Gene Regulatory Network Analysis Research Articles

Unraveling the Molecular Puzzle: Exploring Gene Networks across Diverse EMT Status of Cell Lines.

Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in single genes. Specifically, the exploration of cell line-specific gene networks can provide essential clues for precision medicine, as this methodology can uncover molecular interplays specific to particular cell line statuses, such as drug sensitivity, cancer progression, etc. In this article, we provide a comprehensive review of computational strategies for cell line-specific gene network analysis: (1) cell line-specific gene regulatory network estimation and analysis of gene networks under varying epithelial-mesenchymal transition (EMT) statuses of cell lines; and (2) an explainable artificial intelligence approach for interpreting the estimated massive multiple EMT-status-specific gene networks. The objective of this review is to help readers grasp the concept of computational network biology, which holds significant implications for precision medicine by offering crucial clues.

Determination of key events in mouse hepatocyte maturation at the single-cell level

Hepatocytes, the liver's predominant cells, perform numerous essential biological functions. However, crucial events and regulators during hepatocyte maturation require in-depth investigation. In this study, we performed single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) to explore the precise hepatocyte development process in mice. We defined three maturation stages of postnatal hepatocytes, each of which establishes specific metabolic functions and exhibits distinct proliferation rates. Hepatic zonation is gradually formed during hepatocyte maturation. Hepatocytes or their nuclei with distinct ploidies exhibit zonation preferences in distribution and asynchrony in maturation. Moreover, by combining gene regulatory network analysis with invivo genetic manipulation, we identified critical maturation- and zonation-related transcription factors. This study not only delineates the comprehensive transcriptomic profiles of hepatocyte maturation but also presents a paradigm to identify genes that function in the development of hepatocyte maturation and zonation by combining genetic manipulation and measurement of coordinates in a single-cell developmental trajectory.

COL3A1-positive endothelial cells influence LUAD prognosis and regulate LUAD carcinogenesis by NCL-PI3K-AKT axis.

Lung adenocarcinoma (LUAD), as the most common type of lung cancer, poses a significant threat to public health. Tumor heterogeneity plays a crucial role in carcinogenesis, which could be largely deciphered by next-generation sequencing (NGS). We obtained and screened single-cell RNA sequencing (scRNA-seq) data from 16 LUAD samples, and endothelial cells (ECs) were grouped into three clusters. The origin of EC differentiation was explored by pseudo-time analysis. CellChat analysis was used to detect potential communication between ECs and malignant cells, and gene regulatory network analysis was used to identify changes in transcription factor activity. We explored the prognosis of specific ECs clusters and their effects on the tumor microenvironment (TME) at the bulk transcriptome level. 5-Ethynyl-2'- deoxyuridine (EdU) and Ki-67 staining were conducted to study the proliferative phenotype of LUAD cell lines. Western blotting targeting the phosphorylation of PI3K-AKT proteins was utilized for determination of the downstream pathway of NCL. COL3A1-positive ECs showed the highest crosstalk interaction with malignant cells, indicating that they have important effects on driving LUAD carcinogenesis. Vascular endothelial growth factor (VEGF) signaling pathway was identified as the main signaling pathway, mediating signal transduction from malignant cells. The TME-related genes of COL3A1-positive ECs were significantly more highly expressed. COL3A1-positive ECs showed unique metabolic and immune characteristics, as well as highly activated metabolic signaling pathways and inflammatory responses. Importantly, LUAD patients with low COL3A1-positive ECs scores displayed an inferior prognosis outcome and a higher risk of metastasis. The key target gene NCL, which is involved in the interaction between epithelial cells and cancer cells, has been identified through screening. Flow cytometry showed that knockdown of NCL prompted the apoptosis of A549 and NCI-H1299. Western blotting showed that knockdown of NCL decreased the phosphorylation of AKT and PI3K, which identified the downstream pathway of NCL. COL3A1-positive ECs have important effects on the development of LUAD and the formation of an immune microenvironment. Furthermore, we identified a key target gene, NCL, which is involved in the interaction between endothelial cells and cancer cells. NCL also affected the apoptosis and proliferation in LUAD through the PI3K-AKT pathway.

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.

