Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus

ABSTRACT Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati. The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by 13C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi. IMPORTANCE Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Neocallimastix lanati. Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.

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Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Applied and Environmental Microbiology ◽

10.1128/aem.01795-12 ◽

2012 ◽

Vol 78 (24) ◽

pp. 8735-8742 ◽

Cited By ~ 12

Author(s):

Yilin Fang ◽

Michael J. Wilkins ◽

Steven B. Yabusaki ◽

Mary S. Lipton ◽

Philip E. Long

Keyword(s):

Proteomic Analysis ◽

In Silico ◽

Field Experiments ◽

Metabolic Model ◽

Geobacter Metallireducens ◽

Content Type ◽

Proteomic Data ◽

Subsurface Environment ◽

A Genome ◽

Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

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High‐quality genome‐scale metabolic model of Aurantiochytrium sp. T66

Biotechnology and Bioengineering ◽

10.1002/bit.27726 ◽

2021 ◽

Author(s):

Vetle Simensen ◽

André Voigt ◽

Eivind Almaas

Keyword(s):

Metabolic Model ◽

High Quality ◽

Genome Scale ◽

High Quality Genome

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Metabolic control of nitrogen fixation in rhizobium-legume symbioses

Science Advances ◽

10.1126/sciadv.abh2433 ◽

2021 ◽

Vol 7 (31) ◽

pp. eabh2433

Author(s):

Carolin C. M. Schulte ◽

Khushboo Borah ◽

Rachel M. Wheatley ◽

Jason J. Terpolilli ◽

Gerhard Saalbach ◽

...

Keyword(s):

Metabolic Flux ◽

Tricarboxylic Acid Cycle ◽

Oxygen Demand ◽

Metabolic Model ◽

Redox Balance ◽

Ammonia Assimilation ◽

Nodule Formation ◽

Flux Analysis ◽

Carbon And Nitrogen ◽

Genome Scale

Rhizobia induce nodule formation on legume roots and differentiate into bacteroids, which catabolize plant-derived dicarboxylates to reduce atmospheric N2 into ammonia. Despite the agricultural importance of this symbiosis, the mechanisms that govern carbon and nitrogen allocation in bacteroids and promote ammonia secretion to the plant are largely unknown. Using a metabolic model derived from genome-scale datasets, we show that carbon polymer synthesis and alanine secretion by bacteroids facilitate redox balance in microaerobic nodules. Catabolism of dicarboxylates induces not only a higher oxygen demand but also a higher NADH/NAD+ ratio than sugars. Modeling and 13C metabolic flux analysis indicate that oxygen limitation restricts the decarboxylating arm of the tricarboxylic acid cycle, which limits ammonia assimilation into glutamate. By tightly controlling oxygen supply and providing dicarboxylates as the energy and electron source donors for N2 fixation, legumes promote ammonia secretion by bacteroids. This is a defining feature of rhizobium-legume symbioses.

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High-quality genome-scale metabolic model of Aurantiochytrium sp. T66

10.1101/2020.11.06.371070 ◽

2020 ◽

Author(s):

Vetle Simensen ◽

André Voigt ◽

Eivind Almaas

Keyword(s):

Primary Source ◽

Metabolic Model ◽

Cellular Growth ◽

Fish Oils ◽

Growth Data ◽

High Quality ◽

Cell Factories ◽

Genome Scale ◽

High Quality Genome

AbstractThe long-chain, ω-3 polyunsaturated fatty acids (PUFAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists able to synthesize and accrue large amounts of essential ω-3 PUFAs, including EPA and DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66’s metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.

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Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer

Cancers ◽

10.3390/cancers13164130 ◽

2021 ◽

Vol 13 (16) ◽

pp. 4130

Author(s):

Helena A. Herrmann ◽

Mate Rusz ◽

Dina Baier ◽

Michael A. Jakupec ◽

Bernhard K. Keppler ◽

...

Keyword(s):

Metabolic Flux ◽

Carcinoma Cell ◽

Metabolic Model ◽

Cellular Reprogramming ◽

Metabolic Reprogramming ◽

Metabolomics Data ◽

Extracellular Metabolite ◽

Metabolite Concentrations ◽

Genome Scale ◽

Resistant Cells

Background: Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. Methods: Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. Results: Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. Conclusions: Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

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Genome-Scale Metabolic Model of Caldicellulosiruptor bescii Reveals Optimal Metabolic Engineering Strategies for Bio-based Chemical Production

mSystems ◽

10.1128/msystems.01351-20 ◽

2021 ◽

Author(s):

Ke Zhang ◽

Weishu Zhao ◽

Dmitry A. Rodionov ◽

Gabriel M. Rubinstein ◽

Diep N. Nguyen ◽

...

