Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms

MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization

BACKGROUND

Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene-gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene-gene interactions using Mendelian randomization.

RESULTS

MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions.

CONCLUSION

These findings demonstrate that MR-GGI accurately inferences gene-gene interactions despite the confounding effects in real biological environments.

Genomic factors shape carbon and nitrogen metabolic niche breadth across Saccharomycotina yeasts

Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Two general paradigms have been proposed to explain this variation: (i) trade-offs between performance efficiency and breadth and (ii) the joint influence of extrinsic (environmental) and intrinsic (genomic) factors. We assembled genomic, metabolic, and ecological data from nearly all known species of the ancient fungal subphylum Saccharomycotina (1154 yeast strains from 1051 species), grown in 24 different environmental conditions, to examine niche breadth evolution. We found that large differences in the breadth of carbon utilization traits between yeasts stem from intrinsic differences in genes encoding specific metabolic pathways, but we found limited evidence for trade-offs. These comprehensive data argue that intrinsic factors shape niche breadth variation in microbes.

Nuclear Hsp104 safeguards the dormant translation machinery during quiescence

The resilience of cellular proteostasis declines with age, which drives protein aggregation and compromises viability. The nucleus has emerged as a key quality control compartment that handles misfolded proteins produced by the cytosolic protein biosynthesis system. Here, we find that age-associated metabolic cues target the yeast protein disaggregase Hsp104 to the nucleus to maintain a functional nuclear proteome during quiescence. The switch to respiratory metabolism and the accompanying decrease in translation rates direct cytosolic Hsp104 to the nucleus to interact with latent translation initiation factor eIF2 and to suppress protein aggregation. Hindering Hsp104 from entering the nucleus in quiescent cells results in delayed re-entry into the cell cycle due to compromised resumption of protein synthesis. In sum, we report that cytosolic-nuclear partitioning of the Hsp104 disaggregase is a critical mechanism to protect the latent protein synthesis machinery during quiescence in yeast, ensuring the rapid restart of translation once nutrients are replenished.

Genomic and ecological factors shaping specialism and generalism across an entire subphylum

Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Paradigms proposed to explain this variation either invoke trade-offs between performance efficiency and breadth or underlying intrinsic or extrinsic factors. We assembled genomic (1,154 yeast strains from 1,049 species), metabolic (quantitative measures of growth of 843 species in 24 conditions), and ecological (environmental ontology of 1,088 species) data from nearly all known species of the ancient fungal subphylum Saccharomycotina to examine niche breadth evolution. We found large interspecific differences in carbon breadth stem from intrinsic differences in genes encoding specific metabolic pathways but no evidence of trade-offs and a limited role of extrinsic ecological factors. These comprehensive data argue that intrinsic factors driving microbial niche breadth variation.

Revealing the key gene involved in bioplastic degradation from superior bioplastic degrader Bacillus sp. JY35

The use of bioplastics, which can alleviate environmental pollution caused by non-degradable bioplastics, has received attention. As there are many types of bioplastics, method that can treat them simultaneously is important. Therefore, Bacillus sp. JY35 which can degrade different types of bioplastics, was screened in previous study. Most types of bioplastics, such as polyhydroxybutyrate (PHB), (P(3HB-co-4HB)), poly(butylene adipate-co-terephthalate) (PBAT), polybutylene succinate (PBS), and polycaprolactone (PCL), can be degraded by esterase family enzymes. To identify the genes that are involved in bioplastic degradation, analysis with whole-genome sequencing was performed. Among the many esterase enzymes, three carboxylesterase and one triacylglycerol lipase were identified and selected based on previous studies. Esterase activity using p-nitrophenyl substrates was measured, and the supernatant of JY35_02679 showed strong emulsion clarification activity compared with others. In addition, when recombinant E. coli was applied to the clear zone test, only the JY35_02679 gene showed activity in the clear zone test with bioplastic containing solid cultures. Further quantitative analysis showed 100 % PCL degradation at 7 days and 45.7 % PBS degradation at 10 days. We identified a gene encoding a bioplastic-degrading enzyme in Bacillus sp. JY35 and successfully expressed the gene in heterologous E. coli, which secreted esterases with broad specificity.

Species Tree Estimation and the Impact of Gene Loss Following Whole-Genome Duplication

Whole-genome duplication (WGD) occurs broadly and repeatedly across the history of eukaryotes and is recognized as a prominent evolutionary force, especially in plants. Immediately following WGD, most genes are present in two copies as paralogs. Due to this redundancy, one copy of a paralog pair commonly undergoes pseudogenization and is eventually lost. When speciation occurs shortly after WGD; however, differential loss of paralogs may lead to spurious phylogenetic inference resulting from the inclusion of pseudoorthologs–paralogous genes mistakenly identified as orthologs because they are present in single copies within each sampled species. The influence and impact of including pseudoorthologs versus true orthologs as a result of gene extinction (or incomplete laboratory sampling) are only recently gaining empirical attention in the phylogenomics community. Moreover, few studies have yet to investigate this phenomenon in an explicit coalescent framework. Here, using mathematical models, numerous simulated data sets, and two newly assembled empirical data sets, we assess the effect of pseudoorthologs on species tree estimation under varying degrees of incomplete lineage sorting (ILS) and differential gene loss scenarios following WGD. When gene loss occurs along the terminal branches of the species tree, alignment-based (BPP) and gene-tree-based (ASTRAL, MP-EST, and STAR) coalescent methods are adversely affected as the degree of ILS increases. This can be greatly improved by sampling a sufficiently large number of genes. Under the same circumstances, however, concatenation methods consistently estimate incorrect species trees as the number of genes increases. Additionally, pseudoorthologs can greatly mislead species tree inference when gene loss occurs along the internal branches of the species tree. Here, both coalescent and concatenation methods yield inconsistent results. These results underscore the importance of understanding the influence of pseudoorthologs in the phylogenomics era. [Coalescent method; concatenation method; incomplete lineage sorting; pseudoorthologs; single-copy gene; whole-genome duplication.]

