MIPS: a database for protein sequences, homology data and yeast genome information

Yeast Genomics and Its Applications in Biotechnological Processes: What Is Our Present and Near Future?

Since molecular biology and advanced genetic techniques have become important tools in a variety of fields of interest, including taxonomy, identification, classification, possible production of substances and proteins, applications in pharmacology, medicine, and the food industry, there has been significant progress in studying the yeast genome and its potential applications. Because of this potential, as well as their manageability, safety, ease of cultivation, and reproduction, yeasts are now being extensively researched in order to evaluate a growing number of natural and sustainable applications to provide many benefits to humans. This review will describe what yeasts are, how they are classified, and attempt to provide a rapid overview of the many current and future applications of yeasts. The review will then discuss how yeasts—including those molecularly modified—are used to produce biofuels, proteins such as insulin, vaccines, probiotics, beverage preparations, and food additives and how yeasts could be used in environmental bioremediation and biocontrol for plant infections. This review does not delve into the issues raised during studies and research, but rather presents the positive outcomes that have enabled several industrial, clinical, and agricultural applications in the past and future, including the most recent on cow-free milk.

Systematic analysis of critical genes and pathways identified a signature of neuropathic pain after spinal cord injury

Spinal cord injury (SCI) damages sensory systems, producing chronic neuropathic pain that is resistant to medical treatment. The specific mechanisms underlying SCI-induced neuropathic pain (SCI-NP) remain unclear, and protein biomarkers have not yet been integrated into diagnostic screening. To better understand the host molecular pathways involved in SCI-NP, we used the bioinformatics method, the PubMed database, and bioinformatics methods to identify target genes and their associated pathways. We reviewed 2504 articles on the regulation of SCI-NP and used the text mining of PubMed database abstracts to determine associations among 12 pathways and networks. Based on this method, we identified two central genes in SCI-NP: interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α). Adult male Sprague-Dawley rats were used to build the SCI-NP models. The threshold for paw withdrawal was significantly reduced in the SCI group and TLR4 was activated in microglia after SCI. ELISA analysis of TNF-α and IL-6 levels was significantly higher in the SCI group than in the sham group. Western blot showed that expressions of the TLR4/MyD88/NF-κB inflammatory pathway protein increased dramatically in the SCI group. Using the TLR4 inhibitor TAK-242, the pain threshold and expressions of inflammatory factors and proteins of the proteins of the inflammatory signal pathway were reversed, TLR4 in microglia was suppressed, suggesting that SCI-NP was related to neuroinflammation mediated by the TLR4 signaling pathway. In conclusion, we found TNF-α and IL-6 were the neuroinflammation-related genes involved in SCI-NP that can be alleviated by inhibiting the inflammatory pathway upstream of the TLR4/MyD88/NF-κB inflammatory pathway.

Exploring the potential of comparative de novo transcriptomics to classify Saccharomyces brewing yeasts

In this work the potential of comparative transcriptomics was explored of Saccharomyces (S.) cerevisiae and S. pastorianus for their discrimination. This way an alternative should be demonstrated to comparative genomics, which can be difficult as a result of their aneuoploid genomes composed of mosaics of the parental genomes. Strains were selected according to their application in beer brewing, i.e. top and bottom fermenting yeasts. Comparative transcriptomics was performed for four strains each of commercially available S. cerevisiae (top fermenting) and Saccharomyces pastorianus (bottom fermenting) brewing yeasts grown at two different temperatures to mid-exponential growth phase. A non-reference based approach was chosen in the form of alignment against a de novo assembled brewery-associated pan transcriptome to exclude bias introduced by manual selection of reference genomes. The result is an analysis workflow for self-contained comparative transcriptomics of Saccharomyces yeasts including, but not limited to, the analysis of core and accessory gene expression, functional analysis and metabolic classification. The functionality of this workflow is demonstrated along the principal differentiation of accessory transcriptomes of S. cerevisiae versus S. pastorianus strains. Hence, this work provides a concept enabling studies under different brewing conditions.

Bioinformatic analysis of the molecular mechanism underlying bronchial pulmonary dysplasia using a text mining approach

Bronchopulmonary dysplasia (BPD) is a common disease of premature infants with very low birth weight. The mechanism is inconclusive. The aim of this study is to systematically explore BPD-related genes and characterize their functions.Natural language processing analysis was used to identify BPD-related genes. Gene data were extracted from PubMed database. Gene ontology, pathway, and network analysis were carried out, and the result was integrated with corresponding database.In this study, 216 genes were identified as BPD-related genes with P < .05, and 30 pathways were identified as significant. A network of BPD-related genes was also constructed with 17 hub genes identified. In particular, phosphatidyl inositol-3-enzyme-serine/threonine kinase signaling pathway involved the largest number of genes. Insulin was found to be a promising candidate gene related with BPD, suggesting that it may serve as an effective therapeutic target.Our data may help to better understand the molecular mechanisms underlying BPD. However, the mechanisms of BPD are elusive, and further studies are needed.

Analysis of microarrays of miR-34a and its identification of prospective target gene signature in hepatocellular carcinoma

BACKGROUND

Currently, some studies have demonstrated that miR-34a could serve as a suppressor of several cancers including hepatocellular carcinoma (HCC). Previously, we discovered that miR-34a was downregulated in HCC and involved in the tumorigenesis and progression of HCC; however, the mechanism remains unclear. The purpose of this study was to estimate the expression of miR-34a in HCC by applying the microarray profiles and analyzing the predicted targets of miR-34a and their related biological pathways of HCC.

