DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control - An efficient data analysis for cancer research.

Building data-driven models is an effective strategy for information extraction from empirical data. Adapting model parameters specifically to data with a best fitting approach encodes the relevant information into a mathematical model. Subsequently, an optimal control framework extracts the most efficient targets to steer the model into desired changes via external stimuli. The DataXflow software framework integrates three software pipelines, D2D for model fitting, a framework solving optimal control problems including external stimuli and JimenaE providing graphical user interfaces to employ the other frameworks lowering the barriers for the need of programming skills, and simultaneously automating reoccurring modeling tasks. Such tasks include equation generation from a graph and script generation allowing also to approach systems with many agents, like complex gene regulatory networks. A desired state of the model is defined, and therapeutic interventions are modeled as external stimuli. The optimal control framework purposefully exploits the model-encoded information by providing those external stimuli that effect the desired changes most efficiently. The implementation of DataXflow is available under https://github.com/MarvelousHopefull/DataXflow. We showcase its application by detecting specific drug targets for a therapy of lung cancer from measurement data to lower proliferation and increase apoptosis. By an iterative modeling process refining the topology of the model, the regulatory network of the tumor is generated from the data. An application of the optimal control framework in our example reveals the inhibition of AURKA and the activation of CDH1 as the most efficient drug target combination. DataXflow paves the way to an agile interplay between data generation and its analysis potentially accelerating cancer research by an efficient drug target identification, even in complex networks.

An orchestra of machine learning methods reveals landmarks in single-cell data exemplified with aging fibroblasts.

In this work, a Python framework for characteristic feature extraction is developed and applied to gene expression data of human fibroblasts. Unlabeled feature selection objectively determines groups and minimal gene sets separating groups. ML explainability methods transform the features correlating with phenotypic differences into causal reasoning, supported by further pipeline and visualization tools, allowing user knowledge to boost causal reasoning. The purpose of the framework is to identify characteristic features that are causally related to phenotypic differences of single cells. The pipeline consists of several data science methods enriched with purposeful visualization of the intermediate results in order to check them systematically and infuse the domain knowledge about the investigated process. A specific focus is to extract a small but meaningful set of genes to facilitate causal reasoning for the phenotypic differences. One application could be drug target identification. For this purpose, the framework follows different steps: feature reduction (PFA), low dimensional embedding (UMAP), clustering ((H)DBSCAN), feature correlation (chi-square, mutual information), ML validation and explainability (SHAP, tree explainer). The pipeline is validated by identifying and correctly separating signature genes associated with aging in fibroblasts from single-cell gene expression measurements: PLK3, polo-like protein kinase 3; CCDC88A, Coiled-Coil Domain Containing 88A; STAT3, signal transducer and activator of transcription-3; ZNF7, Zinc Finger Protein 7; SLC24A2, solute carrier family 24 member 2 and lncRNA RP11-372K14.2. The code for the preprocessing step can be found in the GitHub repository https://github.com/AC-PHD/NoLabelPFA, along with the characteristic feature extraction https://github.com/LauritzR/characteristic-feature-extraction.

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.

The post-stroke young adult brain has limited capacity to re-express the gene expression patterns seen during early postnatal brain development.