New data and collaborations at the Saccharomyces Genome Database: updated reference genome, alleles, and the Alliance of Genome Resources

Saccharomyces cerevisiae is used to provide fundamental understanding of eukaryotic genetics, gene product function, and cellular biological processes. Saccharomyces Genome Database (SGD) has been supporting the yeast research community since 1993, serving as its de facto hub. Over the years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation, and developed various tools and methods for analysis and curation of a variety of emerging data types. More recently, SGD and six other model organism focused knowledgebases have come together to create the Alliance of Genome Resources to develop sustainable genome information resources that promote and support the use of various model organisms to understand the genetic and genomic bases of human biology and disease. Here we describe recent activities at SGD, including the latest reference genome annotation update, the development of a curation system for mutant alleles, and new pages addressing homology across model organisms as well as the use of yeast to study human disease.

An Overview on Identification and Regulatory Mechanisms of Long Non-coding RNAs in Fungi

For decades, more and more long non-coding RNAs (lncRNAs) have been confirmed to play important functions in key biological processes of different organisms. At present, most identified lncRNAs and those with known functional roles are from mammalian systems. However, lncRNAs have also been found in primitive eukaryotic fungi, and they have different functions in fungal development, metabolism, and pathogenicity. In this review, we highlight some recent researches on lncRNAs in the primitive eukaryotic fungi, particularly focusing on the identification of lncRNAs and their regulatory roles in diverse biological processes.

The Model System Saccharomyces cerevisiae Versus Emerging Non-Model Yeasts for the Production of Biofuels

Microorganisms are effective platforms for the production of a variety of chemicals including biofuels, commodity chemicals, polymers and other natural products. However, deep cellular understanding is required for improvement of current biofuel cell factories to truly transform the Bioeconomy. Modifications in microbial metabolic pathways and increased resistance to various types of stress caused by the production of these chemicals are crucial in the generation of robust and efficient production hosts. Recent advances in systems and synthetic biology provide new tools for metabolic engineering to design strategies and construct optimal biocatalysts for the sustainable production of desired chemicals, especially in the case of ethanol and fatty acid production. Yeast is an efficient producer of bioethanol and most of the available synthetic biology tools have been developed for the industrial yeast Saccharomyces cerevisiae. Non-conventional yeast systems have several advantageous characteristics that are not easily engineered such as ethanol tolerance, low pH tolerance, thermotolerance, inhibitor tolerance, genetic diversity and so forth. Currently, synthetic biology is still in its initial steps for studies in non-conventional yeasts such as Yarrowia lipolytica, Kluyveromyces marxianus, Issatchenkia orientalis and Pichia pastoris. Therefore, the development and application of advanced synthetic engineering tools must also focus on these underexploited, non-conventional yeast species. Herein, we review the basic synthetic biology tools that can be applied to the standard S. cerevisiae model strain, as well as those that have been developed for non-conventional yeasts. In addition, we will discuss the recent advances employed to develop non-conventional yeast strains that are efficient for the production of a variety of chemicals through the use of metabolic engineering and synthetic biology.

Divergence of Peroxisome Membrane Gene Sequence and Expression Between Yeast Species

Large population-genomic sequencing studies can enable highly-powered analyses of sequence signatures of natural selection. Genome repositories now available for Saccharomyces yeast make it a premier model for studies of the molecular mechanisms of adaptation. We mined the genomes of hundreds of isolates of the sister species S. cerevisiae and S. paradoxus to identify sequence hallmarks of adaptive divergence between the two. From the top hits we focused on a set of genes encoding membrane proteins of the peroxisome, an organelle devoted to lipid breakdown and other specialized metabolic pathways. In-depth population- and comparative-genomic sequence analyses of these genes revealed striking divergence between S. cerevisiae and S. paradoxus. And from transcriptional profiles we detected non-neutral, directional cis-regulatory variation at the peroxisome membrane genes, with overall high expression in S. cerevisiae relative to S. paradoxus. Taken together, these data support a model in which yeast species have differentially tuned the expression of peroxisome components to boost their fitness in distinct niches.

Identification of Protein Complexes by Using a Spatial and Temporal Active Protein Interaction Network

The rapid development of proteomics and high-throughput technologies has produced a large amount of Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties of protein interaction networks (PINs) instead of static properties. Identification of protein complexes from dynamic PINs becomes a vital scientific problem for understanding cellular life in the post genome era. Up to now, plenty of models or methods have been proposed for the construction of dynamic PINs to identify protein complexes. However, most of the constructed dynamic PINs just focus on the temporal dynamic information and thus overlook the spatial dynamic information of the complex biological systems. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we propose a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL is adopted to identify protein complexes from ST-APIN and the other three dynamic PINs, NF-APIN, DPIN, and TC-PIN. The experimental results show that, the performance of MCL on ST-APIN outperforms those on the other three dynamic PINs in terms of matching with known complexes, sensitivity, specificity, and f-measure. Furthermore, we evaluate the identified protein complexes by Gene Ontology (GO) function enrichment analysis. The validation shows that the identified protein complexes from ST-APIN are more biologically significant. This study provides a general paradigm for constructing the ST-APINs, which is essential for further understanding of molecular systems and the biomedical mechanism of complex diseases.

Identification and evolutionary analysis of the nucleolar proteome of Giardia lamblia

Background

The nucleoli, including their proteomes, of higher eukaryotes have been extensively studied, while few studies about the nucleoli of the lower eukaryotes – protists were reported. Giardia lamblia, a protist with the controversy of whether it is an extreme primitive eukaryote or just a highly evolved parasite, might be an interesting object for carrying out the nucleolar proteome study of protists and for further examining the controversy.

Results

Using bioinformatics methods, we reconstructed G. lamblia nucleolar proteome (GiNuP) and the common nucleolar proteome of the three representative higher eukaryotes (human, Arabidopsis, yeast) (HEBNuP). Comparisons of the two proteomes revealed that: 1) GiNuP is much smaller than HEBNuP, but 78.4% of its proteins have orthologs in the latter; 2) More than 68% of the GiNuP proteins are involved in the “Ribosome related” function, and the others participate in the other functions, and these two groups of proteins are much larger and much smaller than those in HEBNuP, respectively; 3) Both GiNuP and HEBNuP have their own specific proteins, but HEBNuP has a much higher proportion of such proteins to participate in more categories of nucleolar functions.

