Biology subjects

Suarez-Diez, M.

Publications and source records attributed to Suarez-Diez, M..

35 records · Page 2Linked to original sources

bioRxiv2022

Exploring the adaptability and robustness of the central carbon metabolism of Mycoplasma pneumoniae

In this study we explored the adaptability and robustness of glycolysis and pyruvate metabolism of Mycoplasma pneumoniae (MPN). We used a dual approach, we analysed metabolomics data collected for a large number of OE and KO mutants and perturbation samples. Furthermore, we trained a dynamic model of central carbon metabolism and tested the models capacity to predict these mutants and perturbation samples as well as identify key controlling factors in central carbon metabolism. Our analysis of metabolite data as well as our model analysis indicate MPN metabolism is inherently robust against perturbations due to its network structure. Two key control hubs of central carbon metabolism were identified.

systems biology↗

bioRxiv2022

Predicting Mycoplasma tissue and host specificity from genome sequences

To gain insights into the genotype-phenotype relationships in Mycoplasmas, we set to investigate which Mycoplasma proteins are most predictive of tissue and host trophism and to which functional groups of proteins they belong. We retrieved and annotated 430 Mycoplasma genomes and combined their genome information with data on which host and tissue these Mycoplasmas were isolated from. We assessed clustering of Mycoplasma strains from a wide range of hosts and tissues based on different functional groups of proteins. Additionally, we assessed clustering using only a subset of M. pneumoniae strains based on different functional groups of proteins. We found that proteins belonging to the Gene Ontology (GO) Biological process group Interspecies interaction between organisms proteins are most important for predicting the pathogenesis of Mycoplasma strains whereas for M. pneumoniae, those belonging to Quorum sensing and Biofilm formation proteins are most important for predicting pathogenesis. Two Random Forest Classifiers were trained to accurately predicts host and tissue specificity based on only 12 proteins. For Mycoplasma host specificity CTP synthase complex, magnesium transporter MgtE, and glycine cleavage system are most important for correctly classifying Mycoplasma strains that infect humans, including opportunistic zoonotic strains. For tissue specificity, we found that a) known virulence and adhesions factor Methionine sulphate reductase MetA is predictive of urinary tract infecting Mycoplasmas; b) an extra cytoplasmic thiamine binding lipoprotein is most predictive of gastro-intestinal infecting Mycoplasmas; c) a type I restriction endonuclease is most predictive of respiratory infecting Mycoplasmas, and; d) a branched-chain amino acid transport system is most predictive for blood infecting Mycoplasmas. These findings can aid in predicting host and tissue specific pathogenicity of Mycoplasmas as well as provide insight in which proteins are important for specific host and tissue adaptations. Furthermore, these results underscore the usefulness of deploying genome-wide methodologies for gaining insights into pathogenicity from genome sequences.

systems biology↗

bioRxiv2022

Comparative genome-scale constraint-based metabolic modeling reveals key lifestyle features of plant-associated Pseudomonas spp.

Plant Growth Promoting Rhizobacteria (PGPR) dwell in the rhizosphere, the area surrounding the root of plants, and enhance growth of the host through different mechanisms: they can protect plants against pathogens, assist in nutrient gathering, and in increasing stress tolerance. Hence, developing strategies to enhance their performance is important to increase crop productivity. Specific solutions are necessary to enhance the performance of the beneficials while simultaneously avoiding nurturing of pathogens. This requires insights into the mechanisms underlying these microbials interactions. Pseudomonas is one of the most studied genera and contains both beneficials and pathogenic species. Hence, we used comparative genome-scale constraint-based metabolic modeling to reveal key features of both classes of Pseudomonads and which can provide leads for the possible interventions regarding these solutions. Models of 75 plant-growth promoting rhizosphere and 33 epiphytic pathogenic Pseudomonas strains were automatically reconstructed and validated using phenotype microarray (Biolog) data. The models were used for compositional analysis and 12 representative strains, 6 of each group, were further selected for extensive simulation. The analyses reveal differences in the potential for metabolite uptake and transport between these two distinct classes that suggest their nutrient preferences and their differences in, among other, D-ornithine acquisition mechanisms. The models enable simulation of metabolic state of root exudates. Simulations highlighted and summarized the differences in pathway utilization and intracellular states between two groups. The insights obtained will be very valuable to broader such studies of rhizobiome and to possibly develop strategies to improve crop productivity by supporting the beneficial microbiome while reducing pathogen activities.