The developmental origins of the brain's response to injury can play an important role in recovery after a brain lesion. In this study, we investigated whether the ischemic young adult brain can re-express brain plasticity genes that were active during early postnatal development. Differentially expressed genes in the cortex of juvenile post-natal day 3 and the peri-infarcted cortical areas of young, 3-month-old post-stroke rats were identified using fixed-effects modeling within an empirical Bayes framework through condition-specific comparison. To further analyze potential biological processes, upregulated and downregulated genes were assessed for enrichment using GSEA software. The genes showing the highest expression changes were subsequently verified through RT-PCR. Our findings indicate that the adult brain partially recapitulates the gene expression profile observed in the juvenile brain but fails to upregulate many genes and pathways necessary for brain plasticity. Of the upregulated genes in post-stroke brains, specific roles have not been assigned to Apobec1, Cenpf, Ect2, Folr2, Glipr1, Myo1f, and Pttg1. New genes that failed to upregulate in the adult post-stroke brain include Bex4, Cd24, Klhl1/Mrp2, Trim67, and St8sia2. Among the upregulated pathways, the largest change was observed in the KEGG pathway "One carbon pool of folate," which is necessary for cellular proliferation, followed by the KEGG pathway "Antifolate resistance," whose genes mainly encode the family of ABC transporters responsible for the efflux of drugs that have entered the brain. We also noted three less-described downregulated KEGG pathways in experimental models: glycolipid biosynthesis, oxytocin, and cortisol pathways, which could be relevant as therapeutic targets. The limited brain plasticity of the adult brain is illustrated through molecular and histological analysis of the axonal growth factor, KIF4. Collectively, these results strongly suggest that further research is needed to decipher the complex genetic mechanisms that prevent the re-expression of brain plasticity-associated genes in the adult brain.

Calcineurin and mTOR inhibitors in kidney transplantation: integrative metamodeling on transplant survival and kidney function.

In our study, we examined the efficacy of mTOR (mammalian target of rapamycin) inhibitors, specifically rapamycin (Rap), compared to calcineurin inhibitors (CNIs) in kidney transplantation. By conducting a comprehensive search across reputable databases (EMBASE, Scopus, PubMed, Cochrane, and Crossref), we gathered data for a six-month post-transplantation period. Our analysis revealed that mTOR inhibitor administration resulted in improved glomerular filtration rate (GFR) and serum creatinine levels. However, it is important to note that the mTOR inhibitor group had a higher incidence of acute rejection after biopsy. Through molecular modeling, we observed that Rap exhibited a superior binding affinity for mTOR compared to CNIs' binding to calcineurin, probably contributing to the transplant rejection. Our meta-analysis supports the cautious use of an optimal mTOR inhibitor in conjunction with careful consideration of clinical features when minimizing CNIs early in the transplantation process. This is because mTOR inhibitors have complementary mechanisms of action, a low nephrotoxicity profile, and favorable outcomes in serum creatinine and GFR, which contribute to improved transplant survival.

How to make DNA data storage more applicable.

The storage of digital data is becoming a worldwide problem. DNA has been recognized as a biological solution due to its ability to store genetic information without alteration over long periods. The first demonstrations of high-capacity long-lasting DNA digital data storage have been shown. However, high storage costs and slow retrieval of the data must be overcome to make DNA data storage more applicable and marketable. Herein, we discuss the issues and recent advances in DNA data storage methods and highlight pathways to make this technology more applicable to real-world digital data storage. We envision that a combination of molecular biology, nanotechnology, novel polymers, electronics, and automation with systematic development will allow DNA data storage sufficient for everyday use.

Targeting antimalarial metabolites from the actinomycetes associated with the Red Sea sponge Callyspongia siphonella using a metabolomic method.

Malaria is a persistent illness that is still a public health issue. On the other hand, marine organisms are considered a rich source of anti­infective drugs and other medically significant compounds. Herein, we reported the isolation of the actinomycete associated with the Red Sea sponge Callyspongia siphonella. Using "one strain many compounds" (OSMAC) approach, a suitable strain was identified and then sub-cultured in three different media (M1, ISP2 and OLIGO). The extracts were evaluated for their in-vitro antimalarial activity against Plasmodium falciparum strain and subsequently analyzed by Liquid chromatography coupled with high-resolution mass spectrometry (LC-HR-MS). In addition, MetaboAnalyst 5.0 was used to statistically analyze the LC-MS data. Finally, Molecular docking was carried out for the dereplicated metabolites against lysyl-tRNA synthetase (PfKRS1). The phylogenetic study of the 16S rRNA sequence of the actinomycete isolate revealed its affiliation to Streptomyces genus. Antimalarial screening revealed that ISP2 media is the most active against Plasmodium falciparum strain. Based on LC-HR-MS based metabolomics and multivariate analyses, the static cultures of the media, ISP2 (ISP2-S) and M1 (M1-S), are the optimal media for metabolites production. OPLS-DA suggested that quinone derivatives are abundant in the extracts with the highest antimalarial activity. Fifteen compounds were identified where eight of these metabolites were correlated to the observed antimalarial activity of the active extracts. According to molecular docking experiments, saframycin Y3 and juglomycin E showed the greatest binding energy scores (-6.2 and -5.13) to lysyl-tRNA synthetase (PfKRS1), respectively. Using metabolomics and molecular docking investigation, the quinones, saframycin Y3 (5) and juglomycin E (1) were identified as promising antimalarial therapeutic candidates. Our approach can be used as a first evaluation stage in natural product drug development, facilitating the separation of chosen metabolites, particularly biologically active ones.

