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BackgroundMany approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually).ResultsWe approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships.ConclusionsNew insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.

Introduction: Gene regulatory networks (GRNs) reveal the intricate interactions between and among genes, and understanding these interactions is essential for revealing the molecular mechanisms of cancer. However, existing algorithms for constructing GRNs may confuse regulatory relationships and complicate the determination of network directionality. Methods: We propose a new method to construct GRNs based on causal strength and ensemble regression (CSER) to overcome these issues. CSER uses conditional mutual inclusive information to quantify the causal associations between genes, eliminating indirect regulation and marginal genes. It considers linear and nonlinear features and uses ensemble regression to infer the direction and interaction (activation or regression) from regulatory to target genes. Results: Compared to traditional algorithms, CSER can construct directed networks and infer the type of regulation, thus demonstrating higher accuracy on simulated datasets. Here, using real gene expression data, we applied CSER to construct a colorectal cancer GRN and successfully identified several key regulatory genes closely related to colorectal cancer (CRC), including ADAMDEC1, CLDN8, and GNA11. Discussion: Importantly, by integrating immune cell and microbial data, we revealed the complex interactions between the CRC gene regulatory network and the tumor microenvironment, providing additional new biomarkers and therapeutic targets for the early diagnosis and prognosis of CRC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Most organisms have evolved specific mechanisms to respond to changes in environmental conditions such as light and temperature over the course of day. These periodic changes in the physiology and behaviour of organisms, referred to as circadian rhythms, are a consequence of intricate molecular mechanisms in the form of transcription and translational feedback loops. The plant circadian regulatory network is a complex web of interconnected feedback loops involving various transcription factors such as CCA1, LHY, PRRs, TOC1, LUX, ELF3, ELF4, RVE8, and more. This network enables plants to adapt and thrive in diverse environmental conditions. It responds to entrainment signals, including light, temperature, and nutrient concentrations and interacts with most of the physiological functions such as flowering, growth and stress response. Mathematical modelling of these gene regulatory networks enables a deeper understanding of not only the function but also the perturbations that may affect the plant growth and function with changing climate. Over the years, numerous mathematical models have been developed to understand the diverse aspects of plant circadian regulation. In this review, we have delved into the systematic development of these models, outlining the model components and refinements over time. We have also highlighted strengths and limitations of each of the models developed so far. Finally, we conclude the review by describing the prospects for investigation and advancement of these models for better understanding of plant circadian regulation.Key message: Plant circadian rhythms are regulated by a complex network of interactions. This review summarizes the mathematical models that have been developed until now to explore plant circadian rhythms. [ABSTRACT FROM AUTHOR]

Caenorhabditis elegans males undergo sex-specific tail tip morphogenesis (TTM) under the control of the DM-domain transcription factor DMD-3. To find genes regulated by DMD-3, we performed RNA-seq of laser-dissected tail tips. We identified 564 genes differentially expressed (DE) in wild-type males versus dmd-3(-) males and hermaphrodites. The transcription profile of dmd-3(-) tail tips is similar to that in hermaphrodites. For validation, we analyzed transcriptional reporters for 49 genes and found male-specific or malebiased expression for 26 genes. Only 11 DE genes overlapped with genes found in a previous RNAi screen for defective TTM. GO enrichment analysis of DE genes finds upregulation of genes within the unfolded protein response pathway and downregulation of genes involved in cuticle maintenance. Of the DE genes, 40 are transcription factors, indicating that the gene network downstream of DMD-3 is complex and potentially modular. We propose modules of genes that act together in TTM and are co-regulated by DMD-3, among them the chondroitin synthesis pathway and the hypertonic stress response. [ABSTRACT FROM AUTHOR]

