Meta-QTL analysis for mining of candidate genes and constitutive gene network development for fungal disease resistance in maize(Zea mays L.)

The development of resistant maize cultivars is the most effective and sustainable approach to combat fungal diseases.Over the last three decades,many quantitative trait loci(QTL)mapping studies reported numerous QTL for fungal disease resistance(FDR)in maize.However,different genetic backgrounds of germplasm and differing QTL analysis algorithms limit the use of identified QTL for comparative studies.The meta-QTL(MQTL)analysis is the meta-analysis of multiple QTL experiments,which entails broader allelic coverage and helps in the combined analysis of diverse QTL mapping studies revealing common genomic regions for target traits.In the present study,128(33.59%)out of 381 reported QTL(from 82 studies)for FDR could be projected on the maize genome through MQTL analysis.It revealed 38 MQTL for FDR(12 diseases)on all chromosomes except chromosome 10.Five MQTL namely 1_4,2_4,3_2,3_4,and 5_4 were linked with multiple FDR.Total of 1910 candidate genes were identified for all the MQTL regions,with protein kinase gene families,TFs,pathogenesis-related,and disease-responsive proteins directly or indirectly associated with FDR.The comparison of physical positions of marker-traits association(MTAs)from genome-wide association studies with genes underlying MQTL interval verified the presence of QTL/candidate genes for particular diseases.The linked markers to MQTL and putative candidate genes underlying identified MQTL can be further validated in the germplasm through marker screening and expression studies.The study also attempted to unravel the underlying mechanism for FDR resistance by analyzing the constitutive gene network,which will be a useful resource to understand the molecular mechanism of defense-response of a particular disease and multiple FDR in maize.

Research on Behavior of Governing Gene/Epigene Networks as a Problem of Cellular Automata Identification

The aim of this work is to investigate the general functional properties of the intracellular governing gene/epigene networks. A body of mathematics used in automata and graphs theories is adequate for revealing the general dynamic properties of governing gene and epigene networks and provides a methodic basis for efficient analytical algorithms. The obtained results permit to reveal the properties of the characteristic function （transitions and outputs） of the cellular automata as models for the intracellular governing gene/epigene networks.

Screening key target genes for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)based on bioinformatics and gene network

Background:To provide a reference for the clinical development of drugs to suppress severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Methods:Retrieving genes related to SARS-CoV-2 with Genecards database and then importing the obtained gene data into the database of Database for Annotation,Visualization and Integrated Discovery(DAVID)(Version 6.8)to collect relevant information on pathways and genes.Genes enriched in the first 20 most significant pathways and genes with gene occurrence frequency≥6 were respectively imported into the STRING database to construct protein-protein interaction(PPI)network diagrams,and the two network diagrams were compared.Results:In the two network graphs,RELA,MAPK1,MAPK3,PIK3CA,PIK3R1,MAPK8,JAK1,STAT1,TNF,IL6,MAPK14,and IL1B ranked higher,and the occurrence frequency of the first 20 pathways was≥10.Conclusion:The pathogenesis of SARS-CoV-2 is associated with multiple pathways such as influenza A,TNF signaling pathway,chemokine signaling pathway,toll-like receptor signaling pathway,T cell receptor signaling pathway et al.RELA,MAPK1,MAPK3,PIK3CA,PIK3R1,MAPK8,JAK1,STAT1,TNF,IL6,MAPK14 and IL1B are closely related to SARS-CoV-2 and need further study.Gene interaction network and pathway analysis of diseaseassociated genes will help us to screen the key target genes of SARS-CoV-2 and provide a reference for the clinical development of effective drugs.