Metadata integrity in bioinformatics: Bridging the gap between data and knowledge.

In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.

Endothelial barrier dysfunction in systemic inflammation is mediated by soluble VE-cadherin interfering VE-PTP signaling.

Breakdown of endothelial barrier integrity determines organ dysfunction and outcome of patients with sepsis. Increased levels of soluble vascular endothelial (VE)-cadherin fragments (sVE-cadherin) have previously been linked with inflammation-induced loss of endothelial barrier function. We provide evidence for a causative role of sVE-cadherin to induce loss of endothelial barrier function. In patients with sepsis, sVE-cadherin levels were associated with organ dysfunction and the need for volume resuscitation. Similarly, LPS-induced systemic inflammation in rats with microvascular dysfunction was paralleled by augmented sVE-cadherin levels. Newly generated recombinant human sVE-cadherin (extracellular domains EC1-5) induced loss of endothelial barrier function in both human microvascular endothelial cells in vitro and in rat mesenteric microvessels in vivo and reduced microcirculatory flow. sVE-cadherinEC1-5 disturbed VE-cadherin-mediated adhesion and perturbed VE-protein tyrosine phosphatase (VE-PTP)/VE-cadherin interaction resulting in RhoGEF1-mediated RhoA activation. VE-PTP inhibitor AKB9778 and Rho-kinase inhibitor Y27632 blunted all sVE-cadherinEC1-5-induced effects, which uncovers a pathophysiological role of sVE-cadherin via dysbalanced VE-PTP/RhoA signaling.

Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning.

Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.

PRO-Simat: Protein network simulation and design tool.

PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength, duration and pathway of the protein interactions on the website. Furthermore, the user can efficiently edit and analyse the effect of network modifications and engineering experiments. In case studies, applications of PRO-Simat are demonstrated: (i) understanding mutually exclusive differentiation pathways in Bacillus subtilis, (ii) making Vaccinia virus oncolytic by switching on its viral replication mainly in cancer cells and triggering cancer cell apoptosis and (iii) optogenetic control of nucleotide processing protein networks to operate DNA storage. Multilevel communication between components is critical for efficient network switching, as demonstrated by a general census on prokaryotic and eukaryotic networks and comparing design with synthetic networks using PRO-Simat. The tool is available at https://prosimat.heinzelab.de/ as a web-based query server.

Integrated analysis of SR-like protein kinases Sky1 and Sky2 links signaling networks with transcriptional regulation in Candida albicans.

