Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators

Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data

MOTIVATION

In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs.

RESULTS

To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest.

AVAILABILITY AND IMPLEMENTATION

STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.

Synergistic activation of RARβ and RARγ nuclear receptors restores cell specialization during stem cell differentiation by hijacking RARα-controlled programs

How cells respond to different external cues to develop along defined cell lineages to form complex tissues is a major question in systems biology. Here, we investigated the potential of retinoic acid receptor (RAR)-selective synthetic agonists to activate the gene regulatory programs driving cell specialization during nervous tissue formation from embryonic carcinoma (P19) and mouse embryonic (E14) stem cells. Specifically, we found that the synergistic activation of the RARβ and RARγ by selective ligands (BMS641 or BMS961) induces cell maturation to specialized neuronal subtypes, and to astrocytes and oligodendrocyte precursors. Using RAR isotype knockout lines exposed to RAR-specific agonists, interrogated by global transcriptome landscaping and in silico modeling of transcription regulatory signal propagation, revealed major RARα-driven gene programs essential for optimal neuronal cell specialization and hijacked by the synergistic activation of the RARβ and RARγ receptors. Overall, this study provides a systems biology view of the gene programs accounting for the previously observed redundancy between RARs, paving the way toward their potential use for directing cell specialization during nervous tissue formation.

Reconstruction of regulatory network predicts transcription factors driving the dynamics of zebrafish heart regeneration

The limited regenerative capacity in mammals has serious implications for cardiac tissue damage. Meanwhile, zebrafish has a high regenerative capacity, but the regulation of the heart healing process has yet to be elucidated. The dynamic nature of cardiac regeneration requires consideration of the inherent temporal dimension of this process. Here, we conducted a systematic review to find genes that define the regenerative cell state of the zebrafish heart. We then performed an in silico temporal gene regulatory network analysis using transcriptomic data from the zebrafish heart regenerative process obtained from databases. In this analysis, the genes found in the systematic review were used to represent the final cell state of the transition process from a non-regenerative cell state to a regenerative state. We found 135 transcription factors driving the cellular state transition process during zebrafish cardiac regeneration, including Hand2, Nkx2.5, Tbx20, Fosl1, Fosb, Junb, Vdr, Wt1, and Tcf21 previously reported for playing a key role in tissue regeneration. Furthermore, we demonstrate that most regulators are activated in the first days post-injury, indicating that the transition from a non-regenerative to a regenerative state occurs promptly.

Complexity against current cancer research - are we on the wrong track?

Cancer genetics has led to major discoveries, including proto-oncogene and tumor-suppressor concepts, and cancer genomics generated concepts like driver and passenger genes, revealed tumor heterogeneity and clonal evolution. Reconstructing trajectories of tumorigenesis using spatial and single-cell genomics is possible. Patient stratification and prognostic parameters have been improved. Yet, despite these advances, successful translation into targeted therapies has been scarce and mostly limited to kinase inhibitors. Here, we argue that current cancer research may be on the wrong track, by considering cancer more as a "monogenic" disease, trying to extract common information from thousands of patients, while not properly considering complexity and individual diversity. We suggest to empower a systems cancer approach which reconstructs the information network that has been altered by the tumorigenic events, to analyze hierarchies and predict (druggable) key nodes that could interfere with/block the aberrant information transfer. We also argue that the inter-individual variability between patients of similar cohorts is too high to extract common polygenic network information from large numbers of patients and argue in favor of an individualized approach. The analysis we propose would require a structured multinational and multidisciplinary effort, in which clinicians, and cancer, developmental, cell and computational biologists together with mathematicians and informaticians develop dynamic regulatory networks which integrate the entire information transfer in and between cells and organs in (patho)physiological conditions, revealing hierarchies and available drugs to interfere with key regulators. Based on this blueprint, the altered information transfer in individual cancers could be modeled and possible targeted (combo)therapies proposed. This article is protected by copyright. All rights reserved.

A Core Transcription Regulatory Circuitry Defining Microglia Cell Identity Inferred from the Reanalysis of Multiple Human Microglia Differentiation Protocols

Microglia, the immune cells in the brain involved in both homeostasis and injury/infection control, play a predominant role in neurodegenerative diseases. In vivo studies on microglia are limited due to the requirement of surgical intervention, which can lead to the destruction of the tissues. Over the last few years, multiple protocols-presenting a variety of strategies-have described microglia differentiation issued from human pluripotent stem cells. Herein, we have reanalyzed the transcriptomes released on six different microglia differentiation protocols and revealed a consensus core of master transcription regulatory circuitry defining microglia identity. Furthermore, we have discussed the major divergencies among the studied protocols and have provided suggestions to further enhance microglia differentiation assays.

Network biology to uncover functional and structural properties of the plant immune system

In the last two decades, advances in network science have facilitated the discovery of important systems' entities in diverse biological networks. This graph-based technique has revealed numerous emergent properties of a system that enable us to understand several complex biological processes including plant immune systems. With the accumulation of multiomics data sets, the comprehensive understanding of plant-pathogen interactions can be achieved through the analyses and efficacious integration of multidimensional qualitative and quantitative relationships among the components of hosts and their microbes. This review highlights comparative network topology analyses in plant-pathogen co-expression networks and interactomes, outlines dynamic network modeling for cell-specific immune regulatory networks, and discusses the new frontiers of single-cell sequencing as well as multiomics data integration that are necessary for unraveling the intricacies of plant immune systems.

