Gene Regulatory Network Analysis of Decidual Stromal Cells and Natural Killer Cells.

Human reproductive success relies on the proper differentiation of the uterine endometrium to facilitate implantation, formation of the placenta, and pregnancy. This process involves two critical types of decidual uterine cells: endometrial/decidual stromal cells (dS) and uterine/decidual natural killer (dNK) cells. To better understand the transcription factors governing the in vivo functions of these cells, we analyzed single-cell transcriptomics data from first-trimester terminations of pregnancy, and for the first time conducted gene regulatory network analysis of dS and dNK cell subpopulations. Our analysis revealed stromal cell populations that corresponded to previously described in vitro decidualized cells and senescent decidual cells. We discovered new decidualization driving transcription factors of stromal cells for early pregnancy, including DDIT3 and BRF2, which regulate oxidative stress protection. For dNK cells, we identified transcription factors involved in the immunotolerant (dNK1) subpopulation, including IRX3 and RELB, which repress the NFKB pathway. In contrast, for the less immunotolerant (dNK3) population we predicted TBX21 (T-bet) and IRF2-mediated upregulation of the interferon pathway. To determine the clinical relevance of our findings, we tested the overrepresentation of the predicted transcription factors target genes among cell type-specific regulated genes from pregnancy disorders, such as recurrent pregnancy loss and preeclampsia. We observed that the predicted decidualized stromal and dNK1-specific transcription factor target genes were enriched with the genes downregulated in pregnancy disorders, whereas the predicted dNK3-specific targets were enriched with genes upregulated in pregnancy disorders. Our findings emphasize the importance of stress tolerance pathways in stromal cell decidualization and immunotolerance promoting regulators in dNK differentiation.

Spatiotemporal transcriptome atlas reveals gene regulatory patterns during the organogenesis of the rapid growing bamboo shoots.

Bamboo with its remarkable growth rate and economic significance, offers an ideal system to investigate the molecular basis of organogenesis in rapidly growing plants, particular in monocots, where gene regulatory networks governing the maintenance and differentiation of shoot apical and intercalary meristems remain a subject of controversy. We employed both spatial and single-nucleus transcriptome sequencing on 10× platform to precisely dissect the gene functions in various tissues and early developmental stages of bamboo shoots. Our comprehensive analysis reveals distinct cell trajectories during shoot development, uncovering critical genes and pathways involved in procambium differentiation, intercalary meristem formation, and vascular tissue development. Spatial and temporal expression patterns of key regulatory genes, particularly those related to hormone signaling and lipid metabolism, strongly support the hypothesis that intercalary meristem origin from surrounded parenchyma cells. Specific gene expressions in intercalary meristem exhibit regular and dispersed distribution pattern, offering clues for understanding the intricate molecular mechanisms that drive the rapid growth of bamboo shoots. The single-nucleus and spatial transcriptome analysis reveal a comprehensive landscape of gene activity, enhancing the understanding of the molecular architecture of organogenesis and providing valuable resources for future genomic and genetic studies relying on identities of specific cell types.

Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data.

Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans, achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans, where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git.

Transcriptome Profiling of Mouse Embryonic Fibroblast Spontaneous Immortalization: A Comparative Analysis.

Cell immortalization, a hallmark of cancer development, is a process that cells can undergo on their path to carcinogenesis. Spontaneously immortalized mouse embryonic fibroblasts (MEFs) have been used for decades; however, changes in the global transcriptome during this process have been poorly described. In our research, we characterized the poly-A RNA transcriptome changes after spontaneous immortalization. To this end, differentially expressed genes (DEGs) were screened using DESeq2 and characterized by gene ontology enrichment analysis and protein-protein interaction (PPI) network analysis to identify the potential hub genes. In our study, we identified changes in the expression of genes involved in proliferation regulation, cell adhesion, immune response and transcriptional regulation in immortalized MEFs. In addition, we performed a comparative analysis with previously reported MEF immortalization data, where we propose a predicted gene regulatory network model in immortalized MEFs based on the altered expression of Mapk11, Cdh1, Chl1, Zic1, Hoxd10 and the novel hub genes Il6 and Itgb2.

Expression and Transcriptional Targets of TGFß-RII in Paracentrotus lividus Larval Skeletogenesis.