Inferring gene regulatory networks through deep learning and causal inference methods is a crucial task in the field of computational biology and bioinformatics. This study presents a novel approach that uses a Graph Convolutional Network (GCN) guided by causal information to infer Gene Regulatory Networks (GRN). The transfer entropy and reconstruction layer are utilized to achieve causal feature reconstruction, mitigating the information loss problem caused by multiple rounds of neighbor aggregation in GCN, resulting in a causal and integrated representation of node features. Separable features are extracted from gene expression data by the Gaussian-kernel Autoencoder to improve computational efficiency. Experimental results on the DREAM5 and the mDC dataset demonstrate that our method exhibits superior performance compared to existing algorithms, as indicated by the higher values of the AUPRC metrics. Furthermore, the incorporation of causal feature reconstruction enhances the inferred GRN, rendering them more reasonable, accurate, and reliable. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The skeleton plays a fundamental role in the maintenance of organ function and daily activities. The insulin‐like growth factor (IGF) family is a group of polypeptide substances with a pronounced role in osteoblast differentiation, bone development, and metabolism. Disturbance of the IGFs and the IGF signaling pathway is inextricably linked with assorted developmental defects, growth irregularities, and jeopardized skeletal structure. Recent findings have illustrated the significance of the action of the IGF signaling pathway via growth factors and receptors and its interactions with dissimilar signaling pathways (Wnt/β‐catenin, BMP, TGF‐β, and Hh/PTH signaling pathways) in promoting the growth, survival, and differentiation of osteoblasts. IGF signaling also exhibits profound influences on cartilage and bone development and skeletal homeostasis via versatile cell–cell interactions in an autocrine, paracrine, and endocrine manner systemically and locally. Our review summarizes the role and regulatory function as well as a potentially integrated gene network of the IGF signaling pathway with other signaling pathways in bone and cartilage development and skeletal homeostasis, which in turn provides an enlightening insight into visualizing bright molecular targets to be eligible for designing effective drugs to handle bone diseases and maladies, such as osteoporosis, osteoarthritis, and dwarfism. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Studying gene regulatory networks (GRNs) is paramount for unraveling the complexities of biological processes and their associated disorders, such as diabetes, cancer, and Alzheimer's disease. Recent advancements in computational biology have aimed to enhance the inference of GRNs from gene expression data, a non-trivial task given the networks' intricate nature. The challenge lies in accurately identifying the myriad interactions among transcription factors and target genes, which govern cellular functions. This research introduces a cutting-edge technique, EGRC (Effective GRN Inference applying Graph Convolution with Self-Attention Graph Pooling), which innovatively conceptualizes GRN reconstruction as a graph classification problem, where the task is to discern the links within subgraphs that encapsulate pairs of nodes. By leveraging Spearman's correlation, we generate potential subgraphs that bring nonlinear associations between transcription factors and their targets to light. We use mutual information to enhance this, capturing a broader spectrum of gene interactions. Our methodology bifurcates these subgraphs into 'Positive' and 'Negative' categories. 'Positive' subgraphs are those where a transcription factor and its target gene are connected, including interactions among their neighbors. 'Negative' subgraphs, conversely, denote pairs without a direct connection. EGRC utilizes dual graph convolution network (GCN) models that exploit node attributes from gene expression profiles and graph embedding techniques to classify these. The performance of EGRC is substantiated by comprehensive evaluations using the DREAM5 datasets. Notably, EGRC attained an AUROC of 0.856 and an AUPR of 0.841 on the E. coli dataset. In contrast, the in silico dataset achieved an AUROC of 0.5058 and an AUPR of 0.958. Furthermore, on the S. cerevisiae dataset, EGRC recorded an AUROC of 0.823 and an AUPR of 0.822. These results underscore the robustness of EGRC in accurately inferring GRNs across various organisms. The advanced performance of EGRC represents a substantial advancement in the field, promising to deepen our comprehension of the intricate biological processes and their implications in both health and disease. [ABSTRACT FROM AUTHOR]

Background: Jasmonic acid (JA) is a phytohormone involved in regulating responses to biotic and abiotic stress. Although the JA pathway is well characterized in model plants such as Arabidopsis thaliana, less is known about many non-model plants. Phytolacca americana (pokeweed) is native to eastern North Americana and is resilient to environmental stress. The goal of this study was to produce a publicly available pokeweed genome assembly and annotations and use this resource to determine how early response to JA changes gene expression, with particular focus on genes involved in defense. Results: We assembled the pokeweed genome de novo from approximately 30 Gb of PacBio Hifi long reads and achieved an NG50 of ~ 13.2 Mb and a minimum 93.9% complete BUSCO score for gene annotations. With this reference, we investigated the early changes in pokeweed gene expression following JA treatment. Approximately 5,100 genes were differentially expressed during the 0–6 h time course with almost equal number of genes with increased and decreased transcript levels. Cluster and gene ontology analyses indicated the downregulation of genes associated with photosynthesis and upregulation of genes involved in hormone signaling and defense. We identified orthologues of key transcription factors and constructed the first JA gene response network integrated with our transcriptomic data from orthologues of Arabidopsis genes. We discovered that pokeweed did not use leaf senescence as a means of reallocating resources during stress; rather, most secondary metabolite synthesis genes were constitutively expressed, suggesting that pokeweed directs its resources for survival over the long term. In addition, pokeweed synthesizes several RNA N-glycosylases hypothesized to function in defense, each with unique expression profiles in response to JA. Conclusions: Our investigation of the early response of pokeweed to JA illustrates patterns of gene expression involved in defence and stress tolerance. Pokeweed provides insight into the defense mechanisms of plants beyond those observed in research models and crops, and further study may yield novel approaches to improving the resilience of plants to environmental changes. Our assembled pokeweed genome is the first within the taxonomic family Phytolaccaceae to be publicly available for continued research. [ABSTRACT FROM AUTHOR]

