Single-cell network biology characterizes cell-type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell-type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular. Several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment). Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals mechanisms on cell-type gene dyregulation in AD.

Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.

Finding the “switch” in platelet activation Prediction of key mediators involved in platelet hyperreactivity using a novel network biology approach

The healthy endothelium controls platelet activity through release of prostaglandin I2 (PGI2) and nitric oxide. The loss of this natural brake on platelet activity can cause platelets to become hyperreactive. PGI2 attenuates platelet activation by adenosine diphosphate (ADP) through stimulation of cyclic adenosine monophosphate (cAMP) production and subsequent phosphorylation changes by protein kinase A (PKA). We hypothesize that proteins/processes involved in platelet hyperactivity downstream of the cAMP-PKA pathway can serve as a “switch” in platelet activation and inhibition. We designed a network biology approach to explore the entangled platelet signaling pathways downstream of PGI2 and ADP. The STRING database was used to build a protein-protein interaction network from proteins of interest in which we integrate a quantitative platelet proteome dataset with pathway information, relative RNA expression of hematopoietic cells, the likelihood of the proteins being phosphorylated by PKA, and drug-target information from DrugBank in a biological network. We distilled 30 proteins from existing phosphoproteomics datasets (PXD000242 and PXD001189) that putatively can be “turned on” after ADP-mediated platelet activation and subsequently switched “off” after platelet inhibition with iloprost. Enrichment analysis revealed biological processes related to vesicle secretion and cytoskeletal reorganization to be overrepresented coinciding with topological clusters in the network. Our method highlights novel proteins related to vesicle transport, platelet shape change, and small GTPases as potential switch proteins in platelet activation and inhibition. Our novel approach demonstrates the benefit of data integration by combining tools and datasets and visualization to obtain a more complete picture of complex molecular mechanisms.