Conclusion

For the first time the nucleolar proteome of a protist - Giardia was reconstructed. The results of comparison of it with the common proteome of three representative higher eukaryotes -- HEBNuP indicated that the simplicity of GiNuP is most probably a reflection of primitiveness but not just parasitic reduction of Giardia, and simultaneously revealed some interesting evolutionary phenomena about the nucleolus and even the eukaryotic cell, compositionally and functionally.

Reduced intrinsic DNA curvature leads to increased mutation rate

BACKGROUND

Mutation rates vary across the genome. Many trans factors that influence mutation rates have been identified, as have specific sequence motifs at the 1-7-bp scale, but cis elements remain poorly characterized. The lack of understanding regarding why different sequences have different mutation rates hampers our ability to identify positive selection in evolution and to identify driver mutations in tumorigenesis.

RESULTS

Here, we use a combination of synthetic genes and sequences of thousands of isolated yeast colonies to show that intrinsic DNA curvature is a major cis determinant of mutation rate. Mutation rate negatively correlates with DNA curvature within genes, and a 10% decrease in curvature results in a 70% increase in mutation rate. Consistently, both yeast and humans accumulate mutations in regions with small curvature. We further show that this effect is due to differences in the intrinsic mutation rate, likely due to differences in mutagen sensitivity and not due to differences in the local activity of DNA repair.

CONCLUSIONS

Our study establishes a framework for understanding the cis properties of DNA sequence in modulating the local mutation rate and identifies a novel causal source of non-uniform mutation rates across the genome.

HisB as novel selection marker for gene targeting approaches in Aspergillus niger

Background

For Aspergillus niger, a broad set of auxotrophic and dominant resistance markers is available. However, only few offer targeted modification of a gene of interest into or at a genomic locus of choice, which hampers functional genomics studies. We thus aimed to extend the available set by generating a histidine auxotrophic strain with a characterized hisB locus for targeted gene integration and deletion in A. niger.

Results

A histidine-auxotrophic strain was established via disruption of the A. niger hisB gene by using the counterselectable pyrG marker. After curing, a hisB^-, pyrG^- strain was obtained, which served as recipient strain for further studies. We show here that both hisB orthologs from A. nidulans and A. niger can be used to reestablish histidine prototrophy in this recipient strain. Whereas the hisB gene from A. nidulans was suitable for efficient gene targeting at different loci in A. niger, the hisB gene from A. niger allowed efficient integration of a Tet-on driven luciferase reporter construct at the endogenous non-functional hisB locus. Subsequent analysis of the luciferase activity revealed that the hisB locus is tight under non-inducing conditions and allows even higher luciferase expression levels compared to the pyrG integration locus.

Conclusion

Taken together, we provide here an alternative selection marker for A. niger, hisB, which allows efficient homologous integration rates as well as high expression levels which compare favorably to the well-established pyrG selection marker.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-017-0960-3) contains supplementary material, which is available to authorized users.

Comparative genomics reveals high biological diversity and specific adaptations in the industrially and medically important fungal genus Aspergillus

BACKGROUND

The fungal genus Aspergillus is of critical importance to humankind. Species include those with industrial applications, important pathogens of humans, animals and crops, a source of potent carcinogenic contaminants of food, and an important genetic model. The genome sequences of eight aspergilli have already been explored to investigate aspects of fungal biology, raising questions about evolution and specialization within this genus.

RESULTS

We have generated genome sequences for ten novel, highly diverse Aspergillus species and compared these in detail to sister and more distant genera. Comparative studies of key aspects of fungal biology, including primary and secondary metabolism, stress response, biomass degradation, and signal transduction, revealed both conservation and diversity among the species. Observed genomic differences were validated with experimental studies. This revealed several highlights, such as the potential for sex in asexual species, organic acid production genes being a key feature of black aspergilli, alternative approaches for degrading plant biomass, and indications for the genetic basis of stress response. A genome-wide phylogenetic analysis demonstrated in detail the relationship of the newly genome sequenced species with other aspergilli.

CONCLUSIONS

Many aspects of biological differences between fungal species cannot be explained by current knowledge obtained from genome sequences. The comparative genomics and experimental study, presented here, allows for the first time a genus-wide view of the biological diversity of the aspergilli and in many, but not all, cases linked genome differences to phenotype. Insights gained could be exploited for biotechnological and medical applications of fungi.

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

Phosphorylation is a broad regulatory mechanism with implications in nearly all processes of the cell. However, a global understanding of possible regulatory mechanisms remains elusive. In this study, we examined the potential regulatory role of kinases, phosphatases and transcription-factors in yeast metabolism across a variety of steady-state and dynamic conditions. The main novelty of our analysis was to infer putative regulatory interactions from in silico estimated activity of transcription-factors and kinases/phosphatases. This provided functional information about the proteins important for the experimental conditions at hand that had not been uncovered before. We showed that activity profiles are predictive features to estimate metabolite changes in dynamic experiments, while the same was not visible in steady-state conditions. We also showed that dynamic experiments could be used to recapitulate and provide novel TFs-metabolite and K/Ps-metabolite regulatory associations. We believe these findings illustrates the usefulness of this approach for future integrative studies interested in studying metabolic regulation.

Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide

Biochemical network reconstructions have become popular tools in systems biology. Metabolicnetwork reconstructions are biochemically, genetically, and genomically (BiGG) structured databases of biochemical reactions and metabolites. They contain information such as exact reaction stoichiometry, reaction reversibility, and the relationships between genes, proteins, and reactions. Network reconstructions have been used extensively to study the phenotypic behavior of wild-type and mutant stains under a variety of conditions, linking genotypes with phenotypes. Such phenotypic simulations have allowed for the prediction of growth after genetic manipulations, prediction of growth phenotypes after adaptive evolution, and prediction of essential genes. Additionally, because network reconstructions are organism specific, they can be used to understand differences between organisms of species in a functional context.There are different types of reconstructions representing various types of biological networks (metabolic, regulatory, transcription/translation). This chapter serves as an introduction to metabolic and regulatory network reconstructions and models and gives a complete description of the core Escherichia coli metabolic model. This model can be analyzed in any computational format (such as MATLAB or Mathematica) based on the information given in this chapter. The core E. coli model is a small-scale model that can be used for educational purposes. It is meant to be used by senior undergraduate and first-year graduate students learning about constraint-based modeling and systems biology. This model has enough reactions and pathways to enable interesting and insightful calculations, but it is also simple enough that the results of such calculations can be understoodeasily.

