Whole transcriptome analysis and critical gene regulatory network analysis during Schistosoma japonicum infection and praziquantel treatment in mice / 中国血吸虫病防治杂志

Objective To investigate long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) interactions and identify the critical gene regulatory network during Schistosoma japonicum infections and praziquantel treatment using whole transcriptome sequencing. Methods A total of 110 male C57BL/6 mice were randomly divided into the control group, the infection group and the treatment group. Mice in the infection treatment and the control group were infected with S. japonicum cercariae via the abdomen, and liver specimens were sampled from 10 mice 3, 6, 8 weeks post-infection. Praziquantel treatment was given to mice in the treatment group 8 weeks post-infection, and liver specimens were sampled from 10 mice 2, 4, 6, 8, 10 weeks post-treatment. Total RNA was isolated from mouse liver specimens, and the transcriptome library was constructed for highthroughput whole transcriptome sequencing. The significant differentially expressed genes were subjected to functional annotations, Gene Ontology (GO) terms enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Correlation analysis of liver specimens was performed using R Corrplot and Himsc functions, and the lncRNAmiRNA-mRNA interaction network analysis was performed using R MixOmics and Himsc functions. Results There were 1 176 differentially expressed miRNAs, 5 270 differentially expressed mRNAs, and 2 682 differentially expressed lncRNAs between the infection group and the control group, 1 289 differentially expressed miRNAs, 7 differentially expressed mRNAs, and 69 differentially expressed lncRNAs between the treatment group and the infection group, and 1 210 differentially expressed miRNAs, 4 456 differentially expressed mRNAs, and 2 016 differentially expressed lncRNAs between the treatment group and the control group. Correlation analysis showed a higher correlation of gene expression between the treatment group and the control group. Principal component analysis showed obvious separate clustering between the infection group and the treatment group. The differentially expressed genes with significant relevance were significantly enriched in 24 GO terms, including arachidonic acid metabolic process, xenobiotic catabolic process, unsaturated fatty acid metabolic process, xenobiotic metabolic process, long-chain fatty acid metabolic process, and 8 KEGG metabolic pathways, including cholesterol metabolism, tyrosine metabolism, linoleic acid metabolism, retinol metabolism, and steroid hormone biometabolism. Conclusions There were 23 mRNAs including Cyp2b9 and 14 lncRNAs including Rmrpr in the core position of the gene regulatory network, which may play a critical role in S. japonicum infections and praziquantel treatment, and 9 miRNAs including miR-8105 may serve as potential molecular markers for diagnosis of S. japonicum infections.

An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851

BACKGROUND Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets. OBJECTIVES In this work, we update the MDR P. aeruginosa CCBH4851 GRN reconstruction and analyse and discuss its structural properties. METHODS We based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process. FINDINGS Our result is a GRN with a greater number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa. MAIN CONCLUSIONS Here, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.

Utilizing benchmarked dataset and gene regulatory network to investigate hub genes in postmenopausal osteoporosis

Objective: The objective of this paper was to investigate hub genes of postmenopausal osteoporosis (PO) utilizing benchmarked dataset and gene regulatory network (GRN). Materials and Methods: To achieve this goal, the first step was to benchmark the dataset downloaded from the ArrayExpress database by adding local noise and global noise. Second, differentially expressed genes (DEGs) between PO and normal controls were identified using the Linear Models for Microarray Data package based on benchmarked dataset. Third, five kinds of GRN inference methods, which comprised Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT), and GEne Network Inference with Ensemble of trees (Genie3), were described and evaluated by receiver operating characteristic (ROC) and precision and recall (PR) curves. Finally, GRN constructed according to the method with best performance was implemented to conduct topological centrality (closeness) for the purpose of investigate hub genes of PO. Results:A total of 236 DEGs were obtained based on benchmarked dataset of 20,554 genes. By assessing Zscore, GeneNet, CLR, PCIT, and Genie3 on the basis of ROC and PR curves, Genie3 had a clear advantage than others and was applied to construct the GRN which was composed of 236 nodes and 27,730 edges. Closeness centrality analysis of GRN was carried out, and we identified 14 hub genes (such as TTN, ACTA1, and MYBPC1) for PO. Conclusion: In conclusion, we have identified 14 hub genes (such as TN, ACTA1, and MYBPC1) based on benchmarked dataset and GRN. These genes might be potential biomarkers and give insights for diagnose and treatment of PO

Gene regulatory network inference and analysis of multidrug-resistant Pseudomonas aeruginosa

BACKGROUND Healthcare-associated infections caused by bacteria such as Pseudomonas aeruginosa are a major public health problem worldwide. Gene regulatory networks (GRN) computationally represent interactions among regulatory genes and their targets. They are an important approach to help understand bacterial behaviour and to provide novel ways of overcoming scientific challenges, including the identification of potential therapeutic targets and the development of new drugs. OBJECTIVES The goal of this study was to reconstruct the multidrug-resistant (MDR) P. aeruginosa GRN and to analyse its topological properties. METHODS The methodology used in this study was based on gene orthology inference using the reciprocal best hit method. We used the genome of P. aeruginosa CCBH4851 as the basis of the reconstruction process. This MDR strain is representative of the sequence type 277, which was involved in an endemic outbreak in Brazil. FINDINGS We obtained a network with a larger number of regulatory genes, target genes and interactions as compared to the previously reported network. Topological analysis results are in accordance with the complex network representation of biological processes. MAIN CONCLUSIONS The properties of the network were consistent with the biological features of P. aeruginosa. To the best of our knowledge, the P. aeruginosa GRN presented here is the most complete version available to date.

Gene regulatory network of hepatocellular carcinoma: a review / 生物工程学报

Hepatocellular carcinoma (HCC) is one of the common malignant tumors. HCC gene regulatory network (HCC GRN), whose nodes consist of genes, miRNAs or TFs and whose edges consist of interaction relationships of nodes, is one of the important ways to study molecular mechanism of HCC. Based on various experimental data, types of HCC GRNs could be conducted such as TF-miRNA regulatory network. Integrating the studies of HCC GRN, TF-miRNA transcriptional regulatory network performs better in identifying core genes which play important roles in network disturbances. It is a trend that gene variations and transcriptional regulatory networks should be combined, however the corresponding research is almost blank. This review summarizes the source of HCC data sources, the classification, character, and research program of HCC GRN. Finally, according to present analysis and discussion of progress and research status of HCC GRN, we provide a useful reference for researchers.

Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

Noise-induced Synchronized Switching of a Multicellular System / 生物化学与生物物理进展

Genetic bistable systems are a large class of important biological systems. Bistability, the capacity to achieve two distinct stable steady states in response to a set of external stimuli, arises within biological systems ranging from the ? phage switch in bacteria to cellular signal transduction pathways in mammalian cells. On the other hand, the increasing experimental evidence in the form of bimodal population distribution has indicated that noise plays a very key role in the switching of bistable systems. However, the physiological mechanism underling noise-induced switching behaviors has not been well explored yet. In the previous work, it has been showed that noise can induce coherent switch for a single genetic Toggle switch system. Here the influence of several kinds of noises (including intracellular and extracellular noises) on synchronized switch was investigated for a multicell gene toggle switch network system. It has been found that multiplicative noises resulting from fluctuations of either synthesis or degradation rates and the additive noise within each cell (they altogether are called as intracellular noises) all can induce the synchronized switch, and that there exists an optimal noise intensity such that the synchronized switch is optimally achieved and the amplification factor has the maximal value. On the other hand, the extracellular noises arising from the stochastic fluctuation of the cellular environment, not only brings about the synchronized switch, but also enhances it by suppressing intracellular fluctuations when the intracellular noises are not enough to induce the synchronized switch. Finally, the influence of the diffusive rate of signal molecules affected by noise on the dynamics of the multicellular system was also investigated, showing that the larger the diffusive rate, the better the synchronized switch and the larger the amplification factor.