Low temperature is one of the most important environmental factors that inhibits rice growth and grain yield. Transcription factors (TFs) play crucial roles in chilling acclimation by regulating gene expression. However, transcriptional dynamics and key regulators responding to low temperature remain largely unclear in rice. In this study, a transcriptome-based comparative analysis was performed to explore genome-wide gene expression profiles between a chilling-resistant cultivar DC90 and a chilling-susceptible cultivar 9311 at a series of time points under low temperature treatment and recovery condition. A total of 3,590 differentially expressed genes (DEGs) between two cultivars were determined and divided into 12 co-expression modules. Meanwhile, several biological processes participating in the chilling response such as abscisic acid (ABA) responses, water deprivation, protein metabolic processes, and transcription regulator activities were revealed. Through weighted gene co-expression network analysis (WGCNA), 15 hub TFs involved in chilling conditions were identified. Further, we used the gene regulatory network (GRN) to evaluate the top 50 TFs, which might have potential roles responding to chilling stress. Finally, five TFs, including a C-repeat binding factor (OsCBF3), a zinc fingerhomeodomain protein (OsZHD8), a tandem zinc finger protein (OsTZF1), carbon starved anther (CSA), and indeterminate gametophyte1 (OsIG1) were identified as crucial candidates responsible for chilling resistance in rice. This study deepens our understanding in the gene regulation networks of chilling stress in rice and offers potential gene resources for breeding climateresilient crops. [ABSTRACT FROM AUTHOR]

Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges. [ABSTRACT FROM AUTHOR]

How did the complex structure of the telencephalon evolve? Existing explanations are based on phenomena and lack a first-principles account. The Darwinian dynamics and endogenous network theory—established decades ago—provides a mathematical and theoretical framework and a general constitutive structure for theory–experiment coupling for answering this question from a first-principles perspective. By revisiting a gene network that explains the anterior–posterior patterning of the vertebrate telencephalon, we found that upon increasing the cooperative effect within this network, fixed points gradually evolve, accompanied by the occurrence of two bifurcations. The dynamic behavior of this network is informed by the knowledge obtained from experiments on telencephalic evolution. Our work provides a quantitative explanation for how telencephalon anterior–posterior patterning evolved from the pre-vertebrate chordate to the vertebrate and provides a series of verifiable predictions from a first-principles perspective. [ABSTRACT FROM AUTHOR]

Abstract Background Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually). Results We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization—a step that skews distributions, particularly for sparse data—and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene–gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. Conclusions New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene–gene correlations.

Abstract Inferring gene regulatory networks through deep learning and causal inference methods is a crucial task in the field of computational biology and bioinformatics. This study presents a novel approach that uses a Graph Convolutional Network (GCN) guided by causal information to infer Gene Regulatory Networks (GRN). The transfer entropy and reconstruction layer are utilized to achieve causal feature reconstruction, mitigating the information loss problem caused by multiple rounds of neighbor aggregation in GCN, resulting in a causal and integrated representation of node features. Separable features are extracted from gene expression data by the Gaussian-kernel Autoencoder to improve computational efficiency. Experimental results on the DREAM5 and the mDC dataset demonstrate that our method exhibits superior performance compared to existing algorithms, as indicated by the higher values of the AUPRC metrics. Furthermore, the incorporation of causal feature reconstruction enhances the inferred GRN, rendering them more reasonable, accurate, and reliable.

