MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data.

BACKGROUND: Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows. RESULTS: Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study. CONCLUSIONS: MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/ . Users interested in analyzing their own data are encouraged to apply for an account.

A systems biology approach reveals major metabolic changes in the thermoacidophilic archaeon Sulfolobus solfataricus in response to the carbon source L-fucose versus D-glucose.

Archaea are characterised by a complex metabolism with many unique enzymes that differ from their bacterial and eukaryotic counterparts. The thermoacidophilic archaeon Sulfolobus solfataricus is known for its metabolic versatility and is able to utilize a great variety of different carbon sources. However, the underlying degradation pathways and their regulation are often unknown. In this work, the growth on different carbon sources was analysed, using an integrated systems biology approach. The comparison of growth on L-fucose and D-glucose allows first insights into the genome-wide changes in response to the two carbon sources and revealed a new pathway for L-fucose degradation in S. solfataricus. During growth on L-fucose major changes in the central carbon metabolic network, as well as an increased activity of the glyoxylate bypass and the 3-hydroxypropionate/4-hydroxybutyrate cycle were observed. Within the newly discovered pathway for L-fucose degradation the following key reactions were identified: (i) L-fucose oxidation to L-fuconate via a dehydrogenase, (ii) dehydration to 2-keto-3-deoxy-L-fuconate via dehydratase, (iii) 2-keto-3-deoxy-L-fuconate cleavage to pyruvate and L-lactaldehyde via aldolase and (iv) L-lactaldehyde conversion to L-lactate via aldehyde dehydrogenase. This pathway as well as L-fucose transport shows interesting overlaps to the D-arabinose pathway, representing another example for pathway promiscuity in Sulfolobus species.

Establishment of a CpG island microarray for analyses of genome-wide DNA methylation in Chinese hamster ovary cells.

Optimizing productivity and growth rates of recombinant Chinese hamster ovary (CHO) cells requires insight into the regulation of cellular processes. In this regard, the elucidation of the epigenetic process of DNA methylation, known to influence transcription by a differential occurrence in CpG islands in promoter regions, is increasingly gaining importance. However, DNA methylation has not yet been investigated on a genomic scale in CHO cells and suitable tools have not existed until now. Based on the genomic and transcriptomic CHO data currently available, we developed a customized oligonucleotide microarray covering 19598 CpG islands (89 % of total bioinformatically identified CpG islands) in the CHO genome. We applied our CHO-specific CpG island microarray to investigate the effect of butyrate treatment on differential DNA methylation in CHO cultures in a time-dependent approach. Supplementation of butyrate is known to enhance cell specific productivities in CHO cells and leads to alterations of epigenetic silencing events. Gene ontology clusters regarding, e.g., chromatin modification or DNA repair, were significantly overrepresented 24 h after butyrate addition. Functional classifications furthermore indicated that several major signaling systems such as the Wnt/ß-catenin pathway were affected by butyrate treatment. Our novel CHO-specific CpG island microarray will provide valuable information in future studies of cellular processes associated with productivity and product characteristics.

Protein turnover quantification in a multilabeling approach: from data calculation to evaluation.

Liquid chromatography coupled to tandem mass spectrometry in combination with stable-isotope labeling is an established and widely spread method to measure gene expression on the protein level. However, it is often not considered that two opposing processes are responsible for the amount of a protein in a cell--the synthesis as well as the degradation. With this work, we provide an integrative, high-throughput method--from the experimental setup to the bioinformatics analysis--to measure synthesis and degradation rates of an organism's proteome. Applicability of the approach is demonstrated with an investigation of heat shock response, a well-understood regulatory mechanism in bacteria, on the biotechnologically relevant Corynebacterium glutamicum. Utilizing a multilabeling approach using both heavy stable nitrogen as well as carbon isotopes cells are metabolically labeled in a pulse-chase experiment to trace the labels' incorporation in newly synthesized proteins and its loss during protein degradation. Our work aims not only at the calculation of protein turnover rates but also at their statistical evaluation, including variance and hierarchical cluster analysis using the rich internet application QuPE.

Proteome Turnover Analysis in Haloferax volcanii by a Heavy Isotope Multilabeling Approach.

The cellular protein repertoire is highly dynamic and responsive to internal or external stimuli. Its changes are largely the consequence of the combination of protein synthesis and degradation, referred collectively as protein turnover. Different proteomics techniques have been developed to determine the whole proteome turnover of a cell, but very few have been applied to archaea. In this chapter we describe a heavy isotope multilabeling method that allowed the successful analysis of relative protein synthesis and degradation rates on the proteome scale of the halophilic archaeon Haloferax volcanii. This method combines 15N and 13C isotope metabolic labeling with high-resolution mass spectrometry and data analysis tools (QuPE web-based platform) and could be applied to different archaea.

Functional Genomics Uncovers Pleiotropic Role of Rhomboids in Corynebacterium glutamicum.

The physiological role of ubiquitous rhomboid proteases, membrane-integral proteins that cleave their substrates inside the lipid bilayer, is still ill-defined in many prokaryotes. The two rhomboid genes cg0049 and cg2767 of Corynebacterium glutamicum were mutated and it was the aim of this study to investigate consequences in respect to growth phenotype, stress resistance, transcriptome, proteome, and lipidome composition. Albeit increased amount of Cg2767 upon heat stress, its absence did not change the growth behavior of C. glutamicum during exponential and stationary phase. Quantitative shotgun mass spectrometry was used to compare the rhomboid mutant with wild type strain and revealed that proteins covering diverse cellular functions were differentially abundant with more proteins affected in the stationary than in the exponential growth phase. An observation common to both growth phases was a decrease in ribosomal subunits and RNA polymerase, differences in iron uptake proteins, and abundance changes in lipid and mycolic acid biosynthesis enzymes that suggested a functional link of rhomboids to cell envelope lipid biosynthesis. The latter was substantiated by shotgun lipidomics in the stationary growth phase, where in a strain-dependent manner phosphatidylglycerol, phosphatidic acid, diacylglycerol and phosphatidylinositol increased irrespective of cultivation temperature.

Construction and evaluation of a whole genome microarray of Chlamydomonas reinhardtii.

BACKGROUND: Chlamydomonas reinhardtii is widely accepted as a model organism regarding photosynthesis, circadian rhythm, cell mobility, phototaxis, and biotechnology. The complete annotation of the genome allows transcriptomic studies, however a new microarray platform was needed. Based on the completed annotation of Chlamydomonas reinhardtii a new microarray on an Agilent platform was designed using an extended JGI 3.1 genome data set which included 15000 transcript models. RESULTS: In total 44000 probes were determined (3 independent probes per transcript model) covering 93% of the transcriptome. Alignment studies with the recently published AUGUSTUS 10.2 annotation confirmed 11000 transcript models resulting in a very good coverage of 70% of the transcriptome (17000). Following the estimation of 10000 predicted genes in Chlamydomonas reinhardtii our new microarray, nevertheless, covers the expected genome by 90-95%. CONCLUSIONS: To demonstrate the capabilities of the new microarray, we analyzed transcript levels for cultures grown under nitrogen as well as sulfate limitation, and compared the results with recently published microarray and RNA-seq data. We could thereby confirm previous results derived from data on nutrient-starvation induced gene expression of a group of genes related to protein transport and adaptation of the metabolism as well as genes related to efficient light harvesting, light energy distribution and photosynthetic electron transport.

A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study.

BACKGROUND: Mass spectrometry-based proteomics has reached a stage where it is possible to comprehensively analyze the whole proteome of a cell in one experiment. Here, the employment of stable isotopes has become a standard technique to yield relative abundance values of proteins. In recent times, more and more experiments are conducted that depict not only a static image of the up- or down-regulated proteins at a distinct time point but instead compare developmental stages of an organism or varying experimental conditions. RESULTS: Although the scientific questions behind these experiments are of course manifold, there are, nevertheless, two questions that commonly arise: 1) which proteins are differentially regulated regarding the selected experimental conditions, and 2) are there groups of proteins that show similar abundance ratios, indicating that they have a similar turnover? We give advice on how these two questions can be answered and comprehensively compare a variety of commonly applied computational methods and their outcomes. CONCLUSIONS: This work provides guidance through the jungle of computational methods to analyze mass spectrometry-based isotope-labeled datasets and recommends an effective and easy-to-use evaluation strategy. We demonstrate our approach with three recently published datasets on Bacillus subtilis 12 and Corynebacterium glutamicum 3. Special focus is placed on the application and validation of cluster analysis methods. All applied methods were implemented within the rich internet application QuPE 4. Results can be found at http://qupe.cebitec.uni-bielefeld.de.

