Single-cell analysis of the survival mechanisms of fratricidal CAR-T targeting of T cell malignancies.

Chimeric antigen receptor T (CAR-T) cell therapy targeting T cell tumors still faces many challenges, one of which is its fratricide due to the target gene expressed on CAR-T cells. Despite this, these CAR-T cells can be expanded in vitro by extending the culture time and effectively eliminating malignant T cells. However, the mechanisms underlying CAR-T cell survival in cell subpopulations, the molecules involved, and their regulation are still unknown. We performed single-cell transcriptome profiling to investigate the fratricidal CAR-T products (CD26 CAR-Ts and CD44v6 CAR-Ts) targeting T cells, taking CD19 CAR-Ts targeting B cells from the same donor as a control. Compared with CD19 CAR-Ts, fratricidal CAR-T cells exhibit no unique cell subpopulation, but have more exhausted T cells, fewer cytotoxic T cells, and more T cell receptor (TCR) clonal amplification. Furthermore, we observed that fratricidal CAR-T cell survival was accompanied by target gene expression. Gene expression results suggest that fratricidal CAR-T cells may downregulate their human leukocyte antigen (HLA) molecules to evade T cell recognition. Single-cell regulatory network analysis and suppression experiments revealed that exhaustion mediated by critical regulatory factors may contribute to fratricidal CAR-T cell survival. Together, these data provide valuable and first-time insights into the survival of fratricidal CAR-T cells.

The biosynthesis of storage reserves and auxin is coordinated by a hierarchical regulatory network in maize endosperm.

Grain filling in maize (Zea mays) is intricately linked to cell development, involving the regulation of genes responsible for the biosynthesis of storage reserves (starch, proteins, and lipids) and phytohormones. However, the regulatory network coordinating these biological functions remains unclear. In this study, we identified 1744 high-confidence target genes co-regulated by the transcription factors (TFs) ZmNAC128 and ZmNAC130 (ZmNAC128/130) through chromatin immunoprecipitation sequencing coupled with RNA-seq analysis in the zmnac128/130 loss-of-function mutants. We further constructed a hierarchical regulatory network using DNA affinity purification sequencing analysis of downstream TFs regulated by ZmNAC128/130. In addition to target genes involved in the biosynthesis of starch and zeins, we discovered novel target genes of ZmNAC128/130 involved in the biosynthesis of lipids and indole-3-acetic acid (IAA). Consistently, the number of oil bodies, as well as the contents of triacylglycerol, and IAA were significantly reduced in zmnac128/130. The hierarchical regulatory network centered by ZmNAC128/130 revealed a significant overlap between the direct target genes of ZmNAC128/130 and their downstream TFs, particularly in regulating the biosynthesis of storage reserves and IAA. Our results indicated that the biosynthesis of storage reserves and IAA is coordinated by a multi-TFs hierarchical regulatory network in maize endosperm.

Exploring the reciprocity between pioneer factors and development.

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

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

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

Genome-wide characterization of the GRF transcription factors in potato (Solanum tuberosum L.) and expression analysis of StGRF genes during potato tuber dormancy and sprouting.

Growth-regulating factors (GRFs) are transcription factors that play a pivotal role in plant growth and development. This study identifies 12 Solanum tuberosum GRF transcription factors (StGRFs) and analyzes their physicochemical properties, phylogenetic relationships, gene structures and gene expression patterns using bioinformatics. The StGRFs exhibit a length range of 266 to 599 amino acids, with a molecular weight of 26.02 to 64.52 kDa. The majority of StGRFs possess three introns. The promoter regions contain a plethora of cis-acting elements related to plant growth and development, as well as environmental stress and hormone response. All the members of the StGRF family contain conserved WRC and QLQ domains, with the sequences of these two conserved domain modules exhibiting high levels of conservation. Transcriptomic data indicates that StGRFs play a significant role in the growth and development of stamens, roots, young tubers, and other tissues or organs in potatoes. Furthermore, a few StGRFs exhibit differential expression patterns in response to Phytophthora infestans, chemical elicitors, heat, salt, and drought stresses, as well as multiple hormone treatments. The results of the expression analysis indicate that StGRF1, StGRF2, StGRF5, StGRF7, StGRF10 and StGRF12 are involved in the process of tuber sprouting, while StGRF4 and StGRF9 may play a role in tuber dormancy. These findings offer valuable insights that can be used to investigate the roles of StGRFs during potato tuber dormancy and sprouting.

On the evolutionary origin of discrete phenotypic plasticity.

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

Regulatory role of Mycobacterium tuberculosis MtrA on dormancy/resuscitation revealed by a novel target gene-mining strategy.

Introduction: The unique dormancy of Mycobacterium tuberculosis plays a significant role in the major clinical treatment challenge of tuberculosis, such as its long treatment cycle, antibiotic resistance, immune escape, and high latent infection rate. Methods: To determine the function of MtrA, the only essential response regulator, one strategy was developed to establish its regulatory network according to high-quality genome-wide binding sites. Results and discussion: The complex modulation mechanisms were implied by the strong bias distribution of MtrA binding sites in the noncoding regions, and 32.7% of the binding sites were located inside the target genes. The functions of 288 potential MtrA target genes predicted according to 294 confirmed binding sites were highly diverse, and DNA replication and damage repair, lipid metabolism, cell wall component biosynthesis, cell wall assembly, and cell division were the predominant pathways. Among the 53 pathways shared between dormancy/resuscitation and persistence, which accounted for 81.5% and 93.0% of the total number of pathways, respectively, MtrA regulatory genes were identified not only in 73.6% of their mutual pathways, but also in 75.4% of the pathways related to dormancy/resuscitation and persistence respectively. These results suggested the pivotal roles of MtrA in regulating dormancy/resuscitation and the apparent relationship between dormancy/resuscitation and persistence. Furthermore, the finding that 32.6% of the MtrA regulons were essential in vivo and/or in vitro for M. tuberculosis provided new insight into its indispensability. The findings mentioned above indicated that MtrA is a novel promising therapeutic target for tuberculosis treatment since the crucial function of MtrA may be a point of weakness for M. tuberculosis.

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

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

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

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

Lattice structures that parameterize regulatory network dynamics.

We consider two types of models of regulatory network dynamics: Boolean maps and systems of switching ordinary differential equations. Our goal is to construct all models in each category that are compatible with the directed signed graph that describe the network interactions. This leads to consideration of lattice of monotone Boolean functions (MBF), poset of non-degenerate MBFs, and a lattice of chains in these sets. We describe explicit inductive construction of these posets where the induction is on the number of inputs in MBF. Our results allow enumeration of potential dynamic behavior of the network for both model types, subject to practical limitation imposed by the size of the lattice of MBFs described by the Dedekind number.

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer.

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.

Exploring regulatory network of icariin synthesis in Herba Epimedii through integrated omics analysis.

Herba Epimedii's leaves are highly valued in traditional Chinese medicine for their substantial concentration of flavonoids, which play a crucial role in manifesting the plant's therapeutic properties. This study investigated the metabolomic, transcriptomic and proteomic profiles of leaves from two Herba Epimedii cultivars, Epipremnum sagittatum (J) and Epipremnum pubescens (R), at three different developmental stages. Metabolite identification and analysis revealed a total of 1,412 and 1,421 metabolites with known structures were found. Flavonoids made up of 33%, including 10 significant accumulated icariin analogues. Transcriptomic analysis unveiled totally 41,644 differentially expressed genes (DEGs) containing five encoded genes participated in icariin biosynthesis pathways. Totally, 9,745 differentially expressed proteins (DEPs) were found, including Cluster-47248.2.p1 (UDP-glucuronosy/UDP-glucosyltransferase), Cluster-30441.2.p1 (O-glucosyltransferase), and Cluster-28344.9.p1 (anthocyanidin 3-O-glucoside 2 "-O-glucosyltransferase-like) through proteomics analysis which are involved to icariin biosynthesis. Protein-protein interaction (PPI) assay exhibited, totally 12 proteins showing a strong relationship of false discovery rate (FDR) <0.05 with these three proteins containing 2 leucine-rich repeat receptor kinase-like protein SRF7, and 5 methyl jasmonate esterase 1. Multi-omics connection networks uncovered 237 DEGs and 72 DEPs exhibited significant associations with the 10 icariin analogues. Overall, our integrated omics approach provides comprehensive insights into the regulatory network underlying icariin synthesis in Herba Epimedii, offering valuable resources for further research and development in medicinal plant cultivation and pharmaceutical applications.

The Wnt/ß-catenin/TCF/Sp5/Zic4 Gene Network That Regulates Head Organizer Activity in Hydra Is Differentially Regulated in Epidermis and Gastrodermis.

