SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles

Intraspecies characterization of bacteria via evolutionary modeling of protein domains

The ability to detect and characterize bacteria within a biological sample is crucial for the monitoring of infections and epidemics, as well as for the study of human health and its relationship with commensal microorganisms. To this aim, a commonly used technique is the 16S rRNA gene targeted sequencing. PCR-amplified 16S sequences derived from the sample of interest are usually clustered into the so-called Operational Taxonomic Units (OTUs) based on pairwise similarities. Then, representative OTU sequences are compared with reference (human-made) databases to derive their phylogeny and taxonomic classification. Here, we propose a new reference-free approach to define the phylogenetic distance between bacteria based on protein domains, which are the evolving units of proteins. We extract the protein domain profiles of 3368 bacterial genomes and we use an ecological approach to model their Relative Species Abundance distribution. Based on the model parameters, we then derive a new measurement of phylogenetic distance. Finally, we show that such model-based distance is capable of detecting differences between bacteria in cases in which the 16S rRNA-based method fails, providing a possibly complementary approach , which is particularly promising for the analysis of bacterial populations measured by shotgun sequencing.

A large-scale analysis of codon usage bias in 4868 bacterial genomes shows association of codon adaptation index with GC content, protein functional domains and bacterial phenotypes

Multiple synonymous codons code for the same amino acid, resulting in the degeneracy of the genetic code and in the preferred used of some codons called codon bias usage (CBU). We performed a large-scale analysis of codon usage bias analysing the distribution of the codon adaptation index (CAI) and the codon relative adaptiveness index (RA) in 4868 bacterial genomes. We found that CAI values differ significantly between protein functional domains and part of the protein outside domains and show how CAI, GC content and preferred usage of polymerase III alpha subunits are related. Additionally, we give evidence of the association between CAI and bacterial phenotypes.

Classification of the plant-associated lifestyle 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). Many important PGPR as well as plant pathogens belong to the genus Pseudomonas. There is, however, uncertainty on the divide between beneficial and pathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here we used the Genome properties (GP) common biological pathways annotation system and Machine Learning (ML) to establish the relationship between the genome wide GP composition and the plant-associated lifestyle of 91 Pseudomonas strains isolated from the rhizosphere and the phyllosphere representing both plant-associated phenotypes. GP enrichment analysis, Random Forest model fitting and feature selection revealed 28 discriminating features. A test set of 75 new strains confirmed the importance of the selected features for classification. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated lifestyle.

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.

Atlantic 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.

Assembly and Comparison of Ca. Neoehrlichia mikurensis Genomes

Ca. Neoehrlichia mikurensis is widely prevalent in I. ricinus across Europe and has been associated with human disease. However, diagnostic modalities are limited, and much is still unknown about its biology. Here, we present the first complete Ca. Neoehrlichia mikurensis genomes directly derived from wildlife reservoir host tissues, using both long- and short-read sequencing technologies. This pragmatic approach provides an alternative to obtaining sufficient material from clinical cases, a difficult task for emerging infectious diseases, and to expensive and challenging bacterial isolation and culture methods. Both genomes exhibit a larger chromosome than the currently available Ca. Neoehrlichia mikurensis genomes and expand the ability to find new targets for the development of supportive laboratory diagnostics in the future. Moreover, this method could be utilized for other tick-borne pathogens that are difficult to culture.

SEDA: A Desktop Tool Suite for FASTA Files Processing

SEDA (SEquence DAtaset builder) is a multiplatform desktop application for the manipulation of FASTA files containing DNA or protein sequences. The convenient graphical user interface gives access to a collection of simple (filtering, sorting, or file reformatting, among others) and advanced (BLAST searching, protein domain annotation, gene annotation, and sequence alignment) utilities not present in similar applications, which eases the work of life science researchers working with DNA and/or protein sequences, especially those who have no programming skills. This paper presents general guidelines on how to build efficient data handling protocols using SEDA, as well as practical examples on how to prepare high-quality datasets for single gene phylogenetic studies, the characterization of protein families, or phylogenomic studies. The user-friendliness of SEDA also relies on two important features: (i) the availability of easy-to-install distributable versions and installers of SEDA, including a Docker image for Linux, and (ii) the facility with which users can manage large datasets. SEDA is open-source, with GNU General Public License v3.0 license, and publicly available at GitHub (https://github.com/sing-group/seda). SEDA installers and documentation are available at https://www.sing-group.org/seda/.

