Genes controlling hydrolysate toxin tolerance identified by QTL analysis of the natural Saccharomyces cerevisiae BCC39850

Lignocellulosic material can be converted to valorized products such as fuels. Pretreatment is an essential step in conversion, which is needed to increase the digestibility of the raw material for microbial fermentation. However, pretreatment generates by-products (hydrolysate toxins) that are detrimental to microbial growth. In this study, natural Saccharomyces strains isolated from habitats in Thailand were screened for their tolerance to synthetic hydrolysate toxins (synHTs). The Saccharomyces cerevisiae natural strain BCC39850 (toxin-tolerant) was crossed with the laboratory strain CEN.PK2-1C (toxin-sensitive), and quantitative trait locus (QTL) analysis was performed on the segregants using phenotypic scores of growth (OD600) and glucose consumption. VMS1, DET1, KCS1, MRH1, YOS9, SYO1, and YDR042C were identified from QTLs as candidate genes associated with the tolerance trait. CEN.PK2-1C knockouts of the VMS1, YOS9, KCS1, and MRH1 genes exhibited significantly greater hydrolysate toxin sensitivity to growth, whereas CEN.PK2-1C knock-ins with replacement of VMS1 and MRH1 genes from the BCC39850 alleles showed significant increased ethanol production titers compared with the CEN.PK2-1C parental strain in the presence of synHTs. The discovery of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin tolerance in S. cerevisiae indicates the roles of the endoplasmic-reticulum-associated protein degradation pathway, plasma membrane protein association, and the phosphatidylinositol signaling system in this trait. KEY POINTS: • QTL analysis was conducted using a hydrolysate toxin-tolerant S. cerevisiae natural strain • Deletion of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin-sensitivity • Replacement of VMS1 and MRH1 with natural strain alleles increased ethanol production titers in the presence of hydrolysate toxins.

Biobased short chain fatty acid production - Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches

In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

Identification of Aspergillus niger Aquaporins Involved in Hydrogen Peroxide Signaling

Aspergillus niger is a robust microbial cell factory for organic acid production. However, the regulation of many industrially important pathways is still poorly understood. The regulation of the glucose oxidase (Gox) expression system, involved in the biosynthesis of gluconic acid, has recently been uncovered. The results of that study show hydrogen peroxide, a by-product of the extracellular conversion of glucose to gluconate, has a pivotal role as a signaling molecule in the induction of this system. In this study, the facilitated diffusion of hydrogen peroxide via aquaporin water channels (AQPs) was studied. AQPs are transmembrane proteins of the major intrinsic proteins (MIPs) superfamily. In addition to water and glycerol, they may also transport small solutes such as hydrogen peroxide. The genome sequence of A. niger N402 was screened for putative AQPs. Seven AQPs were found and could be classified into three main groups. One protein (AQPA) belonged to orthodox AQP, three (AQPB, AQPD, and AQPE) were grouped in aquaglyceroporins (AQGP), two (AQPC and AQPF) were in X-intrinsic proteins (XIPs), and the other (AQPG) could not be classified. Their ability to facilitate diffusion of hydrogen peroxide was identified using yeast phenotypic growth assays and by studying AQP gene knock-outs in A. niger. The X-intrinsic protein AQPF appears to play roles in facilitating hydrogen peroxide transport across the cellular membrane in both Saccharomyces cerevisiae and A. niger experiments.

FAIR data station for lightweight metadata management and validation of omics studies

BACKGROUND

The life sciences are one of the biggest suppliers of scientific data. Reusing and connecting these data can uncover hidden insights and lead to new concepts. Efficient reuse of these datasets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles have been accepted by all stakeholders, in practice, there are only a limited number of easy-to-adopt implementations available that fulfill the needs of data producers.

FINDINGS

We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of 3 modules. Based on the minimal information model(s) selected by the user, the "form generation module" creates a metadata template Excel workbook with a header row of machine-actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process, the format of the recorded values can be checked using the "validation module." Finally, the "resource module" can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive-compatible XML metadata file.

CONCLUSIONS

Turning FAIR into reality requires the availability of easy-to-adopt data FAIRification workflows that are also of direct use for data producers. As such, the FAIR Data Station provides, in addition to the means to correctly FAIRify (omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl.

Model-driven approach for the production of butyrate from CO2/H2 by a novel co-culture of C. autoethanogenum and C. beijerinckii