Inferential modeling of 3D chromatin structure

For eukaryotic cells, the biological processes involving regulatory DNA elements play an important role in cell cycle. Understanding 3D spatial arrangements of chromosomes and revealing long-range chromatin interactions are critical to decipher these biological processes. In recent years, chromosome conformation capture (3C) related techniques have been developed to measure the interaction frequencies between long-range genome loci, which have provided a great opportunity to decode the 3D organization of the genome. In this paper, we develop a new Bayesian framework to derive the 3D architecture of a chromosome from 3C-based data. By modeling each chromosome as a polymer chain, we define the conformational energy based on our current knowledge on polymer physics and use it as prior information in the Bayesian framework. We also propose an expectation-maximization (EM) based algorithm to estimate the unknown parameters of the Bayesian model and infer an ensemble of chromatin structures based on interaction frequency data. We have validated our Bayesian inference approach through cross-validation and verified the computed chromatin conformations using the geometric constraints derived from fluorescence in situ hybridization (FISH) experiments. We have further confirmed the inferred chromatin structures using the known genetic interactions derived from other studies in the literature. Our test results have indicated that our Bayesian framework can compute an accurate ensemble of 3D chromatin conformations that best interpret the distance constraints derived from 3C-based data and also agree with other sources of geometric constraints derived from experimental evidence in the previous studies. The source code of our approach can be found in https://github.com/wangsy11/InfMod3DGen.

CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts

BACKGROUND

The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study.

RESULTS

CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against whole genome Sc. pombe and S. cerevisiae annotations further substantiate a 4-5% improvement in the number of correctly predicted genes.

CONCLUSIONS

We demonstrate the success of a novel method of incorporating RNA-seq data into GHMM fungal gene prediction. This shows that a high quality annotation can be achieved without relying on protein homology or a training set of genes. CodingQuarry is freely available ( https://sourceforge.net/projects/codingquarry/ ), and suitable for incorporation into genome annotation pipelines.

Critical assessment of genome-scale metabolic networks: the need for a unified standard

Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using 'constraint-based' modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations; this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models.

Proteomic analysis reveals a novel function of the kinase Sat4p in Saccharomyces cerevisiae mitochondria

The Saccharomyces cerevisiae kinase Sat4p has been originally identified as a protein involved in salt tolerance and stabilization of plasma membrane transporters, implicating a cytoplasmic localization. Our study revealed an additional mitochondrial (mt) localization, suggesting a dual function for Sat4p. While no mt related phenotype was observed in the absence of Sat4p, its overexpression resulted in significant changes of a specific mitochondrial subproteome. As shown by a comparative two dimensional difference gel electrophoresis (2D-DIGE) approach combined with mass spectrometry, particularly two groups of proteins were affected: the iron-sulfur containing aconitase-type proteins (Aco1p, Lys4p) and the lipoamide-containing subproteome (Lat1p, Kgd2p and Gcv3p). The lipoylation sites of all three proteins could be assigned by nanoLC-MS/MS to Lys75 (Lat1p), Lys114 (Kgd2p) and Lys102 (Gcv3p), respectively. Sat4p overexpression resulted in accumulation of the delipoylated protein variants and in reduced levels of aconitase-type proteins, accompanied by a decrease in the activities of the respective enzyme complexes. We propose a regulatory role of Sat4p in the late steps of the maturation of a specific subset of mitochondrial iron-sulfur cluster proteins, including Aco1p and lipoate synthase Lip5p. Impairment of the latter enzyme may account for the observed lipoylation defects.

Proteome-wide remodeling of protein location and function by stress

Protein location and function can change dynamically depending on many factors, including environmental stress, disease state, age, developmental stage, and cell type. Here, we describe an integrative computational framework, called the conditional function predictor (CoFP; http://nbm.ajou.ac.kr/cofp/), for predicting changes in subcellular location and function on a proteome-wide scale. The essence of the CoFP approach is to cross-reference general knowledge about a protein and its known network of physical interactions, which typically pool measurements from diverse environments, against gene expression profiles that have been measured under specific conditions of interest. Using CoFP, we predict condition-specific subcellular locations, biological processes, and molecular functions of the yeast proteome under 18 specified conditions. In addition to highly accurate retrieval of previously known gold standard protein locations and functions, CoFP predicts previously unidentified condition-dependent locations and functions for nearly all yeast proteins. Many of these predictions can be confirmed using high-resolution cellular imaging. We show that, under DNA-damaging conditions, Tsr1, Caf120, Dip5, Skg6, Lte1, and Nnf2 change subcellular location and RNA polymerase I subunit A43, Ino2, and Ids2 show changes in DNA binding. Beyond specific predictions, this work reveals a global landscape of changing protein location and function, highlighting a surprising number of proteins that translocate from the mitochondria to the nucleus or from endoplasmic reticulum to Golgi apparatus under stress.

Hierarchical ensemble methods for protein function prediction

Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware "flat" prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a "consensus" ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research.

DNA is structured as a linear "jigsaw puzzle" in the genomes of Arabidopsis, rice, and budding yeast

Knowledge of how a genome is structured and organized from its constituent elements is crucial to understanding its biology and evolution. Here, we report the genome structuring and organization pattern as revealed by systems analysis of the sequences of three model species, Arabidopsis, rice and yeast, at the whole-genome and chromosome levels. We found that all fundamental function elements (FFE) constituting the genomes, including genes (GEN), DNA transposable elements (DTE), retrotransposable elements (RTE), simple sequence repeats (SSR), and (or) low complexity repeats (LCR), are structured in a nonrandom and correlative manner, thus leading to a hypothesis that the DNA of the species is structured as a linear "jigsaw puzzle". Furthermore, we showed that different FFE differ in their importance in the formation and evolution of the DNA jigsaw puzzle structure between species. DTE and RTE play more important roles than GEN, LCR, and SSR in Arabidopsis, whereas GEN and RTE play more important roles than LCR, SSR, and DTE in rice. The genes having multiple recognized functions play more important roles than those having single functions. These results provide useful knowledge necessary for better understanding genome biology and evolution of the species and for effective molecular breeding of rice.

