Identification of Genes Involved in Cytochrome c Biogenesis in Shewanella oneidensis, Using a Modified mariner Transposon

A modified mariner transposon, miniHimar RB1, was generated to mutagenize cells of the metal-reducing bacterium Shewanella oneidensis. The use of this transposon led to the isolation of stable mutants and allowed rapid identification of disrupted genes. Fifty-eight mutants, including BG104 and BG148 with transposon insertions in the cytochrome c maturation genes ccmC and ccmF1, respectively, were analyzed. Both mutants were deficient in anaerobic respiration and cytochrome c production.     

Reannotation of Shewanella oneidensis genome

As more and more complete bacterial genome sequences become available, the genome annotation of previously sequenced genomes may become quickly outdated. This is primarily due to the discovery and functional characterization of new genes. We have reannotated the recently published genome of Shewanella oneidensis with the following results: 51 new genes have been identified, and functional annotation has been added to the 97 genes, including 15 new and 82 existing ones with previously unassigned function. The identification of new genes was achieved by predicting the protein coding regions using the HMM-based program GeneMark.hmm. Subsequent comparison of the predicted gene products to the non-redundant protein database using BLAST and the COG (Clusters of Orthologous Groups) database using COGNITOR provided for the functional annotation.     

Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM

The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms.     

Validation of Shewanella oneidensis MR-1 small proteins by AMT tag-based proteome analysis

Using stringent criteria for protein identification by accurate mass and time (AMT) tag mass spectrometric methodology, we detected 36 proteins of <101 amino acids in length, including 10 that were annotated as hypothetical proteins, in 172 global tryptic digests of Shewanella oneidensis MR-1 proteins. Peptides that map to the conserved, but functionally uncharacterized proteins SO4134 and SO2787, were the most frequently detected peptides in these samples, while those that map to hypotheticals SO2669 and SO2063, conserved hypotheticals SO0335 and SO2176, and the SlyX protein (SO1063) were observed at frequencies similar to those from essential small proteins (ribosomal proteins and translation initiation factor IF-1), suggesting that they may function in similarly important cellular functions. In addition, peptides were detected that map to 30 genes predicted to encode frameshifts, point mutations, or recoding signals. Of these 30 genes, peptides that map to positions beyond internal stop codons were detected in 13 genes (SO0101, SO0419, SO0590, SO0738, SO1113, SO1211, SO3079, SO3130, SO3240, SO4231, SO4328, SO4422, and SO4657). While expression of the full-length formate dehydrogenase encoded by SO0101 can be explained by incorporation of selenocysteine at the internal stop codon, the mechanism of translating downstream sequences in the remaining genes remains unknown.     

Spatiometabolic stratification of Shewanella oneidensis biofilms

Biofilms, or surface-attached microbial communities, are both ubiquitous and resilient in the environment. Although much is known about how biofilms form, develop, and detach, very little is understood about how these events are related to metabolism and its dynamics. It is commonly thought that large subpopulations of cells within biofilms are not actively producing proteins or generating energy and are therefore dead. An alternative hypothesis is that within the growth-inactive domains of biofilms, significant populations of living cells persist and retain the capacity to dynamically regulate their metabolism. To test this, we employed unstable fluorescent reporters to measure growth activity and protein synthesis in vivo over the course of biofilm development and created a quantitative routine to compare domains of activity in independently grown biofilms. Here we report that Shewanella oneidensis biofilm structures reproducibly stratify with respect to growth activity and metabolism as a function of size. Within domains of growth-inactive cells, genes typically upregulated under anaerobic conditions are expressed well after growth has ceased. These findings reveal that, far from being dead, the majority of cells in mature S. oneidensis biofilms have actively turned-on metabolic programs appropriate to their local microenvironment and developmental stage.     

Spatiometabolic Stratification of Shewanella oneidensis Biofilms

Biofilms, or surface-attached microbial communities, are both ubiquitous and resilient in the environment. Although much is known about how biofilms form, develop, and detach, very little is understood about how these events are related to metabolism and its dynamics. It is commonly thought that large subpopulations of cells within biofilms are not actively producing proteins or generating energy and are therefore dead. An alternative hypothesis is that within the growth-inactive domains of biofilms, significant populations of living cells persist and retain the capacity to dynamically regulate their metabolism. To test this, we employed unstable fluorescent reporters to measure growth activity and protein synthesis in vivo over the course of biofilm development and created a quantitative routine to compare domains of activity in independently grown biofilms. Here we report that Shewanella oneidensis biofilm structures reproducibly stratify with respect to growth activity and metabolism as a function of size. Within domains of growth-inactive cells, genes typically upregulated under anaerobic conditions are expressed well after growth has ceased. These findings reveal that, far from being dead, the majority of cells in mature S. oneidensis biofilms have actively turned-on metabolic programs appropriate to their local microenvironment and developmental stage.     

Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties.     

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.Rational and quantitative assessment of metabolic changes in response to genetic modification (GM) is an open question and in need of innovative solutions. Nontargeted metabolite profiling can detect thousands of compounds, but it is not easy to understand the significance of the changed metabolites in the biochemical and biological context of the organism. To better assess the changes in metabolites from nontargeted metabolomics studies, it is important to examine the changed metabolites in the context of the genome-scale metabolic network of the organism.Metabolomics is a technique that aims to quantify all the metabolites in a biological system (Nikolau and Wurtele, 2007; Nicholson and Lindon, 2008; Roessner and Bowne, 2009). It has been used widely in studies ranging from disease diagnosis (Holmes et al., 2008; DeBerardinis and Thompson, 2012) and drug discovery (Cascante et al., 2002; Kell, 2006) to metabolic reconstruction (Feist et al., 2009; Kim et al., 2012) and metabolic engineering (Keasling, 2010; Lee et al., 2011). Metabolomic studies have demonstrated the possibility of identifying gene functions from changes in the relative concentrations of metabolites (metabotypes or metabolic signatures; Ebbels et al., 2004) in various species including yeast (Saccharomyces cerevisiae; Raamsdonk et al., 2001; Allen et al., 2003), Arabidopsis (Arabidopsis thaliana; Brotman et al., 2011), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Zea mays; Riedelsheimer et al., 2012). Metabolomics has also been used to better understand how plants interact with their environments (Field and Lake, 2011), including their responses to biotic and abiotic stresses (Dixon et al., 2006; Arbona et al., 2013), and to predict important agronomic traits (Riedelsheimer et al., 2012). Metabolite profiling has been performed on many plant species, including angiosperms such as Arabidopsis, poplar (Populus trichocarpa), and Catharanthus roseus (Sumner et al., 2003; Rischer et al., 2006), basal land plants such as Selaginella moellendorffii and Physcomitrella patens (Erxleben et al., 2012; Yobi et al., 2012), and Chlamydomonas reinhardtii (Fernie et al., 2012; Davis et al., 2013). With the availability of whole genome sequences of various species, metabolomics has the potential to become a useful tool for elucidating the functions of genes using large-scale systematic analyses (Fiehn et al., 2000; Saito and Matsuda, 2010; Hur et al., 2013).Although metabolomics data have the potential for identifying the roles of genes that are associated with metabolic phenotypes, the biochemical mechanisms that link functions of genes with metabolic phenotypes are still poorly characterized. For example, we do not yet know the principles behind how perturbing the expression of a single gene changes the metabolic system as a whole. Large-scale metabolomics data have provided useful resources for linking phenotypes to genotypes (Fiehn et al., 2000; Roessner et al., 2001; Tikunov et al., 2005; Schauer et al., 2006; Lu et al., 2011; Fukushima et al., 2014). For example, Lu et al. (2011) compared morphological and metabolic phenotypes from more than 5,000 Arabidopsis chloroplast mutants using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS). Fukushima et al. (2014) generated metabolite profiles from various characterized and uncharacterized mutant plants and clustered the mutants with similar metabolic phenotypes by conducting multidimensional scaling with quantified metabolic phenotypes. Nonetheless, representation and analysis of such a large amount of data remains a challenge for scientific discovery (Lu et al., 2011). In addition, these studies do not examine the topological and functional characteristics of metabolic changes in the context of a genome-scale metabolic network. To understand the relationship between genotype and metabolic phenotype, we need to investigate the metabolic changes caused by perturbing the expression of a gene in a genome-scale metabolic network perspective, because metabolic pathways are not independent biochemical factories but are components of a complex network (Berg et al., 2002; Merico et al., 2009).Much progress has been made in the last 2 decades to represent metabolism at a genome scale (Terzer et al., 2009). The advances in genome sequencing and emerging fields such as biocuration and bioinformatics enabled the representation of genome-scale metabolic network reconstructions for model organisms (Bassel et al., 2012). Genome-scale metabolic models have been built and applied broadly from microbes to plants. The first step toward modeling a genome-scale metabolism in a plant species started with developing a genome-scale metabolic pathway database for Arabidopsis (AraCyc; Mueller et al., 2003) from reference pathway databases (Kanehisa and Goto, 2000; Karp et al., 2002; Zhang et al., 2010). Genome-scale metabolic pathway databases have been built for several plant species (Mueller et al., 2005; Zhang et al., 2005, 2010; Urbanczyk-Wochniak and Sumner, 2007; May et al., 2009; Dharmawardhana et al., 2013; Monaco et al., 2013, 2014; Van Moerkercke et al., 2013; Chae et al., 2014; Jung et al., 2014). Efforts have been made to develop predictive genome-scale metabolic models using enzyme kinetics and stoichiometric flux-balance approaches (Sweetlove et al., 2008). de Oliveira Dal’Molin et al. (2010) developed a genome-scale metabolic model for Arabidopsis and successfully validated the model by predicting the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. Other genome-scale models have been developed for Arabidopsis (Poolman et al., 2009; Radrich et al., 2010; Mintz-Oron et al., 2012), C. reinhardtii (Chang et al., 2011; Dal’Molin et al., 2011), maize (Dal’Molin et al., 2010; Saha et al., 2011), sorghum (Sorghum bicolor; Dal’Molin et al., 2010), and sugarcane (Saccharum officinarum; Dal’Molin et al., 2010). These predictive models have the potential to be applied broadly in fields such as metabolic engineering, drug target discovery, identification of gene function, study of evolutionary processes, risk assessment of genetically modified crops, and interpretations of mutant phenotypes (Feist and Palsson, 2008; Ricroch et al., 2011).Here, we interrogate the metabotypes caused by 136 single gene perturbations of Arabidopsis by analyzing the relative concentration changes of 1,348 chemically identified metabolites using a reconstructed genome-scale metabolic network. We examine the characteristics of the changed metabolites (the metabolites whose relative concentrations were significantly different in mutants relative to the wild type) in the metabolic network to uncover biological and topological consequences of the perturbed genes.     

