A test of the coordinated expression hypothesis for the origin and maintenance of the GAL cluster in yeast

Metabolic gene clusters--functionally related and physically clustered genes--are a common feature of some eukaryotic genomes. Two hypotheses have been advanced to explain the origin and maintenance of metabolic gene clusters: coordinated gene expression and genetic linkage. Here we test the hypothesis that selection for coordinated gene expression underlies the clustering of GAL genes in the yeast genome. We find that, although clustering coordinates the expression of GAL1 and GAL10, disrupting the GAL cluster does not impair fitness, suggesting that other mechanisms, such as genetic linkage, drive the origin and maintenance metabolic gene clusters.     

Cytosolic NAD content strictly depends on ATP concentration in isolated liver cells

By focusing on the question of the thermodynamic relationships involved in the regulation of biological energy conversion, bioenergetic studies usually consider the free pyridine and adenine nucleotide rather than their total pools, in either cytosol or mitochondria. In this study, we report a new observation that, at steady state, nicotinamide nucleotide content is increased by a rise in the ATP content of the whole cell under physiological conditions. It is a straight line relationship when only NAD⁺ and ATP are considered. When regarding the compartmentation of this phenomenon, it appears that the linear relationship between [NAD⁺] and [ATP] occurs only in the cytosol. Such a dependence could be a supplementary mechanism of regulation between various metabolic pathways in the liver cell.     

Subcellular compartmentation of pyrophosphate and alkaline pyrophosphatase in leaves

The subcellular localisation of pyrophosphate and alkaline pyrophosphatase in leaves has been studied using non-aqueous density gradient centrifugation of spinach leaves, and membrane filtration of wheat mesophyll protoplasts. The pyrophosphate was measured in extracts prepared in trichloroacetic acid, and could be quantitatively recovered from the leaf material. It was located predominantly in the cytosol, with a concentration of 0.2–0.3 mM. In contrast, the alkaline pyrophosphatase was largely, if not, exclusively, located in the chloroplast. By comparing the pyrophosphate levels in the cytosol with previously published data on the cytosolic levels of phosphate and metabolic intermediates, it is shown that the reactions catalysed by pyrophosphate: fructose-6-phosphate phosphotransferase and UDP-glucose pyrophosphorylase are close to the thermodynamic equilibrium and, thus, freely reversible in vivo. Comparison of the pyrophosphate levels with the reported electrical and pH gradient across the tonoplast membrane shows the free energy released during pyrophosphate hydrolysis is similar to that required to move a proton across the tonoplast membrane. It is suggested that pyrophosphate could operate as a secondary energy donor in the cytosol of plant cells.     

From hormones to secondary metabolism: the emergence of metabolic gene clusters in plants

Gene clusters for the synthesis of secondary metabolites are a common feature of microbial genomes. Well-known examples include clusters for the synthesis of antibiotics in actinomycetes, and also for the synthesis of antibiotics and toxins in filamentous fungi. Until recently it was thought that genes for plant metabolic pathways were not clustered, and this is certainly true in many cases; however, five plant secondary metabolic gene clusters have now been discovered, all of them implicated in synthesis of defence compounds. An obvious assumption might be that these eukaryotic gene clusters have arisen by horizontal gene transfer from microbes, but there is compelling evidence to indicate that this is not the case. This raises intriguing questions about how widespread such clusters are, what the significance of clustering is, why genes for some metabolic pathways are clustered and those for others are not, and how these clusters form. In answering these questions we may hope to learn more about mechanisms of genome plasticity and adaptive evolution in plants. It is noteworthy that for the five plant secondary metabolic gene clusters reported so far, the enzymes for the first committed steps all appear to have been recruited directly or indirectly from primary metabolic pathways involved in hormone synthesis. This may or may not turn out to be a common feature of plant secondary metabolic gene clusters as new clusters emerge.     

Annotating gene function by combining expression data with a modular gene network

MOTIVATION: A promising and reliable approach to annotate gene function is clustering genes not only by using gene expression data but also literature information, especially gene networks. RESULTS: We present a systematic method for gene clustering by combining these totally different two types of data, particularly focusing on network modularity, a global feature of gene networks. Our method is based on learning a probabilistic model, which we call a hidden modular random field in which the relation between hidden variables directly represents a given gene network. Our learning algorithm which minimizes an energy function considering the network modularity is practically time-efficient, regardless of using the global network property. We evaluated our method by using a metabolic network and microarray expression data, changing with microarray datasets, parameters of our model and gold standard clusters. Experimental results showed that our method outperformed other four competing methods, including k-means and existing graph partitioning methods, being statistically significant in all cases. Further detailed analysis showed that our method could group a set of genes into a cluster which corresponds to the folate metabolic pathway while other methods could not. From these results, we can say that our method is highly effective for gene clustering and annotating gene function.     