Screening key target genes for coronavirus disease 2019 based on bioinformatics and gene network

Objective:To use bioinformatics and gene networks to screen key target genes of coronavirus disease 2019,which provides references for clinical research and development of drugs for coronavirus disease 2019.Methods:Target genes related to coronavirus disease 2019 were screened in the GeneCards and National Center for Biotechnology Information databases,and the obtained gene data were imported into the Database for Annotation,Visualization and Integrated Discovery(Version 6.8)database to collect the related information about pathways and genes.The genes enriched in the first 20 pathways and the genes whose occurrence frequency≥5 were imported into the String database respectively to construct protein-protein interaction network diagram and compare the two network diagrams.Results:TNF,IL-6,IL-2,IL-8,CXCL8,IL1B,CCL2,IFNG,STAT1,MAPK1,MAPK3,MAPK8,TP53 and RELA are ranked top in the two network diagrams,and the frequency of occurrence in the first 20 pathways was≥5.Conclusion:The incidence of coronavirus disease 2019 is associated with multiple signaling pathways,including influenza A,pathways in cancer,toll-like receptor signaling pathway,hypoxia-inducible factor-1 signaling pathway,et al.TNF,IL-6,IL-2,IL-8,CXCL8,IL1B,CCL2,IFNG,STAT1,MAPK1,MAPK3,MAPK8,TP53 and RELA are closely related to coronavirus disease 2019,which needs to be further studied.By analyzing the pathways of the genes related to coronavirus disease 2019 and the interactive network diagrams between the genes,it is helpful to understand the pathogenesis of the disease and provide a reference for clinical research and development of effective drugs for coronavirus disease 2019.

H2 and H∞-Feedback Control Design for Nonlinear Gene Networks via Successive Galerkin’s Approximation

This paper presents a design method of H2 and H∞-feedback control loop for nonlinear smooth gene networks that are in control affine form. Formulaic solution methodology for solving the nonlinear partial differential equations, namely the Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Isaacs equations through successive Galerkin’s approximation is implemented and the results are compared. Throughout the implementation, there were several caveats that need to be further resolved for practical applications in general cases. Such issues and the clarification of causes are mathematically established and reviewed.

A Novel Analytical Method for Structural Characteristics of Gene Networks and its Application

Analyzing gene network structure is an important way to discover and understand some unknown relevant functions and regulatory mechanisms of organism at the molecular level. In this work, mutual information networks and Boolean logic networks are constructed using the methods of reverse modeling based on gene expression profiles in lung tissues with and without cancer. The comparison of these network structures shows that average degree, the proportion of non-isolated nodes, average betweenness and average coreness can distinguish the networks corresponding to the lung tissues with and without cancer. According to the difference of degree, betweenness and coreness of each gene in these networks, nine structural key genes are obtained. Seven of them which are related to lung cancer are supported by literatures. The remaining two genes AKT1 and RBL may have important roles in the formation, development and metastasis of lung cancer. Furthermore, the contrast of these logic networks suggests that the distributions of logic types are obviously different. The structural differences can help us to understand the mechanism of formation and development of lung cancer.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis

The plant genome produces an extremely large collection of long noncoding RNAs(lncRNAs)that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes.Here,we mapped the transcriptional heterogeneity of lncRNAs and their associated gene reg-ulatory networks at single-cell resolution.We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing(scRNA-seq)datasets from juvenile Arabidopsis seedlings.We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers.We further demon-strated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity.In addi-tion,we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs,and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs.The analysis results are available at the single-cell-based plant lncRNA atlas data-base(scPLAD;https://biobigdata.nju.edu.cn/scPLAD/).Overall,this work demonstrates the power of inte-grative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.

Kinetic analysis of p53 gene network with time delays and PIDD

p53 kinetics plays a key role in regulating cell fate.Based on the p53 gene regulatory network composed by the core regulatory factors ATM,Mdm2,Wipl,and PIDD,the effect of the delays in the process of transcription and translation of Mdm2 and Wipl on the dynamics of p53 is studied theoretically and numerically.The results show that these two time delays can affect the stability of the positive equilibrium.With the increase of delays,the dynamics of p53 presents an oscillating state.Further,we also study the effects of PIDD and chemotherapeutic drug etoposide on the kinetics of p53.The model indicates that(i)PIDD low-level expression does not significantly affect p53 oscillatory behavior,but high-level expression could induce two-phase kinetics of p53;(ii)Too high and too low concentration of etoposide is not conducive to p53 oscillation.These results are in good agreement with experimental findings.Finally,we consider the infuence of internal noise on the system through Binomial r-leap algorithm.Stochastic simulations reveal that high-intensity noise completely destroys p53 dynamics in the deterministic model,whereas low-intensity noise does not alter p53 dynamics.Interestingly,for the stable focus,the internal noise with appropriate intensity can induce quasi-limit cycle oscillations of the system.Our work may provide the useful insights for the development of anticancer therapy.

Target Entrapment Based on Adaptive Transformation of Gene Regulatory Networks

The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic settings.To address these challenges,this paper introduces an adaptive swarm robot entrapment control model grounded in the transformation of gene regulatory networks(AT-GRN).This innovative model enables swarm robots to dynamically adjust entrap-ment strategies by assessing current environmental conditions via real-time sensory data.Further-more,an improved motion control model for swarm robots is designed to dynamically shape the for-mation generated by the AT-GRN.Through two sets of rigorous experimental environments,the proposed model significantly enhances the trapping performance of swarm robots in complex envi-ronments,demonstrating remarkable adaptability and stability.

Gene Networks in Plant Ozone Stress Response and Tolerance

For many plant species ozone stress has become much more severe in the last decade. The accumulating evidence for the significant effects of ozone pollutant on crop and forest yield situate ozone as one of the most important environmental stress factors that limits plant productivity worldwide. Today, transcriptomic approaches seem to give the best coverage of genome level responses. Therefore, microarray serves as an invaluable tool for global gene expression analyses, unravelling new information about gene pathways, in-species and cross-species gene expression comparison, and for the characterization of unknown relationships between genes. In this review we summarize the recent progress in the transcriptomics of ozone to demonstrate the benefits that can be harvested from the application of integrative and systematic analytical approaches to study ozone stress response. We focused our consideration on microarray analyses identifying gene networks responsible for response and tolerance to elevated ozone concentration. From these analyses it is now possible to notice how plant ozone defense responses depend on the interplay between many complex signaling pathways and metabolite signals.

ANALYSIS FOR GENE NETWORKS BASED ON LOGIC RELATIONSHIPS

The reverse construction and analysis of the networks of molecular interactions are essential for understanding their functions within cells. In this paper, a logic network model is constructed to investigate the complicated regulation mechanism of shoot genes of Arabidopsis Thaliana in response to stimuli. The dynamics of the complicated logic network is analyzed, discussed, and simulated. The simulation results show that the logic network of the active genes of shoot eventually evolves into eleven attractors under the stimuli, including five 1-periodic and six 2-periodic attractors. Our work provides valuable reference and guidance for biologists to understand and explain Arabidopsis＇ response to external stimuli by experiments.

Differential gene network analysis from single cell RNA-seq

Study of gene expression has been arguably the most active research field in functional genomics.Over the last two decades,various high-throughput technologies,from gene expression microarray to RNA-seq,have been widely applied to the wholegenome profiling of gene expression.The commonality of these experiments is that they measure the gene expression levels of"bulk"sample,which pools a large number(often in the scale of millions)of cells,and thus the measurements reflect the average expression

Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks

Although genome-wide association studies （GWAS） have successfully identified thousands of genomic loci associated with hun- dreds of complex traits in the past decade, the debate about such problems as missing heritabiUty and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and antici- pated genetic data. Towards this goal, gene-tevel integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advan- tages as straightforward interpretation, tess multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET （Simultaneously Inference of GeNEs and Tissues） to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype- associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in find- ing both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the preven- tion, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.