Bifurcation Analysis and Fractional PD Control of Gene Regulatory Networks with sRNA

This paper investigates the problem of bifurcation analysis and bifurcation control of a fractional-order gene regulatory network with sRNA. Firstly, the process of stability change of system equilibrium under the influence of the sum of time delay is discussed, the critical condition of Hopf bifurcation is explored, and the effect of fractional order on the system stability domain. Secondly, aiming at the system’s instability caused by a large time delay, we design a controller to improve the system’s stability and derive the parameter conditions that satisfy the system’s stability. It is found that changing the parameter values of the controller within a certain range can control the system’s nonlinear behaviours and effectively expand the stability range. Then, a numerical example is given to illustrate the results of this paper.

Single-cell transcriptomics reveals maturation of transplanted stem cell–derived retinal pigment epithelial cells toward native state

Transplantation of stem cell-derived retinal pigment epithelial (RPE) cells is considered a viable therapeutic option for age-related macular degeneration (AMD). Several landmark Phase I/II clinical trials have demonstrated safety and tolerability of RPE transplants in AMD patients, albeit with limited efficacy. Currently, there is limited understanding of how the recipient retina regulates the survival, maturation, and fate specification of transplanted RPE cells. To address this, we transplanted stem cell-derived RPE into the subretinal space of immunocompetent rabbits for 1 mo and conducted single-cell RNA sequencing analyses on the explanted RPE monolayers, compared to their age-matched invitro counterparts. We observed an unequivocal retention of RPE identity, and a trajectory-inferred survival of all invitro RPE populations after transplantation. Furthermore, there was a unidirectional maturation toward the native adult human RPE state in all transplanted RPE, regardless of stem cell resource. Gene regulatory network analysis suggests that tripartite transcription factors (FOS, JUND, and MAFF) may be specifically activated in posttransplanted RPE cells, to regulate canonical RPE signature gene expression crucial for supporting host photoreceptor function, and to regulate prosurvival genes required for transplanted RPE's adaptation to the host subretinal microenvironment. These findings shed insights into the transcriptional landscape of RPE cells after subretinal transplantation, with important implications for cell-based therapy for AMD.

Single cell multiomic profiling of high-grade serous carcinoma.

e17535 Background: High-grade serous ovarian carcinoma (HGSOC) is the most frequent and deadliest type of ovarian cancer (OC). With the recent advances in transcriptomic and epigenomic profiling of cancer at single cell resolution, it has become clear that intra-tumor heterogeneity driven by genetic and epigenetic factors may be the basis of drug resistance. Current treatments are not homogeneously effective against all the cancer cell subpopulations, thus enabling resistance. With single cell sequencing technology, characterization of cell signatures and regulatory mechanisms may translate to prognostic markers and novel drug targets. Methods: The transcriptomic and epigenomic landscape of HGSOC was mapped for five tumor samples from debulking surgeries and one tissue patient-derived xenograft (PDX) using the 10X genomics sequencing platform. This strategy permits the combination of single cell ATAC-seq and single cell RNA-seq within the same nucleus (Multiome). Isolation of nuclei, library preparation and sequencing were performed following 10X genomics protocols. Sample quality and library quality were assessed by nuclear morphology and fragment size analysis, respectively. Sequenced reads were mapped to the human genome and downstream analysis including copy number variant prediction, pathway enrichment, motif enrichment and gene regulatory network analysis were performed. The sampled patients continue to be followed for clinical outcomes, such as response to therapy. Clinical data including patient age, grade, and stage of cancer, debulking status, CA-125 levels, and neoadjuvant status, were used in conjunction with the genomics data to characterize patient specific molecular regulatory signatures. Results: Cells from the six samples (total N= 26,421) were projected via UMAP. Immune and stromal cells were shown to cluster by cell-type while cancer cells clustered by patient. Cancer cells were identified as cycling cells, ciliated cells, cells with enrichment of the JAK-STAT signaling pathway, and cells exhibiting cancer stemness signatures. Transcription factor motifs and binding-site enrichment in open chromatin regions reveal transcription regulation-based subpopulations. Using the combined gene expression and open chromatin information from these cells, there is potential to uncover the genetic regulatory network(s) that drives treatment resistance. Conclusions: With single cell technology, specific clusters of cancer and tumor microenvironment cells can be classified. Beyond characterizing patient specific signatures, multiome enables the discovery of genomic, transcriptomic and epigenomic signatures that provide insight in tumor progression and treatment resistance.

Single-cell dissection of aggression in honeybee colonies.

