Identification of key genes and expression profiles in osteoarthritis by co-expressed network analysis

BackgroundThe underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA).Material and methodsWe obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the “limma” package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples.ResultsThe preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples.ConclusionsWe identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.     

Expression patterns of photoperiod and temperature regulated heading date genes in Oryza sativa

In plants, flowering is a major biological phenomenon, which is regulated by an array of interactions occurring between biotic and abiotic factors. In our study, we have compared the expression profiles of flowering genes involved in the flowering pathway, which are influenced by conditions like photoperiod and temperature from seedling to heading developmental stages in two Oryza sativa indica varieties, viz., Zhenshan 97 and Minghui 63 using a expression network approach. Using the network expression approach, we found 17 co-expressed genes having the same expression profile pattern as three key photoperiod flowering genes Hd1, Ehd1 and Hd3a. We also demonstrated that these three co-expressed genes have a similar simulation pattern as temperature flowering genes. Based on our observations, we hypothesize that photoperiod and temperature regulate flowering pathways independently. The present study provides a basis for understanding the network of co-expressed genes involved in flowering pathway and presents a way to demonstrate the behavior of specific gene sets in specific cultivars.     

Pharmacoepidemiological characterization of drug-induced adverse reaction clusters towards understanding of their mechanisms

A big challenge in pharmacology is the understanding of the underlying mechanisms that cause drug-induced adverse reactions (ADRs), which are in some cases similar to each other regardless of different drug indications, and are in other cases different regardless of same drug indications. The FDA Adverse Event Reporting System (FAERS) provides a valuable resource for pharmacoepidemiology, the study of the uses and the effects of drugs in large human population. However, FAERS is a spontaneous reporting system that inevitably contains noise that deviates the application of conventional clustering approaches. By performing a biclustering analysis on the FAERS data we identified 163 biclusters of drug-induced adverse reactions, counting for 691 ADRs and 240 drugs in total, where the number of ADR occurrences are consistently high across the associated drugs. Medically similar ADRs are derived from several distinct indications for use in the majority (145/163 = 88%) of the biclusters, which enabled us to interpret the underlying mechanisms that lead to similar ADRs. Furthermore, we compared the biclusters that contain same drugs but different ADRs, finding the cases where the populations of the patients were different in terms of age, sex, and body weight. We applied a biclustering approach to catalogue the relationship between drugs and adverse reactions from a large FAERS data set, and demonstrated a systematic way to uncover the cases different drug administrations resulted in similar adverse reactions, and the same drug can cause different reactions dependent on the patients’ conditions.     

Gene expression variability in mammalian embryonic stem cells using single cell RNA-seq data

BackgroundGene expression heterogeneity contributes to development as well as disease progression. Due to technological limitations, most studies to date have focused on differences in mean expression across experimental conditions, rather than differences in gene expression variance. The advent of single cell RNA sequencing has now made it feasible to study gene expression heterogeneity and to characterise genes based on their coefficient of variation.MethodsWe collected single cell gene expression profiles for 32 human and 39 mouse embryonic stem cells and studied correlation between diverse characteristics such as network connectivity and coefficient of variation (CV) across single cells. We further systematically characterised properties unique to High CV genes.ResultsHighly expressed genes tended to have a low CV and were enriched for cell cycle genes. In contrast, High CV genes were co-expressed with other High CV genes, were enriched for bivalent (H3K4me3 and H3K27me3) marked promoters and showed enrichment for response to DNA damage and DNA repair.ConclusionsTaken together, this analysis demonstrates the divergent characteristics of genes based on their CV. High CV genes tend to form co-expression clusters and they explain bivalency at least in part.     

