Whole‐genome sequencing and genome‐scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use

Abstract

Salt tolerant organisms are increasingly being used for the industrial production of high‐value biomolecules due to their better adaptability compared to mesophiles. Chromohalobacter canadensis is one of the early halophiles to show promising biotechnology potential, which has not been explored to date. Advanced high throughput technologies such as whole‐genome sequencing allow in‐depth insight into the potential of organisms while at the frontiers of systems biology. At the same time, genome‐scale metabolic models (GEMs) enable phenotype predictions through a mechanistic representation of metabolism. Here, we sequence and analyze the genome of C. canadensis 85B, and we use it to reconstruct a GEM. We then analyze the GEM using flux balance analysis and validate it against literature data on C. canadensis. We show that C. canadensis 85B is a metabolically versatile organism with many features for stress and osmotic adaptation. Pathways to produce ectoine and polyhydroxybutyrates were also predicted. The GEM reveals the ability to grow on several carbon sources in a minimal medium and reproduce osmoadaptation phenotypes. Overall, this study reveals insights from the genome of C. canadensis 85B, providing genomic data and a draft GEM that will serve as the first steps towards a better understanding of its metabolism, for novel applications in industrial biotechnology.

Full text

Received: 4 March 2022
|
Accepted: 1 October 2022
DOI: 10.1002/mbo3.1328
ORIGINAL ARTICLE
Whole‐genome sequencing and genome‐scale metabolic
modeling of Chromohalobacter canadensis 85B to explore
its salt tolerance and biotechnological use
Blaise Manga Enuh
1
|Belma Nural Yaman
1,2
|Chaimaa Tarzi
3
|
Pınar Aytar Çelik
1,4
|Mehmet Burçin Mutlu
5
|Claudio Angione
3,6,7
1
Biotechnology and Biosafety Department,
Graduate and Natural Applied Science, Eskişehir
Osmangazi University, Eskişehir, Turkey
2
Department of Biomedical Engineering, Faculty
of Engineering and Architecture, Eskişehir
Osmangazi University, Eskişehir, Turkey
3
School of Computing, Engineering & Digital
Technologies, Teesside University,
Middlesbrough, UK
4
Environmental Protection and Control Program,
Eskişehir Osmangazi University, Eskişehir, Turkey
5
Department of Biology, Faculty of Science,
Eskisehir Technical University, Eskisehir, Turkey
6
Centre for Digital Innovation, Teesside
University, Middlesbrough, UK
7
National Horizons Centre, Teesside
University, Darlington, UK
Correspondence
Claudio Angione, School of Computing,
Engineering & Digital Technologies, Teesside
University, Middlesbrough TS1 3BX, UK.
Email: c.angione@tees.ac.uk
Funding information
Children's Liver Disease Foundation,
Grant/Award Number: SG/2019/06/03;
Eskisehir Osmangazi University scientific
research committee, Grant/Award Number:
202115D01; Alan Turing Institute,
Grant/Award Number: TNDC2‐100022; UKRI
Research England, Grant/Award Number:
THYME project
Abstract
Salt tolerant organisms are increasingly being used for the industrial production
of high‐value biomolecules due to their better adaptability compared to
mesophiles. Chromohalobacter canadensis is one of the early halophiles to show
promising biotechnology potential, which has not been explored to date.
Advanced high throughput technologies such as whole‐genome sequencing
allow in‐depth insight into the potential of organisms while at the frontiers of
systems biology. At the same time, genome‐scale metabolic models (GEMs)
enable phenotype predictions through a mechanistic representation of
metabolism. Here, we sequence and analyze the genome of C. canadensis
85B, and we use it to reconstruct a GEM. We then analyze the GEM using flux
balance analysis and validate it against literature data on C. canadensis.We
show that C. canadensis 85B is a metabolically versatile organism with many
features for stress and osmotic adaptation. Pathways to produce ectoine and
polyhydroxybutyrates were also predicted. The GEM reveals the ability to
grow on several carbon sources in a minimal medium and reproduce
osmoadaptation phenotypes. Overall, this study reveals insights from the
genome of C. canadensis 85B, providing genomic data and a draft GEM that will
serve as the first steps towards a better understanding of its metabolism, for
novel applications in industrial biotechnology.
KEYWORDS
Chromohalobacter canadensis, genome‐scale metabolic modeling, halophiles,
polyhydroxybutyrates, salt‐tolerant, whole‐genome
MicrobiologyOpen. 2022;11:e1328. www.MicrobiologyOpen.com
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https://doi.org/10.1002/mbo3.1328
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2022 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.

1|INTRODUCTION
Chromohalobacter is a genus of halophilic bacteria that have evolved
methods to survive high salinity environments, with the ability to
tolerate up to 12% w/v salt concentration in a minimal medium. They
can also have a tolerance in the same environment to other
conditions such as pH and temperature, thus widening the applica-
tions of their bioproducts (Gedikli et al., 2019). Chromohalobacter
canadensis is part of the Halomonadaceae within the phylum
Bacteria. The clade is made up of Chromohalobacter marismortui,
Chromohalobacter canadensis, Chromohalobacter israelensis, Chromo-
halobacter salexigens, Chromohalobacter beijerinckii, Chromohalobacter
japonicus, Chromohalobacter nigrandensis, Chromohalobacter salarius,
and Chromohalobacter saracensis (Arahal & Ventosa, 2006).
To survive high salinity and low water activity in their environment,
halophilic bacteria use salt‐in and low salt‐in strategies as well as nutrient
storage strategies. The salt‐in strategy involves the accumulation of
inorganic salts such as KCl to balance the osmotic difference with the
environment. The low‐salt‐in strategy involves the accumulation of
organic solutes also called compatible solutes, which allow enzymes and
other cellular processes to function properly. Organic compounds that
have been identified as compatible solutes include polyols, sugars, amino
acids, betaines, ectoines, N‐acetylated diamino acids, and N‐derivatized
carboxamides of glutamine (Gunde‐Cimerman et al., 2018). Surprisingly,
these adaptations have also evolved to make their metabolism more
efficient in high salinity and less efficient in low salinity (Pastor et al., 2013).
They have also adapted to using a wide variety of simple carbon
compounds as sole carbon sources and having high energy‐rich polymer
reserves. One such compound is polyhydroxybutyrate (PHB), a type of
polyhydroxyalkanoate (PHA). The PHAs are candidate biodegradable
bioplastics to replace currently used plastics that are a source of
environmental pollution. These unique adaptation mechanisms offer a
rich source of exploitable bacterial bioresource.
The physiology of halophiles and the range of bioproducts they
can synthesize make them suitable for use as industrial cell factories.
Halophilic organisms’resilience to extreme conditions translates to
reduced chances of contamination in industrial bioreactors. Their
enzymes, (Prakash et al., 2009) exopolysaccharides and osmoprotec-
tants also have several industrial applications contributing to making
them highly attractive as industrial cell factories. C. canadensis has
been shown to produce PHBs, ectoines, amylases, and other high‐
value industrial products (Prakash et al., 2009; Radchenkova
et al., 2018;Wangetal.,2020). Their potential for bioremediation
has also been reported (Erdogmus et al., 2015). Recent research also
shows a promising potential in the production of levan, which is a high‐
value polymer in cosmetics and also safe for consumption (Çakmak
et al., 2020). Within the Chromohalobacter clade, however, the
genomics and in silico analysis of C. salexigens (Ates et al., 2011;
Copeland et al., 2011) has been better studied compared to C.
canadensis and other members. Despite the reported potential
applications of C. canadensis, there is little information on the potential
of C. canadensis from a genomic insight, which can be exploited for
future metabolic engineering and systems biology research.
Advances in technology and computational biology tools are
driving current research in biotechnology (Becker & Wittmann, 2018).
High throughput technologies such as whole‐genome sequencing
allow in‐depth insight into the potential of organisms. Using whole
genomes, detailed metabolic processes of organisms and their
phenotypic characteristics under various external conditions are
increasingly revealed with genome‐scale metabolic network models
(GEM) (Fang et al., 2020; Gu et al., 2019). These models are
stoichiometry‐based mathematical descriptions that permit the
modeling of biochemical metabolic pathways in living systems.
Recently, more sophisticated semi‐automated tools for the
reconstruction of GEMs have been developed that build genome‐scale
models from annotated genomes though need minimal manual curation
and validation before use (Gu et al., 2019; Machado et al., 2018). Flux
balance analysis (FBA) and its variations can be subsequently used to
investigate the metabolic phenotypes for various environmental and
genetic perturbations, predicting flux rates of all known biochemical
reactions in a variety of conditions (Orth et al., 2010). Genomic insights
into halophilic metabolism have revealed different synthetic pathways
that affect the PHA type produced. Hence, state‐of‐the‐art systems
biology tools such as GEMs can facilitate the contextualization of
metabolism for specific strains that can be used for production
optimization studies (Mitra et al., 2020). The GEMs are at the frontier
of systems biology and, when combined with data mining or machine
learning methods, are increasingly driving novel biotechnological discov-
eries. For example, omics data and GEMs are being exploited by novel
machine and deep learning algorithms to tackle a variety of research
questions in biotechnology, ranging from maximization of yield to
characterization of growth across conditions (Ben Guebila & Thiele, 2019;
Culley et al., 2020;Enuh&AytarÇelik,2022; Kavvas et al., 2020;
Vijayakumar et al., 2020; Zampieri et al., 2019). By providing a platform
exploitable by researchers from a wide range of disciplines, GEMs enable
a better understanding of metabolism, driving novel applications and
discoveries in industrial biotechnology (Fang et al., 2020).
Here, we sought to obtain insight from the whole genome of C.
canadensis 85B about its metabolism by using high throughput
sequencing, annotation, and analyses of its genes. Using a semi-
automated pipeline, we then built and curated a GEM from the
annotated genome. We standardized and validated the model against
experimental data from the literature. Our model can provide an in
silico platform for C. canadensis that can be used for future studies,
using genome‐scale models for applications in biotechnology.
2|METHODS
2.1 |Bacteria strains
Bacteria samples were obtained from stored slant cultures that were
isolated from another study (Çakmak et al., 2020) and inoculated on a
nutrient agar medium for 24 h to revive. From the nutrient agar
medium, an inoculum was obtained and transferred to a minimal salt
medium composed of NaCl (96 g), MgCl
2
.6H
2
O (12 g), MgSO
4
.7H
2
O
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(14 g), KCl (2.8g), NaBr (0.32 g), NaHCO
3
(0.008 g), CaCl
2
.2H
2
O (2 g),
yeast extract (1 g), Peptone (5 g), and glucose (20 g) as carbon source.
The culture was incubated for 3 days at 35°C and 150 rpm in 250 mL
Erlenmeyer flasks for polymer production (Dyall‐Smith, 2015).
2.2 |Genomic DNA extraction
From the bacterial cultures, 2 mL of bacterial suspension was
obtained for genomic DNA extraction. Genomic DNA was extracted
using the PureLink Microbiome DNA purification kit (Invitrogen)
according to the manufacturer's instructions. Upon extraction of the
pure DNA, an electrophoresis gel was prepared to confirm the
presence of a single band corresponding to the whole bacterial
genome. A 5 µL of the sample was run on 1% agarose gel for 30min
at 100 v. Gels were stained with ethidium bromide (10 mg mL
−1
) and
visualized on a gel documentation system (BIO‐RAD).
2.3 |Genome sequencing and annotation
The genomic DNA samples were sent for genome sequencing to BM
laboratories and sequenced with the Illumina NGS sequencing platform.
After sequencing, quality analysis was done with FASTQc v0.11.9 to
obtain raw reads quality and trimming was done with default settings.
The sequence reads were assembled and ordered with the Unicycler
pipeline (Wick et al., 2017)inPATRIC(https://www.patricbrc.org/)using
the auto assembly strategy with default parameters (Wattam
et al., 2017,2018). Unicycler first produces an Illumina assembly graph,
then uses long reads to build bridges and anchors to determine the
positions of the contigs. This allowed resolving all repeats in the genome,
resulting in a complete genome assembly. The replicons were then
circularized and rotated to begin at a consistent starting gene.
The genome was annotated using the RAST tool kit v3.6.9
(RASTtk) (Brettin et al., 2015) annotation pipeline provided through
the RAST annotation web service (https://rast.nmpdr.org) and
PATRIC (Wattam et al., 2018). Further annotation with an
orthology‐based search to complement the homology annotations
from RAST was done with Evolutionary Genealogy of Genes: Non‐
supervised Orthologous Groups (EggNOG) (Huerta‐Cepas et al., 2019)
to assign functional annotation to the detected orthologous groups
and to facilitate the interpretation results from RAST homology
predictions. The KAAS (Moriya et al., 2007) annotation server with
BLAST and BBH (bidirectional best hit) was used for pathway
reconstruction. When needed, metabolic pathways were further
inferred from the KEGG database (http://www.genome.jp/kegg/)
(Kanehisa & Goto, 2000) and BioCyc (Karp et al., 2019).
Gene features of essential biosystems were also further
confirmed manually using BLASTp (https://blast.ncbi.nlm.nih.gov/
Blast.cgi). Predicted complementary DNA sequences were blasted in
the NCBI nonredundant database as well as Swiss‐Prot and UniProt,
(Boutet et al., 2007), and the information was combined to obtain the
characteristics of proteins. Genomic features and characteristics
were displayed with the circular genome viewer tool server (CGView)
(Stothard et al., 2019) for generating genomic maps for microorgan-
isms using the annotated genome from the RAST server.
2.4 |Phylogenetic analysis
The 16 S ribosomal subunit sequences were obtained from the
annotated genome and a sequence blast was done in the NCBI
database. The first 35 hits were selected and used to generate the
phylogenetic tree in Molecular Evolutionary Genetics Analysis MEGA
X (Kumar et al., 2018).
2.5 |Genome‐scale modeling
2.5.1 |Draft metabolic model reconstruction
CarveMe v1.4.1 (Machado et al., 2018) was used with default pipeline
arguments to curate a draft reconstruction from the genome of C.
canadensis 85B. So, CarveMe is an automated pipeline that uses a top‐
down method to build both single‐species and community models rapidly
and with high scalability. The pipeline leverages the BIGG database for
metabolite and reaction information. These models perform closely to
manually curated models in terms of reproducing experimental pheno-
types such as gene essentiality and substrate utilization. The genome file
with annotations was retrieved in the FASTA format from the RAST
serverandpassedintotheCarveMepipelinewith$carve‐‐dna
genome.fna arguments in the command line for reconstruction.
2.5.2 |Model benchmarking
The metabolic model testing suite, MEMOTE v0.11.1 (Lieven
et al., 2020) in its command‐line version was used to benchmark
the model against standardized principles of model descriptions and
to obtain a report that can be used for further model curation. The
results of the standard tests and annotations helped direct further
curation of the model for consistency, metabolic gaps, assigning
metabolite charges, and reaction bounds. The MEMOTE reports were
iteratively generated after manual curation steps to ensure the
highest possible score (Lieven et al., 2020).
2.5.3 |Addition of annotations
To extend the annotations in the model, ModelPolisher v2.0.1 was
used (Römer et al., 2016). ModelPolisher compares the model's entity
IDs to the BiGG model database and retrieves relevant metadata
compliant with SBO terms (Schellenberger et al., 2010). All relevant
information and data about the matching instance are integrated as
annotations into the initial draft reconstruction for each related entry
in the BiGG database.
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2.5.4 |Manual curation and gap analysis
After the initial draft was curated and annotated, manual refine-
mentstepsfollowed.Allmanualstepswereconductedbyrefining
the model in COBRApy v0.22.1. (Ebrahim et al., 2013). Literature
evidence related to C. canadensis (Arahal & Ventosa, 2006;
Radchenkova et al., 2018) was used to verify the reactions in the
model as well as to add reactions, metabolites, or genes that were
missing due to annotation errors. Annotation information from
RAST and EggNOG served as sources to trace the presence of
genes and gene ontologies respectively. For reactions that were
added to the model, appropriate scores based on the information
obtained from the literature were also noted. Blocked metabolites
were identified using COBRApy (Ebrahim et al., 2013). The
identifierswereusedtosearchtheKEGG(Kanehisa&Goto,2000)
and Biocyc (Karp et al., 2019) databases that served as a reference
to curate missing reactions and fill metabolic gaps. When present,
the reactions were verified for mass and charge balance and
corrected, when necessary, before inclusion. The output model was
tested for SBML compliance with the COBRApy library in
Python 3.8.
2.5.5 |Minimal medium
Metabolite essentialities in the medium were carefully verified by
limiting each metabolite's availability and subsequently optimiz-
ing the model. If the in silico simulations revealed no growth after
limiting the metabolite's availability, the metabolite's essentiality
was considered confirmed. Finally, the list of media components
that were essential was used to make up the minimal medium for
the model.
2.5.6 |Model validation and analysis
Using the minimal medium obtained from simulations, the in silico
growth capabilities of C. canadensis 85B on different carbon sources
were examined. All available sugar exchange fluxes were extracted
from the model and sorted into monosaccharides, disaccharides,
oligosaccharides, and trisaccharides. For the exchange reactions of
the carbon source under investigation, the lower bound was set to
−10 mmol gDW
−1
h
−1
. Each carbon source was tested individually by
only enabling the tested carbon source's exchange reaction and by
optimizing the model for growth using FBA (Orth et al., 2010).
Simulations with a flux value of zero were considered as an inability
for the model to grow on the carbon source used. Further
investigations of reaction fluxes in optimal states were done with
Flux Variability Analysis (FVA), setting the biomass flux to its maximal
FBA value, therefore with a fraction of the optimum value of 1.0
(Mahadevan & Schilling, 2003), and the fitness in producing
bioproducts was investigated with a phenotypic phase plane analysis
using CAMEO (Cardoso et al., 2018) in python 3.8.
2.5.7 |Visualization
To facilitate model curation and analyzing pathways, Escher was used
for visualizing the fluxes in the model's metabolic pathways. Escher
enables the building of metabolic pathways using reactions, metabo-
lites, and genes by contextualizing them in the organism's metabolism
(King et al., 2015). The Escher Python package v1.7.1 (King
et al., 2015) was also used to draw customized metabolic maps of
C. canadensis 85B in Jupyter notebooks as it is compliant with
COBRApy. Graphs for carbon source predictions were plotted with
ggplot2 (Wickham, 2009) in R studio version 4.1.1 (RStudio
Team, 2015).
3|RESULTS AND DISCUSSIONS
3.1 |Genomic properties
The genome was assembled after sequencing and according to basic
statistics, the genome length was estimated to be 3,718,005 bp, there
were 34 contigs with protein‐encoding genes (PEGs) and an average
G + C content of 60.90%. The N50 length, which is defined as the
shortest sequence length at 50% of the genome, was 186,789 bp.
The L50 count, which is defined as the smallest number of contigs
whose length sum produces N50, was 5 (Table 1). Very few studies
have reported the genome sequence of bacteria in the Chromoha-
lobacter genus. A comparison of genome properties for Chromoha-
lobacter genomes reported in the literature is shown in Table 2.
Considering that the genus contains nine species, it shows that there
is still a lot of research to be done to understand the physiology and
potential of Chromohalobacter.
A circular graphical display of the distribution of the genome
annotations is provided (Figure 1). This includes, from outer to inner
rings, the contigs with contig code labels, CDS on the forward and the
reverse strand also labeled as CDS; RNA genes are embedded within
the forward and reverse strand rings; the GC skew and GC content
are also shown in the same order.
TABLE 1 Summary features for Chromohalobacter canadensis
85B whole genome
Characteristic Value
Size 3,718,005
GC content 60.90
N50 186,789
L50 5
Number of contigs (with PEGs) 34
Number of subsystems 315
Number of coding sequences 3478
Number of RNAs 70
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3.2 |Phylogenetic analysis
The 16 S ribosomal subunit sequences were obtained from the
annotated genome, and a sequence blast was performed in the NCBI
database. The evolutionary history was inferred using the Neighbor‐
Joining method (Saitou & Nei, 1987). The bootstrap consensus tree
inferred from 1000 replicates was taken to represent the evolu-
tionary history of the taxa analyzed (Felsenstein, 1985). Branches
corresponding to partitions reproduced in less than 50% of bootstrap
replicates were collapsed. The percentage of replicate trees in which
the associated taxa clustered together in the bootstrap test (1000
replicates) are shown next to the branches (Felsenstein, 1985). The
evolutionary distances were computed using the Maximum Compos-
ite Likelihood method (Tamura et al., 2004) and are in the units of the
number of base substitutions per site. This analysis involved 35
nucleotide sequences. All ambiguous positions were removed for
each sequence pair (pairwise deletion option). There were a total of
1449 positions in the final data set. Evolutionary analyses were
conducted in MEGA X (Kumar et al., 2018). Similar to the above‐
mentioned close relatives, an identity of 99.79% was reported for C.
canadensis strain DSM 6769
T
and C. canadensis strain ATCC 43984
T
99.79% followed by C. japonicus 99.38%. This agrees with the
TABLE 2 Comparison of the genomic features of Chromohalobacter canadensis 85B of this study with other Chromohalobacter species.
Species Genome length (bp) Protein coding sequences GC content (%) Reference
C. canadensis 85B 3,718,005 3478 60.9 This study
C. marismortui DSM 6770 3,553,220 3226 61.7 (RefSeq: NZ_SOBR00000000.1),
C. salexigens type strain (1H11
T
)3,696,649 3319 63.9 Copeland et al. (2011)
C. salexigens ANJ207 3,664,372 3344 63.71 Srivastava et al. (2019)
Chromohalobacter sp. SMB17 3,775,557 3486 60.5 Olsson et al. (2017)
C. israelensis DSM 6768
T
3,660,991 3361 63.74 Zhou et al. (2015)
Note: Only completed assemblies were considered with a taxonomy check confirmed. A lower GC content but a higher number of predicted coding
sequences were observed with C. canadensis 85B.
FIGURE 1 Circular map showing the distribution of genes in Chromohalobacter canadensis 85B genome. Ordered from the outer ring to the
inner rings are contigs with their labels, forward and reverse strands of CDS, RNA genes, GC skew, and GC content.
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classification of the Chromohalobacter genus that had previously been
established based on the closer sequence similarity to other
Chromohalobacter members (Arahal et al., 2001). Relationships with
other strains are shown in the phylogenetic tree (Figure 2a).
3.3 |Overview of subsystems and orthologous
cluster genes
A subsystem is a set of proteins that together implement a specific
biological process or structural complex. Thirty‐two percent (1080) of
annotated proteins were included in the subsystems analysis
according to the RAST pipeline. An overview of the subsystems for
this genome as produced by the annotation pipeline is provided in
Figure 2b. The amino acids and derivates form the highest proportion
of subsystem annotations followed by carbohydrate metabolism,
protein metabolism, cofactors, and membrane transport. Proteins
play an important role in the adaptation of halophiles to high salinity.
