Predicting cancer involvement of genes from heterogeneous data

Background  

Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.     

MicroRNAs研究方法与技术

MicroRNAs简称miRNAs(微小RNAs),是真核生物、原核生物以及病毒中由非编码蛋白基因转录的初级microRNAs加工成的调控因子.在转录后水平和蛋白质翻译水平,microRNAs通过降解或翻译抑制甚至激活来调控靶mRNA.实验和计算机方法已应用于microRNAs和靶基因的鉴定.大规模测序技术使得microRNAs在不同物种的多样性分析得以实现.着重介绍microRNAs、靶基因及其功能研究的实验技术和计算机方法,以及基于microRNAs的保守性,借助模式生物中已知的microRNAs,研究其在其他生物中的功能和作用.     

Quality Measures for Gene Expression Biclusters

An noticeable number of biclustering approaches have been proposed proposed for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. In this context, recognizing groups of co-expressed or co-regulated genes, that is, genes which follow a similar expression pattern, is one of the main objectives. Due to the problem complexity, heuristic searches are usually used instead of exhaustive algorithms. Furthermore, most of biclustering approaches use a measure or cost function that determines the quality of biclusters. Having a suitable quality metric for bicluster is a critical aspect, not only for guiding the search, but also for establishing a comparison criteria among the results obtained by different biclustering techniques. In this paper, we analyse a large number of existing approaches to quality measures for gene expression biclusters, as well as we present a comparative study of them based on their capability to recognize different expression patterns in biclusters.     

Understanding anthelmintic resistance: the need for genomics and genetics

Anthelmintic resistance is a major problem for the control of many parasitic nematode species and has become a major constraint to livestock production in many parts of the world. In spite of its increasing importance, there is still a poor understanding of the molecular and genetic basis of resistance. It is unclear which mutations contribute most to the resistance phenotype and how resistance alleles arise, are selected and spread in parasite populations. The main strategy used to identify mutations responsible for anthelmintic resistance has been to undertake experimental studies on candidate genes. These genes have been chosen predominantly on the basis of our knowledge of drug mode-of-action and the identification of mutations that can confer resistance in model organisms. The application of these approaches to the analysis of benzimidazole and ivermectin resistance is reviewed and the reasons for their relative success or failure are discussed. The inherent limitation of candidate gene studies is that they rely on very specific and narrow assumptions about the likely identity of resistance-associated genes. In contrast, forward genetic and functional genomic approaches do not make such assumptions, as illustrated by the successful application of these techniques in the study of insecticide resistance. Although there is an urgent need to apply these powerful approaches to anthelmintic resistance research, the basic methodologies and resources are still lacking. However, these are now being developed for the trichostrongylid nematode Haemonchus contortus and the current progress and research priorities in this area are discussed.     

Discovering Study-Specific Gene Regulatory Networks

Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method''s results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets.     

Advanced Transgenic and Gene-Targeting Approaches

Over the past two decades the techniques associated with the manipulation of the mouse genome have provided a powerful approach toward the better understanding of gene function. Conventional transgenie and gene targeting approaches have been used extensively, and these techniques have been particularly rewarding for neuroscientists. Nevertheless, the traditional approaches toward genome manipulation have certain limitations that diminish their usefulness for studying more sophisticated biological processes. Therefore, variations to these techniques have recently been developed. The improvements are focused on two areas: one provides regulated control of transgene expression using an inducible expression system; and the other provides the opportunity to inactivate genes in specific cells and at predetermined developmental stages with a conditional gene targeting system. This review summarizes the advantages as well as some of the technical difficulties of these new approaches. The application of these advanced approaches in biomedical research, particularly neuroscience, are also discussed.     

Hub-centered gene network reconstruction using automatic relevance determination

Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Network reconstruction is then performed by maximization of or sampling from the posterior distribution. We evaluate our approach on simulated and real experimental data, indicating that we can reconstruct main regulatory interactions from the data. We furthermore compare our approach to other state-of-the art methods, showing superior performance in identifying hubs. Using a large publicly available dataset of over 800 cell cycle regulated genes, we are able to identify several main hub genes. Our method may thus provide a valuable tool to identify interesting candidate genes for further study. Furthermore, the approach presented may stimulate further developments in regularization methods for network reconstruction from data.     