DERL3 functions as a tumor suppressor in gastric cancer

BackgroundGastric cancer is a common malignant tumor in the clinic with a high mortality rate, ranking the first among malignant tumors of the digestive system. Early gastric cancer exhibits no specific clinical symptoms and signs, and most of the patients were diagnosed as advanced gastric cancer. The prognosis is poor, and the 5-year overall survival rate is still lower than 30%, seriously threatening people’s life and health. However, the pathogenesis of gastric cancer is still unclear.MethodsThis study aimed to identify methylated differentially expressed genes in gastric cancer and to study the cellular functions and pathways that may be involved in its regulation, as well as the biological functions of key methylated differentially expressed genes. The gene expression data set and methylation data set of gastric cancer genes based on TCGA were analyzed to identify prognostic methylated genes.ResultsThis study showed that the methylation of the DERL3 promoter was correlated with the clinical analysis of tumors. Further studies were conducted on genes co-expressed with DERL3, whose functions and pathways to inhibit gastric cancer were adaptive immune response, T cell activation, immune response-regulating pathway, cell surface on molecules, and natural killer cell-mediated cytotoxicity. Finally, cell proliferation assay, cell scratch assay, and cell invasion assay confirmed that DERL3 as a tumor suppressor gene inhibited the malignant evolution of gastric cancer.ConclusionsThe analysis of key methylated differentially expressed genes helped elucidate the epigenetic regulation mechanism in the development of gastric cancer. DERL3, as a methylation biomarker, has a predictive and prognostic value in the accurate diagnosis and treatment of gastric cancer and provides potential targets for the precision treatment of gastric cancer.Trial RegistrationNot applicable.     

An elastic network model to identify characteristic stress response genes

Exposing eukaryotic cells to a toxic compound and subsequent gene expression profiling may allow the prediction of selected toxic effects based on changes in gene expression. This objective is complicated by the observation that compounds with different modes of toxicity cause similar changes in gene expression and that a global stress response affects many genes. We developed an elastic network model of global stress response with nodes representing genes which are connected by edges of graded coexpression. The expression of only few genes have to be known to model the global stress response of all but a few atypical responder genes. Those required genes and the atypical response genes are shown to be good biomarker for tox predictions. In total, 138 experiments and 13 different compounds were used to train models for different toxicity classes. The deduced biomarkers were shown to be biologically plausible. A neural network was trained to predict the toxic effects of compounds from profiling experiments. On a validation data set of 189 experiments with 16 different compounds the accuracy of the predictions was assessed: 14 out of 16 compounds have been classified correctly. Derivation of model based biomarkers through the elastic network approach can naturally be extended to other areas beyond toxicology since subtle signals against a broad response background are common in biological studies.     

Gene function classification using NCI-60 cell line gene expression profiles

Gene expression patterns from NCI's panel of 60 cell lines were used to train a Neural Network model for classifying genes to pathways. The model assigns probabilities to each gene for each of the 21 modeled pathways assigned by the Kyoto Encyclopedia of Genes and Genomes. Cross-validation of the model showed that 10 of the 21 pathways exhibited good performance in statistical significance and accuracy. The model was designed to output gene probabilities that could be screened for higher probabilities resulting in higher confidence in classification though yielding fewer genes per pathway. The model was deployed on 5798 genes and our approach allowed us to ascertain the most relevant genes above an estimated background. Eight pathways were identified with both good cross-validation and significant numbers above background, TCA Cycle, Oxidative Phosphorylation, Porphyrin Biosynthesis, Ribosome, Polymerases, Proteasome, Cell Cycle, and Cell Adhesion. Gene Ontology (GO) annotation was used for additional validation of gene classification results. A total of 551 GO annotated genes and 468 unannotated genes were classified to the 8 pathways. The primary and secondary classifications of genes revealed known pathway relationships and provide the potential for discovering new pathway relationships.     

The biosynthetic gene cluster for a monocyclic beta-lactam antibiotic, nocardicin A

The monocyclic beta-lactam antibiotic nocardicin A is related structurally and biologically to the bicyclic beta-lactams comprised of penicillins/cephalosporins, clavams, and carbapenems. Biosynthetic gene clusters are known for each of the latter, but not for monocyclic beta-lactams. A previously cloned gene encoding an enzyme specific to the biosynthetic pathway was used to isolate the nocardicin A cluster from Nocardia uniformis. Sequence analysis revealed the presence of 14 open reading frames involved in antibiotic production, resistance, and export. Among these are a two-protein nonribosomal peptide synthetase system, p-hydroxyphenylglycine biosynthetic genes, an S-adenosylmethionine-dependent 3-amino-3-carboxypropyl transferase (Nat), and a cytochrome P450. Gene disruption mutants of Nat, as well as an activation domain of the NRPS system, led to loss of nocardicin A formation. Several enzymes involved in antibiotic biosynthesis were heterologously overproduced, and biochemical characterization confirmed their proposed activities.     