Hydra head formation depends on an organizing center in which Wnt/ß-catenin signaling, that plays an inductive role, positively regulates Sp5 and Zic4, with Sp5 limiting Wnt3/ß-catenin expression and Zic4 triggering tentacle formation. Using transgenic lines in which the HySp5 promoter drives eGFP expression in either the epidermis or gastrodermis, we show that Sp5 promoter activity is differentially regulated in each epithelial layer. In intact animals, epidermal HySp5:GFP activity is strong apically and weak along the body column, while in the gastrodermis, it is maximal in the tentacle ring region and maintained at a high level along the upper body column. During apical regeneration, HySp5:GFP is activated early in the gastrodermis and later in the epidermis. Alsterpaullone treatment induces a shift in apical HySp5:GFP expression towards the body column where it forms transient circular figures in the epidermis. Upon ß-catenin(RNAi), HySp5:GFP activity is down-regulated in the epidermis while bud-like structures expressing HySp5:GFP in the gastrodermis develop. Sp5(RNAi) reveals a negative Sp5 autoregulation in the epidermis, but not in the gastrodermis. These differential regulations in the epidermis and gastrodermis highlight the distinct architectures of the Wnt/ß-catenin/TCF/Sp5/Zic4 network in the hypostome, tentacle base and body column of intact animals, as well as in the buds and apical and basal regenerating tips.

LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.

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.

An integrated bioinformatic analysis of microarray datasets to identify biomarkers and miRNA-based regulatory networks in leishmaniasis.

Micro RNAs (miRNAs, miRs) and relevant networks might exert crucial functions during differential host cell infection by the different Leishmania species. Thus, a bioinformatic analysis of microarray datasets was developed to identify pivotal shared biomarkers and miRNA-based regulatory networks for Leishmaniasis. A transcriptomic analysis by employing a comprehensive set of gene expression profiling microarrays was conducted to identify the key genes and miRNAs relevant for Leishmania spp. infections. Accordingly, the gene expression profiles of healthy human controls were compared with those of individuals infected with Leishmania mexicana, L. major, L. donovani, and L. braziliensis. The enrichment analysis for datasets was conducted by utilizing EnrichR database, and Protein-Protein Interaction (PPI) network to identify the hub genes. The prognostic value of hub genes was assessed by using receiver operating characteristic (ROC) curves. Finally, the miRNAs that interact with the hub genes were identified using miRTarBase, miRWalk, TargetScan, and miRNet. Differentially expressed genes were identified between the groups compared in this study. These genes were significantly enriched in inflammatory responses, cytokine-mediated signaling pathways and granulocyte and neutrophil chemotaxis responses. The identification of hub genes of recruited datasets suggested that TNF, SOCS3, JUN, TNFAIP3, and CXCL9 may serve as potential infection biomarkers and could deserve value as prognostic biomarkers for leishmaniasis. Additionally, inferred data from miRWalk revealed a significant degree of interaction of a number of miRNAs (hsa-miR-8085, hsa-miR-4673, hsa-miR-4743-3p, hsa-miR-892c-3p, hsa-miR-4644, hsa-miR-671-5p, hsa-miR-7106-5p, hsa-miR-4267, hsa-miR-5196-5p, and hsa-miR-4252) with the majority of the hub genes, suggesting such miRNAs play a crucial role afterwards parasite infection. The hub genes and hub miRNAs identified in this study could be potentially suggested as therapeutic targets or biomarkers for the management of leishmaniasis.

Comprehensive analysis of gene signatures associated with aging in human aortic dissection.

Background: Aortic dissection (AD) is a lethal aortic disease with limited effective therapeutic strategies. Aging increases the risk of AD, yet the underlying mechanisms remain unclear. This study aims to analyze the association of aging-related genes (Args) and AD using bioinformatic analysis. This helps provide novel insights into AD pathogenesis and contributes to developing novel therapeutic strategies. Methods: mRNA (GSE52093, GSE153434), miRNA (GSE98770) and single-cell RNA-sequencing (scRNA-seq, GSE213740) datasets of AD were downloaded from GEO database. Args were downloaded from Aging Atlas database. Differentially-expressed Args were determined by intersecting Args and differentially-expressed mRNAs of two mRNA datasets. Cytoscape was used to identify hub genes and construct hub gene regulatory networks related to miRNAs. Seurat and clusterProfiler R package were used for investigating expression patterns of hub genes at single-cell level, and functional analysis, respectively. To validate the cellular expression pattern of hub genes, the same analysis was applied to our own scRNA-seq data. Drugs targeting hub Args were determined using the DGIdb database. Results: HGF, CXCL8, SERPINE1, HIF1A, TIMP1, ESR1 and PLAUR were identified as aging-related hub genes in AD. miR-221-3p was predicted to interact with ESR1. A decreased ESR1 expression in smooth muscle cell subpopulation 4 (SMC4) was observed in AD versus normal aortic tissues, which was validated by sequencing 197,605 aortic cells from 13 AD patients. Additionally, upregulated genes of SMC4 in AD tissues were enriched in the "cellular senescence" pathway. These data indicated that decreased ESR1 might promote SMC4 aging during AD formation. Eleven existing drugs targeting hub genes were identified, including ruxolitinib and filgrastim, which are associated with AD. Conclusions: By sequencing transcriptomic data, this study revealed aging-related hub genes and regulatory network involved in AD formation. Additionally, this study proposed a noteworthy hypothesis that downregulated ESR1 may exacerbate AD by promoting SMC aging, which requires further investigation.