Understanding how genotypic variation results in phenotypic variation is especially difficult for collective behaviour because group phenotypes arise from complex interactions among group members. A genome-wide association study identified hundreds of genes associated with colony-level variation in honeybee aggression, many of which also showed strong signals of positive selection, but the influence of these 'colony aggression genes' on brain function was unknown. Here we use single-cell (sc) transcriptomics and gene regulatory network (GRN) analyses to test the hypothesis that genetic variation for colony aggression influences individual differences in brain gene expression and/or gene regulation. We compared soldiers, which respond to territorial intrusion with stinging attacks, and foragers, which do not. Colony environment showed stronger influences on soldier-forager differences in brain gene regulation compared with brain gene expression. GRN plasticity was strongly associated with colony aggression, with larger differences in GRN dynamics detected between soldiers and foragers from more aggressive relative to less aggressive colonies. The regulatory dynamics of subnetworks composed of genes associated with colony aggression genes were more strongly correlated with each other across different cell types and brain regions relative to other genes, especially in brain regions involved with olfaction and vision and multimodal sensory integration, which are known to mediate bee aggression. These results show how group genetics can shape a collective phenotype by modulating individual brain gene regulatory network architecture.

Self-Organization and Genomic Causality in Models of Morphogenesis.

The debate about what causes the generation of form and structure in embryological development goes back to antiquity. Most recently, it has focused on the divergent views as to whether the generation of patterns and form in development is a largely self-organized process or is mainly determined by the genome, in particular, complex developmental gene regulatory processes. This paper presents and analyzes pertinent models of pattern formation and form generation in a developing organism in the past and the present, with a special emphasis on Alan Turing's 1952 reaction-diffusion model. I first draw attention to the fact that Turing's paper remained, at first, without a noticeable impact on the community of biologists because purely physical-chemical models were unable to explain embryological development and often also simple repetitive patterns. I then show that from the year 2000 and onwards, Turing's 1952 paper was increasingly cited also by biologists. The model was updated to include gene products and now seemed able to account for the generation of biological patterns, though discrepancies between models and biological reality remained. I then point out Eric Davidson's successful theory of early embryogenesis based on gene-regulatory network analysis and its mathematical modeling that not only was able to provide a mechanistic and causal explanation for gene regulatory events controlling developmental cell fate specification but, unlike reaction-diffusion models, also addressed the effects of evolution and organisms' longstanding developmental and species stability. The paper concludes with an outlook on further developments of the gene regulatory network model.

Decoding the transcriptome of muscular dystrophy due to Ptrf deficiency using single-nucleus RNA sequencing.

Lacking PTRF (polymerase I and transcript release factor), an essential caveolae component, causes a secondary deficiency of caveolins resulting in muscular dystrophy. The transcriptome responses of different types of muscle fibers and mononuclear cells in skeletal muscle to muscular dystrophy caused by Ptrf deletion have not been explored. Here, we created muscular dystrophy mice by Ptrf knockout and applied single-nucleus RNA sequencing (snRNA-seq) to unveil the transcriptional changes of the skeletal muscle at single-nucleus resolution. 11 613 muscle nuclei (WT, 5838; Ptrf KO, 5775) were classified into 12 clusters corresponding to 11 nuclear types. Trajectory analysis revealed the potential transition between type IIb_1 and IIb_2 myonuclei upon muscular dystrophy. Functional enrichment analysis indicated that apoptotic signaling and enzyme-linked receptor protein signaling pathway were significantly enriched in type IIb_1 and IIb_2 myonuclei of Ptrf KO, respectively. The muscle structure development and the PI3K-AKT signaling pathway were significantly enriched in type IIa and IIx myonuclei of Ptrf KO. Meanwhile, metabolic pathway analysis showed a decrease in overall metabolic pathway activity of myonuclei subtypes upon muscular dystrophy, with the most decrease in type IIb_1 myonuclei. Gene regulatory network analysis found that the activity of Mef2c, Mef2d, Myf5, and Pax3 regulons was enhanced in type II myonuclei of Ptrf KO, especially in type IIb_2 myonuclei. In addition, we investigated the transcriptome changes in adipocytes and found that muscular dystrophy enhanced the lipid metabolic capacity of adipocytes. Our findings provide a valuable resource for exploring the molecular mechanism of muscular dystrophy due to Ptrf deficiency.