Acetate Degradation at Low pH by the Moderately Acidophilic Sulfate Reducer Acididesulfobacillus acetoxydans gen. nov. sp. nov

In acid drainage environments, biosulfidogenesis by sulfate-reducing bacteria (SRB) attenuates the extreme conditions by enabling the precipitation of metals as their sulfides, and the neutralization of acidity through proton consumption. So far, only a handful of moderately acidophilic SRB species have been described, most of which are merely acidotolerant. Here, a novel species within a novel genus of moderately acidophilic SRB is described, Acididesulfobacillus acetoxydans gen. nov. sp. nov. strain INE, able to grow at pH 3.8. Bioreactor studies with strain INE at optimum (5.0) and low (3.9) pH for growth showed that strain INE alkalinized its environment, and that this was more pronounced at lower pH. These studies also showed the capacity of strain INE to completely oxidize organic acids to CO₂, which is uncommon among acidophilic SRB. Since organic acids are mainly in their protonated form at low pH, which increases their toxicity, their complete oxidation may be an acid stress resistance mechanism. Comparative proteogenomic and membrane lipid analysis further indicated that the presence of saturated ether-bound lipids in the membrane, and their relative increase at lower pH, was a protection mechanism against acid stress. Interestingly, other canonical acid stress resistance mechanisms, such as a Donnan potential and increased active charge transport, did not appear to be active.

Mechanistic insight into the destabilization of p53TD tetramer by cancer-related R337H mutation: a molecular dynamics study

The p53 protein is a tumor suppressor crucial for cell cycle and genome integrity. In a very large proportion of human cancers, p53 is frequently inactivated by mutations located in its DNA-binding domain (DBD). Some experimental studies reported that the inherited R337H mutation located in the p53 tetramerization domain (p53TD) can also result in destabilization of the p53 protein, and consequently lead to an organism prone to cancer setup. However, the underlying R337H mutation-induced structural destabilization mechanism is not well understood. Herein, we investigate the structural stability and dynamic property of the wild type p53TD tetramer and its cancer-related R337H mutant by performing multiple microsecond molecular dynamics simulations. It is found that R337H mutation destroys the R337-D352 hydrogen bonds, weakens the F341-F341 π-π stacking interaction and the hydrophobic interaction between aliphatic hydrocarbons of R337 and M340, leading to more solvent exposure of all the hydrophobic cores, and thus disrupting the structural integrity of the tetramer. Importantly, our simulations show for the first time that R337H mutation results in unfolding of the α-helix starting from the N-terminal region (residues ³³⁵RER(H)FEM³⁴⁰). Consistently, community network analyses reveal that R337H mutation reduces dynamical correlation and global connectivity of p53TD tetramer, which destabilizes the structure of the p53TD tetramer. This study provides the atomistic mechanism of R337H mutation-induced destabilization of p53TD tetramer, which might be helpful for in-depth understanding of the p53 loss-of-function mechanism.

Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

BACKGROUND

The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step.

RESULTS

Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest.

CONCLUSION

The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

A chromosome-level assembly of the black tiger shrimp (Penaeus monodon) genome facilitates the identification of growth-associated genes

To salvage marine ecosystems from fishery overexploitation, sustainable and efficient aquaculture must be emphasized. The knowledge obtained from available genome sequence of marine organisms has accelerated marine aquaculture in many cases. The black tiger shrimp (Penaeus monodon) is one of the most prominent cultured penaeid shrimps (Crustacean) with an average annual global production of half a million tons in the last decade. However, its currently available genome assemblies lack the contiguity and completeness required for accurate genome annotation due to the highly repetitive nature of the genome and technical difficulty in extracting high-quality, high-molecular weight DNA. Here, we report the first chromosome-level whole-genome assembly of P. monodon. The combination of long-read Pacific Biosciences (PacBio) and long-range Chicago and Hi-C technologies enabled a successful assembly of this first high-quality genome sequence. The final assembly covered 2.39 Gb (92.3% of the estimated genome size) and contained 44 pseudomolecules, corresponding to the haploid chromosome number. Repetitive elements occupied a substantial portion of the assembly (62.5%), the highest of the figures reported among crustacean species. The availability of this high-quality genome assembly enabled the identification of genes associated with rapid growth in the black tiger shrimp through the comparison of hepatopancreas transcriptome of slow-growing and fast-growing shrimps. The results highlighted several growth-associated genes. Our high-quality genome assembly provides an invaluable resource for genetic improvement and breeding penaeid shrimp in aquaculture. The availability of P. monodon genome enables analyses of ecological impact, environment adaptation and evolution, as well as the role of the genome to protect the ecological resources by promoting sustainable shrimp farming.

Sharing Biomedical Data: Strengthening AI Development in Healthcare

Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed by humans. Massive data is a fundamental aspect of the most powerful AI-based algorithms. However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical data, especially medical imaging. To overcome this important limitation, several alternative solutions have been suggested, including transfer learning approaches, generation of artificial data, adoption of blockchain technology, and creation of an infrastructure composed of anonymous and abstract data. However, none of these strategies is currently able to completely solve this challenge. The need to build large datasets that can be used to develop healthcare solutions deserves special attention from the scientific community, clinicians, all the healthcare players, engineers, ethicists, legislators, and society in general. This paper offers an overview of the data limitation in medical predictive models; its impact on the development of healthcare solutions; benefits and barriers of sharing data; and finally, suggests future directions to overcome data limitations in the medical field and enable AI to enhance healthcare. This perspective is dedicated to the technical requirements of the learning models, and it explains the limitation that comes from poor and small datasets in the medical domain and the technical options that try or can solve the problem related to the lack of massive healthcare data.

Galactocerebroside biosynthesis pathways of Mycoplasma species: an antigen triggering Guillain-Barré-Stohl syndrome

Infection by Mycoplasma pneumoniae has been identified as a preceding factor of Guillain-Barré-Stohl syndrome. The Guillain-Barré-Stohl syndrome is triggered by an immune reaction against the major glycolipids and it has been postulated that M. pneumoniae infection triggers this syndrome due to bacterial production of galactocerebroside. Here, we present an extensive comparison of 224 genome sequences from 104 Mycoplasma species to characterize the genetic determinants of galactocerebroside biosynthesis. Hidden Markov models were used to analyse glycosil transferases, leading to identification of a functional protein domain, termed M2000535 that appears in about a third of the studied genomes. This domain appears to be associated with a potential UDP-glucose epimerase, which converts UDP-glucose into UDP-galactose, a main substrate for the biosynthesis of galactocerebroside. These findings clarify the pathogenic mechanisms underlining the triggering of Guillain-Barré-Stohl syndrome by M. pneumoniae infections.

Influence of Ultra-Processed Foods Consumption on Redox Status and Inflammatory Signaling in Young Celiac Patients

The current study was designed to assess the influence of consumption of ultra-processed (UPF) on oxidative/antioxidant balance and evoked inflammatory signaling in young patients with celiac disease (CD). The study included 85 children. The celiac group (n = 53) included children with CD with a long (>18 months, n = 17) or recent (<18 months, n = 36) adherence to a gluten-free diet (GFD). The control group (n = 32) included healthy children with a significantly lower consumption of UPF compared to the CD group, both expressed as kcal/day (p = 0.043) and as percentage of daily energy intake (p = 0.023). Among children with CD, the group with the lowest consumption of UPF (below the 50% of daily energy intake) had a greater Mediterranean diet (MD) adherence and higher moderate physical activity levels. In addition, CD children with the lowest consumption of UPF had healthier redox (lower soluble superoxide dismutase-1 and 15-F2t-isoprostanes) and inflammatory profiles (lower macrophage inflammatory protein-1α) compared to the group with the highest consumption of UPF (all, p < 0.05) regardless of the time on a GFD. These findings highlight the importance of a correct monitoring of the GFD. An unbalanced GFD with high consumption of UPF and an unhealthy pattern with less physical activity and worse adherence to MD results in a worse inflammatory profile, which could act as a parallel pathway that could have important consequences on the pathophysiology of the disease.

A metabolic and physiological design study of Pseudomonas putida KT2440 capable of anaerobic respiration

BACKGROUND

Pseudomonas putida KT2440 is a metabolically versatile, HV1-certified, genetically accessible, and thus interesting microbial chassis for biotechnological applications. However, its obligate aerobic nature hampers production of oxygen sensitive products and drives up costs in large scale fermentation. The inability to perform anaerobic fermentation has been attributed to insufficient ATP production and an inability to produce pyrimidines under these conditions. Addressing these bottlenecks enabled growth under micro-oxic conditions but does not lead to growth or survival under anoxic conditions.

