Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Interactome networks and human disease

From bench to bedside

Glial cells are abundant in the CNS and are essential for brain development and homeostasis. These cells also regulate tissue recovery after injury and their dysfunction is a possible contributing factor to neurodegenerative and psychiatric disease. Recent evidence suggests that microglia, which are also the brain's major resident immune cells, provide disease-modifying regulation of the other major glial populations, namely astrocytes and oligodendrocytes. In addition, peripheral immune cells entering the CNS after injury and in disease may directly affect microglial, astrocyte and oligodendrocyte function, suggesting an integrated network of immune cell-glial cell communication.

Immune cell regulation of glia during CNS injury and disease.

While it is now widely accepted that host inflammatory responses contribute to lung injury, the pathways that drive severity and distinguish coronavirus disease 2019 (COVID-19) from other viral lung diseases remain poorly characterized. We analyzed plasma samples from 471 hospitalized patients recruited through the prospective multicenter ISARIC4C study and 39 outpatients with mild disease, enabling extensive characterization of responses across a full spectrum of COVID-19 severity. Progressive elevation of levels of numerous inflammatory cytokines and chemokines (including IL-6, CXCL10, and GM-CSF) were associated with severity and accompanied by elevated markers of endothelial injury and thrombosis. Principal component and network analyses demonstrated central roles for IL-6 and GM-CSF in COVID-19 pathogenesis. Comparing these profiles to archived samples from patients with fatal influenza, IL-6 was equally elevated in both conditions whereas GM-CSF was prominent only in COVID-19. These findings further identify the key inflammatory, thrombotic, and vascular factors that characterize and distinguish severe and fatal COVID-19.

Inflammatory profiles across the spectrum of disease reveal a distinct role for GM-CSF in severe COVID-19.

Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial. Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have provided largely negative results, so far. Here, the authors present a machine learning framework that quantifies potential associations between the pathology of AD severity and gene-based molecular mechanisms to enable drug repurposing.

/pdf/machine-learning-identifies-candidates-for-drug-repurposing-1iro28yr0w.pdf

Machine learning identifies candidates for drug repurposing in Alzheimer's disease.

Growing recognition of the pivotal role microglia play in neurodegenerative and neuroinflammatory disorders has accentuated the need to characterize their function in health and disease. Studies in mouse have applied transcriptome-wide profiling of microglia to reveal key features of microglial ontogeny, functional profile, and phenotypic diversity. While similar, human microglia exhibit clear differences to their mouse counterparts, underlining the need to develop a better understanding of the human microglial profile. On examining published microglia gene signatures, limited consistency was observed between studies. Hence, we sought to derive a core microglia signature of the human central nervous system (CNS), through a comprehensive analysis of existing transcriptomic datasets. Nine datasets derived from cells and tissues, isolated from various regions of the CNS across numerous donors, were subjected independently to an unbiased correlation network analysis. From each dataset, a list of coexpressing genes corresponding to microglia was identified, with 249 genes highly conserved between them. This core signature included known microglial markers, and compared with other signatures provides a gene set specific to microglia in the context of the CNS. The utility of this signature was demonstrated by its use in detecting qualitative and quantitative region-specific alterations in aging and Alzheimer's disease. These analyses highlighted the reactive response of microglia in vulnerable brain regions such as the entorhinal cortex and hippocampus, additionally implicating pathways associated with disease progression. We believe this resource and the analyses described here, will support further investigations to the contribution of human microglia in CNS health and disease.

/pdf/a-core-transcriptional-signature-of-human-microglia-3r2qyhz59g.pdf

A core transcriptional signature of human microglia: Derivation and utility in describing region‐dependent alterations associated with Alzheimer's disease

Quantitative and qualitative data derived from the analysis of genomes, genes, proteins or metabolites from tissue or cells are currently generated in huge volumes during biomedical research. Graphia is an open-source platform created for the graph-based analysis of such complex data, e.g. transcriptomics, proteomics, genomics data. The software imports data already defined as a network or a similarity matrix and is designed to rapidly visualise very large graphs in 2D or 3D space, providing a wide range of functionality for graph exploration. An extensive range of analysis algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are also available. Graphia’s core is extensible through the deployment of plugins, supporting rapid development of additional computational analyses and features necessary for a given analysis task or data source. A plugin for correlation network analysis is distributed with the core application, to support the generation of correlation graphs from any tabular matrix of continuous or discrete values. This provides a powerful analysis solution for the interpretation of high-dimensional data from many sources. Several use cases of Graphia are described, to showcase its wide range of applications. Graphia runs on all major desktop operating systems and is freely available to download from https://graphia.app/.