PK-means: A new algorithm for gene clustering

Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. Gene cluster analysis is found useful for discovering the function of gene because co-expressed genes are likely to share the same biological function. K-means is one of well-known clustering methods. However, it is sensitive to the selection of an initial clustering and easily becoming trapped in a local minimum. Particle-pair optimizer (PPO) is a variation on the traditional particle swarm optimization (PSO) algorithm, which is stochastic particle-pair based optimization technique that can be applied to a wide range of problems. In this paper we bridges PPO and K-means within the algorithm PK-means for the first time. Our results indicate that PK-means clustering is generally more accurate than K-means and Fuzzy K-means (FKM). PK-means also has better robustness for it is less sensitive to the initial randomly selected cluster centroids. Finally, our algorithm outperforms these methods with fast convergence rate and low computation load.     

Gene expression profiling of tumor-associated macrophages after exposure to single-dose irradiation

Radiotherapy (RT) is a common cancer treatment approach that accounts for nearly 50% of patient treatment; however, tumor relapse after radiotherapy is still a major issue. To study the crucial role of tumor-associated macrophages (TAMs) in the regulation of tumor progression post-RT, microarray experiments comparing TAM gene expression profiles between unirradiated and irradiated tumors were conducted to discover possible roles of TAMs in initiation or contribution to tumor recurrence following RT, taking into account the relationships among gene expression, tumor microenvironment, and immunology. A single dose of 25 Gy was given to TRAMP C-1 prostate tumors established in C57/B6 mice. CD11b-positive macrophages were extracted from the tumors at one, two and three weeks post-RT. Gene ontology (GO) term analysis using the DAVID database revealed that genes that were differentially expressed at one and two weeks after irradiation were associated with biological processes such as morphogenesis of a branching structure, tube development, and cell proliferation. Analysis using Short Time-Series Expression Miner (STEM) revealed the temporal gene expression profiles and identified 13 significant patterns in four main groups of profiles. The genes in the upregulated temporal profile have diverse functions involved in the intracellular signaling cascade, cell proliferation, and cytokine-mediated signaling pathway. We show that tumor irradiation with a single 25-Gy dose can initiate a time-series of differentially expressed genes in TAMs, which are associated with the immune response, DNA repair, cell cycle arrest, and apoptosis. Our study helps to improve our understanding of the function of the group of genes whose expression changes temporally in an irradiated tumor microenvironment.     

Crystal structure of a squalene cyclase in complex with the potential anticholesteremic drug Ro48-8071

Squalene-hopene cyclase (SHC) catalyzes the conversion of squalene into pentacyclic compounds. It is the prokaryotic counterpart of the eukaryotic oxidosqualene cyclase (OSC) that catalyzes the steroid scaffold formation. Because of clear sequence homology, SHC can serve as a model for OSC, which is an attractive target for anticholesteremic drugs. We have established the crystal structure of SHC complexed with Ro48-8071, a potent inhibitor of OSC and therefore of cholesterol biosynthesis. Ro48-8071 is bound in the active-center cavity of SHC and extends into the channel that connects the cavity with the membrane. The binding site of Ro48-8071 is largely identical with the expected site of squalene; it differs from a previous model based on photoaffinity labeling. The knowledge of the inhibitor binding mode in SHC is likely to help develop more potent inhibitors for OSC.     

The p53 network: p53 and its downstream genes

The tumor-suppressor gene p53 and its downstream genes consist of a complicated gene network. p53 is a key molecular node in the network, which is activated in response to several cellular signals resulting in the maintenance of genetic stability. Several cellular signals may activate the p53 network. When the expression of P53 is elevated, P53-MDM2 module and the ubiquitin system can accurately regulate the expression level of P53. P53 can bind to specific DNA sequence, activate its downstream genes expression, and control cell-cycle arrest, DNA repair, and apoptosis. Elucidating the function of p53 gene network will help understand the interaction mechanisms of p53 and its downstream genes.     

