Large-scale loss-of-function perturbations reveal a comprehensive epigenetic regulatory network in breast cancer

Objective:Epigenetic abnormalities have a critical role in breast cancer by regulating gene expression;however,the intricate interrelationships and key roles of approximately 400 epigenetic regulators in breast cancer remain elusive.It is important to decipher the comprehensive epigenetic regulatory network in breast cancer cells to identify master epigenetic regulators and potential therapeutic targets.Methods:We employed high-throughput sequencing-based high-throughput screening(HTS^(2))to effectively detect changes in the expression of 2,986 genes following the knockdown of 400 epigenetic regulators.Then,bioinformatics analysis tools were used for the resulting gene expression signatures to investigate the epigenetic regulations in breast cancer.Results:Utilizing these gene expression signatures,we classified the epigenetic regulators into five distinct clusters,each characterized by specific functions.We discovered functional similarities between BAZ2B and SETMAR,as well as CLOCK and CBX3.Moreover,we observed that CLOCK functions in a manner opposite to that of HDAC8 in downstream gene regulation.Notably,we constructed an epigenetic regulatory network based on the gene expression signatures,which revealed 8 distinct modules and identified 10 master epigenetic regulators in breast cancer.Conclusions:Our work deciphered the extensive regulation among hundreds of epigenetic regulators.The identification of 10 master epigenetic regulators offers promising therapeutic targets for breast cancer treatment.

Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

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.

Construction of the underlying circRNA-miRNA-mRNA regulatory network and a new diagnostic model in ulcerative colitis by bioinformatics analysis

BACKGROUND Circular RNAs(circRNAs)are involved in the pathogenesis of many diseases through competing endogenous RNA(ceRNA)regulatory mechanisms.AIM To investigate a circRNA-related ceRNA regulatory network and a new predictive model by circRNA to understand the diagnostic mechanism of circRNAs in ulcerative colitis(UC).METHODS We obtained gene expression profiles of circRNAs,miRNAs,and mRNAs in UC from the Gene Expression Omnibus dataset.The circRNA-miRNA-mRNA network was constructed based on circRNA-miRNA and miRNA-mRNA interactions.Functional enrichment analysis was performed to identify the biological mechanisms involved in circRNAs.We identified the most relevant differential circRNAs for diagnosing UC and constructed a new predictive nomogram,whose efficacy was tested with the C-index,receiver operating characteristic curve(ROC),and decision curve analysis(DCA).RESULTS A circRNA-miRNA-mRNA regulatory network was obtained,containing 12 circRNAs,three miRNAs,and 38 mRNAs.Two optimal prognostic-related differentially expressed circRNAs,hsa_circ_0085323 and hsa_circ_0036906,were included to construct a predictive nomogram.The model showed good discrimination,with a C-index of 1(>0.9,high accuracy).ROC and DCA suggested that the nomogram had a beneficial diagnostic ability.CONCLUSION This novel predictive nomogram incorporating hsa_circ_0085323 and hsa_circ_0036906 can be conveniently used to predict the risk of UC.The circRNa-miRNA-mRNA network in UC could be more clinically significant.

Identification of the target genes of AhTWRKY24 and AhTWRKY106 transcription factors reveals their regulatory network in Arachis hypogaea cv.Tifrunner using DAP-seq

WRKY transcription factors(TFs)have been identified as important core regulators in the responses of plants to biotic and abiotic stresses.Cultivated peanut(Arachis hypogaea)is an important oil and protein crop.Previous studies have identified hundreds of WRKY TFs in peanut.However,their functions and regulatory networks remain unclear.Simultaneously,the AdWRKY40 TF is involved in drought tolerance in Arachis duranensis and has an orthologous relationship with the AhTWRKY24 TF,which has a homoeologous relationship with AhTWRKY106 TF in A.hypogaea cv.Tifrunner.To reveal how the homoeologous AhTWRKY24 and AhTWRKY106 TFs regulate the downstream genes,DNA affinity purification sequencing(DAP-seq)was performed to detect the binding sites of TFs at the genome-wide level.A total of 3486 downstream genes were identified that were collectively regulated by the AhTWRKY24 and AhTWRKY106 TFs.The results revealed that W-box elements were the binding sites for regulation of the downstream genes by AhTWRKY24 and AhTWRKY106 TFs.A gene ontology enrichment analysis indicated that these downstream genes were enriched in protein modification and reproduction in the biological process.In addition,RNA-seq data showed that the AhTWRKY24 and AhTWRKY106 TFs regulate differentially expressed genes involved in the response to drought stress.The AhTWRKY24 and AhTWRKY106 TFs can specifically regulate downstream genes,and they nearly equal the numbers of downstream genes from the two A.hypogaea cv.Tifrunner subgenomes.These results provide a theoretical basis to study the functions and regulatory networks of AhTWRKY24 and AhTWRKY106 TFs.

