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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Bioinformatics for NGS-based metagenomics and the application to biogas research.

Metagenomics has proven to be one of the most important research fields for microbial ecology during the last decade. Starting from 16S rRNA marker gene analysis for the characterization of community compositions to whole metagenome shotgun sequencing which additionally allows for functional analysis, metagenomics has been applied in a wide spectrum of research areas. The cost reduction paired with the increase in the amount of data due to the advent of next-generation sequencing led to a rapidly growing demand for bioinformatic software in metagenomics. By now, a large number of tools that can be used to analyze metagenomic datasets has been developed. The Bielefeld-Gießen center for microbial bioinformatics as part of the German Network for Bioinformatics Infrastructure bundles and imparts expert knowledge in the analysis of metagenomic datasets, especially in research on microbial communities involved in anaerobic digestion residing in biogas reactors. In this review, we give an overview of the field of metagenomics, introduce into important bioinformatic tools and possible workflows, accompanied by application examples of biogas surveys successfully conducted at the Center for Biotechnology of Bielefeld University.

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.

Complete Genome Sequence of the Barley Pathogen Xanthomonas translucens pv. translucens DSM 18974T (ATCC 19319T).

We report here the complete 4.7-Mb genome sequence of Xanthomonas translucens pv. translucens DSM 18974T, which causes black chaff disease on barley (Hordeum vulgare). Genome data of this X. translucens type strain will improve our understanding of this bacterial species.

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.

An integrated metagenome and -proteome analysis of the microbial community residing in a biogas production plant.

To study the metaproteome of a biogas-producing microbial community, fermentation samples were taken from an agricultural biogas plant for microbial cell and protein extraction and corresponding metagenome analyses. Based on metagenome sequence data, taxonomic community profiling was performed to elucidate the composition of bacterial and archaeal sub-communities. The community's cytosolic metaproteome was represented in a 2D-PAGE approach. Metaproteome databases for protein identification were compiled based on the assembled metagenome sequence dataset for the biogas plant analyzed and non-corresponding biogas metagenomes. Protein identification results revealed that the corresponding biogas protein database facilitated the highest identification rate followed by other biogas-specific databases, whereas common public databases yielded insufficient identification rates. Proteins of the biogas microbiome identified as highly abundant were assigned to the pathways involved in methanogenesis, transport and carbon metabolism. Moreover, the integrated metagenome/-proteome approach enabled the examination of genetic-context information for genes encoding identified proteins by studying neighboring genes on the corresponding contig. Exemplarily, this approach led to the identification of a Methanoculleus sp. contig encoding 16 methanogenesis-related gene products, three of which were also detected as abundant proteins within the community's metaproteome. Thus, metagenome contigs provide additional information on the genetic environment of identified abundant proteins.

Omics Fusion - A Platform for Integrative Analysis of Omics Data.

We present Omics Fusion, a new web-based platform for integrative analysis of omics data. Omics Fusion provides a collection of new and established tools and visualization methods to support researchers in exploring omics data, validating results or understanding how to adjust experiments in order to make new discoveries. It is easily extendible and new visualization methods are added continuously. It is available for free under: https://fusion.cebitec.uni-bielefeld.de/.

Learning to Classify Organic and Conventional Wheat - A Machine Learning Driven Approach Using the MeltDB 2.0 Metabolomics Analysis Platform.

We present results of our machine learning approach to the problem of classifying GC-MS data originating from wheat grains of different farming systems. The aim is to investigate the potential of learning algorithms to classify GC-MS data to be either from conventionally grown or from organically grown samples and considering different cultivars. The motivation of our work is rather obvious nowadays: increased demand for organic food in post-industrialized societies and the necessity to prove organic food authenticity. The background of our data set is given by up to 11 wheat cultivars that have been cultivated in both farming systems, organic and conventional, throughout 3 years. More than 300 GC-MS measurements were recorded and subsequently processed and analyzed in the MeltDB 2.0 metabolomics analysis platform, being briefly outlined in this paper. We further describe how unsupervised (t-SNE, PCA) and supervised (SVM) methods can be applied for sample visualization and classification. Our results clearly show that years have most and wheat cultivars have second-most influence on the metabolic composition of a sample. We can also show that for a given year and cultivar, organic and conventional cultivation can be distinguished by machine-learning algorithms.

2D-DIGE screening of high-productive CHO cells under glucose limitation--basic changes in the proteome equipment and hints for epigenetic effects.