Keyword(s):

Metabolic Engineering ◽

High Temperatures ◽

Plant Biomass ◽

Metabolic Model ◽

Industrial Applications ◽

Cellulolytic Bacterium ◽

Chemical Production ◽

Content Type ◽

Caldicellulosiruptor Bescii ◽

Genome Scale

The extremely thermophilic cellulolytic bacterium, Caldicellulosiruptor bescii , degrades plant biomass at high temperatures without any pretreatments and can serve as a strategic platform for industrial applications. The metabolic engineering of C. bescii , however, faces potential bottlenecks in bio-based chemical productions.

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Metabolic Responses to Polymyxin Treatment in Acinetobacter baumannii ATCC 19606: Integrating Transcriptomics and Metabolomics with Genome-Scale Metabolic Modeling

mSystems ◽

10.1128/msystems.00157-18 ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 8

Author(s):

Yan Zhu ◽

Jinxin Zhao ◽

Mohd Hafidz Mahamad Maifiah ◽

Tony Velkov ◽

Falk Schreiber ◽

...

Keyword(s):

Acinetobacter Baumannii ◽

Metabolic Modeling ◽

Metabolic Model ◽

Metabolic Responses ◽

Metabolomics Data ◽

Gram Negative ◽

Content Type ◽

Integrative Modeling ◽

Line Therapy ◽

Genome Scale

ABSTRACT Multidrug-resistant (MDR) Acinetobacter baumannii has emerged as a very problematic pathogen over the past decades, with a high incidence in nosocomial infections. Discovered in the late 1940s but abandoned in the 1970s, polymyxins (i.e., polymyxin B and colistin) have been revived as the last-line therapy against Gram-negative “superbugs,” including MDR A. baumannii. Worryingly, resistance to polymyxins in A. baumannii has been increasingly reported, urging the development of novel antimicrobial therapies to rescue this last-line class of antibiotics. In the present study, we integrated genome-scale metabolic modeling with multiomics data to elucidate the mechanisms of cellular responses to colistin treatment in A. baumannii. A genome-scale metabolic model, iATCC19606, was constructed for strain ATCC 19606 based on the literature and genome annotation, containing 897 genes, 1,270 reactions, and 1,180 metabolites. After extensive curation, prediction of growth on 190 carbon sources using iATCC19606 achieved an overall accuracy of 84.3% compared to Biolog experimental results. Prediction of gene essentiality reached a high accuracy of 86.1% and 82.7% compared to two transposon mutant libraries of AB5075 and ATCC 17978, respectively. Further integrative modeling with our correlative transcriptomics and metabolomics data deciphered the complex regulation on metabolic responses to colistin treatment, including (i) upregulated fluxes through gluconeogenesis, the pentose phosphate pathway, and amino acid and nucleotide biosynthesis; (ii) downregulated TCA cycle and peptidoglycan and lipopolysaccharide biogenesis; and (iii) altered fluxes over respiratory chain. Our results elucidated the interplay of multiple metabolic pathways under colistin treatment in A. baumannii and provide key mechanistic insights into optimizing polymyxin combination therapy. IMPORTANCE Combating antimicrobial resistance has been highlighted as a critical global health priority. Due to the drying drug discovery pipeline, polymyxins have been employed as the last-line therapy against Gram-negative “superbugs”; however, the detailed mechanisms of antibacterial killing remain largely unclear, hampering the improvement of polymyxin therapy. Our integrative modeling using the constructed genome-scale metabolic model iATCC19606 and the correlative multiomics data provide the fundamental understanding of the complex metabolic responses to polymyxin treatment in A. baumannii at the systems level. The model iATCC19606 may have a significant potential in antimicrobial systems pharmacology research in A. baumannii.

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Probing the Genome-Scale Metabolic Landscape of Bordetella pertussis, the Causative Agent of Whooping Cough

Applied and Environmental Microbiology ◽

10.1128/aem.01528-17 ◽

2017 ◽

Vol 83 (21) ◽

Cited By ~ 10

Author(s):

Filipe Branco dos Santos ◽

Brett G. Olivier ◽

Joost Boele ◽

Vincent Smessaert ◽

Philippe De Rop ◽

...