Analysis of mouse eye development with chimeras and mosaics

Analysis of experimental mouse chimeras (chimaeras) and mosaics provides a means of investigating patterning and differentiation within the developing mammalian eye. Chimeric and mosaic mice carry two or more genetically distinct cell populations and extend the repertoire of analytical tools available to the geneticist. Here we review the impact these techniques have had on our understanding of eye organogenesis. Chimeras and mosaics are routinely used to investigate cell lineages, patterns of growth and gene function, and provide a means to clear analytical hurdles that otherwise limit standard genetic approaches. In particular, chimeras are used to investigate the roles of genes in tissues that do not develop in conventional mutant or knock-out mice, to test whether genes act cell autonomously or non-autonomously in different tissues and to dissect tissue-tissue interactions in less tractable, complex systems. Chimeras, in which cells of different genetic composition are mixed at a fine-scale cellular level, may provide qualitatively different data from mosaic mice with conditional knockouts. The uses of chimeras, Cre-loxP mosaics and in vitro tissue recombination for study of ocular organogenesis are compared. Wider use of mosaics and chimeras should provide further insights into eye development.     

Whole genome searching with shotgun proteomic data: applications for genome annotation

High-throughput genome sequencing continues to accelerate the rate at which complete genomes are available for biological research. Many of these new genome sequences have little or no genome annotation currently available and hence rely upon computational predictions of protein coding genes. Evidence of translation from proteomic techniques could facilitate experimental validation of protein coding genes, but the techniques for whole genome searching with MS/MS data have not been adequately developed to date. Here we describe GENQUEST, a novel method using peptide isoelectric focusing and accurate mass to greatly reduce the peptide search space, making fast, accurate, and sensitive whole human genome searching possible on common desktop computers. In an initial experiment, almost all exonic peptides identified in a protein database search were identified when searching genomic sequence. Many peptides identified exclusively in the genome searches were incorrectly identified or could not be experimentally validated, highlighting the importance of orthogonal validation. Experimentally validated peptides exclusive to the genomic searches can be used to reannotate protein coding genes. GENQUEST represents an experimental tool that can be used by the proteomics community at large for validating computational approaches to genome annotation.     

Discovering essential and infection-related genes

Transposon-based approaches are very powerful for identification of essential and infection-related genes in bacteria, particularly in the context of microbial genomics. We describe recent progress in several of these approaches, and their underlying principles. The essential gene test (EGT) is a transposon-based technique that can rapidly identify a nucleotide sequence from a database as essential or dispensable. Also, variations of in vitro transposon mutagenesis applications, such as genomic analysis and mapping by in vitro transposition (GAMBIT), are described. The development of techniques including PCR-based signature-tagged mutagenesis is now used to find essential virulence genes in different bacterial hosts. These approaches form the basis for the identification of microbial targets in development of novel antimicrobials and vaccines by the biotechnology and pharmaceutical industry.     

The "ups" and "downs" in Using Subtractive Cloning Techniques to Isolate Regulated Genes in Fish

Over the last decade, subtractive cloning approaches have beenused extensively to isolate genes that are up- or down-regulatedunder various conditions. These techniques have provided thefoundation for many subsequent studies concerning gene functionand regulation and, as such, have been valuable tools for manybiological fields. Over the past 10 years, we have used differentsubtractive cloning approaches to isolate genes in fish thatare regulated in relation to hormonal stimulation or the stageof ovarian maturation. These include conventional cDNA subtractionfollowed by library screening, differential display PCR, suppressionsubtraction hybridization, and more recently, iterative PCRsubtraction. We continue to use these techniques for the isolationof new genes involved in physiological processes in fish andbivalve molluscs. Examples that illustrate the use of thesedifferent subtractive cloning techniques are described, includingwhere possible the advantages and disadvantages of each. Inaddition, the use of ancillary methods (e.g., "Reverse Northerns")to facilitate the use of these subtractive approaches are discussed.     

Sequencing Strategies for Fusion Gene Detection

Fusion genes formed by chromosomal rearrangements are common drivers of cancer. Recent innovations in the field of next-generation sequencing (NGS) have seen a dynamic shift from traditional fusion detection approaches, such as visual characterization by fluorescence, to more precise multiplexed methods. There are many different NGS-based approaches to fusion gene detection and deciding on the most appropriate method can be difficult. Beyond the experimental approach, consideration needs to be given to factors such as the ease of implementation, processing time, associated costs, and the level of expertise required for data analysis. Here, the different NGS-based methods for fusion gene detection, the basic principles underlying the techniques, and the benefits and limitations of each approach are reviewed. This article concludes with a discussion of how NGS will impact fusion gene detection in a clinical context and from where the next innovations are evolving.     

