scholarly journals Genome-Scale reconstruction ofPaenarthrobacter aurescensTC1 metabolic model towards the study of atrazine bioremediation

2019 ◽  
Author(s):  
Shany Ofaim ◽  
Raphy Zarecki ◽  
Seema Porob ◽  
Daniella Gat ◽  
Tamar Lahav ◽  
...  
Keyword(s):  
Environmental Concern ◽  
Subsequent Development ◽  
Biomass Accumulation ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Strain Design ◽  
A Genome ◽  
Genome Scale ◽  
Atrazine Degradation

ABSTRACTAtrazine is an herbicide and pollutant of great environmental concern that is naturally biodegraded by microbial communities. The efficiency of biodegradation can be improved through the stimulating addition of fertilizers, electron acceptors, etc. In recent years, metabolic modelling approaches have become widely used as anin silicotool for organism-level phenotyping and the subsequent development of metabolic engineering strategies including biodegradation improvement. Here, we constructed a genome scale metabolic model,iRZ960, forPaenarthrobacter aurescensTC1 – a widely studied atrazine degrader - aiming at simulating its degradation activity. A mathematical stoichiometric metabolic model was constructed based on a published genome sequence ofP. aurescensTC1. An Initial draft model was automatically constructed using the RAST and KBase servers. The draft was developed into a predictive model through semi-automatic gap-filling procedures including manual curation. In addition to growth predictions under different conditions, model simulations were used to identify optimized media for enhancing the natural degradation of atrazine without a need in strain design via genetic modifications. Model predictions for growth and atrazine degradation efficiency were tested in myriad of media supplemented with different combinations of carbon and nitrogen sources that were verifiedin vitro. Experimental validations support the reliability of the model’s predictions for both bacterial growth (biomass accumulation) and atrazine degradation. Predictive tools, such as the presented model, can be applied for achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation in changing environments.

scholarly journals Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling

2021 ◽  
Vol 9 ◽  
Author(s):  
Kusum Dhakar ◽  
Raphy Zarecki ◽  
Daniella van Bommel ◽  
Nadav Knossow ◽  
Shlomit Medina ◽  
...  
Keyword(s):  
Genetic Manipulation ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Scale Model ◽  
Promoting Effect ◽  
Metabolic Potential ◽  
Carbon And Nitrogen ◽  
A Genome ◽  
Genome Scale

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

scholarly journals Biochemical Characteristics and a Genome-Scale Metabolic Model of an Indian Euryhaline Cyanobacterium with High Polyglucan Content

Metabolites ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 177 ◽  
Author(s):  
Ahmad Ahmad ◽  
Ruchi Pathania ◽  
Shireesh Srivastava
Keyword(s):  
Biomass Accumulation ◽  
Metabolic Model ◽  
Metabolic Characteristics ◽  
Protein Levels ◽  
A Genome ◽  
Lower Protein ◽  
Balance Analysis ◽  
Synechococcus Sp ◽  
The Difference ◽  
Genome Scale

Marine cyanobacteria are promising microbes to capture and convert atmospheric CO2 and light into biomass and valuable industrial bio-products. Yet, reports on metabolic characteristics of non-model cyanobacteria are scarce. In this report, we show that an Indian euryhaline Synechococcus sp. BDU 130192 has biomass accumulation comparable to a model marine cyanobacterium and contains approximately double the amount of total carbohydrates, but significantly lower protein levels compared to Synechococcus sp. PCC 7002 cells. Based on its annotated chromosomal genome sequence, we present a genome scale metabolic model (GSMM) of this cyanobacterium, which we have named as iSyn706. The model includes 706 genes, 908 reactions, and 900 metabolites. The difference in the flux balance analysis (FBA) predicted flux distributions between Synechococcus sp. PCC 7002 and Synechococcus sp. BDU130192 strains mimicked the differences in their biomass compositions. Model-predicted oxygen evolution rate for Synechococcus sp. BDU130192 was found to be close to the experimentally-measured value. The model was analyzed to determine the potential of the strain for the production of various industrially-useful products without affecting growth significantly. This model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for the production of industrially-relevant compounds.

scholarly journals Genome-Scale Model and Omics Analysis of Metabolic Capacities of Akkermansia muciniphila Reveal a Preferential Mucin-Degrading Lifestyle