Expression in periplasmic space of Shewanella oneidensis

A Shewanella expression system has been used for an overproduction of c-type multiheme proteins. The proteins were exported to the periplasmic space for the maturation. Since the periplasmic expression system is attractive, especially for protease-sensitive proteins, an expression vector containing a signal peptide was constructed for expressions in the periplasmic space of Shewanella oneidensis. To evaluate the system, two eukaryotic proteins which originally do not have signal sequences and are difficult to express in Escherichia coli, were selected. The first is human cytochrome c. Properties of the recombinant cytochrome c were identical to those previously reported, indicating the protein is intact. The other was potato calcium-dependent protein kinase. The protein was expressed in periplasmic space. These results indicated that the system is generally applicable for any protein expression including c-type cytochromes, protease-sensitive proteins and those with multi-disulfide bonds because of transportation to the periplasmic space.     

Low-Temperature Growth of Shewanella oneidensis MR-1

Shewanella oneidensis MR-1 is a mesophilic bacterium with a maximum growth temperature of ≈35°C but the ability to grow over a wide range of temperatures, including temperatures near zero. At room temperature (≈22°C) MR-1 grows with a doubling time of about 40 min, but when moved from 22°C to 3°C, MR-1 cells display a very long lag phase of more than 100 h followed by very slow growth, with a doubling time of ≈67 h. In comparison to cells grown at 22°C, the cold-grown cells formed long, motile filaments, showed many spheroplast-like structures, produced an array of proteins not seen at higher temperature, and synthesized a different pattern of cellular lipids. Frequent pilus-like structures were observed during the transition from 3 to 22°C.     

Low-temperature growth of Shewanella oneidensis MR-1

Shewanella oneidensis MR-1 is a mesophilic bacterium with a maximum growth temperature of approximately 35 degrees C but the ability to grow over a wide range of temperatures, including temperatures near zero. At room temperature ( approximately 22 degrees C) MR-1 grows with a doubling time of about 40 min, but when moved from 22 degrees C to 3 degrees C, MR-1 cells display a very long lag phase of more than 100 h followed by very slow growth, with a doubling time of approximately 67 h. In comparison to cells grown at 22 degrees C, the cold-grown cells formed long, motile filaments, showed many spheroplast-like structures, produced an array of proteins not seen at higher temperature, and synthesized a different pattern of cellular lipids. Frequent pilus-like structures were observed during the transition from 3 to 22 degrees C.     

Genome-Scale Validation of Deep-Sequencing Libraries

Chromatin immunoprecipitation followed by high-throughput (HTP) sequencing (ChIP-seq) is a powerful tool to establish protein-DNA interactions genome-wide. The primary limitation of its broad application at present is the often-limited access to sequencers. Here we report a protocol, Mab-seq, that generates genome-scale quality evaluations for nucleic acid libraries intended for deep-sequencing. We show how commercially available genomic microarrays can be used to maximize the efficiency of library creation and quickly generate reliable preliminary data on a chromosomal scale in advance of deep sequencing. We also exploit this technique to compare enriched regions identified using microarrays with those identified by sequencing, demonstrating that they agree on a core set of clearly identified enriched regions, while characterizing the additional enriched regions identifiable using HTP sequencing.     

Preferential Utilization of d-Lactate by Shewanella oneidensis

Shewanella oneidensis couples oxidation of lactate to respiration of many substrates. Here we report that llpR (l-lactate-positive regulator, SO_3460) encodes a positive regulator of l-lactate utilization distinct from previously studied regulators. We also demonstrate d-lactate inhibition of l-lactate utilization in S. oneidensis, resulting in preferential utilization of the d isomer.     

MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.