Discriminative motif discovery via simulated evolution and random under-sampling

Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is applied to solve the multi-class imbalance problem at the stage of data preprocessing, and at the stage of Hidden Markov Models (HMMs) training, a random under-sampling method is introduced for the imbalance between the positive and negative datasets. It is shown that, in the task of discovering targeting motifs of nine subcellular compartments, the motifs found by our method are more conserved than the methods without considering data imbalance problem and recover the most known targeting motifs from Minimotif Miner and InterPro. Meanwhile, we use the found motifs to predict protein subcellular localization and achieve higher prediction precision and recall for the minority classes.

Metabolic and transcriptomic response of the wine yeast Saccharomyces cerevisiae strain EC1118 after an oxygen impulse under carbon-sufficient, nitrogen-limited fermentative conditions

During alcoholic fermentation, Saccharomyces cerevisiae is exposed to continuously changing environmental conditions, such as decreasing sugar and increasing ethanol concentrations. Oxygen, a critical nutrient to avoid stuck and sluggish fermentations, is only discretely available throughout the process after pump-over operation. In this work, we studied the physiological response of the wine yeast S. cerevisiae strain EC1118 to a sudden increase in dissolved oxygen, simulating pump-over operation. With this aim, an impulse of dissolved oxygen was added to carbon-sufficient, nitrogen-limited anaerobic continuous cultures. Results showed that genes related to mitochondrial respiration, ergosterol biosynthesis, and oxidative stress, among other metabolic pathways, were induced after the oxygen impulse. On the other hand, mannoprotein coding genes were repressed. The changes in the expression of these genes are coordinated responses that share common elements at the level of transcriptional regulation. Beneficial and detrimental effects of these physiological processes on wine quality highlight the dual role of oxygen in 'making or breaking wines'. These findings will facilitate the development of oxygen addition strategies to optimize yeast performance in industrial fermentations.

A New Path Based Hybrid Measure for Gene Ontology Similarity

Gene Ontology (GO) consists of a controlled vocabulary of terms, annotating a gene or gene product, structured in a directed acyclic graph. In the graph, semantic relations connect the terms, that represent the knowledge of functional description and cellular component information of gene products. GO similarity gives us a numerical representation of biological relationship between a gene set, which can be used to infer various biological facts such as protein interaction, structural similarity, gene clustering, etc. Here we introduce a new shortest path based hybrid measure of ontological similarity between two terms which combines both structure of the GO graph and information content of the terms. Here the similarity between two terms t1 and t2, referred to as GOSim(PBHM)(t1,t2), has two components; one obtained from the common ancestors of t1 and t2. The other from their remaining ancestors. The proposed path based hybrid measure does not suffer from the well-known shallow annotation problem. Its superiority with respect to some other popular measures is established for protein protein interaction prediction, correlation with gene expression and functional classification of genes in a biological pathway. Finally, the proposed measure is utilized to compute the average GO similarity score among the genes that are experimentally validated targets of some microRNAs. Results demonstrate that the targets of a given miRNA have a high degree of similarity in the biological process category of GO.

Genetic interaction networks: better understand to better predict

A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible database describing metabolic pathways and enzymes from all domains of life. MetaCyc pathways are experimentally determined, mostly small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains >2100 pathways derived from >37 000 publications, and is the largest curated collection of metabolic pathways currently available. BioCyc (BioCyc.org) is a collection of >3000 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems and pathway-hole fillers. Additions to BioCyc over the past 2 years include YeastCyc, a PGDB for Saccharomyces cerevisiae, and 891 new genomes from the Human Microbiome Project. The BioCyc Web site offers a variety of tools for querying and analysis of PGDBs, including Omics Viewers and tools for comparative analysis. New developments include atom mappings in reactions, a new representation of glycan degradation pathways, improved compound structure display, better coverage of enzyme kinetic data, enhancements of the Web Groups functionality, improvements to the Omics viewers, a new representation of the Enzyme Commission system and, for the desktop version of the software, the ability to save display states.

Ptc6 is required for proper rapamycin-induced down-regulation of the genes coding for ribosomal and rRNA processing proteins in S. cerevisiae

Ptc6 is one of the seven components (Ptc1-Ptc7) of the protein phosphatase 2C family in the yeast Saccharomyces cerevisiae. In contrast to other type 2C phosphatases, the cellular role of this isoform is poorly understood. We present here a comprehensive characterization of this gene product. Cells lacking Ptc6 are sensitive to zinc ions, and somewhat tolerant to cell-wall damaging agents and to Li⁺. Ptc6 mutants are sensitive to rapamycin, albeit to lesser extent than ptc1 cells. This phenotype is not rescued by overexpression of PTC1 and mutation of ptc6 does not reproduce the characteristic genetic interactions of the ptc1 mutation with components of the TOR pathway, thus suggesting different cellular roles for both isoforms. We show here that the rapamycin-sensitive phenotype of ptc6 cells is unrelated to the reported role of Pt6 in controlling pyruvate dehydrogenase activity. Lack of Ptc6 results in substantial attenuation of the transcriptional response to rapamycin, particularly in the subset of repressed genes encoding ribosomal proteins or involved in rRNA processing. In contrast, repressed genes involved in translation are Ptc6-independent. These effects cannot be attributed to the regulation of the Sch9 kinase, but they could involve modulation of the binding of the Ifh1 co-activator to specific gene promoters.

