A genomic approach to identify hybrid incompatibility genes

Uncovering the genetic and molecular basis of barriers to gene flow between populations is key to understanding how new species are born. Intrinsic postzygotic reproductive barriers such as hybrid sterility and hybrid inviability are caused by deleterious genetic interactions known as hybrid incompatibilities. The difficulty in identifying these hybrid incompatibility genes remains a rate-limiting step in our understanding of the molecular basis of speciation. We recently described how whole genome sequencing can be applied to identify hybrid incompatibility genes, even from genetically terminal hybrids. Using this approach, we discovered a new hybrid incompatibility gene, gfzf, between Drosophila melanogaster and Drosophila simulans, and found that it plays an essential role in cell cycle regulation. Here, we discuss the history of the hunt for incompatibility genes between these species, discuss the molecular roles of gfzf in cell cycle regulation, and explore how intragenomic conflict drives the evolution of fundamental cellular mechanisms that lead to the developmental arrest of hybrids.     

A gene trap approach to identify genes that control development

One methodology called gene trap represents a versatile strategy by which murine genes that control developmental events can be captured and identified with corresponding mutants produced at the same time. Gene trap methodology has been developed and several genes and their mutants have been analyzed, but almost all of the genes reported are those already known or murine homologs of other species. In this study, the efficiency of the gene trap methodology was improved and a novel mutant mouse strain named jumonji established which displayed an intriguing defect. Homozygous fetal mice died in utero and a significant proportion of the homozygotes showed abnormal groove formation on the neural plate and a defect in neural tube closure with a mixed genetic background of 129/Ola and BALB/c. The trapped gene believed to be responsible for these phenotypes encodes a novel nuclear protein. The results reveal that the gene trap approach can identify unknown interesting genes in murine development. The gene trap strategy, however, has several problems, the greatest of which is the difficulty in prescreening embryonic stem (ES) cells for interesting trapped genes. Recent studies are solving this problem and show that the prescreening of ES cells for genes with several characteristics is possible.     

A multiplexed in vivo approach to identify driver genes in small cell lung cancer

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A computational approach to identify genes for functional RNAs in genomic sequences

Currently there is no successful computational approach for identification of genes encoding novel functional RNAs (fRNAs) in genomic sequences. We have developed a machine learning approach using neural networks and support vector machines to extract common features among known RNAs for prediction of new RNA genes in the unannotated regions of prokaryotic and archaeal genomes. The Escherichia coli genome was used for development, but we have applied this method to several other bacterial and archaeal genomes. Networks based on nucleotide composition were 80–90% accurate in jackknife testing experiments for bacteria and 90–99% for hyperthermophilic archaea. We also achieved a significant improvement in accuracy by combining these predictions with those obtained using a second set of parameters consisting of known RNA sequence motifs and the calculated free energy of folding. Several known fRNAs not included in the training datasets were identified as well as several hundred predicted novel RNAs. These studies indicate that there are many unidentified RNAs in simple genomes that can be predicted computationally as a precursor to experimental study. Public access to our RNA gene predictions and an interface for user predictions is available via the web.     

A complementary approach to identifying and assessing the remediation potential of hydrocarbonoclastic bacteria

The isolation and assessment of hydrocarbonoclastic bacteria often represents a key strategy in the bioremediation of hydrocarbon-contaminated sites. However the isolation and assessment of such bacteria is often a lengthy and expensive procedure. The aim of this study was to identify potential isolates for use in the remediation of hydrocarbon contaminated sites using a combination of selective isolation plating, the Biolog system and subsequent multivariate analyses. The use of weathered oil as the main C source restricted the number of isolates growing to 5×10(2)CFUg soil(-1). Isolates (n=96) were then assessed individually using Biolog MT2 plates with seven different hydrocarbons (dodecane, tridecane, hexadecane, octadecane, eicosane, naphthalene and phenanthrene). The results indicated that all isolates were able to grow on at least one hydrocarbon from the seven chosen. This confirmed that the isolation media developed was selective in isolating hydrocarbonoclastic bacteria only. Cluster analysis of Biolog data separated the isolates into two discrete clusters with cluster 2 identifying hydrocarbonoclastic bacteria that are effective in degrading a variety of contaminants. Further study on the isolates from cluster 2 was carried out based on their phylogenetic analysis. Phylogenetic analysis of 28 bacterial isolates from cluster 2 based on the 1500bp sequences from 16S rDNA genes using MRBAYES confirmed all isolates as being hydrocarbonoclastic, providing supportive evidence that isolates from cluster 2 have a potential use in bioremediation. This approach could improve both the speed and efficiency of the commercial bioremediation process.     