Identification of Xanthomonas campestris pv. campestris galactose utilization genes from transcriptome data

A 70 mer oligonucleotide microarray was constructed to analyze genome-wide expression profiles of Xanthomonas campestris pv. campestris B100, a plant-pathogenic bacterium that is industrially employed to produce the exopolysaccharide xanthan gum which has many applications as a stabilizing, thickening, gelling, and emulsifying agent in food, pharmaceutical, and cosmetic industries. As an application example, global changes of gene expression were monitored during growth of X. campestris pv. campestris B100 on two different carbon sources. Exponential growing bacterial cultures were incubated either for 1h or permanently in minimal medium supplemented with 1% galactose in comparison to growth in minimal medium supplemented with 1% glucose. Six genes were identified that were significantly increased in gene expression under both growth conditions. These genes were located in three distinguished chromosomal regions in operon-like gene clusters. Genes from these clusters encode secreted glycosidases, which were predicted to be specific for galactose-containing carbohydrates, as well as transport proteins probably located in the outer and inner cell membrane. Finally genes from one cluster code for cytoplasmic enzymes of a metabolic pathway specific for the breakdown of galactose to intermediates of glycolysis.     

Limit to steady-state aerobic power of skeletal muscles

Like any other kind of cell, muscle cells produce energy by oxidizing the fuel substrate that they absorb together with the needed oxygen from the surroundings. Oxidation occurs entirely within the cell. It means that the reactants and products of reaction must at some time be dissolved in the cell’s cytosol. If a cell operates at steady state, its cytosol composition remains constant. Therefore, the cytosol in a muscle that produces work at steady state must contain a constant amount of fuel, oxygen, and product of reaction dissolved in it. The greater the power produced, the higher the concentration of these solutes. There is a limit, however, to the maximum amount of solutes that the cytosol can contain without damaging the cell. General thermodynamic arguments, which are reviewed in this paper, help relate this limit to the dehydration and overhydration limits of the cell. The present analysis shows that the same limits entail a limit to the maximum power that a muscle can produce at steady state. This limit depends on the composition of the fuel mixture used by the muscle. The analysis also determines the number of fuel carbon atoms that must be oxidized in parallel within a cell to produce a given power. It may well happen that a muscle cannot reach the maximum attainable power because it cannot activate all the parallel oxidation paths that are needed to produce it. This may be due to a series of reasons ranging from health issues to a lack of training. The paper shows how the methods of indirect calorimetry can provide all the experimental data needed to determine the actual number of parallel oxidation paths that at steady state must be active in a muscle in a given exercise. A diagram relating muscle power to the number of parallel oxidation paths and fuel composition is finally presented. It provides a means to assess the power capacity of animal muscles and can be applied to evaluate their fitness, stamina, margins for improvement, and athletic potential.     

Co-clustering of biological networks and gene expression data

MOTIVATION: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. RESULTS: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.     

The role of mutational dynamics in genome shrinkage

Genome shrinkage occurs after whole genome duplications (WGDs) and in the evolution of parasitic or symbiotic species. The dynamics of this process, whether it occurs by single gene deletions or also by larger deletions are however unknown. In yeast, genome shrinkage has occurred after a WGD. Using a computational model of genome evolution, we show that in a random genome single gene deletions cannot explain the observed pattern of gene loss in yeast. The distribution of genes deleted per event can be very well described by a geometric distribution, with a mean of 1.1 genes per event. In terms of deletions of a stretch of base pairs, we find that a geometric distribution with an average of 500-600 base pairs per event describes the data very well. Moreover, in the model, as in the data, gene pairs that have a small intergenic distance are more likely to be both deleted. This proves that simultaneous deletion of multiple genes causes the observed pattern of gene deletions, rather than deletion of functionally clustered genes by selection. Furthermore, we found that in the bacterium Buchnera aphidicola larger deletions than in yeast are necessary to explain the clustering of deleted genes. We show that the excess clustering of deleted genes in B. aphidicola can be explained by the clustering of genes in operons. Therefore, we show that selection has little effect on the clustering of deleted genes after the WGD in yeast, while it has during genome shrinkage in B. aphidicola.     

