Apoptosis DOI 10.1007/s10495-015-1133-1
ORIGINAL PAPER
AutomiRDB: a web resource connecting microRNAs and autophagy in cancer Yi Chen1 • Jian Huang2 • Bo Liu1
Ó Springer Science+Business Media New York 2015
MicroRNAs (miRNAs) are small and non-coding endogenous RNAs *22 nucleotides in length, exerting either/both an oncogenic or/and a tumor suppressive function in cancer. Of note, these miRNAs may play their essential roles in modulating autophagy, a multi-step lysosomal degradation process in which a cell may degrade long-lived proteins and damaged organelles [1]. Recently, accumulating evidence has elucidated the key roles of oncogenic or tumor suppressive miRNAs in autophagy, thereby establishing a basis for understanding specific mechanisms linking miRNA deregulation to autophagic pathways [2, 3] (Fig. S1). However, the specific mechanisms on how miRNA can regulate cancer-related autophagy at the posttranscriptional or the translational levels, is in its embryogenesis. Thus, we still lack a systematical collection of corresponding information about their relationships. In this study, we constructed the online autophagy-miRNA database (AutomiRDB) (http://www.chen-lab.com/index.php),
Electronic supplementary material The online version of this article (doi:10.1007/s10495-015-1133-1) contains supplementary material, which is available to authorized users. & Jian Huang [email protected] & Bo Liu [email protected] 1
Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, West China Hospital, Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu 610041, China
2
School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang 110016, China
a knowledge depository that integrates information of experimentally identified miRNAs and their autophagic target genes/proteins in different types of cancer. AutomiRDB is comprised of three data fields, including human miRNAs, autophagic target genes/proteins, and different types of cancer (Fig. S2). In AutomiRDB, we search for all known autophagyrelated miRNAs in cancer by referring to published articles. There are about 237 humanized autophagic genes/ proteins in Gene Ontology (GO) consortium, and under the guidance of Online Mendelian Inheritance in Man (OMIM) have got 49 cancer-related autophagic genes/proteins. To predict candidate miRNAs targeting these autophagic genes/proteins, we used different predictive databases, such as miRBase (http://miRBase.org), miTarBase (http://mir tarbase.mbc.nctu.edu.tw), TargetScan (http://www.targets can.org), MiRanda (http://www.microrna.org) and PicTar (http://pictar.mdc-berlin.de). Due to the fact that available prediction methods may hold strongly distinct degrees of sensitivity and specificity, we developed a combinatory approach predicted by the consensus results from these different online databases. Subsequently, we applied similar methods to collect data about targeted autophagic genes/proteins in cancer. Data were achieved from published articles, GO consortium and OMIM. For the information of cancer cell types, we manually extracted the data by text-mining methods from the published articles. Then, we used OMIM to collect information of different cancer types, which was associated with the known and predicted miRNAs in autophagy. Additionally, we converted target information into the UniProt accession number, which is a comprehensive, high-quality and freely accessible resource of protein sequence and function. There are 493 autophagyrelated miRNAs, 90 targeted autophagic genes/proteins and 18 types of cancer in this database. Users can take target
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name or miRNA symbol as keyword to query the database and follow the link to retrieve related information. And, users can take provided hyperlinks to search for detailed information. Besides, the hyperlinks of targets and diseases are linked to other databases, such as UniProt, OMIM and etc. For instance, users can search a miRNA in two forms such as hsa-mir-30a or mir-30a and it is not case sensitive (Fig. S3). Together, we collect related data including 493 autophagic miRNAs, 90 targeted genes/proteins, and 18 types of cancers for AutomiRDB. We also update and curate this database frequently to keep up with this rapidly growing area, and hope it would become a bridge connecting microRNAs and autophagy in different types of cancer, and thus providing a new clue on facilitating future cancer therapy.
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Acknowledgments This work was supported by Grants from the National 973 Basic Research Program of China (No. 2013CB911300) and the National Natural Science Foundation of China (No. 81402496). Conflict of interest
The authors declare no conflict of interest.
References 1. Fu LL et al (2012) MicroRNA-modulated autophagic signaling networks in cancer. Int J Biochem Cell B 44:733–736 2. Liu B, Wen X, Cheng Y (2013) Survival or death: disequilibrating the oncogenic and tumor suppressive autophagy in cancer. Cell Death Dis 4:e892 3. Chen Y et al (2014) Oncogenic and tumor suppressive roles of microRNAs in apoptosis and autophagy. Apoptosis 19:1177–1189
ORIGINAL PAPER
AutomiRDB: a web resource connecting microRNAs and autophagy in cancer Yi Chen1 • Jian Huang2 • Bo Liu1
Ó Springer Science+Business Media New York 2015
MicroRNAs (miRNAs) are small and non-coding endogenous RNAs *22 nucleotides in length, exerting either/both an oncogenic or/and a tumor suppressive function in cancer. Of note, these miRNAs may play their essential roles in modulating autophagy, a multi-step lysosomal degradation process in which a cell may degrade long-lived proteins and damaged organelles [1]. Recently, accumulating evidence has elucidated the key roles of oncogenic or tumor suppressive miRNAs in autophagy, thereby establishing a basis for understanding specific mechanisms linking miRNA deregulation to autophagic pathways [2, 3] (Fig. S1). However, the specific mechanisms on how miRNA can regulate cancer-related autophagy at the posttranscriptional or the translational levels, is in its embryogenesis. Thus, we still lack a systematical collection of corresponding information about their relationships. In this study, we constructed the online autophagy-miRNA database (AutomiRDB) (http://www.chen-lab.com/index.php),
Electronic supplementary material The online version of this article (doi:10.1007/s10495-015-1133-1) contains supplementary material, which is available to authorized users. & Jian Huang [email protected] & Bo Liu [email protected] 1
Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, West China Hospital, Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu 610041, China
2
School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang 110016, China
a knowledge depository that integrates information of experimentally identified miRNAs and their autophagic target genes/proteins in different types of cancer. AutomiRDB is comprised of three data fields, including human miRNAs, autophagic target genes/proteins, and different types of cancer (Fig. S2). In AutomiRDB, we search for all known autophagyrelated miRNAs in cancer by referring to published articles. There are about 237 humanized autophagic genes/ proteins in Gene Ontology (GO) consortium, and under the guidance of Online Mendelian Inheritance in Man (OMIM) have got 49 cancer-related autophagic genes/proteins. To predict candidate miRNAs targeting these autophagic genes/proteins, we used different predictive databases, such as miRBase (http://miRBase.org), miTarBase (http://mir tarbase.mbc.nctu.edu.tw), TargetScan (http://www.targets can.org), MiRanda (http://www.microrna.org) and PicTar (http://pictar.mdc-berlin.de). Due to the fact that available prediction methods may hold strongly distinct degrees of sensitivity and specificity, we developed a combinatory approach predicted by the consensus results from these different online databases. Subsequently, we applied similar methods to collect data about targeted autophagic genes/proteins in cancer. Data were achieved from published articles, GO consortium and OMIM. For the information of cancer cell types, we manually extracted the data by text-mining methods from the published articles. Then, we used OMIM to collect information of different cancer types, which was associated with the known and predicted miRNAs in autophagy. Additionally, we converted target information into the UniProt accession number, which is a comprehensive, high-quality and freely accessible resource of protein sequence and function. There are 493 autophagyrelated miRNAs, 90 targeted autophagic genes/proteins and 18 types of cancer in this database. Users can take target
123
Apoptosis
name or miRNA symbol as keyword to query the database and follow the link to retrieve related information. And, users can take provided hyperlinks to search for detailed information. Besides, the hyperlinks of targets and diseases are linked to other databases, such as UniProt, OMIM and etc. For instance, users can search a miRNA in two forms such as hsa-mir-30a or mir-30a and it is not case sensitive (Fig. S3). Together, we collect related data including 493 autophagic miRNAs, 90 targeted genes/proteins, and 18 types of cancers for AutomiRDB. We also update and curate this database frequently to keep up with this rapidly growing area, and hope it would become a bridge connecting microRNAs and autophagy in different types of cancer, and thus providing a new clue on facilitating future cancer therapy.
123
Acknowledgments This work was supported by Grants from the National 973 Basic Research Program of China (No. 2013CB911300) and the National Natural Science Foundation of China (No. 81402496). Conflict of interest
The authors declare no conflict of interest.
References 1. Fu LL et al (2012) MicroRNA-modulated autophagic signaling networks in cancer. Int J Biochem Cell B 44:733–736 2. Liu B, Wen X, Cheng Y (2013) Survival or death: disequilibrating the oncogenic and tumor suppressive autophagy in cancer. Cell Death Dis 4:e892 3. Chen Y et al (2014) Oncogenic and tumor suppressive roles of microRNAs in apoptosis and autophagy. Apoptosis 19:1177–1189