An evidence-based approach to identify aging-related genes in Caenorhabditis elegans

Background

Extensive studies have been carried out on Caenorhabditis elegans as a model organism to elucidate mechanisms of aging and the effects of perturbing known aging-related genes on lifespan and behavior. This research has generated large amounts of experimental data that is increasingly difficult to integrate and analyze with existing databases and domain knowledge. To address this challenge, we demonstrate a scalable and effective approach for automatic evidence gathering and evaluation that leverages existing experimental data and literature-curated facts to identify genes involved in aging and lifespan regulation in C. elegans.

Results

We developed a semantic knowledge base for aging by integrating data about C. elegans genes from WormBase with data about 2005 human and model organism genes from GenAge and 149 genes from GenDR, and with the Bio2RDF network of linked data for the life sciences. Using HyQue (a Semantic Web tool for hypothesis-based querying and evaluation) to interrogate this knowledge base, we examined 48,231 C. elegans genes for their role in modulating lifespan and aging. HyQue identified 24 novel but well-supported candidate aging-related genes for further experimental validation.

Conclusions

We use semantic technologies to discover candidate aging genes whose effects on lifespan are not yet well understood. Our customized HyQue system, the aging research knowledge base it operates over, and HyQue evaluations of all C. elegans genes are freely available at http://hyque.semanticscience.org.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0469-4) contains supplementary material, which is available to authorized users.     

The plant cell wall

<正>Research on the many aspects of the plant cell wall has experienced rejuvenation during the past few years.This is perhaps mainly due to the commercial interest in the chemical components of the cell wall that have potential for industrial use:Cellulose for fi bers and together with hemicelluloses for bioethanol,lignin for plastics or biofuel,pectins as gel agents,let alone woody cell wall material for construction or pulp     

Functional plant cell wall design revealed by the Raman imaging approach

Using the Raman imaging approach, the optimization of the plant cell wall design was investigated on the micron level within different tissue types at different positions of a Phormium tenax leaf. Pectin and lignin distribution were visualized and the cellulose microfibril angle (MFA) of the cell walls was determined. A detailed analysis of the Raman spectra extracted from the selected regions, allowed a semi-quantitative comparison of the chemical composition of the investigated tissue types on the micron level. The cell corners of the parenchyma revealed almost pure pectin and the cell wall an amount of 38–49% thereof. Slight lignification was observed in the parenchyma and collenchyma in the top of the leaf and a high variability (7–44%) in the sclerenchyma. In the cell corners and in the cell wall of the sclerenchymatic fibres surrounding the vascular tissue, the highest lignification was observed, which can act as a barrier and protection of the vascular tissue. In the sclerenchyma high variable MFA (4°–40°) was detected, which was related with lignin variability. In the primary cell walls a constant high MFA (57°–58°) was found together with pectin. The different plant cell wall designs on the tissue and microlevel involve changes in chemical composition as well as cellulose microfibril alignment and are discussed and related according to the development and function.     