2017 ◽  
Vol 83 (18) ◽  
Author(s):  
Noora Ottman ◽  
Mark Davids ◽  
Maria Suarez-Diez ◽  
Sjef Boeren ◽  
Peter J. Schaap ◽  
...  
Keyword(s):  
Proteome Analysis ◽  
Metabolic Model ◽  
Scale Model ◽  
Bacterial Cells ◽  
Akkermansia Muciniphila ◽  
A Genome ◽  
Wide Range ◽  
Genome Scale ◽  
Genome Scale Model

ABSTRACT The composition and activity of the microbiota in the human gastrointestinal tract are primarily shaped by nutrients derived from either food or the host. Bacteria colonizing the mucus layer have evolved to use mucin as a carbon and energy source. One of the members of the mucosa-associated microbiota is Akkermansia muciniphila, which is capable of producing an extensive repertoire of mucin-degrading enzymes. To further study the substrate utilization abilities of A. muciniphila, we constructed a genome-scale metabolic model to test amino acid auxotrophy, vitamin biosynthesis, and sugar-degrading capacities. The model-supported predictions were validated by in vitro experiments, which showed A. muciniphila to be able to utilize the mucin-derived monosaccharides fucose, galactose, and N-acetylglucosamine. Growth was also observed on N-acetylgalactosamine, even though the metabolic model did not predict this. The uptake of these sugars, as well as the nonmucin sugar glucose, was enhanced in the presence of mucin, indicating that additional mucin-derived components are needed for optimal growth. An analysis of whole-transcriptome sequencing (RNA-Seq) comparing the gene expression of A. muciniphila grown on mucin with that of the same bacterium grown on glucose confirmed the activity of the genes involved in mucin degradation and revealed most of these to be upregulated in the presence of mucin. The transcriptional response was confirmed by a proteome analysis, altogether revealing a hierarchy in the use of sugars and reflecting the adaptation of A. muciniphila to the mucosal environment. In conclusion, these findings provide molecular insights into the lifestyle of A. muciniphila and further confirm its role as a mucin specialist in the gut. IMPORTANCE Akkermansia muciniphila is among the most abundant mucosal bacteria in humans and in a wide range of other animals. Recently, A. muciniphila has attracted considerable attention because of its capacity to protect against diet-induced obesity in mouse models. However, the physiology of A. muciniphila has not been studied in detail. Hence, we constructed a genome-scale model and describe its validation by transcriptomic and proteomic approaches on bacterial cells grown on mucus and glucose, a nonmucus sugar. The results provide detailed molecular insight into the mucus-degrading lifestyle of A. muciniphila and further confirm the role of this mucin specialist in producing propionate and acetate under conditions of the intestinal tract.

scholarly journals Genome-Scale Metabolic Model of Helicobacter pylori 26695

2002 ◽  
Vol 184 (16) ◽  
pp. 4582-4593 ◽  
Author(s):  
Christophe H. Schilling ◽  
Markus W. Covert ◽  
Iman Famili ◽  
George M. Church ◽  
Jeremy S. Edwards ◽  
...  
Keyword(s):  
Amino Acids ◽  
Helicobacter Pylori ◽  
In Silico ◽  
Metabolic Model ◽  
Open Reading Frames ◽  
Physiological Data ◽  
In Silico Model ◽  
A Genome ◽  
Genome Scale

ABSTRACT A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balance analysis were used to explore the metabolic capabilities of the in silico model. General network properties were analyzed and compared to similar results previously generated for Haemophilus influenzae. A minimal medium required by the model to generate required biomass constituents was calculated, indicating the requirement of eight amino acids, six of which correspond to essential human amino acids. In addition a list of potential substrates capable of fulfilling the bulk carbon requirements of H. pylori were identified. A deletion study was performed wherein reactions and associated genes in central metabolism were deleted and their effects were simulated under a variety of substrate availability conditions, yielding a number of reactions that are deemed essential. Deletion results were compared to recently published in vitro essentiality determinations for 17 genes. The in silico model accurately predicted 10 of 17 deletion cases, with partial support for additional cases. Collectively, the results presented herein suggest an effective strategy of combining in silico modeling with experimental technologies to enhance biological discovery for less characterized organisms and their genomes.

scholarly journals Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

2021 ◽  
Vol 412 ◽  
pp. 115390
Author(s):  
Kristopher D. Rawls ◽  
Bonnie V. Dougherty ◽  
Kalyan C. Vinnakota ◽  
Venkat R. Pannala ◽  
Anders Wallqvist ◽  
...  
Keyword(s):  
Metabolic Model ◽  
Renal Metabolism ◽  
A Genome ◽  
Compound Exposure ◽  
Genome Scale

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

2012 ◽  
Vol 78 (24) ◽  
pp. 8735-8742 ◽  
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.

