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.

Computational prediction and experimental validation of Salmonella Typhimurium SopE-mediated fine-tuning of autophagy in intestinal epithelial cells.

Macroautophagy is a ubiquitous homeostasis and health-promoting recycling process of eukaryotic cells, targeting misfolded proteins, damaged organelles and intracellular infectious agents. Some intracellular pathogens such as Salmonella enterica serovar Typhimurium hijack this process during pathogenesis. Here we investigate potential protein-protein interactions between host transcription factors and secreted effector proteins of Salmonella and their effect on host gene transcription. A systems-level analysis identified Salmonella effector proteins that had the potential to affect core autophagy gene regulation. The effect of a SPI-1 effector protein, SopE, that was predicted to interact with regulatory proteins of the autophagy process, was investigated to validate our approach. We then confirmed experimentally that SopE can directly bind to SP1, a host transcription factor, which modulates the expression of the autophagy gene MAP1LC3B. We also revealed that SopE might have a double role in the modulation of autophagy: Following initial increase of MAP1LC3B transcription triggered by Salmonella infection, subsequent decrease in MAP1LC3B transcription at 6h post-infection was SopE-dependent. SopE also played a role in modulation of the autophagy flux machinery, in particular MAP1LC3B and p62 autophagy proteins, depending on the level of autophagy already taking place. Upon typical infection of epithelial cells, the autophagic flux is increased. However, when autophagy was chemically induced prior to infection, SopE dampened the autophagic flux. The same was also observed when most of the intracellular Salmonella cells were not associated with the SCV (strain lacking sifA) regardless of the autophagy induction status before infection. We demonstrated how regulatory network analysis can be used to better characterise the impact of pathogenic effector proteins, in this case, Salmonella. This study complements previous work in which we had demonstrated that specific pathogen effectors can affect the autophagy process through direct interaction with autophagy proteins. Here we show that effector proteins can also influence the upstream regulation of the process. Such interdisciplinary studies can increase our understanding of the infection process and point out targets important in intestinal epithelial cell defense.

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.

Epigenetic Dysregulation of Trophoblastic Gene Expression in Gestational Trophoblastic Disease.

Gestational trophoblastic diseases (GTDs) have not been investigated for their epigenetic marks and consequent transcriptomic changes. Here, we analyzed genome-wide DNA methylation and transcriptome data to reveal the epigenetic basis of disease pathways that may lead to benign or malignant GTDs. RNA-Seq, mRNA microarray, and Human Methylation 450 BeadChip data from complete moles and choriocarcinoma cells were bioinformatically analyzed. Paraffin-embedded tissues from complete moles and control placentas were used for tissue microarray construction, DNMT3B immunostaining and immunoscoring. We found that DNA methylation increases with disease severity in GTDs. Differentially expressed genes are mainly upregulated in moles while predominantly downregulated in choriocarcinoma. DNA methylation principally influences the gene expression of villous trophoblast differentiation-related or predominantly placenta-expressed genes in moles and choriocarcinoma cells. Affected genes in these subsets shared focal adhesion and actin cytoskeleton pathways in moles and choriocarcinoma. In moles, cell cycle and differentiation regulatory pathways, essential for trophoblast/placental development, were enriched. In choriocarcinoma cells, hormone biosynthetic, extracellular matrix-related, hypoxic gene regulatory, and differentiation-related signaling pathways were enriched. In moles, we found slight upregulation of DNMT3B protein, a developmentally important de novo DNA methylase, which is strongly overexpressed in choriocarcinoma cells that may partly be responsible for the large DNA methylation differences. Our findings provide new insights into the shared and disparate molecular pathways of disease in GTDs and may help in designing new diagnostic and therapeutic tools.

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.

Placenta-Specific Genes, Their Regulation During Villous Trophoblast Differentiation and Dysregulation in Preterm Preeclampsia.

The human placenta maintains pregnancy and supports the developing fetus by providing nutrition, gas-waste exchange, hormonal regulation, and an immunological barrier from the maternal immune system. The villous syncytiotrophoblast carries most of these functions and provides the interface between the maternal and fetal circulatory systems. The syncytiotrophoblast is generated by the biochemical and morphological differentiation of underlying cytotrophoblast progenitor cells. The dysfunction of the villous trophoblast development is implicated in placenta-mediated pregnancy complications. Herein, we describe gene modules and clusters involved in the dynamic differentiation of villous cytotrophoblasts into the syncytiotrophoblast. During this process, the immune defense functions are first established, followed by structural and metabolic changes, and then by peptide hormone synthesis. We describe key transcription regulatory molecules that regulate gene modules involved in placental functions. Based on transcriptomic evidence, we infer how villous trophoblast differentiation and functions are dysregulated in preterm preeclampsia, a life-threatening placenta-mediated obstetrical syndrome for the mother and fetus. In the conclusion, we uncover the blueprint for villous trophoblast development and its impairment in preterm preeclampsia, which may aid in the future development of non-invasive biomarkers for placental functions and early identification of women at risk for preterm preeclampsia as well as other placenta-mediated pregnancy complications.