肽类抗生素生物合成基因簇在Escherichia coli中的异源表达

微生物能够产生众多结构和生物活性多样的次生代谢产物,而其生物合成基因簇的挖掘和异源表达是药物创新和产量提高的必要前提. 在过去20年里,大量重要天然产物的生物合成基因簇在微生物中被不断的发现. 在这些被挖掘的基因簇中,肽类抗生素的生物合成基因簇占了很大比重.肽类抗生素因具有抗菌、抗肿瘤、抗病毒等多种生物学活性而备受化学家和药物学家的重视. 如能了解它们的生物合成机制,实现其基因簇的异源表达,将使合理化遗传修饰生物合成通路获取结构类似物（药物开发）和提高产量成为可能. 大肠杆菌作为最广泛、最成功的表达体系,常用来表达外源基因,但一般只能表达一个或几个基因,却很少有用它来表达整个生物合成基因簇. 2001年,Khosla和Cane在E.coli中成功异源表达了一个复杂聚酮天然产物（红霉素苷原6dEB）基因簇. 这是首个有关在E.coli中异源表达天然产物生物合成基因簇的研究. 至此之后,大肠杆菌开始作为生物合成基因簇的异源表达宿主,越来越受到相关领域的重视. 紧接着核糖体肽和非核糖体肽生物合成基因簇也相继在大肠杆菌中成功异源表达. 本文对肽类抗生素生物合成基因簇在E.coli中的异源表达进行了综述.     

Network constrained clustering for gene microarray data

Many bioinformatics problems can be tackled from a fresh angle offered by the network perspective. Directly inspired by metabolic network structural studies, we propose an improved gene clustering approach for inferring gene signaling pathways from gene microarray data. Based on the construction of co-expression networks that consists of both significantly linear and non-linear gene associations together with controlled biological and statistical significance, our approach tends to group functionally related genes into tight clusters despite their expression dissimilarities. We illustrate our approach and compare it to the traditional clustering approaches on a yeast galactose metabolism dataset and a retinal gene expression dataset. Our approach greatly outperforms the traditional approach in rediscovering the relatively well known galactose metabolism pathway in yeast and in clustering genes of the photoreceptor differentiation pathway. AVAILABILITY: The clustering method has been implemented in an R package "GeneNT" that is freely available from: http://www.cran.org.     

A novel prefusion complex formed during protein transport between Golgi cisternae in a cell-free system

Examination of a cell-free reconstitution of intercompartmental transport through the Golgi apparatus has enabled detection of two intermediates in the pathway (Balch, W. E., Glick, B. S., and Rothman, J. E. (1984) Cell 39, 525-536). These intermediates are thought to represent stages in the budding and fusion reactions of transport vesicles mediating such a transport process. Here we describe a new transport intermediate that is interposed between the previously established primed donor formation and the N-ethylmaleimide (NEM)-resistant acceptor intermediates. Consumption of this intermediate requires much less cytosol than its formation, and thus it has been termed the "dilution-resistant" intermediate. The dilution-resistant intermediate only forms in the presence of donor and acceptor membranes, and its consumption is sensitive to NEM. The transition from this state to the later, NEM-resistant form of the prefusion complex requires ATP as well as cytosol and may represent a processing of transport vesicles to permit their fusion.     

The oxidative pentose phosphate pathway: structure and organisation

The oxidative pentose phosphate pathway is a major source of reducing power and metabolic intermediates for biosynthetic processes. Some, if not all, of the enzymes of the pathway are found in both the cytosol and plastids, although the precise distribution of their activities varies. The apparent absence of sections of the pathway from the cytosol potentially complicates metabolism. These complications are partly offset, however, by exchange of intermediates between the cytosol and the plastids through the activities of a family of plastid phosphate translocators. Molecular analysis is confirming the widespread presence of multiple genes encoding each of the enzymes of the oxidative pentose phosphate pathway. Differential expression of these isozymes may ensure that the kinetic properties of the activity that catalyses a specific reaction match the metabolic requirements of a particular tissue. This hypothesis can be tested thanks to recent developments in the application of 13C-steady-state labelling strategies. These strategies make it possible to quantify flux through metabolic networks and to discriminate between pathways of carbohydrate oxidation in the cytosol and plastids.     

Consensus clustering and functional interpretation of gene-expression data

Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus clustering, which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel genes regulated by NFκB and the unfolded protein response in certain B-cell lymphomas.     

Detection of fundamental principles and a level of order for large-scale gene clustering on the Escherichia coli chromosome

Summary The Escherichia coli K-12 genetic map was divided into intervals of equal length to count the number of genes per interval. Plots of genes per interval at four sets of interval lengths revealed large-scale clustering of genes with the major clusters occurring at regularly spaced distances apart. Major gene cluster properties were analyzed at a scale of 100 intervals wherein each interval corresponded to a genetic map unit length of 1 min. In any major gene cluster, the highest gene concentration was observed at or near the midpoint interval, and the number of genes per interval was found to decline exponentially as a function of the linear distance from the midpoint or interval of peak gene concentration of that cluster. An autocorrelation analysis of gene content in first-neighbor intervals throughout the chromosome revealed an ordered first-neighbor relationship in comparison to 2,000 randomized interval versions of the chromosome. Attempts to simulate gene placement by a Gaussian model did not produce large-scale gene clustering in any way comparable to that observed on the chromosome. We propose that major gene clusters formed from smaller gene clusters, and the contemporary chromosome formed from fusion of homologous or heterologous major gene clusters.     

