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.

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.

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.

The release of host-derived antibodies bound to the variant surface glycoprotein (VSG) of Trypanosoma brucei cannot be explained by pH-dependent conformational changes of the VSG dimer.

Background: Trypanosoma brucei is a protozoan parasite that evades the mammalian host's adaptive immune response by antigenic variation of the highly immunogenic variant surface glycoprotein (VSG). VSGs form a dense surface coat that is constantly recycled through the endosomal system. Bound antibodies are separated in the endosome from the VSG and destroyed in the lysosome. For VSGs it has been hypothesized that pH-dependent structural changes of the VSG could occur in the more acidic environment of the endosome and hence, facilitate the separation of the antibody from the VSG. Methods: We used size exclusion chromatography, where molecules are separated according to their hydrodynamic radius to see if the VSG is present as a homodimer at both pH values. To gain information about the structural integrity of the protein we used circular dichroism spectroscopy by exposing the VSG in solution to a mixture of right- and left-circularly polarized light and analysing the absorbed UV spectra. Evaluation of protein stability and molecular dynamics simulations at different pH values was performed using different computational methods. Results: We show, for an A2-type VSG, that the dimer size is only slightly larger at pH 5.2 than at pH 7.4. Moreover, the dimer was marginally more stable at lower pH due to the higher affinity (ΔG = 353.37 kcal/mol) between the monomers. Due to the larger size, the predicted epitopes were more exposed to the solvent at low pH. Moderate conformational changes (ΔRMSD = 0.35 nm) in VSG were detected between the dimers at pH 5.2 and pH 7.4 in molecular dynamics simulations, and no significant differences in the protein secondary structure were observed by circular dichroism spectroscopy. Conclusions: Thus, the dissociation of anti-VSG-antibodies in endosomes cannot be explained by changes in pH.

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/.