Comprehensive network of stress-induced responses in Zymomonas mobilis during bioethanol production: from physiological and molecular responses to the effects of system metabolic engineering.

Nowadays, biofuels, especially bioethanol, are becoming increasingly popular as an alternative to fossil fuels. Zymomonas mobilis is a desirable species for bioethanol production due to its unique characteristics, such as low biomass production and high-rate glucose metabolism. However, several factors can interfere with the fermentation process and hinder microbial activity, including lignocellulosic hydrolysate inhibitors, high temperatures, an osmotic environment, and high ethanol concentration. Overcoming these limitations is critical for effective bioethanol production. In this review, the stress response mechanisms of Z. mobilis are discussed in comparison to other ethanol-producing microbes. The mechanism of stress response is divided into physiological (changes in growth, metabolism, intracellular components, and cell membrane structures) and molecular (up and down-regulation of specific genes and elements of the regulatory system and their role in expression of specific proteins and control of metabolic fluxes) changes. Systemic metabolic engineering approaches, such as gene manipulation, overexpression, and silencing, are successful methods for building new metabolic pathways. Therefore, this review discusses systems metabolic engineering in conjunction with systems biology and synthetic biology as an important method for developing new strains with an effective response mechanism to fermentation stresses during bioethanol production. Overall, understanding the stress response mechanisms of Z. mobilis can lead to more efficient and effective bioethanol production.

Screening of active components of Ganoderma lucidum and decipher its molecular mechanism to improve learning and memory disorders.

Learning and memory impairment (LMI), a common degenerative central nervous system disease. Recently, more and more studies have shown that Ganoderma lucidum (GL) can improve the symptoms of LMI. The active ingredients in GL and their corresponding targets were screened through TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform) and BATMAN-TCM (Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine) databases, and the potential LMI targets were searched for through GeneCard (GeneCards Human Gene Database) and DrugBank. Then, we construct a "main active ingredient-target" network and a protein-protein interaction (PPI) network diagram.The GO (Gene Ontology) functional enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway annotation analysis were performed on the common targets through DAVID (Database for Annotation Visualization and Integrated Discovery) to clarify the potential molecular mechanism of action of active ingredients in GL. The TNF protein was verified by western blot;Twenty one active ingredients in GL and 142 corresponding targets  were screened out, including 59 targets shared with LMI. The 448 biological processes shown by the GO functional annotation results and 55 signal pathways shown by KEGG enrichment analysis were related to the improvement of LMI by GL, among which the correlation of Alzheimer disease pathway is the highest, and TNF was the most important protein; TNF can improve LMI.GL can improve LMI mainly by 10 active ingredients in it, and they may play a role by regulating Alzheimer disease pathway and TNF protein.

Integration analysis of miRNA-mRNA pairs between two contrasting genotypes reveals the molecular mechanism of jujube (Ziziphus jujuba Mill.) response to high-temperature stress.

With global warming, high temperature (HT) has become one of the most common abiotic stresses resulting in significant crop yield losses, especially for jujube (Ziziphus jujuba Mill.), an important temperate economic crop cultivated worldwide. This study aims to explore the coping mechanism of jujube to HT stress at the transcriptional and post-transcriptional levels, including identifying differentially expressed miRNAs and mRNAs as well as elucidating the critical pathways involved. High-throughput sequencing analyses of miRNA and mRNA were performed on jujube leaves, which were collected from "Fucumi" (heat-tolerant) and "Junzao" (heat-sensitive) cultivars subjected to HT stress (42 °C) for 0, 1, 3, 5, and 7 days, respectively. The results showed that 45 known miRNAs, 482 novel miRNAs, and 13,884 differentially expressed mRNAs (DEMs) were identified. Among them, integrated analysis of miRNA target genes prediction and mRNA-seq obtained 1306 differentially expressed miRNAs-mRNAs pairs, including 484, 769, and 865 DEMIs-DEMs pairs discovered in "Fucuimi", "Junzao" and two genotypes comparative groups, respectively. Furthermore, functional enrichment analysis of 1306 DEMs revealed that plant-pathogen interaction, starch and sucrose metabolism, spliceosome, and plant hormone signal transduction were crucial pathways in jujube leaves response to HT stress. The constructed miRNA-mRNA network, composed of 20 DEMIs and 33 DEMs, displayed significant differently expressions between these two genotypes. This study further proved the regulatory role of miRNAs in the response to HT stress in plants and will provide a theoretical foundation for the innovation and cultivation of heat-tolerant varieties.

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

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