Abstract
Multidrug-resistant (MDR) Acinetobacter baumannii is a critical threat
to human health globally. We constructed a genome-scale metabolic model
iAB5075 for the hypervirulent, MDR A. baumannii strain AB5075.
Predictions of nutrient utilization and gene essentiality were
validated using Biolog assay and a transposon mutant library. In vivo
transcriptomics data were integrated with iAB5075 to elucidate
bacterial metabolic responses to the host environment. iAB5075 contains
1530 metabolites, 2229 reactions, and 1015 genes, and demonstrated high
accuracies in predicting nutrient utilization and gene essentiality. At
4 h post-infection, a total of 146 metabolic fluxes were increased and
52 were decreased compared to 2 h post-infection; these included
enhanced fluxes through peptidoglycan and lipopolysaccharide
biosynthesis, tricarboxylic cycle, gluconeogenesis, nucleotide and
fatty acid biosynthesis, and altered fluxes in amino acid metabolism.
These flux changes indicate that the induced central metabolism, energy
production, and cell membrane biogenesis played key roles in
establishing and enhancing A. baumannii bloodstream infection. This
study is the first to employ genome-scale metabolic modeling to
investigate A. baumannii infection in vivo. Our findings provide
important mechanistic insights into the adaption of A. baumannii to the
host environment and thus will contribute to the development of new
therapeutic agents against this problematic pathogen.
Keywords: Acinetobacter baumannii, genome-scale metabolic modeling,
transcriptomics, bacteremia, RNA-seq
1. Introduction
Multidrug-resistant (MDR) Acinetobacter baumannii has become a critical
threat to human health globally [[42]1]. It has a high incidence of
nosocomial infections, including bacteremia, pneumonia, wound sepsis,
and urinary tract infections [[43]2]. Recently, the World Health
Organization (WHO) identified carbapenem-resistant A. baumannii as one
of three top-priority pathogens that urgently require novel
antimicrobial therapeutics [[44]3].
Antibiotics target essential components of bacterial growth, such as
DNA replication, translation, and peptidoglycan biosynthesis [[45]4].
However, resistance to antibiotics can develop rapidly in bacteria
[[46]5]. Virulence factors, such as outer membrane proteins, secretion
systems, phospholipases, and iron acquisition systems, promote
bacterial intracellular replication, cell adhesion, and invasion, and
are crucial for pathogens to adapt to the host environment [[47]6].
Strategies to inhibit virulence factors represent a promising
alternative therapeutic option for the treatment of severe infections
caused by MDR bacteria [[48]7]. Dual RNA-seq is increasingly used to
identify the key factors contributing to host adaptation during
infection [[49]8,[50]9,[51]10,[52]11]. Several key metabolic pathways
(e.g., the phenylacetic acid metabolism pathway) are associated with
the establishment of A. baumannii infection in vivo [[53]12,[54]13];
however, it remains unclear how exactly host adaption is controlled by
the complex bacterial metabolic network during infection.
Genome-scale metabolic modeling (GSMM) is increasingly used to decipher
the metabolic changes in pathogens under infection or antibiotic
treatment conditions [[55]14,[56]15,[57]16]. Integration with
multi-omics data enabled GSMMs to accurately describe cellular
metabolism [[58]17]. In the present study, we report the development
and validation of the first GSMM for A. baumannii MDR strain AB5075. By
incorporating in vivo transcriptomics data as constraints, our model
was able to identify the significant metabolic changes of AB5075 during
infection in mice. This is the first integrative modeling of A.
baumannii infection in vivo and provides key mechanistic information
regarding bacterial metabolic changes in response to the host immune
system. Such information will facilitate optimization of antibiotic
therapy for infections caused by A. baumannii.
2. Materials and Methods
2.1. Bacterial Strain and Growth Condition
Hypervirulent MDR A. baumannii AB5075 was obtained from the University
of Washington and stored at −80 °C in tryptone soy broth (TSB, Oxoid
Australia) with 20% glycerol. Prior to experiments, AB5075 was
sub-cultured onto nutrient agar and incubated at 37 °C overnight. A
single colony was then selected and grown overnight in 20 mL of
cation-adjusted Mueller-Hinton broth (MHB; Oxoid, Australia; 20–25
mg·L^−1 Ca^2+ and 10–12.5 mg·L^−1 Mg^2+), from which a 1:100 dilution
was performed in fresh broth to prepare mid-logarithmic cultures
(OD[600nm] = 0.4 to 0.6). All broth cultures were incubated at 37 °C in
an open-air shaker (200 rpm). Final bacterial suspensions were
concentrated to 1 × 10^10 CFU·mL^−1 in sterile saline.
2.2. Animals
Animal experiments were approved by Monash University Animal Ethics
Committee. For all animal experiments, Swiss mice (female, 8–10 weeks,
body weight 25–35 g) were obtained from Monash Animal Services. Animals
were handled, fed, and housed according to the criteria of the
Australian Code of Practice for the Care and Use of Animals for
Scientific Purposes [[59]18]. Food and water were available ad libitum.
2.3. Non-Neutropenic Murine Bacteremia Model
A non-neutropenic murine bacteremia infection model was employed in
this study. Mice were briefly anesthetized via placement into an
isoflurane induction chamber. Anesthetized mice were placed on a
Perspex support in a vertical upright position, which allowed the mice
to be temporarily immobilized. Two independent groups of mice (n = 3
per group) were injected with 10 μL of bacterial suspension
(approximately 1.0 × 10^9 colony-forming units (CFUs) in early
logarithmic phase) via an intravenous injection at 0 h and then placed
onto a warm pad for rapid recovery. Our preliminary studies showed that
bacterial infection established in mice after approximately 2 h post
bacterial inoculation, and bacterial load peaked at approximately 4 h
post bacterial inoculation. Therefore, we used 2 and 4 h post
inoculation in this study to represent the establishment of bacterial
infection and the maximum bacterial load, respectively. Samples were
collected for RNA extraction and sequencing at Genewiz (paired-end 150
bp, Illumina HiSeq, Suzhou, China). Raw reads were submitted to the
Sequence Read Archive Database (accession number: SRS7522398) [[60]19].
2.4. Construction of the Genome-Scale Metabolic Model (GSMM) iAB5075
The genome annotation of A. baumannii AB5075 (i.e., AB5075-UW) was
obtained from the PGAT database [[61]20] and a draft model was
initially constructed using CarveMe [[62]21]. Further manual curation
was conducted, including (i) adding transport reactions and
extracellular metabolites; (ii) detecting and filling pathway gaps; and
(iii) checking the mass and charge balance for each reaction. The
obtained model was compiled in Systems Biology Markup Language (SBML)
[[63]22].
2.5. Biolog Assay and Prediction of Nutrient Utilizations
AB5075 was subcultured onto nutrient agar and incubated at 37 °C for 20
h. Biolog phenotype microarrays (PMs; Cell Biosciences, Australia) were
employed to test the utilization of 190 carbon and 95 nitrogen sources,
with 3 independent biological replicates. Bacterial growth was detected
after 18 and 24 h of incubation at 37 °C by measuring the optical
density at 595 nm using an Infinite M200 microplate reader (Tecan,
Mannedorf, Switzerland). Readings with ≥1.5-fold of blank media
controls were considered as utilization of nutrients. The constructed
GSMM iAB5075 was then employed to predict the bacterial growth on a
chemically defined media with 190 individual carbon sources and 95
nitrogen sources using flux balance analysis (FBA) method with COBRA
toolbox 3.0 [[64]23]. Biomass formation was optimized with the maximum
specific carbon nutrient uptake rate set at 10 mmol·gDW^−1·h^−1 under
aerobic condition [[65]24]:
[MATH:
max <
mi>vbiomas<
/mi>s, :MATH]
[MATH: s.t. S·v=0, :MATH]
[MATH:
vjmin≤vj≤
vjmax,j
=1,2,⋯,<
mi>n, :MATH]
where
[MATH: S :MATH]
represents the stoichiometric matrix with m metabolites and n
reactions. Each flux
[MATH: vj
:MATH]
is constrained by the lower bound
[MATH:
vjmin :MATH]
and upper bound
[MATH:
vjmax :MATH]
. The prediction accuracy was calculated by comparison with Biolog
experimental results as previously described [[66]21]. Briefly, a true
positive (TP) or true negative (TN) was considered as a correct
prediction of a utilizable or non-utilizable nutrient source for
growth, respectively; a false negative (FN) or false positive (FP) was
considered an incorrect prediction of a utilizable or non-utilizable
nutrient source, respectively. The prediction accuracy was then
calculated by:
[MATH:
overall accuracy= T
P+TNTP+TN+<
mi>FP+FN :MATH]
The Matthews correlation coefficient (MCC) [[67]25] was then calculated
by:
[MATH:
TP×
TN−PF×P<
mi>NTP<
mo>+FPT
P+FNTN+FPTN+FN :MATH]
2.6. Gene Essentiality Analysis
In silico single-gene deletion was conducted using both FBA and
minimization of metabolic adjustment (MOMA) algorithms [[68]14]. FBA
predicts growth and metabolic fluxes based on the assumption that
growth efficiency has evolved to an optimal point using linear
programming. In contrast to FBA, MOMA does not assume optimality of
growth. Instead, MOMA relaxes the assumption of optimal growth flux for
gene deletions by performing distance minimization in flux space using
quadratic programming [[69]14]. Nutrient uptake constraints were set
according to ingredients of chemically defined M9, MH, and
Luria-Bertani (LB) media. Essential metabolites were predicted by
calculating the growth rate when switching off the corresponding
consuming fluxes, and essential reactions were predicted by setting
each reaction flux to zero while maximizing the biomass formation
[[70]15]. The recently generated three-allele transposon mutant library
for A. baumannii AB5075 was employed as a reference to assess the
prediction accuracy as previously described [[71]26].
2.7. Calculating Metabolic Fluxes with Transcriptomics Constraints
The in vivo RNA-Seq data (accession number: SRS7522398) of AB5075 were
incorporated to iAB5075 using INIT (Integrative Network Inference for
Tissues) algorithm, which is formulated as a mixed integer linear
programming problem (MILP) shown below [[72]27]:
[MATH: max∑i∈R<
/mi>wiyi :MATH]
[MATH: s.t.
S·v=0, :MATH]
[MATH: vi≤<
/mo>1000yi, :MATH]
[MATH: vi+<
/mo>10001−yi≥ε, :MATH]
[MATH:
vi≥0,<
/mo> i∈irreversible rxns, :MATH]
[MATH: yi∈0,1. :MATH]
The parameter ε is an arbitrarily small positive number, which was set
by default in COBRA toolbox; w[i] is the weight of the ith reaction
calculated using transcriptomic data; and y[i] is an integer variable
indicating either including (y[i] = 1) or excluding (y[i] = 0) of the
ith reaction in the extracted model [[73]23]. The optimal trade-off
between including and removing reactions was based on their weights (w)
[[74]27], which were calculated as follows:
[MATH:
wi,j
=5log
RPKMi,jAveragei
. :MATH]
Specifically, RPKM (reads per kilobase per million) values were
calculated using edgeR and employed to calculate the weights w
[[75]28]. Briefly, all the RNA-seq data of AB5075 available from the
public database (Gene Expression Omnibus) and our previous studies were
collected and used for estimation of the average expression level of
each gene. If the RPKM of the ith gene in the jth condition is higher
than its average across all the samples, w[i,j] is positive. Otherwise,
w[i,j] is negative. An weights-containing objective function is then
maximized to achieve the best agreement of the calculated metabolic
fluxes with the transcriptomic data. Moreover, to further improve the
prediction, additional protein crowding constraints were incorporated
into the extracted model. The total abundance of metabolic enzyme (P)
was set to 0.25 g⋅gDW^−1 according to previous Escherichia coli data
due to the limited quantitative proteomics data in A. baumannii
[[76]14]. The molecular weight (MW[k]) of kth protein was calculated
based on its amino acid sequence:
[MATH:
maxvbiomass=c·vT, :MATH]
[MATH: s.t. S·v=0, :MATH]
[MATH:
vkmin≦ vk≦vkmax
, :MATH]
[MATH:
vk≦k
cat,k·ek, :MATH]
[MATH:
∑ekM
Wk=P,
:MATH]
where reaction flux
[MATH: vk
:MATH]
is limited by the enzyme turnover rate k[cat,k] and enzyme molar
abundance
[MATH: ek
:MATH]
as previously described [[77]14,[78]29]. Due to the lack of enzyme
kinetic data in A. baumannii, an averaged k[cat] (65 s^−1) in E. coli
was used unless specific k[cat] values were available in BRENDA
database (e.g., 2740 s^−1 for xanthine dehydrogenase catalyzing
reaction R_HXAND [[79]30], 1 s^−1 for methionyl aminopeptidase
catalyzing reaction R_AMPTALAGLN [[80]31], 11.14 s^−1 for
D-Alanine-d-alanine ligase catalyzing reaction R_ALAALAr [[81]32], 34
s^−1 for NAD^+ synthase catalyzing reaction R_NADS1 [[82]33]). FBA was
conducted using COBRA toolbox 3.0 [[83]23] in MATLAB environment and
the significantly perturbed metabolic fluxes were identified using a
Z-score based approach [[84]34]. Briefly, genome-scale metabolic models
for AB5075 at 2 and 4 h post-infection were obtained with the growth
constraints (0.21 and 0.30 h^−1 for 2 and 4 h post-infection,
respectively) calculated from the bacterial viable counts. Metabolic
solution space was sampled with 10000 random points with the ll-ACHRB
(loopless Artificially Centered Hit-and-Run on a Box) algorithm and
linear programming solver Gurobi 9.0 [[85]35]. Statistical significance
of differential flux distributions was estimated using a Z-score
method. Differential metabolic fluxes were filtered with FDR < 0.05 and
fold change > 2.