Node-based differential network analysis in genomics

Gene dependency networks often undergo changes in response to different conditions. Understanding how these networks change across two conditions is an important task in genomics research. Most previous differential network analysis approaches assume that the difference between two condition-specific networks is driven by individual edges. Thus, they may fail in detecting key players which might represent important genes whose mutations drive the change of network. In this work, we develop a node-based differential network analysis (N-DNA) model to directly estimate the differential network that is driven by certain hub nodes. We model each condition-specific gene network as a precision matrix and the differential network as the difference between two precision matrices. Then we formulate a convex optimization problem to infer the differential network by combing a D-trace loss function and a row-column overlap norm penalty function. Simulation studies demonstrate that N-DNA provides more accurate estimate of the differential network than previous competing approaches. We apply N-DNA to ovarian cancer and breast cancer gene expression data. The model rediscovers known cancer-related genes and contains interesting predictions.     

Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data

Gene expression data are characterized by thousands even tens of thousands of measured genes on only a few tissue samples. This can lead either to possible overfitting and dimensional curse or even to a complete failure in analysis of microarray data. Gene selection is an important component for gene expression-based tumor classification systems. In this paper, we develop a hybrid particle swarm optimization (PSO) and tabu search (HPSOTS) approach for gene selection for tumor classification. The incorporation of tabu search (TS) as a local improvement procedure enables the algorithm HPSOTS to overleap local optima and show satisfactory performance. The proposed approach is applied to three different microarray data sets. Moreover, we compare the performance of HPSOTS on these datasets to that of stepwise selection, the pure TS and PSO algorithm. It has been demonstrated that the HPSOTS is a useful tool for gene selection and mining high dimension data.     

A Gibbs sampling method to determine biomarkers for asthma

PurposeTo identify potential biomarkers and to uncover the mechanisms underlying asthma based on Gibbs sampling.MethodsThe molecular functions (MFs) with genes greater than 5 were determined using AnnotationMFGO of BAGS package, and the obtained MFs were then transformed to Markov chain (MC). Gibbs sampling was conducted to obtain a new MC. Meanwhile, the average probabilities of MFs were computed via MC Monte Carlo (MCMC) algorithm, followed by identification of differentially expressed MFs based on the probabilities of MF more than 0.6. Moreover, the differentially expressed genes (DEGs) and their correlated genes were screened and merged, called as co-expressed genes. Pathways enrichment analysis was implemented for the co-expressed genes.ResultsBased on the gene set more than 5, overall 396 MFs were determined. After Gibbs sampling, 5 differentially expressed MF were acquired according to alfa.pi > 0.6. Moreover, the genes in these 5 differentially expressed MF were merged, and 110 DEGs were identified. Subsequently, 338 co-expressed genes were gained. Based on the P value < 0.01, the co-expressed genes were significantly enriched in 6 pathways. Among these, ubiquitin mediated proteolysis contained the maximum numbers of 35 co-expressed genes, and cell cycle were enriched by the second largest number of 11 co-expressed genes, respectively.ConclusionsThe identified pathways such as ubiquitin mediated proteolysis and cell cycle might play important roles in the development of asthma and may be useful for developing the credible therapeutic approaches for diagnosis and treatment of asthma in future.     

基于Gene Ontology的表达谱与基因功能相似性的相关性研究

聚类是芯片数据分析中被广泛使用的方法。未知基因的功能通常通过其与已知基因在不同生物状态下具有表达相似性来进行预测。然而，还未有人就这种通过表达相似性来进行功能注释的方法的可靠性进行评估。本文利用Gene Ontology对表达相似性和基因功能相似性的相关关系进行了全面的研究。研究表明，尽管表达谱的相似性和基因功能相似性之间有一定的依赖关系，但相关性较弱。在Gene Ontology的三大类中，相对生物过程和分子功能，基因表达谱的相似性更有助于细胞组分的注释。本文的研究结果对于基因功能的预测有一定的指导意义。     

金属配合物作为核酸释放的载体

基因治疗是指利用一种载体将健康的基因载入细胞替换致病的基因.由基因缺陷导致的人类疾病达1200多种,最合理的选择是采用基因替换的方法进行治疗.基因治疗的关键问题是解决"使用何种载体才能安全有效地将治疗基因载入靶细胞".非病毒基因载体主要是一些有机阳离子物种,一直受到极大重视;近年来,磷酸钙、纳米粒子和金属配合物释放核酸的功能也开始受到关注.本文总结了金属配合物作为非病毒基因载体使用的研究进展,希望由此理解配合物释放核酸的优势和不足之处.