively compared to microautophagy and CMA, and thus hereafter referred to as autophagy. It is mainly mediated by autophagy-related proteins and involves an intermediate organelle named autophagosome. Although different types of autophagy occur in different ways, they all play an important role in the process of responding to outside stimulation and removing damaged substances [3–6].
Autophagy consists of several sequential steps: sequestration, transport to lysosomes, degradation, and utilization of degradation products [7, 8]. Recent studies have clearly demonstrated that autophagy has a greater variety of physiological and pathophysiological roles than expected and each step can have a variety of physiological roles. Despite efforts to assign known functions to individual steps, many proposed functions remain to be assigned. Accordingly, autophagy is now widely implicated in pathophysiological processes (e.g., cancer, infection diseases, metabolic and neurodegenerative disorders, and cardiovascular and pulmonary diseases) and in physiological responses to exercise and aging [9–12]. Considering the key role of autophagy in cell biology and its considerable therapeutic potential, the discovery of its modulators may be a new strategy for clinical therapy . Recently, a lot of experiments indicate that inhibiting autophagy can increase the sensitivity of tumor cells to radiotherapy, chemotherapy and molecular targeted drugs, increase cell death and thus enhance the treatment efficacy. Among the existing autophagy inhibitors, only anti-malarial drugs chloroquine and hydroxychloroquine are used in clinical trials to increase survival rates for cancer patients because their clear pharmacological and toxicological properties [14–16]. Actually, current therapeutic targeting of autophagy in human disease is still limited although many efforts have been made in this direction mainly due to following several reasons: (1) an incomplete understanding of how the process contributes to pathogenesis; (2) the lack of specificity of compounds that can influence autophagy; (3) the limited availability of candidate therapeutics with clinical efficacy [17–19]. That is, the comprehensive understanding of autophagy-related genes, proteins and modulators (e.g., know about their explicit role, target, pathway and involved disease information) will be of great help for identifying new diagnostic and therapeutic targets and developing new autophagy modulators. To our best knowledge, there are already some databases specializing in autophagy available [20, 21]. However, they only involve a part of information of autophagy modulators or only cover biological and structural information of autophagy-related proteins and lack corresponding pharmacological and pathophysiological information. Therefore, the specific databases covering autophagy modulator (proteins, chemicals and microRNA)-related target, pathway and disease information are urgently needed to autophagy research and drug discovery.
To give impetus to further study and promote the autophagy research in pathophysiological area, we try to collect the functional mechanism (specific effect on autophagy), pathway and disease information of existing autophagy-related modulators (proteins, chemicals and microRNAs) as many as possible. Additionally, some basic information (biological, structural and physicochemical information) and most commonly used links are also added to provide more detailed information. A specialized database is built and freely available at http://hamdb.scbdd.com for the publics to make effective utilization of these available information.
To provide comprehensive information of autophagy modulators for researchers, we searched not only for related proteins, but also related chemicals and microRNAs from peer-reviewed literatures, available databases and some websites. The detailed collection processes are described as follows.
Related proteins: Firstly, we searched and reviewed autophagy-related scientific articles recorded by PubMed as many as possible and extracted some useful information for us. In this step, we collected 545 autophagy-related genes from 499 literatures after removing duplicates. For these genes, their molecular type, specific effects on autophagy (e.g., their increased/decreased activity will increase/decrease autophagy), species evidence and corresponding experimental references were reserved. Additionally, their pathway and disease information have also been added including canonical pathways, downstream microRNAs, proteins and chemicals, upstream proteins and chemicals, role in cell, involved disease, OMIM information, KEGG disease information. After that, we searched for the autophagy-related database and found two excellent databases: Human Autophagy Database (HADb, http://www.autophagy.lu/) and the autophagy database (http://www.tanpaku.org/autophagy/, human). From them, we obtained 251 new related genes and their pathway information were collected from Autophagy Regulatory Network database. For all the collected genes, their corresponding uniport ID (Homo sapiens) and protein description were compiled manually. And then, 20 external database links containing structural and biological information were added: Gene ID, GI number, Uni Gene, PDB, disport, BioGrid, MINT, String, ChEMBL, DrugBank, Guide to Phar, Swisslipids, Biomuta, Ensembl protein, KEGG, Pharm GKB, Biocyc, Reactome, Unipathway, and Gene wiki.