Organisms from the five kingdoms of life use minerals to harden their tissues and make teeth, shells and skeletons, in the process of biomineralization. The sea urchin larval skeleton is an excellent system to study the biological regulation of biomineralization and its evolution. The gene regulatory network (GRN) that controls sea urchin skeletogenesis is known in great details and shows similarity to the GRN that controls vertebrates' vascularization while it is quite distinct from the GRN that drives vertebrates' bone formation. Yet, transforming growth factor beta (TGF-ß) signaling regulates both sea urchin and vertebrates' skeletogenesis. Here, we study the upstream regulation and identify transcriptional targets of TGF-ß in the Mediterranean Sea urchin species, Paracentrotus lividus. TGF-ßRII is transiently active in the skeletogenic cells downstream of vascular endothelial growth factor (VEGF) signaling, in P. lividus. Continuous perturbation of TGF-ßRII activity significantly impairs skeletal elongation and the expression of key skeletogenic genes. Perturbation of TGF-ßRII after skeletal initiation leads to a delay in skeletal elongation and minor changes in gene expression. TGF-ß targets are distinct from its transcriptional targets during vertebrates' bone formation, suggesting that the role of TGF-ß in biomineralization in these two phyla results from convergent evolution.

TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks.

BACKGROUND: Inference of Gene Regulatory Networks (GRNs) is a difficult and long-standing question in Systems Biology. Numerous approaches have been proposed with the latest methods exploring the richness of single-cell data. One of the current difficulties lies in the fact that many methods of GRN inference do not result in one proposed GRN but in a collection of plausible networks that need to be further refined. In this work, we present a Design of Experiment strategy to use as a second stage after the inference process. It is specifically fitted for identifying the next most informative experiment to perform for deciding between multiple network topologies, in the case where proposed GRNs are executable models. This strategy first performs a topological analysis to reduce the number of perturbations that need to be tested, then predicts the outcome of the retained perturbations by simulation of the GRNs and finally compares predictions with novel experimental data. RESULTS: We apply this method to the results of our divide-and-conquer algorithm called WASABI, adapt its gene expression model to produce perturbations and compare our predictions with experimental results. We show that our networks were able to produce in silico predictions on the outcome of a gene knock-out, which were qualitatively validated for 48 out of 49 genes. Finally, we eliminate as many as two thirds of the candidate networks for which we could identify an incorrect topology, thus greatly improving the accuracy of our predictions. CONCLUSION: These results both confirm the inference accuracy of WASABI and show how executable gene expression models can be leveraged to further refine the topology of inferred GRNs. We hope this strategy will help systems biologists further explore their data and encourage the development of more executable GRN models.

Genes Related to Frontonasal Malformations Are Regulated by miR-338-5p, miR-653-5p, and miR-374-5p in O9-1 Cells.

Frontonasal malformations are caused by a failure in the growth of the frontonasal prominence during development. Although genetic studies have identified genes that are crucial for frontonasal development, it remains largely unknown how these genes are regulated during this process. Here, we show that microRNAs, which are short non-coding RNAs capable of targeting their target mRNAs for degradation or silencing their expression, play a crucial role in the regulation of genes related to frontonasal development in mice. Using the Mouse Genome Informatics (MGI) database, we curated a total of 25 mouse genes related to frontonasal malformations, including frontonasal hypoplasia, frontonasal dysplasia, and hypotelorism. MicroRNAs regulating the expression of these genes were predicted through bioinformatic analysis. We then experimentally evaluated the top three candidate miRNAs (miR-338-5p, miR-653-5p, and miR-374c-5p) for their effect on cell proliferation and target gene regulation in O9-1 cells, a neural crest cell line. Overexpression of these miRNAs significantly inhibited cell proliferation, and the genes related to frontonasal malformations (Alx1, Lrp2, and Sirt1 for miR-338-5p; Alx1, Cdc42, Sirt1, and Zic2 for miR-374c-5p; and Fgfr2, Pgap1, Rdh10, Sirt1, and Zic2 for miR-653-5p) were directly regulated by these miRNAs in a dose-dependent manner. Taken together, our results highlight miR-338-5p, miR-653-5p, and miR-374c-5p as pathogenic miRNAs related to the development of frontonasal malformations.

Exploring the reciprocity between pioneer factors and development.