A targeted transcriptomics approach for the determination of mixture effects of pesticides.

While real-life exposure occurs to complex chemical mixtures, toxicological risk assessment mostly focuses on individual compounds. There is an increasing demand for in vitro tools and strategies for mixture toxicity analysis. Based on a previously established set of hepatotoxicity marker genes, we analyzed mixture effects of non-cytotoxic concentrations of different pesticides in exposure-relevant binary mixtures in human HepaRG hepatocarcinoma cells using targeted transcriptomics. An approach for mixture analysis at the level of a complex endpoint such as a transcript pattern is presented, including mixture design based on relative transcriptomic potencies and similarities. From a mechanistic point of view, goal of the study was to evaluate combinations of chemicals with varying degrees of similarity in order to determine whether differences in mechanisms of action lead to different mixtures effects. Using a model deviation ratio-based approach for assessing mixture effects, it was revealed that most data points are consistent with the assumption of dose addition. A tendency for synergistic effects was only observed at high concentrations of some combinations of the test compounds azoxystrobin, cyproconazole, difenoconazole, propiconazole and thiacloprid, which may not be representative of human real-life exposure. In summary, the findings of our study suggest that, for the pesticide mixtures investigated, risk assessment based on the general assumption of dose addition can be considered sufficiently protective for consumers. The way of data analysis presented in this paper can pave the way for a more comprehensive use of multi-gene expression data in experimental studies related to mixture toxicity.

Qupe--a Rich Internet Application to take a step forward in the analysis of mass spectrometry-based quantitative proteomics experiments.

MOTIVATION: The goal of present -omics sciences is to understand biological systems as a whole in terms of interactions of the individual cellular components. One of the main building blocks in this field of study is proteomics where tandem mass spectrometry (LC-MS/MS) in combination with isotopic labelling techniques provides a common way to obtain a direct insight into regulation at the protein level. Methods to identify and quantify the peptides contained in a sample are well established, and their output usually results in lists of identified proteins and calculated relative abundance values. The next step is to move ahead from these abstract lists and apply statistical inference methods to compare measurements, to identify genes that are significantly up- or down-regulated, or to detect clusters of proteins with similar expression profiles. RESULTS: We introduce the Rich Internet Application (RIA) Qupe providing comprehensive data management and analysis functions for LC-MS/MS experiments. Starting with the import of mass spectra data the system guides the experimenter through the process of protein identification by database search, the calculation of protein abundance ratios, and in particular, the statistical evaluation of the quantification results including multivariate analysis methods such as analysis of variance or hierarchical cluster analysis. While a data model to store these results has been developed, a well-defined programming interface facilitates the integration of novel approaches. A compute cluster is utilized to distribute computationally intensive calculations, and a web service allows to interchange information with other -omics software applications. To demonstrate that Qupe represents a step forward in quantitative proteomics analysis an application study on Corynebacterium glutamicum has been carried out. AVAILABILITY AND IMPLEMENTATION: Qupe is implemented in Java utilizing Hibernate, Echo2, R and the Spring framework. We encourage the usage of the RIA in the sense of the 'software as a service' concept, maintained on our servers and accessible at the following location: http://qupe.cebitec.uni-bielefeld.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RNA-protein correlation of liver toxicity markers in HepaRG cells.

The liver is a main target organ for the toxicity of many different compounds. While in general, in vivo testing is still routinely used for assessing the hepatotoxic potential of test chemicals, the use of in vitro models offers advantages with regard to throughput, consumption of resources, and animal welfare aspects. Using the human hepatoma cell line HepaRG, we performed a comparative evaluation of a panel of hepatotoxicity marker mRNAs and proteins after exposure of the cells to 30 different pesticidal active compounds comprising herbizides, fungicides, insecticides, and others. The panel of hepatotoxicity markers included nuclear receptor target genes, key players of fatty acid and bile acid metabolism-related pathways, as well as recently identified biomarkers of drug-induced liver injury. Moreover, marker genes and proteins were identified, for example, S100P, ANXA10, CYP1A1, and CYP7A1. These markers respond with high sensitivity to stimulation with chemically diverse test compounds already at non-cytotoxic concentrations. The potency of the test compounds, determined as an overall parameter of their ability to deregulate marker expression in vitro, was very similar between the mRNA and protein levels. Thus, this study does not only characterize the response of human liver cells to 30 different pesticides but also demonstrates that hepatotoxicity testing in human HepaRG cells yields well comparable results at the mRNA and protein levels. Furthermore, robust hepatotoxicity marker genes and proteins were identified in HepaRG cells.

Transcript and protein marker patterns for the identification of steatotic compounds in human HepaRG cells.

Non-alcoholic fatty liver disease is a major health concern especially in Western countries. Animal studies suggest that certain chemicals may contribute to hepatocellular triglyceride accumulation, among them a number of hepatotoxic pesticidal active compounds. In order to improve the identification of potential liver steatosis inducers in vitro in a human cell culture system, HepaRG cells were treated with a selection of 30 steatotic or non-steatotic pesticides. Induction of triglyceride accumulation was monitored, and changes in the expression of hepatotoxicity marker genes were measured at the mRNA and protein levels. Based on these data, transcript and protein marker signatures predictive of triglyceride accumulation in HepaRG cells were derived. The predictive transcript set consisted of POR, ANXA10, ARG1, CCL20, FASN, INSIG1, SREBF1, CD36, CYP2D6, and SLCO1B1. The predictive protein set consisted of NCPR (POR), CYP2E1, CYP1A1, ALDH3A1, UGT2B7, UGT2B15, S100P, LMNA, and PRKDC. In conclusion, the present study presents for the first time transcript and protein marker patterns to separate steatotic from non-steatotic compounds in a human liver cell line.

Distinct Myocardial Transcriptomic Profiles of Cardiomyopathies Stratified by the Mutant Genes.

Cardiovascular diseases are the number one cause of morbidity and mortality worldwide, but the underlying molecular mechanisms remain not well understood. Cardiomyopathies are primary diseases of the heart muscle and contribute to high rates of heart failure and sudden cardiac deaths. Here, we distinguished four different genetic cardiomyopathies based on gene expression signatures. In this study, RNA-Sequencing was used to identify gene expression signatures in myocardial tissue of cardiomyopathy patients in comparison to non-failing human hearts. Therefore, expression differences between patients with specific affected genes, namely LMNA (lamin A/C), RBM20 (RNA binding motif protein 20), TTN (titin) and PKP2 (plakophilin 2) were investigated. We identified genotype-specific differences in regulated pathways, Gene Ontology (GO) terms as well as gene groups like secreted or regulatory proteins and potential candidate drug targets revealing specific molecular pathomechanisms for the four subtypes of genetic cardiomyopathies. Some regulated pathways are common between patients with mutations in RBM20 and TTN as the splice factor RBM20 targets amongst other genes TTN, leading to a similar response on pathway level, even though many differentially expressed genes (DEGs) still differ between both sample types. The myocardium of patients with mutations in LMNA is widely associated with upregulated genes/pathways involved in immune response, whereas mutations in PKP2 lead to a downregulation of genes of the extracellular matrix. Our results contribute to further understanding of the underlying molecular pathomechanisms aiming for novel and better treatment of genetic cardiomyopathies.

EDGAR: a software framework for the comparative analysis of prokaryotic genomes.

BACKGROUND: The introduction of next generation sequencing approaches has caused a rapid increase in the number of completely sequenced genomes. As one result of this development, it is now feasible to analyze large groups of related genomes in a comparative approach. A main task in comparative genomics is the identification of orthologous genes in different genomes and the classification of genes as core genes or singletons. RESULTS: To support these studies EDGAR - "Efficient Database framework for comparative Genome Analyses using BLAST score Ratios" - was developed. EDGAR is designed to automatically perform genome comparisons in a high throughput approach. Comparative analyses for 582 genomes across 75 genus groups taken from the NCBI genomes database were conducted with the software and the results were integrated into an underlying database. To demonstrate a specific application case, we analyzed ten genomes of the bacterial genus Xanthomonas, for which phylogenetic studies were awkward due to divergent taxonomic systems. The resultant phylogeny EDGAR provided was consistent with outcomes from traditional approaches performed recently and moreover, it was possible to root each strain with unprecedented accuracy. CONCLUSION: EDGAR provides novel analysis features and significantly simplifies the comparative analysis of related genomes. The software supports a quick survey of evolutionary relationships and simplifies the process of obtaining new biological insights into the differential gene content of kindred genomes. Visualization features, like synteny plots or Venn diagrams, are offered to the scientific community through a web-based and therefore platform independent user interface http://edgar.cebitec.uni-bielefeld.de, where the precomputed data sets can be browsed.