Related chemicals: Similar to the protein collection process, we firstly collected 246 related chemicals from 367 literatures recorded by PubMed. For these chemicals, their molecular type, specific effects on autophagy (e.g., their increased/decreased activity will increase/decrease autophagy), species evidence and corresponding experimental references were reserved. Additionally, some pathway and disease information including target, pathway, biological description and corresponding gene name listed in aforementioned protein database. After that, we also obtained 595 new chemicals from MedChem Express, Selleck and APExBIO. Their research area, category (activator/inhibitor), in vitro/vivo test, clinical trials were reserved. For all the chemicals, some basic information was collected: IUPAC name, alternative names, canonical SMILES, molecular formula, molecular weight, solubility. Furthermore, 18 important physicochemical and ADME properties were calculated by our ADMETlab platform and chemopy package : hydrogen acceptor, hydrogen donor, logD (pH = 7), pKa (pH = 7), pKb (pH = 7), druglike, logP(o/w), logS, SlogP, TPSA, loghERG, Caco-2, logBB, MDCK, logKp, logKhsa, human oral absorption, and percent human oral absorption (%). Four external links including structural and drug information were added: CAS number, PubChem CID, HMDB ID, and DrugBank ID.
Related microRNAs: In this part, we totally collected 132 autophagy related microRNAs from literatures recorded by PubMed and a noncoding RNA database, ncRDeathDB (www.rna-society.org/ncrdeathdb) after removing some duplicates . Their molecular type, specific effects on autophagy (e.g., their increased/decreased activity will increase/decrease autophagy), species evidence and corresponding experimental references were reserved. Additionally, the gene description, RefSeq status, organism, synonyms and miRbase ID were also compiled to supply the biological information.
The HAMdb database was deployed and runs on an ECS (elastic computation service) server of Aliyun Company. The number of CPU cores and memory are automatically allocated to the running instances on demand, which ensures the elastically stretchable computing capability. We have set a long time supporting plan to maintain the database and update new data.
A striking feature of HAMDB is the clear and optimized design of database tables and fields. On the one hand, we created parent tables to store the basic information of chemicals, proteins and microRNAs individually; set child tables to extend the related information of each object from the corresponding parent table; set primary and foreign keys to link parent and child tables. On the other hand, we optimized the field type and the size of each table to ensure a relatively small table size. These measures enable us to update records, extend object information and add tables of new kinds of modulators easily and conveniently, which makes the database a sustainable scientific project.
Content of HAMdb
Considering the research states and present challenges of autophagy, we think that HAMdb can be used in following aspects: (1) Help to understand the process. As described before, for each modulator, not only its specific effect and canonical pathway, but also its upstream and downstream are collected to give an overall understanding of its role in the autophagy process. Taking the apoptosis regulator Bcl-2 as an example, from the search result of HAMdb, we can know that increased Bcl-2 activity decreases autophagy of cell/breast cancer cell lines and it may involve PI3 K/AKT/mTOR signaling pathway, STAT3 signaling pathway and TGF-β signaling pathway. Additionally, its upstream elements such as CYCS, CASP3, BAX and downstream elements such as TNF, beta-estradiol, curcumin, TP53 can be detected. Based on these information, we can speculate that Bcl-2 may affect autophagy by regulating PI3 K/AKT/mTOR signaling pathway and consequently can affect the development of breast cancer. In fact, the nucleation phase of autophagy is controlled by a complex involving PI3KCIII with either Beclin1-Atg14L-PI3KCIII-p150, Ambra1 or Beclin1-UVRAG-PI3KCIII-p150-Bif1 and is negatively regulated by the antiapoptotic protein Bcl-2. The over-expression of Bcl-2 can promote the development of breast cancer cells by binding to Beclin 1 and inhibiting autophagy . Clearly, by means of HAMdb, the researchers could conveniently understand the pathway information of autophagy-related proteins. (2) Give detailed information about related disease. In this database, we creatively collect the related disease information for every modulator. Based on these information, users can connect disease and proteins, chemicals or microRNAs, and further study about detailed pathological mechanism may be facilitated. For mTOR, a lot of related diseases such as metastatic breast cancer, non-squamous non-small cell lung cancer, epithelial ovarian cancer, breast cancer are reported. We think that the inhibitor of mTOR may play an important role in the cancer therapeutic field and massive efforts should be made in relevant studies. Up to now, there