This suggests that C. canadensis 85B possesses the machinery to
meet its adaptation needs in a saline environment. The same is also
observed for the membrane transport systems. Osmolite balance is
fundamental for halophiles therefore robust membrane transport
systems ensure that the integrity of the cell is maintained with
changing conditions.
An analysis of orthologous genes shows amino acid metabolism
and transport and transcription containing the highest number of
(a)
(c) (d)
(b)
FIGURE 2 (a) Phylogenetic tree showing the relationship between Chromohalobacter canadensis 85B and other microorganisms. The
accession numbers and length of sequences used are shown in brackets (b) Subsystems in the C. canadensis 85B genome. (c) Number of genes
associated with general COG functional categories. (d) Polyhydroxybutyrate (PHB) synthesis pathway prediction according to KEGG.
Intermediates from both glycolysis and fatty acid metabolism. (S)‐3‐Hydroxybutanoyl‐CoA is an important intermediate as it links the PHB
synthesis pathway and fatty acid metabolism. fadN, fadB, fadJ and fadB, and fadJ, are fatty acid degradation enzymes, 3‐hydroxybutyryl‐CoA
dehydrogenase [EC:1.1.1.157] (paaH), 3‐hydroxyacyl‐CoA dehydrogenase [EC:1.1.1.35] (HADH), EHHADH.
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orthologous genes (Figure 2c). When compared with results by
Copeland et al. (2011)onC. salexigens the first seven groups seem to
be the most abundant despite the subtle differences in the relative
abundance of orthologous genes between both species. This further
emphasizes the importance of these systems in this group of
microorganisms.
3.4 |Carbohydrate metabolism
Therewere177carbohydratemetabolismgenesinC. canadensis 85B and
nine subsystems representing biosynthesis and degradation pathways.
Predictions show genes for the metabolization of various carbohydrate
substrates such as sugar alcohols, C‐1 compounds, sugar acids,
monosaccharides, polysaccharides, and fermentation. Enzymes able to
metabolize the following substrates were predicted: glucose, starch,
sucrose,fructose,mannose,xylose,glycerol,andgalactose.Thepresence
of many different pathways for carbohydrate metabolism has significant
implications for the adaptation of halophiles.
In C. salexigens, glucose metabolism occurs exclusively through the
Entner–Doudoroff pathway while fructose metabolism occurs through
the Entner–Doudoroff and Embden–Meyerhof–Parnas pathways. Fruc-
tose metabolism seems to give more metabolic flexibility in response to
energy and biosynthetic demands. The Entner–Doudoroff pathway, on
the other hand, is inefficient for growth when salinity is low, as a result of
metabolite overflow. However, in high salinity, there is a high metabolic
burden on this pathway due to the use of NADPH and ATP for the
synthesis of compatible solutes. This allows the organism to use other
pathways to meet other metabolic requirements (Pastor et al., 2019).
Despite the closeness of both species, the Entner–Duodoroff
pathway was not predicted in C. canadensis 85B, therefore other
adaptation mechanisms may apply. Other studies show that halophilic
bacteria may prefer to metabolize glucose only after other substrate
sources are depleted (Oren & Mana, 2003). Experimental studies with C.
canadensis are needed to derive conclusionsasthiswillbehelpfulfor
organism‐specific approaches. The broad range of usable carbohydrate
substrates is a biotechnology advantage through the growth on a wide
variety of possible cheap substrates which can help reduce production
costs (Güngörmedi et al., 2014).
3.5 |Fatty acid metabolism
The fatty acid composition of salt‐tolerant organisms is influenced by
salt concentrations. This is observed through decreased saturation of
fatty acids at suboptimal concentrations. Therefore by varying the
ratio of saturated to unsaturated fatty acids adaptation to salt stress
can be achieved (Mutnuri et al., 2005). This shows the important role
of fatty acid metabolism in the adaptation of organisms living in high
salinity. In the C. canadensis 85B genome, there were five subsystems
and 63 genes predicted to be involved in fatty acid metabolism.
Pathways for fatty acid, phospholipids triacylglycerols, and isoprenoid
metabolism were predicted. The KEGG annotations show both fatty
acid biosynthesis and fatty acid degradation pathways. Fatty acid
degradation occurs through beta‐oxidation which also has
Acetoacetyl‐CoA and (S) ‐3‐Hydroxybutanoyl‐CoA intermediates
that link it to the PHB synthesis pathway.
3.6 |Stress response, defense, and virulence
The main types of stress response systems identified were osmotic stress,
heat/cold shock, stress, resistance to antibiotics and toxic compounds,
and the Hfl operon; details are presented in Table 3below. In bacteria,
glutathione plays an important role in protecting the cell from the effects
of low pH, chlorine chemicals, and oxidative and osmotic stressors, in
addition to maintaining the appropriate oxidation state of protein thiols.
Furthermore, by directly modifying proteins via glutathionylation,
glutathione has emerged as a posttranslational regulator of protein
function under oxidative stress (Masip et al., 2006). Iron homeostasis
regulators have previously been shown to play a role in the complicated
circuit that governs halophilic bacteria's response to osmotic stress in C.
salexigens (Masip et al., 2006).
3.7 |Polyhydroxyalkanoates
In some organisms, the genes for PHA are frequently located on the same
operon but in C. canadensis the PHA genes were located on different loci
in the genome. The genes identified were PhaA, PhaB, PhaC,andPhaR
(Table 4). The PhaA gene was predicted in two locations on the genome
while others were found in one location only. Note, PHA synthase (PhaC)
is the key enzyme in the PHB synthesis pathway, catalyzing the
polymerization of hydroxyalkanoate subunits (Figure 2d). Note, PHA
synthase influences the type of monomer, the composition, and the
weight of the PHA produced (Zheng et al., 2020). Four classes of PHA
synthases have been identified based on their primary sequence, the
composition of subunits, and their substrate specificities. Class I PHA
synthases are homodimers, class II is made of PhaC1 and PhaC2 subunits,
class III is made of PhaC and PhaE, and class IV PhaC and PhaR. Classes I,
III, and IV produce short‐chain length monomers made of three to five
carbon lengths while class II synthases produce six to 14 carbon chain
lengths (Chek et al., 2017). Up to 14 different pathways for PHB
synthesis have been described so far leading to the production of
homopolymers, random copolymers, block copolymers, and graft
polymers (Meng et al., 2014).
The protein sequence of the PHA synthase gene was blasted in NCBI
to assess the type of PHA synthase enzyme. Blast results returned
99.51% similarity with C. japonicus, 99.35% C. salexigens, and 98.38% C.
canadensis. A further search by blast in the Uniprot database first hit
99.5% similarity with Class I poly(R)‐hydroxyalkanoic acid synthase (C.
japonicus). Only one hit was obtained each in the Gene3D, InterPro, Pfam,
SUPFAM, and TIGRFAMs, all corresponding to PHA synthase class I. The
class I subfamily PHA synthases can polymerize hydroxyacyl‐CoAs with
three to five carbons in the hydroxyacyl into PHA esters in this case most
likely PHB. These can be accumulated up to 90% of the cell's dry weight.
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The PhaR genes play a posttranscriptional role and help prevent protease
degradation or act directly or indirectly to activate PHA synthase (McCool
& Cannon, 2001). Note, PhaR is found to be a DNA‐binding
homotetramer that is also capable of binding short‐chain hydroxyalkanoic
acids and PHA granules. Thus, PhaR may regulate the expression of itself,
the phasins that coat granules, and enzymes that direct carbon flux into
polymers stored in granules (Maehara et al., 2002). Further research to
determine the specific function of PhaR in PHB synthesis in C. canadensis
is required.
According to KEGG annotations, fadNBJ, paaH, HADH, EH-
HADH, fadJ, and fadB enzymes are from the fatty acid metabolism
pathways. As shown in Figure 2d, (S)‐3‐Hydroxybutanoyl‐CoA can be
either isomerized to (R)‐3‐Hydroxybutanoyl‐CoA or converted to
Acetocetyl‐CoA which are both intermediates in the PHB synthesis
TABLE 3 Predicted stress response and defense systems
Subclass Subsystem name
Gene
count
Role
count
Resistance to antibiotics
and toxic compounds
Antibiotic targets in DNA processing 4 4
Resistance to Triclosan 1 1
Fusaric acid resistance cluster 6 3
Beta‐lactamases Ambler class C 1 1
Antibiotic targets in metabolic pathways 5 4
Polymyxin resistance, lipid A modifications with
phosphoethanolamine
22
Antibiotic targets in transcription 3 3
Antibiotic targets in protein synthesis 8 8
Mupirocin resistance 1 1
Copper homeostasis: Copper tolerance 2 2
Antibiotic targets in cell wall biosynthesis 3 3
Resistance to Daptomycin 4 3
Fusidic acid resistance 2 2
Cadmium resistance 1 1
Resistance to chromium compounds 1 1
Stress Response Repair of iron centers 4 3
Glutathione: Redox cycle 3 3
Glutathione: Non‐redox reactions 8 5
Cluster containing glutathione synthetase 4 4
Glutathione: Biosynthesis and gamma‐glutamyl cycle 4 3
Protection from reactive oxygen species 7 7
Stress proteins YciF, YciE 2 2
Universal stress protein family 1 1
Stress Response: Heat/
cold shock
Heat shock dnaK gene cluster extended 17 16
Cold shock proteins of CSP family 4 1
Stress Response: Osmotic
stress
Choline uptake and conversion to betaine clusters 34 21
Ectoine, hydroxyectoine uptake and catabolism 8 7
Ectoine synthesis 7 7
Osmoregulation 1 1
Glycine betaine synthesis from choline 4 4
Hyperosmotic potassium uptake 3 2
Other Hfl operon 5 5
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pathway. This suggests that fatty acid metabolism and PHB synthesis
in C. canadensis 85B are closely related. Hence, under the right
conditions, fatty acid metabolism can deviate toward the production
of PHBs. Similar observations have been made with Halomonas sp.
SF2003 (Thomas et al., 2019).
3.8 |Genome‐scale modeling and analysis
3.8.1 |General model features
After reconstructing the draft, the model development followed an
iterative path (Figure 3a). The initial draft model contained 1522
metabolites, 2347 reactions, and 1159 genes within three compart-
ments: the cytosol, periplasm, and extracellular space. The model was
named iEB1159 according to the model naming convention, with i
representing in silico, EB the initials of the name of the model curator,
and 1159 the number of genes in the model. There are 1830
annotated reactions in the model. The distribution of reaction types
according to their SBO categories is shown in Figure 3b.A
comparison of general model features with other previously reported
Chromohalobacter models iFP764 (Piubeli et al., 2018) and iOA584
(Ates et al., 2011) is reported in Figure 3c, showing that iEB1159 has
a larger number of reactions, genes, and metabolites.
3.8.2 |Model benchmarking
The initial model results in MEMOTE returned a score of 37%, with
the lowest scores due to poor annotations. After model curation and
the addition of annotations, a MEMOTE score of 70% was achieved.
Considering that this is the first genome‐scale model of C. canadensis
85B and the lack of data to fill gaps, we believe that this is a
promising score, showing the model has a good foundation for
research improvement (Figure 3d).
3.8.3 |Addition of annotations
Models by CarveMe produce annotations in the Notes area of the model.
However, this is not detected by MEMOTE during benchmarking.