Studying gene regulatory networks (GRNs) is paramount for unraveling the complexities of biological processes and their associated disorders, such as diabetes, cancer, and Alzheimer’s disease. Recent advancements in computational biology have aimed to enhance the inference of GRNs from gene expression data, a non-trivial task given the networks’ intricate nature. The challenge lies in accurately identifying the myriad interactions among transcription factors and target genes, which govern cellular functions. This research introduces a cutting-edge technique, EGRC (Effective GRN Inference applying Graph Convolution with Self-Attention Graph Pooling), which innovatively conceptualizes GRN reconstruction as a graph classification problem, where the task is to discern the links within subgraphs that encapsulate pairs of nodes. By leveraging Spearman’s correlation, we generate potential subgraphs that bring nonlinear associations between transcription factors and their targets to light. We use mutual information to enhance this, capturing a broader spectrum of gene interactions. Our methodology bifurcates these subgraphs into ‘Positive’ and ‘Negative’ categories. ‘Positive’ subgraphs are those where a transcription factor and its target gene are connected, including interactions among their neighbors. ‘Negative’ subgraphs, conversely, denote pairs without a direct connection. EGRC utilizes dual graph convolution network (GCN) models that exploit node attributes from gene expression profiles and graph embedding techniques to classify these. The performance of EGRC is substantiated by comprehensive evaluations using the DREAM5 datasets. Notably, EGRC attained an AUROC of 0.856 and an AUPR of 0.841 on the E. coli dataset. In contrast, the in silico dataset achieved an AUROC of 0.5058 and an AUPR of 0.958. Furthermore, on the S. cerevisiae dataset, EGRC recorded an AUROC of 0.823 and an AUPR of 0.822. These results underscore the robustness of EGRC in accurately inferring GRNs across various organisms. The advanced performance of EGRC represents a substantial advancement in the field, promising to deepen our comprehension of the intricate biological processes and their implications in both health and disease.

Abstract Background Jasmonic acid (JA) is a phytohormone involved in regulating responses to biotic and abiotic stress. Although the JA pathway is well characterized in model plants such as Arabidopsis thaliana, less is known about many non-model plants. Phytolacca americana (pokeweed) is native to eastern North Americana and is resilient to environmental stress. The goal of this study was to produce a publicly available pokeweed genome assembly and annotations and use this resource to determine how early response to JA changes gene expression, with particular focus on genes involved in defense. Results We assembled the pokeweed genome de novo from approximately 30 Gb of PacBio Hifi long reads and achieved an NG50 of ~ 13.2 Mb and a minimum 93.9% complete BUSCO score for gene annotations. With this reference, we investigated the early changes in pokeweed gene expression following JA treatment. Approximately 5,100 genes were differentially expressed during the 0–6 h time course with almost equal number of genes with increased and decreased transcript levels. Cluster and gene ontology analyses indicated the downregulation of genes associated with photosynthesis and upregulation of genes involved in hormone signaling and defense. We identified orthologues of key transcription factors and constructed the first JA gene response network integrated with our transcriptomic data from orthologues of Arabidopsis genes. We discovered that pokeweed did not use leaf senescence as a means of reallocating resources during stress; rather, most secondary metabolite synthesis genes were constitutively expressed, suggesting that pokeweed directs its resources for survival over the long term. In addition, pokeweed synthesizes several RNA N-glycosylases hypothesized to function in defense, each with unique expression profiles in response to JA. Conclusions Our investigation of the early response of pokeweed to JA illustrates patterns of gene expression involved in defence and stress tolerance. Pokeweed provides insight into the defense mechanisms of plants beyond those observed in research models and crops, and further study may yield novel approaches to improving the resilience of plants to environmental changes. Our assembled pokeweed genome is the first within the taxonomic family Phytolaccaceae to be publicly available for continued research.

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and classical systems typically studied in non-equilibrium statistical and quantum mechanics.

BACKGROUND: m6 A modification has been confirmed to play an important role in the occurrence and development of osteonecrosis of the femoral head; however, the role of m6 A modification patterns in steroid-induced osteonecrosis of the femoral head remains unknown. OBJECTIVE: Bioinformatics analysis was performed based on the Gene Expression Omnibus (GEO) database to analyze the differential expression of the m6 A gene in steroid-induced osteonecrosis of the femoral head, predict the downstream targeted miRNAs, and investigate the potential pathogenesis. METHODS: Expressing profiles of mRNA data of steroid-induced osteonecrosis of the femoral head were downloaded from GEO database (GSE123568). Differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the R software. After obtaining these differentially methylated m6 A genes (m6 A-DEGs), we analyzed GO function and KEGG pathway enrichment and compared the correlation among the m6 A-DEGs typing according to gene expression. The protein-protein interaction network and core gene subnetwork of m6 A-DEGs were constructed using Cytoscape software. The m6 A-DEGs-associated potential miRNAs were predicted using the TargetScan, miRTarBase, and miRBD databases. Simultaneously, ChIPBase and hTFtarget databases were used to predict potential transcription factors of seven core genes, then m6 A-miRNA and transcription factor-m6 A regulatory networks were constructed separately. Finally, the expression levels of the seven core m6 A-DEGs were verified by using the GSE74089 dataset. RESULTS AND CONCLUSION: (1) A total of 2 460 common DEGs were screened out from datasets, among which 1 455 genes were upregulated and 1 005 genes were downregulated. (2) A total of 14 m6 A-DEGs were identified in the datasets. Among them, 11 m6 A-DEGs were up-regulated and 3 m6 A-DEGs were down-regulated. Differential gene expression was considered significant for m6 A-DEGs in steroid-induced osteonecrosis of the femoral head (P < 0.05). Spearman correlation analysis showed a significant correlation between m6 A-DEGs. (3) GO and KEGG enrichment analysis showed that m6 A-DEGs were mainly enriched in myeloid cell differentiation and development, immune and cytokine receptor activity, osteoclast differentiation, AMPK signaling pathway and interleukin-17 signaling pathway. (4) The seven core genes of m6 A-DEGs contained YTHDF3, YTHDF1, YTHDF2, ALKBH5, METTL3, HNRNPA2B1, and HNRNPC. A total of 44 miRNAs overlapping were detected in the miRTarBase, miRDB, and TargetScan databases. Totally 79 transcription factors overlapping were found in the ChIPBase and hTFtarget databases. (5) The expression levels of six core m6 A-DEGs in the GSE74089 dataset were consistent with those in the GSE123568 dataset. (6) These findings confirm that the seven m6 A-DEGs identified through bioinformatics techniques play a regulatory role in the expression of various miRNAs, transcription factors, AMPK, and interleukin-17 signaling pathways, and these genes have a significant impact on the differentiation and development of bone marrow cells as well as osteoclast differentiation in steroid-induced osteonecrosis of the femoral head. Consequently, these findings offer data support and establish a research direction for future investigations into the pathogenesis and targeted therapeutic strategies for steroid-induced osteonecrosis of the femoral head. [ABSTRACT FROM AUTHOR]

Gene regulatory network (GRN) refers to the complex network formed by regulatory interactions between genes in living cells. In this paper, we consider inferring GRNs in single cells based on single‐cell RNA sequencing (scRNA‐seq) data. In scRNA‐seq, single cells are often profiled from mixed populations, and their cell identities are unknown. A common practice for single‐cell GRN analysis is to first cluster the cells and infer GRNs for every cluster separately. However, this two‐step procedure ignores uncertainty in the clustering step and thus could lead to inaccurate estimation of the networks. Here, we consider the mixture Poisson log‐normal model (MPLN) for network inference of count data from mixed populations. The precision matrices of the MPLN are the GRNs of different cell types. To avoid the intractable optimization of the MPLN's log‐likelihood, we develop an algorithm called variational mixture Poisson log‐normal (VMPLN) to jointly estimate the GRNs of different cell types based on the variational inference method. We compare VMPLN with state‐of‐the‐art single‐cell regulatory network inference methods. Comprehensive simulation shows that VMPLN achieves better performance, especially in scenarios where different cell types have a high mixing degree. Benchmarking on real scRNA‐seq data also demonstrates that VMPLN can provide more accurate network estimation in most cases. Finally, we apply VMPLN to a large scRNA‐seq dataset from patients infected with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and find that VMPLN identifies critical differences in regulatory networks in immune cells between patients with moderate and severe symptoms. The source codes are available on the GitHub website (github.com/XiDsLab/SCVMPLN). [ABSTRACT FROM AUTHOR]

Larvacean tunicates feature a spectacular innovation not seen in other animals - the trunk oikoplastic epithelium (OE). This epithelium produces a house, a large and complex extracellular structure used for filtering and concentrating food particles. Previously we identified several homeobox transcription factor genes expressed during early OE patterning. Among these are two Pax3/7 copies that we named pax37A and pax37B. The vertebrate homologs, PAX3 and PAX7 are involved in developmental processes related to neural crest and muscles. In the ascidian tunicate Ciona intestinalis , Pax3/7 plays a role in the development of cells deriving from the neural plate border, including trunk epidermal sensory neurons and tail nerve cord neurons, as well as in the neural tube closure. Here we have investigated the roles of Oikopleura dioica pax37A and pax37B in the development of the OE, by using CRISPR-Cas9 mutant lines and analyzing scRNA-seq data from wild-type animals. We found that pax37B but not pax37A is essential for the differentiation of cell fields that produce the food concentrating filter of the house: the anterior Fol, giant Fol and Nasse cells. Trajectory analysis supported a neuroepithelial-like or a preplacodal ectoderm transcriptional signature in these cells. We propose that the highly specialized secretory epithelial cells of the Fol region either maintained or evolved neuroepithelial features. This is supported by a fragmented gene regulatory network involved in their development that also operates in ascidian epidermal neurons. [Display omitted] • pax37B controls the development of the Fol region of the oikoplastic epithelium. • The pax37B TF network is similar to that of ascidian trunk epidermal neurons. • The pax37B + cells are secretory cells with a neuroepithelial-like origin. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract 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.