MSdb: An integrated expression atlas of human musculoskeletal system

The global prevalence and burden of musculoskeletal (MSK) disorders are immense. Advancements in next-generation sequencing (NGS) have generated vast amounts of data, accelerating the research of pathological mechanisms and the development of therapeutic approaches for MSK disorders. However, scattered datasets across various repositories complicate uniform analysis and comparison. Here, we introduce MSdb, a database for visualization and integrated analysis of next-generation sequencing data from human musculoskeletal system, along with manually curated patient phenotype data. MSdb provides various types of analysis, including sample-level browsing of metadata information, gene/miRNA expression, and single-cell RNA-seq dataset. In addition, MSdb also allows integrated analysis for cross-samples and cross-omics analysis, including customized differentially expressed gene/microRNA analysis, microRNA-gene network, scRNA-seq cross-sample/disease integration, and gene regulatory network analysis. Overall, systematic categorizing, standardized processing, and freely accessible knowledge features MSdb a valuable resource for MSK research community.

TYROBP-positive endothelial cell-derived TWEAK as a promoter of osteosarcoma progression: insights from single-cell omics.

Endothelial cells (ECs) play a vital role in promoting the progression of malignant cells, and they exhibit heterogeneity in their phenotypic characteristics. We aimed to explore the initiating cells of ECs in osteosarcoma (OS) and investigate their potential interaction with malignant cells. We obtained scRNA-seq data from 6 OS patients, and datasets were batch-corrected to minimize variations among samples. Pseudotime analysis was performed to investigate the origin of differentiation of ECs. CellChat was employed to examine the potential communication between endothelial cells and malignant cells, and gene regulatory network analysis was performed to identify transcription factor activity changes during the conversion process. Importantly, we generated TYROBP-positive ECs in vitro and investigated its role in OS cell lines. Finally, we explored the prognosis of specific ECs cluster and their impact on the tumor microenvironment (TME) at the bulk transcriptome level. The results showed that TYROBP-positive ECs may play a crucial role in initiating the differentiation of ECs. TYROBOP-positive endothelial cells (ECs) exhibited the strongest crosstalk with malignant cells, likely mediated by TWEAK, a multifunctional cytokine. TYROBP-positive ECs exhibited significant expression of TME-related genes, unique metabolic and immunological profiles. Importantly, OS patients with low enrichment of TYROBP-positive ECs had better prognoses and a lower risk of metastasis. Finally, vitro assays confirmed that TWEAK was significantly increased in ECs-conditioned medium (ECs-CM) when TYROBP was over-expressed in EC cells, and could promote the proliferation and migration of OS cells. We concluded that TYROBP-positive ECs may be the initiating cells and play a crucial role in the promotion of malignant cell progression. TYROBP-positive ECs have a unique metabolic and immunological profile and may interact with malignant cells through the secretion of TWEAK.

FHL5 Controls Vascular Disease-Associated Gene Programs in Smooth Muscle Cells.

Genome-wide association studies have identified hundreds of loci associated with common vascular diseases, such as coronary artery disease, myocardial infarction, and hypertension. However, the lack of mechanistic insights for many GWAS loci limits their translation into the clinic. Among these loci with unknown functions is UFL1-four-and-a-half LIM (LIN-11, Isl-1, MEC-3) domain 5 (FHL5; chr6q16.1), which reached genome-wide significance in a recent coronary artery disease/ myocardial infarction GWAS meta-analysis. UFL1-FHL5 is also associated with several vascular diseases, consistent with the widespread pleiotropy observed for GWAS loci. We apply a multimodal approach leveraging statistical fine-mapping, epigenomic profiling, and ex vivo analysis of human coronary artery tissues to implicate FHL5 as the top candidate causal gene. We unravel the molecular mechanisms of the cross-phenotype genetic associations through in vitro functional analyses and epigenomic profiling experiments in coronary artery smooth muscle cells. We prioritized FHL5 as the top candidate causal gene at the UFL1-FHL5 locus through expression quantitative trait locus colocalization methods. FHL5 gene expression was enriched in the smooth muscle cells and pericyte population in human artery tissues with coexpression network analyses supporting a functional role in regulating smooth muscle cell contraction. Unexpectedly, under procalcifying conditions, FHL5 overexpression promoted vascular calcification and dysregulated processes related to extracellular matrix organization and calcium handling. Lastly, by mapping FHL5 binding sites and inferring FHL5 target gene function using artery tissue gene regulatory network analyses, we highlight regulatory interactions between FHL5 and downstream coronary artery disease/myocardial infarction loci, such as FOXL1 and FN1 that have roles in vascular remodeling. Taken together, these studies provide mechanistic insights into the pleiotropic genetic associations of UFL1-FHL5. We show that FHL5 mediates vascular disease risk through transcriptional regulation of downstream vascular remodeling gene programs. These transacting mechanisms may explain a portion of the heritable risk for complex vascular diseases.