METHODS

Gene expression omnibus (GEO) datasets were conducted to identify the difference of miR-34a expression between HCC and corresponding normal tissues and to explore its relationship with HCC clinicopathologic features. The natural language processing (NLP), gene ontology (GO), pathway and network analyses were performed to analyze the genes associated with the carcinogenesis and progression of HCC and the targets of miR-34a predicted in silico. In addition, the integrative analysis was performed to explore the targets of miR-34a which were also relevant to HCC.

RESULTS

The analysis of GEO datasets demonstrated that miR-34a was downregulated in HCC tissues, and no heterogeneity was observed (Std. Mean Difference(SMD) = 0.63, 95% confidence intervals(95%CI):[0.38, 0.88], P < 0.00001; P_{heterogeneity} = 0.08 I² = 41%). However, no association was found between the expression value of miR-34a and any clinicopathologic characteristics. In the NLP analysis of HCC, we obtained 25 significant HCC-associated signaling pathways. Besides, we explored 1000 miR-34a-related genes and 5 significant signaling pathways in which CCND1 and Bcl-2 served as necessary hub genes. In the integrative analysis, we found 61 hub genes and 5 significant pathways, including cell cycle, cytokine-cytokine receptor interaction, notching pathway, p53 pathway and focal adhesion, which proposed the relevant functions of miR-34a in HCC.

CONCLUSION

Our results may lead researchers to understand the molecular mechanism of miR-34a in the diagnosis, prognosis and therapy of HCC. Therefore, the interaction between miR-34a and its targets may promise better prediction and treatment for HCC. And the experiments in vivo and vitro will be conducted by our group to identify the specific mechanism of miR-34a in the progress and deterioration of HCC.

A computational interactome and functional annotation for the human proteome

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein's function. We provide annotations for most human proteins, including many annotated as having unknown function.

An integrated approach of predicted miR-34a targets identifies a signature for gastric cancer

microRNA-34a (miRNA/miR-34a) functions as a tumor suppressor gene in gastric cancer and may be involved in system-wide regulatory networks. To clarify the expression of all predicted target genes of this miRNA, a comprehensive and systematic analysis of miR-34a-target genes in gastric cancer was conducted in the present study. In the initial analysis, the potential functions, pathways and networks of gastric cancer-associated molecules and miR-34a targets were identified. In the final integrative analysis of gastric cancer-associated miR-34a targets, 30 hub genes were identified using overlap calculations, indicating that miR-34a may be significant in the development and progression of gastric cancer through the Smad signaling pathway, the cell cycle, the mitogen-activated protein kinase signaling pathway, apoptosis, the Notch signaling pathway and other pathways. The present study provides a bioinformatic analysis of miR-34a-targets in gastric cancer, describes numerous target genes and novel coregulatory networks, and may provide an opportunity to identify a critical regulatory network for predicting the molecular mechanisms of miR-34a in the development and progression of gastric cancer.

Systems pharmacology of mifepristone (RU486) reveals its 47 hub targets and network: comprehensive analysis and pharmacological focus on FAK-Src-Paxillin complex

Mifepristone (RU486), a synthetic steroid compound used as an abortifacient drug, has received considerable attention to its anticancer activity recently. To explore the possibility of using mifepristone as a cancer metastasis chemopreventive, we performed a systems pharmacology analysis of mifepristone-related molecules in the present study. Data were collected by using Natural Language Processing (NLP) and 513 mifepristone-related genes were dug out and classified functionally using a gene ontology (GO) hierarchy, followed by KEGG pathway enrichment analysis. Potential signal pathways and targets involved in cancer were obtained by integrative network analysis. Total thirty-three proteins were involved in focal adhesion-the key signaling pathway associated with cancer metastasis. Molecular and cellular assays further demonstrated that mifepristone had the ability to prevent breast cancer cells from migration and interfere with their adhesion to endothelial cells. Moreover, mifepristone inhibited the expression of focal adhesion kinase (FAK), paxillin, and the formation of FAK/Src/Paxillin complex, which are correlated with cell adhesion and migration. This study set a good example to identify chemotherapeutic potential seamlessly from systems pharmacology to cellular pharmacology, and the revealed hub genes may be the promising targets for cancer metastasis chemoprevention.

Brassinosteroid enhances resistance to fusarium diseases of barley

Fusarium pathogens are among the most damaging pathogens of cereals. These pathogens have the ability to attack the roots, seedlings, and flowering heads of barley and wheat plants with disease, resulting in yield loss and head blight disease and also resulting in the contamination of grain with mycotoxins harmful to human and animal health. There is increasing evidence that brassinosteroid (BR) hormones play an important role in plant defense against both biotic and abiotic stress agents and this study set out to determine if and how BR might affect Fusarium diseases of barley. Application of the epibrassinolide (epiBL) to heads of 'Lux' barley reduced the severity of Fusarium head blight (FHB) caused by Fusarium culmorum by 86% and reduced the FHB-associated loss in grain weight by 33%. Growth of plants in soil amended with epiBL resulted in a 28 and 35% reduction in Fusarium seedling blight (FSB) symptoms on the Lux and 'Akashinriki' barley, respectively. Microarray analysis was used to determine whether growth in epiBL-amended soil changed the transcriptional profile in stem base tissue during the early stages of FSB development. At 24 and 48 h post F. culmorum inoculation, there were 146 epiBL-responsive transcripts, the majority being from the 48-h time point (n = 118). Real-time reverse-transcription polymerase chain reaction analysis validated the results for eight transcripts, including five defense genes. The results of gene expression studies show that chromatin remodeling, hormonal signaling, photosynthesis, and pathogenesis-related genes are activated in plants as a result of growth in epiBL.

Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response

Over the last decade, numerous computational methods have been developed in order to infer and model biological networks. Transcriptional networks in particular have attracted significant attention due to their critical role in cell survival. The majority of network inference methods use genome-wide experimental data to search for modules of genes with coherent expression profiles and common regulators, often ignoring the multi-layer structure of transcriptional cascades. Modeling methodologies on the other hand assume a given network structure and vary significantly in their algorithmic approach, ranging from over-simplified representations (e.g., Boolean networks) to detailed -but computationally expensive-network simulations (e.g., with differential equations). In this work we use Artificial Neural Networks (ANNs) to model transcriptional regulatory cascades that emerge during the stress response in Saccharomyces cerevisiae and extend in three layers. We confine the structure of the ANNs to match the structure of the biological networks as determined by gene expression, DNA-protein interaction and experimental evidence provided in publicly available databases. Trained ANNs are able to predict the expression profile of 11 target genes across multiple experimental conditions with a correlation coefficient >0.7. When time-dependent interactions between upstream transcription factors (TFs) and their indirect targets are also included in the ANNs, accurate predictions are achieved for 30/34 target genes. Moreover, heterodimer formation is taken into account. We show that ANNs can be used to (1) accurately predict the expression of downstream genes in a 3-layer transcriptional cascade based on the expression of their indirect regulators and (2) infer the condition- and time-dependent activity of various TFs as well as during heterodimer formation. We show that a three-layer regulatory cascade whose structure is determined by co-expressed gene modules and their regulators can successfully be modeled using ANNs with a similar configuration.

PrePPI: a structure-informed database of protein-protein interactions

PrePPI (http://bhapp.c2b2.columbia.edu/PrePPI) is a database that combines predicted and experimentally determined protein-protein interactions (PPIs) using a Bayesian framework. Predicted interactions are assigned probabilities of being correct, which are derived from calculated likelihood ratios (LRs) by combining structural, functional, evolutionary and expression information, with the most important contribution coming from structure. Experimentally determined interactions are compiled from a set of public databases that manually collect PPIs from the literature and are also assigned LRs. A final probability is then assigned to every interaction by combining the LRs for both predicted and experimentally determined interactions. The current version of PrePPI contains ∼2 million PPIs that have a probability more than ∼0.1 of which ∼60 000 PPIs for yeast and ∼370 000 PPIs for human are considered high confidence (probability > 0.5). The PrePPI database constitutes an integrated resource that enables users to examine aggregate information on PPIs, including both known and potentially novel interactions, and that provides structural models for many of the PPIs.

A systematic analysis of the predicted human La protein targets identified a hepatitis B virus infection signature

The human La (hLa) protein functions in RNA metabolism and is activated by casein kinase 2 (CK2) phosphorylation. Hepatitis B virus (HBV) can exploit hLa to stabilize its RNA and promote its pathogenesis. To enhance our knowledge of host molecular pathways involved in HBV pathogenesis, a bioinformatic approach was used to generate an expression profile of all predicted target genes of CK2-activated hLa in HBV-infected cells. A computerized literature search was performed to identify English language studies of HBV-, hLa- and CK2-related molecules. The data were pooled and the genes were classified in three functional groups by gene ontology (GO) analysis. HBV, hLa and CK2 targets were predicted, respectively, by a computational method, followed by screening for matching gene symbols in the NCBI human sequences, GO, pathway and network analyses. hLa targets and respective networks in the viral mechanisms of HBV were obtained by the final integrative analysis. Thirty-seven hub genes were identified by overlap calculation, suggesting that hLa may play an important role in the development and progression of HBV through cytokine-cytokine receptor interaction, hematopoietic cell lineage, cell adhesion molecules (CAMs), antigen processing and presentation, Jak-STAT signalling pathway, natural killer cell-mediated cytotoxicity, apoptosis, T-cell receptor signalling pathway, complement and coagulation cascades, protein export and other pathways. Our data may help researchers to predict the molecular mechanisms of hLa in the development and progression of HBV through CK2 comprehensively. Moreover, the present data indicate that hLa targets may be a series of promising candidates for HBV.

Structure-based prediction of protein-protein interactions on a genome-wide scale

The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms^1,2. Much of our current knowledge derives from high-throughput techniques such as yeast two hybrid and affinity purification³, as well as from manual curation of experiments on individual systems⁴. A variety of computational approaches based, for example, on sequence homology, gene co-expression, and phylogenetic profiles have also been developed for the genome-wide inference of protein-protein interactions (PPIs)^5,6. Yet, comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages^7–9. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, PrePPI, that combines structural information with other functional clues is comparable in accuracy to high-throughput experiments, yielding over 30,000 high confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of significant biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.

Clustering algorithms in biomedical research: a review

Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting. Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important. This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications.

Identification of dimorphism-involved genes of Yarrowia lipolytica by means of microarray analysis

Fungal dimorphism is the capacity of certain species of fungi to grow in the form of budding yeasts or mycelium depending on the environmental conditions. This characteristic is a complex phenomenon that involves modifications of the molecular machinery in response to different environmental signals. Through the use of microarrays, in this work we identified genes involved in the early stages of the yeast-to-mycelium transition of Yarrowia lipolytica induced by a shift in pH of the medium. As controls, yeast and mycelium monomorphic mutants were used, identifying by this mean a total of 61 upregulated and 165 downregulated genes specifically involved in dimorphism. Determination of the putative function of these genes was accomplished by means of BLAST analyses which showed that they were involved mainly in processes such as remodeling and biogenesis of the cell wall, membrane trafficking and N- or O-glycosylation. Some of these genes were identified by homology with Saccharomyces cerevisiae genes, and found to play a role during the dimorphic transition in both systems.

A systematic-analysis of predicted miR-21 targets identifies a signature for lung cancer

INTRODUCTION

The well-known oncomiR-miR-21 was previously reported oncogenic activity in lung cancer. We sought to determine the expression of all predicted target genes of miR-21 and their potential function, pathways and networks, which are involved in the biological behavior of lung cancer.

METHODS

After a systematic review of English language studies of lung cancer-related molecules were pooled; genes were classified in three functional groups by gene ontology (GO) analysis. The key molecules were indentified by establishing lung cancer related networks and pathways. MiR-21 targets were predicted by computational method, followed by screening for matched gene symbols in NCBI human sequences and GO, pathway and network analysis. MiR-21 targets and their network, which are involved in the malignant mechanisms of lung cancer, were obtained by the final integrative analysis.

RESULT

We indentified the potential functions, pathways and networks of lung cancer relating molecules and miR-21 targets respectively in the initial analysis. In the final integrative analysis of lung cancer related miR-21-targets analysis, 24 hub genes were identified by overlap calculation, suggesting that miR-21 may play an important role in the development and progression of lung cancer through JAK/STAT signal pathway, MAPK signaling pathway, Wnt signaling pathway, cell cycle, PPAR signaling pathway, apoptosis pathway and other pathways.

CONCLUSION

Our data may help researchers to predict the molecular mechanisms of miR-21 in the development and progression of lung cancer comprehensively. Moreover, the present data indicate that miR-21-targets may be a series of promising candidates as biomarkers for lung cancer.

GENE CLASSIFICATION USING EXPRESSION PROFILES: A FEASIBILITY STUDY

As various genome sequencing projects have already been completed or are near completion, genome researchers are shifting their focus to functional genomics. Functional genomics represents the next phase, that expands the biological investigation to studying the functionality of genes of a single organism as well as studying and correlating the functionality of genes across many different organisms. Recently developed methods for monitoring genome-wide mRNA expression changes hold the promise of allowing us to inexpensively gain insights into the function of unknown genes. In this paper we focus on evaluating the feasibility of using supervised machine learning methods for determining the function of genes based solely on their expression profiles. We experimentally evaluate the performance of traditional classification algorithms such as support vector machines and k-nearest neighbors on the yeast genome, and present new approaches for classification that improve the overall recall with moderate reductions in precision. Our experiments show that the accuracies achieved for different classes varies dramatically. In analyzing these results we show that the achieved accuracy is highly dependent on whether or not the genes of that class were significantly active during the various experimental conditions, suggesting that gene expression profiles can become a viable alternative to sequence similarity searches provided that the genes are observed under a wide range of experimental conditions.

KUPS: constructing datasets of interacting and non-interacting protein pairs with associated attributions

KUPS (The University of Kansas Proteomics Service) provides high-quality protein–protein interaction (PPI) data for researchers developing and evaluating computational models for predicting PPIs by allowing users to construct ready-to-use data sets of interacting protein pairs (IPPs), non-interacting protein pairs (NIPs) and associated features. Multiple filters and options allow the user to control the make-up of the IPPs and NIPs as well as the quality of the resultant data sets. Each data set is built from the overall database, which includes 185 446 IPPs and ∼1.5 billion NIPs from five primary databases: IntAct, HPRD, MINT, UniProt and the Gene Ontology. The IPP set can be set to specific model organisms, interaction types and experimental evidence. The NIP set can be generated using four different strategies, which can alleviate biased estimation problems. Lastly, multiple features can be provided for all of the IPP and NIP pairs. Additionally, KUPS provides two benchmark data sets to help researchers compare their algorithms to existing approaches. KUPS is freely available at http://www.ittc.ku.edu/chenlab.

A critical and integrated view of the yeast interactome

Global studies of protein–protein interactions are crucial to both elucidating gene function and producing an integrated view of the workings of living cells. High-throughput studies of the yeast interactome have been performed using both genetic and biochemical screens. Despite their size, the overlap between these experimental datasets is very limited. This could be due to each approach sampling only a small fraction of the total interactome. Alternatively, a large proportion of the data from these screens may represent false-positive interactions. We have used the Genome Information Management System (GIMS) to integrate interactome datasets with transcriptome and protein annotation data and have found significant evidence that the proportion of false-positive results is high. Not all high-throughput datasets are similarly contaminated, and the tandem affinity purification (TAP) approach appears to yield a high proportion of reliable interactions for which corroborating evidence is available. From our integrative analyses, we have generated a set of verified interactome data for yeast.

Two components of a velvet-like complex control hyphal morphogenesis, conidiophore development, and penicillin biosynthesis in Penicillium chrysogenum

Penicillium chrysogenum is the industrial producer of the antibiotic penicillin, whose biosynthetic regulation is barely understood. Here, we provide a functional analysis of two major homologues of the velvet complex in P. chrysogenum, which we have named P. chrysogenum velA (PcvelA) and PclaeA. Data from array analysis using a DeltaPcvelA deletion strain indicate a significant role of PcVelA on the expression of biosynthesis and developmental genes, including PclaeA. Northern hybridization and high-performance liquid chromatography quantifications of penicillin titers clearly show that both PcVelA and PcLaeA play a major role in penicillin biosynthesis in a producer strain that underwent several rounds of UV mutagenesis during a strain improvement program. Both regulators are further involved in different developmental processes. While PcvelA deletion leads to light-independent conidial formation, dichotomous branching of hyphae, and pellet formation in shaking cultures, a DeltaPclaeA strain shows a severe impairment in conidiophore formation under both light and dark conditions. Bimolecular fluorescence complementation assays provide evidence for a velvet-like complex in P. chrysogenum, with structurally conserved components that have distinct developmental roles, illustrating the functional plasticity of these regulators in genera other than Aspergillus.

Transcriptional profiling reveals the expression of novel genes in response to various stimuli in the human dermatophyte Trichophyton rubrum

Background

Cutaneous mycoses are common human infections among healthy and immunocompromised hosts, and the anthropophilic fungus Trichophyton rubrum is the most prevalent microorganism isolated from such clinical cases worldwide. The aim of this study was to determine the transcriptional profile of T. rubrum exposed to various stimuli in order to obtain insights into the responses of this pathogen to different environmental challenges. Therefore, we generated an expressed sequence tag (EST) collection by constructing one cDNA library and nine suppression subtractive hybridization libraries.

Results

The 1388 unigenes identified in this study were functionally classified based on the Munich Information Center for Protein Sequences (MIPS) categories. The identified proteins were involved in transcriptional regulation, cellular defense and stress, protein degradation, signaling, transport, and secretion, among other functions. Analysis of these unigenes revealed 575 T. rubrum sequences that had not been previously deposited in public databases.

Conclusion

In this study, we identified novel T. rubrum genes that will be useful for ORF prediction in genome sequencing and facilitating functional genome analysis. Annotation of these expressed genes revealed metabolic adaptations of T. rubrum to carbon sources, ambient pH shifts, and various antifungal drugs used in medical practice. Furthermore, challenging T. rubrum with cytotoxic drugs and ambient pH shifts extended our understanding of the molecular events possibly involved in the infectious process and resistance to antifungal drugs.

Protein interactions and ligand binding: from protein subfamilies to functional specificity

The divergence accumulated during the evolution of protein families translates into their internal organization as subfamilies, and it is directly reflected in the characteristic patterns of differentially conserved residues. These specifically conserved positions in protein subfamilies are known as "specificity determining positions" (SDPs). Previous studies have limited their analysis to the study of the relationship between these positions and ligand-binding specificity, demonstrating significant yet limited predictive capacity. We have systematically extended this observation to include the role of differential protein interactions in the segregation of protein subfamilies and explored in detail the structural distribution of SDPs at protein interfaces. Our results show the extensive influence of protein interactions in the evolution of protein families and the widespread association of SDPs with protein interfaces. The combined analysis of SDPs in interfaces and ligand-binding sites provides a more complete picture of the organization of protein families, constituting the necessary framework for a large scale analysis of the evolution of protein function.

Involvement of vacuolar sequestration and active transport in tolerance of Saccharomyces cerevisiae to hop iso-alpha-acids

The hop plant, Humulus lupulus L., has an exceptionally high content of secondary metabolites, the hop alpha-acids, which possess a range of beneficial properties, including antiseptic action. Studies performed on the mode of action of hop iso-alpha-acids have hitherto been restricted to lactic acid bacteria. The present study investigated molecular mechanisms of hop iso-alpha-acid resistance in the model eukaryote Saccharomyces cerevisiae. Growth inhibition occurred at concentrations of hop iso-alpha-acids that were an order of magnitude higher than those found with hop-tolerant prokaryotes. Chemostat-based transcriptome analysis and phenotype screening of the S. cerevisiae haploid gene deletion collection were used as complementary methods to screen for genes involved in hop iso-alpha-acid detoxification and tolerance. This screening and further analysis of deletion mutants confirmed that yeast tolerance to hop iso-alpha-acids involves three major processes, active proton pumping into the vacuole by the vacuolar-type ATPase to enable vacuolar sequestration of iso-alpha-acids and alteration of cell wall structure and, to a lesser extent, active export of iso-alpha-acids across the plasma membrane. Furthermore, iso-alpha-acids were shown to affect cellular metal homeostasis by acting as strong zinc and iron chelators.

Identity of the growth-limiting nutrient strongly affects storage carbohydrate accumulation in anaerobic chemostat cultures of Saccharomyces cerevisiae

Accumulation of glycogen and trehalose in nutrient-limited cultures of Saccharomyces cerevisiae is negatively correlated with the specific growth rate. Additionally, glucose-excess conditions (i.e., growth limitation by nutrients other than glucose) are often implicated in high-level accumulation of these storage carbohydrates. The present study investigates how the identity of the growth-limiting nutrient affects accumulation of storage carbohydrates in cultures grown at a fixed specific growth rate. In anaerobic chemostat cultures (dilution rate, 0.10 h(-1)) of S. cerevisiae, the identity of the growth-limiting nutrient (glucose, ammonia, sulfate, phosphate, or zinc) strongly affected storage carbohydrate accumulation. The glycogen contents of the biomass from glucose- and ammonia-limited cultures were 10- to 14-fold higher than those of the biomass from cultures grown under the other three glucose-excess regimens. Trehalose levels were specifically higher under nitrogen-limited conditions. These results demonstrate that storage carbohydrate accumulation in nutrient-limited cultures of S. cerevisiae is not a generic response to excess glucose but instead is strongly dependent on the identity of the growth-limiting nutrient. While transcriptome analysis of wild-type and msn2Delta msn4Delta strains confirmed that transcriptional upregulation of glycogen and trehalose biosynthesis genes is mediated by Msn2p/Msn4p, transcriptional regulation could not quantitatively account for the drastic changes in storage carbohydrate accumulation. The results of assays of glycogen synthase and glycogen phosphorylase activities supported involvement of posttranscriptional regulation. Consistent with the high glycogen levels in ammonia-limited cultures, the ratio of glycogen synthase to glycogen phosphorylase in these cultures was up to eightfold higher than the ratio in the other glucose-excess cultures.

On large margin hierarchical classification with multiple paths

Hierarchical classification is critical to knowledge management and exploration, as in gene function prediction and document categorization. In hierarchical classification, an input is classified according to a structured hierarchy. In a situation as such, the central issue is how to effectively utilize the inter-class relationship to improve the generalization performance of flat classification ignoring such dependency. In this article, we propose a novel large margin method through constraints characterizing a multi-path hierarchy, where class membership can be non-exclusive. The proposed method permits a treatment of various losses for hierarchical classification. For implementation, we focus on the symmetric difference loss and two large margin classifiers: support vector machines and psi-learning. Finally, theoretical and numerical analyses are conducted, in addition to an application to gene function prediction. They suggest that the proposed method achieves the desired objective and outperforms strong competitors in the literature.

Fewer permutations, more accurate P-values

Motivation: Permutation tests have become a standard tool to assess the statistical significance of an event under investigation. The statistical significance, as expressed in a P-value, is calculated as the fraction of permutation values that are at least as extreme as the original statistic, which was derived from non-permuted data. This empirical method directly couples both the minimal obtainable P-value and the resolution of the P-value to the number of permutations. Thereby, it imposes upon itself the need for a very large number of permutations when small P-values are to be accurately estimated. This is computationally expensive and often infeasible.

Results: A method of computing P-values based on tail approximation is presented. The tail of the distribution of permutation values is approximated by a generalized Pareto distribution. A good fit and thus accurate P-value estimates can be obtained with a drastically reduced number of permutations when compared with the standard empirical way of computing P-values.

Availability: The Matlab code can be obtained from the corresponding author on request.

Contact:tknijnenburg@systemsbiology.org

Supplementary information:Supplementary data are available at Bioinformatics online.

Identification of genes that are preferentially expressed in conidiogenous cell development of Metarhizium anisopliae by suppression subtractive hybridization

The insect pathogenic fungus Metarhizium anisopliae is widely used as an insect biocontrol agent. The M. anisopliae conidium plays an important role in pathogenesis and disease transmission. The aim of this study was to identify genes whose expression is up-regulated during conidiogenous cell development. This is a powerful strategy for obtaining insight into the molecular events that regulate conidiation. We isolated genes that are preferentially expressed in the developing conidiophores of the common fungal locust pathogen M. anisopliae CQMa102 using suppression subtractive hybridization. Based on the results of cDNA array dot blotting, we identified 109 unique expressed sequence tags (ESTs) that were up-regulated more than fivefold during conidiophore formation. Among these 109 ESTs were 45 (41.3%) with significant similarity to NCBI annotated hypothetical proteins, 35 (32.1%) with low similarity to known or predicted genes that might represent novel genes, and 29 (26.6%) with significant similarity to known proteins involved in various cell and molecular processes, such as ell structure and function, cell metabolism, protein metabolism, stress response, nucleic acid metabolism, and cell cycle and growth. We confirmed the up-regulation of 11 randomly selected genes with real-time reverse transcriptase-PCR analysis. The results of this study provide a preliminary description of genes that may be involved in the molecular regulation of fungal conidiogenesis.

On Efficient Large Margin Semisupervised Learning: Method and Theory

In classification, semisupervised learning usually involves a large amount of unlabeled data with only a small number of labeled data. This imposes a great challenge in that it is difficult to achieve good classification performance through labeled data alone. To leverage unlabeled data for enhancing classification, this article introduces a large margin semisupervised learning method within the framework of regularization, based on an efficient margin loss for unlabeled data, which seeks efficient extraction of the information from unlabeled data for estimating the Bayes decision boundary for classification. For implementation, an iterative scheme is derived through conditional expectations. Finally, theoretical and numerical analyses are conducted, in addition to an application to gene function prediction. They suggest that the proposed method enables to recover the performance of its supervised counterpart based on complete data in rates of convergence, when possible.

Combinatorial effects of environmental parameters on transcriptional regulation in Saccharomyces cerevisiae: a quantitative analysis of a compendium of chemostat-based transcriptome data

Background

Microorganisms adapt their transcriptome by integrating multiple chemical and physical signals from their environment. Shake-flask cultivation does not allow precise manipulation of individual culture parameters and therefore precludes a quantitative analysis of the (combinatorial) influence of these parameters on transcriptional regulation. Steady-state chemostat cultures, which do enable accurate control, measurement and manipulation of individual cultivation parameters (e.g. specific growth rate, temperature, identity of the growth-limiting nutrient) appear to provide a promising experimental platform for such a combinatorial analysis.

Results

A microarray compendium of 170 steady-state chemostat cultures of the yeast Saccharomyces cerevisiae is presented and analyzed. The 170 microarrays encompass 55 unique conditions, which can be characterized by the combined settings of 10 different cultivation parameters. By applying a regression model to assess the impact of (combinations of) cultivation parameters on the transcriptome, most S. cerevisiae genes were shown to be influenced by multiple cultivation parameters, and in many cases by combinatorial effects of cultivation parameters. The inclusion of these combinatorial effects in the regression model led to higher explained variance of the gene expression patterns and resulted in higher function enrichment in subsequent analysis. We further demonstrate the usefulness of the compendium and regression analysis for interpretation of shake-flask-based transcriptome studies and for guiding functional analysis of (uncharacterized) genes and pathways.

Conclusion

Modeling the combinatorial effects of environmental parameters on the transcriptome is crucial for understanding transcriptional regulation. Chemostat cultivation offers a powerful tool for such an approach.

Identification of genes preferentially expressed during microcycle conidiation of Metarhizium anisopliae using suppression subtractive hybridization

Microcycle conidiation has been defined as the production of conidia directly by a spore without the intervention of hyphal growth. The molecular mechanisms underlying this process are still poorly understood. Suppression subtractive hybridization was used here to isolate the genes preferentially expressed during microcycle conidiation vs. in normal conidiation hypha of Metarhizium anisopliae CQMa102, a common fungal pathogen of locusts. A total of 1600 clones from the subtracted cDNA library were screened by cDNA array dot blotting and 221 unique expressed sequence tags were identified as being differentially expressed. These genes were found to be homologous genes involved in various cellular processes, including general metabolism, protein synthesis, energy, cell-cycle and DNA processing, cellular transport, transcription, signal transduction and stress response. Real-time reverse transcriptase PCR assay of six randomly selected genes revealed that they are all highly expressed during microcycle conidiation.

Physiological and transcriptional responses to high concentrations of lactic acid in anaerobic chemostat cultures of Saccharomyces cerevisiae

Based on the high acid tolerance and the simple nutritional requirements of Saccharomyces cerevisiae, engineered strains of this yeast are considered biocatalysts for industrial production of high-purity undissociated lactic acid. However, high concentrations of lactic acid are toxic to S. cerevisiae, thus limiting its growth and product formation. Physiological and transcriptional responses to high concentrations of lactic acid were studied in anaerobic, glucose-limited chemostat cultures grown at different pH values and lactic acid concentrations, resulting in a 50% decrease in the biomass yield. At pH 5, the yield decrease was caused mostly by osmotically induced glycerol production and not by the classic weak-acid action, as was observed at pH 3. Cultures grown at pH 5 with 900 mM lactic acid revealed an upregulation of many genes involved in iron homeostasis, indicating that iron chelation occurred at high concentrations of dissociated lactic acid. Chemostat cultivation at pH 3 with 500 mM lactate, resulting in lower anion concentrations, showed an alleviation of this iron homeostasis response. Six of the 10 known targets of the transcriptional regulator Haa1p were strongly upregulated in lactate-challenged cultures at pH 3 but showed only moderate induction by high lactate concentrations at pH 5. Moreover, the haa1Delta mutant exhibited a growth defect at high lactic acid concentrations at pH 3. These results indicate that iron homeostasis plays a major role in the response of S. cerevisiae to high lactate concentrations, whereas the Haa1p regulon is involved primarily in the response to high concentrations of undissociated lactic acid.

Protein networking: insights into global functional organization of proteomes

The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields. This approach has provided many insights into the functional organization of the proteome so far and will likely continue to do so. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow different insights into functional organization. One such concept is the protein interaction network, which contains proteins as nodes and undirected edges representing the occurrence of binding in large-scale protein-protein interaction studies. A second concept is the protein-signaling network, in which the nodes correspond to levels of post-translationally modified forms of proteins and directed edges to causal effects through post-translational modification, such as phosphorylation. Several other network concepts were introduced for proteomics. Although all formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We review recent literature formulating and analyzing such networks.

Down-regulation of Sclerotinia sclerotiorum gene expression in response to infection with Sclerotinia sclerotiorum debilitation-associated RNA virus

We have previously presented convincing evidence in support of a viral etiology for the debilitation phenotype exhibited by strain Ep-1PN of Sclerotinia sclerotiorum. To explore the possible mechanisms underlying fungal pathogenicity and hyphal growth, potential genes whose expression was down-regulated in Ep-1PN were identified from a cDNA library of the virus-free strain Ep-1PNAa, which is a single ascospore derivative of strain Ep-1PN, using reverse northern blot analysis. A total of 1116 cDNA clones were targeted and, following PCR re-amplification, 210 cDNA clones were selected as candidates, of which 16 cDNA clones were subjected to northern blot analysis for further confirmation. The results showed that 12 clones represented genes that were differentially expressed in the virus-free strain compared to the virus-infected one. Of the 210 clones that were sequenced, 150 had non-redundant sequences and of these 92% (138 clones) had significant homology to fungal genes in the databases examined. The remaining 12 clones did not have any matches. The differentially expressed genes represented a broad spectrum of biological functions including carbon and energy metabolism, protein synthesis and transport, signal transduction and stress response. This study provides the first insight into genes differentially expressed between the virus-free strain Ep-1PNAa and the virus-infected strain Ep-1PN. The possible relationships between mycovirus-mediated changes in cellular gene expression and observed phenotypes are discussed.

Chemical-genetic approaches for exploring the mode of action of natural products

Determining the mode of action of bioactive compounds, including natural products, is a central problem in chemical biology. Because many genes are conserved from the yeast Saccharomyces cerevisiae to humans and a number of powerful genomics tools and methodologies have been developed for this model system, yeast is making a major contribution to the field of chemical genetics. The set of barcoded yeast deletion mutants, including the set of approximately 5000 viable haploid and homozygous diploid deletion mutants and the complete set of approximately 6000 heterozygous deletion mutants, containing the set of approximately 1000 essential genes, are proving highly informative for identifying chemical-genetic interactions and deciphering compound mode of action. Gene deletions that render cells hypersensitive to a specific drug identify pathways that buffer the cell against the toxic effects of the drug and thereby provide clues about both gene and compound function. Moreover, compounds that show similar chemical-genetic profiles often perturb similar target pathways. Gene dosage can be exploited to discover connections between compounds and their targets. For example, haploinsufficiency profiling of an antifungal compound, in which the set of approximately 6000 heterozygous diploid deletion mutants are scored for hypersensitivity to a compound, may identify the target directly. Creating deletion mutant collections in other fungal species, including the major human fungal pathogen Candida albicans, will expand our chemical genomics tool set, allowing us to screen for antifungal lead drugs directly. The yeast deletion mutant collection is also being exploited to map large-scale genetic interaction data obtained from genome-wide synthetic lethal screens and the integration of this data with chemical genetic data should provide a powerful system for linking compounds to their target pathway. Extensive application of chemical genetics in yeast has the potential to develop a small molecule inhibitor for the majority of all approximately 6000 yeast genes.