https://biorxiv.org/content/10.1101/2020.09.02.279349v1.full.pdf

Graphia: A platform for the graph-based visualisation and analysis of complex data

Cilia are complex microtubule-based organelles essential to a range of processes associated with embryogenesis and tissue homeostasis. Mutations in components of these organelles or those involved in their assembly may result in a diverse set of diseases collectively known as ciliopathies. Accordingly, many cilia-associated proteins have been described, while those distinguishing cilia subtypes are poorly defined. Here we set out to define genes associated with motile cilia in humans based on their transcriptional signature. To define the signature, we performed network deconvolution of transcriptomics data derived from tissues possessing motile ciliated cell populations. For each tissue, genes coexpressed with the motile cilia-associated transcriptional factor, FOXJ1, were identified. The consensus across tissues provided a transcriptional signature of 248 genes. To validate these, we examined the literature, databases (CilDB, CentrosomeDB, CiliaCarta and SysCilia), single cell RNA-Seq data, and the localisation of mRNA and proteins in motile ciliated cells. In the case of six poorly characterised signature genes, we performed new localisation experiments on ARMC3, EFCAB6, FAM183A, MYCBPAP, RIBC2 and VWA3A. In summary, we report a set of motile cilia-associated genes that helps shape our understanding of these complex cellular organelles.

/pdf/the-transcriptional-signature-associated-with-human-motile-28f66rhvmb.pdf

The transcriptional signature associated with human motile cilia

Graphia is an open-source platform created for the graph-based analysis of the huge amounts of quantitative and qualitative data currently being generated from the study of genomes, genes, proteins metabolites and cells. Core to Graphia’s functionality is support for the calculation of correlation matrices from any tabular matrix of continuous or discrete values, whereupon the software is designed to rapidly visualise the often very large graphs that result in 2D or 3D space. Following graph construction, an extensive range of measurement algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are available, for graph exploration and analysis. Combined, these provide a powerful solution for the interpretation of high-dimensional data from many sources, or data already in the form of a network or equivalent adjacency matrix. Several use cases of Graphia are described, to showcase its wide range of applications in the analysis biological data. Graphia runs on all major desktop operating systems, is extensible through the deployment of plugins and is freely available to download from https://graphia.app/.

/pdf/graphia-a-platform-for-the-graph-based-visualisation-and-hojuiv78.pdf

Graphia: A platform for the graph-based visualisation and analysis of high dimensional data

Cilia are complex microtubule-based organelles implicated in the aetiology of numerous diseases. Accordingly, many cilia-associated proteins have been described, while those distinguishing cilia subtypes are poorly defined. Here, we characterise the gene signature associated with human motile cilia that captures both known and unknown components of this class of cilia. To define the signature, we performed network deconvolution of transcriptomics data derived from tissues possessing motile ciliated cell populations. For each tissue, genes coexpressed with the motile cilia-associated transcriptional factor, FOXJ1, were identified. The consensus across tissues provided a transcriptional signature of 248 genes. For validation, we examined the literature, databases, single cell RNA-Seq data, and the localisation of mRNA and proteins in motile ciliated cells. To validate some of the many poorly characterised genes, we performed new localisation experiments on ARMC3, EFCAB6, FAM183A, MYCBPAP, RIBC2 and VWA3A. In summary, we report a highly validated set of motile cilia-associated genes that helps shape our understanding of these complex cellular organelles. Summary This work defines a conserved transcriptional signature associated with human motile cilia, including many genes with little or no previous association with these structures. These genes were compared with existing resources and a number of poorly characterised genes validated. Graphical abstract

/pdf/the-transcriptional-signature-associated-with-human-motile-32hz46vayy.pdf

Anirudh Patir

Papers

A core transcriptional signature of human microglia: Derivation and utility in describing region‐dependent alterations associated with Alzheimer's disease

Graphia: A platform for the graph-based visualisation and analysis of complex data

The transcriptional signature associated with human motile cilia

Graphia: A platform for the graph-based visualisation and analysis of high dimensional data

The transcriptional signature associated with human motile cilia