Fungal infections are a major global health burden where Candida albicans is among the most common fungal pathogen in humans and is a common cause of invasive candidiasis. Fungal phenotypes, such as those related to morphology, proliferation and virulence are mainly driven by gene expression, which is primarily regulated by kinase signaling cascades. Serine-arginine (SR) protein kinases are highly conserved among eukaryotes and are involved in major transcriptional processes in human and S. cerevisiae. Candida albicans harbors two SR protein kinases, while Sky2 is important for metabolic adaptation, Sky1 has similar functions as in S. cerevisiae. To investigate the role of these SR kinases for the regulation of transcriptional responses in C. albicans, we performed RNA sequencing of sky1Δ and sky2Δ and integrated a comprehensive phosphoproteome dataset of these mutants. Using a Systems Biology approach, we study transcriptional regulation in the context of kinase signaling networks. Transcriptomic enrichment analysis indicates that pathways involved in the regulation of gene expression are downregulated and mitochondrial processes are upregulated in sky1Δ. In sky2Δ, primarily metabolic processes are affected, especially for arginine, and we observed that arginine-induced hyphae formation is impaired in sky2Δ. In addition, our analysis identifies several transcription factors as potential drivers of the transcriptional response. Among these, a core set is shared between both kinase knockouts, but it appears to regulate different subsets of target genes. To elucidate these diverse regulatory patterns, we created network modules by integrating the data of site-specific protein phosphorylation and gene expression with kinase-substrate predictions and protein-protein interactions. These integrated signaling modules reveal shared parts but also highlight specific patterns characteristic for each kinase. Interestingly, the modules contain many proteins involved in fungal morphogenesis and stress response. Accordingly, experimental phenotyping shows a higher resistance to Hygromycin B for sky1Δ. Thus, our study demonstrates that a combination of computational approaches with integration of experimental data can offer a new systems biological perspective on the complex network of signaling and transcription. With that, the investigation of the interface between signaling and transcriptional regulation in C. albicans provides a deeper insight into how cellular mechanisms can shape the phenotype.

Simplified urinary steroid profiling by LC-MS as diagnostic tool for malignancy in adrenocortical tumors.

OBJECTIVES: Preoperative identification of malignant adrenal tumors is challenging. 24-h urinary steroid profiling by LC-MS/MS and machine learning has demonstrated high diagnostic power, but the unavailability of bioinformatic models for public use has limited its routine application. We here aimed to increase usability with a novel classification model for the differentiation of adrenocortical adenoma (ACA) and adrenocortical carcinoma (ACC). METHODS: Eleven steroids (5-pregnenetriol, dehydroepiandrosterone, cortisone, cortisol, α-cortolone, tetrahydro-11-deoxycortisol, etiocholanolone, pregnenolone, pregnanetriol, pregnanediol, and 5-pregnenediol) were quantified by LC-MS/MS in 24-h urine samples from 352 patients with adrenal tumor (281 ACA, 71 ACC). Random forest modelling and decision tree algorithms were applied in training (n = 188) and test sets (n = 80) and independently validated in 84 patients with paired 24-h and spot urine. RESULTS: After examining different models, a decision tree using excretions of only 5-pregnenetriol and tetrahydro-11-deoxycortisol classified three groups with low, intermediate, and high risk for malignancy. 148/217 ACA were classified as being at low, 67 intermediate, and 2 high risk of malignancy. Conversely, none of the ACC demonstrated a low-risk profile leading to a negative predictive value of 100% for malignancy. In the independent validation cohort, the negative predictive value was again 100% in both 24-h urine and spot urine with a positive predictive value of 87.5% and 86.7%, respectively. CONCLUSIONS: This simplified LC-MS/MS-based classification model using 24-h-urine provided excellent results for exclusion of ACC and can help to avoid unnecessary surgeries. Analysis of spot urine led to similarly satisfactory results suggesting that cumbersome 24-h urine collection might be dispensable after future validation.

DNA storage-from natural biology to synthetic biology.

Natural DNA storage allows cellular differentiation, evolution, the growth of our children and controls all our ecosystems. Here, we discuss the fundamental aspects of DNA storage and recent advances in this field, with special emphasis on natural processes and solutions that can be exploited. We point out new ways of efficient DNA and nucleotide storage that are inspired by nature. Within a few years DNA-based information storage may become an attractive and natural complementation to current electronic data storage systems. We discuss rapid and directed access (e.g. DNA elements such as promotors, enhancers), regulatory signals and modulation (e.g. lncRNA) as well as integrated high-density storage and processing modules (e.g. chromosomal territories). There is pragmatic DNA storage for use in biotechnology and human genetics. We examine DNA storage as an approach for synthetic biology (e.g. light-controlled nucleotide processing enzymes). The natural polymers of DNA and RNA offer much for direct storage operations (read-in, read-out, access control). The inbuilt parallelism (many molecules at many places working at the same time) is important for fast processing of information. Using biology concepts from chromosomal storage, nucleic acid processing as well as polymer material sciences such as electronical effects in enzymes, graphene, nanocellulose up to DNA macramé , DNA wires and DNA-based aptamer field effect transistors will open up new applications gradually replacing classical information storage methods in ever more areas over time (decades).

