Cat-E: A comprehensive web tool for exploring cancer targeting strategies.

Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.

Signaling network analysis reveals fostamatinib as a potential drug to control platelet hyperactivation during SARS-CoV-2 infection.

Introduction: Pro-thrombotic events are one of the prevalent causes of intensive care unit (ICU) admissions among COVID-19 patients, although the signaling events in the stimulated platelets are still unclear. Methods: We conducted a comparative analysis of platelet transcriptome data from healthy donors, ICU, and non-ICU COVID-19 patients to elucidate these mechanisms. To surpass previous analyses, we constructed models of involved networks and control cascades by integrating a global human signaling network with transcriptome data. We investigated the control of platelet hyperactivation and the specific proteins involved. Results: Our study revealed that control of the platelet network in ICU patients is significantly higher than in non-ICU patients. Non-ICU patients require control over fewer proteins for managing platelet hyperactivity compared to ICU patients. Identification of indispensable proteins highlighted key subnetworks, that are targetable for system control in COVID-19-related platelet hyperactivity. We scrutinized FDA-approved drugs targeting indispensable proteins and identified fostamatinib as a potent candidate for preventing thrombosis in COVID-19 patients. Discussion: Our findings shed light on how SARS-CoV-2 efficiently affects host platelets by targeting indispensable and critical proteins involved in the control of platelet activity. We evaluated several drugs for specific control of platelet hyperactivity in ICU patients suffering from platelet hyperactivation. The focus of our approach is repurposing existing drugs for optimal control over the signaling network responsible for platelet hyperactivity in COVID-19 patients. Our study offers specific pharmacological recommendations, with drug prioritization tailored to the distinct network states observed in each patient condition. Interactive networks and detailed results can be accessed at https://fostamatinib.bioinfo-wuerz.eu/.

Prediction and validation of host-pathogen interactions by a versatile inference approach using Aspergillus fumigatus as a case study.

Biological networks are characterized by diverse interactions and dynamics in time and space. Many regulatory modules operate in parallel and are interconnected with each other. Some pathways are functionally known and annotated accordingly, e.g., endocytosis, migration, or cytoskeletal rearrangement. However, many interactions are not so well characterized. For reconstructing the biological complexity in cellular networks, we combine here existing experimentally confirmed and analyzed interactions with a protein-interaction inference framework using as basis experimentally confirmed interactions from other organisms. Prediction scoring includes sequence similarity, evolutionary conservation of interactions, the coexistence of interactions in the same pathway, orthology as well as structure similarity to rank and compare inferred interactions. We exemplify our inference method by studying host-pathogen interactions during infection of Mus musculus (phagolysosomes in alveolar macrophages) with Aspergillus fumigatus (conidia, airborne, asexual spores). Three of nine predicted critical host-pathogen interactions could even be confirmed by direct experiments. Moreover, we suggest drugs that manipulate the host-pathogen interaction.

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells.

We present a protocol and workflow to perform live cell dual-color fluorescence cross-correlation spectroscopy (FCCS) combined with Förster Resonance Energy transfer (FRET) to study membrane receptor dynamics in live cells using modern fluorescence labeling techniques. In dual-color FCCS, where the fluctuations in fluorescence intensity represent the dynamic "fingerprint" of the respective fluorescent biomolecule, we can probe co-diffusion or binding of the receptors. FRET, with its high sensitivity to molecular distances, serves as a well-known "nanoruler" to monitor intramolecular changes. Taken together, conformational changes and key parameters such as local receptor concentrations and mobility constants become accessible in cellular settings. Quantitative fluorescence approaches are challenging in cells due to high noise levels and the vulnerability of the sample. Here we show how to perform this experiment, including the calibration steps using dual-color labeled ß2-adrenergic receptor (ß2AR) labeled with eGFP and SNAP-tag-TAMRA. A step-by-step data analysis procedure is provided using open-source software and templates that are easy to customize. Our guideline enables researchers to unravel molecular interactions of biomolecules in live cells in situ with high reliability despite the limited signal-to-noise levels in live cell experiments. The operational window of FRET and particularly FCCS at low concentrations allows quantitative analysis at near-physiological conditions.

RhoA/Cdc42 signaling drives cytoplasmic maturation but not endomitosis in megakaryocytes.