One-carbon (C1) compounds are promising feedstocks for the sustainable production of commodity chemicals. CO₂ is a particularly advantageous C1-feedstock since it is an unwanted industrial off-gas that can be converted into valuable products while reducing its atmospheric levels. Acetogens are microorganisms that can grow on CO₂/H₂ gas mixtures and syngas converting these substrates into ethanol and acetate. Co-cultivation of acetogens with other microbial species that can further process such products, can expand the variety of products to, for example, medium chain fatty acids (MCFA) and longer chain alcohols. Solventogens are microorganisms known to produce MCFA and alcohols via the acetone-butanol-ethanol (ABE) fermentation in which acetate is a key metabolite. Thus, co-cultivation of an acetogen and a solventogen in a consortium provides a potential platform to produce valuable chemicals from CO₂. In this study, metabolic modeling was implemented to design a new co-culture of an acetogen and a solventogen to produce butyrate from CO₂/H₂ mixtures. The model-driven approach suggested the ability of the studied solventogenic species to grow on lactate/glycerol with acetate as co-substrate. This ability was confirmed experimentally by cultivation of Clostridium beijerinckii on these substrates in batch serum bottles and subsequently in pH-controlled bioreactors. Community modeling also suggested that a novel microbial consortium consisting of the acetogen Clostridium autoethanogenum, and the solventogen C. beijerinckii would be feasible and stable. On the basis of this prediction, a co-culture was experimentally established. C. autoethanogenum grew on CO₂/H₂ producing acetate and traces of ethanol. Acetate was in turn, consumed by C. beijerinckii together with lactate, producing butyrate. These results show that community modeling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of chemicals from renewable resources.

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

BACKGROUND

Microbial 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.

RESULTS

Our 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.

CONCLUSIONS

The 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.

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.

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.

Genomic convergence between Akkermansia muciniphila in different mammalian hosts

Background

Akkermansia muciniphila is a member of the human gut microbiota where it resides in the mucus layer and uses mucin as the sole carbon, nitrogen and energy source. A. muciniphila is the only representative of the Verrucomicrobia phylum in the human gut. However, A. muciniphila 16S rRNA gene sequences have also been found in the intestines of many vertebrates.

Results

We detected A. muciniphila-like bacteria in the intestines of animals belonging to 15 out of 16 mammalian orders. In addition, other species belonging to the Verrucomicrobia phylum were detected in fecal samples. We isolated 10 new A. muciniphila strains from the feces of chimpanzee, siamang, mouse, pig, reindeer, horse and elephant. The physiology and genome of these strains were highly similar in comparison to the type strain A. muciniphila Muc^T. Overall, the genomes of the new strains showed high average nucleotide identity (93.9 to 99.7%). In these genomes, we detected considerable conservation of at least 75 of the 78 mucin degradation genes that were previously detected in the genome of the type strain Muc^T.

Conclusions

The low genomic divergence observed in the new strains may indicate that A. muciniphila favors mucosal colonization independent of the differences in hosts. In addition, the conserved mucus degradation capability points towards a similar beneficial role of the new strains in regulating host metabolic health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02360-6.

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.

Global Transcriptional Response of Aspergillus niger to Blocked Active Citrate Export through Deletion of the Exporter Gene

Aspergillus niger is the major industrial citrate producer worldwide. Export as well as uptake of citric acid are believed to occur by active, proton-dependent, symport systems. Both are major bottlenecks for industrial citrate production. Therefore, we assessed the consequences of deleting the citT gene encoding the A. niger citrate exporter, effectively blocking active citrate export. We followed the consumption of glucose and citrate as carbon sources, monitored the secretion of organic acids and carried out a thorough transcriptome pathway enrichment analysis. Under controlled cultivation conditions that normally promote citrate secretion, the knock-out strain secreted negligible amounts of citrate. Blocking active citrate export in this way led to a reduced glucose uptake and a reduced expression of high-affinity glucose transporter genes, mstG and mstH. The glyoxylate shunt was strongly activated and an increased expression of the OAH gene was observed, resulting in a more than two-fold higher concentration of oxalate in the medium. Deletion of citT did not affect citrate uptake suggesting that citrate export and citrate uptake are uncoupled from the system.

From Innovation to Application: Bridging the Valley of Death in Industrial Biotechnology

Few biotechnology innovations make it through the Valley of Death to markets. Based on our experience with academia, technology transfer offices, and industry, we provide insights into differences in operating levels, how to best traverse the Valley of Death, and ways to foster more innovation towards market implementation.

A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses

BACKGROUND

Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a "commons." Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions.

RESULTS

As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates.

CONCLUSIONS

Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).

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.

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

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We model a co-culture of C. autoethanogenum and C. kluyveri for syngas fermentation.

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Biomass species ratio affects ethanol and acetate profiles in the co-culture.

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The model predicts that addition of succinate increases caproate production.

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Genetic interventions  in C. autoethanogenum could increase caproate production.

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 co-cultivation 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 one strategy to optimize the production of medium-chain fatty-acids from syngas would be the addition of succinate. According to the prediction, addition of succinate would increase the pool of crotonyl-CoA and the ethanol/acetate uptake ratio in C. kluyveri, resulting in a flux of up to 60% of electrons into hexanoate. Another potential way to further optimize butyrate and hexanoate production would be an increase of C. autoethanogenum ethanol production. Blocking either acetaldehyde dehydrogenase or formate dehydrogenase (ferredoxin) activity or formate transport, in the C. autoethanogenum metabolic model could potentially lead to an up to 150% increase in ethanol production.

Combined Transcriptome Sequencing of Mycoplasma hyopneumoniae and Infected Pig Lung Tissue Reveals Up-Regulation of Bacterial F1-Like ATPase and Down-Regulation of the P102 Cilium Adhesin in vivo

Mycoplasma hyopneumoniae (M. hyopneumoniae) causes enzootic pneumonia in pigs but it is still largely unknown which host-pathogen interactions enable persistent infection and cause disease. In this study, we analyzed the host and bacterial transcriptomes during infection using RNA sequencing. Comparison of the transcriptome of lung lesion tissue from infected pigs with lung tissue from non-infected animals, identified 424 differentially expressed genes (FDR < 0.01 and fold change > 1.5LOG2). These genes were part of the following major pathways of the immune system: interleukin signaling (type 4, 10, 13, and 18), regulation of Toll-like receptors by endogenous ligand and activation of C3 and C5 in the complement system. Besides analyzing the lung transcriptome, a sampling protocol was developed to obtain enough bacterial mRNA from infected lung tissue for RNA sequencing. This was done by flushing infected lobes in the lung, and subsequently enriching for bacterial RNA. On average, 2.2 million bacterial reads were obtained per biological replicate to analyze the bacterial in vivo transcriptome. We compared the in vivo bacterial transcriptome with the transcriptome of bacteria grown in vitro and identified 22 up-regulated and 30 down-regulated genes (FDR < 0.01 and fold change > 2LOG2). Six out of seven genes in the operon encoding the mycoplasma specific F1-like ATPase (MHP_RS02445-MHP_RS02475) and all genes in the operon MHP_RS01965-MHP_RS01990 with functions related to nucleotide metabolism, spermidine transport and glycerol-3-phoshate transport were up-regulated in vivo. Down-regulated in vivo were genes related to glycerol uptake, cilium adhesion (P102), cell division and myo-inositol metabolism. In addition to providing a novel method to isolate bacterial mRNA from infected lung, this study provided insights into changes in gene expression during infection, which could help development of novel treatment strategies against enzootic pneumonia caused by M. hyopneumoniae.

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. We hypothesise that depending on the experimental setup, extracellular citrate accumulation can have fundamentally different underlying transcriptomic landscapes. We show that varying the amount and type of supplement of an arginine auxotrophic A. niger strain results in transcriptional down-regulation of citrate metabolising enzymes in the condition in which more citrate is accumulated extracellularly. This contrasts with the transcriptional adaptations when increased citrate production is triggered by iron limitation. By combining gene expression data obtained from these two very distinct experimental setups with hidden Markov models and transporter homology approaches, we were able to compile a shortlist of the most likely citrate transporter candidates. Two candidates (An17g01710 and An09g06720m.01) were heterologously expressed in the yeast Saccharomyces cerevisiae, and one of the resultant mutants showed the ability to secrete citrate. Our findings provide steps in untangling the complex interplay of different mechanisms underlying A. niger citrate accumulation, and we demonstrate how a comparative transcriptomics approach complemented with further bioinformatics analyses can be used to pinpoint a fungal citrate exporter.

Toxicological response of the model fungus Saccharomyces cerevisiae to different concentrations of commercial graphene nanoplatelets

Graphene nanomaterials have attracted a great interest during the last years for different applications, but their possible impact on different biological systems remains unclear. Here, an assessment to understand the toxicity of commercial polycarboxylate functionalized graphene nanoplatelets (GN) on the unicellular fungal model Saccharomyces cerevisiae was performed. While cell proliferation was not negatively affected even in the presence of 800 mg L-1 of the nanomaterial for 24 hours, oxidative stress was induced at a lower concentration (160 mg L-1), after short exposure periods (2 and 4 hours). No DNA damage was observed under a comet assay analysis under the studied conditions. In addition, to pinpoint the molecular mechanisms behind the early oxidative damage induced by GN and to identify possible toxicity pathways, the transcriptome of S. cerevisiae exposed to 160 and 800 mg L-1 of GN was studied. Both GN concentrations induced expression changes in a common group of genes (337), many of them related to the fungal response to reduce the nanoparticles toxicity and to maintain cell homeostasis. Also, a high number of genes were only differentially expressed in the GN800 condition (3254), indicating that high GN concentrations can induce severe changes in the physiological state of the yeast.

NG-Tax 2.0: A Semantic Framework for High-Throughput Amplicon Analysis

NG-Tax 2.0 is a semantic framework for FAIR high-throughput analysis and classification of marker gene amplicon sequences including bacterial and archaeal 16S ribosomal RNA (rRNA), eukaryotic 18S rRNA and ribosomal intergenic transcribed spacer sequences. It can directly use single or merged reads, paired-end reads and unmerged paired-end reads from long range fragments as input to generate de novo amplicon sequence variants (ASV). Using the RDF data model, ASV's can be automatically stored in a graph database as objects that link ASV sequences with the full data-wise and element-wise provenance, thereby achieving the level of interoperability required to utilize such data to its full potential. The graph database can be directly queried, allowing for comparative analyses of over thousands of samples and is connected with an interactive Rshiny toolbox for analysis and visualization of (meta) data. Additionally, NG-Tax 2.0 exports an extended BIOM 1.0 (JSON) file as starting point for further analyses by other means. The extended BIOM file contains new attribute types to include information about the command arguments used, the sequences of the ASVs formed, classification confidence scores and is backwards compatible. The performance of NG-Tax 2.0 was compared with DADA2, using the plugin in the QIIME 2 analysis pipeline. Fourteen 16S rRNA gene amplicon mock community samples were obtained from the literature and evaluated. Precision of NG-Tax 2.0 was significantly higher with an average of 0.95 vs 0.58 for QIIME2-DADA2 while recall was comparable with an average of 0.85 and 0.77, respectively. NG-Tax 2.0 is written in Java. The code, the ontology, a Galaxy platform implementation, the analysis toolbox, tutorials and example SPARQL queries are freely available at http://wurssb.gitlab.io/ngtax under the MIT License.

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.

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.

Distant Non-Obvious Mutations Influence the Activity of a Hyperthermophilic Pyrococcus furiosus Phosphoglucose Isomerase

The cupin-type phosphoglucose isomerase (PfPGI) from the hyperthermophilic archaeon Pyrococcus furiosus catalyzes the reversible isomerization of glucose-6-phosphate to fructose-6-phosphate. We investigated PfPGI using protein-engineering bioinformatics tools to select functionally-important residues based on correlated mutation analyses. A pair of amino acids in the periphery of PfPGI was found to be the dominant co-evolving mutation. The position of these selected residues was found to be non-obvious to conventional protein engineering methods. We designed a small smart library of variants by substituting the co-evolved pair and screened their biochemical activity, which revealed their functional relevance. Four mutants were further selected from the library for purification, measurement of their specific activity, crystal structure determination, and metal cofactor coordination analysis. Though the mutant structures and metal cofactor coordination were strikingly similar, variations in their activity correlated with their fine-tuned dynamics and solvent access regulation. Alternative, small smart libraries for enzyme optimization are suggested by our approach, which is able to identify non-obvious yet beneficial mutations.

Modeling Host-Pathogen Interaction to Elucidate the Metabolic Drug Response of Intracellular Mycobacterium tuberculosis

Little is known about the metabolic state of Mycobacterium tuberculosis (Mtb) inside the phagosome, a compartment inside phagocytes for killing pathogens and other foreign substances. We have developed a combined model of Mtb and human metabolism, sMtb-RECON and used this model to predict the metabolic state of Mtb during infection of the host. Amino acids are predicted to be used for energy production as well as biomass formation. Subsequently we assessed the effect of increasing dosages of drugs targeting metabolism on the metabolic state of the pathogen and predict resulting metabolic adaptations and flux rerouting through various pathways. In particular, the TCA cycle becomes more important upon drug application, as well as alanine, aspartate, glutamate, proline, arginine and porphyrin metabolism, while glycine, serine, and threonine metabolism become less important. We modeled the effect of 11 metabolically active drugs. Notably, the effect of eight could be recreated and two major profiles of the metabolic state were predicted. The profiles of the metabolic states of Mtb affected by the drugs BTZ043, cycloserine and its derivative terizidone, ethambutol, ethionamide, propionamide, and isoniazid were very similar, while TMC207 is predicted to have quite a different effect on metabolism as it inhibits ATP synthase and therefore indirectly interferes with a multitude of metabolic pathways.

Risk-Based Bioengineering Strategies for Reliable Bacterial Vaccine Production

Design of a reliable process for bacterial antigen production requires understanding of and control over critical process parameters. Current methods for process design use extensive screening experiments for determining ranges of critical process parameters yet fail to give clear insights into how they influence antigen potency. To address this gap, we propose to apply constraint-based, genome-scale metabolic models to reduce the need of experimental screening for strain selection and to optimize strains based on model driven iterative Design-Build-Test-Learn (DBTL) cycles. Application of these systematic methods has not only increased the understanding of how metabolic network properties influence antigen potency, but also allows identification of novel critical process parameters that need to be controlled to achieve high process reliability.

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

Summary

To unlock the full potential of genome data and to enhance data interoperability and reusability of genome annotations we have developed SAPP, a Semantic Annotation Platform with Provenance. SAPP is designed as an infrastructure supporting FAIR de novo computational genomics but can also be used to process and analyze existing genome annotations. SAPP automatically predicts, tracks and stores structural and functional annotations and associated dataset- and element-wise provenance in a Linked Data format, thereby enabling information mining and retrieval with Semantic Web technologies. This greatly reduces the administrative burden of handling multiple analysis tools and versions thereof and facilitates multi-level large scale comparative analysis.

Availability and implementation

SAPP is written in JAVA and freely available at https://gitlab.com/sapp and runs on Unix-like operating systems. The documentation, examples and a tutorial are available at https://sapp.gitlab.io.

Comparative Genomics Highlights Symbiotic Capacities and High Metabolic Flexibility of the Marine Genus Pseudovibrio

Pseudovibrio is a marine bacterial genus members of which are predominantly isolated from sessile marine animals, and particularly sponges. It has been hypothesized that Pseudovibrio spp. form mutualistic relationships with their hosts. Here, we studied Pseudovibrio phylogeny and genetic adaptations that may play a role in host colonization by comparative genomics of 31 Pseudovibrio strains, including 25 sponge isolates. All genomes were highly similar in terms of encoded core metabolic pathways, albeit with substantial differences in overall gene content. Based on gene composition, Pseudovibrio spp. clustered by geographic region, indicating geographic speciation. Furthermore, the fact that isolates from the Mediterranean Sea clustered by sponge species suggested host-specific adaptation or colonization. Genome analyses suggest that Pseudovibrio hongkongensis UST20140214-015BT is only distantly related to other Pseudovibrio spp., thereby challenging its status as typical Pseudovibrio member. All Pseudovibrio genomes were found to encode numerous proteins with SEL1 and tetratricopeptide repeats, which have been suggested to play a role in host colonization. For evasion of the host immune system, Pseudovibrio spp. may depend on type III, IV, and VI secretion systems that can inject effector molecules into eukaryotic cells. Furthermore, Pseudovibrio genomes carry on average seven secondary metabolite biosynthesis clusters, reinforcing the role of Pseudovibrio spp. as potential producers of novel bioactive compounds. Tropodithietic acid, bacteriocin, and terpene biosynthesis clusters were highly conserved within the genus, suggesting an essential role in survival, for example through growth inhibition of bacterial competitors. Taken together, these results support the hypothesis that Pseudovibrio spp. have mutualistic relations with sponges.

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.

Modeling the Metabolic State of Mycobacterium tuberculosis Upon Infection

Genome-scale metabolic models of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, have been envisioned as a platform for drug discovery. By systematically probing the networks that underpin such models, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Nevertheless, this is complicated by the limited knowledge on the environment that Mtb encounters during infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of the metabolic state requires a precise biomass reaction for the described condition. In recent years, additional insights in the in-host environment occupied by Mtb have been gained as transcript abundance data of interacting host and pathogen have become available. Therefore, we used transcript abundance data and developed a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the “metabolic objective” of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used to predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Our results show that nutrient uptake can accurately be predicted. Gene essentiality can also be predicted but accurate predictions remain difficult to obtain. In conclusion, a viable strategy to model Mtb metabolism in hard-to-access environments that is virtually free of pre-set assumptions is provided.

Modulating D-amino acid oxidase (DAAO) substrate specificity through facilitated solvent access

D-amino acid oxidase (DAAO) degrades D-amino acids to produce α-ketoacids, hydrogen peroxide and ammonia. DAAO has often been investigated and engineered for industrial and clinical applications. We combined information from literature with a detailed analysis of the structure to engineer mammalian DAAOs. The structural analysis was complemented with molecular dynamics simulations to characterize solvent accessibility and product release mechanisms. We identified non-obvious residues located on the loops on the border between the active site and the secondary binding pocket essential for pig and human DAAO substrate specificity and activity. We engineered DAAOs by mutating such critical residues and characterised the biochemical activity of the resulting variants. The results highlight the importance of the selected residues in modulating substrate specificity, product egress and enzyme activity, suggesting further steps of DAAO re-engineering towards desired clinical and industrial applications.

Deciphering the trophic interaction between Akkermansia muciniphila and the butyrogenic gut commensal Anaerostipes caccae using a metatranscriptomic approach

Host glycans are paramount in regulating the symbiotic relationship between humans and their gut bacteria. The constant flux of host-secreted mucin at the mucosal layer creates a steady niche for bacterial colonization. Mucin degradation by keystone species subsequently shapes the microbial community. This study investigated the transcriptional response during mucin-driven trophic interaction between the specialised mucin-degrader Akkermansia muciniphila and a butyrogenic gut commensal Anaerostipes caccae. A. muciniphila monocultures and co-cultures with non-mucolytic A. caccae from the Lachnospiraceae family were grown anaerobically in minimal media supplemented with mucin. We analysed for growth, metabolites (HPLC analysis), microbial composition (quantitative reverse transcription PCR), and transcriptional response (RNA-seq). Mucin degradation by A. muciniphila supported the growth of A. caccae and concomitant butyrate production predominantly via the acetyl-CoA pathway. Differential expression analysis (DESeq 2) showed the presence of A. caccae induced changes in the A. muciniphila transcriptional response with increased expression of mucin degradation genes and reduced expression of ribosomal genes. Two putative operons that encode for uncharacterised proteins and an efflux system, and several two-component systems were also differentially regulated. This indicated A. muciniphila changed its transcriptional regulation in response to A. caccae. This study provides insight to understand the mucin-driven microbial ecology using metatranscriptomics. Our findings show that the expression of mucolytic enzymes by A. muciniphila increases upon the presence of a community member. This could indicate its role as a keystone species that supports the microbial community in the mucosal environment by increasing the availability of mucin sugars.

Neochloris oleoabundans is worth its salt: Transcriptomic analysis under salt and nitrogen stress

Neochloris oleoabundans is an oleaginous microalgal species that can be cultivated in fresh water as well as salt water. Using salt water gives the opportunity to reduce production costs and the fresh water footprint for large scale cultivation. Production of triacylglycerols (TAG) usually includes a biomass growth phase in nitrogen-replete conditions followed by a TAG accumulation phase under nitrogen-deplete conditions. This is the first report that provides insight in the saline resistance mechanism of a fresh water oleaginous microalgae. To better understand the osmoregulatory mechanism of N. oleoabundans during growth and TAG accumulating conditions, the transcriptome was sequenced under four different conditions: fresh water nitrogen-replete and -deplete conditions, and salt water (525 mM dissolved salts, 448mM extra NaCl) nitrogen-replete and -deplete conditions. In this study, several pathways are identified to be responsible for salt water adaptation of N. oleoabundans under both nitrogen-replete and -deplete conditions. Proline and the ascorbate-glutathione cycle seem to be of importance for successful osmoregulation in N. oleoabundans. Genes involved in Proline biosynthesis were found to be upregulated in salt water. This was supported by Nuclear magnetic resonance (NMR) spectroscopy, which indicated an increase in proline content in the salt water nitrogen-replete condition. Additionally, the lipid accumulation pathway was studied to gain insight in the gene regulation in the first 24 hours after nitrogen was depleted. Oil accumulation is increased under nitrogen-deplete conditions in a comparable way in both fresh and salt water. The mechanism behind the biosynthesis of compatible osmolytes can be used to improve N. oleoabundans and other industrially relevant microalgal strains to create a more robust and sustainable production platform for microalgae derived products in the future.

Studying microbial functionality within the gut ecosystem by systems biology

Humans are not autonomous entities. We are all living in a complex environment, interacting not only with our peers, but as true holobionts; we are also very much in interaction with our coexisting microbial ecosystems living on and especially within us, in the intestine. Intestinal microorganisms, often collectively referred to as intestinal microbiota, contribute significantly to our daily energy uptake by breaking down complex carbohydrates into simple sugars, which are fermented to short-chain fatty acids and subsequently absorbed by human cells. They also have an impact on our immune system, by suppressing or enhancing the growth of malevolent and beneficial microbes. Our lifestyle can have a large influence on this ecosystem. What and how much we consume can tip the ecological balance in the intestine. A "western diet" containing mainly processed food will have a different effect on our health than a balanced diet fortified with pre- and probiotics. In recent years, new technologies have emerged, which made a more detailed understanding of microbial communities and ecosystems feasible. This includes progress in the sequencing of PCR-amplified phylogenetic marker genes as well as the collective microbial metagenome and metatranscriptome, allowing us to determine with an increasing level of detail, which microbial species are in the microbiota, understand what these microorganisms do and how they respond to changes in lifestyle and diet. These new technologies also include the use of synthetic and in vitro systems, which allow us to study the impact of substrates and addition of specific microbes to microbial communities at a high level of detail, and enable us to gather quantitative data for modelling purposes. Here, we will review the current state of microbiome research, summarizing the computational methodologies in this area and highlighting possible outcomes for personalized nutrition and medicine.

Model-driven design of a minimal medium for Akkermansia muciniphila confirms mucus adaptation

The abundance of the human intestinal symbiont Akkermansia muciniphila has found to be inversely correlated with several diseases, including metabolic syndrome and obesity. A. muciniphila is known to use mucin as sole carbon and nitrogen source. To study the physiology and the potential for therapeutic applications of this bacterium, we designed a defined minimal medium. The composition of the medium was based on the genome‐scale metabolic model of A. muciniphila and the composition of mucin. Our results indicate that A. muciniphila does not code for GlmS, the enzyme that mediates the conversion of fructose‐6‐phosphate (Fru6P) to glucosamine‐6‐phosphate (GlcN6P), which is essential in peptidoglycan formation. The only annotated enzyme that could mediate this conversion is Amuc‐NagB on locus Amuc_1822. We found that Amuc‐NagB was unable to form GlcN6P from Fru6P at physiological conditions, while it efficiently catalyzed the reverse reaction. To overcome this inability, N‐acetylglucosamine needs to be present in the medium for A. muciniphila growth. With these findings, the genome‐scale metabolic model was updated and used to accurately predict growth of A. muciniphila on synthetic media. The finding that A. muciniphila has a necessity for GlcNAc, which is present in mucin further prompts the adaptation to its mucosal niche.

Regulation of Three Virulence Strategies of Mycobacterium tuberculosis: A Success Story

Tuberculosis remains one of the deadliest diseases. Emergence of drug-resistant and multidrug-resistant M. tuberculosis strains makes treating tuberculosis increasingly challenging. In order to develop novel intervention strategies, detailed understanding of the molecular mechanisms behind the success of this pathogen is required. Here, we review recent literature to provide a systems level overview of the molecular and cellular components involved in divalent metal homeostasis and their role in regulating the three main virulence strategies of M. tuberculosis: immune modulation, dormancy and phagosomal rupture. We provide a visual and modular overview of these components and their regulation. Our analysis identified a single regulatory cascade for these three virulence strategies that respond to limited availability of divalent metals in the phagosome.

Improving heterologous membrane protein production in Escherichia coli by combining transcriptional tuning and codon usage algorithms

High-level, recombinant production of membrane-integrated proteins in Escherichia coli is extremely relevant for many purposes, but has also been proven challenging. Here we study a combination of transcriptional fine-tuning in E. coli LEMO21(DE3) with different codon usage algorithms for heterologous production of membrane proteins. The overexpression of 6 different membrane proteins is compared for the wild-type gene codon usage variant, a commercially codon-optimized variant, and a codon-harmonized variant. We show that transcriptional fine-tuning plays a major role in improving the production of all tested proteins. Moreover, different codon usage variants significantly improved production of some of the tested proteins. However, not a single algorithm performed consistently best for the membrane-integrated production of the 6 tested proteins. In conclusion, for improving heterologous membrane protein production in E. coli, the major effect is accomplished by transcriptional tuning. In addition, further improvements may be realized by attempting different codon usage variants, such as codon harmonized variants, which can now be easily generated through our online Codon Harmonizer tool.

Genomic and functional analysis of Romboutsia ilealis CRIBT reveals adaptation to the small intestine

BACKGROUND

The microbiota in the small intestine relies on their capacity to rapidly import and ferment available carbohydrates to survive in a complex and highly competitive ecosystem. Understanding how these communities function requires elucidating the role of its key players, the interactions among them and with their environment/host.

METHODS

The genome of the gut bacterium Romboutsia ilealis CRIBT was sequenced with multiple technologies (Illumina paired-end, mate-pair and PacBio). The transcriptome was sequenced (Illumina HiSeq) after growth on three different carbohydrate sources, and short chain fatty acids were measured via HPLC.

RESULTS

We present the complete genome of Romboutsia ilealis CRIBT, a natural inhabitant and key player of the small intestine of rats. R. ilealis CRIBT possesses a circular chromosome of 2,581,778 bp and a plasmid of 6,145 bp, carrying 2,351 and eight predicted protein coding sequences, respectively. Analysis of the genome revealed limited capacity to synthesize amino acids and vitamins, whereas multiple and partially redundant pathways for the utilization of different relatively simple carbohydrates are present. Transcriptome analysis allowed identification of the key components in the degradation of glucose, L-fucose and fructo-oligosaccharides.

DISCUSSION

This revealed that R. ilealis CRIBT is adapted to a nutrient-rich environment where carbohydrates, amino acids and vitamins are abundantly available.

Identification of methylated GnTI-dependent N-glycans in Botryococcus brauni

In contrast to mammals and vascular plants, microalgae show a high diversity in the N-glycan structures of complex N-glycoproteins. Although homologues for β1,2-N-acetylglucosaminyltransferase I (GnTI), a key enzyme in the formation of complex N-glycans, have been identified in several algal species, GnTI-dependent N-glycans have not been detected so far. We have performed an N-glycoproteomic analysis of the hydrocarbon oils accumulating green microalgae Botryococcus braunii. Thereby, the analysis of intact N-glycopeptides allowed the determination of N-glycan compositions. Furthermore, insights into the role of N-glycosylation in B. braunii were gained from functional annotation of the identified N-glycoproteins. In total, 517 unique N-glycosylated peptides have been identified, including intact N-glycopeptides that harbored N-acetylhexosamine (HexNAc) at the nonreducing end. Surprisingly, these GnTI-dependent N-glycans were also found to be modified with (di)methylated hexose. The identification of GnTI-dependent N-glycans in combination with N-glycan methylation in B. braunii revealed an uncommon type of N-glycan processing in this microalgae.

Aspergillus niger Secretes Citrate to Increase Iron Bioavailability

Aspergillus niger has an innate ability to secrete various organic acids, including citrate. The conditions required for A. niger citrate overproduction are well described, but the physiological reasons underlying extracellular citrate accumulation are not yet fully understood. One of the less understood culture conditions is the requirement of growth-limiting iron concentrations. While this has been attributed to iron-dependent citrate metabolizing enzymes, this straightforward relationship does not always hold true. Here, we show that an increase in citrate secretion under iron limited conditions is a physiological response consistent with a role of citrate as A. niger iron siderophore. We found that A. niger citrate secretion increases with decreasing amounts of iron added to the culture medium and, in contrast to previous findings, this response is independent of the nitrogen source. Differential transcriptomics analyses of the two A. niger mutants NW305 (gluconate non-producer) and NW186 (gluconate and oxalate non-producer) revealed up-regulation of the citrate biosynthesis gene citA under iron limited conditions compared to iron replete conditions. In addition, we show that A. niger can utilize Fe(III) citrate as iron source. Finally, we discuss our findings in the general context of the pH-dependency of A. niger organic acid production, offering an explanation, besides competition, for why A. niger organic acid production is a sequential process influenced by the external pH of the culture medium.

Metabolic modeling of energy balances in Mycoplasma hyopneumoniae shows that pyruvate addition increases growth rate

Mycoplasma hyopneumoniae is cultured on large-scale to produce antigen for inactivated whole-cell vaccines against respiratory disease in pigs. However, the fastidious nutrient requirements of this minimal bacterium and the low growth rate make it challenging to reach sufficient biomass yield for antigen production. In this study, we sequenced the genome of M. hyopneumoniae strain 11 and constructed a high quality constraint-based genome-scale metabolic model of 284 chemical reactions and 298 metabolites. We validated the model with time-series data of duplicate fermentation cultures to aim for an integrated model describing the dynamic profiles measured in fermentations. The model predicted that 84% of cellular energy in a standard M. hyopneumoniae cultivation was used for non-growth associated maintenance and only 16% of cellular energy was used for growth and growth associated maintenance. Following a cycle of model-driven experimentation in dedicated fermentation experiments, we were able to increase the fraction of cellular energy used for growth through pyruvate addition to the medium. This increase in turn led to an increase in growth rate and a 2.3 times increase in the total biomass concentration reached after 3-4 days of fermentation, enhancing the productivity of the overall process. The model presented provides a solid basis to understand and further improve M. hyopneumoniae fermentation processes. Biotechnol. Bioeng. 2017;114: 2339-2347. © 2017 Wiley Periodicals, Inc.