Abstract Background Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene–gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene–gene interactions using Mendelian randomization. Results MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions. Conclusion These findings demonstrate that MR-GGI accurately inferences gene–gene interactions despite the confounding effects in real biological environments.

Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors (TFs) in intricate regulatory networks in a cell-type specific manner. Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings. This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets, addressing batch effects and conserving biological variance. This integration spans a broad spectrum of tissues, including both below- and above-ground parts. Utilizing a rigorous approach for cell type annotation, we identified 47 distinct cell types or states, largely expanding our current view of plant cell compositions. We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression. Taken together, our study not only offers extensive plant cell atlas exploration that serves as a valuable resource, but also provides molecular insights into gene-regulatory programs that varies from different cell types.

Abstract Comparative analyses between traditional model organisms, such as the fruit fly Drosophila melanogaster, and more recent model organisms, such as the red flour beetle Tribolium castaneum, have provided a wealth of insight into conserved and diverged aspects of gene regulation. While the study of trans-regulatory components is relatively straightforward, the study of cis-regulatory elements (CREs, or enhancers) remains challenging outside of Drosophila. A central component of this challenge has been finding a core promoter suitable for enhancer-reporter assays in diverse insect species. Previously, we demonstrated that a Drosophila Synthetic Core Promoter (DSCP) functions in a cross-species manner in Drosophila and Tribolium. Given the over 300 million years of divergence between the Diptera and Coleoptera, we reasoned that DSCP-based reporter constructs will be useful when studying cis-regulation in a variety of insect models across the holometabola and possibly beyond. To this end, we sought to create a suite of new DSCP-based reporter vectors, leveraging dual compatibility with piggyBac and PhiC31-integration, the 3xP3 universal eye marker, GATEWAY cloning, different colors of reporters and markers, as well as Gal4-UAS binary expression. While all constructs functioned properly with a Tc-nub enhancer in Drosophila, complications arose with tissue-specific Gal4-UAS binary expression in Tribolium. Nevertheless, the functionality of these constructs across multiple holometabolous orders suggests a high potential compatibility with a variety of other insects. In addition, we present the piggyLANDR (piggyBac-LoxP AttP Neutralizable Destination Reporter) platform for the establishment of proper PhiC31 landing sites free from position effects. As a proof-of-principle, we demonstrated the workflow for piggyLANDR in Drosophila. The potential utility of these tools ranges from molecular biology research to pest and disease-vector management, and will help advance the study of gene regulation beyond traditional insect models.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The expression level of a gene can vary between genetically identical cells under the same environmental condition—a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision‐making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision‐making. As advances in single‐cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference. [ABSTRACT FROM AUTHOR]

Summary: Silique number is a crucial yield‐related trait for the genetic enhancement of rapeseed (Brassica napus L.). The intricate molecular process governing the regulation of silique number involves various factors. Despite advancements in understanding the mechanisms regulating silique number in Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa), the molecular processes involved in controlling silique number in rapeseed remain largely unexplored. In this review, we identify candidate genes and review the roles of genes and environmental factors in regulating rapeseed silique number. We use genetic regulatory networks for silique number in Arabidopsis and grain number in rice to uncover possible regulatory pathways and molecular mechanisms involved in regulating genes associated with rapeseed silique number. A better understanding of the genetic network regulating silique number in rapeseed will provide a theoretical basis for the genetic improvement of this trait and genetic resources for the molecular breeding of high‐yielding rapeseed. [ABSTRACT FROM AUTHOR]

Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator relationships. Understanding the GRN dynamics will unravel the complexity behind the observed gene expressions. Insect gene regulation is often complicated due to their complex life cycles and diverse ecological adaptations. The main interest of this review is to have an update on the current mathematical modelling methods of GRNs to explain insect science. Several popular GRN architecture models are discussed, together with examples of applications in insect science. In the last part of this review, each model is compared from different aspects, including network scalability, computation complexity, robustness to noise and biological relevancy. [Display omitted] [ABSTRACT FROM AUTHOR]

Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships between genes obtained by these methods are biased. Eliminating indirect effects in GRNs remains a significant challenge for researchers. In this work, we propose a novel approach for inferring functional GRNs, named EIEPCF (eliminating indirect effects produced by confounding factors), which eliminates indirect effects caused by confounding factors. This method eliminates the influence of confounding factors on regulatory factors and target genes by measuring the similarity between their residuals. The validation results of the EIEPCF method on simulation studies, the gold-standard networks provided by the DREAM3 Challenge and the real gene networks of Escherichia coli demonstrate that it achieves significantly higher accuracy compared to other popular computational methods for inferring GRNs. As a case study, we utilized the EIEPCF method to reconstruct the cold-resistant specific GRN from gene expression data of cold-resistant in Arabidopsis thaliana. The source code and data are available at https://github.com/zhanglab-wbgcas/EIEPCF. [ABSTRACT FROM AUTHOR]

A network, whose nodes are genes and whose directed edges represent positive or negative influences of a regulatory gene and its targets, is often used as a representation of causality. To infer a network, researchers often develop a machine learning model and then evaluate the model based on its match with experimentally verified "gold standard" edges. The desired result of such a model is a network that may extend the gold standard edges. Since networks are a form of visual representation, one can compare their utility with architectural or machine blueprints. Blueprints are clearly useful because they provide precise guidance to builders in construction. If the primary role of gene regulatory networks is to characterize causality, then such networks should be good tools of prediction because prediction is the actionable benefit of knowing causality. But are they? In this paper, we compare prediction quality based on "gold standard" regulatory edges from previous experimental work with nonlinear models inferred from time series data across four different species. We show that the same non-linear machine learning models have better predictive performance, with improvements from 5.3% to 25.3% in terms of the reduction in the root mean square error (RMSE) compared with the same models based on the gold standard edges. Having established that networks fail to characterize causality properly, we suggest that causality research should focus on four goals: (i) predictive accuracy; (ii) a parsimonious enumeration of predictive regulatory genes for each target gene g; (iii) the identification of disjoint sets of predictive regulatory genes for each target g of roughly equal accuracy; and (iv) the construction of a bipartite network (whose node types are genes and models) representation of causality. We provide algorithms for all goals. [ABSTRACT FROM AUTHOR]

Summary 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. [ABSTRACT FROM AUTHOR]

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security. [ABSTRACT FROM AUTHOR]

Objective: Network analysis techniques often require tuning hyperparameters for optimal performance. For instance, the independent cascade model necessitates determining the probability of diffusion. Despite its importance, a consensus on effective parameter adjustment remains elusive. Methods: In this study, we propose a novel approach utilizing experimental design methodologies, specifically 2-Factorial Analysis for Screening, and Response Surface Methodology (RSM) for parameter adjustment. We apply this methodology to the task of detecting cancer driver genes in colorectal cancer. Result: Through experimental validation of colorectal cancer data, we demonstrate the effectiveness of our proposed methodology. Compared with existing methods, our approach offers several advantages, including reduced computational overhead, systematic parameter selection grounded in statistical theory, and improved performance in detecting cancer driver genes. Conclusion: This study presents a significant advancement in the field of network analysis by providing a practical and systematic approach to hyperparameter tuning. By optimizing parameter settings, our methodology offers promising implications for critical biomedical applications such as cancer driver gene detection. [ABSTRACT FROM AUTHOR]

Floral forms with an increased number of petals, also known as double-flower phenotypes, have been selected and conserved in many domesticated plants, particularly in ornamentals, because of their great economic value. The molecular and genetic mechanisms that control this trait are therefore of great interest, not only for scientists, but also for breeders. In this review, we summarize current knowledge of the gene regulatory networks of flower initiation and development and known mutations that lead to variation of petal number in many species. In addition to the well-accepted miR172/AP2-like module, for which many questions remain unanswered, we also discuss other pathways in which mutations also lead to the formation of extra petals, such as those involved in meristem maintenance, hormone signalling, epigenetic regulation, and responses to environmental signals. We discuss how the concept of 'natural mutants' and recent advances in genomics and genome editing make it possible to explore the molecular mechanisms underlying double-flower formation, and how such knowledge could contribute to the future breeding and selection of this trait in more crops. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