are already several inhibitors available for mTOR: Sirolimus, a registered anti-rejection drug in kidney transplant recipients ; Temsirolimus and Everolimus, registered for renal cancer [25, 26]; NV-128111, under preclinical research . (3) Give hints for new modulators and potential drug candidates. Based on the pathway and upstream/downstream information, a series of new modulators (e.g., specific autophagy activators or inhibitors) may be designed and synthesized according to some indirect mechanisms. As an example, we can obtain the upstream elements information of VEGFA from HAMdb, such as KIT, MET, NGFR, PDGFRA, PDGFRB, TEK and so on. Therefore, not only direct autophagy modulators aiming at VEGFA can be designed, but also some indirect modulators acting on KIT, MET or PDGFR deserve much attention. So far as we know, there are at least two related drugs targeting at KIT and PDGFR: Imatinib registered for gastrointestinal stromal tumor and Sorafenib registered for renal cancer and hepatocellular carcinoma [28–30]. (4) Identify new diagnostic and therapeutic targets. By integrating the disease information and the connection of disease and protein/chemical/microRNA, some promising diagnostic and therapeutic targets may be put forward and bring good clinical benefit. For Bcl-2, both of its upstream and downstream elements can be regarded as new therapeutic targets and their changes under pathological state may provide a potential diagnostic means for clinical researchers. For example, not only the expression level of Bcl-2, but also the level of Beclin 1 can be taken as a promising index for breast cancer . (5) Promote the autophagy research in pathophysiological area. As mentioned in the first four items, thanks to comprehensive pathway and disease information included in HAMdb, it will enable researchers to know more about the role of autophagy in different diseases and may facilitate the autophagy study in pharmacological and pathophysiological area.
Comparison with other resources
The details and comparation of 7 autophagy related resources
Autophagy, as an essential, conserved lysosomal degradation pathway that controls the quality of the cytoplasm by eliminating protein aggregates and damaged organelles, has been extensively studied in recent years. Not only its basic cellular mechanism, but also its role in human health and disease has become widespread. Considering the key role of autophagy in cell biology and its considerable therapeutic potential for various diseases, the comprehensive understanding of autophagy-related modulators and corresponding pathway and diseases information will be of great help for identifying new diagnostic and therapeutic targets. Inspired by the lack of autophagy-related pathway and disease information, we manually collected the literatures and integrated external resources to gain a high coverage autophagy database. Based on them, we developed an online resource, Human Autophagy Modulator Database (HAMdb, http://hamdb.scbdd.com.), to provide researchers pathway and disease information as many as possible. HAMdb contains 796 proteins, 841 chemicals and 132 microRNAs from 871 scientific literatures and 286 cell lines. Their specific effect on autophagy, physicochemical information, biological information and disease information were carefully collected and compiled. Additionally, a lot of external links were available for more information including sequence database, 3D structure database, protein–protein interaction database, chemistry database and so on. The user-friendly website of HAMdb allows researchers without computational background to query, search and browse the database. The database can be downloaded in Excel, CSV, and SDF file formats. HAMdb will help researchers to understand the whole autophagy process and give detailed information about related diseases. Furthermore, it can give hints for discovery of new modulators and identify new diagnostic and therapeutic targets. In the long run, HAMdb has the potential to promote the autophagy research in pharmacological and pathophysiological area.
Project name: HAMdb.
Project home page: http://hamdb.scbdd.com.
Operating system(s): Platform independent.
Any restrictions to use by non-academics: License needed.
DSC conceived the experiment. NNW, JD and DSC designed and implemented the platform. NNW, JD and DSC wrote and revised the manuscript. DFOY and LZ helped in preparing figures and tables, testing and validating the results. YC, AFC and APL helped in giving suggestions to improve the platform. All authors read and approved the final manuscript.
The authors declare that they have no conflict of interests.
This work is financially supported by the National Key Basic Research Program (2015CB910700), the National Natural Science Foundation of China (Grants No. 81402853, 81501619), program for Science &Technology Innovation Talents of Hunan Province (2017TP1021), and the Project of Innovation-driven Plan in Central South University. The studies meet with the approval of the university’s review board.
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