Dysregulation of murine long noncoding single-cell transcriptome in nonalcoholic steatohepatitis and liver fibrosis.

LncRNAs comprise a heterogeneous class of RNA-encoding genes typified by low expression, nuclear enrichment, high tissue-specificity, and functional diversity, but the vast majority remain uncharacterized. Here, we assembled the mouse liver noncoding transcriptome from >2000 bulk RNA-seq samples and discovered 48,261 liver-expressed lncRNAs, a majority novel. Using these lncRNAs as a single-cell transcriptomic reference set, we elucidated lncRNA dysregulation in mouse models of high fat diet-induced nonalcoholic steatohepatitis and carbon tetrachloride-induced liver fibrosis. Trajectory inference analysis revealed lncRNA zonation patterns across the liver lobule in each major liver cell population. Perturbations in lncRNA expression and zonation were common in several disease-associated liver cell types, including nonalcoholic steatohepatitis-associated macrophages, a hallmark of fatty liver disease progression, and collagen-producing myofibroblasts, a central feature of liver fibrosis. Single-cell-based gene regulatory network analysis using bigSCale2 linked individual lncRNAs to specific biological pathways, and network-essential regulatory lncRNAs with disease-associated functions were identified by their high network centrality metrics. For a subset of these lncRNAs, promoter sequences of the network-defined lncRNA target genes were significantly enriched for lncRNA triplex formation, providing independent mechanistic support for the lncRNA-target gene linkages predicted by the gene regulatory networks. These findings elucidate liver lncRNA cell-type specificities, spatial zonation patterns, associated regulatory networks, and temporal patterns of dysregulation during hepatic disease progression. A subset of the liver disease-associated regulatory lncRNAs identified have human orthologs and are promising candidates for biomarkers and therapeutic targets.

CeGAL: Redefining a Widespread Fungal-Specific Transcription Factor Family Using an In Silico Error-Tracking Approach.

In fungi, the most abundant transcription factor (TF) class contains a fungal-specific 'GAL4-like' Zn2C6 DNA binding domain (DBD), while the second class contains another fungal-specific domain, known as 'fungal_trans' or middle homology domain (MHD), whose function remains largely uncharacterized. Remarkably, almost a third of MHD-containing TFs in public sequence databases apparently lack DNA binding activity, since they are not predicted to contain a DBD. Here, we reassess the domain organization of these 'MHD-only' proteins using an in silico error-tracking approach. In a large-scale analysis of ~17,000 MHD-only TF sequences present in all fungal phyla except Microsporidia and Cryptomycota, we show that the vast majority (>90%) result from genome annotation errors and we are able to predict a new DBD sequence for 14,261 of them. Most of these sequences correspond to a Zn2C6 domain (82%), with a small proportion of C2H2 domains (4%) found only in Dikarya. Our results contradict previous findings that the MHD-only TF are widespread in fungi. In contrast, we show that they are exceptional cases, and that the fungal-specific Zn2C6-MHD domain pair represents the canonical domain signature defining the most predominant fungal TF family. We call this family CeGAL, after the highly characterized members: Cep3, whose 3D structure is determined, and GAL4, a eukaryotic TF archetype. We believe that this will not only improve the annotation and classification of the Zn2C6 TF but will also provide critical guidance for future fungal gene regulatory network analyses.

Comparative expression analysis of the Atoh7 gene regulatory network in the mouse and chicken auditory hindbrain

The mammalian and avian auditory brainstem likely arose by independent evolution. To compare the underlying molecular mechanisms, we focused on Atoh7, as its expression pattern in the mammalian hindbrain is restricted to bushy cells in the ventral cochlear nucleus. We thereby took advantage of an Atoh7 centered gene regulatory network (GRN) in the retina including upstream regulators, Hes1 and Pax6, and downstream targets, Ebf3 and Eya2. In situ hybridization demonstrated for the latter four genes broad expression in all three murine cochlear nuclei at postnatal days (P) 4 and P30, contrasting the restricted expression of Atoh7. In chicken, all five transcription factors were expressed in all auditory hindbrain nuclei at embryonic day (E) 13 and P14. Notably, all five genes showed graded expression in the embryonic nucleus magnocellularis (NM). Atoh7 was highly expressed in caudally located neurons, whereas the other four transcription factors were highly expressed in rostrally located neurons. Thus, Atoh7 shows a strikingly different expression between the mammalian and avian auditory hindbrain. This together with the consistent absence of graded expression of GRN components in developing mammalian nuclei provide the first molecular support to the current view of convergent evolution as a major mechanism in the amniote auditory hindbrain. The graded expression of five transcription factors specifically in the developing NM confirms this nucleus as a central organizer of tonotopic features in birds. Finally, the expression of all five retinal GRN components in the auditory system suggests co-options of genes for development of sensory systems of distinct modalities.

