DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis.

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. This study introduces DREAMER, a novel network-based method for exploring the mechanisms underlying ADRs and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes.

Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

Robust, credible, and interpretable AI-based histopathological prostate cancer grading.

Background: Prostate cancer (PCa) is among the most common cancers in men and its diagnosis requires the histopathological evaluation of biopsies by human experts. While several recent artificial intelligence-based (AI) approaches have reached human expert-level PCa grading, they often display significantly reduced performance on external datasets. This reduced performance can be caused by variations in sample preparation, for instance the staining protocol, section thickness, or scanner used. Another limiting factor of contemporary AI-based PCa grading is the prediction of ISUP grades, which leads to the perpetuation of human annotation errors. Methods: We developed the prostate cancer aggressiveness index (PCAI), an AI-based PCa detection and grading framework that is trained on objective patient outcome, rather than subjective ISUP grades. We designed PCAI as a clinical application, containing algorithmic modules that offer robustness to data variation, medical interpretability, and a measure of prediction confidence. To train and evaluate PCAI, we generated a multicentric, retrospective, observational trial consisting of six cohorts with 25,591 patients, 83,864 images, and 5 years of median follow-up from 5 different centers and 3 countries. This includes a high-variance dataset of 8,157 patients and 28,236 images with variations in sample thickness, staining protocol, and scanner, allowing for the systematic evaluation and optimization of model robustness to data variation. The performance of PCAI was assessed on three external test cohorts from two countries, comprising 2,255 patients and 9,437 images. Findings: Using our high-variance datasets, we show how differences in sample processing, particularly slide thickness and staining time, significantly reduce the performance of AI-based PCa grading by up to 6.2 percentage points in the concordance index (C-index). We show how a select set of algorithmic improvements, including domain adversarial training, conferred robustness to data variation, interpretability, and a measure of credibility to PCAI. These changes lead to significant prediction improvement across two biopsy cohorts and one TMA cohort, systematically exceeding expert ISUP grading in C-index and AUROC by up to 22 percentage points. Interpretation: Data variation poses serious risks for AI-based histopathological PCa grading, even when models are trained on large datasets. Algorithmic improvements for model robustness, interpretability, credibility, and training on high-variance data as well as outcome-based severity prediction gives rise to robust models with above ISUP-level PCa grading performance.

Heat-stress-induced ROS in maize silks cause late pollen tube growth arrest and sterility.

The reproductive phase of plants is highly sensitive to ambient temperature stresses. To investigate sensitivity of female reproductive organs in grass crops during the pollination phase, we exposed the elongated stigma (silk) of maize to ambient environment at the silking stage. Moderate heat stress causes cell death of silk hair cells but did not affect early pollen tube growth inside the silk. Late pollen tube growth arrest was observed, leading to sterility. Heat stress causes elevated levels of reactive oxygen species (ROS) in silks, whose levels can be reduced by scavengers partly restoring pollen tube growth and fertility. A number of biological processes including hydrogen peroxide catabolic processes and bHLH transcription factor genes are downregulated by heat stress, while some NAC transcription factor genes are strongly upregulated. In conclusion, this study now provides a basis to select genes for engineering heat-stress-tolerant grass crops during the pollination phase.

Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility.

Genomic data holds huge potential for medical progress but requires strict safety measures due to its sensitive nature to comply with data protection laws. This conflict is especially pronounced in genome-wide association studies (GWAS) which rely on vast amounts of genomic data to improve medical diagnoses. To ensure both their benefits and sufficient data security, we propose a federated approach in combination with privacy-enhancing technologies utilising the findings from a systematic review on federated learning and legal regulations in general and applying these to GWAS.

Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation.

BACKGROUND: Central collection of distributed medical patient data is problematic due to strict privacy regulations. Especially in clinical environments, such as clinical time-to-event studies, large sample sizes are critical but usually not available at a single institution. It has been shown recently that federated learning, combined with privacy-enhancing technologies, is an excellent and privacy-preserving alternative to data sharing. OBJECTIVE: This study aims to develop and validate a privacy-preserving, federated survival support vector machine (SVM) and make it accessible for researchers to perform cross-institutional time-to-event analyses. METHODS: We extended the survival SVM algorithm to be applicable in federated environments. We further implemented it as a FeatureCloud app, enabling it to run in the federated infrastructure provided by the FeatureCloud platform. Finally, we evaluated our algorithm on 3 benchmark data sets, a large sample size synthetic data set, and a real-world microbiome data set and compared the results to the corresponding central method. RESULTS: Our federated survival SVM produces highly similar results to the centralized model on all data sets. The maximal difference between the model weights of the central model and the federated model was only 0.001, and the mean difference over all data sets was 0.0002. We further show that by including more data in the analysis through federated learning, predictions are more accurate even in the presence of site-dependent batch effects. CONCLUSIONS: The federated survival SVM extends the palette of federated time-to-event analysis methods by a robust machine learning approach. To our knowledge, the implemented FeatureCloud app is the first publicly available implementation of a federated survival SVM, is freely accessible for all kinds of researchers, and can be directly used within the FeatureCloud platform.