SUMMARY: Bioenergy sorghum is a low‐input, drought‐resilient, deep‐rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4–5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell‐type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell‐type‐specific and cell‐preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell‐type‐specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell‐specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data (https://mc‐lab.shinyapps.io/lcm‐dataset/). [ABSTRACT FROM AUTHOR]

The gene regulatory approach based on retrieving information from the database provides a detailed characterization of the molecular mechanisms of disease progression at the level of miRNAs, Transcription Factors (TFs), and genes. Moreover, gene regulatory networks can find an interaction between the miRNAs, TFs, and genes involved in diabetic nephropathy (DN), but the underlying mechanisms of motif remain unclear. We first gathered genes related to glomeruli diabetic nephropathy from GEO and CTD database. Besides, miRNAs targeting genes were collected from the public databases and GEO. Furthermore, regulator TFs were accumulated from related public databases. After that, we explored the regulatory relationships between TF-miRNA, miRNA-Gene, TF-Gene, and miRNA–TF using FANMOD software. Finally, a gene regulatory network consisting of miRNAs, genes, and TFs was constructed, helping the Cytoscape. The global const parameter in FANMOD software used to discover the interaction between miRNAs, genes, TFs, and 3-node regulatory motif types were detected in the resulting network. Among them, it led to the discovery of the two-node feedback motif (2FB) in charge of the up-regulation of miRNA-target gene-TF and TF-mediated cascade motif and co-pointing motif (COP) responsible for the down-regulation of miRNA-target gene–TF. In this study, we found a correlation between miRNAs, TFs, and target genes using a gene regulatory network. We revealed the candidate 3-node motifs associated with the progression of DN. Therefore, detected molecular mechanisms, as well as the relationship between previous studies, demonstrated targets that can help in the discovery of a novel treatment for DN. [ABSTRACT FROM AUTHOR]

Introduction: Cross‐talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross‐talk in colorectal cancer (CRC) development at both the pathway and gene levels. Methods: In this study, we integrated the gene expression data with domain knowledge to construct state‐dependent pathway cross‐talk networks. The significance of the genes involved in pathway cross‐talk was assessed by analyzing their association with cancer hallmarks, disease‐gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression. Results: Cross‐talk was observed between immune‐related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial‐mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high‐risk from low‐risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways. Conclusions: This study highlights the crucial role of pathway cross‐talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules. [ABSTRACT FROM AUTHOR]

Boolean networks have rich dynamics properties widely used in modeling and investigating circuit design, artificial intelligence, biological research. Generally, a gene regulation network is not independent. It interacts between external networks and external regulation signals. Therefore, the Boolean network with external control signals, that is, Boolean control network, has become a potential effective tool for studying gene regulatory networks. Attractor calculation is the main research content of Boolean control networks. In this study, an approach of attractor calculation was proposed for Boolean control networks. Examples and simulations also confirmed that the proposed approach is very effective and can get all attractors of large-scale Boolean control networks. [ABSTRACT FROM AUTHOR]

A network, whose nodes are genes and whose directed edges represent positive or negative influences of a regulatory gene and its targets, is often used as a representation of causality. To infer a network, researchers often develop a machine learning model and then evaluate the model based on its match with experimentally verified "gold standard" edges. The desired result of such a model is a network that may extend the gold standard edges. Since networks are a form of visual representation, one can compare their utility with architectural or machine blueprints. Blueprints are clearly useful because they provide precise guidance to builders in construction. If the primary role of gene regulatory networks is to characterize causality, then such networks should be good tools of prediction because prediction is the actionable benefit of knowing causality. But are they? In this paper, we compare prediction quality based on "gold standard" regulatory edges from previous experimental work with nonlinear models inferred from time series data across four different species. We show that the same non-linear machine learning models have better predictive performance, with improvements from 5.3% to 25.3% in terms of the reduction in the root mean square error (RMSE) compared with the same models based on the gold standard edges. Having established that networks fail to characterize causality properly, we suggest that causality research should focus on four goals: (i) predictive accuracy; (ii) a parsimonious enumeration of predictive regulatory genes for each target gene g; (iii) the identification of disjoint sets of predictive regulatory genes for each target g of roughly equal accuracy; and (iv) the construction of a bipartite network (whose node types are genes and models) representation of causality. We provide algorithms for all goals. [ABSTRACT FROM AUTHOR]