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data.

BACKGROUND: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. RESULTS: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. CONCLUSION: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

MeltDB: a software platform for the analysis and integration of metabolomics experiment data.

MOTIVATION: The recent advances in metabolomics have created the potential to measure the levels of hundreds of metabolites which are the end products of cellular regulatory processes. The automation of the sample acquisition and subsequent analysis in high-throughput instruments that are capable of measuring metabolites is posing a challenge on the necessary systematic storage and computational processing of the experimental datasets. Whereas a multitude of specialized software systems for individual instruments and preprocessing methods exists, there is clearly a need for a free and platform-independent system that allows the standardized and integrated storage and analysis of data obtained from metabolomics experiments. Currently there exists no such system that on the one hand supports preprocessing of raw datasets but also allows to visualize and integrate the results of higher level statistical analyses within a functional genomics context. RESULTS: To facilitate the systematic storage, analysis and integration of metabolomics experiments, we have implemented MeltDB, a web-based software platform for the analysis and annotation of datasets from metabolomics experiments. MeltDB supports open file formats (netCDF, mzXML, mzDATA) and facilitates the integration and evaluation of existing preprocessing methods. The system provides researchers with means to consistently describe and store their experimental datasets. Comprehensive analysis and visualization features of metabolomics datasets are offered to the community through a web-based user interface. The system covers the process from raw data to the visualization of results in a knowledge-based background and is integrated into the context of existing software platforms of genomics and transcriptomics at Bielefeld University. We demonstrate the potential of MeltDB by means of a sample experiment where we dissect the influence of three different carbon sources on the gram-negative bacterium Xanthomonas campestris pv. campestris on the level of measured metabolites. Experimental data are stored, analyzed and annotated within MeltDB and accessible via the public MeltDB web server. AVAILABILITY: The system is publicly available at http://meltdb.cebitec.uni-bielefeld.de.

Flexible metagenome analysis using the MGX framework.

BACKGROUND: The characterization of microbial communities based on sequencing and analysis of their genetic information has become a popular approach also referred to as metagenomics; in particular, the recent advances in sequencing technologies have enabled researchers to study even the most complex communities. Metagenome analysis, the assignment of sequences to taxonomic and functional entities, however, remains a tedious task: large amounts of data need to be processed. There are a number of approaches addressing particular aspects, but scientific questions are often too specific to be answered by a general-purpose method. RESULTS: We present MGX, a flexible and extensible client/server-framework for the management and analysis of metagenomic datasets; MGX features a comprehensive set of adaptable workflows required for taxonomic and functional metagenome analysis, combined with an intuitive and easy-to-use graphical user interface offering customizable result visualizations. At the same time, MGX allows to include own data sources and devise custom analysis pipelines, thus enabling researchers to perform basic as well as highly specific analyses within a single application. CONCLUSIONS: With MGX, we provide a novel metagenome analysis platform giving researchers access to the most recent analysis tools. MGX covers taxonomic and functional metagenome analysis, statistical evaluation, and a wide range of visualizations easing data interpretation. Its default taxonomic classification pipeline provides equivalent or superior results in comparison to existing tools.

An sRNA and Cold Shock Protein Homolog-Based Feedforward Loop Post-transcriptionally Controls Cell Cycle Master Regulator CtrA.