RESULTS

Here, a data-driven approach was used to develop a rational design for a P. putida KT2440 derivative strain capable of anaerobic respiration. To come to the design, data derived from a genome comparison of 1628 Pseudomonas strains was combined with genome-scale metabolic modelling simulations and a transcriptome dataset of 47 samples representing 14 environmental conditions from the facultative anaerobe Pseudomonas aeruginosa.

CONCLUSIONS

The results indicate that the implementation of anaerobic respiration in P. putida KT2440 would require at least 49 additional genes of known function, at least 8 genes encoding proteins of unknown function, and 3 externally added vitamins.

Genome-scale metabolic modeling underscores the potential of Cutaneotrichosporon oleaginosus ATCC 20509 as a cell factory for biofuel production

BACKGROUND

Cutaneotrichosporon oleaginosus ATCC 20509 is a fast-growing oleaginous basidiomycete yeast that is able to grow in a wide range of low-cost carbon sources including crude glycerol, a byproduct of biodiesel production. When glycerol is used as a carbon source, this yeast can accumulate more than 50% lipids (w/w) with high concentrations of mono-unsaturated fatty acids.

RESULTS

To increase our understanding of this yeast and to provide a knowledge base for further industrial use, a FAIR re-annotated genome was used to build a genome-scale, constraint-based metabolic model containing 1553 reactions involving 1373 metabolites in 11 compartments. A new description of the biomass synthesis reaction was introduced to account for massive lipid accumulation in conditions with high carbon-to-nitrogen (C/N) ratio in the media. This condition-specific biomass objective function is shown to better predict conditions with high lipid accumulation using glucose, fructose, sucrose, xylose, and glycerol as sole carbon source.

CONCLUSION

Contributing to the economic viability of biodiesel as renewable fuel, C. oleaginosus ATCC 20509 can effectively convert crude glycerol waste streams in lipids as a potential bioenergy source. Performance simulations are essential to identify optimal production conditions and to develop and fine tune a cost-effective production process. Our model suggests ATP-citrate lyase as a possible target to further improve lipid production.

FAM83A as a Potential Biological Marker Is Regulated by miR-206 to Promote Cervical Cancer Progression Through PI3K/AKT/mTOR Pathway

Background and Objective: Chemotherapy and radiotherapy are effective treatment options for cervical cancer (CC), but their efficacy is limited by short survival rate of about 5 years particularly for advance stage CC. Bioinformatics analysis combined with experimental in vivo and in vitro data can identify potential markers of tumorigenesis and cancer progression to improve CC prognosis and survival rate of the patients. This study aims to investigate the prognostic value of family with sequence similarity 83, member A (FAM83A) gene and miR-206 in promoting CC progression and the involved genetic signaling pathways.

Method: This was a bioinformatic analysis study based on RNA sequencing data of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and verification by in vivo and in vitro experimental data. It was designed to evaluate whether the aberrantly expressed gene signatures could serve as new potential biomarker to improve prognosis prediction in CC. The TCGA RNA sequencing data [306 cervical squamous cell carcinoma (SCC) and endocervical adenocarcinoma samples and 13 adjacent samples] and GEO data (GSE9750 and GSE52903 datasets) were integrated and performed a bioinformatics analysis.

Results: The results showed that CC-associated FAM83A gene serves as a key regulator of CC development and progression. Functionally, we observed that FAM83A is significantly overexpressed in CC, which is linked to poor overall survival as well as disease-free survival in CC patients. The in-vitro and in-vivo assessments performed after silencing FAM83A revealed that cell proliferation was significantly inhibited and the S-phase cell cycle arrest was induced. Mechanistically, FAM83A plays a role in PI3K/AKT signaling, and its downstream molecules could promote CC cell proliferation. Furthermore, functionality assessments by in-vitro luciferase reporter system and immunoblot analysis showed that miR-206 was the upstream of FAM83A and negatively correlated with FAM83A.

Conclusion: The miR-206/FAM83A/PI3K/AKT signaling pathway possibly serves as a critical effector in CC progression indicating the potential prognostic value of FAM83A gene as a novel biomarker for CC progression.

Designing a novel mRNA vaccine against SARS-CoV-2: An immunoinformatics approach

SARS-CoV-2 is the deadly virus behind COVID-19, the disease that went on to ravage the world and caused the biggest pandemic 21st century has witnessed so far. On the face of ongoing death and destruction, the urgent need for the discovery of a vaccine against the virus is paramount. This study resorted to the emerging discipline of immunoinformatics in order to design a multi-epitope mRNA vaccine against the spike glycoprotein of SARS-CoV-2. Various immunoinformatics tools were utilized to predict T and B lymphocyte epitopes. The epitopes were channeled through a filtering pipeline comprised of antigenicity, toxicity, allergenicity, and cytokine inducibility evaluation with the goal of selecting epitopes capable of generating both T and B cell-mediated immune responses. Molecular docking simulation between the epitopes and their corresponding MHC molecules was carried out. 13 epitopes, a highly immunogenic adjuvant, elements for proper sub-cellular trafficking, a secretion booster, and appropriate linkers were combined for constructing the vaccine. The vaccine was found to be antigenic, almost neutral at physiological pH, non-toxic, non-allergenic, capable of generating a robust immune response and had a decent worldwide population coverage. Based on these parameters, this design can be considered a promising choice for a vaccine against SARS-CoV-2.

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SARS-CoV-2, the causative agent for COVID-19, has caused the biggest pandemic 21st century has witnessed so far.

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Here, immunoinformatics was used for designing a muti-epitope mRNA vaccine against the spike glycoprotein of SARS-CoV-2.

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Through stringent selection of T and B cell epitopes and other necessary elements, a vaccine was constructed in silico.

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Proposed mechanism of its synthesis, delivery and action has also been presented.

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The vaccine was found to be immunogenic, almost neutral, non-toxic, non-allergenic and has a decent worldwide coverage.

Genome-guided analysis allows the identification of novel physiological traits in Trichococcus species

BACKGROUND

The genus Trichococcus currently contains nine species: T. flocculiformis, T. pasteurii, T. palustris, T. collinsii, T. patagoniensis, T. ilyis, T. paludicola, T. alkaliphilus, and T. shcherbakoviae. In general, Trichococcus species can degrade a wide range of carbohydrates. However, only T. pasteurii and a non-characterized strain of Trichococcus, strain ES5, have the capacity of converting glycerol to mainly 1,3-propanediol. Comparative genomic analysis of Trichococcus species provides the opportunity to further explore the physiological potential and uncover novel properties of this genus.

RESULTS

In this study, a genotype-phenotype comparative analysis of Trichococcus strains was performed. The genome of Trichococcus strain ES5 was sequenced and included in the comparison with the other nine type strains. Genes encoding functions related to e.g. the utilization of different carbon sources (glycerol, arabinan and alginate), antibiotic resistance, tolerance to low temperature and osmoregulation could be identified in all the sequences analysed. T. pasteurii and Trichococcus strain ES5 contain a operon with genes encoding necessary enzymes for 1,3-PDO production from glycerol. All the analysed genomes comprise genes encoding for cold shock domains, but only five of the Trichococcus species can grow at 0 °C. Protein domains associated to osmoregulation mechanisms are encoded in the genomes of all Trichococcus species, except in T. palustris, which had a lower resistance to salinity than the other nine studied Trichococcus strains.

CONCLUSIONS

Genome analysis and comparison of ten Trichococcus strains allowed the identification of physiological traits related to substrate utilization and environmental stress resistance (e.g. to cold and salinity). Some substrates were used by single species, e.g. alginate by T. collinsii and arabinan by T. alkaliphilus. Strain ES5 may represent a subspecies of Trichococcus flocculiformis and contrary to the type strain (DSM 2094T), is able to grow on glycerol with the production of 1,3-propanediol.

Metabolic shift induced by synthetic co-cultivation promotes high yield of chain elongated acids from syngas

Bio-catalytic processes for sustainable production of chemicals and fuels receive increased attention within the concept of circular economy. Strategies to improve these production processes include genetic engineering of bio-catalysts or process technological optimization. Alternatively, synthetic microbial co-cultures can be used to enhance production of chemicals of interest. It remains often unclear however how microbe to microbe interactions affect the overall production process and how this can be further exploited for application. In the present study we explored the microbial interaction in a synthetic co-culture of Clostridium autoethanogenum and Clostridium kluyveri, producing chain elongated products from carbon monoxide. Monocultures of C. autoethanogenum converted CO to acetate and traces of ethanol, while during co-cultivation with C. kluyveri, it shifted its metabolism significantly towards solventogenesis. In C. autoethanogenum, expression of the genes involved in the central carbon- and energy-metabolism remained unchanged during co-cultivation compared to monoculture condition. Therefore the shift in the metabolic flux of C. autoethanogenum appears to be regulated by thermodynamics, and results from the continuous removal of ethanol by C. kluyveri. This trait could be further exploited, driving the metabolism of C. autoethanogenum to solely ethanol formation during co-cultivation, resulting in a high yield of chain elongated products from CO-derived electrons. This research highlights the important role of thermodynamic interactions in (synthetic) mixed microbial communities and shows that this can be exploited to promote desired conversions.

The Empusa code generator and its application to GBOL, an extendable ontology for genome annotation

The RDF data model facilitates integration of diverse data available in structured and semi-structured formats. To obtain a coherent RDF graph the chosen ontology must be consistently applied. However, addition of new diverse data causes the ontology to evolve, which could lead to accumulation of unintended erroneous composites. Thus, there is a need for a gate keeping system that compares the intended content described in the ontology with the actual content of the resource. The Empusa code generator facilitates creation of composite RDF resources from disparate sources. Empusa can convert a schema into an associated application programming interface (API), that can be used to perform data consistency checks and generates Markdown documentation to make persistent URLs resolvable. Using Empusa consistency is ensured within and between the ontology and the content of the resource. As an illustration of the potential of Empusa, we present the Genome Biology Ontology Language (GBOL). GBOL uses and extends current ontologies to provide a formal representation of genomic entities, along with their properties, relations and provenance.

In silico-guided engineering of Pseudomonas putida towards growth under micro-oxic conditions

BACKGROUND

Pseudomonas putida is a metabolically versatile, genetically accessible, and stress-robust species with outstanding potential to be used as a workhorse for industrial applications. While industry recognises the importance of robustness under micro-oxic conditions for a stable production process, the obligate aerobic nature of P. putida, attributed to its inability to produce sufficient ATP and maintain its redox balance without molecular oxygen, severely limits its use for biotechnology applications.

RESULTS

Here, a combination of genome-scale metabolic modelling and comparative genomics is used to pinpoint essential [Formula: see text]-dependent processes. These explain the inability of the strain to grow under anoxic conditions: a deficient ATP generation and an inability to synthesize essential metabolites. Based on this, several P. putida recombinant strains were constructed harbouring acetate kinase from Escherichia coli for ATP production, and a class I dihydroorotate dehydrogenase and a class III anaerobic ribonucleotide triphosphate reductase from Lactobacillus lactis for the synthesis of essential metabolites. Initial computational designs were fine-tuned by means of adaptive laboratory evolution.

CONCLUSIONS

We demonstrated the value of combining in silico approaches, experimental validation and adaptive laboratory evolution for microbial design by making the strictly aerobic Pseudomonas putida able to grow under micro-oxic conditions.

Propionibacterium ruminifibrarum sp. nov., isolated from cow rumen fibrous content

A novel propionate producing bacterium, strain JV5^T, was isolated from the rumen fibrous content of a Holstein Friesian dairy cow. Cells of strain JV5^T were Gram-stain-positive, non-motile and aerotolerant. Growth occurred between 35 and 45 °C, with an optimum at 39 °C. The pH range for growth was 6.5-8, with an optimum at pH 7. The 16S rRNA gene sequence of strain JV5^T was 98.4 and 96.5 % identical to those of Propionibacterium australiense DSM 15818^T and Propionibacterium acidifaciens DSM 21887^T, respectively. Genome wide average nucleotide identity and digital DNA-DNA hybridization values were 88.3 and 35.5 %, respectively, against P. australiense DSM 15818^T. The G+C content of strain JV5^T was 68.9  mol%. Strain JV5^T did not produce urease and was able to metabolize glutamate, but not aspartate and glycine. Strain JV5^T was able to ferment a range of substrates including certain simple and complex carbohydrates, sugar alcohols and amino acids. Chemotaxonomic analysis of strain JV5^T revealed the presence of meso-diamino pimelic acid isomers similar those found in P. australiense, but different from P. acidifaciens. The observed major (>10 %) cellular fatty acids in strain JV5^T (C18 : 1 ω9c, anteiso-C15 : 1, C16 : 0, C17 : 0 and C16 : 0 alcohol) were also different from those observed in P. australiense and P. acidifaciens. Based on these findings, a novel species is proposed within the genus Propionibacterium, Propionibacterium ruminifibrarum sp. nov. (type strain JV5^T=DSM 106771^T=TISTR 2629^T).

Co-culture of a Novel Fermentative Bacterium, Lucifera butyrica gen. nov. sp. nov., With the Sulfur Reducer Desulfurella amilsii for Enhanced Sulfidogenesis

Biosulfidogenesis can be used to remediate low pH and high metal content waters such as acid mine drainage and recover the present metals. The selection of a cheap electron donor for the process is important for the economic viability. In this work we isolated a novel versatile acidotolerant fermentative bacterium (strain ALE^T) that is able to use a great variety of substrates including glycerol. Strain ALE^T is an obligate anaerobe, and cells are motile, rod-shaped, spore-forming, and stain Gram-positive. Growth occurred in a pH range from 3.5 to 7 (optimum 5.5), and temperature range from 25 to 40°C (optimum 37°C). It grows by fermentation of sugars, organic acids and glycerol. It has the ability to use thiosulfate, iron and DMSO as electron acceptors. Its genome is 4.7 Mb with 5122 protein-coding sequences, and a G+C content of 46.9 mol%. Based on 16S rRNA gene sequence analysis, the closest cultured species is Propionispora hippei (91.4% 16S rRNA gene identity) from the Sporomusaceae family (Selenomonadales order, Negativicutes class, Firmicutes phylum). Based on the distinctive physiological and phylogenetic characteristics of strain ALE^T, a new genus and species Lucifera butyrica gen. nov., sp. nov., is proposed. The type strain is ALE^T (=JCM 19373^T = DSM 27520^T). Strain ALE^T is an incomplete oxidizer and acetate, among other products, accumulates during glycerol conversion. Strain ALE^T was used to extend the substrate range for sulfur reduction by constructing co-cultures with the acetate oxidizer and sulfur reducer Desulfurella amilsii. The co-culture was tested with glycerol as substrate in batch and chemostat experiments. Acetate formed by fermentation of glycerol by strain ALE^T resulted in sulfur reduction by D. amilsii. The co-culture strategy offers good perspectives to use a wide range of cost-efficient substrates, including glycerol, to produce sulfide by specialized sulfur reducers. The recovery of heavy metals from metalliferous streams may become economically feasible by this approach.

Note: The locus tag for the genes encoded in Lucifera butyrica is LUCI_^∗. To avoid repetition of the prefix along the text, the locus tags are represented by the specific identifier.

SyNDI: synchronous network data integration framework

BACKGROUND

Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.

RESULTS

In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified.

CONCLUSIONS

Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.

Forward Genetics by Genome Sequencing Uncovers the Central Role of the Aspergillus niger goxB Locus in Hydrogen Peroxide Induced Glucose Oxidase Expression

Aspergillus niger is an industrially important source for gluconic acid and glucose oxidase (GOx), a secreted commercially important flavoprotein which catalyses the oxidation of β-D-glucose by molecular oxygen to D-glucolactone and hydrogen peroxide. Expression of goxC, the GOx encoding gene and the concomitant two step conversion of glucose to gluconic acid requires oxygen and the presence of significant amounts of glucose in the medium and is optimally induced at pH 5.5. The molecular mechanisms underlying regulation of goxC expression are, however, still enigmatic. Genetic studies aimed at understanding GOx induction have indicated the involvement of at least seven complementation groups, for none of which the molecular basis has been resolved. In this study, a mapping-by-sequencing forward genetics approach was used to uncover the molecular role of the goxB locus in goxC expression. Using the Illumina and PacBio sequencing platforms a hybrid high quality draft genome assembly of laboratory strain N402 was obtained and used as a reference for mapping of genomic reads obtained from the derivative NW103:goxB mutant strain. The goxB locus encodes a thioredoxin reductase. A deletion of the encoding gene in the N402 parent strain led to a high constitutive expression level of the GOx and the lactonase encoding genes required for the two-step conversion of glucose in gluconic acid and of the catR gene encoding catalase R. This high constitutive level of expression was observed to be irrespective of the carbon source and oxidative stress applied. A model clarifying the role of GoxB in the regulation of the expression of goxC involving hydrogen peroxide as second messenger is presented.