Ablation of Vitamin D Signaling in Cardiomyocytes Leads to Functional Impairment and Stimulation of Pro-Inflammatory and Pro-Fibrotic Gene Regulatory Networks in a Left Ventricular Hypertrophy Model in Mice.

The association between vitamin D deficiency and cardiovascular disease remains a controversial issue. This study aimed to further elucidate the role of vitamin D signaling in the development of left ventricular (LV) hypertrophy and dysfunction. To ablate the vitamin D receptor (VDR) specifically in cardiomyocytes, VDRfl/fl mice were crossed with Mlcv2-Cre mice. To induce LV hypertrophy experimentally by increasing cardiac afterload, transverse aortic constriction (TAC) was employed. Sham or TAC surgery was performed in 4-month-old, male, wild-type, VDRfl/fl, Mlcv2-Cre, and cardiomyocyte-specific VDR knockout (VDRCM-KO) mice. As expected, TAC induced profound LV hypertrophy and dysfunction, evidenced by echocardiography, aortic and cardiac catheterization, cardiac histology, and LV expression profiling 4 weeks post-surgery. Sham-operated mice showed no differences between genotypes. However, TAC VDRCM-KO mice, while having comparable cardiomyocyte size and LV fibrosis to TAC VDRfl/fl controls, exhibited reduced fractional shortening and ejection fraction as measured by echocardiography. Spatial transcriptomics of heart cryosections revealed more pronounced pro-inflammatory and pro-fibrotic gene regulatory networks in the stressed cardiac tissue niches of TAC VDRCM-KO compared to VDRfl/fl mice. Hence, our study supports the notion that vitamin D signaling in cardiomyocytes plays a protective role in the stressed heart.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Single-Cell Multi-Omics Map of Cell Type-Specific Mechanistic Drivers of Multiple Sclerosis Lesions.

BACKGROUND AND OBJECTIVES: In progressive multiple sclerosis (MS), compartmentalized inflammation plays a pivotal role in the complex pathology of tissue damage. The interplay between epigenetic regulation, transcriptional modifications, and location-specific alterations within white matter (WM) lesions at the single-cell level remains underexplored. METHODS: We examined intracellular and intercellular pathways in the MS brain WM using a novel dataset obtained by integrated single-cell multi-omics techniques from 3 active lesions, 3 chronic active lesions, 3 remyelinating lesions, and 3 control WM of 6 patients with progressive MS and 3 non-neurologic controls. Single-nucleus RNA-seq and ATAC-seq were combined and additionally enriched with newly conducted spatial transcriptomics from 1 chronic active lesion. Functional gene modules were then validated in our previously published bulk tissue transcriptome data obtained from 73 WM lesions of patients with progressive MS and 25 WM of non-neurologic disease controls. RESULTS: Our analysis uncovered an MS-specific oligodendrocyte genetic signature influenced by the KLF/SP gene family. This modulation has potential associations with the autocrine iron uptake signaling observed in transcripts of transferrin and its receptor LRP2. In addition, an inflammatory profile emerged within these oligodendrocytes. We observed unique cellular endophenotypes both at the periphery and within the chronic active lesion. These include a distinct metabolic astrocyte phenotype, the importance of FGF signaling among astrocytes and neurons, and a notable enrichment of mitochondrial genes at the lesion edge populated predominantly by astrocytes. Our study also identified B-cell coexpression networks indicating different functional B-cell subsets with differential location and specific tendencies toward certain lesion types. DISCUSSION: The use of single-cell multi-omics has offered a detailed perspective into the cellular dynamics and interactions in MS. These nuanced findings might pave the way for deeper insights into lesion pathogenesis in progressive MS.

Inference of differential gene regulatory networks using boosted differential trees.

Summary: Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses. Availability and implementation: BoostDiff is available at https://github.com/scibiome/boostdiff_inference.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2.

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that-combined with sequence alignments and BLASTp-they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions.

