MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions.

From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell-cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein-protein cell-cell communication interactions are routinely predicted from transcriptomic data.

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities.

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed.

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.

A microfluidic Braille valve platform for on-demand production, combinatorial screening and sorting of chemically distinct droplets.

Droplet microfluidics is a powerful tool for a variety of biological applications including single-cell genetics, antibody discovery and directed evolution. All these applications make use of genetic libraries, illustrating the difficulty of generating chemically distinct droplets for screening applications. This protocol describes our Braille Display valving platform for on-demand generation of droplets with different chemical contents (16 different reagents and combinations thereof), as well as sorting droplets with different chemical properties, on the basis of fluorescence signals.

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data.

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events.

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.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources.

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis.

Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes.

The Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST).

Motivation: A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called 'causal interaction' takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells.

Bringing data from curated pathway resources to Cytoscape with OmniPath.

Summary: Multiple databases provide valuable information about curated pathways and other resources that can be used to build and analyze networks. OmniPath combines 61 (and continuously growing) network resources into a comprehensive collection, with over 120 000 interactions. We present here the OmniPath App, a Cytoscape plugin to flexibly import data from OmniPath via a simple and intuitive interface.

Benchmark and integration of resources for the estimation of human transcription factor activities.

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits.

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells.

Adipocyte-secreted BMP8b mediates adrenergic-induced remodeling of the neuro-vascular network in adipose tissue.

Activation of brown adipose tissue-mediated thermogenesis is a strategy for tackling obesity and promoting metabolic health. BMP8b is secreted by brown/beige adipocytes and enhances energy dissipation. Here we show that adipocyte-secreted BMP8b contributes to adrenergic-induced remodeling of the neuro-vascular network in adipose tissue (AT).

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.

The orchestra of lipid-transfer proteins at the crossroads between metabolism and signaling.

Within the eukaryotic cell, more than 1000 species of lipids define a series of membranes essential for cell function. Tightly controlled systems of lipid transport underlie the proper spatiotemporal distribution of membrane lipids, the coordination of spatially separated lipid metabolic pathways, and lipid signaling mediated by soluble proteins that may be localized some distance away from membranes. Alongside the well-established vesicular transport of lipids, non-vesicular transport mediated by a group of proteins referred to as lipid-transfer proteins (LTPs) is emerging as a key mechanism of lipid transport in a broad range of biological processes.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations.

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks.

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.

Complex regulation of autophagy in cancer - integrated approaches to discover the networks that hold a double-edged sword.

Autophagy, a highly regulated self-degradation process of eukaryotic cells, is a context-dependent tumor-suppressing mechanism that can also promote tumor cell survival upon stress and treatment resistance. Because of this ambiguity, autophagy is considered as a double-edged sword in oncology, making anti-cancer therapeutic approaches highly challenging. In this review, we present how systems-level knowledge on autophagy regulation can help to develop new strategies and efficiently select novel anti-cancer drug targets.

NRF2-ome: an integrated web resource to discover protein interaction and regulatory networks of NRF2.

NRF2 is the master transcriptional regulator of oxidative and xenobiotic stress responses. NRF2 has important roles in carcinogenesis, inflammation, and neurodegenerative diseases. We developed an online resource, NRF2-ome, to provide an integrated and systems-level database for NRF2.

SignaLink 2 - a signaling pathway resource with multi-layered regulatory networks.

Background: Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise.