Munich Information Center For Protein Sequences Research Articles

The Fruit Proteome Response to the Ripening Stages in Three Tomato Genotypes.

The tomato is a horticultural crop that appears in various colors as it ripens. Differences in the proteome expression abundance of a tomato depend on its genotype and ripening stage. Thus, this study aimed to confirm the differences in changes in the proteome according to four ripening stages (green, breaker, turning, and mature) of three tomato genotypes, i.e., yellow, black, and red tomatoes, using a gel-based proteomic technique. The number of protein spots shown as two-dimensional electrophoresis (2-DE) gels differed according to tomato genotype and ripening stage. A total of 286 variant proteins were determined using matrix-assisted laser desorption-time of flight (MALDI-TOF) mass spectrometry (MS) analysis, confirming 233 identified protein functions. In three tomato genotypes in each ripening stage, grouping according to the Munich Information Center for Protein Sequences (MIPS) functional categories confirmed the variant proteins involved in the following: energy processes (21%); metabolism (20%); protein fate (15%); protein synthesis (10%); a protein with a binding function or cofactor requirement (8%); cell rescue, defense, and virulence (8%); cellular transport, transport facilitation, and transport routes (6%); the biogenesis of cellular components (5%); cell cycle and DNA processing (2%); others (5%). Among the identified protein spots in the function category, two proteins related to metabolism, four related to energy, four related to protein synthesis, and two related to interaction with the cellular environment showed significantly different changes according to the fruit color by the ripening stage. This study reveals the physiological changes in different types of tomatoes according to their ripening stage and provides information on the proteome for further improvement.

Protein Complexes Prediction Method Based on Core-Attachment Structure and Functional Annotations.

Recent advances in high-throughput laboratory techniques captured large-scale protein–protein interaction (PPI) data, making it possible to create a detailed map of protein interaction networks, and thus enable us to detect protein complexes from these PPI networks. However, most of the current state-of-the-art studies still have some problems, for instance, incapability of identifying overlapping clusters, without considering the inherent organization within protein complexes, and overlooking the biological meaning of complexes. Therefore, we present a novel overlapping protein complexes prediction method based on core–attachment structure and function annotations (CFOCM), which performs in two stages: first, it detects protein complex cores with the maximum value of our defined cluster closeness function, in which the proteins are also closely related to at least one common function. Then it appends attach proteins into these detected cores to form the returned complexes. For performance evaluation, CFOCM and six classical methods have been used to identify protein complexes on three different yeast PPI networks, and three sets of real complexes including the Munich Information Center for Protein Sequences (MIPS), the Saccharomyces Genome Database (SGD) and the Catalogues of Yeast protein Complexes (CYC2008) are selected as benchmark sets, and the results show that CFOCM is indeed effective and robust for achieving the highest F-measure values in all tests.

Proteomic Analysis Implicates Dominant Alterations of RNA Metabolism and the Proteasome Pathway in the Cellular Response to Carbon-Ion Irradiation.

Radiotherapy with heavy ions is considered advantageous compared to irradiation with photons due to the characteristics of the Braggs peak and the high linear energy transfer (LET) value. To understand the mechanisms of cellular responses to different LET values and dosages of heavy ion radiation, we analyzed the proteomic profiles of mouse embryo fibroblast MEF cells exposed to two doses from different LET values of heavy ion 12C. Total proteins were extracted from these cells and examined by Q Exactive with Liquid Chromatography (LC)—Electrospray Ionization (ESI) Tandem MS (MS/MS). Using bioinformatics approaches, differentially expressed proteins with 1.5 or 2.0-fold changes between different dosages of exposure were compared. With the higher the dosage and/or LET of ion irradiation, the worse response the cells were in terms of protein expression. For instance, compared to the control (0 Gy), 771 (20.2%) proteins in cells irradiated at 0.2 Gy of carbon-ion radiation with 12.6 keV/μm, 313 proteins (8.2%) in cells irradiated at 2 Gy of carbon-ion radiation with 12.6 keV/μm, and 243 proteins (6.4%) in cells irradiated at 2 Gy of carbon-ion radiation with 31.5 keV/μm exhibited changes of 1.5-fold or greater. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Munich Information Center for Protein Sequences (MIPS) analysis, and BioCarta analysis all indicated that RNA metabolic processes (RNA splicing, destabilization and deadenylation) and proteasome pathways may play key roles in the cellular response to heavy-ion irradiation. Proteasome pathways ranked highest among all biological processes associated with heavy carbon-ion irradiation. In addition, network analysis revealed that cellular pathways involving proteins such as Col1a1 and Fn1 continued to respond to high dosages of heavy-ion irradiation, suggesting that these pathways still protect cells against damage. However, pathways such as those involving Ikbkg1 responded better at lower dosages than at higher dosages, implying that cell damage would occur when the networks involving these proteins stop responding. Our investigation provides valuable proteomic information for elucidating the mechanism of biological effects induced by carbon ions in general.

Entropic Biological Score: a cell cycle investigation for GRNs inference

Inference of gene regulatory networks (GRNs) is one of the most challenging research problems of Systems Biology. In this investigation, a new GRNs inference methodology, called Entropic Biological Score (EBS), which linearly combines the mean conditional entropy (MCE) from expression levels and a Biological Score (BS), obtained by integrating different biological data sources, is proposed. The EBS is validated with the Cell Cycle related functional annotation information, available from Munich Information Center for Protein Sequences (MIPS), and compared with some existing methods like MRNET, ARACNE, CLR and MCE for GRNs inference. For real networks, the performance of EBS, which uses the concept of integrating different data sources, is found to be superior to the aforementioned inference methods. The best results for EBS are obtained by considering the weights w1=0.2 and w2=0.8 for MCE and BS values, respectively, where approximately 40% of the inferred connections are found to be correct and significantly better than related methods. The results also indicate that expression profile is able to recover some true connections, that are not present in biological annotations, thus leading to the possibility of discovering new relations between its genes.

Detecting overlapping protein complexes in protein-protein interaction networks

We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.

Clustering PPI data based on Improved functional-flow model through Quantum-behaved PSO

Clustering Protein-Protein Interaction (PPI) data is a difficult problem due to its small world and scale-free characteristics. Existing clustering methods could not perform well. This paper proposes an improved functional-flow based approach through Quantum-behaved Particle Swarm Optimisation (QPSO) algorithm, which can find the optimum threshold automatically when calculating the lowest similarity between modules. We also take bridging nodes into account to improve the clustering result. The experiments on Munich Information Center for Protein Sequences (MIPS) PPI data sets show that the algorithm has better performance than functional flow method in terms of accuracy and number of matched clusters.

Categorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets

We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not necessarily imply that one detection methodology was better or worse, but rather that, to a large extent, the insights reflected the methodological biases themselves. Consequently, interpreting the protein interaction data within their experimental or cellular context provided the best avenue for overcoming biases and inferring biological knowledge.

A METHOD BASED ON LOCAL DENSITY AND RANDOM WALKS FOR COMPLEXES DETECTION IN PROTEIN INTERACTION NETWORKS

In this paper, we present a method based on local density and random walks (LDRW) for core-attachment complexes detection in protein-protein interaction (PPI) networks whether they are weighted or not. Our LDRW method consists of two stages. Firstly, it finds all the protein-complex cores based on local density of subnetwork. Then it uses random walks with restarts for finding the attachment proteins of each detected core to form complexes. We evaluate the effectiveness of our method using two different yeast PPI networks and validate the biological significance of the predicted protein complexes using known complexes in the Munich Information Center for Protein Sequence (MIPS) and Gene Ontology (GO) databases. We also perform a comprehensive comparison between our method and other existing methods. The results show that our method can find more protein complexes with high biological significance and obtains a significant improvement. Furthermore, our method is able to identify biologically significant overlapped protein complexes.

Semantic and layered protein function prediction from PPI networks

Background The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein–protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness. Results We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein’s semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions. Conclusion The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins.

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

BackgroundCutaneous 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.ResultsThe 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.ConclusionIn 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.

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN.

Patterns of co-expression for protein complexes by size in Saccharomyces cerevisiae

Many successful functional studies by gene expression profiling in the literature have led to the perception that profile similarity is likely to imply functional association. But how true is the converse of the above statement? Do functionally associated genes tend to be co-regulated at the transcription level? In this paper, we focus on a set of well-validated yeast protein complexes provided by Munich Information Center for Protein Sequences (MIPS). Using four well-known large-scale microarray expression data sets, we computed the correlations between genes from the same complex. We then analyzed the relationship between the distribution of correlations and the complex size (the number of genes in a protein complex). We found that except for a few large protein complexes, such as mitochondrial ribosomal and cytoplasmic ribosomal proteins, the correlations are on the average not much higher than that from a pair of randomly selected genes. The global impact of large complexes on the expression of other genes in the genome is also studied. Our result also showed that the expression of over 85% of the genes are affected by six large complexes: the cytoplasmic ribosomal complex, mitochondrial ribosomal complex, proteasome complex, F0/F1 ATP synthase (complex V) (size 18), rRNA splicing (size 24) and H+- transporting ATPase, vacular (size 15).

Combining Multisource Information Through Functional-Annotation-Based Weighting: Gene Function Prediction in Yeast

One of the important goals of biological investigation is to predict the function of unclassified gene. Although there is a rich literature on multi data source integration for gene function prediction, there is hardly any similar work in the framework of data source weighting using functional annotations of classified genes. In this investigation, we propose a new scoring framework, called biological score (BS) and incorporating data source weighting, for predicting the function of some of the unclassified yeast genes. The BS is computed by first evaluating the similarities between genes, arising from different data sources, in a common framework, and then integrating them in a linear combination style through weights. The relative weight of each data source is determined adaptively by utilizing the information on yeast gene ontology (GO)-slim process annotations of classified genes, available from Saccharomyces Genome Database (SGD). Genes are clustered by a method called K-BS, where, for each gene, a cluster comprising that gene and its K nearest neighbors is computed using the proposed score (BS). The performances of BS and K-BS are evaluated with gene annotations available from Munich Information Center for Protein Sequences (MIPS). We predict the functional categories of 417 classified genes from 417 clusters with 0.98 positive predictive value using K-BS. The functional categories of 12 unclassified yeast genes are also predicted. Our experimental results indicate that considering multiple data sources and estimating their weights with annotations of classified genes can considerably enhance the performance of BS. It has been found that even a small proportion of annotated genes can provide improvements in finding true positive gene pairs using BS.

Establishment of a cDNA library from Bambusa edulis Murno in vitro-grown shoots

An expressed sequence tag (EST) analysis approach was undertaken to identify the genes involved in photosynthesis and metabolism of Bambusa edulis Murno, an important bamboo crop species in Asia. The cDNA library was constructed from in vitro-grown multiple shoots. Sequencing of the cDNA clones generated 987 5′-end high-quality expressed sequence tags (ESTs, Accession number: FG551848–FG552834), of which 792 were revealed by sequence comparison to be unique. A BLASTX analysis showed that 645 of these genes were similar to genes present in the NCBI database. A total of 389 genes were most similar to rice genes. Nine genes had an E-value of zero. Functional classification of these genes by the Munich Information Center for Protein Sequences (MIPS) showed that most of the genes were related to metabolism and subcellular localization processes. PSI assembly protein (Beycf3) expression was absent in the albino mutant, while PSII 43-kDa protein (BepsbC), chlorophyll synthase (BeCAO) and PSII 10-kDa protein (BePsbR) expression were reduced. These data suggest that in this chloroplast genome-aberrated mutant, both chloroplast-encoded and nucleus-encoded genes were repressed and that this library will be useful for further investigation of bamboo.

Effects of anaerobic stress on the proteome of citrus fruit

Exposure of citrus fruit to anaerobic conditions results in induction of anaerobic respiration and accumulation of the off flavor volatiles ethanol and acetaldehyde. In this study, we evaluated the effects of anaerobic stress (exposure to N 2 atmospheres for 24 h) on the proteome of mandarins and grapefruit. With two-dimension polyacrylamide gel electrophoresis (2D-PAGE), we detected more than 400 protein spots in the flavedo tissue and 300 spots in the juice vesicle tissue, that were reproducibly stained in mandarins and grapefruit. Exposure to the anaerobic treatment significantly affected the abundances of 33 different proteins by a factor of at least 1.5. Identification of the citrus anaerobic proteins (ANPs) by mass spectrometry (MS) and annotation according to the Munich Information Center for Protein Sequence (MIPS) revealed tissue- and cultivar-specific differences in the anaerobic response of citrus fruit. In the peel tissue, 64% of the detected ANPs were stress-related proteins involved in cell rescue, defense and virulence and only 6% in energy production, whereas 38% of the ANPs in juice vesicle tissue were involved in energy and 31% in either cell rescue and defense or in cell cycle and protein fate. Furthermore, exposure to N 2 for 24 h had only minor affects on protein abundance in grapefruit juice vesicle tissue (suppression of 5 proteins) but remarkably affected protein accumulation in mandarins, including induction of glycolytic enzymes, a 10-fold increase in the abundance of alcohol dehydrogenase (ADH), and induction of stress proteins, such as heat shock proteins and ascorbate peroxidase. Overall, the present study provides the first 2D-PAGE proteome analysis of fruit tissue responses to anaerobic stress, and the observed data reflect both common as well as citrus-specific anaerobic-response mechanisms.

Transcriptome profiling of Saccharomyces cerevisiae mutants lacking C2H2 zinc finger proteins

BackgroundThe budding yeast Saccharomyces cerevisiae is a eukaryotic organism with extensive genetic redundancy. Large-scale gene deletion analysis has shown that over 80% of the ~6200 predicted genes are nonessential and that the functions of 30% of all ORFs remain unclassified, implying that yeast cells can tolerate deletion of a substantial number of individual genes. For example, a class of zinc finger proteins containing C2H2 zinc fingers in tandem arrays of two or three is predicted to be transcription factors; however, seven of the thirty-one predicted genes of this class are nonessential, and their functions are poorly understood. In this study we completed a transcriptomic profiling of three mutants lacking C2H2 zinc finger proteins, ypr013cΔ,ypr015cΔ and ypr013cΔypr015cΔ.ResultsGene expression patterns were remarkably different between wild type and the mutants. The results indicate altered expression of 79 genes in ypr013cΔ, 185 genes in ypr015cΔ and 426 genes in the double mutant when compared with that of the wild type strain. More than 80% of the alterations in the double mutants were not observed in either one of the single deletion mutants. Functional categorization based on Munich Information Center for Protein Sequences (MIPS) revealed up-regulation of genes related to transcription and down-regulation of genes involving cell rescue and defense, suggesting a decreased response to stress conditions. Genes related to cell cycle and DNA processing whose expression was affected by single or double deletions were also identified.ConclusionOur results suggest that microarray analysis can define the biological roles of zinc finger proteins with unknown functions and identify target genes that are regulated by these putative transcriptional factors. These findings also suggest that both YPR013C and YPR015C have biological processes in common, in addition to their own regulatory pathways.

Cell organisation, sulphur metabolism and ion transport-related genes are differentially expressed in Paracoccidioides brasiliensis mycelium and yeast cells.

BackgroundMycelium-to-yeast transition in the human host is essential for pathogenicity by the fungus Paracoccidioides brasiliensis and both cell types are therefore critical to the establishment of paracoccidioidomycosis (PCM), a systemic mycosis endemic to Latin America. The infected population is of about 10 million individuals, 2% of whom will eventually develop the disease. Previously, transcriptome analysis of mycelium and yeast cells resulted in the assembly of 6,022 sequence groups. Gene expression analysis, using both in silico EST subtraction and cDNA microarray, revealed genes that were differential to yeast or mycelium, and we discussed those involved in sugar metabolism. To advance our understanding of molecular mechanisms of dimorphic transition, we performed an extended analysis of gene expression profiles using the methods mentioned above.ResultsIn this work, continuous data mining revealed 66 new differentially expressed sequences that were MIPS(Munich Information Center for Protein Sequences)-categorised according to the cellular process in which they are presumably involved. Two well represented classes were chosen for further analysis: (i) control of cell organisation – cell wall, membrane and cytoskeleton, whose representatives were hex (encoding for a hexagonal peroxisome protein), bgl (encoding for a 1,3-β-glucosidase) in mycelium cells; and ags (an α-1,3-glucan synthase), cda (a chitin deacetylase) and vrp (a verprolin) in yeast cells; (ii) ion metabolism and transport – two genes putatively implicated in ion transport were confirmed to be highly expressed in mycelium cells – isc and ktp, respectively an iron-sulphur cluster-like protein and a cation transporter; and a putative P-type cation pump (pct) in yeast. Also, several enzymes from the cysteine de novo biosynthesis pathway were shown to be up regulated in the yeast form, including ATP sulphurylase, APS kinase and also PAPS reductase.ConclusionTaken together, these data show that several genes involved in cell organisation and ion metabolism/transport are expressed differentially along dimorphic transition. Hyper expression in yeast of the enzymes of sulphur metabolism reinforced that this metabolic pathway could be important for this process. Understanding these changes by functional analysis of such genes may lead to a better understanding of the infective process, thus providing new targets and strategies to control PCM.

MPact: the MIPS protein interaction resource on yeast

In recent years, the Munich Information Center for Protein Sequences (MIPS) yeast protein–protein interaction (PPI) dataset has been used in numerous analyses of protein networks and has been called a gold standard because of its quality and comprehensiveness [H. Yu, N. M. Luscombe, H. X. Lu, X. Zhu, Y. Xia, J. D. Han, N. Bertin, S. Chung, M. Vidal and M. Gerstein (2004) Genome Res., 14, 1107–1118]. MPact and the yeast protein localization catalog provide information related to the proximity of proteins in yeast. Beside the integration of high-throughput data, information about experimental evidence for PPIs in the literature was compiled by experts adding up to 4300 distinct PPIs connecting 1500 proteins in yeast. As the interaction data is a complementary part of CYGD, interactive mapping of data on other integrated data types such as the functional classification catalog [A. Ruepp, A. Zollner, D. Maier, K. Albermann, J. Hani, M. Mokrejs, I. Tetko, U. Güldener, G. Mannhaupt, M. Münsterkötter and H. W. Mewes (2004) Nucleic Acids Res., 32, 5539–5545] is possible. A survey of signaling proteins and comparison with pathway data from KEGG demonstrates that based on these manually annotated data only an extensive overview of the complexity of this functional network can be obtained in yeast. The implementation of a web-based PPI-analysis tool allows analysis and visualization of protein interaction networks and facilitates integration of our curated data with high-throughput datasets. The complete dataset as well as user-defined sub-networks can be retrieved easily in the standardized PSI-MI format. The resource can be accessed through .

Application of Yeast DNA Microarray to Evaluate the Environmental Risk of Leachate and the Treated Leachate of Waste Disposal Site

Genomic responses to untreated and treated leachate from a waste disposal site were investigated using yeast DNA microarray. Differences were observed in both the number of genes induced and Munich Information Center for Protein Sequence (MIPS) functional categories after exposure to each of the landfill Leachate sample. Several of the genes related to homeostasis of metal ions were highly induced by exposure to leachate from the waste disposal site, while genes of unknown function were similarly induced by exposure to the treated leachate (treated using bacterial bed method and sedimentation with coagulation) . This results could be used to infer the nature of the substances in each of the landfill leachate sample. This study further demonstrated that DNA microarray analysis is a powerful technique for elucidating the toxicity of complex mixtures such as landfill leachate. We also demonstrated the potential application of DNA microarray analysis to the evaluation of the effectiveness and efficiency of certain waste treatment systems by characterizing gene expression profiles.

A Specific Role of the Yeast Mitochondrial Carriers Mrs3/4p in Mitochondrial Iron Acquisition under Iron-limiting Conditions

The yeast genes MRS3 and MRS4 encode two members of the mitochondrial carrier family with high sequence similarity. To elucidate their function we utilized genome-wide expression profiling and found that both deletion and overexpression of MRS3/4 lead to up-regulation of several genes of the "iron regulon." We therefore analyzed the two major iron-utilizing processes, heme formation and Fe/S protein biosynthesis in vivo, in organello (intact mitochondria), and in vitro (mitochondrial extracts). Radiolabeling of yeast cells with 55Fe revealed a clear correlation between MRS3/4 expression levels and the efficiency of these biosynthetic reactions indicating a role of the carriers in utilization and/or transport of iron in vivo. Similar effects on both heme formation and Fe/S protein biosynthesis were seen in organello using mitochondria isolated from cells grown under iron-limiting conditions. The correlation between MRS3/4 expression levels and the efficiency of the two iron-utilizing processes was lost upon detergent lysis of mitochondria. As no significant changes in the mitochondrial membrane potential were observed upon overexpression or deletion of MRS3/4, our results suggest that Mrs3/4p carriers are directly involved in mitochondrial iron uptake. Mrs3/4p function in mitochondrial iron transport becomes evident under iron-limiting conditions only, indicating that the two carriers do not represent the sole system for mitochondrial iron acquisition.