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

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. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.Peer reviewe

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. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.</p

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

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)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. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

On the role of 4-hydroxynonenal in health and disease

AbstractPolyunsaturated fatty acids are susceptible to peroxidation and they yield various degradation products, including the main α,β-unsaturated hydroxyalkenal, 4-hydroxy-2,3-trans-nonenal (HNE) in oxidative stress. Due to its high reactivity, HNE interacts with various macromolecules of the cell, and this general toxicity clearly contributes to a wide variety of pathological conditions. In addition, growing evidence suggests a more specific function of HNE in electrophilic signaling as a second messenger of oxidative/electrophilic stress. It can induce antioxidant defense mechanisms to restrain its own production and to enhance the cellular protection against oxidative stress. Moreover, HNE-mediated signaling can largely influence the fate of the cell through modulating major cellular processes, such as autophagy, proliferation and apoptosis. This review focuses on the molecular mechanisms underlying the signaling and regulatory functions of HNE. The role of HNE in the pathophysiology of cancer, cardiovascular and neurodegenerative diseases is also discussed

Is Data Snooping responsible for Technical Analysis Rules Success?

Data Snooping is often suspected when effective technical analysis rules are found or presented. It is difficult to tell if a result is due to data snooping, so evaluating technical analysis rules often boils down to detecting data snooping and if it has invalidated the results. Herein we look at several algorithms designed to increase (risk–adjusted) returns for investors, and several techniques for detecting or compensating for data snooping. We find no easy answer to detecting data snooping. Many of the methods we look at are useful, but there is no known way to get around sparse data and the unrepeatable nature of investment decisions. We conclude that data snooping bias is a persistent risk and it is unlikely that there is any effective single solution to the problem. The best that we can do is be aware of the risk of data snooping and to report how we have dealt with the risk as part of our analysis

Is Data Snooping responsible for Technical Analysis Rules Success?

Data Snooping is often suspected when effective technical analysis rules are found or presented. It is difficult to tell if a result is due to data snooping, so evaluating technical analysis rules often boils down to detecting data snooping and if it has invalidated the results. Herein we look at several algorithms designed to increase (risk–adjusted) returns for investors, and several techniques for detecting or compensating for data snooping. We find no easy answer to detecting data snooping. Many of the methods we look at are useful, but there is no known way to get around sparse data and the unrepeatable nature of investment decisions. We conclude that data snooping bias is a persistent risk and it is unlikely that there is any effective single solution to the problem. The best that we can do is be aware of the risk of data snooping and to report how we have dealt with the risk as part of our analysis

Phase One: Strengthening the Ability for Māori Law to Become a Firm Foundational Component of a Legal Education in Aotearoa New Zealand

This Issues Paper is the result of the first stage of a multi-year, three-phase national project. In it we review the literature and consider some of the preliminary opportunities relevant for the teaching of Māori law as a foundation source of the Aotearoa New Zealand Bachelor of Laws (LLB) degree for the benefit of the legal profession and Aotearoa New Zealand society. We present ten key messages as integral to this Issues Paper

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. More than a hundred LTPs exist in humans and these can be divided into at least ten protein families. LTPs are widely distributed in tissues, organelles and membrane contact sites (MCSs), as well as in the extracellular space. They all possess a soluble and globular domain that encapsulates a lipid monomer and they specifically bind and transport a wide range of lipids. Here, we present the most recent discoveries in the functions and physiological roles of LTPs, which have expanded the playground of lipids into the aqueous spaces of cells