A Vertex Network Model of Arabidopsis Leaf Growth

Biology provides many examples of complex systems whose properties allow organisms to develop in a highly reproducible,or robust,manner.One such system is the growth and development of flat leaves in Arabidopsis thaliana.This mechanistically challenging process results from multiple inputs including gene interactions,cellular geometry,growth rates,and coordinated cell divisions.To better understand how this complex genetic and cellular information controls leaf growth,we developed a mathematical model of flat leaf production.This two-dimensional model describes the gene interactions in a vertex network of cells which grow and divide according to physical forces and genetic information.Interestingly,the model predicts the presence of an unknown additional factor required for the formation of biologically realistic gene expression domains and iterative cell division.This two-dimensional model will form the basis for future studies into robustness of adaxial-abaxial patterning.

早发性卵巢功能不全的lncRNA-miRNA-mRNA网络的构建和生物信息学分析

目的:探讨lncRNA相关内源竞争RNA(ceRNA)网络在早发性卵巢功能不全中的作用机制。方法:从Gene Expression Omnibus(GEO)数据库中筛选出两个早发性卵巢功能不全患者的数据集,使用R语言“limma”包筛选出差异表达的lncRNA、miRNA和mRNA。使用Starbase数据库预测与差异lncRNA相互作用的miRNAs,使用TargetScan和miRDB数据库预测与差异miRNAs相互作用的mRNAs,预测的mRNAs与差异mRNAs取交集。根据RNA之间的相互作用关系,建立早发性卵巢功能不全的lncRNA-miRNA-mRNA ceRNA调控网络。使用Metascape在线工具对ceRNA调控网络中的mRNA进行GO和KEGG功能分析,通过String数据库建立蛋白质相互作用(PPI)网络,利用Cytohubba插件识别PPI网络中的hub基因并构建lncRNAmiRNA-hub基因网络。结果:从早发性卵巢功能不全患者与正常对照组中筛选了116个差异lncRNAs,22个差异miRNAs和282个差异mRNAs。通过116个差异lncRNAs,利用数据库预测到了13个差异lncRNAs,7个差异miRNAs和54个差异mRNAs的相互作用,构建了早发性卵巢功能不全的lncRNA-miRNA-mRNA ceRNA调控网络。GO和KEGG富集分析结果显示,调控网络中的mRNA参与早发性卵巢功能不全的生物学过程,包括凋亡信号通路、mRNA的代谢过程和cAMP信号通路。通过PPI网络筛选了8个hub基因(EIF4ENIF1、SENP1、RBM5、DYNLL2、AGO2、SRSF1、CAPZB、SRSF10),构建了一个lncRNA-miRNA-hub基因网络。结论:12个lncRNA通过参与SRSF1等hub基因的表达,调控影响早发性卵巢功能不全的发生、发展。

Fluctuation Resonance of Feed Forward Loops in Gene Regulatory Networks

The feed forward loop （FFL）, wherein a gene X can regulate target gene Z alone or cooperatively with gene Y, is one of the most important motifs in gene regulatory networks. Gene expression often involves a small number of reactant molecules and thus internal molecular fluctuation is considerable. Here we studied how an FFL responds to small external signal inputs at gene X, with particular attention paid to the fluctuation resonance （FR） phenomenon of gene Z. We found that for all coherent FFLs, where the sign of the direct regulation path from X to Z is the same as the overall sign of the indirect path via Y, the FR shows a regular single peak, while for incoherent FFLs, the FR exhibits distinct bimodal shapes. The results indicate that one could use small external signals to help identify the regulatory structure of an unknown FFL in complex gene networks.

Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.

Molecular Regulatory Network of Flowering by Photoperiod and Temperature in Rice

Plants have an ability to flower under optimal seasonal conditions to ensure reproductive success.Photoperiod and temperature are two important season-dependent factors of plant flowering.The floral transition of plants depends on accurate measurement of changes in photoperiod and temperature.Recent advances in molecular biology and genetics on Arabidopsis and rice reveals that the regulation of plant flowering by photoperiod and temperature are involved in a complicated gene network with different regulatory pathways,and new evidence and understanding were provided in the regulation of rice flowering.Here,we summarize and analyze different flowering regulatory pathways in detail in rice based on previous studies and our results,including short-day promotion,long-day suppression,long-day induction of flowering,night break,different light-quality and temperature regulation pathways.

Reconstructing gene regulatory networks in single-cell transcriptomic data analysis

Gene regulatory networks play pivotal roles in our understanding of biological processes/mechanisms at the molecular level.Many studies have developed sample-specific or cell-type-specific gene regulatory networks from single-cell transcriptomic data based on a large amount of cell samples.Here,we review the state-of-the-art computational algorithms and describe various applications of gene regulatory networks in biological studies.

Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks

Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly. In this paper, we propose a novel approach based on combining neuro-fu^zy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory nctworks＋ but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without lhctitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast, The results show that this approach can work effectively.