Annotations for metabolites, SBO terms, and genes included in the model
permitted a high score with MEMOTE. ModelPolisher permitted the
inclusion of annotations in the right fields that can be identified by
MEMOTE. Annotation databases that were queried include BiGG
(Schellenberger et al., 2010), BioCyc (Karp et al., 2019), CHEBI
(Degtyarenko et al., 2008), HMDB (Wishart et al., 2007), Inchikey (Heller
et al., 2015), Lipidmaps (Liebisch et al., 2020), KEGG (Kanehisa and
Goto, 2000), Reactome (Fabregat et al., 2018), SEED (Seaver et al., 2020),
MetaNetX (Moretti et al., 2021), and EC‐code, RHEA (Alcántara
et al., 2012).
Further SBO terms annotations were done manually using the
libSBML package in Python according to the SBO conventions (http://
TABLE 4 Predicted polyhydroxybutyrate biosynthesis genes and their genomic characteristics
Function Ontology Aliases Start Strand Length Contig
3‐ketoacyl‐CoA thiolase (EC 2.3.1.16) @ Acetyl‐CoA
acetyltransferase (EC 2.3.1.9)
SSO:000000312‐3‐ketoacyl‐CoA thiolase (EC
2.3.1.16)
PhbA 17,888 + 1182 NODE_2_length_501238_cov_157.383901
SSO:000000702‐Acetyl‐CoA acetyltransferase
(EC 2.3.1.9)
3‐ketoacyl‐CoA thiolase (EC 2.3.1.16) SSO:000000312‐3‐ketoacyl‐CoA thiolase (EC
2.3.1.16)
PhbA 25,498 ‐1179 NODE_20_length_57320_cov_157.543735
Acetoacetyl‐CoA reductase (EC 1.1.1.36) SSO:000000675‐Acetoacetyl‐CoA reductase (EC
1.1.1.36)
PhbB 12,237 + 747 NODE_1_length_781020_cov_158.120335
Polyhydroxyalkanoic acid synthase PhaC 730,278 + 1857 NODE_1_length_781020_cov_158.120335
Polyhydroxyalkanoate synthesis repressor PhaR PhaR 33,110 + 459 NODE_6_length_177777_cov_157.849561
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www.ebi.ac.uk/sbo/main/). The annotations are as follows: passive
transport (SBO:0000658), active transport (SBO:0000657), cotransport:-
symport (SBO:0000659), cotransport:antiport (SBO:0000660) other
transport reactions (SBO:0000655), general metabolic reactions
(SBO:0000176), exchange reactions (SBO:0000627), biomass reactions
(SBO:0000629), genes (SBO:0000243), and species (SBO:0000247)
(Figure 3b).
3.8.4 |Gap analysis
There were 37 blocked metabolites identified in the model. Further
investigation of metabolites using the BIGG database showed that
the blocked reactions were mostly exchange reactions, cofactors, and
prosthetic groups. Escher maps enabled visualization of metabolic
pathways that served to identify incomplete pathways for gap filling
(Figure 3f). Due to the lack of data on C. canadensis in the major
databases, most of the pathway gaps could not be investigated in‐
depth. These were allowed and considered as knowledge gaps that
will be filled with growing research. There was however high
metabolite connectivity as reported by MEMOTE with a score of
100%. The output model was further tested for SBML compliance
with the COBRApy (Ebrahim et al., 2013) library in Python, and all
errors were corrected. The final model contains all SBML fields as
required.
3.8.5 |Minimal medium
The minimal medium for the model was obtained by iteratively
checking for growth in the model in limiting conditions. During
simulations, glucose was maintained as the sole carbon source while
the entrance of simple salts and ions was varied. The secretion of
other carbon‐containing compounds was monitored to ensure that
only CO
2
was produced in the final medium. The final number of
essential metabolites termed the minimal media are provided in
Table A2. (Table A1)
3.8.6 |Validation of carbon source usage
Microorganisms in the Halomonadaceae family are metabolically
diverse. Within individual species, the ability to support growth on a
carbon source can vary between studies (Arahal & Ventosa, 2006).
Genome‐scale models provide a systems approach to understanding
the interplay between carbon sources, metabolic pathway dynamics,
and the biosynthesis of important metabolites (Ates, 2015). Model
predictions are important in guiding experiments requiring labeling or
for the production of specific bioproducts. With this in mind, FBA
simulations on a wide range of carbon sources were carried out with
iEB1159 to assess its ability to represent carbon use phenotypes and
reproduce experimental results.
(a)
(b) (c)
(d)
(e)
(f)
FIGURE 3 (a) Model development process from reconstruction from the annotated genome to refinements and analysis. (b) Distribution of
metabolic reaction types in the model. (c) Comparison of iEB1159 model and two models of C. salexigens iOA584 and iFP764. (d) MEMOTE test
for model benchmarking. (e) Carbon sources that were shown to produce growth in minimal media and their corresponding fluxes. (f) Escher map
of Glucose metabolism showing the flow of metabolites and the distribution of flux in the central carbon metabolism pathway. The colors
represent different flux ranges as shown in the legend.
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In silico predictions were done by considering biomass as an
objective function, with glucose as the sole carbon source on the minimal
medium previously obtained. Growth on other carbon sources was
simulated with FBA by using each carbon source in separate simulations
as the sole source of carbon with an uptake value of 10 mmol/gDW/h.
Overall, the model showed growth on 27 carbon sources (Figure 3e), with
varying flux rates. The high biomass yield of greater than 2 g/mmol for
some carbon sources could be attributed to the need to determine the
preciseuptakerateforsuchsubstrates, as 10 mmol/gDW/h was obtained
from other organisms. It was also observed that the polymerization of the
carbon source influenced the growth rate, with the growth rate increasing
as the level of polymerization increased. To provide a context for the
results obtained, the predictions were compared with experimental data
previously reported (Arahal & Ventosa, 2006; Radchenkova et al., 2018).
The model did not grow on lactose, citrate, and esculin as shown in
previous studies (Arahal & Ventosa, 2006; Radchenkova et al., 2018),
despite the presence of citrate and both L‐lactose and D‐lactose transport
reactions. This suggests an important gap in knowledge that requires
further attention considering that lactose is a favorable substrate in the
production of exopolysaccharides (Radchenkova et al., 2018). Thus,
iEB1159 also predicted growth in several carbon sources not previously
studied (Table 5).
The model did not grow in anaerobic conditions, confirming its
strictly aerobic phenotype (Ventosa & Haba, 2020). When oxygen
was limited, no growth was produced by the model even in the
presence of a potential electron acceptor such as Fe
3+
. So, C.
salexigens iOA584 was reported to grow anaerobically on nitrate
(Ates et al., 2011); for iEB1159, no growth was observed using nitrate
in anaerobic conditions despite the presence of transport and other
metabolic reactions. Such differences are the basis for hypotheses for
research to either improve the model knowledge base or better
understand microbial cellular behaviors.
3.8.7 |Osmoadaptation phenotypes
Salt tolerance is a hallmark phenotype of halophilic organisms with
several mechanisms happening simultaneously for survival. The
uptake and synthesis of compatible solutes constitute an important
adaptation strategy for Chromohalobacter (Arahal & Ventosa, 2006;
Piubeli et al., 2018). According to the genome annotation, C.
canadensis 85B should be able to oxidize choline to betaine and
synthesize ectoine de novo via the use of EctA,EctB, and EctC genes.
In addition, these pathways also seem to be evolutionarily conserved
in halophilic ectoine producers (Arahal & Ventosa, 2006; Piubeli
et al., 2018).
Ectoine and 5‐hydroxyectoine were included in the biomass
reaction and their respective amounts were calculated from the
amounts in the C. salexigens model by Piubeli et al. (2018) in relation
to NaCl molarity. This provides a useful approximation because both
species are close and share similar salinity adaptation features.
Demand reactions were also included to simulate the production of
intracellular ectoine. Our FBA simulations at optimal growth showed
states with flux in the direction of ectoine synthesis and the
production of small amounts of glycine betaine when choline was
added to the medium. According to Thiele and Palsson (2010);
demand functions can be added for compounds that the organism is
known to produce, and for which its production is dependent on
environmental conditions. This enables the reactions to become
active like in their favorable environment (Thiele & Palsson, 2010).
This can become useful for our model when simulating osmoadapta-
tion phenotypes. Simulations show that ectoine synthesis is inversely
related to growth. Besides, the synthesis of ectoine is highly
regulated and requires specific conditions. This can be correlated
with the fact that ectoine synthesis is energy‐intensive, also reported
with the iFP764 model (Piubeli et al., 2018).
It is worth noting that when product biosynthesis rates are
predicted, FBA simulations do not take into account the impact of
gene regulation as they only predict optimal solutions. Hence, when
validating simulations in vivo, culture conditions that provide optimal
responses need to be determined to match in silico FBA predictions.
In such cases, in principle, FBA predictions suggest optimal product
biosynthesis rates after regulatory genes have been knocked out in
cases when these genes are known (O'Brien et al., 2015). To further
improve the quality and scope of predictions related to osmoadapta-
tion, experiments towards determining the precise biomass composi-
tions in different salinities, and integrating other omics data into the
model are encouraged. This will be important in understanding
osmoadaptation in C. canadensis and halophiles in general.
3.8.8 |Gene essentiality
The analysis of the essential genes in iEB1159 was done by doing
single‐gene knockout simulations and then optimizing the model for
growth. When growth was not predicted, the knocked‐out gene and
its associated reactions were considered essential. In total, 60
essential genes were predicted (Table A2). Most essential genes
were those related to the metabolism of amino acids and nucleotides,
ectoine synthesis as well as the transportation of ions. Specifically,
our model predicted the Cl
‐
channel (voltage‐gated), and zinc/iron
permease which have been reported to be associated with adapta-
tions to high salt environments by sensing salt stress and regulating
intracellular ion homeostasis respectively (Ding et al., 2019;He
et al., 2020). Noteworthy is that the mechanism through which
voltage‐gated Cl
‐
channel contributes to salt tolerance is not yet
clearly understood. Our model could provide a platform to integrate
transcriptomics data to further investigate these mechanisms using a
systems biology perspective (Occhipinti et al., 2021).
3.8.9 |Model fitness to produce PHBs and ectoine
Halophilic bacteria are well known for their ability to produce PHBs
and ectoine which alongside other physiological mechanisms enable
survival in conditions of high salt concentrations. The PHBs are
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energy‐rich compounds accumulated under nutrient‐limiting condi-
tions, while ectoines are compatible solutes that help maintain a
growth‐supporting osmotic balance for the cell. Both are high‐value
products with several uses in the biotechnology industry (Prakash
et al., 2009; Radchenkova et al., 2018; Wang et al., 2020).
To investigate the ability of iEB1159 to produce PHBs and
ectoines, First, the model was simulated with FBA for optimal growth,
and the flux of the reactions producing both products was recorded.