Megakaryocytes (MKs), the precursors of blood platelets, are large, polyploid cells residing mainly in the bone marrow. We have previously shown that balanced signaling of the Rho GTPases RhoA and Cdc42 is critical for correct MK localization at bone marrow sinusoids in vivo. Using conditional RhoA/Cdc42 double-knockout (DKO) mice, we reveal here that RhoA/Cdc42 signaling is dispensable for the process of polyploidization in MKs but essential for cytoplasmic MK maturation. Proplatelet formation is virtually abrogated in the absence of RhoA/Cdc42 and leads to severe macrothrombocytopenia in DKO animals. The MK maturation defect is associated with downregulation of myosin light chain 2 (MLC2) and ß1-tubulin, as well as an upregulation of LIM kinase 1 and cofilin-1 at both the mRNA and protein level and can be linked to impaired MKL1/SRF signaling. Our findings demonstrate that MK endomitosis and cytoplasmic maturation are separately regulated processes, and the latter is critically controlled by RhoA/Cdc42.

Comparison of the central human and mouse platelet signaling cascade by systems biological analysis.

BACKGROUND: Understanding the molecular mechanisms of platelet activation and aggregation is of high interest for basic and clinical hemostasis and thrombosis research. The central platelet protein interaction network is involved in major responses to exogenous factors. This is defined by systemsbiological pathway analysis as the central regulating signaling cascade of platelets (CC). RESULTS: The CC is systematically compared here between mouse and human and major differences were found. Genetic differences were analysed comparing orthologous human and mouse genes. We next analyzed different expression levels of mRNAs. Considering 4 mouse and 7 human high-quality proteome data sets, we identified then those major mRNA expression differences (81%) which were supported by proteome data. CC is conserved regarding genetic completeness, but we observed major differences in mRNA and protein levels between both species. Looking at central interactors, human PLCB2, MMP9, BDNF, ITPR3 and SLC25A6 (always Entrez notation) show absence in all murine datasets. CC interactors GNG12, PRKCE and ADCY9 occur only in mice. Looking at the common proteins, TLN1, CALM3, PRKCB, APP, SOD2 and TIMP1 are higher abundant in human, whereas RASGRP2, ITGB2, MYL9, EIF4EBP1, ADAM17, ARRB2, CD9 and ZYX are higher abundant in mouse. Pivotal kinase SRC shows different regulation on mRNA and protein level as well as ADP receptor P2RY12. CONCLUSIONS: Our results highlight species-specific differences in platelet signaling and points of specific fine-tuning in human platelets as well as murine-specific signaling differences.

Proteomics of Aspergillus fumigatus Conidia-containing Phagolysosomes Identifies Processes Governing Immune Evasion.

Invasive infections by the human pathogenic fungus Aspergillus fumigatus start with the outgrowth of asexual, airborne spores (conidia) into the lung tissue of immunocompromised patients. The resident alveolar macrophages phagocytose conidia, which end up in phagolysosomes. However, A. fumigatus conidia resist phagocytic degradation to a certain degree. This is mainly attributable to the pigment 1,8-dihydroxynaphthalene (DHN) melanin located in the cell wall of conidia, which manipulates the phagolysosomal maturation and prevents their intracellular killing. To get insight in the underlying molecular mechanisms, we comparatively analyzed proteins of mouse macrophage phagolysosomes containing melanized wild-type (wt) or nonmelanized pksP mutant conidia. For this purpose, a protocol to isolate conidia-containing phagolysosomes was established and a reference protein map of phagolysosomes was generated. We identified 637 host and 22 A. fumigatus proteins that were differentially abundant in the phagolysosome. 472 of the host proteins were overrepresented in the pksP mutant and 165 in the wt conidia-containing phagolysosome. Eight of the fungal proteins were produced only in pksP mutant and 14 proteins in wt conidia-containing phagolysosomes. Bioinformatical analysis compiled a regulatory module, which indicates host processes affected by the fungus. These processes include vATPase-driven phagolysosomal acidification, Rab5 and Vamp8-dependent endocytic trafficking, signaling pathways, as well as recruitment of the Lamp1 phagolysosomal maturation marker and the lysosomal cysteine protease cathepsin Z. Western blotting and immunofluorescence analyses confirmed the proteome data and moreover showed differential abundance of the major metabolic regulator mTOR. Taken together, with the help of a protocol optimized to isolate A. fumigatus conidia-containing phagolysosomes and a potent bioinformatics algorithm, we were able to confirm A. fumigatus conidia-dependent modification of phagolysosomal processes that have been described before and beyond that, identify pathways that have not been implicated in A. fumigatus evasion strategy, yet.Mass spectrometry proteomics data are available via ProteomeXchange with identifiers PXD005724 and PXD006134.

Integrated inference and evaluation of host-fungi interaction networks.

Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host-pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host-fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen-host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi-human and fungi-mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host-fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host-fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host-fungi transcriptome and proteome data.