Targeted interplay between bacterial pathogens and host autophagy.

Due to the critical role played by autophagy in pathogen clearance, pathogens have developed diverse strategies to subvert it. Despite previous key findings of bacteria-autophagy interplay, asystems-level insight into selective targeting by the host and autophagy modulation by the pathogens is lacking. We predicted potential interactions between human autophagy proteins and effector proteins from 56 pathogenic bacterial species by identifying bacterial proteins predicted to have recognition motifs for selective autophagy receptors SQSTM1/p62, CALCOCO2/NDP52 and MAP1LC3/LC3. Using structure-based interaction prediction, we identified bacterial proteins capable to modify core autophagy components. Our analysis revealed that autophagy receptors in general potentially target mostly genus-specific proteins, and not those present in multiple genera. The complementarity between the predicted SQSTM1/p62 and CALCOCO2/NDP52 targets, which has been shown for Salmonella, Listeria and Shigella, could be observed across other pathogens. This complementarity potentially leaves the host more susceptible to chronic infections upon the mutation of autophagy receptors. Proteins derived from enterotoxigenic and non-toxigenic Bacillus outer membrane vesicles indicated that autophagy targets pathogenic proteins rather than non-pathogenic ones. We also observed apathogen-specific pattern as to which autophagy phase could be modulated by specific genera. We found intriguing examples of bacterial proteins that could modulate autophagy, and in turn being targeted by autophagy as ahost defense mechanism. We confirmed experimentally an interplay between a Salmonella protease, YhjJ and autophagy. Our comparative meta-analysis points out key commonalities and differences in how pathogens could affect autophagy and how autophagy potentially recognizes these pathogenic effectors. Abbreviations: ATG5: autophagy related 5; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; GST: glutathione S-transferase; LIR: MAP1LC3/LC3-interacting region; MAP1LC3/LC3: microtubule associated protein 1 light chain 3 alpha; OMV: outer membrane vesicles; SQSTM1/p62: sequestosome 1; SCV: Salmonella containing vesicle; TECPR1: tectonin beta-propeller repeat containing 1; YhjJ: hypothetical zinc-protease.

Integrated Systems Biology Approach Identifies Novel Maternal and Placental Pathways of Preeclampsia.

Preeclampsia is a disease of the mother, fetus, and placenta, and the gaps in our understanding of the complex interactions among their respective disease pathways preclude successful treatment and prevention. The placenta has a key role in the pathogenesis of the terminal pathway characterized by exaggerated maternal systemic inflammation, generalized endothelial damage, hypertension, and proteinuria. This sine qua non of preeclampsia may be triggered by distinct underlying mechanisms that occur at early stages of pregnancy and induce different phenotypes. To gain insights into these molecular pathways, we employed a systems biology approach and integrated different "omics," clinical, placental, and functional data from patients with distinct phenotypes of preeclampsia. First trimester maternal blood proteomics uncovered an altered abundance of proteins of the renin-angiotensin and immune systems, complement, and coagulation cascades in patients with term or preterm preeclampsia. Moreover, first trimester maternal blood from preterm preeclamptic patients in vitro dysregulated trophoblastic gene expression. Placental transcriptomics of women with preterm preeclampsia identified distinct gene modules associated with maternal or fetal disease. Placental "virtual" liquid biopsy showed that the dysregulation of these disease gene modules originates during the first trimester. In vitro experiments on hub transcription factors of these gene modules demonstrated that DNA hypermethylation in the regulatory region of ZNF554 leads to gene down-regulation and impaired trophoblast invasion, while BCL6 and ARNT2 up-regulation sensitizes the trophoblast to ischemia, hallmarks of preterm preeclampsia. In summary, our data suggest that there are distinct maternal and placental disease pathways, and their interaction influences the clinical presentation of preeclampsia. The activation of maternal disease pathways can be detected in all phenotypes of preeclampsia earlier and upstream of placental dysfunction, not only downstream as described before, and distinct placental disease pathways are superimposed on these maternal pathways. This is a paradigm shift, which, in agreement with epidemiological studies, warrants for the central pathologic role of preexisting maternal diseases or perturbed maternal-fetal-placental immune interactions in preeclampsia. The description of these novel pathways in the "molecular phase" of preeclampsia and the identification of their hub molecules may enable timely molecular characterization of patients with distinct preeclampsia phenotypes.