Secondly, FVA was done to investigate the existence of other
potential optimal states. Thirdly, the objective function was changed
TABLE 5 Comparison of
Chromohalobacter canadensis growth on
various carbon sources reported in the
literature and in silico predictions of
iEB1159
Compound name Experimental Insilico Reference
D‐Glucose + + Arahal & ventosa (2006)
Maltose −+ Arahal & ventosa (2006)
Maltotriose No data + no report
D‐Arabinose + + Arahal & ventosa (2006)
Cellobiose + + Arahal & ventosa (2006)
D‐Fructose + + Arahal & ventosa (2006)
D‐Galactose No data + no report
Beta D‐Galactose No data + no report
D‐Gluconate No data + no report
Maltoheptaose No data + no report
Maltohexaose No data + no report
Maltopentaose No data + no report
Maltotetraose No data + no report
D‐Mannose No data + no report
D‐Mannitol No data + no report
Raffinose No data + no report
D‐Ribose −+ Arahal & ventosa (2006)
D‐Sorbitol −+ Arahal & ventosa (2006)
Sucrose + + Arahal & ventosa (2006)
Trehalose No data + no report
D‐Xylose + + Arahal & ventosa (2006)
Esculin + not in model Arahal & ventosa (2006)
L‐Rhamnose not determined −Arahal & ventosa (2006)
Starch Varies not in model Arahal & ventosa (2006)
Citrate + −Arahal & ventosa (2006)
Fumarate not determined −Arahal & ventosa (2006)
Adonitol not determined not in model Arahal & ventosa (2006)
L‐Lysine not determined −Arahal & ventosa (2006)
Lactose + −Radchenkova et al. (2018)
1,4‐alpha‐D‐glucan No data + no report
2‐Dehydro‐Dgluconate No data + no report
Adenosine No data + no report
Cytidine No data + no report
Uridine No data + no report
Salicin No data + no report
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to the demand reaction in the respective pathways producing both
products and simulated to observe their highest possible production
rate. Finally, a phenotypic phase plane analysis to investigate the
fitness of the model to produce these metabolites at optimal
conditions was performed and plotted (Figures A1–A4).
For PHB synthesis, FVA simulations showed a minimum and
maximum flux of 0.0 mmol/gDW/h and 12.35 mmol/gDW/h respec-
tively. The fitness of iEB1159 to produce PHBs showed that its
production is inversely proportional to the growth rate and that up to
12.35 mmol/gDW/h of PHBs could be produced with the lowest
possible growth rate (Figure A1). The phase plane analysis with PHB
synthesis and nitrogen source uptake (NH
4
+
) showed a decrease in
PHB production with increasing nitrogen uptake rates, although with
a steeper slope after uptake rates of about 39 mmol/gDW/h
(Figure A2). This suggests that in vivo, if C. canadensis reaches
optimal growth, decreasing the uptake rate of NH
4
+
to trigger
secondary metabolism will result in a fairly proportional increase in
PHB production. These predictions are in agreement with laboratory
and industrial PHB production fermentation schemes (Koller, 2018;
McAdam et al., 2020). Therefore, iEB1159 shows the potential to
accurately predict the production dynamics of PHBs.
The fitness of iEB1159 to produce ectoine showed that its
production is inversely proportional to the growth rate and that up to
7.05 mmol/gDW/h of ectoine could be produced when the growth rate is
lowered (Figure A3). A similar trend was also observed for 5‐
hydroxyectoine (Figure A4).Thiscouldbeexplainedbythefactthat
the synthesis of ectoine draws significant amounts of intermediates from
the TCA cycle, which reduces their availability for other growth‐
associated processes, thereby affecting the growth rate (Piubeli
et al., 2018).
4|CONCLUSIONS
Halophilic bacteria have enormous biotechnological potential, and
there is growing interest in using them as alternative resilient cell
factories and sources of high‐value bioproducts. Their use towards
this end requires an understanding of their genetics and physiology
to better design strategies that exploit their potential. In this study,
the complete genome sequence of C. canadensis 85B was analyzed
and a draft genome‐scale model was built to provide a base for future
systems biology research. We hope that this model will provide the
first computational tool to improve our understanding of its
metabolism and drive novel biotechnology discoveries.
Generally, the genome of C. canadensis 85B is comparable to the
genome of other Chromohalobacter, and genes for adaptation and
production of high‐value products were predicted. The analysis of
metabolic subsystems showed that carbohydrate metabolism was the
second‐largest important pathway, indicating the importance for the
organism to obtain and transform a wide variety of carbon sources in
diverse ways to obtain energy. This is also supported by the pathway
diversity predicted for metabolizing different carbon compounds and
producing energy. For environment‐specific adaptation, according to the
COG functional categories, the transport of inorganic ions and metabo-
lism contained up to 233 genes. Salt and ion balance are very important
for adaptation to saline environmentsaspreviouslyreportedbyother
studies (Oren, 1999; Ventosa et al., 1998).Thestressresponsesystem
was dominated by glutathione and ectoine. Studies on other halophiles
show the use of similar systems to mitigate stress and ectoine for osmotic
stress (Cai et al., 2011; Pastor et al., 2012; Schwibbert et al., 2011). C.
canadensis 85B grows at high salinity in which compatible solutes such as
ectoine are necessary for adaptation. Of interest is also the production of
polyhydroxyalkanoate biopolymers as high‐energy stores.
We here built a GEM of the metabolism of C. canadensis 85B. First,
we generated a draft reconstruction which was further curated,
annotated, and used for simulations in an iterative fashion. Finally, we
validated the model with literature data. Our model provides a platform
for multi‐omic data integration and potential combination with machine
learning and deep learning approaches. Compared to other organisms like
E. coli or S. cerevisiae, there is a limited pool of specific experimental data
on C. canadensis, indicating that there are still many knowledge gaps and
opportunities for exploration, especially for use in condition‐specific
modeling and optimization (Czajka et al., 2021; Vijayakumar &
Angione, 2021; Zhang et al., 2020).
The validated draft metabolic network model reconstructed in
this study can be updated in line with all GEMs, and can be further
improved with context‐specific modeling approaches, for instance in
presence of condition‐specific omics data. Nevertheless, we note
that GEMs remain powerful tools even when the knowledge base is
not yet complete. For instance, the model built here correctly
predicts the growth on different carbon sources in minimal media,
and the production of ectoines, betaine, and PHBs. We hope that
researchers from a wide range of disciplines will be able to use the
model to further understand its metabolism, driving novel hypotheses
on its use in industrial biotechnology.
AUTHOR CONTRIBUTIONS
Blaise Manga Enuh: Conceptualization (equal), Formal analysis
(equal), Funding acquisition (equal), Visualization (equal), Writing –
review & editing (equal). Belma Nural Yaman: Conceptualization
(equal), Funding acquisition (equal), Writing –review & editing
(equal). Chaimaa Tarzi: Formal analysis (equal), Visualization (equal),
Writing –review & editing (equal). Pınar Aytar Çelik: Conceptualiza-
tion (equal), Funding acquisition (equal), Supervision (equal), Writing –
review & editing (equal). Mehmet Mutlu: Supervision (equal), Writing
–review & editing (equal). Claudio Angione: Conceptualization
(equal), Funding acquisition (equal), Supervision (equal), Visualization
(equal), Writing –review & editing (equal).
ACKNOWLEDGMENTS
Part of this study was funded by Eskisehir Osmangazi University
scientific research committee project ID: 202115D01. CA would like
to acknowledge the support from UKRI Research England's THYME
project, the Children's Liver Disease Foundation (grant SG/2019/06/
03), and UKRI EPSRC through a Network Development Grant from
The Alan Turing Institute (grant number TNDC2‐100022).
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CONFLICT OF INTEREST
None declared.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available in the
Appendix. The whole genome shotgun project is available in DDBJ/ENA/
GenBank under the accession JAJQJH000000000: https://www.ncbi.
nlm.nih.gov/nuccore/JAJQJH000000000.Thegenome‐scale metabolic
model is available in the BioModels database with the identifier
MODEL2204110001: https://www.ebi.ac.uk/biomodels/MODEL2204
110001 and on GitHub: https://github.com/Angione-Lab/GEM-
Chromohalobacter-canadensis-85B.
ETHICS STATEMENT
None required.
ORCID
Blaise Manga Enuh https://orcid.org/0000-0002-2081-6029
Claudio Angione http://orcid.org/0000-0002-3140-7909
REFERENCES
Alcántara, R., Axelsen, K. B., Morgat, A., Belda, E., Coudert, E., Bridge, A.,
Cao, H., de Matos, P., Ennis, M., Turner, S., Owen, G.,
Bougueleret, L., Xenarios, I., & Steinbeck, C. (2012). Rhea—A
manually curated resource of biochemical reactions. Nucleic Acids
Research,40, D754–D760. https://doi.org/10.1093/nar/gkr1126
Arahal, D. R., García, M. T., Ludwig, W., Schleifer, K. H., & Ventosa, A.
(2001). Transfer of halomonas canadensis and halomonas israe-
lensis to the genus chromohalobacter as Chromohalobacter cana-
densis comb. nov. and chromohalobacter israelensis comb. nov.
International Journal of Systematic and Evolutionary Microbiology,51,
1443–1448. https://doi.org/10.1099/00207713-51-4-1443
Arahal, D. R., & Ventosa, A. (2006). The family Halomonadaceae. In The
Prokaryotes. Springer.
Ates, O. (2015). Systems biology of microbial exopolysaccharides
production. Frontiers in Bioengineering and Biotechnology,3,3.
Ates, Ö., Oner, E. T., & Arga, K. Y. (2011). Genome‐scale reconstruction of
metabolic network for a halophilic extremophile, Chromohalobacter
salexigens DSM 3043. BMC Systems Biology,5, 12. https://doi.org/
10.1186/1752-0509-5-12
Becker, J., & Wittmann, C. (2018). From systems biology to metabolically
engineered cells—an omics perspective on the development of
industrial microbes. Current Opinion in Microbiology,45, 180–188.
https://doi.org/10.1016/j.mib.2018.06.001
Boutet, E., Lieberherr, D., Tognolli, M., Schneider, M., & Bairoch, A. (2007).
UniProtKB/Swiss‐Prot. Methods in Molecular Biology,406,89–112.
https://doi.org/10.1007/978-1-59745-535-0_4
Brettin, T., Davis, J. J., Disz, T., Edwards, R. A., Gerdes, S., Olsen, G. J.,
Olson, R., Overbeek, R., Parrello, B., Pusch, G. D., Shukla, M.,
Thomason, J. A., Stevens, R., Vonstein, V., Wattam, A. R., & Xia, F.
(2015). RASTtk: A modular and extensible implementation of the
RAST algorithm for building custom annotation pipelines and
annotating batches of genomes. Scientific Reports,5, 8365. https://
doi.org/10.1038/srep08365
Cai, L., Tan, D., Aibaidula, G., Dong, X.‐R., Chen, J.‐C., Tian, W.‐D., &
Chen, G.‐Q. (2011). Comparative genomics study of polyhydrox-
yalkanoates (PHA) and ectoine relevant genes from halomonas sp.
TD01 revealed extensive horizontal gene transfer events and co‐
evolutionary relationships. Microbial Cell Factories,10, 88. https://
doi.org/10.1186/1475-2859-10-88
Çakmak, H., Çelik, P. A., Çınar, S., Hoşgün, E. Z., Mutlu, M. B., & Çabuk, A.
(2020). Levan production potentials from different hypersaline
environments in Turkey. JMBFS,10,61–64. https://doi.org/10.