RNA sequencing offers unique insights into transcriptome diversity, and a plethora of tools have been developed to analyze alternative splicing. One important task is to detect changes in the relative transcript abundance in differential transcript usage (DTU) analysis. The choice of the right analysis tool is non-trivial and depends on experimental factors such as the availability of single- or paired-end and bulk or single-cell data. To help users select the most promising tool for their task, we performed a comprehensive benchmark of DTU detection tools. We cover a wide array of experimental settings, using simulated bulk and single-cell RNA-seq data as well as real transcriptomics datasets, including time-series data. Our results suggest that DEXSeq, edgeR, and LimmaDS are better choices for paired-end data, while DSGseq and DEXSeq can be used for single-end data. In single-cell simulation settings, we showed that satuRn performs better than DTUrtle. In addition, we showed that Spycone is optimal for time series DTU/IS analysis based on the evidence provided using GO terms enrichment analysis.

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling.

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis.

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2.

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we argue that RNA-seq should be considered a routine diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers vital insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 240 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that - combined with sequence alignments and pBLAST - they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs)1-3. Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL is the first application that demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

New Enhancing MRI Lesions Associate with IL-17, Neutrophil Degranulation and Integrin Microparticles: Multi-Omics Combined with Frequent MRI in Multiple Sclerosis.

BACKGROUND: Blood-barrier (BBB) breakdown and active inflammation are hallmarks of relapsing multiple sclerosis (RMS), but the molecular events contributing to the development of new lesions are not well explored. Leaky endothelial junctions are associated with increased production of endothelial-derived extracellular microvesicles (EVs) and result in the entry of circulating immune cells into the brain. MRI with intravenous gadolinium (Gd) can visualize acute blood-barrier disruption as the initial event of the evolution of new lesions. METHODS: Here, weekly MRI with Gd was combined with proteomics, multiplex immunoassay, and endothelial stress-optimized EV array to identify early markers related to BBB disruption. Five patients with RMS with no disease-modifying treatment were monitored weekly using high-resolution 3T MRI scanning with intravenous gadolinium (Gd) for 8 weeks. Patients were then divided into three groups (low, medium, or high MRI activity) defined by the number of new, total, and maximally enhancing Gd-enhancing lesions and the number of new FLAIR lesions. Plasma samples taken at each MRI were analyzed for protein biomarkers of inflammation by quantitative proteomics, and cytokines using multiplex immunoassays. EVs were characterized with an optimized endothelial stress EV array based on exosome surface protein markers for the detection of soluble secreted EVs. RESULTS: Proteomics analysis of plasma yielded quantitative information on 208 proteins at each patient time point (n = 40). We observed the highest number of unique dysregulated proteins (DEPs) and the highest functional enrichment in the low vs. high MRI activity comparison. Complement activation and complement/coagulation cascade were also strongly overrepresented in the low vs. high MRI activity comparison. Activation of the alternative complement pathway, pathways of blood coagulation, extracellular matrix organization, and the regulation of TLR and IGF transport were unique for the low vs. high MRI activity comparison as well, with these pathways being overrepresented in the patient with high MRI activity. Principal component analysis indicated the individuality of plasma profiles in patients. IL-17 was upregulated at all time points during 8 weeks in patients with high vs. low MRI activity. Hierarchical clustering of soluble markers in the plasma indicated that all four MRI outcomes clustered together with IL-17, IL-12p70, and IL-1ß. MRI outcomes also showed clustering with EV markers CD62E/P, MIC A/B, ICAM-1, and CD42A. The combined cluster of these cytokines, EV markers, and MRI outcomes clustered also with IL-12p40 and IL-7. All four MRI outcomes correlated positively with levels of IL-17 (p < 0.001, respectively), and EV-ICAM-1 (p < 0.0003, respectively). IL-1ß levels positively correlated with the number of new Gd-enhancing lesions (p < 0.01), new FLAIR lesions (p < 0.001), and total number of Gd-enhancing lesions (p < 0.05). IL-6 levels positively correlated with the number of new FLAIR lesions (p < 0.05). Random Forests and linear mixed models identified IL-17, CCL17/TARC, CCL3/MIP-1α, and TNF-α as composite biomarkers predicting new lesion evolution. CONCLUSIONS: Combination of serial frequent MRI with proteome, neuroinflammation markers, and protein array data of EVs enabled assessment of temporal changes in inflammation and endothelial dysfunction in RMS related to the evolution of new and enhancing lesions. Particularly, the Th17 pathway and IL-1ß clustered and correlated with new lesions and Gd enhancement, indicating their importance in BBB disruption and initiating acute brain inflammation in MS. In addition to the Th17 pathway, abundant protein changes between MRI activity groups suggested the role of EVs and the coagulation system along with innate immune responses including acute phase proteins, complement components, and neutrophil degranulation.

Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet.

MOTIVATION: The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem. RESULTS: To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for eight potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. AVAILABILITY AND IMPLEMENTATION: SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD.

Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ .