AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation

Abstract

Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last eight years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, expansion, termination, etc.); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the user's needs. The resource is available in various file formats (e.g. CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.

Keywords: Autophagy regulation; big data; multi-omics; network resource; signaling.

Figure 1.

The layered structure of the AutophagyNet database. The database includes the core and regulatory proteins, transcription factors and RNAs of the process, and the diverse sets of interactions between them. Abbreviations indicate BCR: B cell receptor pathway; TCR: T cell receptor pathway; RTK: receptor tyrosine kinase pathway; NHR: nuclear hormone receptor pathway; TLR: toll like receptor pathway.

Figure 2.

Statistics of AutophagyNet. (A) number of interactions are counted by layer in the previous autophagy regulatory network (gray) and the new AutophagyNet (blue) resources. (B) number of annotated PTM interactions per type of PTM.

Figure 3.

Protein page of AutophagyNet. (A) annotation box with identifiers, autophagy phase, type and tissue annotations. (B) Interactive network box with core (blue) and transcriptional (purple) layers visualized. (C) downloadable details box for selected protein, annotated by source database and publications referencing the interaction.

Figure 4.

Customizable download options of AutophagyNet. Users can select regulation type, annotations and file formats to personalize the retrieved dataset on the website.

Figure 5.

Connection of xenophagy core proteins to signaling pathways. Each circle node represents a core protein involved in xenophagy. Red boxes indicate the phase of autophagy core proteins are involved in. Rectangular nodes represent signaling pathways. The size of each pathway corresponds to the involved protein ratio, while edge width represents the number of regulatory connections from pathway members to the connected core protein.

Figure 6.

microRNA regulators of BECN1 targeted by bacterial pathogens. Bacterial infections that have been associated with microRnas are presented. Vibrio angillarium infection was described in teleost fish (indicated by dashed lines), not humans. However, this could indicate the possible role of MIR216A in human bacterial infections as discussed later in the discussion section.

Figure 7.

Workflow of building the AutophagyNet dataset. The pipeline is made up of two major modules: the integration and builder module. These are responsible for integrating and organizing the imported resources’ data. The export module converts the unified internal structure to file formats compatible with the user-friendly website.