A systematic approach to dynamic programming in bioinformatics

MOTIVATION: Dynamic programming is probably the most popular programming method in bioinformatics. Sequence comparison, gene recognition, RNA structure prediction and hundreds of other problems are solved by ever new variants of dynamic programming. Currently, the development of a successful dynamic programming algorithm is a matter of experience, talent and luck. The typical matrix recurrence relations that make up a dynamic programming algorithm are intricate to construct, and difficult to implement reliably. No general problem independent guidance is available. RESULTS: This article introduces a systematic method for constructing dynamic programming solutions to problems in biosequence analysis. By a conceptual splitting of the algorithm into a recognition and an evaluation phase, algorithm development is simplified considerably, and correct recurrences can be derived systematically. Without additional effort, the method produces an early, executable prototype expressed in a functional programming language. The method is quite generally applicable, and, while programming effort decreases, no overhead in terms of ultimate program efficiency is incurred.     

A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens

In recent years, genome-sequencing projects of pathogens and humans have revolutionized microbial drug target identification. Of the several known genomic strategies, subtractive genomics has been successfully utilized for identifying microbial drug targets. The present work demonstrates a novel genomics approach in which codon adaptation index (CAI), a measure used to predict the translational efficiency of a gene based on synonymous codon usage, is coupled with subtractive genomics approach for mining potential drug targets. The strategy adopted is demonstrated using respiratory pathogens, namely, Streptococcus pneumoniae and Haemophilus influenzae as examples. Our approach identified 8 potent target genes (Streptococcus pneumoniae?C2, H. influenzae?C6), which are functionally significant and also play key role in host-pathogen interactions. This approach facilitates swift identification of potential drug targets, thereby enabling the search for new inhibitors. These results underscore the utility of CAI for enhanced in silico drug target identification.     

New approach to reveal genes that control cell wall biogenesis of the yeast Saccharomyces cerevisiae

The Mcd4 protein of Saccharomyces cerevisiae is probably involved in addition of the phosphoethanolamine moiety to the first mannose residue of the glycosylphosphatidylinositol precursor(s). However, significance of this modification is unclear. Besides, functions of the MCD4 gene also is not completely clear, since mutations in this gene may have pleiotropic manifestations, which are not obviously related to the glycosylphosphatidylinositol biosynthesis. To clarify the functions of Mcd4p we have performed a search for genes whose mutations are lethal or semilethal in combination with the ssu21 mutation in MCD4. In total, we have isolated six mutations some of which cause sensitivity to SDS and/or calcofluor white. Genes which are able to complement two of these mutations were cloned. They were MNN9 which encodes protein involved in formation of outer chains of the N-linked glycans of secretory proteins and GWT1, encoding the protein of the endoplasmic reticulum involved in the glycosylphosphatidylinositol biosynthesis. The results obtained indicate that in both cases growth inhibition was caused by defect of cell wall biogenesis and alteration of folding of secretory proteins. Search for mutations that lethal in combination with the ssu21 is an effective approach to reveal genes involved in the control of cell wall biogenesis.     

A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments

MOTIVATION: Currently most of the methods for identifying differentially expressed genes fall into the category of so called single-gene-analysis, performing hypothesis testing on a gene-by-gene basis. In a single-gene-analysis approach, estimating the variability of each gene is required to determine whether a gene is differentially expressed or not. Poor accuracy of variability estimation makes it difficult to identify genes with small fold-changes unless a very large number of replicate experiments are performed. RESULTS: We propose a method that can avoid the difficult task of estimating variability for each gene, while reliably identifying a group of differentially expressed genes with low false discovery rates, even when the fold-changes are very small. In this article, a new characterization of differentially expressed genes is established based on a theorem about the distribution of ranks of genes sorted by (log) ratios within each array. This characterization of differentially expressed genes based on rank is an example of all-gene-analysis instead of single gene analysis. We apply the method to a cDNA microarray dataset and many low fold-changed genes (as low as 1.3 fold-changes) are reliably identified without carrying out hypothesis testing on a gene-by-gene basis. The false discovery rate is estimated in two different ways reflecting the variability from all the genes without the complications related to multiple hypothesis testing. We also provide some comparisons between our approach and single-gene-analysis based methods. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.