Development is regulated by coordinated changes in gene expression. Control of these changes in expression is largely governed by the binding of transcription factors to specific regulatory elements. However, the packaging of DNA into chromatin prevents the binding of many transcription factors. Pioneer factors overcome this barrier owing to unique properties that enable them to bind closed chromatin, promote accessibility and, in so doing, mediate binding of additional factors that activate gene expression. Because of these properties, pioneer factors act at the top of gene-regulatory networks and drive developmental transitions. Despite the ability to bind target motifs in closed chromatin, pioneer factors have cell type-specific chromatin occupancy and activity. Thus, developmental context clearly shapes pioneer-factor function. Here, we discuss this reciprocal interplay between pioneer factors and development: how pioneer factors control changes in cell fate and how cellular environment influences pioneer-factor binding and activity.

Body plan evolvability: The role of variability in gene regulatory networks.

Recent computational modeling of early fruit fly (Drosophila) development has characterized the degree to which gene regulation networks can be robust to natural variability. In the first few hours of development, broad spatial gradients of maternally derived transcription factors activate embryonic gap genes. These gap patterns determine the subsequent segmented insect body plan through pair-rule gene expression. Gap genes are expressed with greater spatial precision than the maternal patterns. Computational modeling of the gap-gap regulatory interactions provides a mechanistic understanding for this robustness to maternal variability in wild-type (WT) patterning. A long-standing question in evolutionary biology has been how a system which is robust, such as the developmental program creating any particular species' body plan, is also evolvable, i.e. how can a system evolve or speciate, if the WT form is strongly buffered and protected? In the present work, we use the WT model to explore the breakdown of such Waddington-type 'canalization'. What levels of variability will push the system out of the WT form; are there particular pathways in the gene regulatory mechanism which are more susceptible to losing the WT form; and when robustness is lost, what types of forms are most likely to occur (i.e. what forms lie near the WT)? Manipulating maternal effects in several different pathways, we find a common gap 'peak-to-step' pattern transition in the loss of WT. We discuss these results in terms of the evolvability of insect segmentation, and in terms of experimental perturbations and mutations which could test the model predictions. We conclude by discussing the prospects for using continuum models of pattern dynamics to investigate a wider range of evo-devo problems.

On the evolutionary origin of discrete phenotypic plasticity.

Phenotypic plasticity provides an attractive strategy for adapting to various environments, but the evolutionary mechanism of the underlying genetic system is poorly understood. We use a simple gene regulatory network model to explore how a species acquires phenotypic plasticity, particularly focusing on discrete phenotypic plasticity, which has been difficult to explain by quantitative genetic models. Our approach employs a population genetic framework that integrates the developmental process, where each individual undergoes growth to develop its phenotype, which subsequently becomes subject to selection pressures. Our model considers two alternative types of environments, with the gene regulatory network including a sensor gene that turns on and off depending on the type of environment. With this assumption, we demonstrate that the system gradually adapts by acquiring the ability to produce two distinct optimum phenotypes under two types of environments without changing genotype, resulting in phenotypic plasticity. We find that the resulting plasticity is often discrete after a lengthy period of evolution. Our results suggest that gene regulatory networks have a notable capacity to flexibly produce various phenotypes in response to environmental changes. This study also shows that the evolutionary dynamics of phenotype may differ significantly between mechanistic-based developmental models and quantitative genetics models, suggesting the utility of incorporating gene regulatory networks into evolutionary models.

Network analyses predict major regulators of resistance to early blight disease complex in tomato.

BACKGROUND: Early blight and brown leaf spot are often cited as the most problematic pathogens of tomato in many agricultural regions. Their causal agents are Alternaria spp., a genus of Ascomycota containing numerous necrotrophic pathogens. Breeding programs have yielded quantitatively resistant commercial cultivars, but fungicide application remains necessary to mitigate the yield losses. A major hindrance to resistance breeding is the complexity of the genetic determinants of resistance and susceptibility. In the absence of sufficiently resistant germplasm, we sequenced the transcriptomes of Heinz 1706 tomatoes treated with strongly virulent and weakly virulent isolates of Alternaria spp. 3 h post infection. We expanded existing functional gene annotations in tomato and using network statistics, we analyzed the transcriptional modules associated with defense and susceptibility. RESULTS: The induced responses are very distinct. The weakly virulent isolate induced a defense response of calcium-signaling, hormone responses, and transcription factors. These defense-associated processes were found in a single transcriptional module alongside secondary metabolite biosynthesis genes, and other defense responses. Co-expression and gene regulatory networks independently predicted several D clade ethylene response factors to be early regulators of the defense transcriptional module, as well as other transcription factors both known and novel in pathogen defense, including several JA-associated genes. In contrast, the strongly virulent isolate elicited a much weaker response, and a separate transcriptional module bereft of hormone signaling. CONCLUSIONS: Our findings have predicted major defense regulators and several targets for downstream functional analyses. Combined with our improved gene functional annotation, they suggest that defense is achieved through induction of Alternaria-specific immune pathways, and susceptibility is mediated by modulating hormone responses. The implication of multiple specific clade D ethylene response factors and upregulation of JA-associated genes suggests that host defense in this pathosystem involves ethylene response factors to modulate jasmonic acid signaling.

Is cross-species horizontal gene transfer responsible for gallbladder carcinogenesis.

BACKGROUND: Cross-species horizontal gene transfer (HGT) involves the transfer of genetic material between different species of organisms. In recent years, mounting evidence has emerged that cross-species HGT does take place and may play a role in the development and progression of diseases. METHODS: Transcriptomic data obtained from patients with gallbladder cancer (GBC) was assessed for the differential expression of antisense RNAs (asRNAs). The Basic Local Alignment Search Tool (BLAST) was used for cross-species analysis with viral, bacterial, fungal, and ancient human genomes to elucidate the evolutionary cross species origins of these differential asRNAs. Functional enrichment analysis and text mining were conducted and a network of asRNAs targeting mRNAs was constructed to understand the function of differential asRNAs better. RESULTS: A total of 17 differentially expressed antisense RNAs (asRNAs) were identified in gallbladder cancer tissue compared to that of normal gallbladder. BLAST analysis of 15 of these asRNAs (AFAP1-AS1, HMGA2-AS1, MNX1-AS1, SLC2A1-AS1, BBOX1-AS1, ELFN1-AS1, TRPM2-AS, DNAH17-AS1, DCST1-AS1, VPS9D1-AS1, MIR1-1HG-AS1, HAND2-AS1, PGM5P4-AS1, PGM5P3-AS1, and MAGI2-AS) showed varying degree of similarities with bacterial and viral genomes, except for UNC5B-AS1 and SOX21-AS1, which were conserved during evolution. Two of these 15 asRNAs, (VPS9D1-AS1 and SLC2A1-AS1) exhibited a high degree of similarity with viral genomes (Chikungunya virus, Human immunodeficiency virus 1, Stealth virus 1, and Zika virus) and bacterial genomes including (Staphylococcus sp., Bradyrhizobium sp., Pasteurella multocida sp., and, Klebsiella pneumoniae sp.), indicating potential HGT during evolution. CONCLUSION: The results provide novel evidence supporting the hypothesis that differentially expressed asRNAs in GBC exhibit varying sequence similarity with bacterial, viral, and ancient human genomes, indicating a potential shared evolutionary origin. These non-coding genes are enriched with methylation and were found to be associated with cancer-related pathways, including the P53 and PI3K-AKT signaling pathways, suggesting their possible involvement in tumor development.

Refining computational inference of gene regulatory networks: integrating knockout data within a multi-task framework.

Constructing accurate gene regulatory network s (GRNs), which reflect the dynamic governing process between genes, is critical to understanding the diverse cellular process and unveiling the complexities in biological systems. With the development of computer sciences, computational-based approaches have been applied to the GRNs inference task. However, current methodologies face challenges in effectively utilizing existing topological information and prior knowledge of gene regulatory relationships, hindering the comprehensive understanding and accurate reconstruction of GRNs. In response, we propose a novel graph neural network (GNN)-based Multi-Task Learning framework for GRN reconstruction, namely MTLGRN. Specifically, we first encode the gene promoter sequences and the gene biological features and concatenate the corresponding feature representations. Then, we construct a multi-task learning framework including GRN reconstruction, Gene knockout predict, and Gene expression matrix reconstruction. With joint training, MTLGRN can optimize the gene latent representations by integrating gene knockout information, promoter characteristics, and other biological attributes. Extensive experimental results demonstrate superior performance compared with state-of-the-art baselines on the GRN reconstruction task, efficiently leveraging biological knowledge and comprehensively understanding the gene regulatory relationships. MTLGRN also pioneered attempts to simulate gene knockouts on bulk data by incorporating gene knockout information.

Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks.

BACKGROUND AND OBJECTIVE: Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand. METHODS: MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance. RESULTS: MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner. CONCLUSIONS: MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.

MEFFGRN: Matrix enhancement and feature fusion-based method for reconstructing the gene regulatory network of epithelioma papulosum cyprini cells by spring viremia of carp virus infection.

Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study findings from SVCV-infected EPC cells suggest that MEFFGRN can predict novel gene regulatory relationships.

Corrigendum: Bipartite networks represent causality better than simple networks: Evidence, algorithms, and applications.

[This corrects the article DOI: 10.3389/fgene.2024.1371607.].

Regulation of a single inositol 1-phosphate synthase homeologue by HSFA6B contributes to fibre yield maintenance under drought conditions in upland cotton.

Drought stress substantially impacts crop physiology resulting in alteration of growth and productivity. Understanding the genetic and molecular crosstalk between stress responses and agronomically important traits such as fibre yield is particularly complicated in the allopolyploid species, upland cotton (Gossypium hirsutum), due to reduced sequence variability between A and D subgenomes. To better understand how drought stress impacts yield, the transcriptomes of 22 genetically and phenotypically diverse upland cotton accessions grown under well-watered and water-limited conditions in the Arizona low desert were sequenced. Gene co-expression analyses were performed, uncovering a group of stress response genes, in particular transcription factors GhDREB2A-A and GhHSFA6B-D, associated with improved yield under water-limited conditions in an ABA-independent manner. DNA affinity purification sequencing (DAP-seq), as well as public cistrome data from Arabidopsis, were used to identify targets of these two TFs. Among these targets were two lint yield-associated genes previously identified through genome-wide association studies (GWAS)-based approaches, GhABP-D and GhIPS1-A. Biochemical and phylogenetic approaches were used to determine that GhIPS1-A is positively regulated by GhHSFA6B-D, and that this regulatory mechanism is specific to Gossypium spp. containing the A (old world) genome. Finally, an SNP was identified within the GhHSFA6B-D binding site in GhIPS1-A that is positively associated with yield under water-limiting conditions. These data lay out a regulatory connection between abiotic stress and fibre yield in cotton that appears conserved in other systems such as Arabidopsis.

Identifying vital nodes for yeast network by dynamic network entropy.

BACKGROUND: The progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed K2 algorithm to identify effectively the sensitive nodes and genes in the evolution of yeast. RESULTS: Considering the multivariate of cell cycle data, we first utilize the K2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory factor. CONCLUSIONS: It is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first step in deciphering the profound mystery of gene regulation.

SLIVER: Unveiling large scale gene regulatory networks of single-cell transcriptomic data through causal structure learning and modules aggregation.

Prevalent Gene Regulatory Network (GRN) construction methods rely on generalized correlation analysis. However, in biological systems, regulation is essentially a causal relationship that cannot be adequately captured solely through correlation. Therefore, it is more reasonable to infer GRNs from a causal perspective. Existing causal discovery algorithms typically rely on Directed Acyclic Graphs (DAGs) to model causal relationships, but it often requires traversing the entire network, which result in computational demands skyrocketing as the number of nodes grows and make causal discovery algorithms only suitable for small networks with one or two hundred nodes or fewer. In this study, we propose the SLIVER (cauSaL dIscovery Via dimEnsionality Reduction) algorithm which integrates causal structural equation model and graph decomposition. SLIVER introduces a set of factor nodes, serving as abstractions of different functional modules to integrate the regulatory relationships between genes based on their respective functions or pathways, thus reducing the GRN to the product of two low-dimensional matrices. Subsequently, we employ the structural causal model (SCM) to learn the GRN within the gene node space, enforce the DAG constraint in the low-dimensional space, and guide each factor to aggregate various functions through cosine similarity. We evaluate the performance of the SLIVER algorithm on 12 real single cell transcriptomic datasets, and demonstrate it outperforms other 12 widely used methods both in GRN inference performance and computational resource usage. The analysis of the gene information integrated by factor nodes also demonstrate the biological explanation of factor nodes in GRNs. We apply it to scRNA-seq of Type 2 diabetes mellitus to capture the transcriptional regulatory structural changes of ß cells under high insulin demand.

Inference of gene regulatory networks based on directed graph convolutional networks.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.