Jianghui Xiong

Genome wide prediction of protein function via a generic knowledge discovery approach based on evidence integration

BackgroundThe automation of many common molecular biology techniques has resulted in the accumulation of vast quantities of experimental data. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system (e.g. knowledge of genes and their products, and the biological roles of proteins, their molecular functions, localizations and interaction networks). We present a technique called Global Mapping of Unknown Proteins (GMUP) which uses the Gene Ontology Index to relate diverse sources of experimental data by creation of an abstraction layer of evidence data. This abstraction layer is used as input to a neural network which, once trained, can be used to predict function from the evidence data of unannotated proteins. The method allows us to include almost any experimental data set related to protein function, which incorporates the Gene Ontology, to our evidence data in order to seek relationships between the different sets.ResultsWe have demonstrated the capabilities of this method in two ways. We first collected various experimental datasets associated with yeast (Saccharomyces cerevisiae) and applied the technique to a set of previously annotated open reading frames (ORFs). These ORFs were divided into training and test sets and were used to examine the accuracy of the predictions made by our method. Then we applied GMUP to previously un-annotated ORFs and made 1980, 836 and 1969 predictions corresponding to the GO Biological Process, Molecular Function and Cellular Component sub-categories respectively. We found that GMUP was particularly successful at predicting ORFs with functions associated with the ribonucleoprotein complex, protein metabolism and transportation.ConclusionThis study presents a global and generic gene knowledge discovery approach based on evidence integration of various genome-scale data. It can be used to provide insight as to how certain biological processes are implemented by interaction and coordination of proteins, which may serve as a guide for future analysis. New data can be readily incorporated as it becomes available to provide more reliable predictions or further insights into processes and interactions.

The Effects of Secretion Factors from Umbilical Cord Derived Mesenchymal Stem Cells on Osteogenic Differentiation of Mesenchymal Stem Cells

Factors synthesized by mesenchymal stem cells (MSCs) contain various growth factors, cytokines, exosomes and microRNAs, which may affect the differentiation abilities of MSCs. In the present study, we investigated the effects of secretion factors of human umbilical cord derived mesenchymal stem cells (hUCMSCs) on osteogenesis of human bone marrow derived MSCs (hBMSCs). The results showed that 20 μg/ml hUCMSCs secretion factors could initiate osteogenic differentiation of hBMSCs without osteogenic induction medium (OIM), and the amount of calcium deposit (stained by Alizarin Red) was significantly increased after the hUCMSCs secretion factors treatment. Real time quantitative reverse transcription-polymerase chain reaction (real time qRT-PCR) demonstrated that the expression of osteogenesis-related genes including ALP, BMP2, OCN, Osterix, Col1α and Runx2 were significantly up-regulated following hUCMSCs secretion factors treatment. In addition, we found that 10 μg hUCMSCs secretion factors together with 2×105 hBMSCs in the HA/TCP scaffolds promoted ectopic bone formation in nude mice. Local application of 10 μg hUCMSCs secretion factors with 50 μl 2% hyaluronic acid hydrogel and 1×105 rat bone marrow derived MSCs (rBMSCs) also significantly enhanced the bone repair of rat calvarial bone critical defect model at both 4 weeks and 8 weeks. Moreover, the group that received the hUCMSCs secretion factors treatment had more cartilage and bone regeneration in the defect areas than those in the control group. Taken together, these findings suggested that hUCMSCs secretion factors can initiate osteogenesis of bone marrow MSCs and promote bone repair. Our study indicates that hUCMSCs secretion factors may be potential sources for promoting bone regeneration.