Adjustment of cell cycle progression is crucial for bacterial survival and adaptation under adverse conditions. However, the understanding of modulation of cell cycle control in response to environmental changes is rather incomplete. In α-proteobacteria, the broadly conserved cell cycle master regulator CtrA underlies multiple levels of control, including coupling of cell cycle and cell differentiation. CtrA levels are known to be tightly controlled through diverse transcriptional and post-translational mechanisms. Here, small RNA (sRNA)-mediated post-transcriptional regulation is uncovered as an additional level of CtrA fine-tuning. Computational predictions as well as transcriptome and proteome studies consistently suggested targeting of ctrA and the putative cold shock chaperone cspA5 mRNAs by the trans-encoded sRNA (trans-sRNA) GspR (formerly SmelC775) in several Sinorhizobium species. GspR strongly accumulated in the stationary growth phase, especially in minimal medium (MM) cultures. Lack of the gspR locus confers a fitness disadvantage in competition with the wild type, while its overproduction hampers cell growth, suggesting that this riboregulator interferes with cell cycle progression. An eGFP-based reporter in vivo assay, involving wild-type and mutant sRNA and mRNA pairs, experimentally confirmed GspR-dependent post-transcriptional down-regulation of ctrA and cspA5 expression, which most likely occurs through base-pairing to the respective mRNA. The energetically favored secondary structure of GspR is predicted to comprise three stem-loop domains, with stem-loop 1 and stem-loop 3 targeting ctrA and cspA5 mRNA, respectively. Moreover, this work reports evidence for post-transcriptional control of ctrA by CspA5. Thus, this regulation and GspR-mediated post-transcriptional repression of ctrA and cspA5 expression constitute a coherent feed-forward loop, which may enhance the negative effect of GspR on CtrA levels. This novel regulatory circuit involving the riboregulator GspR, CtrA, and a cold shock chaperone may contribute to fine-tuning of ctrA expression.

Early Response of Sulfolobus acidocaldarius to Nutrient Limitation.

In natural environments microorganisms encounter extreme changes in temperature, pH, osmolarities and nutrient availability. The stress response of many bacterial species has been described in detail, however, knowledge in Archaea is limited. Here, we describe the cellular response triggered by nutrient limitation in the thermoacidophilic crenarchaeon Sulfolobus acidocaldarius. We measured changes in gene transcription and protein abundance upon nutrient depletion up to 4 h after initiation of nutrient depletion. Transcript levels of 1118 of 2223 protein coding genes and abundance of approximately 500 proteins with functions in almost all cellular processes were affected by nutrient depletion. Our study reveals a significant rerouting of the metabolism with respect to degradation of internal as well as extracellular-bound organic carbon and degradation of proteins. Moreover, changes in membrane lipid composition were observed in order to access alternative sources of energy and to maintain pH homeostasis. At transcript level, the cellular response to nutrient depletion in S. acidocaldarius seems to be controlled by the general transcription factors TFB2 and TFEß. In addition, ribosome biogenesis is reduced, while an increased protein degradation is accompanied with a loss of protein quality control. This study provides first insights into the early cellular response of Sulfolobus to organic carbon and organic nitrogen depletion.

Proteomics of FACS-sorted heterogeneous Corynebacterium glutamicum populations.

The metabolic status of individual cells in microbial cultures can differ, being relevant for biotechnology, environmental and medical microbiology. However, it is hardly understood in molecular detail due to limitations of current analytical tools. Here, we demonstrate that FACS in combination with proteomics can be used to sort and analyze cell populations based on their metabolic state. A previously established GFP reporter system was used to detect and sort single Corynebacterium glutamicum cells based on the concentration of branched chain amino acids (BCAA) using FACS. A proteomics workflow optimized for small cell numbers was used to quantitatively compare proteomes of a ΔaceE mutant, lacking functional pyruvate dehydrogenase (PD), and the wild type. About 800 proteins could be quantified from 1,000,000 cells. In the ΔaceE mutant BCAA production was coordinated with upregulation of the glyoxylate cycle and TCA cycle to counter the lack of acetyl CoA resulting from the deletion of aceE. BIOLOGICAL SIGNIFICANCE: Metabolic pathways in C. glutamicum WT and ΔaceE, devoid of functional pyruvate dehydrogenase, were compared to understand proteome changes that contribute to the high production of branched chain amino acids (BCAA) in the ΔaceE strain. The data complements previous metabolome studies and corroborates the role of malate provided by the glyoxylate cycle and increased activity of glycolysis and pyruvate carboxylase reaction to replenish the TCA cycle. A slight increase in acetohydroxyacid synthase (ILV subunit B) substantiates the previously reported increased pyruvate pool in C. glutamicumΔaceE, and the benefit of additional ilv gene cluster overexpression for BCAA production.