Feature Selection for Gene Expression Using Model-Based Entropy

Gene expression data usually contain a large number of genes but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. Using machine learning techniques, traditional gene selection based on empirical mutual information suffers the data sparseness issue due to the small number of samples. To overcome the sparseness issue, we propose a model-based approach to estimate the entropy of class variables on the model, instead of on the data themselves. Here, we use multivariate normal distributions to fit the data, because multivariate normal distributions have maximum entropy among all real-valued distributions with a specified mean and standard deviation and are widely used to approximate various distributions. Given that the data follow a multivariate normal distribution, since the conditional distribution of class variables given the selected features is a normal distribution, its entropy can be computed with the log-determinant of its covariance matrix. Because of the large number of genes, the computation of all possible log-determinants is not efficient. We propose several algorithms to largely reduce the computational cost. The experiments on seven gene data sets and the comparison with other five approaches show the accuracy of the multivariate Gaussian generative model for feature selection, and the efficiency of our algorithms.     

A Highly Redundant Gene Network Controls Assembly of the Outer Spore Wall in S. cerevisiae

The spore wall of Saccharomyces cerevisiae is a multilaminar extracellular structure that is formed de novo in the course of sporulation. The outer layers of the spore wall provide spores with resistance to a wide variety of environmental stresses. The major components of the outer spore wall are the polysaccharide chitosan and a polymer formed from the di-amino acid dityrosine. Though the synthesis and export pathways for dityrosine have been described, genes directly involved in dityrosine polymerization and incorporation into the spore wall have not been identified. A synthetic gene array approach to identify new genes involved in outer spore wall synthesis revealed an interconnected network influencing dityrosine assembly. This network is highly redundant both for genes of different activities that compensate for the loss of each other and for related genes of overlapping activity. Several of the genes in this network have paralogs in the yeast genome and deletion of entire paralog sets is sufficient to severely reduce dityrosine fluorescence. Solid-state NMR analysis of partially purified outer spore walls identifies a novel component in spore walls from wild type that is absent in some of the paralog set mutants. Localization of gene products identified in the screen reveals an unexpected role for lipid droplets in outer spore wall formation.     

Genetic Mapping of MAPK-Mediated Complex Traits Across S. cerevisiae

Signaling pathways enable cells to sense and respond to their environment. Many cellular signaling strategies are conserved from fungi to humans, yet their activity and phenotypic consequences can vary extensively among individuals within a species. A systematic assessment of the impact of naturally occurring genetic variation on signaling pathways remains to be conducted. In S. cerevisiae, both response and resistance to stressors that activate signaling pathways differ between diverse isolates. Here, we present a quantitative trait locus (QTL) mapping approach that enables us to identify genetic variants underlying such phenotypic differences across the genetic and phenotypic diversity of S. cerevisiae. Using a Round-robin cross between twelve diverse strains, we identified QTL that influence phenotypes critically dependent on MAPK signaling cascades. Genetic variants under these QTL fall within MAPK signaling networks themselves as well as other interconnected signaling pathways. Finally, we demonstrate how the mapping results from multiple strain background can be leveraged to narrow the search space of causal genetic variants.     

Comparative Xylose Metabolism among the Ascomycetes C. albicans,S. stipitis and S. cerevisiae

The ascomycetes Candida albicans, Saccharomyces cerevisiae and Scheffersomyces stipitis metabolize the pentose sugar xylose very differently. S. cerevisiae fails to grow on xylose, while C. albicans can grow, and S. stipitis can both grow and ferment xylose to ethanol. However, all three species contain highly similar genes that encode potential xylose reductases and xylitol dehydrogenases required to convert xylose to xylulose, and xylulose supports the growth of all three fungi. We have created C. albicans strains deleted for the xylose reductase gene GRE3, the xylitol dehydrogenase gene XYL2, as well as the gre3 xyl2 double mutant. As expected, all the mutant strains cannot grow on xylose, while the single gre3 mutant can grow on xylitol. The gre3 and xyl2 mutants are efficiently complemented by the XYL1 and XYL2 from S. stipitis. Intriguingly, the S. cerevisiae GRE3 gene can complement the Cagre3 mutant, while the ScSOR1 gene can complement the Caxyl2 mutant, showing that S. cerevisiae contains the enzymatic capacity for converting xylose to xylulose. In addition, the gre3 xyl2 double mutant of C. albicans is effectively rescued by the xylose isomerase (XI) gene of either Piromyces or Orpinomyces, suggesting that the XI provides an alternative to the missing oxido-reductase functions in the mutant required for the xylose-xylulose conversion. Overall this work suggests that C. albicans strains engineered to lack essential steps for xylose metabolism can provide a platform for the analysis of xylose metabolism enzymes from a variety of species, and confirms that S. cerevisiae has the genetic potential to convert xylose to xylulose, although non-engineered strains cannot proliferate on xylose as the sole carbon source.     