scholarly journals A Genome-Scale Metabolic Model of Anabaena 33047 to Guide Genetic Modifications to Overproduce Nylon Monomers

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 168
Author(s):  
John I. Hendry ◽  
Hoang V. Dinh ◽  
Debolina Sarkar ◽  
Lin Wang ◽  
Anindita Bandyopadhyay ◽  
...  
Keyword(s):  
Electron Transport ◽  
Pyruvate Decarboxylase ◽  
Metabolic Model ◽  
Cell Types ◽  
Economic Feasibility ◽  
Nitrogen Fixing ◽  
Design Algorithm ◽  
A Genome ◽  
Anabaena Sp ◽  
Genome Scale

Nitrogen fixing-cyanobacteria can significantly improve the economic feasibility of cyanobacterial production processes by eliminating the requirement for reduced nitrogen. Anabaena sp. ATCC 33047 is a marine, heterocyst forming, nitrogen fixing cyanobacteria with a very short doubling time of 3.8 h. We developed a comprehensive genome-scale metabolic (GSM) model, iAnC892, for this organism using annotations and content obtained from multiple databases. iAnC892 describes both the vegetative and heterocyst cell types found in the filaments of Anabaena sp. ATCC 33047. iAnC892 includes 953 unique reactions and accounts for the annotation of 892 genes. Comparison of iAnC892 reaction content with the GSM of Anabaena sp. PCC 7120 revealed that there are 109 reactions including uptake hydrogenase, pyruvate decarboxylase, and pyruvate-formate lyase unique to iAnC892. iAnC892 enabled the analysis of energy production pathways in the heterocyst by allowing the cell specific deactivation of light dependent electron transport chain and glucose-6-phosphate metabolizing pathways. The analysis revealed the importance of light dependent electron transport in generating ATP and NADPH at the required ratio for optimal N2 fixation. When used alongside the strain design algorithm, OptForce, iAnC892 recapitulated several of the experimentally successful genetic intervention strategies that over produced valerolactam and caprolactam precursors.

scholarly journals Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation

Microbiology ◽  
2014 ◽  
Vol 160 (6) ◽  
pp. 1252-1266 ◽  
Author(s):  
Hassan B. Hartman ◽  
David A. Fell ◽  
Sergio Rossell ◽  
Peter Ruhdal Jensen ◽  
Martin J. Woodward ◽  
...  
Keyword(s):  
Salmonella Enterica ◽  
Energy Demand ◽  
Drug Targets ◽  
Minimal Medium ◽  
Model Organism ◽  
Metabolic Model ◽  
Scale Model ◽  
Glucose Minimal Medium ◽  
A Genome ◽  
Genome Scale

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.

scholarly journals Transcriptome analysis ofSchistosoma mansonilarval development using serial analysis of gene expression (SAGE)

Parasitology ◽  
2009 ◽  
Vol 136 (5) ◽  
pp. 469-485 ◽  
Author(s):  
A. S. TAFT ◽  
J. J. VERMEIRE ◽  
J. BERNIER ◽  
S. R. BIRKELAND ◽  
M. J. CIPRIANO ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sequence Data ◽  
Subsequent Development ◽  
Differentially Expressed ◽  
Cdna Libraries ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Expression ◽  
Cell Conditioned Medium

SUMMARYInfection of the snail,Biomphalaria glabrata, by the free-swimming miracidial stage of the human blood fluke,Schistosoma mansoni, and its subsequent development to the parasitic sporocyst stage is critical to establishment of viable infections and continued human transmission. We performed a genome-wide expression analysis of theS. mansonimiracidia and developing sporocyst using Long Serial Analysis of Gene Expression (LongSAGE). Five cDNA libraries were constructed from miracidia andin vitrocultured 6- and 20-day-old sporocysts maintained in sporocyst medium (SM) or in SM conditioned by previous cultivation with cells of theB. glabrataembryonic (Bge) cell line. We generated 21 440 SAGE tags and mapped 13 381 to theS. mansonigene predictions (v4.0e) either by estimating theoretical 3′ UTR lengths or using existing 3′ EST sequence data. Overall, 432 transcripts were found to be differentially expressed amongst all 5 libraries. In total, 172 tags were differentially expressed between miracidia and 6-day conditioned sporocysts and 152 were differentially expressed between miracidia and 6-day unconditioned sporocysts. In addition, 53 and 45 tags, respectively, were differentially expressed in 6-day and 20-day cultured sporocysts, due to the effects of exposure to Bge cell-conditioned medium.

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