Expression profiles of genes encoded by the supernumerary chromosome controlling AM-toxin biosynthesis and pathogenicity in the apple pathotype of Alternaria alternata

The apple pathotype of Alternaria alternata produces host-specific AM-toxin and causes Alternaria blotch of apple. Previously, we cloned two genes, AMT1 and AMT2, required for AM-toxin biosynthesis and found that these genes are encoded by small, supernumerary chromosomes of <1.8 Mb in the apple pathotype strains. Here, we performed expressed sequence tag analysis of the 1.4-Mb chromosome encoding AMT genes in strain IFO8984. A cDNA library was constructed using RNA from AM-toxin-producing cultures. A total of 40,980 clones were screened with the 1.4-Mb chromosome probe, and 196 clones encoded by the chromosome were isolated. Sequence analyses of these clones identified 80 unigenes, including AMT1 and AMT2, and revealed that the functions of 43 (54%) genes are unknown. The expression levels of the 80 genes in AM-toxin-producing and nonproducing cultures were analyzed by real-time quantitative polymerase chain reaction (PCR). Most of the genes were found to be expressed in both cultures at markedly lower levels than the translation elongation factor 1-alpha gene used as an internal control. Comparison of the expression levels of these genes between two cultures showed that 21 genes, including AMT1 and AMT2, were upregulated (>10-fold) in AM-toxin-producing cultures. Two of the upregulated genes were newly identified to be involved in AM-toxin biosynthesis by the gene disruption experiments and were named AMT3 and AMT4. Thus, the genes upregulated in AM-toxin-producing cultures contain ideal candidates for novel AM-toxin biosynthetic genes.

Physiological and transcriptional responses of Saccharomyces cerevisiae to zinc limitation in chemostat cultures

Transcriptional responses of the yeast Saccharomyces cerevisiae to Zn availability were investigated at a fixed specific growth rate under limiting and abundant Zn concentrations in chemostat culture. To investigate the context dependency of this transcriptional response and eliminate growth rate-dependent variations in transcription, yeast was grown under several chemostat regimens, resulting in various carbon (glucose), nitrogen (ammonium), zinc, and oxygen supplies. A robust set of genes that responded consistently to Zn limitation was identified, and the set enabled the definition of the Zn-specific Zap1p regulon, comprised of 26 genes and characterized by a broader zinc-responsive element consensus (MHHAACCBYNMRGGT) than so far described. Most surprising was the Zn-dependent regulation of genes involved in storage carbohydrate metabolism. Their concerted down-regulation was physiologically relevant as revealed by a substantial decrease in glycogen and trehalose cellular content under Zn limitation. An unexpectedly large number of genes were synergistically or antagonistically regulated by oxygen and Zn availability. This combinatorial regulation suggested a more prominent involvement of Zn in mitochondrial biogenesis and function than hitherto identified.

Identification of InuR, a new Zn(II)2Cys6 transcriptional activator involved in the regulation of inulinolytic genes in Aspergillus niger

The expression of inulinolytic genes in Aspergillus niger is co-regulated and induced by inulin and sucrose. We have identified a positive acting transcription factor InuR, which is required for the induced expression of inulinolytic genes. InuR is a member of the fungal specific class of transcription factors of the Zn(II)2Cys6 type. Involvement of InuR in inulin and sucrose metabolism was suspected because of the clustering of inuR gene with sucB, which encodes an intracellular invertase with transfructosylation activity and a putative sugar transporter encoding gene (An15g00310). Deletion of the inuR gene resulted in a strain displaying a severe reduction in growth on inulin and sucrose medium. Northern analysis revealed that expression of inulinolytic and sucrolytic genes, e.g., inuE, inuA, sucA, as well as the putative sugar transporter gene (An15g00310) is dependent on InuR. Genome-wide expression analysis revealed, three additional putative sugar transporters encoding genes (An15g04060, An15g03940 and An17g01710), which were strongly induced by sucrose in an InuR dependent way. In silico analysis of the promoter sequences of strongly InuR regulated genes suggests that InuR might bind as dimer to two CGG triplets, which are separated by eight nucleotides.

Comparison of protein coding gene contents of the fungal phyla Pezizomycotina and Saccharomycotina

BACKGROUND

Several dozen fungi encompassing traditional model organisms, industrial production organisms and human and plant pathogens have been sequenced recently and their particular genomic features analysed in detail. In addition comparative genomics has been used to analyse specific sub groups of fungi. Notably, analysis of the phylum Saccharomycotina has revealed major events of evolution such as the recent genome duplication and subsequent gene loss. However, little has been done to gain a comprehensive comparative view to the fungal kingdom. We have carried out a computational genome wide comparison of protein coding gene content of Saccharomycotina and Pezizomycotina, which include industrially important yeasts and filamentous fungi, respectively.

RESULTS

Our analysis shows that based on genome redundancy, the traditional model organisms Saccharomyces cerevisiae and Neurospora crassa are exceptional among fungi. This can be explained by the recent genome duplication in S. cerevisiae and the repeat induced point mutation mechanism in N. crassa. Interestingly in Pezizomycotina a subset of protein families related to plant biomass degradation and secondary metabolism are the only ones showing signs of recent expansion. In addition, Pezizomycotina have a wealth of phylum specific poorly characterised genes with a wide variety of predicted functions. These genes are well conserved in Pezizomycotina, but show no signs of recent expansion. The genes found in all fungi except Saccharomycotina are slightly better characterised and predicted to encode mainly enzymes. The genes specific to Saccharomycotina are enriched in transcription and mitochondrion related functions. Especially mitochondrial ribosomal proteins seem to have diverged from those of Pezizomycotina. In addition, we highlight several individual gene families with interesting phylogenetic distributions.

CONCLUSION

Our analysis predicts that all Pezizomycotina unlike Saccharomycotina can potentially produce a wide variety of secondary metabolites and secreted enzymes and that the responsible gene families are likely to evolve fast. Both types of fungal products can be of commercial value, or on the other hand cause harm to humans. In addition, a great number of novel predicted and known enzymes are found from all fungi except Saccharomycotina. Therefore further studies and exploitation of fungal metabolism appears very promising.