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

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.

Oscillation of Autophagy Induction under Cellular Stress and What Lies behind It, a Systems Biology Study.

One of the main inducers of autophagy-dependent self-cannibalism, called ULK1, is tightly regulated by the two sensor molecules of nutrient conditions and energy status, known as mTOR and AMPK kinases, respectively. Recently, we developed a freely available mathematical model to explore the oscillatory characteristic of the AMPK-mTOR-ULK1 regulatory triangle. Here, we introduce a systems biology analysis to explain in detail the dynamical features of the essential negative and double-negative feedback loops and also the periodic repeat of autophagy induction upon cellular stress. We propose an additional regulatory molecule in the autophagy control network that delays some of AMPK's effect on the system, making the model output more consistent with experimental results. Furthermore, a network analysis on AutophagyNet was carried out to identify which proteins could be the proposed regulatory components in the system. These regulatory proteins should satisfy the following rules: (1) they are induced by AMPK; (2) they promote ULK1; (3) they down-regulate mTOR upon cellular stress. We have found 16 such regulatory components that have been experimentally proven to satisfy at least two of the given rules. Identifying such critical regulators of autophagy induction could support anti-cancer- and ageing-related therapeutic efforts.

Mapping the epithelial-immune cell interactome upon infection in the gut and the upper airways.

Increasing evidence points towards the key role of the epithelium in the systemic and over-activated immune response to viral infection, including SARS-CoV-2 infection. Yet, how viral infection alters epithelial-immune cell interactions regulating inflammatory responses, is not well known. Available experimental approaches are insufficient to properly analyse this complex system, and computational predictions and targeted data integration are needed as an alternative approach. In this work, we propose an integrated computational biology framework that models how infection alters intracellular signalling of epithelial cells and how this change impacts the systemic immune response through modified interactions between epithelial cells and local immune cell populations. As a proof-of-concept, we focused on the role of intestinal and upper-airway epithelial infection. To characterise the modified epithelial-immune interactome, we integrated intra- and intercellular networks with single-cell RNA-seq data from SARS-CoV-2 infected human ileal and colonic organoids as well as from infected airway ciliated epithelial cells. This integrated methodology has proven useful to point out specific epithelial-immune interactions driving inflammation during disease response, and propose relevant molecular targets to guide focused experimental analysis.

SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks.

Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.

CytokineLink: A Cytokine Communication Map to Analyse Immune Responses-Case Studies in Inflammatory Bowel Disease and COVID-19.

Intercellular communication mediated by cytokines is critical to the development of immune responses, particularly in the context of infectious and inflammatory diseases. By releasing these small molecular weight peptides, the source cells can influence numerous intracellular processes in the target cells, including the secretion of other cytokines downstream. However, there are no readily available bioinformatic resources that can model cytokine-cytokine interactions. In this effort, we built a communication map between major tissues and blood cells that reveals how cytokine-mediated intercellular networks form during homeostatic conditions. We collated the most prevalent cytokines from the literature and assigned the proteins and their corresponding receptors to source tissue and blood cell types based on enriched consensus RNA-Seq data from the Human Protein Atlas database. To assign more confidence to the interactions, we integrated the literature information on cell-cytokine interactions from two systems of immunology databases, immuneXpresso and ImmunoGlobe. From the collated information, we defined two metanetworks: a cell-cell communication network connected by cytokines; and a cytokine-cytokine interaction network depicting the potential ways in which cytokines can affect the activity of each other. Using expression data from disease states, we then applied this resource to reveal perturbations in cytokine-mediated intercellular signalling in inflammatory and infectious diseases (ulcerative colitis and COVID-19, respectively). For ulcerative colitis, with CytokineLink, we demonstrated a significant rewiring of cytokine-mediated intercellular communication between non-inflamed and inflamed colonic tissues. For COVID-19, we were able to identify cell types and cytokine interactions following SARS-CoV-2 infection, highlighting important cytokine interactions that might contribute to severe illness in a subgroup of patients. Such findings have the potential to inform the development of novel, cytokine-targeted therapeutic strategies. CytokineLink is freely available for the scientific community through the NDEx platform and the project github repository.

ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways.

The SARS-CoV-2 pandemic of 2020 has mobilised scientists around the globe to research all aspects of the coronavirus virus and its infection. For fruitful and rapid investigation of viral pathomechanisms, a collaborative and interdisciplinary approach is required. Therefore, we have developed ViralLink: a systems biology workflow which reconstructs and analyses networks representing the effect of viruses on intracellular signalling. These networks trace the flow of signal from intracellular viral proteins through their human binding proteins and downstream signalling pathways, ending with transcription factors regulating genes differentially expressed upon viral exposure. In this way, the workflow provides a mechanistic insight from previously identified knowledge of virally infected cells. By default, the workflow is set up to analyse the intracellular effects of SARS-CoV-2, requiring only transcriptomics counts data as input from the user: thus, encouraging and enabling rapid multidisciplinary research. However, the wide-ranging applicability and modularity of the workflow facilitates customisation of viral context, a priori interactions and analysis methods. Through a case study of SARS-CoV-2 infected bronchial/tracheal epithelial cells, we evidence the functionality of the workflow and its ability to identify key pathways and proteins in the cellular response to infection. The application of ViralLink to different viral infections in a context specific manner using different available transcriptomics datasets will uncover key mechanisms in viral pathogenesis.

Sherlock: an open-source data platform to store, analyze and integrate Big Data for computational biologists.

In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform ( https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock's ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.

ULK1 and ULK2 are less redundant than previously thought: computational analysis uncovers distinct regulation and functions of these autophagy induction proteins.

Macroautophagy, the degradation of cytoplasmic content by lysosomal fusion, is an evolutionary conserved process promoting homeostasis and intracellular defence. Macroautophagy is initiated primarily by a complex containing ULK1 or ULK2 (two paralogs of the yeast Atg1 protein). To understand the differences between ULK1 and ULK2, we compared the human ULK1 and ULK2 proteins and their regulation. Despite the similarity in their enzymatic domain, we found that ULK1 and ULK2 have major differences in their autophagy-related interactors and their post-translational and transcriptional regulators. We identified 18 ULK1-specific and 7 ULK2-specific protein motifs serving as different interaction interfaces. We found that interactors of ULK1 and ULK2 all have different tissue-specific expressions partially contributing to diverse and ULK-specific interaction networks in various tissues. We identified three ULK1-specific and one ULK2-specific transcription factor binding sites, and eight sites shared by the regulatory region of both genes. Importantly, we found that both their post-translational and transcriptional regulators are involved in distinct biological processes-suggesting separate functions for ULK1 and ULK2. Unravelling differences between ULK1 and ULK2 could lead to a better understanding of how ULK-type specific dysregulation affects autophagy and other cellular processes that have been implicated in diseases such as inflammatory bowel disease and cancer.

SignaLink: Multilayered Regulatory Networks.

Biological networks are graphs used to represent the inner workings of a biological system. Networks describe the relationships of the elements of biological systems using edges and nodes. However, the resulting representation of the system can sometimes be too simplistic to usefully model reality. By combining several different interaction types within one larger multilayered biological network, tools such as SignaLink provide a more nuanced view than those relying on single-layer networks (where edges only describe one kind of interaction). Multilayered networks display connections between multiple networks (i.e., protein-protein interactions and their transcriptional and posttranscriptional regulators), each one of them describing a specific set of connections. Multilayered networks also allow us to depict cross talk between cellular systems, which is a more realistic way of describing molecular interactions. They can be used to collate networks from different sources into one multilayered structure, which makes them useful as an analytic tool as well.