Reconstruction Of Gene Regulatory Networks Research Articles

Abstract PO2-01-08: Differential HOXB13 gene expression and promoter methylation analysis in breast cancer

Abstract Background: The Breast Cancer Index (BCI) is a gene expression-based signature comprising two functional biomarker panels: the Molecular Grade Index (MGI) and BCI (H/I), which is a ratio of the HOXB13 and IL17BR gene expression. Several studies have shown that BCI (H/I) is a predictive biomarker for extended adjuvant endocrine therapy benefit in hormone receptor-positive (HR+) early-stage breast cancer. Molecular mechanisms underlying endocrine responsiveness remain to be fully explored. The objective of this analysis is to evaluate the correlation between gene expression and methylation in breast cancer patients displaying different HOXB13 mRNA expression levels from The Cancer Genome Atlas (TCGA) project. Methods: Data from methylation microarrays and mRNA sequencing were examined in combination with clinical metadata from 1,095 TCGA breast cancer patients. After removing outliers, HR+ samples were ordered based on their HOXB13 expression levels and the top 10% of samples (HOXB13-high; n=95) and bottom 10% (HOXB13-low; n=95) were selected for further analysis. Differentially expressed genes (DEGs) were determined by comparing all protein-coding genes between the HOXB13-high and HOXB13-low sets, based on an absolute log-fold change above 2 and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Among the subset of samples with matching methylation data (n=60 for the HOXB13-high group and n=48 for the HOXB13-low group), hypo-methylated and hyper-methylated differentially methylated probes (DMPs) were identified based on at least 10% change of β (i.e., average promoter methylation) and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Motif and transcription factor (TF) enrichment analyses were identified at a minimum incidence of 10 and a lower boundary of 1.5 for the 95% confidence interval of the odds ratio. Results: Our analysis identified a total of 613 DEGs between the HOXB13-high and HOXB13-low groups. Gene ontology analysis of DEGs revealed a statistically significant enrichment of genes associated with PTK6 regulated cell cycle, oncogene-induced cellular senescence, and regulation of T cell activation. Analysis of protein-protein interactions of differentially expressed genes revealed differences in key biological processes including changes in cell cycle regulation, p53 pathway, and IL-17 signaling. A total of 5,295 hyper-methylated and 580 hypo-methylated DMPs were discovered when comparing HOXB13-high and HOXB13-low samples. Changes in global methylation patterns enabled classification of breast cancer samples with high or low HOXB13 expression with an accuracy of 0.90. Reconstruction of gene regulatory networks from DNA methylation and transcriptome profiles elucidated significant pairs of DMPs and differentially expressed genes. Specifically, a total of 197 differentially expressed genes were attributed to hyper-methylated DMPs, including genes involved in response to estrogen, activation of HOX genes during differentiation, and drug-mediated inhibition of CDK4/6 activity. Additionally, 8 differentially expressed genes were attributed to hypo-methylated DMPs and were associated with proteasomal protein catabolic processes. Lastly, motif analysis of transcriptional factors for the hyper-methylated DMPs revealed enrichment of binding motifs attributed to FOXA1, ESR1, XBR1, and FOXP1. Conclusion: A comprehensive and comparative analysis of mRNA expression levels and methylation patterns between TCGA breast cancer samples with high and low expression of HOXB13 revealed key signaling pathways and biological processes that may provide insights on the molecular mechanism of HOXB13 in the regulation of response to endocrine therapy in HR+ breast cancer. Citation Format: Natalia Siuliukina, Yi Zhang, Kai Treuner, Mark Pegram. Differential HOXB13 gene expression and promoter methylation analysis in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-08.