15414/jmbfs.2020.10.1.61-64
Cardoso, J. G. R., Jensen, K., Lieven, C., Lærke Hansen, A. S., Galkina, S.,
Beber, M., Özdemir, E., Herrgård, M. J., Redestig, H., &
Sonnenschein, N. (2018). Cameo: A python library for computer
aided metabolic engineering and optimization of cell factories. ACS
Synthetic Biology,7, 1163–1166. https://doi.org/10.1021/
acssynbio.7b00423
Chek, M. F., Kim, S.‐Y., Mori, T., Arsad, H., Samian, M. R., Sudesh, K., &
Hakoshima, T. (2017). Structure of polyhydroxyalkanoate (PHA)
synthase PhaC from chromobacterium sp. USM2, producing
biodegradable plastics. Scientific Reports,7, 5312. https://doi.org/
10.1038/s41598-017-05509-4
Copeland, A., O'Connor, K., Lucas, S., Lapidus, A., Berry, K. W.,
Detter, J. C., Del Rio, T. G., Hammon, N., Dalin, E., Tice, H.,
Pitluck, S., Bruce, D., Goodwin, L., Han, C., Tapia, R., Saunders, E.,
Schmutz, J., Brettin, T., Larimer, F., …Woyke, T. (2011). Complete
genome sequence of the halophilic and highly halotolerant chromo-
halobacter salexigens type strain (1H11T). Standards in Genomic
Sciences,5, 379–388. https://doi.org/10.4056/sigs.2285059
Culley, C., Vijayakumar, S., Zampieri, G., & Angione, C. (2020). A
mechanism‐aware and multiomic machine‐learning pipeline
characterizes yeast cell growth. Proceedings of the National
Academy of Sciences,117, 18869–18879. https://doi.org/10.
1073/pnas.2002959117
Czajka, J. J., Oyetunde, T., & Tang, Y. J. (2021). Integrated knowledge
mining, genome‐scale modeling, and machine learning for predicting
yarrowia lipolytica bioproduction. Metabolic Engineering,67,
227–236. https://doi.org/10.1016/j.ymben.2021.07.003
Degtyarenko, K., de Matos, P., Ennis, M., Hastings, J., Zbinden, M.,
McNaught, A., Alcantara, R., Darsow, M., Guedj, M., & Ashburner, M.
(2007). ChEBI: A database and ontology for chemical entities of
biological interest. Nucleic Acids Research,36, D344–D350. https://
doi.org/10.1093/nar/gkm791
Ding, X., Liu, K., Lu, Y., & Gong, G. (2019). Morphological, transcriptional,
and metabolic analyses of osmotic‐adapted mechanisms of the
halophilic aspergillus montevidensis ZYD4 under hypersaline condi-
tions. Applied Microbiology and Biotechnology,103, 3829–3846.
https://doi.org/10.1007/s00253-019-09705-2
Dyall‐Smith, M., 2015. The Halohandbook v7.3. https://doi.org/10.13140/
RG.2.1.1750.5441
Ebrahim, A., Lerman, J. A., Palsson, B. O., & Hyduke, D. R. (2013).
COBRApy: COnstraints‐based reconstruction and analysis for
python. BMC Systems Biology,7, 74. https://doi.org/10.1186/
1752-0509-7-74
Enuh, B. M., & Aytar Çelik, P. (2022). Insight into the biotechnology
potential of alicyclobacillus tolerans from whole genome sequence
analysis and genome‐scale metabolic network modeling. Journal of
Microbiological Methods,197, 106459. https://doi.org/10.1016/j.
mimet.2022.106459
Erdogmus, S. F., Korcan, S. E., Konuk, M., Guven, K., & Mutlu, M. B. (2015).
Aromatic hydrocarbon utilization ability of chromohalobacter sp.
Ekoloji,24,10–16.
Fabregat, A., Jupe, S., Matthews, L., Sidiropoulos, K., Gillespie, M.,
Garapati, P., Haw, R., Jassal, B., Korninger, F., May, B., Milacic, M.,
Roca, C. D., Rothfels, K., Sevilla, C., Shamovsky, V., Shorser, S.,
Varusai, T., Viteri, G., Weiser, J., …D'Eustachio, P. (2018). The
reactome pathway knowledgebase. Nucleic Acids Research,46,
D649–D655. https://doi.org/10.1093/nar/gkx1132
Fang, X., Lloyd, C. J., & Palsson, B. O. (2020). Reconstructing organisms in
silico: Genome‐scale models and their emerging applications.
Nature Reviews Microbiology,18, 731–743. https://doi.org/10.
1038/s41579-020-00440-4
14 of 20
|
ENUH ET AL.

Felsenstein, J. (1985). Confidence limits on phylogenies: An approach
using the bootstrap. Evolution,39, 783–791. https://doi.org/10.
2307/2408678
Gedikli, S., Çelik, P. A., Demirbilek, M., Mutlu, M. B., Denkbaş, E. B., &
Çabuk, A. (2019). Experimental exploration of thermostable poly
(β‐Hydroxybutyrates) by Geobacillus kaustophilus using Box‐Behnken
design. Journal of Polymers and the Environment,27, 245–255.
https://doi.org/10.1007/s10924-018-1335-z
Gu, C., Kim, G. B., Kim, W. J., Kim, H. U., & Lee, S. Y. (2019). Current status
and applications of genome‐scale metabolic models. Genome Biology,
20, 121. https://doi.org/10.1186/s13059-019-1730-3
Ben Guebila, M., & Thiele, I., 2019. Predicting gastrointestinal drug effects
using contextualized metabolic models. PLoS Computational Biology,
15, e1007100. https://doi.org/10.1371/journal.pcbi.1007100
Gunde‐Cimerman, N., Plemenitaš, A., & Oren, A. (2018). Strategies of
adaptation of microorganisms of the three domains of life to high
salt concentrations. FEMS Microbiology Reviews,42, 353–375.
https://doi.org/10.1093/femsre/fuy009
Güngörmedi, G., Demirbilek, M., Mutlu, M. B., Denkbaş, E. B., & Çabuk, A.
(2014). Polyhydroxybutyrate and hydroxyvalerate production by
bacillus megaterium strain A1 isolated from hydrocarbon‐
contaminated soil. Journal of Applied Polymer Science,131, 40530.
https://doi.org/10.1002/app.40530
He, Q., Lin, Y., Tan, H., Zhou, Y., Wen, Y., Gan, J., Li, R., & Zhang, Q. (2020).
Transcriptomic profiles of Dunaliella salina in response to hypersaline
stress. BMC Genomics,21, 115. https://doi.org/10.1186/s12864-
020-6507-2
Heller, S. R., McNaught, A., Pletnev, I., Stein, S., & Tchekhovskoi, D.
(2015). InChI, the IUPAC international chemical identifier. Journal of
Cheminformatics,7, 23. https://doi.org/10.1186/s13321-015-
0068-4
Huerta‐Cepas, J., Szklarczyk, D., Heller, D., Hernández‐Plaza, A.,
Forslund, S. K., Cook, H., Mende, D. R., Letunic, I., Rattei, T.,
Jensen, L. J., von Mering C., & Bork P. (2019). eggNOG 5.0: A
hierarchical, functionally and phylogenetically annotated orthology
resource based on 5090 organisms and 2502 viruses. Nucleic Acids
Research,47, D309–D314. https://doi.org/10.1093/nar/gky1085
Kanehisa, M. (2000). KEGG: Kyoto encyclopedia of genes and genomes.
Nucleic Acids Research,28,27–30. https://doi.org/10.1093/nar/28.
1.27
Karp, P. D., Billington, R., Caspi, R., Fulcher, C. A., Latendresse, M.,
Kothari, A., Keseler, I. M., Krummenacker, M., Midford, P. E., Ong, Q.,
Ong, W. K., Paley, S. M., & Subhraveti, P. (2019). The BioCyc
collection of microbial genomes and metabolic pathways. Briefings in
Bioinformatics,20, 1085–1093. https://doi.org/10.1093/bib/
bbx085
Kavvas, E. S., Yang, L., Monk, J. M., Heckmann, D., & Palsson, B. O. (2020).
A biochemically‐interpretable machine learning classifier for micro-
bial GWAS. Nature Communications,11, 2580. https://doi.org/10.
1038/s41467-020-16310-9
King, Z. A., Dräger, A., Ebrahim, A., Sonnenschein, N., Lewis, N. E., &
Palsson, B. O. (2015). Escher: A web application for building, sharing,
and embedding data‐rich visualizations of biological pathways. PLoS
Computational Biology,11, e1004321. https://doi.org/10.1371/
journal.pcbi.1004321
Koller, M. (2018). A review on established and emerging fermentation
schemes for microbial production of polyhydroxyalkanoate (PHA)
biopolyesters. Fermentation,4(2), 30. https://doi.org/10.3390/
fermentation4020030
Kumar, S., Stecher, G., Li, M., Knyaz, C., & Tamura, K. (2018). MEGA X:
Molecular evolutionary genetics analysis across computing plat-
forms. Molecular Biology and Evolution,35, 1547–1549. https://doi.
org/10.1093/molbev/msy096
Liebisch, G., Fahy, E., Aoki, J., Dennis, E. A., Durand, T., Ejsing, C. S.,
Fedorova, M., Feussner, I., Griffiths, W. J., Köfeler, H., Merrill, A. H.,
Murphy, R. C., O'Donnell, V. B., Oskolkova, O., Subramaniam, S.,
Wakelam, M. J. O., & Spener, F. (2020). Update on LIPID MAPS
classification, nomenclature, and shorthand notation for MS‐derived
lipid structures. Journal of Lipid Research,61, 1539–1555. https://
doi.org/10.1194/jlr.S120001025
Lieven, C., Beber, M. E., Olivier, B. G., Bergmann, F. T., Ataman, M.,
Babaei, P., Bartell, J. A., Blank, L. M., Chauhan, S., Correia, K.,
Diener, C., Dräger, A., Ebert, B. E., Edirisinghe, J. N., Faria, J. P.,
Feist, A. M., Fengos, G., Fleming, R. M. T., García‐Jiménez, B., …
Zhang, C. (2020). MEMOTE for standardized genome‐scale meta-
bolic model testing. Nature Biotechnology,38, 272–276. https://doi.
org/10.1038/s41587-020-0446-y
Machado, D., Andrejev, S., Tramontano, M., & Patil, K. R. (2018). Fast
automated reconstruction of genome‐scale metabolic models for
microbial species and communities. Nucleic Acids Research,46,
7542–7553. https://doi.org/10.1093/nar/gky537
Maehara, A., Taguchi, S., Nishiyama, T., Yamane, T., & Doi, Y. (2002). A
repressor protein, PhaR, regulates polyhydroxyalkanoate (PHA)
synthesis via its direct interaction with PHA. Journal of
Bacteriology,184, 3992–4002. https://doi.org/10.1128/jb.184.14.
3992-4002.2002
Mahadevan, R., & Schilling, C. H. (2003). The effects of alternate optimal
solutions in constraint‐based genome‐scale metabolic models.
Metabolic Engineering,5, 264–276. https://doi.org/10.1016/j.
ymben.2003.09.002
Masip, L., Veeravalli, K., & Georgiou, G. (2006). The many faces of
glutathione in bacteria. Antioxidants & Redox Signaling,8, 753–762.
https://doi.org/10.1089/ars.2006.8.753
McAdam, B., Brennan Fournet, M., McDonald, P., & Mojicevic, M. (2020).
Production of polyhydroxybutyrate (PHB) and factors impacting its
chemical and mechanical characteristics. Polymers,12(12), 2908.
https://doi.org/10.3390/polym12122908
McCool, G. J., & Cannon, M. C. (2001). PhaC and PhaR are required for
polyhydroxyalkanoic acid synthase activity in Bacillus megaterium.
Journal of Bacteriology,183, 4235–4243. https://doi.org/10.1128/
JB.183.14.4235-4243.2001
Meng, D.‐C., Shen, R., Yao, H., Chen, J.‐C., Wu, Q., & Chen, G.‐Q. (2014).
Engineering the diversity of polyesters. Current Opinion in
Biotechnology,29,24–33. https://doi.org/10.1016/j.copbio.2014.
02.013
Mitra, R., Xu, T., Xiang, H., & Han, J. (2020). Current developments on
polyhydroxyalkanoates synthesis by using halophiles as a promising
cell factory. Microbial Cell Factories,19, 86. https://doi.org/10.1186/
s12934-020-01342-z
Moretti, S., Tran, V. D. T., Mehl, F., Ibberson, M., & Pagni, M. (2021).
MetaNetX/MNXref: Unified namespace for metabolites and bio-
chemical reactions in the context of metabolic models. Nucleic Acids
Research,49, D570–D574. https://doi.org/10.1093/nar/gkaa992
Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C., & Kanehisa, M. (2007).
KAAS: An automatic genome annotation and pathway
reconstruction server. Nucleic Acids Research,35, W182–W185.
https://doi.org/10.1093/nar/gkm321
Mutnuri, S., Vasudevan, N., Kastner, M., & Heipieper, H. J. (2005).
Changes in fatty acid composition of chromohalobacter israelensis
with varying salt concentrations. Current Microbiology,50, 151–154.
https://doi.org/10.1007/s00284-004-4396-2
O'Brien, E. J., Monk, J. M., & Palsson, B. O. (2015). Using genome‐scale
models to predict biological capabilities. Cell,161, 971–987. https://
doi.org/10.1016/j.cell.2015.05.019
Occhipinti, A., Hamadi, Y., Kugler, H., Wintersteiger, C. M., Yordanov, B., &
Angione, C. (2021). Discovering essential multiple gene effects
through large scale optimization: An application to human cancer
metabolism. IEEE/ACM Transactions on Computational Biology and
Bioinformatics,18, 2339–2352. https://doi.org/10.1109/TCBB.
2020.2973386
ENUH ET AL.
|
15 of 20

Olsson, B. E., Korsakova, E. S., Anan'ina, L. N., Pyankova, A. A.,
Mavrodi, O. V., Plotnikova, E. G., & Mavrodi, D. V. (2017). Draft
genome sequences of strains Salinicola socius SMB35T, Salinicola
sp. MH3R3–1 and Chromohalobacter sp. SMB17 from the Verkh-
nekamsk potash mining region of Russia. Standards in Genomic
Sciences,12(1). https://doi.org/10.1186/s40793-017-0251-5
Oren, A. (1999). Bioenergetic aspects of halophilism. Microbiology and
Molecular Biology Reviews,63, 334–348.
Oren, A., & Mana, L. (2003). Sugar metabolism in the extremely halophilic
bacterium salinibacter ruber. FEMS Microbiology Letters,223,83–87.
https://doi.org/10.1016/S0378-1097(03)00345-8
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010). What is flux balance analysis?
Nature Biotechnology,28, 245–248. https://doi.org/10.1038/
nbt.1614
Pastor, J. M., Bernal, V., Salar, M. J., Salvador, M., Argandona, M.,
Sevilla, A., Iborra, J. L., Csonka, L., Nieto, J. J., Vargas, C., &
Canovas, M. (2012). Central metabolism adaptations for ectoines
synthesis in Chromohalobacter salexigens.FEBS Journal,279, 522.
Pastor, J. M., Bernal, V., Salvador, M., Argandoña, M., Vargas, C.,
Csonka, L., Sevilla, Á., Iborra, J. L., Nieto, J. J., & Cánovas, M.
(2013). Role of central metabolism in the osmoadaptation of the
halophilic bacterium Chromohalobacter salexigens.Journal of
Biological Chemistry,288, 17769–17781. https://doi.org/10.1074/
jbc.M113.470567
Pastor, J. M., Borges, N., Pagán, J. P., Castaño‐Cerezo, S., Csonka, L. N.,
Goodner, B. W., Reynolds, K. A., Gonçalves, L. G., Argandoña, M.,
Nieto, J. J., Vargas, C., Bernal, V., & Cánovas, M. (2019). Fructose
metabolism in Chromohalobacter salexigens: Interplay between the
Embden–Meyerhof–Parnas and Entner–Doudoroff pathways.
Microbial Cell Factories,18, 134. https://doi.org/10.1186/s12934-
019-1178-x
Piubeli, F., Salvador, M., Argandoña, M., Nieto, J. J., Bernal, V.,
Pastor, J. M., Cánovas, M., & Vargas, C. (2018). Insights into
metabolic osmoadaptation of the ectoines‐producer bacterium
Chromohalobacter salexigens through a high‐quality genome scale
metabolic model. Microbial Cell Factories,17,2.https://doi.org/10.
1186/s12934-017-0852-0
Prakash, B., Vidyasagar, M., Madhukumar, M. S., Muralikrishna, G., &
Sreeramulu, K. (2009). Production, purification, and characterization
of two extremely halotolerant, thermostable, and alkali‐stable
α‐amylases from chromohalobacter sp. TVSP 101. Process
Biochemistry,44, 210–215. https://doi.org/10.1016/j.procbio.
2008.10.013
Radchenkova, N., Boyadzhieva, I., Atanasova, N., Poli, A., Finore, I.,
Di Donato, P., Nicolaus, B., Panchev, I., Kuncheva, M., &
Kambourova, M. (2018). Extracellular polymer substance synthe-
sized by a halophilic bacterium Chromohalobacter canadensis 28.
Applied Microbiology and Biotechnology,102, 4937–4949. https://
doi.org/10.1007/s00253-018-8901-0
Römer, M., Eichner, J., Dräger, A., Wrzodek, C., Wrzodek, F., & Zell, A.
(2016). ZBIT bioinformatics toolbox: A web‐platform for systems
biology and expression data analysis. PloS One,11, e0149263.
https://doi.org/10.1371/journal.pone.0149263
RStudio Team. (2015). RStudio: Integrated development environment
for R.
Saitou, N., & Nei, M. (1987). The neighbor‐joining method: A new method
for reconstructing phylogenetic trees. Molecular Biology and
Evolution,4, 406–425. https://doi.org/10.1093/oxfordjournals.
molbev.a040454
Schellenberger, J., Park, J. O., Conrad, T. M., & Palsson, B. Ø. (2010). BiGG:
A biochemical genetic and genomic knowledgebase of large scale
metabolic reconstructions. BMC Bioinformatics,11, 213. https://doi.
org/10.1186/1471-2105-11-213
Schwibbert, K., Marin‐Sanguino, A., Bagyan, I., Heidrich, G., Lentzen, G.,
Seitz, H., Rampp, M., Schuster, S. C., Klenk, H.‐P., Pfeiffer, F.,
Oesterhelt, D., Kunte, H. J. (2011). A blueprint of ectoine
metabolism from the genome of the industrial producer halomonas
elongata DSM 2581T. Environmental Microbiology,13, 1973–1994.
https://doi.org/10.1111/j.1462-2920.2010.02336.x
Seaver,S.M.D.,Liu,F.,Zhang,Q.,Jeffryes, J., Faria, J. P., Edirisinghe, J. N.,
Mundy,M.,Chia,N.,Noor,E.,Beber,M.E.,Best,A.A.,DeJongh,M.,
Kimbrel,J.A.,D'haeseleer,P.,McCorkle,S.R.,Bolton,J.R.,Pearson,E.,
Canon, S., Wood‐Charlson, E. M., …Henry, C. S. (2020). The ModelSEED
biochemistry database for the integration of metabolic annotations and
the reconstruction, comparison and analysis of metabolic models for
plants, fungi and microbes. Nucleic Acids Research,49,D575–D588.
https://doi.org/10.1093/nar/gkaa746
Srivastava, A. K., Sharma, A., Srivastava, R., Tiwari, P. K., Singh, A. K.,
Yadav, J., Jamali, H., Bharati, A. P., Srivastava, A. K., Kashyap, P. L.,
Chakdar, H., Kumar, M., & Saxena, A. K. (2019). Draft genome
sequence of halotolerant bacterium chromohalobacter salexigens
anj207, isolated from salt crystal deposits in pipelines. Microbiology
Resource Announcements,8(15). https://doi.org/10.1128/mra.
00049-19
Stothard, P., Grant, J. R., & Van Domselaar, G. (2019). Visualizing and
comparing circular genomes using the CGView family of tools.
Briefings in Bioinformatics,20, 1576–1582. https://doi.org/10.1093/
bib/bbx081
Tamura, K., Nei, M., & Kumar, S. (2004). Prospects for inferring very large
phylogenies by using the neighbor‐joining method. Proceedings of the
National Academy of Sciences,101, 11030–11035. https://doi.org/
10.1073/pnas.0404206101
Thiele, I., & Palsson, B. Ø. (2010). A protocol for generating a high‐quality
genome‐scale metabolic reconstruction. Nature Protocols,5,93–121.
https://doi.org/10.1038/nprot.2009.203
Thomas, T., Elain, A., Bazire, A., & Bruzaud, S. (2019). Complete genome
sequence of the halophilic PHA‐producing bacterium halomonas sp.
SF2003: Insights into its biotechnological potential. World Journal of
Microbiology and Biotechnology,35, 50. https://doi.org/10.1007/
s11274-019-2627-8
Ventosa, A., & Haba, R. R. (2020). Chromohalobacter. In Bergey's manual of
systematics of archaea and bacteria (pp. 1–16). American Cancer
Society. https://doi.org/10.1002/9781118960608.gbm01189.pub2
Ventosa, A., Nieto, J., & Oren, A. (1998). Biology of moderately halophilic
aerobic bacteria. Microbiology and Molecular Biology Reviews,62,
504–544.
Vijayakumar, S., & Angione, C. (2021). Protocol for hybrid flux balance,
statistical, and machine learning analysis of multi‐omic data from the
cyanobacterium synechococcus sp. STAR Protocols,2, 100837.
Vijayakumar, S., Rahman, P. K. S. M., & Angione, C. (2020). A hybrid flux
balance analysis and machine learning pipeline elucidates metabolic
adaptation in Cyanobacteria. iScience,23, 101818. https://doi.org/
10.1016/j.isci.2020.101818
Wang, M., Ai, L., Zhang, M., Wang, F., & Wang, C. (2020). Characterization
of a novel halotolerant esterase from Chromohalobacter canadensis
isolated from salt well mine. Biotech,10, 430. https://doi.org/10.
1007/s13205-020-02420-0
Wattam,A.R.,Brettin,T.,Davis,J.J.,Gerdes,S.,Kenyon,R.,Machi,D.,Mao,C.,
Olson, R., Overbeek, R., Pusch, G. D., Shukla, M. P., Stevens, R.,
Vonstein, V., Warren, A., Xia, F., & Yoo, H. (2018). Assembly, annotation,
and comparative genomics in PATRIC, the all bacterial bioinformatics
resource center. Methods in Molecular Biology,1704,79–101. https://doi.
org/10.1007/978-1-4939-7463-4_4
Wattam, A. R., Davis, J. J., Assaf, R., Boisvert, S., Brettin, T., Bun, C.,
Conrad, N., Dietrich, E. M., Disz, T., Gabbard, J. L., Gerdes, S.,
Henry, C. S., Kenyon, R. W., Machi, D., Mao, C., Nordberg, E. K.,
Olsen, G. J., Murphy‐Olson, D. E., Olson, R., …Stevens, R. L. (2017).
Improvements to PATRIC, the all‐bacterial bioinformatics database
and analysis resource center. Nucleic Acids Research,45,
D535–D542. https://doi.org/10.1093/nar/gkw1017
16 of 20
|
ENUH ET AL.