The Evolution of Fungal Metabolic Pathways

Fungi contain a remarkable range of metabolic pathways, sometimes encoded by gene clusters, enabling them to digest most organic matter and synthesize an array of potent small molecules. Although metabolism is fundamental to the fungal lifestyle, we still know little about how major evolutionary processes, such as gene duplication (GD) and horizontal gene transfer (HGT), have interacted with clustered and non-clustered fungal metabolic pathways to give rise to this metabolic versatility. We examined the synteny and evolutionary history of 247,202 fungal genes encoding enzymes that catalyze 875 distinct metabolic reactions from 130 pathways in 208 diverse genomes. We found that gene clustering varied greatly with respect to metabolic category and lineage; for example, clustered genes in Saccharomycotina yeasts were overrepresented in nucleotide metabolism, whereas clustered genes in Pezizomycotina were more common in lipid and amino acid metabolism. The effects of both GD and HGT were more pronounced in clustered genes than in their non-clustered counterparts and were differentially distributed across fungal lineages; specifically, GD, which was an order of magnitude more abundant than HGT, was most frequently observed in Agaricomycetes, whereas HGT was much more prevalent in Pezizomycotina. The effect of HGT in some Pezizomycotina was particularly strong; for example, we identified 111 HGT events associated with the 15 Aspergillus genomes, which sharply contrasts with the 60 HGT events detected for the 48 genomes from the entire Saccharomycotina subphylum. Finally, the impact of GD within a metabolic category was typically consistent across all fungal lineages, whereas the impact of HGT was variable. These results indicate that GD is the dominant process underlying fungal metabolic diversity, whereas HGT is episodic and acts in a category- or lineage-specific manner. Both processes have a greater impact on clustered genes, suggesting that metabolic gene clusters represent hotspots for the generation of fungal metabolic diversity.     

Identification of a novel protein with a role in lipoarabinomannan biosynthesis in mycobacteria

All species of Mycobacteria synthesize distinctive cell walls that are rich in phosphatidylinositol mannosides (PIMs), lipomannan (LM), and lipoarabinomannan (LAM). PIM glycolipids, having 2-4 mannose residues, can either be channeled into polar PIM species (with 6 Man residues) or hypermannosylated to form LM and LAM. In this study, we have identified a Mycobacterium smegmatis gene, termed lpqW, that is required for the conversion of PIMs to LAM and is highly conserved in all mycobacteria. A transposon mutant, Myco481, containing an insertion near the 3' end of lpqW exhibited altered colony morphology on complex agar medium. This mutant was unstable and was consistently overgrown by a second mutant, represented by Myco481.1, that had normal growth and colony characteristics. Biochemical analysis and metabolic labeling studies showed that Myco481 synthesized the complete spectrum of apolar and polar PIMs but was unable to make LAM. LAM biosynthesis was restored to near wild type levels in Myco481.1. However, this mutant was unable to synthesize the major polar PIM (AcPIM6) and accumulated a smaller intermediate, AcPIM4. Targeted disruption of the lpqW gene and complementation of the initial Myco481 mutant with the wild type gene confirmed that the phenotype of this mutant was due to loss of LpqW. These studies suggest that LpqW has a role in regulating the flux of early PIM intermediates into polar PIM or LAM biosynthesis. They also suggest that AcPIM4 is the likely branch point intermediate in polar PIM and LAM biosynthesis.     

Identifying subset of genes that have influential impacts on cancer progression: a new approach to analyze cancer microarray data

Cancer is a complex genetic disease, resulting from defects of multiple genes. Development of microarray techniques makes it possible to survey the whole genome and detect genes that have influential impacts on the progression of cancer. Statistical analysis of cancer microarray data is challenging because of the high dimensionality and cluster nature of gene expressions. Here, clusters are composed of genes with coordinated pathological functions and/or correlated expressions. In this article, we consider cancer studies where censored survival endpoint is measured along with microarray gene expressions. We propose a hybrid clustering approach, which uses both pathological pathway information retrieved from KEGG and statistical correlations of gene expressions, to construct gene clusters. Cancer survival time is modeled as a linear function of gene expressions. We adopt the clustering threshold gradient directed regularization (CTGDR) method for simultaneous gene cluster selection, within-cluster gene selection, and predictive model building. Analysis of two lymphoma studies shows that the proposed approach - which is composed of the hybrid gene clustering, linear regression model for survival, and clustering regularized estimation with CTGDR - can effectively identify gene clusters and genes within selected clusters that have satisfactory predictive power for censored cancer survival outcomes.