Distributed Heterogeneous Transfer Learning

Transfer learning has proved to be effective for building predictive models even in complex conditions with a low amount of available labeled data, by constructing a predictive model for a target domain also using the knowledge coming from a separate domain, called source domain. However, several existing transfer learning methods assume identical feature spaces between the source and the target domains. This assumption limits the possible real-world applications of such methods, since two separate, although related, domains could be described by totally different feature spaces. Heterogeneous transfer learning methods aim to overcome this limitation, but they usually i) make other assumptions on the features, such as requiring the same number of features, ii) are not generally able to distribute the workload over multiple computational nodes, iii) cannot work in the Positive-Unlabeled (PU) learning setting, which we also considered in this study, or iv) their applicability is limited to specific application domains, i.e., they are not general-purpose methods.In this manuscript, we present a novel distributed heterogeneous transfer learning method, implemented in Apache Spark, that overcomes all the above-mentioned limitations. Specifically, it is able to work also in the PU learning setting by resorting to a clustering-based approach, and can align totally heterogeneous feature spaces, without exploiting peculiarities of specific application domains. Moreover, our distributed approach allows us to process large source and target datasets.Our experimental evaluation was performed in three different application domains that can benefit from transfer learning approaches, namely the reconstruction of the human gene regulatory network, the prediction of cerebral stroke in hospital patients, and the prediction of customer energy consumption in power grids. The results show that the proposed approach is able to outperform 4 state-of-the-art heterogeneous transfer learning approaches and 3 baselines, and exhibits ideal performances in terms of scalability.

S100a9 might act as a modulator of the Toll-like receptor 4 transduction pathway in chronic rhinosinusitis with nasal polyps

Chronic rhinosinusitis with nasal polyp (CRSwNP) is a highly prevalent disorder characterized by persistent nasal and sinus mucosa inflammation. Despite significant morbidity and decreased quality of life, there are limited effective treatment options for such a disease. Therefore, identifying causal genes and dysregulated pathways paves the way for novel therapeutic interventions. In the current study, a three-way interaction approach was used to detect dynamic co-expression interactions involved in CRSwNP. In this approach, the internal evolution of the co-expression relation between a pair of genes (X, Y) was captured under a change in the expression profile of a third gene (Z), named the switch gene. Subsequently, the biological relevancy of the statistically significant triplets was confirmed using both gene set enrichment analysis and gene regulatory network reconstruction. Finally, the importance of identified switch genes was confirmed using a random forest model. The results suggested four dysregulated pathways in CRSwNP, including “positive regulation of intracellular signal transduction”, “arachidonic acid metabolic process”, “spermatogenesis” and “negative regulation of cellular protein metabolic process”. Additionally, the S100a9 as a switch gene together with the gene pair {Cd14, Tpd52l1} form a biologically relevant triplet. More specifically, we suggested that S100a9 might act as a potential upstream modulator in toll-like receptor 4 transduction pathway in the major CRSwNP pathologies.

Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana. A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.

BHLHE40/41 regulate microglia and peripheral macrophage responses associated with Alzheimer’s disease and other disorders of lipid-rich tissues

Genetic and experimental evidence suggests that Alzheimer’s disease (AD) risk alleles and genes may influence disease susceptibility by altering the transcriptional and cellular responses of macrophages, including microglia, to damage of lipid-rich tissues like the brain. Recently, sc/nRNA sequencing studies identified similar transcriptional activation states in subpopulations of macrophages in aging and degenerating brains and in other diseased lipid-rich tissues. We collectively refer to these subpopulations of microglia and peripheral macrophages as DLAMs. Using macrophage sc/nRNA-seq data from healthy and diseased human and mouse lipid-rich tissues, we reconstructed gene regulatory networks and identified 11 strong candidate transcriptional regulators of the DLAM response across species. Loss or reduction of two of these transcription factors, BHLHE40/41, in iPSC-derived microglia and human THP-1 macrophages as well as loss of Bhlhe40/41 in mouse microglia, resulted in increased expression of DLAM genes involved in cholesterol clearance and lysosomal processing, increased cholesterol efflux and storage, and increased lysosomal mass and degradative capacity. These findings provide targets for therapeutic modulation of macrophage/microglial function in AD and other disorders affecting lipid-rich tissues.

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

SPREd: a simulation-supervised neural network tool for gene regulatory network reconstruction.

Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd," is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g. correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA, and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold-standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step toward incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction. Data and code are available from https://github.com/iiiime/SPREd.

A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing.