Transcriptional Regulation of Cancer Immune Checkpoints: Emerging Strategies for Immunotherapy

The study of immune evasion has gained a well-deserved eminence in cancer research by successfully developing a new class of therapeutics, immune checkpoint inhibitors, such as pembrolizumab and nivolumab, anti-PD-1 antibodies. By aiming at the immune checkpoint blockade (ICB), these new therapeutics have advanced cancer treatment with notable increases in overall survival and tumor remission. However, recent reports reveal that 40-60% of patients fail to benefit from ICB therapy due to acquired resistance or tumor relapse. This resistance may stem from increased expression of co-inhibitory immune checkpoints or alterations in the tumor microenvironment that promotes immune suppression. Because these mechanisms are poorly elucidated, the transcription factors that regulate immune checkpoints, known as "master regulators", have garnered interest. These include AP-1, IRF-1, MYC, and STAT3, which are known to regulate PD/PD-L1 and CTLA-4. Identifying these and other potential master regulators as putative therapeutic targets or biomarkers can be facilitated by mining cancer literature, public datasets, and cancer genomics resources. In this review, we describe recent advances in master regulator identification and characterization of the mechanisms underlying immune checkpoints regulation, and discuss how these master regulators of immune checkpoint molecular expression can be targeted as a form of auxiliary therapeutic strategy to complement traditional immunotherapy.

Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference

Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the synovial joints of the body. Rheumatoid arthritis fibroblast-like synoviocytes (RA FLS) are central players in the disease pathogenesis, as they are involved in the secretion of cytokines and proteolytic enzymes, exhibit invasive traits, high rate of self-proliferation and an apoptosis-resistant phenotype. We aim at characterizing transcription factors (TFs) that are master regulators in RA FLS and could potentially explain phenotypic traits. We make use of differentially expressed genes in synovial tissue from patients suffering from RA and osteoarthritis (OA) to infer a TF co-regulatory network, using dedicated software. The co-regulatory network serves as a reference to analyze microarray and single-cell RNA-seq data from isolated RA FLS. We identified five master regulators specific to RA FLS, namely BATF, POU2AF1, STAT1, LEF1 and IRF4. TF activity of the identified master regulators was also estimated with the use of two additional, independent software. The identified TFs contribute to the regulation of inflammation, proliferation and apoptosis, as indicated by the comparison of their differentially expressed target genes with hallmark molecular signatures derived from the Molecular Signatures Database (MSigDB). Our results show that TFs influence could be used to identify putative master regulators of phenotypic traits and suggest novel, druggable targets for experimental validation.

Neurocardiac regulation: from cardiac mechanisms to novel therapeutic approaches

Cardiac sympathetic overactivity is a well-established contributor to the progression of neurogenic hypertension and heart failure, yet the underlying pathophysiology remains unclear. Recent studies have highlighted the importance of acutely regulated cyclic nucleotides and their effectors in the control of intracellular calcium and exocytosis. Emerging evidence now suggests that a significant component of sympathetic overactivity and enhanced transmission may arise from impaired cyclic nucleotide signalling, resulting from compromised phosphodiesterase activity, as well as alterations in receptor-coupled G-protein activation. In this review, we address some of the key cellular and molecular pathways that contribute to sympathetic overactivity in hypertension and discuss their potential for therapeutic targeting.

Retinoic Acid-Regulated Target Genes During Development: Integrative Genomics Analysis

Retinoic acid (RA), a major natural active metabolite of vitamin A (VA) is well known to play critical roles in embryonic development. The effects of RA are mediated by nuclear receptors (RARs), which regulate the expression of gene batteries involved in cell growth and differentiation. Since the early 1990s several laboratories have focused on understanding how RA-regulated genes and RAR binding sites operate by studying the differentiation of embryonal carcinoma cells and embryonic stem cells. The development of hybridization-based microarray technology and high performance software analysis programs has allowed the characterization of thousands of RA-regulated genes. During the two last decades, publication of the genome sequence of various organisms has allowed advances in massive parallel sequencing and bioinformatics analysis of genome-wide data sets. These new generation sequencing (NGS) technologies have revolutionized the field by providing a global integrated picture of RA-regulated gene networks and the regulatory programs involved in cell fate decisions during embryonal carcinoma and embryonic stem cells differentiation. Now the challenge is to reconstruct the RA-regulated gene networks at the single cell level during the development of specialized embryonic tissues.

A comprehensive resource for retrieving, visualizing, and integrating functional genomics data

This study presents qcGenomics, a user-friendly online resource for ultrafast retrieval, visualization, and comparative analysis of tens of thousands of genomics datasets to gain new functional insight from global or focused multi-dimensional data integration.

The enormous amount of freely accessible functional genomics data is an invaluable resource for interrogating the biological function of multiple DNA-interacting players and chromatin modifications by large-scale comparative analyses. However, in practice, interrogating large collections of public data requires major efforts for (i) reprocessing available raw reads, (ii) incorporating quality assessments to exclude artefactual and low-quality data, and (iii) processing data by using high-performance computation. Here, we present qcGenomics, a user-friendly online resource for ultrafast retrieval, visualization, and comparative analysis of tens of thousands of genomics datasets to gain new functional insight from global or focused multidimensional data integration.