The PhosphoGRID Saccharomyces cerevisiae protein phosphorylation site database: version 2.0 update

PhosphoGRID is an online database that curates and houses experimentally verified in vivo phosphorylation sites in the Saccharomyces cerevisiae proteome (www.phosphogrid.org). Phosphosites are annotated with specific protein kinases and/or phosphatases, along with the condition(s) under which the phosphorylation occurs and/or the effects on protein function. We report here an updated data set, including nine additional high-throughput (HTP) mass spectrometry studies. The version 2.0 data set contains information on 20 177 unique phosphorylated residues, representing a 4-fold increase from version 1.0, and includes 1614 unique phosphosites derived from focused low-throughput (LTP) studies. The overlap between HTP and LTP studies represents only ∼3% of the total unique sites, but importantly 45% of sites from LTP studies with defined function were discovered in at least two independent HTP studies. The majority of new phosphosites in this update occur on previously documented proteins, suggesting that coverage of phosphoproteins in the yeast proteome is approaching saturation. We will continue to update the PhosphoGRID data set, with the expectation that the integration of information from LTP and HTP studies will enable the development of predictive models of phosphorylation-based signaling networks.

Database URL:http://www.phosphogrid.org/

Comprehensive in vivo RNA-binding site analyses reveal a role of Prp8 in spliceosomal assembly

Prp8 stands out among hundreds of splicing factors as a protein that is intimately involved in spliceosomal activation and the catalytic reaction. Here, we present the first comprehensive in vivo RNA footprints for Prp8 in budding yeast obtained using CLIP (cross-linking and immunoprecipitation)/CRAC (cross-linking and analyses of cDNAs) and next-generation DNA sequencing. These footprints encompass known direct Prp8-binding sites on U5, U6 snRNA and intron-containing pre-mRNAs identified using site-directed cross-linking with in vitro assembled small nuclear ribonucleoproteins (snRNPs) or spliceosome. Furthermore, our results revealed novel Prp8-binding sites on U1 and U2 snRNAs. We demonstrate that Prp8 directly cross-links with U2, U5 and U6 snRNAs and pre-mRNA in purified activated spliceosomes, placing Prp8 in position to bring the components of the active site together. In addition, disruption of the Prp8 and U1 snRNA interaction reduces tri-snRNP level in the spliceosome, suggesting a previously unknown role of Prp8 in spliceosomal assembly through its interaction with U1 snRNA.

Investigation of stable and transient protein-protein interactions: Past, present, and future

This article presents an overview of the literature and a review of recent advances in the analysis of stable and transient protein-protein interactions (PPIs) with a focus on their function within cells, organs, and organisms. The significance of PTMs within the PPIs is also discussed. We focus on methods to study PPIs and methods of detecting PPIs, with particular emphasis on electrophoresis-based and MS-based investigation of PPIs, including specific examples. The validation of PPIs is emphasized and the limitations of the current methods for studying stable and transient PPIs are discussed. Perspectives regarding PPIs, with focus on bioinformatics and transient PPIs are also provided.

'Domino' systems biology and the 'A' of ATP

We develop a strategic 'domino' approach that starts with one key feature of cell function and the main process providing for it, and then adds additional processes and components only as necessary to explain provoked experimental observations. The approach is here applied to the energy metabolism of yeast in a glucose limited chemostat, subjected to a sudden increase in glucose. The puzzles addressed include (i) the lack of increase in adenosine triphosphate (ATP) upon glucose addition, (ii) the lack of increase in adenosine diphosphate (ADP) when ATP is hydrolyzed, and (iii) the rapid disappearance of the 'A' (adenine) moiety of ATP. Neither the incorporation of nucleotides into new biomass, nor steady de novo synthesis of adenosine monophosphate (AMP) explains. Cycling of the 'A' moiety accelerates when the cell's energy state is endangered, another essential domino among the seven required for understanding of the experimental observations. This new domino analysis shows how strategic experimental design and observations in tandem with theory and modeling may identify and resolve important paradoxes. It also highlights the hitherto unexpected role of the 'A' component of ATP.

Oxygen response of the wine yeast Saccharomyces cerevisiae EC1118 grown under carbon-sufficient, nitrogen-limited enological conditions

Discrete additions of oxygen play a critical role in alcoholic fermentation. However, few studies have quantitated the fate of dissolved oxygen and its impact on wine yeast cell physiology under enological conditions. We simulated the range of dissolved oxygen concentrations that occur after a pump-over during the winemaking process by sparging nitrogen-limited continuous cultures with oxygen-nitrogen gaseous mixtures. When the dissolved oxygen concentration increased from 1.2 to 2.7 μM, yeast cells changed from a fully fermentative to a mixed respirofermentative metabolism. This transition is characterized by a switch in the operation of the tricarboxylic acid cycle (TCA) and an activation of NADH shuttling from the cytosol to mitochondria. Nevertheless, fermentative ethanol production remained the major cytosolic NADH sink under all oxygen conditions, suggesting that the limitation of mitochondrial NADH reoxidation is the major cause of the Crabtree effect. This is reinforced by the induction of several key respiratory genes by oxygen, despite the high sugar concentration, indicating that oxygen overrides glucose repression. Genes associated with other processes, such as proline uptake, cell wall remodeling, and oxidative stress, were also significantly affected by oxygen. The results of this study indicate that respiration is responsible for a substantial part of the oxygen response in yeast cells during alcoholic fermentation. This information will facilitate the development of temporal oxygen addition strategies to optimize yeast performance in industrial fermentations.

Modelling and simulating generic RNA-Seq experiments with the flux simulator

High-throughput sequencing of cDNA libraries constructed from cellular RNA complements (RNA-Seq) naturally provides a digital quantitative measurement for every expressed RNA molecule. Nature, impact and mutual interference of biases in different experimental setups are, however, still poorly understood—mostly due to the lack of data from intermediate protocol steps. We analysed multiple RNA-Seq experiments, involving different sample preparation protocols and sequencing platforms: we broke them down into their common—and currently indispensable—technical components (reverse transcription, fragmentation, adapter ligation, PCR amplification, gel segregation and sequencing), investigating how such different steps influence abundance and distribution of the sequenced reads. For each of those steps, we developed universally applicable models, which can be parameterised by empirical attributes of any experimental protocol. Our models are implemented in a computer simulation pipeline called the Flux Simulator, and we show that read distributions generated by different combinations of these models reproduce well corresponding evidence obtained from the corresponding experimental setups. We further demonstrate that our in silico RNA-Seq provides insights about hidden precursors that determine the final configuration of reads along gene bodies; enhancing or compensatory effects that explain apparently controversial observations can be observed. Moreover, our simulations identify hitherto unreported sources of systematic bias from RNA hydrolysis, a fragmentation technique currently employed by most RNA-Seq protocols.