Understanding gene regulatory networks (GRNs) is essential for unraveling the molecular mechanisms governing cellular behavior. With the advent of high-throughput transcriptome measurement technology, researchers have aimed to reverse engineer the biological systems, extracting gene regulatory rules from their outputs, which represented by gene expression data. Bulk RNA sequencing, a widely used method for measuring gene expression, has been employed for GRN reconstruction. However, it falls short in capturing dynamic changes in gene expression at the level of individual cells since it averages gene expression across mixed cell populations. In this review, we provide an overview of 15 GRN reconstruction tools and discuss their respective strengths and limitations, particularly in the context of single cell RNA sequencing (scRNA-seq). Recent advancements in scRNA-seq break new ground of GRN reconstruction. They offer snapshots of the individual cell transcriptomes and capturing dynamic changes. We emphasize how these technological breakthroughs have enhanced GRN reconstruction. GRN reconstructors can be classified based on their requirement for cellular trajectory, which represents a dynamical cellular process including differentiation, aging, or disease progression. Benchmarking studies support the superiority of GRN reconstructors that do not require trajectory analysis in identifying regulator-target relationships. However, methods equipped with trajectory analysis demonstrate better performance in identifying key regulatory factors. In conclusion, researchers should select a suitable GRN reconstructor based on their specific research objectives.

PlantPAN 4.0: updated database for identifying conserved non-coding sequences and exploring dynamic transcriptional regulation in plant promoters.

PlantPAN 4.0 (http://PlantPAN.itps.ncku.edu.tw/) is an integrative resource for constructing transcriptional regulatory networks for diverse plant species. In this release, the gene annotation and promoter sequences were expanded to cover 115 species. PlantPAN 4.0 can help users characterize the evolutionary differences and similarities among cis-regulatory elements; furthermore, this system can now help in identification of conserved non-coding sequences among homologous genes. The updated transcription factor binding site repository contains 3428 nonredundant matrices for 18305 transcription factors; this expansion helps in exploration of combinational and nucleotide variants of cis-regulatory elements in conserved non-coding sequences. Additionally, the genomic landscapes of regulatory factors were manually updated, and ChIP-seq data sets derived from a single-cell green alga (Chlamydomonas reinhardtii) were added. Furthermore, the statistical review and graphical analysis components were improved to offer intelligible information through ChIP-seq data analysis. These improvements included easy-to-read experimental condition clusters, searchable gene-centered interfaces for the identification of promoter regions' binding preferences by considering experimental condition clusters and peak visualization for all regulatory factors, and the 20 most significantly enriched gene ontology functions for regulatory factors. Thus, PlantPAN 4.0 can effectively reconstruct gene regulatory networks and help compare genomic cis-regulatory elements across plant species and experiments.

Computational Methods for Inferring and Reconstructing Gene Regulatory Networks

Gene regulatory network (GRN) describes dynamic and complex gene interactions that determine the functions of cells. Ergo, understanding of GRNs has played a great role in cancer treatment, drug design, and gene research. However, the GRN study suffers from a lack of experiment measurement data and the complexity of the model. Computational approaches have evolved in recent years to promote the study of GRN. In this review, we summarized popular Computational approaches for GRN reconstruction. We started with traditional bioinformatic methods, such as Bayesian networks and mutual information methods. Later, we introduced how today's hot technology in the computer field - machine learning benefited GRN research. Tree-based approaches and other machine-learning methods are elaborated on in this section. We discussed not only the advantages and progression brought by various methods but also the drawbacks and limitations, such as the accuracy and robustness of GRN reconstruction. We expect to inspire readers for improved GRN study approaches via our introduction to this field.

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

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. 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. 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.

Gene essentiality in cancer is better predicted by mRNA abundance than by gene regulatory network-inferred activity.

