Semi-Supervised Multi-View Learning for Gene Network Reconstruction

The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets. It has also been shown that the integration of predictions from multiple inference methods is more robust and shows high performance across diverse datasets. Inspired by this research, in this paper, we propose a machine learning solution which learns to combine predictions from multiple inference methods. While this approach adds additional complexity to the inference process, we expect it would also carry substantial benefits. These would come from the automatic adaptation to patterns on the outputs of individual inference methods, so that it is possible to identify regulatory interactions more reliably when these patterns occur. This article demonstrates the benefits (in terms of accuracy of the reconstructed networks) of the proposed method, which exploits an iterative, semi-supervised ensemble-based algorithm. The algorithm learns to combine the interactions predicted by many different inference methods in the multi-view learning setting. The empirical evaluation of the proposed algorithm on a prokaryotic model organism (E. coli) and on a eukaryotic model organism (S. cerevisiae) clearly shows improved performance over the state of the art methods. The results indicate that gene regulatory network reconstruction for the real datasets is more difficult for S. cerevisiae than for E. coli. The software, all the datasets used in the experiments and all the results are available for download at the following link: http://figshare.com/articles/Semi_supervised_Multi_View_Learning_for_Gene_Network_Reconstruction/1604827.     

An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts

Cancer is a heterogeneous disease caused by diverse genomic alterations in oncogenes and tumor suppressor genes. Despite recent advances in high-throughput sequencing technologies and development of targeted therapies, novel cancer drug development is limited due to the high attrition rate from clinical studies. Patient-derived xenografts (PDX), which are established by the transfer of patient tumors into immunodeficient mice, serve as a platform for co-clinical trials by enabling the integration of clinical data, genomic profiles, and drug responsiveness data to determine precisely targeted therapies. PDX models retain many of the key characteristics of patients’ tumors including histology, genomic signature, cellular heterogeneity, and drug responsiveness. These models can also be applied to the development of biomarkers for drug responsiveness and personalized drug selection. This review summarizes our current knowledge of this field, including methodologic aspects, applications in drug development, challenges and limitations, and utilization for precision cancer medicine.     

Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.     

Integrative Approach to Analyze Biodiversity and Anti-Inflammatory Bioactivity of Wedelia Medicinal Plants

For the development of “medical foods” and/or botanical drugs as defined USA FDA, clear and systemic characterizations of the taxonomy, index phytochemical components, and the functional or medicinal bioactivities of the reputed or candidate medicinal plant are needed. In this study, we used an integrative approach, including macroscopic and microscopic examination, marker gene analysis, and chemical fingerprinting, to authenticate and validate various species/varieties of Wedelia, a reputed medicinal plant that grows naturally and commonly used in Asian countries. The anti-inflammatory bioactivities of Wedelia extracts were then evaluated in a DSS-induced murine colitis model. Different species/varieties of Wedelia exhibited distinguishable morphology and histological structures. Analysis of the ribosomal DNA internal transcribed spacer (ITS) region revealed significant differences among these plants. Chemical profiling of test Wedelia species demonstrated candidate index compounds and distinguishable secondary metabolites, such as caffeic acid derivatives, which may serve as phytochemical markers or index for quality control and identification of specific Wedelia species. In assessing their effect on treating DSS induced-murine colitis, we observed that only the phytoextract from W. chinensis species exhibited significant anti-inflammatory bioactivity on DSS-induced murine colitis among the various Wedelia species commonly found in Taiwan. Our results provide a translational research approach that may serve as a useful reference platform for biotechnological applications of traditional phytomedicines. Our findings indicate that specific Wedelia species warrant further investigation for potential treatment of human inflammatory bowel disease.     

利用整合载体构建苏云金芽胞杆菌杀虫工程菌

利用由苏云金芽胞杆菌整合子Tn4 4 30衍生出的整合载体pBMB F7E ,克隆了对鳞翅目夜蛾科昆虫有毒力的杀虫晶体蛋白基因cry1C ,获得了整合重组质粒pBMB FLC。用电脉冲法将该重组质粒转入对鳞翅目昆虫高毒力的野生菌株YBT80 3 1,在 4 6℃下 ,经过约 12 0代转接培养后 ,筛选出在染色体基因组上整合了cry1C基因的重组子 ,整合频率约为 3 4× 10 -5。Southernblotting验证了cry1C在菌株YBT80 3 1中于染色体的不同的位点整合。生物测定结果显示 ,重组子BMB80 3 Z对小菜蛾的毒力与出发菌株无明显差异 ,而对甜菜夜蛾则较出发菌株高。     

Microinjection of Zebrafish Embryos to Analyze Gene Function

One of the advantages of studying zebrafish is the ease and speed of manipulating protein levels in the embryo. Morpholinos, which are synthetic oligonucleotides with antisense complementarity to target RNAs, can be added to the embryo to reduce the expression of a particular gene product. Conversely, processed mRNA can be added to the embryo to increase levels of a gene product. The vehicle for adding either mRNA or morpholino to an embryo is microinjection. Microinjection is efficient and rapid, allowing for the injection of hundreds of embryos per hour. This video shows all the steps involved in microinjection. Briefly, eggs are collected immediately after being laid and lined up against a microscope slide in a Petri dish. Next, a fine-tipped needle loaded with injection material is connected to a microinjector and an air source, and the microinjector controls are adjusted to produce a desirable injection volume. Finally, the needle is plunged into the embryo''s yolk and the morpholino or mRNA is expelled.     