systems biology↗

bioRxiv2022

Tailoring and optimizing fatty acid production by oleaginous yeasts through the systematic exploration of their physiological fitness

BackgroundThe use of palm oil for our current needs is unsustainable. Replacing palm oil with oils produced by microbes through the conversion of sustainable feedstocks is a promising alternative. However, there are major technical challenges that must be overcome to enable this transition. Foremost among these challenges is the stark increase in lipid accumulation and production of higher content of specific fatty acids. Therefore, there is a need for more in-depth knowledge and systematic exploration of the oil productivity of the oleaginous yeasts. In this study, we cultivated Cutaneotrichosporon oleaginosus and Yarrowia lipolytica at various C/N ratios and temperatures in a defined medium with glycerol as carbon source and urea as nitrogen source. We ascertained the synergistic effect between various C/N ratios of a defined medium at different temperatures with Response Surface Methodology (RSM) and explored the variation in fatty acid composition through Principal Component Analysis. ResultsBy applying RSM, we determined a temperature of 30 {degrees}C and a C/N ratio of 175 g/g to enable maximal oil production by C. oleaginosus and a temperature of 21 {degrees}C and a C/N ratio of 140 g/g for Y. lipolytica. We increased production by 71 % and 66 % respectively for each yeast compared to the average lipid accumulation in all tested conditions. Modulating temperature enabled us to steer the fatty acid compositions. Accordingly, switching from higher temperature to lower cultivation temperature shifted the production of oils from more saturated to unsaturated by 14 % in C. oleaginosus and 31 % in Y. lipolytica. Higher cultivation temperatures resulted in production of even longer saturated fatty acids, 3 % in C. oleaginosus and 1.5 % in Y. lipolytica. ConclusionsIn this study, we provided the optimum C/N ratio and temperature for C. oleaginosus and Y. lipolytica by RSM. Additionally, we demonstrated that lipid accumulation of both oleaginous yeasts was significantly affected by the C/N ratio and temperature. Furthermore, we systematically analyzed the variation in fatty acids composition and proved that changing the C/N ratio and temperature steer the composition. We have further established these oleaginous yeasts as platforms for production of tailored fatty acids.

systems biology↗

bioRxiv2022

Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum

BackgroundMicrobial production of propionate from diluted streams of ethanol (e.g., deriving from syngas fermentation) is a sustainable alternative to the petrochemical production route. Yet, few ethanol-fermenting propionigenic bacteria are known, and understanding of their metabolism is limited. Anaerotignum neopropionicum is a propionate-producing bacterium that uses the acrylate pathway to ferment ethanol and CO2 to propionate and acetate. In this work, we used computational and experimental methods to study the metabolism of A. neopropionicum and, in particular, the pathway for conversion of ethanol into propionate. ResultsOur work describes iANEO_SB607, the first genome-scale metabolic model (GEM) of A. neopropionicum. The model was built combining the use of automatic tools with an extensive manual curation process, and it was validated with experimental data from this and published studies. The model predicted growth of A. neopropionicum on ethanol, lactate, sugars and amino acids, matching observed phenotypes. In addition, the model was used to implement a dynamic flux balance analysis (dFBA) approach that accurately predicted the fermentation profile of A. neopropionicum during batch growth on ethanol. A systematic analysis of the metabolism of A. neopropionicum combined with model simulations shed light into the mechanism of ethanol fermentation via the acrylate pathway, and revealed the presence of the electron-transferring complexes NADH-dependent reduced ferredoxin:NADP+ oxidoreductase (Nfn) and acryloyl-CoA reductase-EtfAB, identified for the first time in this bacterium. ConclusionsThe realisation of the GEM iANEO_SB607 is a stepping stone towards the understanding of the metabolism of the propionate-producer A. neopropionicum. With it, we have gained insight into the functioning of the acrylate pathway and energetic aspects of the cell, with focus on the fermentation of ethanol. Overall, this study provides a basis to further exploit the potential of propionigenic bacteria as microbial cell factories.