CorNet: Assigning function to networks of co-evolving residues by automated literature mining

CorNet is a web-based tool for the analysis of co-evolving residue positions in protein super-family sequence alignments. CorNet projects external information such as mutation data extracted from literature on interactively displayed groups of co-evolving residue positions to shed light on the functions associated with these groups and the residues in them. We used CorNet to analyse six enzyme super-families and found that groups of strongly co-evolving residues tend to consist of residues involved in a same function such as activity, specificity, co-factor binding, or enantioselectivity. This finding allows to assign a function to residues for which no data is available yet in the literature. A mutant library was designed to mutate residues observed in a group of co-evolving residues predicted to be involved in enantioselectivity, but for which no literature data is available yet. The resulting set of mutations indeed showed many instances of increased enantioselectivity.

Comparative proteomics of Rhizopus delemar ATCC 20344 unravels the role of amino acid catabolism in fumarate accumulation

The filamentous fungus Rhizopus delemar naturally accumulates relatively high amounts of fumarate. Although the culture conditions that increase fumarate yields are well established, the network underlying the accumulation of fumarate is not yet fully understood. We set out to increase the knowledge about fumarate accumulation in R. delemar. To this end, we combined a transcriptomics and proteomics approach to identify key metabolic pathways involved in fumarate production in R. delemar, and propose that a substantial part of the fumarate accumulated in R. delemar during nitrogen starvation results from the urea cycle due to amino acid catabolism.

Persistence of Functional Protein Domains in Mycoplasma Species and their Role in Host Specificity and Synthetic Minimal Life

Mycoplasmas are the smallest self-replicating organisms and obligate parasites of a specific vertebrate host. An in-depth analysis of the functional capabilities of mycoplasma species is fundamental to understand how some of simplest forms of life on Earth succeeded in subverting complex hosts with highly sophisticated immune systems. In this study we present a genome-scale comparison, focused on identification of functional protein domains, of 80 publically available mycoplasma genomes which were consistently re-annotated using a standardized annotation pipeline embedded in a semantic framework to keep track of the data provenance. We examined the pan- and core-domainome and studied predicted functional capability in relation to host specificity and phylogenetic distance. We show that the pan- and core-domainome of mycoplasma species is closed. A comparison with the proteome of the “minimal” synthetic bacterium JCVI-Syn3.0 allowed us to classify domains and proteins essential for minimal life. Many of those essential protein domains, essential Domains of Unknown Function (DUFs) and essential hypothetical proteins are not persistent across mycoplasma genomes suggesting that mycoplasma species support alternative domain configurations that bypass their essentiality. Based on the protein domain composition, we could separate mycoplasma species infecting blood and tissue. For selected genomes of tissue infecting mycoplasmas, we could also predict whether the host is ruminant, pig or human. Functionally closely related mycoplasma species, which have a highly similar protein domain repertoire, but different hosts could not be separated. This study provides a concise overview of the functional capabilities of mycoplasma species, which can be used as a basis to further understand host-pathogen interaction or to design synthetic minimal life.

Comparison of 432 Pseudomonas strains through integration of genomic, functional, metabolic and expression data

Pseudomonas is a highly versatile genus containing species that can be harmful to humans and plants while others are widely used for bioengineering and bioremediation. We analysed 432 sequenced Pseudomonas strains by integrating results from a large scale functional comparison using protein domains with data from six metabolic models, nearly a thousand transcriptome measurements and four large scale transposon mutagenesis experiments. Through heterogeneous data integration we linked gene essentiality, persistence and expression variability. The pan-genome of Pseudomonas is closed indicating a limited role of horizontal gene transfer in the evolutionary history of this genus. A large fraction of essential genes are highly persistent, still non essential genes represent a considerable fraction of the core-genome. Our results emphasize the power of integrating large scale comparative functional genomics with heterogeneous data for exploring bacterial diversity and versatility.

Complete genome sequence of thermophilic Bacillus smithii type strain DSM 4216(T)

Bacillus smithii is a facultatively anaerobic, thermophilic bacterium able to use a variety of sugars that can be derived from lignocellulosic feedstocks. Being genetically accessible, it is a potential new host for biotechnological production of green chemicals from renewable resources. We determined the complete genomic sequence of the B. smithii type strain DSM 4216^T, which consists of a 3,368,778 bp chromosome (GenBank accession number CP012024.1) and a 12,514 bp plasmid (GenBank accession number CP012025.1), together encoding 3880 genes. Genome annotation via RAST was complemented by a protein domain analysis. Some unique features of B. smithii central metabolism in comparison to related organisms included the lack of a standard acetate production pathway with no apparent pyruvate formate lyase, phosphotransacetylase, and acetate kinase genes, while acetate was the second fermentation product.