Community Detection in Disease-Gene Network Based on Principal Component Analysis

The identification of communities is imperative in the understanding of network structures and functions.Using community detection algorithms in biological networks, the community structure of biological networks can be determined, which is helpful in analyzing the topological structures and predicting the behaviors of biological networks. In this paper, we analyze the diseasome network using a new method called disease-gene network detecting algorithm based on principal component analysis, which can be used to investigate the connection between nodes within the same group. Experimental results on real-world networks have demonstrated that our algorithm is more efficient in detecting community structures when compared with other well-known results.

Analysis of Gene Networks for Arabidopsis Flowering

The flowering time of Arabidopsis is sensitive to climate variability, with lighting conditions being a major determinant of the flowering time. Long-days induce early flowering, while short-days induce late flowering or even no flowers. This study investigates the intrinsic mechanisms for Arabidopsis flowering in different lighting conditions using mutual information networks and logic networks. The structure parameters of the mutual information networks show that the average degree and the average core clearly distinguish these networks. A method is then given to find the key structural genes in the mutual information networks and the logic networks respectively. Ten genes are found to possibly promote flowering with three genes that may restrain flowering. The sensitivity of this method to find the genes that promote flowering is 80%, while the sensitivity of the method to find the genes that restrain flowering is 100%.

Different Gene Networks Are Disturbed by Zika Virus Infection in A Mouse Microcephaly Model

The association of Zika virus(ZIKV)infection with microcephaly has raised alarm worldwide.Their causal link has been confirmed in different animal models infected by ZIKV.However,the molecular mechanisms underlying ZIKV pathogenesis are far from clear.Hence,we performed global gene expression analysis of ZIKV-infected mouse brains to unveil the biological and molecular networks underpinning microcephaly.We found significant dysregulation of the sub-networks associated with brain development,immune response,cell death,microglial cell activation,and autophagy amongst others.We provided detailed analysis of the related complicated gene networks and the links between them.Additionally,we analyzed the signaling pathways that were likely to be involved.This report provides systemic insights into not only the pathogenesis,but also a path to the development of prophylactic and therapeutic strategies against ZIKV infection.

Influences of a periodic signal on a noisy synthetic gene network

The effects of noise and a periodic signal on a synthetic gene network have been investigated. By tuning the distance of a parameter from the Hopf bifurcation point, both implicit internal signal stochastic resonance and explicit internal signal stochastic resonance can be induced by noise. Furthermore, a switch process can also be elicited. When a periodic signal is coupled to the gene network, two interesting phenomena occur with the modulation of the frequency of the signal: the effect of noise amplifying cellular signal can be inhibited or even destroyed, and "locked" coherence resonance occurs.

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.

Expression changes of mi RNA-regulated genes associated with the formation of the leafy head in cabbage

The vegetative development of cabbage(Brassica oleracea var.capitata)passes through seedling,rosette,folding and heading stages.Leaves that form the rosette are large and mostly flat.In the following developmental stages,the plants produce leaves that curve inward to produce the leafy head.Many microRNAs and their target genes have been described participating in leaf development and leaf curvature.The aim of this study is to investigate the role of miRNA-regulated genes in the transition from the rosette to the heading stage.We compared the mi RNA and gene abundances between emerging rosette and heading leaves.To remove transcripts(miRNAs and genes)whose regulation was most likely associated with plant age rather than the change from rosette to heading stage,we utilized a non-heading collard green(B.oleracea var.acephala)morphotype as control.This resulted in 33 DEMs and 1998 DEGs with likely roles in the transition from rosette to heading stage in cabbage.Among these 1998 DEGs,we found enriched GO terms related to DNA-binding transcription factor activity,transcription regulator activity,iron ion binding,and photosynthesis.We predicted the target genes of these 33 DEMs and focused on the subset that was differentially expressed(1998DEGs)between rosette and heading stage leaves to construct mi RNA-target gene interaction networks.Our main finding is a role for miR396b-5p targeting two Arabidopsis thaliana orthologues of GROWTH REGULATING FACTORs 3(GRF3)and 4(GRF4)in pointed cabbage head formation.