Wick, R. R., Judd, L. M., Gorrie, C. L., & Holt, K. E. (2017). Unicycler:
Resolving bacterial genome assemblies from short and long
sequencing reads. PLoS Computational Biology,13, e1005595.
https://doi.org/10.1371/journal.pcbi.1005595
Wickham, H. (2009). Introduction. In Wickham, H. (Ed.), Ggplot2: Elegant
graphics for data analysis, use R (pp. 1–7). Springer. https://doi.org/
10.1007/978-0-387-98141-3_1
Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N.,
Cheng, D., Jewell, K., Arndt, D., Sawhney, S., Fung, C., Nikolai, L.,
Lewis, M., Coutouly, M.‐A., Forsythe, I., Tang, P., Shrivastava, S.,
Jeroncic, K., Stothard, P., …Querengesser, L. (2007). HMDB: The
human metabolome database. Nucleic Acids Research,35,
D521‐D526. https://doi.org/10.1093/nar/gkl923
Zampieri, G., Vijayakumar, S., Yaneske, E., & Angione, C. (2019). Machine
and deep learning meet genome‐scale metabolic modeling. PLoS
Computational Biology,15, e1007084. https://doi.org/10.1371/
journal.pcbi.1007084
Zhang, J., Petersen, S. D., Radivojevic, T., Ramirez, A., Pérez‐Manríquez,
A., Abeliuk, E., Sánchez, B. J., Costello, Z., Chen, Y., Fero, M. J.,
Martin, H. G., Nielsen, J., Keasling, J. D., & Jensen, M. K. (2020).
Combining mechanistic and machine learning models for predictive
engineering and optimization of tryptophan metabolism. Nature
Communications,11, 4880. https://doi.org/10.1038/s41467-020-
17910-1
Zheng, Y., Chen, J.‐C., Ma, Y.‐M., & Chen, G.‐Q. (2020). Engineering
biosynthesis of polyhydroxyalkanoates (PHA) for diversity and cost
reduction. Metabolic Engineering,58,82–93. https://doi.org/10.
1016/j.ymben.2019.07.004
Zhou, P., Huo, Y.‐Y., Xu, L., Wu, Y.‐H., Meng, F.‐X., Wang, C.‐S., & Xu,
X.‐W. (2015). Investigation of mercury tolerance in Chromohalo-
bacter israelensis DSM 6768T and Halomonas zincidurans B6T by
comparative genomics with Halomonas xinjiangensis TRM 0175T.
Marine Genomics,19,15–16. https://doi.org/10.1016/j.margen.
2014.11.008
How to cite this article: Enuh, B. M., Nural Yaman, B., Tarzi,
C., Aytar Çelik, P., Mutlu, M. B., & Angione, C. (2022). Whole‐
genome sequencing and genome‐scale metabolic modeling of
Chromohalobacter canadensis 85B to explore its salt tolerance
and biotechnological use. MicrobiologyOpen, 11, e1328.
https://doi.org/10.1002/mbo3.1328
APPENDIX
FIGURE A1 Phenotypic phase plane for
Polyhydroxybutyrate production.
FIGURE A2 Phase plane analysis of
Polyhydroxybutyrate production with varying
concentrations of nitrogen source.
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FIGURE A3 Phenotypic phase plane for
ectoine production with varying biomass.
FIGURE A4 Phenotypic phase plane for
5‐hydroxyectoine production with varying
biomass.
TABLE A1 Minimal media
Metabolite identifier Metabolite name
ca2_e Calcium
cl_e Chloride
cobalt2_e Cobalt
cu2_e Copper
fe2_e Ferrous Iron
glc__D_e D‐Glucose
k_e Potassium
mg2_e Magnesium
mn2_e Manganese
nh4_e Ammonium
o2_e Oxygen
pi_e Phosphate
so4_e Sulfate
zn2_e Zinc
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TABLE A2 Essential genes predicted by iEB1159
Gene ID Growth
Growth
status Gene product
{‘fig_141389_9_peg_2976'} 0 optimal Phosphomethylpyrimidine synthase ThiC (EC 4.1.99.17)
{‘fig_141389_9_peg_2742'} 0 optimal 3'(2'),5'‐bisphosphate nucleotidase (EC 3.1.3.7)
{‘fig_141389_9_peg_1758'} 0 optimal 3‐methyl‐2‐oxobutanoate hydroxymethyltransferase (EC 2.1.2.11)
{‘fig_141389_9_peg_230'} 0 optimal Dihydrofolate synthase (EC 6.3.2.12) @ Folylpolyglutamate synthase (EC 6.3.2.17)
{‘fig_141389_9_peg_1773'} 0 optimal Phosphoglucosamine mutase (EC 5.4.2.10)
{‘fig_141389_9_peg_1804'} 0 optimal Argininosuccinate lyase (EC 4.3.2.1)
{‘fig_141389_9_peg_861'} 0 optimal Threonine synthase (EC 4.2.3.1)
{‘fig_141389_9_peg_3064'} 0 optimal 3‐dehydroquinate dehydratase II (EC 4.2.1.10)
{‘fig_141389_9_peg_884'} 0 optimal Deoxyuridine 5'‐triphosphate nucleotidohydrolase (EC 3.6.1.23)
{‘fig_141389_9_peg_1913'} 0 optimal Dihydroorotase (EC 3.5.2.3)
{‘fig_141389_9_peg_2658'} 0 optimal Undecaprenyl diphosphate synthase (EC 2.5.1.31)
{‘fig_141389_9_peg_1532'} 0 optimal 3‐isopropylmalate dehydrogenase (EC 1.1.1.85)
{‘fig_141389_9_peg_2718'} 0 optimal Phosphoribosylformimino‐5‐aminoimidazole carboxamide ribotide isomerase (EC 5.3.1.16)
{‘fig_141389_9_peg_3119'} 0 optimal UDP‐N‐acetylmuramoyl‐L‐alanine‐‐D‐glutamate ligase (EC 6.3.2.9)
{‘fig_141389_9_peg_2809'} 0 optimal Thymidylate kinase (EC 2.7.4.9)
{‘fig_141389_9_peg_1215'} 0 optimal N‐acetyl‐gamma‐glutamyl‐phosphate reductase (EC 1.2.1.38)
{‘fig_141389_9_peg_860'} 0 optimal Homoserine dehydrogenase (EC 1.1.1.3)
{‘fig_141389_9_peg_1423'} 0 optimal Serine acetyltransferase (EC 2.3.1.30)
{‘fig_141389_9_peg_3232'} 0 optimal S‐adenosylmethionine synthetase (EC 2.5.1.6)
{‘fig_141389_9_peg_2716'} 0 optimal Imidazoleglycerol‐phosphate dehydratase (EC 4.2.1.19)
{‘fig_141389_9_peg_1530'} 0 optimal 3‐isopropylmalate dehydratase large subunit (EC 4.2.1.33)
{‘fig_141389_9_peg_2630'} 0 optimal Phosphoribosyl‐ATP pyrophosphatase (EC 3.6.1.31)
{‘fig_141389_9_peg_2668'} 0 optimal N‐succinyl‐L,L‐diaminopimelate desuccinylase (EC 3.5.1.18)
{‘fig_141389_9_peg_1982'} 0 optimal Cl
‐
channel, voltage gated
{‘fig_141389_9_peg_415'} 0 optimal Pantothenate kinase type III, CoaX‐like (EC 2.7.1.33)
{‘fig_141389_9_peg_3186'} 0 optimal Argininosuccinate synthase (EC 6.3.4.5)
{‘fig_141389_9_peg_882'} 0 optimal N‐acetylglutamate kinase (EC 2.7.2.8)
{‘fig_141389_9_peg_226'} 0 optimal Phosphoribosylanthranilate isomerase (EC 5.3.1.24)
{‘fig_141389_9_peg_2779'} 0 optimal Cysteine synthase B (EC 2.5.1.47)
{‘fig_141389_9_peg_948'} 0 optimal Branched‐chain amino acid aminotransferase (EC 2.6.1.42)
{‘fig_141389_9_peg_683'} 0 optimal Indole‐3‐glycerol phosphate synthase (EC 4.1.1.48)
{‘fig_141389_9_peg_3097'} 0 optimal UDP‐N‐acetylglucosamine 1‐carboxyvinyltransferase (EC 2.5.1.7)
{‘fig_141389_9_peg_3118'} 0 optimal Phospho‐N‐acetylmuramoyl‐pentapeptide‐transferase (EC 2.7.8.13)
{‘fig_141389_9_peg_2514'} 0 optimal Tol‐Pal system‐associated acyl‐CoA thioesterase
{‘fig_141389_9_peg_310'} 0 optimal Erythronate‐4‐phosphate dehydrogenase (EC 1.1.1.290)
{‘fig_141389_9_peg_1942'} 0 optimal zinc/iron permease
{‘fig_141389_9_peg_2717'} 0 optimal Imidazole glycerol phosphate synthase amidotransferase subunit HisH
{‘fig_141389_9_peg_2824'} 0 optimal UDP‐N‐acetylenolpyruvoylglucosamine reductase (EC 1.3.1.98)
(Continues)
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TABLE A2 (Continued)
Gene ID Growth
Growth
status Gene product
{‘fig_141389_9_peg_340'} 0 optimal FMN adenylyltransferase (EC 2.7.7.2)/Riboflavin kinase (EC 2.7.1.26)
{‘fig_141389_9_peg_3117'} 0 optimal UDP‐N‐acetylmuramoyl‐tripeptide‐‐D‐alanyl‐D‐alanine ligase (EC 6.3.2.10)
{‘fig_141389_9_peg_3156'} 0 optimal Orotidine 5'‐phosphate decarboxylase (EC 4.1.1.23)
{‘fig_141389_9_peg_1963'} 0 optimal N‐acetylglucosamine‐1‐phosphate uridyltransferase (EC 2.7.7.23)/Glucosamine‐1‐
phosphate N‐acetyltransferase (EC 2.3.1.157)
{‘fig_141389_9_peg_306'} 0 optimal Dihydroorotate dehydrogenase (quinone) (EC 1.3.5.2)
{‘fig_141389_9_peg_885'} 0 optimal Phosphopantothenoylcysteine decarboxylase (EC 4.1.1.36)/Phosphopantothenoylcysteine
synthetase (EC 6.3.2.5)
{‘fig_141389_9_peg_1707'} 0 optimal GTP cyclohydrolase I (EC 3.5.4.16) type 1
{‘fig_141389_9_peg_274'} 0 optimal NAD kinase (EC 2.7.1.23)
{‘fig_141389_9_peg_3145'} 0 optimal Phosphoserine aminotransferase (EC 2.6.1.52)
{‘fig_141389_9_peg_2879'} 0 optimal Flavin prenyltransferase UbiX
{‘fig_141389_9_peg_3146'} 0 optimal Chorismate mutase I (EC 5.4.99.5)/Prephenate dehydratase (EC 4.2.1.51)
{‘spontaneous'} 0 optimal #N/A
{‘fig_141389_9_peg_3220'} 0 optimal 5,10‐methylenetetrahydrofolate reductase (EC 1.5.1.20)
{‘fig_141389_9_peg_3099'} 0 optimal Histidinol dehydrogenase (EC 1.1.1.23)
{‘fig_141389_9_peg_1899'} 0 optimal Orotate phosphoribosyltransferase (EC 2.4.2.10)
{‘fig_141389_9_peg_3123'} 0 optimal D‐alanine‐‐ ligase (EC 6.3.2.4)
{‘fig_141389_9_peg_1807'} 0 optimal Diaminopimelate epimerase (EC 5.1.1.7)
{‘fig_141389_9_peg_1531'} 0 optimal 3‐isopropylmalate dehydratase small subunit (EC 4.2.1.33)
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