systems biology↗

bioRxiv2021

Classification of the plant-associated phenotype of Pseudomonas strains using genome properties and machine learning

The rhizosphere, the region of soil surrounding roots of plants, is colonized by a unique population of Plant Growth Promoting Rhizobacteria (PGPR). By enhancing nutrient uptake from the soil and through modulation of plant phytohormone status and metabolism, PGPR can increase the stress tolerance, growth and yield of crop plants. Many important PGPR as well as plant pathogens belong to the genus Pseudomonas. There is, however, uncertainty on the divide between phytobeneficial and phytopathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here the Genome properties (GP) common biological pathways annotation system was applied to establish the relationship between the genome wide GP composition and the plant-associated phenotype of 91 Pseudomonas strains representing both phenotypes. GP enrichment analysis, Random Forest model fitting and feature selection revealed 28 discriminating features. A validation dataset of 67 new strains confirmed the importance of the selected features for classification. A number of unexpected discriminating features were found, suggesting involvement of novel molecular mechanisms. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated phenotype. Author summaryWith a growing population the need to double the agricultural food production is specified. Simultaneously, there is an urgent need to implement sustainable and climate change resilient agricultural practices that preserve natural ecosystems. Cooperative microbiomes play important positive roles in plant growth development and fitness. Properly tuned, these microbiomes can significantly reduce the need for synthetic fertilizers and can replace chemicals in crop pest control. To select beneficial candidates, their traits need to be described and likewise, potential detrimental traits should be avoided. Here we applied GP-based comparative functional genomics, enrichment analysis and Random Forest model fitting to compare known phytobeneficial and phytopathogenic Pseudomonas strains. A number of unexpected discriminating features were found suggesting the involvement of novel molecular mechanisms.

genomics↗

bioRxiv2021

Enzyme-constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed-batch bioreactors

Genome-scale, constraint-based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bio-process design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme-constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well-known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyze flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell-based processes and, thus, the usefulness of such models

bioengineering↗

bioRxiv2021

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds. Author summaryAtlantic salmon aquaculture generates billions of euros annually, but faces challenges of sustainability. Salmon are carnivores by nature, and fish oil and fish meal have become scarce resources in fish feed production. Novel, sustainable feedstuffs are being trialed hand in hand with studies of the genetics of growth and feed efficiency. This calls for a mathematical-biological framework to integrate data with understanding of the effects of novel feeds on salmon physiology and its interplay with genetics. We have developed the SALARECON model of the core salmon metabolic reaction network, linking its genome to metabolic fluxes and growth. Computational analyses show good agreement with observed growth, amino acid limitations, and feed efficiencies, illustrating the potential for in silico studies of potential feed mixtures. In particular, in silico screening of possible diets will enable more efficient animal experiments with improved knowledge gain. We have adopted best practices for test-driven development, virtual experiments to assay metabolic capabilities, revision control, and FAIR data and model management. This facilitates fast, collaborative, reliable development of the model for future applications in sustainable production biology.

systems biology↗

bioRxiv2020

Modeling a co-culture of Clostridium autoethanogenum and Clostridium kluyveri to increase syngas conversion to medium-chain fatty-acids

Microbial fermentation of synthesis gas (syngas) is becoming more attractive for sustainable production of commodity chemicals. To date, syngas fermentation focuses mainly on the use of Clostridium species for the production of small organic molecules such as ethanol and acetate. The cocultivation of syngas-fermenting microorganisms with chain-elongating bacteria can expand the range of possible products, allowing, for instance, the production of medium-chain fatty acids (MCFA) and alcohols from syngas. To explore these possibilities, we report herein a genome-scale, constraint-based metabolic model to describe growth of a co-culture of Clostridium autoethanogenum and Clostridium kluyveri on syngas for the production of valuable compounds. Community flux balance analysis was used to gain insight into the metabolism of the two strains and their interactions, and to reveal potential strategies enabling production of butyrate and hexanoate. The model suggests that addition of succinate is one strategy to optimize the production of medium-chain fatty-acids from syngas with this co-culture. According to the predictions, addition of succinate increases the pool of crotonyl-CoA and the ethanol/acetate uptake ratio in C. kluyveri, resulting in the flux of up to 60% of electrons into hexanoate. Other potential way to optimize butyrate and hexanoate is to increase ethanol production by C. autoethanogenum. Deletion of either formate transport, acetaldehyde dehydrogenase or formate dehydrogenase (ferredoxin) from the metabolic model of C. autoethanogenum leads to a (potential) increase in ethanol production up to 150%, which is clearly very attractive.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗

bioRxiv2019

Model-driven design allows growth of Mycoplasma pneumoniae on serum-free media

Mycoplasma pneumoniae is a slow-growing, human pathogen that causes atypical pneumonia. Because it lacks a cell wall, many antibiotics are ineffective, and vaccination is required. Due to its reduced genome and dearth of many biosynthetic pathways, this fastidious bacterium depends on rich, undefined medium for growth, which makes large-scale cultivation for vaccine production challenging and expensive. To understand factors limiting growth, we developed a genome-scale, constraint-based model of M. pneumoniae called iEG158_mpn to describe the metabolic potential of this bacterium. We have put special emphasis on cell membrane formation to identify key lipid components to maximize bacterial growth. We have used this knowledge to predict and validate in vitro two serum-free media able to sustain growth. Our findings also show that glycolysis and lipid metabolism are much less efficient under hypoxia; these findings suggest that factors other than metabolism and membrane formation alone affect the growth of M. pneumoniae. Altogether, our modelling approach enabled us to optimize medium composition, capacitated growth in defined media and streamlined operational requirements, thereby providing the basis for stable, reproducible and less expensive vaccine production.

systems biology↗

bioRxiv2018

Consistency, Inconsistency and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome Scale Metabolic Modelling

Genome scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in eleven biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.

bioinformatics↗

bioRxiv2018

Expected and observed genotype complexity in prokaryotes: correlation between 16S-rRNA phylogeny and protein domain content

BackgroundThe omnipresent 16S ribosomal RNA gene (16S-rRNA) is commonly used to identify and classify bacteria though it does not take into account the distinctive functional characteristics of taxa. We explored functional domain landscapes of over 5700 complete bacterial genomes, representing a wide coverage of the bacterial tree of life, and investigated to what extent the observed protein domain diversity correlates with the expected evolutionary diversity, using 16S-rRNA as metric for evolutionary distance.\n\nResultsAnalysis of protein domains showed that 83% of the bacterial genes code for at least one of the 9722 domain classes identified. By comparing clade specific and global persistence scores, candidate horizontal gene transfer and signifying domains could be identified. 16S-rRNA and functional domain content distances were used to evaluate and compare species divergence and overall a sigmoid curve is observed. Already at close 16S-rRNA evolutionary distances, high levels of functional diversity can be observed. At a larger 16S-rRNA distance, functional differences accumulate at a relatively lower pace.\n\nConclusionsAnalysis of 16S-rRNA sequences in the same taxa suggests that, in many cases, additional means of classification are required to obtain reliable phylogenetic relationships. Whole genome protein domain class phylogenies correlate with, and complement 16S-rRNA sequence-based phylogenies. Moreover, domain-based phylogenies can be constructed over large evolutionary distances and provide an in-depth insight of the functional diversity within and among species and enables large scale functional comparisons. The increased granularity obtained paves way for new applications to better predict the relationships between genotype, physiology and ecology.

genomics↗

bioRxiv2018

Simulation and reconstruction of metabolite-metabolite association networks using a metabolic dynamic model and correlation based-algorithms

Biological networks play a paramount role in our understanding of complex biological phenomena and metabolite-metabolite association networks are now commonly used in metabolomics applications. In this study we evaluate the performance of several network inference algorithms (PCLRC, MRNET, GENIE3, TIGRESS and modifications of the MR-NET algorithm, together with standard Pearsons and Spearmans correlation) using as a test case data generated using a dynamic metabolic model describing the metabolism of arachidonic acid (consisting of 83 metabolites and 131 reactions) and simulation individual metabolic profiles of 550 subjects. The quality of the reconstructed metabolite-metabolite association networks was assessed against the original metabolic network taking into account different degrees of association among the metabolites and different sample size and noise levels. We found that inference algorithms based on resampling and bootstrapping to perform better when correlations are used as indexes to measure the strength of metabolite-metabolite associations. We also advocate for the use of data generated using dynamic models to test the performance of algorithms for network inference since they produce correlation patterns which are more similar to those observed in real metabolomics data.

systems biology↗

bioRxiv2018

The diurnal transcriptional landscape of the microalga Tetradesmus obliquus

Tetradesmus obliquus is a promising oleaginous microalga. We functionally annotated its genome and characterized the transcriptional landscape of T. obliquus adapted to 16:8h light dark (LD) cycles in turbidostat culture conditions at very high temporal resolution (1h intervals). Revealing a cycle of cellular events, six distinct expression profiles were obtained, each with transcriptional phenotypes correlating with measurements of biochemical composition.\n\nThe impact of starch deficiency was studied using the starchless mutant slm1. Significant changes in the transcriptional landscape were observed. Starch deficiency resulted in incapacity to supply energy during dark period, resulting in early or late time shift for energy demanding processes. Our study provides new perspectives on the role of starch and the adaptation to LD cycles of oleaginous microalgae.

plant biology↗

bioRxiv2018

Cofactors revisited - predicting the impact of flavoprotein-related diseases on a genome scale

Flavin adenine dinucleotide (FAD) and its precursor flavin mononucleotide (FMN) are redox cofactors that are required for the activity of more than hundred human enzymes. Mutations in the genes encoding these proteins cause severe phenotypes, including a lack of energy supply and accumulation of toxic intermediates. Ideally, patients should be diagnosed before they show symptoms so that treatment and/or preventive care can start immediately. This can be achieved by standardized newborn screening tests. However, many of the flavin-related diseases lack appropriate biomarker profiles. Genome-scale metabolic models can aid in biomarker research by predicting altered profiles of potential biomarkers. Unfortunately, current models, including the most recent human metabolic reconstructions Recon and HMR, typically treat enzyme-bound flavins incorrectly as free metabolites. This in turn leads to artificial degrees of freedom in pathways that are strictly coupled. Here, we present a reconstruction of human metabolism with a curated and extended flavoproteome. To illustrate the functional consequences, we show that simulations with the curated model - unlike simulations with earlier Recon versions - correctly predict the metabolic impact of multiple-acyl-CoA-dehydrogenase deficiency as well as of systemic flavin-depletion. Moreover, simulations with the new model allowed us to identify a larger number of biomarkers in flavoproteome-related diseases, without loss of accuracy. We conclude that adequate inclusion of cofactors in constraint-based modelling contributes to higher precision in computational predictions.

systems biology↗

bioRxiv2018

Aspergillus niger citrate exporter revealed by comparison of two alternative citrate producing conditions

Currently, there is no consensus regarding the mechanism underlying Aspergillus niger citrate biosynthesis and secretion, although it is amongst the most studied biotechnological production processes. Carbon excess relative to various other medium constituents is key, but the complex interplay between the limiting factors required for extracellular citrate accumulation remains elusive. It is thought that one of the industrial bottlenecks for citrate production is citrate export, however, no A. niger citrate exporter has yet been identified. Here, we show that the phenotype of increased extracellular citrate accumulation can have fundamentally different underlying mechanisms, depending on how this response is triggered, and that combining gene expression analyses of the different conditions can lead to the compilation of a shortlist of the most promising citrate exporter candidates. Specifically, we found that varying the amount and type of supplement of an arginine auxotrophic A. niger strain shows down-regulation of citrate metabolising enzymes in the condition in which more citrate is accumulated extracellularly. This contrasts with the transcriptional adaptations triggered by iron limitation, which also induces increased A. niger citrate production. By combining data obtained from these two manners of inducing comparatively high extracellular citrate accumulation, we were able to compile a shortlist of the most likely citrate transporter candidates. Two of the most promising candidates were tested in the yeast Saccharomyces cerevisiae, one of which showed the ability to secrete citrate. Deletion of the endogenous A. niger gene encoding the corresponding transporter abolished the ability of this fungus to secrete citrate. Instead, under conditions that usually favour A. niger citrate production, we found increased accumulation of extracellular oxalate. Our findings provide steps in untangling the complex interplay of different mechanisms underlying A. niger citrate accumulation, and we identify, for the first time, a fungal citrate exporter, offering a valuable tool for improvement of A. niger as biotechnological cell-factory for organic acid production.\n\nAuthor SummaryCitrate is widely applied as acidifier, flavouring and chelating agent. Industrial citrate production currently relies on the filamentous fungus Aspergillus niger. Although the industrial production process using A. niger has vastly improved since initiated almost 100 years ago, citrate export remains a bottleneck. Here, we studied the gene expression pattern of A. niger under various citrate producing conditions. Using these expression patterns and different computational approaches, we compiled a shortlist of putative citrate exporter candidates. In this way, we were able to identify a gene encoding a transporter protein capable of citrate export. We show that the yeast Saccharomyces cerevisiae, normally a citrate non-producer, secretes detectable amounts of citrate when harbouring this gene. In addition, we verify the biological function of this gene in A. niger itself, as removing this gene resulted in a citrate non-producing phenotype, which is atypical for this fungus. This finding is particularly exciting, as it is the first identification of a eukaryotic citrate exporter. With this, we not only provide a tool for improvement of industrial citrate production, but knowledge of this gene should help develop new methods for improvement of A. niger as biotechnological cell-factory for the production of other organic acids.

molecular biology↗

bioRxiv2017

Interoperable genome annotation with GBOL, an extendable infrastructure for functional data mining

BackgroundA standard structured format is used by the public sequence databases to present genome annotations. A prerequisite for a direct functional comparison is consistent annotation of the genetic elements with evidence statements. However, the current format provides limited support for data mining, hampering comparative analyses at large scale.\n\nResultsThe provenance of a genome annotation describes the contextual details and derivation history of the process that resulted in the annotation. To enable interoperability of genome annotations, we have developed the Genome Biology Ontology Language (GBOL) and associated infrastructure (GBOL stack). GBOL is provenance aware and thus provides a consistent representation of functional genome annotations linked to the provenance. GBOL is modular in design, extendible and linked to existing ontologies. The GBOL stack of supporting tools enforces consistency within and between the GBOL definitions in the ontology (OWL) and the Shape Expressions (ShEx) language describing the graph structure. Modules have been developed to serialize the linked data (RDF) and to generate a plain text format files.\n\nConclusionThe main rationale for applying formalized information models is to improve the exchange of information. GBOL uses and extends current ontologies to provide a formal representation of genomic entities, along with their properties and relations. The deliberate integration of data provenance in the ontology enables review of automatically obtained genome annotations at a large scale. The GBOL stack facilitates consistent usage of the ontology.

genomics↗