SalmoNet, an integrated network of ten Salmonella enterica strains reveals common and distinct pathways to host adaptation.

Salmonella enterica is a prominent bacterial pathogen with implications on human and animal health. Salmonella serovars could be classified as gastro-intestinal or extra-intestinal. Genome-wide comparisons revealed that extra-intestinal strains are closer relatives of gastro-intestinal strains than to each other indicating a parallel evolution of this trait. Given the complexity of the differences, a systems-level comparison could reveal key mechanisms enabling extra-intestinal serovars to cause systemic infections. Accordingly, in this work, we introduce a unique resource, SalmoNet, which combines manual curation, high-throughput data and computational predictions to provide an integrated network for Salmonella at the metabolic, transcriptional regulatory and protein-protein interaction levels. SalmoNet provides the networks separately for five gastro-intestinal and five extra-intestinal strains. As a multi-layered, multi-strain database containing experimental data, SalmoNet is the first dedicated network resource for Salmonella. It comprehensively contains interactions between proteins encoded in Salmonella pathogenicity islands, as well as regulatory mechanisms of metabolic processes with the option to zoom-in and analyze the interactions at specific loci in more detail. Application of SalmoNet is not limited to strain comparisons as it also provides a Salmonella resource for biochemical network modeling, host-pathogen interaction studies, drug discovery, experimental validation of novel interactions, uncovering new pathological mechanisms from emergent properties and epidemiological studies. SalmoNet is available at http://salmonet.org.

SignaFish: A Zebrafish-Specific Signaling Pathway Resource.

Understanding living systems requires an in-depth knowledge of the signaling networks that drive cellular homeostasis, regulate intercellular communication, and contribute to cell fates during development. Several resources exist to provide high-throughput data sets or manually curated interaction information from human or invertebrate model organisms. We previously developed SignaLink, a uniformly curated, multi-layered signaling resource containing information for human and for the model organisms nematode Caenorhabditis elegans and fruit fly Drosophila melanogaster. Until now, the use of the SignaLink database for zebrafish pathway analysis was limited. To overcome this limitation, we created SignaFish ( http://signafish.org ), a fish-specific signaling resource, built using the concept of SignaLink. SignaFish contains more than 200 curation-based signaling interactions, 132 further interactions listed in other resources, and it also lists potential miRNA-based regulatory connections for seven major signaling pathways. From the SignaFish website, users can reach other web resources, such as ZFIN. SignaFish provides signaling or signaling-related interactions that can be examined for each gene or downloaded for each signaling pathway. We believe that the SignaFish resource will serve as a novel navigating point for experimental design and evaluation for the zebrafish community and for researchers focusing on nonmodel fish species, such as cyclids.

Autophagy Regulatory Network - a systems-level bioinformatics resource for studying the mechanism and regulation of autophagy.

Autophagy is a complex cellular process having multiple roles, depending on tissue, physiological, or pathological conditions. Major post-translational regulators of autophagy are well known, however, they have not yet been collected comprehensively. The precise and context-dependent regulation of autophagy necessitates additional regulators, including transcriptional and post-transcriptional components that are listed in various datasets. Prompted by the lack of systems-level autophagy-related information, we manually collected the literature and integrated external resources to gain a high coverage autophagy database. We developed an online resource, Autophagy Regulatory Network (ARN; http://autophagy-regulation.org), to provide an integrated and systems-level database for autophagy research. ARN contains manually curated, imported, and predicted interactions of autophagy components (1,485 proteins with 4,013 interactions) in humans. We listed 413 transcription factors and 386 miRNAs that could regulate autophagy components or their protein regulators. We also connected the above-mentioned autophagy components and regulators with signaling pathways from the SignaLink 2 resource. The user-friendly website of ARN allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. ARN has the potential to facilitate the experimental validation of novel autophagy components and regulators. In addition, ARN helps the investigation of transcription factors, miRNAs and signaling pathways implicated in the control of the autophagic pathway. The list of such known and predicted regulators could be important in pharmacological attempts against cancer and neurodegenerative diseases.