Assessing and predicting protein interactions by combining manifold embedding with multiple information integration

BACKGROUND

Protein-protein interactions (PPIs) play crucial roles in virtually every aspect of cellular function within an organism. Over the last decade, the development of novel high-throughput techniques has resulted in enormous amounts of data and provided valuable resources for studying protein interactions. However, these high-throughput protein interaction data are often associated with high false positive and false negative rates. It is therefore highly desirable to develop scalable methods to identify these errors from the computational perspective.

RESULTS

We have developed a robust computational technique for assessing the reliability of interactions and predicting new interactions by combining manifold embedding with multiple information integration. Validation of the proposed method was performed with extensive experiments on densely-connected and sparse PPI networks of yeast respectively. Results demonstrate that the interactions ranked top by our method have high functional homogeneity and localization coherence.

CONCLUSIONS

Our proposed method achieves better performances than the existing methods no matter assessing or predicting protein interactions. Furthermore, our method is general enough to work over a variety of PPI networks irrespectively of densely-connected or sparse PPI network. Therefore, the proposed algorithm is a much more promising method to detect both false positive and false negative interactions in PPI networks.

LeishCyc: a guide to building a metabolic pathway database and visualization of metabolomic data

The complexity of the metabolic networks in even the simplest organisms has raised new challenges in organizing metabolic information. To address this, specialized computer frameworks have been developed to capture, manage, and visualize metabolic knowledge. The leading databases of metabolic information are those organized under the umbrella of the BioCyc project, which consists of the reference database MetaCyc, and a number of pathway/genome databases (PGDBs) each focussed on a specific organism. A number of PGDBs have been developed for bacterial, fungal, and protozoan pathogens, greatly facilitating dissection of the metabolic potential of these organisms and the identification of new drug targets. Leishmania are protozoan parasites belonging to the family Trypanosomatidae that cause a broad spectrum of diseases in humans. In this work we use the LeishCyc database, the BioCyc database for Leishmania major, to describe how to build a BioCyc database from genomic sequences and associated annotations. By using metabolomic data generated in our group, we show how such databases can be utilized to elucidate specific changes in parasite metabolism.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (http://metacyc.org/) provides a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains more than 1800 pathways derived from more than 30 000 publications, and is the largest curated collection of metabolic pathways currently available. Most reactions in MetaCyc pathways are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes and literature citations. BioCyc (http://biocyc.org/) is a collection of more than 1700 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference database, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs contain additional features, including predicted operons, transport systems and pathway-hole fillers. The BioCyc website and Pathway Tools software offer many tools for querying and analysis of PGDBs, including Omics Viewers and comparative analysis. New developments include a zoomable web interface for diagrams; flux-balance analysis model generation from PGDBs; web services; and a new tool called Web Groups.

Approaches to Fungal Genome Annotation

Fungal genome annotation is the starting point for analysis of genome content. This generally involves the application of diverse methods to identify features on a genome assembly such as protein-coding and non-coding genes, repeats and transposable elements, and pseudogenes. Here we describe tools and methods leveraged for eukaryotic genome annotation with a focus on the annotation of fungal nuclear and mitochondrial genomes. We highlight the application of the latest technologies and tools to improve the quality of predicted gene sets. The Broad Institute eukaryotic genome annotation pipeline is described as one example of how such methods and tools are integrated into a sequencing center's production genome annotation environment.

PSI:Biology-materials repository: a biologist's resource for protein expression plasmids

The Protein Structure Initiative:Biology-Materials Repository (PSI:Biology-MR; MR; http://psimr.asu.edu ) sequence-verifies, annotates, stores, and distributes the protein expression plasmids and vectors created by the Protein Structure Initiative (PSI). The MR has developed an informatics and sample processing pipeline that manages this process for thousands of samples per month from nearly a dozen PSI centers. DNASU ( http://dnasu.asu.edu ), a freely searchable database, stores the plasmid annotations, which include the full-length sequence, vector information, and associated publications for over 130,000 plasmids created by our laboratory, by the PSI and other consortia, and by individual laboratories for distribution to researchers worldwide. Each plasmid links to external resources, including the PSI Structural Biology Knowledgebase ( http://sbkb.org ), which facilitates cross-referencing of a particular plasmid to additional protein annotations and experimental data. To expedite and simplify plasmid requests, the MR uses an expedited material transfer agreement (EP-MTA) network, where researchers from network institutions can order and receive PSI plasmids without institutional delays. As of March 2011, over 39,000 protein expression plasmids and 78 empty vectors from the PSI are available upon request from DNASU. Overall, the MR's repository of expression-ready plasmids, its automated pipeline, and the rapid process for receiving and distributing these plasmids more effectively allows the research community to dissect the biological function of proteins whose structures have been studied by the PSI.

The reconstruction of condition-specific transcriptional modules provides new insights in the evolution of yeast AP-1 proteins