Design Parameters to Control Synthetic Gene Expression in Escherichia coli

Background

Production of proteins as therapeutic agents, research reagents and molecular tools frequently depends on expression in heterologous hosts. Synthetic genes are increasingly used for protein production because sequence information is easier to obtain than the corresponding physical DNA. Protein-coding sequences are commonly re-designed to enhance expression, but there are no experimentally supported design principles.

Principal Findings

To identify sequence features that affect protein expression we synthesized and expressed in E. coli two sets of 40 genes encoding two commercially valuable proteins, a DNA polymerase and a single chain antibody. Genes differing only in synonymous codon usage expressed protein at levels ranging from undetectable to 30% of cellular protein. Using partial least squares regression we tested the correlation of protein production levels with parameters that have been reported to affect expression. We found that the amount of protein produced in E. coli was strongly dependent on the codons used to encode a subset of amino acids. Favorable codons were predominantly those read by tRNAs that are most highly charged during amino acid starvation, not codons that are most abundant in highly expressed E. coli proteins. Finally we confirmed the validity of our models by designing, synthesizing and testing new genes using codon biases predicted to perform well.

Conclusion

The systematic analysis of gene design parameters shown in this study has allowed us to identify codon usage within a gene as a critical determinant of achievable protein expression levels in E. coli. We propose a biochemical basis for this, as well as design algorithms to ensure high protein production from synthetic genes. Replication of this methodology should allow similar design algorithms to be empirically derived for any expression system.     

Reconstruction and Evaluation of the Synthetic Bacterial MEP Pathway in Saccharomyces cerevisiae

Isoprenoids, which are a large group of natural and chemical compounds with a variety of applications as e.g. fragrances, pharmaceuticals and potential biofuels, are produced via two different metabolic pathways, the mevalonate (MVA) pathway and the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway. Here, we attempted to replace the endogenous MVA pathway in Saccharomyces cerevisiae by a synthetic bacterial MEP pathway integrated into the genome to benefit from its superior properties in terms of energy consumption and productivity at defined growth conditions. It was shown that the growth of a MVA pathway deficient S. cerevisiae strain could not be restored by the heterologous MEP pathway even when accompanied by the co-expression of genes erpA, hISCA1 and CpIscA involved in the Fe-S trafficking routes leading to maturation of IspG and IspH and E. coli genes fldA and fpr encoding flavodoxin and flavodoxin reductase believed to be responsible for electron transfer to IspG and IspH.     

Improved Blue,Green, and Red Fluorescent Protein Tagging Vectors for S. cerevisiae

Fluorescent protein fusions are a powerful tool to monitor the localization and trafficking of proteins. Such studies are particularly easy to carry out in the budding yeast Saccharomyces cerevisiae due to the ease with which tags can be introduced into the genome by homologous recombination. However, the available yeast tagging plasmids have not kept pace with the development of new and improved fluorescent proteins. Here, we have constructed yeast optimized versions of 19 different fluorescent proteins and tested them for use as fusion tags in yeast. These include two blue, seven green, and seven red fluorescent proteins, which we have assessed for brightness, photostability and perturbation of tagged proteins. We find that EGFP remains the best performing green fluorescent protein, that TagRFP-T and mRuby2 outperform mCherry as red fluorescent proteins, and that mTagBFP2 can be used as a blue fluorescent protein tag. Together, the new tagging vectors we have constructed provide improved blue and red fluorescent proteins for yeast tagging and three color imaging.     

Size Of Gene Specific Inverted Repeat - Dependent Gene Deletion In Saccharomyces cerevisiae

We describe here an approach for rapidly producing scar-free and precise gene deletions in S. cerevisiae with high efficiency. Preparation of the disruption gene cassette in this approach was simply performed by overlap extension-PCR of an invert repeat of a partial or complete sequence of the targeted gene with URA3. Integration of the prepared disruption gene cassette to the designated position of a target gene leads to the formation of a mutagenesis cassette within the yeast genome, which consists of a URA3 gene flanked by the targeted gene and its inverted repeat between two short identical direct repeats. The inherent instability of the inverted sequences in close proximity facilitates the self-excision of the entire mutagenesis cassette deposited in the genome and promotes homologous recombination resulting in a seamless deletion via a single transformation. This rapid assembly circumvents the difficulty during preparation of disruption gene cassettes composed of two inverted repeats of the URA3, which requires the engineering of unique restriction sites for subsequent digestion and T4 DNA ligation in vitro. We further identified that the excision of the entire mutagenesis cassette flanked by two DRs in the transformed S. cerevisiae is dependent on the length of the inverted repeat of which a minimum of 800 bp is required for effective gene deletion. The deletion efficiency improves with the increase of the inverted repeat till 1.2 kb. Finally, the use of gene-specific inverted repeats of target genes enables simultaneous gene deletions. The procedure has the potential for application on other yeast strains to achieve precise and efficient removal of gene sequences.     