Keyword(s):

Bordetella Pertussis ◽

Experimental Validation ◽

Whooping Cough ◽

Tca Cycle ◽

Metabolic Model ◽

Toxin Production ◽

Metabolic Reconstruction ◽

Growth Media ◽

Content Type ◽

Genome Scale

ABSTRACT Whooping cough is a highly contagious respiratory disease caused by Bordetella pertussis. Despite widespread vaccination, its incidence has been rising alarmingly, and yet, the physiology of B. pertussis remains poorly understood. We combined genome-scale metabolic reconstruction, a novel optimization algorithm, and experimental data to probe the full metabolic potential of this pathogen, using B. pertussis strain Tohama I as a reference. Experimental validation showed that B. pertussis secretes a significant proportion of nitrogen as arginine and purine nucleosides, which may contribute to modulation of the host response. We also found that B. pertussis can be unexpectedly versatile, being able to metabolize many compounds while displaying minimal nutrient requirements. It can grow without cysteine, using inorganic sulfur sources, such as thiosulfate, and it can grow on organic acids, such as citrate or lactate, as sole carbon sources, providing in vivo demonstration that its tricarboxylic acid (TCA) cycle is functional. Although the metabolic reconstruction of eight additional strains indicates that the structural genes underlying this metabolic flexibility are widespread, experimental validation suggests a role of strain-specific regulatory mechanisms in shaping metabolic capabilities. Among five alternative strains tested, three strains were shown to grow on substrate combinations requiring a functional TCA cycle, but only one strain could use thiosulfate. Finally, the metabolic model was used to rationally design growth media with >2-fold improvements in pertussis toxin production. This study thus provides novel insights into B. pertussis physiology and highlights the potential, but also the limitations, of models based solely on metabolic gene content. IMPORTANCE The metabolic capabilities of Bordetella pertussis, the causative agent of whooping cough, were investigated from a systems-level perspective. We constructed a comprehensive genome-scale metabolic model for B. pertussis and challenged its predictions experimentally. This systems approach shed light on new potential host-microbe interactions and allowed us to rationally design novel growth media with >2-fold improvements in pertussis toxin production. Most importantly, we also uncovered the potential for metabolic flexibility of B. pertussis (significantly larger range of substrates than previously alleged; novel active pathways allowing growth in minimal, nearly mineral nutrient combinations where only the carbon source must be organic), although our results also highlight the importance of strain-specific regulatory determinants in shaping metabolic capabilities. Deciphering the underlying regulatory mechanisms appears to be crucial for a comprehensive understanding of B. pertussis's lifestyle and the epidemiology of whooping cough. The contribution of metabolic models in this context will require the extension of the genome-scale metabolic model to integrate this regulatory dimension.

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First Genome-Scale Metabolic Model of Dolosigranulum pigrum Confirms Multiple Auxotrophies

Metabolites ◽

10.3390/metabo11040232 ◽

2021 ◽

Vol 11 (4) ◽

pp. 232

Author(s):

Alina Renz ◽

Lina Widerspick ◽

Andreas Dräger

Keyword(s):

Minimal Medium ◽

Laboratory Experiments ◽

Carbon Sources ◽

Metabolic Model ◽

High Quality ◽

High Importance ◽

Manual Curation ◽

A Genome ◽

Severe Infections ◽

Genome Scale

Dolosigranulum pigrum is a quite recently discovered Gram-positive coccus. It has gained increasing attention due to its negative correlation with Staphylococcus aureus, which is one of the most successful modern pathogens causing severe infections with tremendous morbidity and mortality due to its multiple resistances. As the possible mechanisms behind its inhibition of S. aureus remain unclear, a genome-scale metabolic model (GEM) is of enormous interest and high importance to better study its role in this fight. This article presents the first GEM of D. pigrum, which was curated using automated reconstruction tools and extensive manual curation steps to yield a high-quality GEM. It was evaluated and validated using all currently available experimental data of D. pigrum. With this model, already predicted auxotrophies and biosynthetic pathways could be verified. The model was used to define a minimal medium for further laboratory experiments and to predict various carbon sources’ growth capacities. This model will pave the way to better understand D. pigrum’s role in the fight against S. aureus.

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