AP-1 proteins are transcription factors (TFs) that belong to the basic leucine zipper family, one of the largest families of TFs in eukaryotic cells. Despite high homology between their DNA binding domains, these proteins are able to recognize diverse DNA motifs. In yeasts, these motifs are referred as YRE (Yap Response Element) and are either seven (YRE-Overlap) or eight (YRE-Adjacent) base pair long. It has been proposed that the AP-1 DNA binding motif preference relies on a single change in the amino acid sequence of the yeast AP-1 TFs (an arginine in the YRE-O binding factors being replaced by a lysine in the YRE-A binding Yaps). We developed a computational approach to infer condition-specific transcriptional modules associated to the orthologous AP-1 protein Yap1p, Cgap1p and Cap1p, in three yeast species: the model yeast Saccharomyces cerevisiae and two pathogenic species Candida glabrata and Candida albicans. Exploitation of these modules in terms of predictions of the protein/DNA regulatory interactions changed our vision of AP-1 protein evolution. Cis-regulatory motif analyses revealed the presence of a conserved adenine in 5' position of the canonical YRE sites. While Yap1p, Cgap1p and Cap1p shared a remarkably low number of target genes, an impressive conservation was observed in the YRE sequences identified by Yap1p and Cap1p. In Candida glabrata, we found that Cgap1p, unlike Yap1p and Cap1p, recognizes YRE-O and YRE-A motifs. These findings were supported by structural data available for the transcription factor Pap1p (Schizosaccharomyces pombe). Thus, whereas arginine and lysine substitutions in Cgap1p and Yap1p proteins were reported as responsible for a specific YRE-O or YRE-A preference, our analyses rather suggest that the ancestral yeast AP-1 protein could recognize both YRE-O and YRE-A motifs and that the arginine/lysine exchange is not the only determinant of the specialization of modern Yaps for one motif or another.

CASCADE_SCAN: mining signal transduction network from high-throughput data based on steepest descent method

BACKGROUND

Signal transduction is an essential biological process involved in cell response to environment changes, by which extracellular signaling initiates intracellular signaling. Many computational methods have been generated in mining signal transduction networks with the increasing of high-throughput genomic and proteomic data. However, more effective means are still needed to understand the complex mechanisms of signaling pathways.

RESULTS

We propose a new approach, namely CASCADE_SCAN, for mining signal transduction networks from high-throughput data based on the steepest descent method using indirect protein-protein interactions (PPIs). This method is useful for actual biological application since the given proteins utilized are no longer confined to membrane receptors or transcription factors as in existing methods. The precision and recall values of CASCADE_SCAN are comparable with those of other existing methods. Moreover, functional enrichment analysis of the network components supported the reliability of the results.

CONCLUSIONS

CASCADE_SCAN is a more suitable method than existing methods for detecting underlying signaling pathways where the membrane receptors or transcription factors are unknown, providing significant insight into the mechanism of cellular signaling in growth, development and cancer. A new tool based on this method is freely available at http://www.genomescience.com.cn/CASCADE_SCAN/.

A survey of metabolic databases emphasizing the MetaCyc family

Thanks to the confluence of genome sequencing and bioinformatics, the number of metabolic databases has expanded from a handful in the mid-1990s to several thousand today. These databases lie within distinct families that have common ancestry and common attributes. The main families are the MetaCyc, KEGG, Reactome, Model SEED, and BiGG families. We survey these database families, as well as important individual metabolic databases, including multiple human metabolic databases. The MetaCyc family is described in particular detail. It contains well over 1,000 databases, including highly curated databases for Escherichia coli, Saccharomyces cerevisiae, Mus musculus, and Arabidopsis thaliana. These databases are available through a number of web sites that offer a range of software tools for querying and visualizing metabolic networks. These web sites also provide multiple tools for analysis of gene expression and metabolomics data, including visualization of those datasets on metabolic network diagrams and over-representation analysis of gene sets and metabolite sets.

Complex principal component and correlation structure of 16 yeast genomic variables

A quickly growing number of characteristics reflecting various aspects of gene function and evolution can be either measured experimentally or computed from DNA and protein sequences. The study of pairwise correlations between such quantitative genomic variables as well as collective analysis of their interrelations by multidimensional methods have delivered crucial insights into the processes of molecular evolution. Here, we present a principal component analysis (PCA) of 16 genomic variables from Saccharomyces cerevisiae, the largest data set analyzed so far. Because many missing values and potential outliers hinder the direct calculation of principal components, we introduce the application of Bayesian PCA. We confirm some of the previously established correlations, such as evolutionary rate versus protein expression, and reveal new correlations such as those between translational efficiency, phosphorylation density, and protein age. Although the first principal component primarily contrasts genomic change and protein expression, the second component separates variables related to gene existence and expressed protein functions. Enrichment analysis on genes affecting variable correlations unveils classes of influential genes. For example, although ribosomal and nuclear transport genes make important contributions to the correlation between protein isoelectric point and molecular weight, protein synthesis and amino acid metabolism genes help cause the lack of significant correlation between propensity for gene loss and protein age. We present the novel Quagmire database (Quantitative Genomics Resource) which allows exploring relationships between more genomic variables in three model organisms-Escherichia coli, S. cerevisiae, and Homo sapiens (http://webclu.bio.wzw.tum.de:18080/quagmire).

Integration of proteomic and metabolomic profiling as well as metabolic modeling for the functional analysis of metabolic networks

The integrated analysis of different omics-level data sets is most naturally performed in the context of common process or pathway association. In this chapter, the two basic approaches for a metabolic pathway-centric integration of proteomics and metabolomics data are described: the knowledge-based approach relying on existing metabolic pathway information, and a data-driven approach that aims to deduce functional (pathway) associations directly from the data. Relevant algorithmic approaches for the generation of metabolic networks of model organisms, their functional analysis, database resources, visualization and analysis tools will be described. The use of proteomics data in the process of metabolic network reconstruction will be discussed.

DAnCER: disease-annotated chromatin epigenetics resource

Chromatin modification (CM) is a set of epigenetic processes that govern many aspects of DNA replication, transcription and repair. CM is carried out by groups of physically interacting proteins, and their disruption has been linked to a number of complex human diseases. CM remains largely unexplored, however, especially in higher eukaryotes such as human. Here we present the DAnCER resource, which integrates information on genes with CM function from five model organisms, including human. Currently integrated are gene functional annotations, Pfam domain architecture, protein interaction networks and associated human diseases. Additional supporting evidence includes orthology relationships across organisms, membership in protein complexes, and information on protein 3D structure. These data are available for 962 experimentally confirmed and manually curated CM genes and for over 5000 genes with predicted CM function on the basis of orthology and domain composition. DAnCER allows visual explorations of the integrated data and flexible query capabilities using a variety of data filters. In particular, disease information and functional annotations are mapped onto the protein interaction networks, enabling the user to formulate new hypotheses on the function and disease associations of a given gene based on those of its interaction partners. DAnCER is freely available at http://wodaklab.org/dancer/.