Structure and Dynamics of Artificial Regulatory Networks Evolved by Segmental Duplication and Divergence Model

Based on a model of network encoding and dynamics called the artificial genome, we propose a segmental duplication and divergence model for evolving artificial regulatory networks. We find that this class of networks share structural properties with natural transcriptional regulatory networks. Specifically, these networks can display scale-free and small-world structures. We also find that these networks have a higher probability to operate in the ordered regimen, and a lower probability to operate in the chaotic regimen. That is, the dynamics of these networks is similar to that of natural networks. The results show that the structure and dynamics inherent in natural networks may be in part due to their method of generation rather than being exclusively shaped by subsequent evolution under natural selection.

Oscillatory and anti-oscillatory motifs in genetic regulatory networks

Recently, self-sustained oscillatory genetic regulatory networks （GRNs） have attracted significant attention in the biological field. Given a GRN, it is important to anticipate whether the network could generate oscillation with proper parameters, and what the key ingredients for the oscillation are. In this paper the ranges of some function-related parameters which are favorable to sustained oscillations are considered. In particular, some oscillatory motifs appearing with high-frequency in most of the oscillatory GRNs are observed. Moreover, there are some anti-oscillatory motifs which have a strong oscillation repressing effect. Some conclusions analyzing these motif effects and constructing oscillatory GRNs are provided.

Expression and regulatory network of long noncoding RNA in rats after spinal cord hemisection injury

Long noncoding RNAs(lncRNAs)participate in a variety of biological processes and diseases.However,the expression and function of lncRNAs after spinal cord injury has not been extensively analyzed.In this study of right side hemisection of the spinal cord at T10,we detected the expression of lncRNAs in the proximal tissue of T10 lamina at different time points and found 445 lncRNAs and 6522 mRNA were differentially expressed.We divided the differentially expressed lncRNAs into 26 expression trends and analyzed Profile 25 and Profile 2,the two expression trends with the most significant difference.Our results showed that the expression of 68 lncRNAs in Profile 25 rose first and remained high 3 days post-injury.There were 387 mRNAs co-expressed with the 68 lncRNAs in Profile 25.The co-expression network showed that the co-expressed genes were mainly enriched in cell division,inflammatory response,FcγR-mediated cell phagocytosis signaling pathway,cell cycle and apoptosis.The expression of 56 lncRNAs in Profile2 first declined and remained low after 3 days post-injury.There were 387 mRNAs co-expressed with the 56 lncRNAs in Profile 2.The co-expression network showed that the co-expressed genes were mainly enriched in the chemical synaptic transmission process and in the signaling pathway of neuroactive ligand-receptor interaction.The results provided the expression and regulatory network of the main lncRNAs after spinal cord injury and clarified their co-expressed gene enriched biological processes and signaling pathways.These findings provide a new direction for the clinical treatment of spinal cord injury.

Research of Gene Regulatory Network with Multi-Time Delay Based on Bayesian Network

The gene regulatory network was reconstructed according to time-series microarray data getting from hybridization at different time between gene chips to analyze coordination and restriction between genes. An algorithm for controlling the gene expression regulatory network of the whole cell was designed using Bayesian network which provides an effective aided analysis for gene regulatory network.

Dynamics of network motifs in genetic regulatory networks

Network motifs hold a very important status in genetic regulatory networks. This paper aims to analyse the dynamical property of the network motifs in genetic regulatory networks. The main result we obtained is that the dynamical property of a single motif is very simple with only an asymptotically stable equilibrium point, but the combination of several motifs can make more complicated dynamical properties emerge such as limit cycles. The above-mentioned result shows that network motif is a stable substructure in genetic regulatory networks while their combinations make the genetic regulatory network more complicated.

Stability of piecewise-linear models of genetic regulatory networks

This paper investigates the stability of the equilibria of the piecewise-linear models of genetic regulatory networks on the intersection of the thresholds of all variables. It first studies circling trajectories and derives some stability conditions by quantitative analysis in the state transition graph. Then it proposes a common Lyapunov function for convergence analysis of the piecewise-linear models and gives a simple sign condition. All the obtained conditions are only related to the constant terms on the right-hand side of the differential equation after bringing the equilibrium to zero.

Designing Genetic Regulatory Networks Using Fuzzy Petri Nets Approach

In this paper, we have successfully presented a fuzzy Petri net （FPN） model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition （HFT） to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.

The application of hidden markov model in building genetic regulatory network

The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.

A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models

Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but different.The differential analysis of GRNs under different conditions is important for understanding condition-specific gene regulatory relationships.In a naive approach,existing GRN inference algorithms can be used to separately estimate two GRNs under different conditions and identify the differences between them.However,in this way,the similarities between the pairwise GRNs are not taken into account.Several joint differential analysis algorithms have been proposed recently,which were proved to outperform the naive approach apparently.In this paper,we model the GRNs under different conditions with structural equation models(SEMs)to integrate gene expression data and genetic perturbations,and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential structure.Then,a Bayesian inference method is used to make joint differential analysis by solving the integrated model.We evaluated the performance of the proposed re-parametrization-based Bayesian differential analysis(ReBDA)algorithm by running simulations on synthetic data with different settings.The performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for SEMs ReDNet obviously.In the end,the ReBDA algorithm was applied to make differential analysis on a real human lung gene data set to illustrate its applicability and practicability.