THINK Back: KNowledge-based Interpretation of High Throughput data

Results of high throughput experiments can be challenging to interpret. Current approaches have relied on bulk processing the set of expression levels, in conjunction with easily obtained external evidence, such as co-occurrence. While such techniques can be used to reason probabilistically, they are not designed to shed light on what any individual gene, or a network of genes acting together, may be doing. Our belief is that today we have the information extraction ability and the computational power to perform more sophisticated analyses that consider the individual situation of each gene. The use of such techniques should lead to qualitatively superior results. The specific aim of this project is to develop computational techniques to generate a small number of biologically meaningful hypotheses based on observed results from high throughput microarray experiments, gene sequences, and next-generation sequences. Through the use of relevant known biomedical knowledge, as represented in published literature and public databases, we can generate meaningful hypotheses that will aide biologists to interpret their experimental data. We are currently developing novel approaches that exploit the rich information encapsulated in biological pathway graphs. Our methods perform a thorough and rigorous analysis of biological pathways, using complex factors such as the topology of the pathway graph and the frequency in which genes appear on different pathways, to provide more meaningful hypotheses to describe the biological phenomena captured by high throughput experiments, when compared to other existing methods that only consider partial information captured by biological pathways.     

Evaluation of coffee reference genes for relative expression studies by quantitative real-time RT-PCR

Accuracy in quantitative real-time polymerase chain reaction (qPCR) requires the use of stable endogenous controls. Normalization with multiple reference genes is the gold standard, but their identification is a laborious task, especially in species with limited sequence information. Coffee (Coffea ssp.) is an important agricultural commodity and, due to its economic relevance, is the subject of increasing research in genetics and biotechnology, in which gene expression analysis is one of the most important fields. Notwithstanding, relatively few works have focused on the analysis of gene expression in coffee. Moreover, most of these works have used less accurate techniques such as northern blot assays instead of more accurate techniques (e.g., qPCR) that have already been extensively used in other plant species. Aiming to boost the use of qPCR in studies of gene expression in coffee, we uncovered reference genes to be used in a number of different experimental conditions. Using two distinct algorithms implemented by geNorm and Norm Finder, we evaluated a total of eight candidate reference genes (psaB, PP2A, AP47, S24, GAPDH, rpl39, UBQ10, and UBI9) in four different experimental sets (control versus drought-stressed leaves, control versus drought-stressed roots, leaves of three different coffee cultivars, and four different coffee organs). The most suitable combination of reference genes was indicated in each experimental set for use as internal control for reliable qPCR data normalization. This study also provides useful guidelines for reference gene selection for researchers working with coffee plant samples under conditions other than those tested here. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Developing salt tolerant plants in a new century: a molecular biology approach

Soil salinity is a major abiotic stress in plant agriculture strongly, influencing plant productivity world-wide. Classical breeding for salt tolerance in crop plants has been attempted to improve field performance without success. Therefore, an alternative strategy is to generate salt tolerant plants through genetic engineering. Several species and experimental approaches have been used in order to identify those genes that are important for salt tolerance. Due to high level of salt tolerance, halophytes are good candidates to identify salt tolerance genes. However, other species such as yeast and glycophytes have also been employed. Three approaches are commonly used to identify genes important for salt tolerance. The first approach is to identify genes involved in processes known to be critical for salt tolerance (osmolyte synthesis, ion homeostasis, etc.). The second approach is to identify genes whose expression is regulated by salt stress. This is relatively simply and applicable to any plant species. Genetic amenability of some species allows the third approach, which consists in the identification of salt tolerance determinants based on functionality. At the moment, there is a large number of reports in the literature claiming that plants with increased salt tolerance have been obtained. The main problem is that different plant species, stage of development, organs, promoters and salt conditions used it is difficult to compare the degree of salt tolerance conferred by different genes. In this review, we discuss progress made towards understanding the molecular elements involved in salt stress responses that have been used in transgenic approaches to improve salt tolerance.     

Gene Function Hypotheses for the Campylobacter jejuni Glycome Generated by a Logic-Based Approach

Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning—the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system.     

Gene therapy for lung cancer

Lung cancer continues to be the largest killer of Americans due to cancer. Although progress has been made, with advances in chemotherapy, the majority of patients diagnosed with lung cancer ultimately succumb to the disease. A better understanding of the molecular pathogenesis of lung cancer is demonstrating how alterations in oncogenes and tumor suppressor genes control lung cancer initiation, growth, and survival. In this article, attempts to target molecular alterations in lung cancer using gene therapy techniques are reviewed. These include introducing suicide genes into tumor cells, replacement of defective tumor suppressor genes, inactivating oncogenes, and immunotherapy-based approaches using gene therapy technology. The major barrier for these techniques continues to be the inability to specifically target tumor cells while sparing normal cells. Nonetheless, these approaches are likely to yield important biologic and clinical data which will further the progress of lung cancer treatment.