Cancer Gene Discovery Using the Sleeping Beauty Transposon

Epidemiological and molecular data support the hypothesis that cancer results from a series of acquired somatic mutations. Discovering the initial mutations required for oncogenesis has long been a goal of cancer research. To date, the majority of causative mutations have been identified based on their ability to act in a dominant fashion and/or because they are activated by chromosomal translocations. Forward genetic screens are necessary for unbiased discovery of the remaining unknown oncogenic mutations. Two recent projects have demonstrated the feasibility of using the Sleeping Beauty transposon as an insertional mutagen for cancer gene discovery. In this article we discuss the history of cancer gene discovery and propose novel forward genetic screens using Sleeping Beauty transposon aimed at specific tissues and accelerating the discovery of recessive tumor suppressor genes.     

Learning Gene Networks under SNP Perturbations Using eQTL Datasets

The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs) that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network identified computationally by our method under SNP perturbations is well supported by the results from experimental perturbation studies related to DNA replication stress response.     

Integrative Modelling of the Influence of MAPK Network on Cancer Cell Fate Decision

The Mitogen-Activated Protein Kinase (MAPK) network consists of tightly interconnected signalling pathways involved in diverse cellular processes, such as cell cycle, survival, apoptosis and differentiation. Although several studies reported the involvement of these signalling cascades in cancer deregulations, the precise mechanisms underlying their influence on the balance between cell proliferation and cell death (cell fate decision) in pathological circumstances remain elusive. Based on an extensive analysis of published data, we have built a comprehensive and generic reaction map for the MAPK signalling network, using CellDesigner software. In order to explore the MAPK responses to different stimuli and better understand their contributions to cell fate decision, we have considered the most crucial components and interactions and encoded them into a logical model, using the software GINsim. Our logical model analysis particularly focuses on urinary bladder cancer, where MAPK network deregulations have often been associated with specific phenotypes. To cope with the combinatorial explosion of the number of states, we have applied novel algorithms for model reduction and for the compression of state transition graphs, both implemented into the software GINsim. The results of systematic simulations for different signal combinations and network perturbations were found globally coherent with published data. In silico experiments further enabled us to delineate the roles of specific components, cross-talks and regulatory feedbacks in cell fate decision. Finally, tentative proliferative or anti-proliferative mechanisms can be connected with established bladder cancer deregulations, namely Epidermal Growth Factor Receptor (EGFR) over-expression and Fibroblast Growth Factor Receptor 3 (FGFR3) activating mutations.     

Gene Regulatory Network Reconstruction Using Conditional Mutual Information

The inference of gene regulatory network from expression data is an important area of research that provides insight to the inner workings of a biological system. The relevance-network-based approaches provide a simple and easily-scalable solution to the understanding of interaction between genes. Up until now, most works based on relevance network focus on the discovery of direct regulation using correlation coefficient or mutual information. However, some of the more complicated interactions such as interactive regulation and coregulation are not easily detected. In this work, we propose a relevance network model for gene regulatory network inference which employs both mutual information and conditional mutual information to determine the interactions between genes. For this purpose, we propose a conditional mutual information estimator based on adaptive partitioning which allows us to condition on both discrete and continuous random variables. We provide experimental results that demonstrate that the proposed regulatory network inference algorithm can provide better performance when the target network contains coregulated and interactively regulated genes.     

网上实验室克隆与鉴定食管癌相关基因ECRG—4

利用生物信息学,探索网上克隆基因与鉴定基因的新方法。以因特网为平台,数据库为试验材料,各种软件为工具组成网上实验室,是人类基因组计划带来的实验技术革命。利用网上实验室以食管癌相关基因E－CRG－4的97bpEST为基础,成功克隆并鉴定了该基因。结果显示ECRG－4近似cDNA全长序列为772bp,其中含有一447bp的完整阅读框,编码148个氨基酸。氨基酸序列相似性分配表明ECRG－4与细胞膜上的免疫球蛋白超家族具有31％同源性。该基因定位于染色体2q141－14．3。组织分布表明ECRG-4须正常食管、膀胱组织中表达明显高于相应的癌组织。采用辐射杂交细胞系GeneBridge4RH嵌板作染色体定位进行初步验证,所得结果与网上克隆完全一致。研究提示,利用网上实验室克隆鉴定基因,是一种简便、精确的好方法。ECRG－4可能是一个在细胞癌变过程中具有非常重要意义的基因。