Gene regulatory networks (GRNs) are often deregulated in tumor cells, resulting in altered transcriptional programs that facilitate tumor growth. These altered networks may make tumor cells vulnerable to the inhibition of specific regulatory proteins. Consequently, the reconstruction of GRNs in tumors is often proposed as a means to identify therapeutic targets. While there are examples of individual targets identified using GRNs, the extent to which GRNs can be used to predict sensitivity to targeted intervention in general remains unknown. Here we use the results of genome-wide CRISPR screens to systematically assess the ability of GRNs to predict sensitivity to gene inhibition in cancer cell lines. Using GRNs derived from multiple sources, including GRNs reconstructed from tumor transcriptomes and from curated databases, we infer regulatory gene activity in cancer cell lines from ten cancer types. We then ask, in each cancer type, if the inferred regulatory activity of each gene is predictive of sensitivity to CRISPR perturbation of that gene. We observe slight variation in the correlation between gene regulatory activity and gene sensitivity depending on the source of the GRN and the activity estimation method used. However, we find that there is consistently a stronger relationship between mRNA abundance and gene sensitivity than there is between regulatory gene activity and gene sensitivity. This is true both when gene sensitivity is treated as a binary and a quantitative property. Overall, our results suggest that gene sensitivity is better predicted by measured expression than by GRN-inferred activity.

Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R.

Deep learning has emerged as a powerful tool for solving complex problems, including reconstruction of gene regulatory networks within the realm ofbiology. These networks consist of transcription factors and their associations with genes they regulate. Despite the utility of deep learning methods in studying gene expression and regulation, their accessibility remains limited forbiologists, mainlydue to the prerequisitesofprogramming skills and anuanced grasp of the underlying algorithms. This chapter presents a deep learning protocol thatutilize TensorFlow and the Keras API in R/RStudio, with the aim of making deep learning accessible for individuals without specialized expertise. The protocol focuses on the genome-wideprediction of regulatory interactions between transcription factors and genes, leveraging publicly available gene expression data in conjunction with well-established benchmarks. The protocol encompasses pivotal phases including data preprocessing, conceptualization of neural network architectures, iterative processes ofmodel training and validation, as well asforecasting of novel regulatory associations. Furthermore, it providesinsights into parameter tuning for deep learning models. By adhering to this protocol, researchers are expected to gain a comprehensive understanding of applying deep learning techniques to predict regulatory interactions. This protocol can be readily modifiable to servediverseresearch problems, therebyempowering scientists to effectivelyharness the capabilities of deep learning in their investigations.

OR20-03 Leptin-mediated Regulation Of Gene Regulatory Networks In Gonadotropes

Abstract Disclosure: J. Banik: None. A. Lagasse: None. A. Haney: None. S. Byrum: None. U. Boehm: None. G.V. Childs: None. M.C. MacNicol: None. A.M. MacNicol: None. A.K. Odle: None. The female reproductive system is energetically expensive and requires tight control of the hypothalamic-pituitary-gonadal (HPG) axis to promote gonadal priming and ovulation. Thus, a crosstalk between the metabolic and reproductive systems exists to regulate the rate-limiting steps of reproduction. Leptin is an adipokine hormone that is produced and secreted proportionally from white fat cells, and leptin receptors (Lepr) are present in the hypothalamus, pituitary, and gonads. Leptin binding to hypothalamic LepR functions as a sensor of nutritional status and, thus, orchestrates energy expenditure during reproductive events. We hypothesized that leptin is crucial to influence fertility in females via differential gene regulation during the estrus cycle in pituitary gonadotropes. To test this hypothesis, we utilized Cre-LoxP technology to selectively ablate leptin receptors within female gonadotropes (CreGnRHR+/- Leprfl/fl TdTomato+/+, referred to as “Gon-Lepr-null”) and performed single-cell RNA-sequencing (scRNA-seq) to identify leptin-dependent targets within the gonadotrope transcriptome. We observed a significant decrease in cell percentage within the gonadotrope cluster, and differential analysis reported a significant decrease in Fshb expression in the Lepr-null mutant gonadotropes. Moreover, the lack of leptin signaling in gonadotropes also caused a shift in the lactotrope-specific transcript signature in the Gon-Lepr-null mutants. We employed SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data, to delineate altered upstream regulators contributing to the reduction of Fshb expression in gonadotropes and the shift in the lactotrope transcriptome signature in Gon-Lepr-null mutants versus control animals. Regulons are co-regulated gene sets that share binding sites for a common transcription factor driver, and we identified 381 and 399 regulons specific to controls and mutants, respectively. Examination of the gonadotrope regulons revealed Gata2, Nhlh2, NeuroD1, and Sox11 to be drivers of Fshb expression in control animals only. Differential expression analysis showed significant downregulation of Gata2 and Sox11 in Lepr-null mutant gonadotropes, suggesting Fshb is a leptin-dependent target of Gata2 and Sox11. Moreover, Gata2, Nhlh2, NeuroD1, and Sox11 target 117 genes in controls and 263 genes in mutants, implying a more restricted ability of Gata2, Nhlh2, NeuroD1, and/or Sox11 to target gene expression in the presence of leptin signaling in controls, as an abrogation of leptin signaling leads to an expansion of the regulatory network in Lepr-null mutant gonadotropes. Overall, our analysis reveals how leptin signaling modulates the gonadotrope transcriptome to optimize pituitary reproductive functions at the molecular level. Presentation: Saturday, June 17, 2023

Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks.

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise and dropout events inherent in scRNA-seq data present challenges for GRN inference. In recent years, the dramatic increase in data on experimentally validated transcription factors binding to DNA has made it possible to infer GRNs by supervised methods. In this study, we address the problem of GRN inference by framing it as a graph link prediction task. In this paper, we propose a novel framework called GNNLink, which leverages known GRNs to deduce the potential regulatory interdependencies between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce a graph convolutional network-based interaction graph encoder to effectively refine gene features by capturing interdependencies between nodes in the network. Finally, the inference of GRN is obtained by performing matrix completion operation on node features. The features obtained from model training can be applied to downstream tasks such as measuring similarity and inferring causality between gene pairs. To evaluate the performance of GNNLink, we compare it with six existing GRN reconstruction methods using seven scRNA-seq datasets. These datasets encompass diverse ground truth networks, including functional interaction networks, Loss of Function/Gain of Function data, non-specific ChIP-seq data and cell-type-specific ChIP-seq data. Our experimental results demonstrate that GNNLink achieves comparable or superior performance across these datasets, showcasing its robustness and accuracy. Furthermore, we observe consistent performance across datasets of varying scales. For reproducibility, we provide the data and source code of GNNLink on our GitHub repository: https://github.com/sdesignates/GNNLink.

Dissecting and improving gene regulatory network inference using single-cell transcriptome data.

Single-cell transcriptome data has been widely used to reconstruct gene regulatory networks (GRNs) controlling critical biological processes such as development and differentiation. Although a growing list of algorithms has been developed to infer GRNs using such data, achieving an inference accuracy consistently higher than random guessing has remained challenging. To address this, it is essential to delineate how the accuracy of regulatory inference is limited. Here, we systematically characterized factors limiting the accuracy of inferred GRNs and demonstrated that using pre-mRNA information can help improve regulatory inference compared to the typically used information (i.e., mature mRNA). Using kinetic modeling and simulated single-cell data sets, we showed that target genes' mature mRNA levels often fail to accurately report upstream regulatory activities because of gene-level and network-level factors, which can be improved by using pre-mRNA levels. We tested this finding on public single-cell RNA-seq data sets using intronic reads as proxies of pre-mRNA levels and can indeed achieve a higher inference accuracy compared to using exonic reads (corresponding to mature mRNAs). Using experimental data sets, we further validated findings from the simulated data sets and identified factors such as transcription factor activity dynamics influencing the accuracy of pre-mRNA-based inference. This work delineates the fundamental limitations of gene regulatory inference and helps improve GRN inference using single-cell RNA-seq data.

Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics.

Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference, including dynamic rewiring, causal inference, feedback loop modeling and context specificity. To address these challenges, we develop Dictys, a dynamic GRN inference and analysis method that leverages multiomic single-cell assays of chromatin accessibility and gene expression, context-specific transcription factor footprinting, stochastic process network and efficient probabilistic modeling of single-cell RNA-sequencing read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context-specific and dynamic GRNs across developmental contexts. Dictys' network analyses recover unique insights in human blood and mouse skin development with cell-type-specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver transcription factors and their regulated targets. Dictys is available as a free, open-source and user-friendly Python package.