Minimal Collagen-Binding Epitope of Glycoprotein VI in Human and Mouse Platelets.

Glycoprotein VI (GPVI) is a platelet-specific receptor for collagen and fibrin, regulating important platelet functions such as platelet adhesion and thrombus growth. Although the blockade of GPVI function is widely recognized as a potent anti-thrombotic approach, there are limited studies focused on site-specific targeting of GPVI. Using computational modeling and bioinformatics, we analyzed collagen- and CRP-binding surfaces of GPVI monomers and dimers, and compared the interacting surfaces with other mammalian GPVI isoforms. We could predict a minimal collagen-binding epitope of GPVI dimer and designed an EA-20 antibody that recognizes a linear epitope of this surface. Using platelets and whole blood samples donated from wild-type and humanized GPVI transgenic mice and also humans, our experimental results show that the EA-20 antibody inhibits platelet adhesion and aggregation in response to collagen and CRP, but not to fibrin. The EA-20 antibody also prevents thrombus formation in whole blood, on the collagen-coated surface, in arterial flow conditions. We also show that EA-20 does not influence GPVI clustering or receptor shedding. Therefore, we propose that blockade of this minimal collagen-binding epitope of GPVI with the EA-20 antibody could represent a new anti-thrombotic approach by inhibiting specific interactions between GPVI and the collagen matrix.

Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis.

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell-cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.

In silico designed microtubule-stabilizer drugs against tauopathy in Alzheimer's disease.

Microtubules are the main building blocks of the cytoskeleton that maintain the shape of the cell. Microtubule-associated proteins, such as Tau protein, facilitate their plasticity in cells. Highly phosphorylated Tau has weak affinity to microtubule and, hence, high probability of aggregation into neurofibrillary tangles (tauopathy). Alzheimer's disease evolves when Tau proteins are abnormally phosphorylated. To prevent tauopathy in Alzheimer's disease, we designed drugs de novo targeting them in silico to the phosphorylated Tau-microtubule complexes. Our molecular docking (AutoDock, MOE, GOLD) and molecular dynamics (GROMACS, 2019.6) simulation results revealed compound 23 (C12H28N4O5) as a potential drug candidate, since it can bind (-11.1 kcal/mol by AutoDock) and fix not only phosphorylated Tau on the surface of microtubules, but also prevent their aggregation into bundles. In addition, compound 23 has shown its ability to de-bundle already grouped phosphorylated peptides into single pieces.Communicated by Ramaswamy H. Sarma.

The ß-cell primary cilium is an autonomous Ca2+ compartment for paracrine GABA signaling.

The primary cilium is an organelle present in most adult mammalian cells that is considered as an antenna for sensing the local microenvironment. Here, we use intact mouse pancreatic islets of Langerhans to investigate signaling properties of the primary cilium in insulin-secreting ß-cells. We find that GABAB1 receptors are strongly enriched at the base of the cilium, but are mobilized to more distal locations upon agonist binding. Using cilia-targeted Ca2+ indicators, we find that activation of GABAB1 receptors induces selective Ca2+ influx into primary cilia through a mechanism that requires voltage-dependent Ca2+ channel activation. Islet ß-cells utilize cytosolic Ca2+ increases as the main trigger for insulin secretion, yet we find that increases in cytosolic Ca2+ fail to propagate into the cilium, and that this isolation is largely due to enhanced Ca2+ extrusion in the cilium. Our work reveals local GABA action on primary cilia that involves Ca2+ influx and depends on restricted Ca2+ diffusion between the cilium and cytosol.

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/.