Ensemble classifier based on context specific miRNA regulation modules: a new method for cancer outcome prediction

BackgroundMany calssifiers which are constructed with chosen gene markers have been proposed to forecast the prognosis of patients who suffer from breast cancer. However, few of them has been applied in clinical practice because of the bad generalization, which results from the situation that markers selected by one method are very different from those obtained by anohter mothod, and thus such markers always lack discriminative capability in the other data sets.MethodsIn this work, a new ensemble classifier, on the basis of context specific miRNA regulation modules, has been proposed to forecast the metastasis risk of cancer sufferers. First, we defined all of the miRNAs which regulate the same context as a module that contains miRNAs and their regulating context, and applied the CoMi (Context-specific miRNA activity) score in order to illustrate a miRNAs effect which happened in a particular background; then the miRNA regulation modules with distinguising abilities were detected and each of them was responsible for building a weak classifier separately; at last, by using majority voting strategy, we integrated all weak classifiers to establish an ensembled one that was applied to forecast the prognosis of patients who suffer from cancer.ResultsAfter comparing, the results on the cohorts containing over 1,000 samples showed that the proposed ensemble classifier is superior to other three classifiers based on miRNA expression profiles, mRNA expression profiles and CoMi activity patterns respectively. Significantly, our method outperforms the representative works. Moreover, the detected modules from different data sets show great stability (with p-value of 6.40e-08). For investigating the biological significance of those selected modules, case studies have been done by us and the results suggested that our method do help to reveal latent mechanism in metastasis of breast cancer.ConclusionsOne context specific miRNA regulation module can uncover one critical biological process and its involved miRNAs that are related to the cancer outcome, and several modules together can help to study the biological mechanism in cancer metastasis, thus the classifer based on ensembling multiple classifers which were built with different context specific miRNA regulation modules has showed promising performances in terms with both prediction accuracy and generalization.

Context-specific miRNA regulation network predicts cancer prognosis

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNAs target genes and non-targets genes within a given gene set (context).

protein-protein Interaction Reveals synergistic Discrimination of cancer phenotype

Cancer is a disease associated with the deregulation of multiple gene networks. Microarray data has permitted researchers to identify gene panel markers for diagnosis or prognosis of cancer but these are not sufficient to make specific mechanistic assertions about phenotype switches. We propose a strategy to identify putative mechanisms of cancer phenotypes by protein-protein interactions (PPI). We first extracted the logic status of a PPI via the relative expression of the corresponding gene pair. The joint association of a gene pair on a cancer phenotype was calculated by entropy minimization and assessed using a support vector machine. A typical predictor is “If Src high-expression, and Cav-1 low-expression, then cancer.“ We achieved 90% accuracy on test data with a majority of predictions associated with the MAPK pathway, focal adhesion, apoptosis and cell cycle. Our results can aid in the development of phenotype discrimination biomarkers and identification of putative therapeutic interference targets for drug development.

Predicting distant metastasis in breast cancer using ensemble classifier based on context-specific miRNA regulation modules

Many methods based on building classifiers by selecting gene markers have been proposed to predict breast cancer patients outcome However, most of them suffer from the problem of poor robustness, which are mainly due to the fact that the overlap degree of gene markers derived by different methods is not high, hading that few of them are generalized and can be widely used for clinical practice. In this paper, we present a method based on context-specific miRNA regulation modules to predict distant metastasis in breast cancer. First, we describe the regulation activity of a miRNA on a specific context by using CoMi (Context-specific miRNA activity) score, based on which, several miRNAs regulate on the same context are regarded as a miRNA regulation module; then the discriminate regulation modules are selected and each is used to construct a classification model separately; finally, an ensemble classifier is established by combining all the models with a majority voting strategy. The evaluation experiment results show that our method performs better than previous works. In addition, the obtained discriminate modules show great stability across different data sets (withp-value of 1.119e-06).