Diel dynamics of multi-omics in elkhorn fern provide new insights into weak CAM photosynthesis

Crassulacean acid metabolism (CAM) has high water-use efficiency (WUE) and is widely recognized to have evolved from C3 photosynthesis. Different plant lineages have convergently evolved CAM, but the molecular mechanism that underlies C3-to-CAM evolution remains to be clarified. Platycerium bifurcatum (elkhorn fern) provides an opportunity to study the molecular changes underlying the transition from C3 to CAM photosynthesis because both modes of photosynthesis occur in this species, with sporotrophophyll leaves (SLs) and cover leaves (CLs) performing C3 and weak CAM photosynthesis, respectively. Here, we report that the physiological and biochemical attributes of CAM in weak CAM-performing CLs differed from those in strong CAM species. We investigated the diel dynamics of the metabolome, proteome, and transcriptome in these dimorphic leaves within the same genetic background and under identical environmental conditions. We found that multi-omic diel dynamics in P. bifurcatum exhibit both tissue and diel effects. Our analysis revealed temporal rewiring of biochemistry relevant to the energy-producing pathway (TCA cycle), CAM pathway, and stomatal movement in CLs compared with SLs. We also confirmed that PHOSPHOENOLPYRUVATE CARBOXYLASE KINASE (PPCK) exhibits convergence in gene expression among highly divergent CAM lineages. Gene regulatory network analysis identified candidate transcription factors regulating the CAM pathway and stomatal movement. Taken together, our results provide new insights into weak CAM photosynthesis and new avenues for CAM bioengineering.

Transcriptional and Chromatin Accessibility Profiling of Neural Stem Cells Differentiating into Astrocytes Reveal Dynamic Signatures Affected under Inflammatory Conditions

Astrocytes arise from multipotent neural stem cells (NSCs) and represent the most abundant cell type of the central nervous system (CNS), playing key roles in the developing and adult brain. Since the differentiation of NSCs towards a gliogenic fate is a precisely timed and regulated process, its perturbation gives rise to dysfunctional astrocytic phenotypes. Inflammation, which often underlies neurological disorders, including neurodevelopmental disorders and brain tumors, disrupts the accurate developmental process of NSCs. However, the specific consequences of an inflammatory environment on the epigenetic and transcriptional programs underlying NSCs' differentiation into astrocytes is unexplored. Here, we address this gap by profiling in mice glial precursors from neural tissue derived from early embryonic stages along their astrocytic differentiation trajectory in the presence or absence of tumor necrosis factor (TNF), a master pro-inflammatory cytokine. By using a combination of RNA- and ATAC-sequencing approaches, together with footprint and integrated gene regulatory network analyses, we here identify key differences during the differentiation of NSCs into astrocytes under physiological and inflammatory settings. In agreement with its role to turn cells resistant to inflammatory challenges, we detect Nrf2 as a master transcription factor supporting the astrocytic differentiation under TNF exposure. Further, under these conditions, we unravel additional transcriptional regulatory hubs, including Stat3, Smad3, Cebpb, and Nfkb2, highlighting the interplay among pathways underlying physiological astrocytic developmental processes and those involved in inflammatory responses, resulting in discrete astrocytic phenotypes. Overall, our study reports key transcriptional and epigenetic changes leading to the identification of molecular regulators of astrocytic differentiation. Furthermore, our analyses provide a valuable resource for understanding inflammation-induced astrocytic phenotypes that might contribute to the development and progression of CNS disorders with an inflammatory component.

Nonlinear Causal Discovery with Confounders

This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a deconfounding adjustment to remove the confounding effects and a sequential procedure to estimate the causal order of variables. We implement DeFuSE via feedforward neural networks for scalable computation. Moreover, we establish the consistency of DeFuSE under an assumption called the strong causal minimality. In simulations, DeFuSE compares favorably against state-of-the-art competitors that ignore confounding or nonlinearity. Finally, we demonstrate the utility and effectiveness of the proposed approach with an application to gene regulatory network analysis. The Python implementation is available at https://github.com/chunlinli/defuse. Supplementary materials for this article are available online.

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.