CHO derivates (Chinese hamster ovary) belong to the most important mammalian cells for industrial recombinant protein production. Many efforts have been made to improve productivity and stability of CHO cells in bioreactor processes. Here, we followed up one barely understood phenomenon observed with process optimizations: a significantly increased cell-specific productivity in late phases of glucose-limited perfusion cultivations, when glucose (and lactate) reserves are exhausted. Our aim was to elucidate the cellular activities connected to the metabolic shift from glucose surplus to glucose limitation phase. With 2D-DIGE, we compared three stages in a perfusion culture of CHO cells: the initial growth with high glucose concentration and low lactate production, the second phase with glucose going to limitation and high lactate level, and finally the state of glucose limitation and also low lactate concentration but increased cell-specific productivity. With our proteomic approach we were able to demonstrate consequences of glucose limitation for the protein expression machinery which also could play a role for a higher recombinant protein production. Most interestingly, we detected epigenetic effects on the level of proteins involved in histone modification (HDAC1/-2, SET, RBBP7, DDX5). Together with shifts in the protein inventory of energy metabolism, cytoskeleton and protein expression, a picture emerges of basic changes in the cellular equipment under long-term glucose limitation of CHO cells.

ALLocator: an interactive web platform for the analysis of metabolomic LC-ESI-MS datasets, enabling semi-automated, user-revised compound annotation and mass isotopomer ratio analysis.

Adduct formation, fragmentation events and matrix effects impose special challenges to the identification and quantitation of metabolites in LC-ESI-MS datasets. An important step in compound identification is the deconvolution of mass signals. During this processing step, peaks representing adducts, fragments, and isotopologues of the same analyte are allocated to a distinct group, in order to separate peaks from coeluting compounds. From these peak groups, neutral masses and pseudo spectra are derived and used for metabolite identification via mass decomposition and database matching. Quantitation of metabolites is hampered by matrix effects and nonlinear responses in LC-ESI-MS measurements. A common approach to correct for these effects is the addition of a U-13C-labeled internal standard and the calculation of mass isotopomer ratios for each metabolite. Here we present a new web-platform for the analysis of LC-ESI-MS experiments. ALLocator covers the workflow from raw data processing to metabolite identification and mass isotopomer ratio analysis. The integrated processing pipeline for spectra deconvolution "ALLocatorSD" generates pseudo spectra and automatically identifies peaks emerging from the U-13C-labeled internal standard. Information from the latter improves mass decomposition and annotation of neutral losses. ALLocator provides an interactive and dynamic interface to explore and enhance the results in depth. Pseudo spectra of identified metabolites can be stored in user- and method-specific reference lists that can be applied on succeeding datasets. The potential of the software is exemplified in an experiment, in which abundance fold-changes of metabolites of the l-arginine biosynthesis in C. glutamicum type strain ATCC 13032 and l-arginine producing strain ATCC 21831 are compared. Furthermore, the capability for detection and annotation of uncommon large neutral losses is shown by the identification of (γ-)glutamyl dipeptides in the same strains. ALLocator is available online at: https://allocator.cebitec.uni-bielefeld.de. A login is required, but freely available.

Transcriptome analyses of CHO cells with the next-generation microarray CHO41K: development and validation by analysing the influence of the growth stimulating substance IGF-1 substitute LongR(3.).

The increasing importance of Chinese hamster ovary (CHO) cells for the production of pharmaceutical proteins has awakened the demand to understand the cellular metabolism of these cells. However, satisfactory gene expression studies have yet been impractical due to insufficient coverage of sequences. In this work, previously determined sequence information of CHO cells and newly derived data from 454 and Illumina sequencing was used to establish the CHO41K microarray which contains 41,304 probes. Self-hybridisation was performed for replica determination and samples were run in triplicates to increase statistical power. For determination of technical variance, confidence intervals at an M-value of ±0.6 for 95% and at ±2.3 for 99% of the probes were calculated. Intra-microarray and slide to slide variance was not detectable. In a first application, this microarray enabled an in-depth look inside the cellular transcriptome of CHO cells cultured in the presence or absence of the growth supporting substance "insulin like growth factor 1" (IGF-1) analogue LongR(3). Its effect on the cells ranged from enhanced growth to delay of cell death as well as cytoskeletal installation. Suggesting that under supplementation, a minimised cellular effort in installation of a large cytoskeleton occurs, possibly in favour of promoting faster cell division.