Molecular mechanisms of microRNAs in glioblastoma pathogenesis.

Glioblastoma (GBM) is human's most prevalent and severe brain cancer. Epigenetic regulators, micro(mi)RNAs, significantly impact cellular health and disease because of their wide range of targets and functions. The "epigenetic symphony" in which miRNAs perform is responsible for orchestrating the transcription of genetic information. The discovery of regulatory miRNA activities in GBM biology has shown that various miRNAs play a vital role in disease onset and development. Here, we summarize our current understanding of the current state-of-the-art and latest findings regarding the interactions between miRNAs and molecular mechanisms commonly associated with GBM pathogenesis. Moreover, by literature review and reconstruction of the GBM gene regulatory network, we uncovered the connection between miRNAs and critical signaling pathways such as cell proliferation, invasion, and cell death, which provides promising hints for identifying potential therapeutic targets for the treatment of GBM. In addition, the role of miRNAs in GBM patient survival was investigated. The present review, which contains new analyses of the previous literature, may lead to new avenues to explore in the future for the development of multitargeted miRNA-based therapies for GBM.

Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.

Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling of multiple target genes may improve performance, even under linearity assumptions. To address this problem, we propose two novel approaches that cast the GRN reconstruction problem as a blend between regularized multivariate regression and graphical models that combine the L2,1-norm with classical regularization techniques. We used data and networks from the DREAM5 challenge to show that the proposed models provide consistently good performance in comparison to contenders whose performance varies with data sets from simulation and experiments from model unicellular organisms Escherichia coli and Saccharomyces cerevisiae. Since the models' formulation facilitates the prediction of master regulators, we also used the resulting findings to identify master regulators over all data sets as well as their plasticity across different environments. Our results demonstrate that the identified master regulators are in line with experimental evidence from the model bacterium E. coli. Together, our study demonstrates that simultaneous modelling of several target genes results in improved inference of GRNs and can be used as an alternative in different applications.

Single-Cell Transcriptomics of Bone Marrow Stromal Cells in Diversity Outbred Mice: A Model for Population-Level scRNA-Seq Studies.

Genome-wide association studies (GWASs) have advanced our understanding of the genetics of osteoporosis; however, the challenge has been converting associations to causal genes. Studies have utilized transcriptomics data to link disease-associated variants to genes, but few population transcriptomics data sets have been generated on bone at the single-cell level. To address this challenge, we profiled the transcriptomes of bone marrow-derived stromal cells (BMSCs) cultured under osteogenic conditions from five diversity outbred (DO) mice using single-cell RNA-seq (scRNA-seq). The goal of the study was to determine if BMSCs could serve as a model to generate cell type-specific transcriptomic profiles of mesenchymal lineage cells from large populations of mice to inform genetic studies. By enriching for mesenchymal lineage cells in vitro, coupled with pooling of multiple samples and downstream genotype deconvolution, we demonstrate the scalability of this model for population-level studies. We demonstrate that dissociation of BMSCs from a heavily mineralized matrix had little effect on viability or their transcriptomic signatures. Furthermore, we show that BMSCs cultured under osteogenic conditions are diverse and consist of cells with characteristics of mesenchymal progenitors, marrow adipogenic lineage precursors (MALPs), osteoblasts, osteocyte-like cells, and immune cells. Importantly, all cells were similar from a transcriptomic perspective to cells isolated in vivo. We employed scRNA-seq analytical tools to confirm the biological identity of profiled cell types. SCENIC was used to reconstruct gene regulatory networks (GRNs), and we observed that cell types show GRNs expected of osteogenic and pre-adipogenic lineage cells. Further, CELLECT analysis showed that osteoblasts, osteocyte-like cells, and MALPs captured a significant component of bone mineral density (BMD) heritability. Together, these data suggest that BMSCs cultured under osteogenic conditions coupled with scRNA-seq can be used as a scalable and biologically informative model to generate cell type-specific transcriptomic profiles of mesenchymal lineage cells in large populations. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).