Stabilization of Dicentric Translocations through Secondary Rearrangements Mediated by Multiple Mechanisms in S. cerevisiae

Background

The gross chromosomal rearrangements (GCRs) observed in S. cerevisiae mutants with increased rates of accumulating GCRs include predicted dicentric GCRs such as translocations, chromosome fusions and isoduplications. These GCRs resemble the genome rearrangements found as mutations underlying inherited diseases as well as in the karyotypes of many cancers exhibiting ongoing genome instability

Methodology/Principal Findings

The structures of predicted dicentric GCRs were analyzed using multiple strategies including array-comparative genomic hybridization, pulse field gel electrophoresis, PCR amplification of predicted breakpoints and sequencing. The dicentric GCRs were found to be unstable and to have undergone secondary rearrangements to produce stable monocentric GCRs. The types of secondary rearrangements observed included: non-homologous end joining (NHEJ)-dependent intramolecular deletion of centromeres; chromosome breakage followed by NHEJ-mediated circularization or broken-end fusion to another chromosome telomere; and homologous recombination (HR)-dependent non-reciprocal translocations apparently mediated by break-induced replication. A number of these GCRs appeared to have undergone multiple bridge-fusion-breakage cycles. We also observed examples of chromosomes with extensive ongoing end decay in mec1 tlc1 mutants, suggesting that Mec1 protects chromosome ends from degradation and contributes to telomere maintenance by HR.

Conclusions/Significance

HR between repeated sequences resulting in secondary rearrangements was the most prevalent pathway for resolution of dicentric GCRs regardless of the structure of the initial dicentric GCR, although at least three other resolution mechanisms were observed. The resolution of dicentric GCRs to stable rearranged chromosomes could in part account for the complex karyotypes seen in some cancers.     

New Mechanisms of Flucytosine Resistance in C. glabrata Unveiled by a Chemogenomics Analysis in S. cerevisiae

5-Flucytosine is currently used as an antifungal drug in combination therapy, but fungal pathogens are rapidly able to develop resistance against this drug, compromising its therapeutic action. The understanding of the underlying resistance mechanisms is crucial to deal with this problem. In this work, the S. cerevisiae deletion mutant collection was screened for increased resistance to flucytosine. Through this chemogenomics analysis, 183 genes were found to confer resistance to this antifungal agent. Consistent with its known effect in DNA, RNA and protein synthesis, the most significant Gene Ontology terms over-represented in the list of 5-flucytosine resistance determinants are related to DNA repair, RNA and protein metabolism. Additional functional classes include carbohydrate and nitrogen—particularly arginine—metabolism, lipid metabolism and cell wall remodeling. Based on the results obtained for S. cerevisiae as a model system, further studies were conducted in the pathogenic yeast Candida glabrata. Arginine supplementation was found to relieve the inhibitory effect exerted by 5-flucytosine in C. glabrata. Lyticase susceptibility was found to increase within the first 30min of 5-flucytosine exposure, suggesting this antifungal drug to act as a cell wall damaging agent. Upon exponential growth resumption in the presence of 5-flucytosine, the cell wall exhibited higher resistance to lyticase, suggesting that cell wall remodeling occurs in response to 5-flucytosine. Additionally, the aquaglyceroporin encoding genes CgFPS1 and CgFPS2, from C. glabrata, were identified as determinants of 5-flucytosine resistance. CgFPS1 and CgFPS2 were found to mediate 5-flucytosine resistance, by decreasing 5-flucytosine accumulation in C. glabrata cells.     

内含子对真核基因表达调控的影响

大多数真核基因都含有非编码的间隔序列--内含子,根据剪接机制的不同,可将内含子分为3类:真核mRNA内舍子、自我剪接内含子和真核tRNA内含子.在多数情况下,真核mRNA内含子的存在可以提高基因的表达水平.因为其剪接过程会影响mRNA新陈代谢的多个阶段,包括转录、RNA编辑、pre-mRNA的加工、mRNA的出核运输、翻译和无义衰变等.真核mRNA内含子在真核生物基因表达调控中起着重要的作用,是转基因研究中提高外源基因表达的重要元件之一.就真核mRNA内含子的特性、剪接机制及其对真核基因表达调控的影响作一概述.     

In-Vivo Real-Time Control of Protein Expression from Endogenous and Synthetic Gene Networks

We describe an innovative experimental and computational approach to control the expression of a protein in a population of yeast cells. We designed a simple control algorithm to automatically regulate the administration of inducer molecules to the cells by comparing the actual protein expression level in the cell population with the desired expression level. We then built an automated platform based on a microfluidic device, a time-lapse microscopy apparatus, and a set of motorized syringes, all controlled by a computer. We tested the platform to force yeast cells to express a desired fixed, or time-varying, amount of a reporter protein over thousands of minutes. The computer automatically switched the type of sugar administered to the cells, its concentration and its duration, according to the control algorithm. Our approach can be used to control expression of any protein, fused to a fluorescent reporter, provided that an external molecule known to (indirectly) affect its promoter activity is available.