Circulating microRNAs Correlated with Bone Loss Induced by 45 Days of Bed Rest

The purpose of this study was to find the circulating microRNAs (miRNAs) co-related with bone loss induced by bed rest, and testify whether the selected miRNAs could reflect the bone mineral status of human after bed-rest. We analyzed plasma miRNA levels of 16 subjects after 45 days of −6° head-down tilt bed rest, which is a reliable model for the simulation of microgravity. We characterize the circulating miRNA profile in individuals after bed rest and identify circulating miRNAs which can best reflect the level of bone loss induced by bed rest. Expression profiling of circulating miRNA revealed significant downregulation of 37 miRNAs and upregulation of 2 miRNAs, while only 11 of the downregulated miRNAs were further validated in a larger volunteer cohort using qPCR. We found that 10 of these 11 miRNAs (miR-103, 130a, 1234, 1290, 151-5p, 151-3p, 199a-3p, 20a, 363, and 451a) had ROC curve that distinguished the status after bed rest. Importantly, significant positive correlations were identified between bone loss parameters and several miRNAs, eventually miR-1234 showed clinical significance in detecting the bone loss of individuals after 45 days of bed rest.

Predicting Response to Preoperative Chemotherapy Agents by Identifying Drug Action on Modeled MicroRNA Regulation Networks

Identifying patients most responsive to specific chemotherapy agents in neoadjuvant settings can help to maximize the benefits of treatment and minimize unnecessary side effects. Metagene approaches that predict response based on gene expression signatures derived from an associative analysis of clinical data can identify chance associations caused by the heterogeneity of a tumor, leading to reproducibility issues in independent validations. In this study, to incorporate information from drug mechanisms of action, we explore the potential of microRNA regulation networks as a new feature space for identifying predictive markers. We introduce a measure we term the CoMi (Context-specific-miRNA-regulation) pattern to represent a descriptive feature of the miRNA regulation network in the transcriptome. We examine whether the modifications to the CoMi pattern on specific biological processes are a useful representation of drug action by predicting the response to neoadjuvant Paclitaxel treatment in breast cancer and show that the drug counteracts the CoMi network dysregulation induced by tumorigenesis. We then generate a quantitative testbed to investigate the ability of the CoMi pattern to distinguish FDA approved breast cancer drugs from other FDA approved drugs not related to breast cancer. We also compare the ability of the CoMi and metagene methods to predict response to neoadjuvant Paclitaxel treatment in clinical cohorts. We find the CoMi method outperforms the metagene method, achieving area under curve (AUC) values of 0.78 and 0.66 respectively. Furthermore, several of the predicted CoMi features highlight the network-based mechanism of drug resistance. Thus, our study suggests that explicitly modeling the drug action using network biology provides a promising approach for predictive marker discovery.

Dynamic remodeling of context-specific miRNAs regulation networks facilitate in silico cancer drug screening

Background: Much effort has been expended in exploring the connections between transcriptome, disease and drug, based on the premise that drug induced perturbations in the transcriptome will affect the phenotype and finally help to cure a disease. MicroRNAs (miRNAs) play a key role in the regulation of the transcriptome and have been identified as a key mediator in human disease and drug response. However, even if miRNA expression can be precisely detected, the information regarding miRNAs action on a particular part of the transcriptome is still lacking. Here, we introduced a novel concept, the Context-specific MiRNA activity (CoMi activity), to reflect a miRNAs regulation effect on a context specific gene set, by calculating the statistical difference between the distributions of its target gene expression and non-target gene expression. In this study we investigate whether CoMi activity could provide a novel perspective on miRNA mechanisms of action in disease and drug response, and facilitate in silico drug screening.

The Principle of Rational Design of Drug Combination and Personalized Therapy Based on Network Pharmacology

Network Pharmacology attempts to model the effects of drug action by simultaneously modulating multiple components in a gene network. However, the theoretical basis for this new concept is still in its infancy and the process by which we translate network modeling to clinical application remains indirect. In this chapter, we try to outline the principles of rational designs for drug combination and personalized therapy based on network pharmacology by deciphering several milestone examples. We demonstrate that the network, which characterizes the dependency or joint dependency between genes and disease phenotype, is the key “battle map” for rational drug combinations and design of personalized therapy. We also tentatively outline several aspects of the process which might help drive innovation in network construction and shape the future development of network pharmacology applications.