3. Results
3.1. Construction of the Genome-Scale Metabolic Model iAB5075
A draft model involving 1480 metabolites and 2184 reactions was
developed by CarveMe based on genome annotation. Further manual
curation was conducted, including (i) addition of transport and
exchange reactions to enable nutrient uptake and by-product secretion,
and (ii) filling pathway gaps. Acinetobactin is the essential
siderophore for iron uptake in A. baumannii [[86]36]. To make our GSMM
more representative of A. baumannii, 15 reactions and 20 metabolites
involved in acinetobactin biosynthesis were added to the draft model.
Altogether, a final model iAB5075 was obtained that involved 1530
metabolites and 2229 reactions ([87]Table 1). iAB5075 includes 1015
genes representing 26.1% of the genome and 18 of 23 clusters of
orthologous groups (COG, [88]Figure 1); the majority of genes were from
amino acid transport and metabolism, energy production and conversion,
and pathways involving transport and metabolism of inorganic ion,
coenzyme, lipid, carbohydrate and nucleotide, as well as cell envelope
biogenesis ([89]Figure 1). iAB5075 contained intracellular,
extracellular, and periplasmic compartments ([90]Table 1). Compared
with other GSMMs (i.e., iATCC19606v2 for strain ATCC 19606 and iCN718
for strain AYE), iAB5075 represents the most comprehensive GSMM for A.
baumannii determined thus far ([91]Table 1).
Table 1.
Genome contents and model components.
Content iAB5075[draft] iAB5075 iATCC19606v2 iCN718
Genome size (Mb) 3.97 3.98 3.90
Assembly status Complete Complete Complete
GC content 39.1% 39.2% 39.0%
No. of genes 3895 3805 3694
No. of CDS 3771 3663 3600
No. of reactions 2184 2229 2114 1016
No. of metabolites 1480 1530 1422 890
No. of involved genes 1010 1015 1009 718
Compartment ^a 3 (c, p, e) 3 (c, p, e) 3 (c, p, e) 2(c, e)
Prediction accuracy ^b 72.8%/74.3% 86.3%/87.6% 85.6%/82.1% 83.7/80%
MCC ^b 0.58/0.26 0.76/0.28 0.68/0.33 0.72/0.32
[92]Open in a new tab
Comparison of genome and GSMM features among isolates AB5075 (iAB5075),
ATCC19606 (iATCC19606v2) and AYE (iCN718) [[93]14,[94]15,[95]16]; ^a
the compartment in the models; c: intracellular compartment; p:
periplasmic compartment; e: extracellular compartment; ^b Prediction
accuracy and Mathews correlation coefficients for Biolog and gene
essentiality results.
Figure 1.
Figure 1
[96]Open in a new tab
Clusters of orthologous (COG) functional classification of the genes
involved in iAB5075.
3.2. Prediction of Bacterial Growth on Various Nutrients
With additional protein crowding constraints, model iAB5075 predicted
bacterial exponential growth at 1.87, 2.64, 0.97, and 0.72 h^−1 in
Luria-Bertani (LB) media, Mueller-Hinton (MH) media, M9 media
supplemented with citrate (M9C), and M9 media supplemented with
succinate (M9S), respectively; these specific growth rates are
consistent with experimental observations ([97]Figure S1). Moreover,
iAB5075 predicted that strain AB5075 was able to utilize 41 of 190
carbon sources and 34 of 95 nitrogen sources ([98]Figure 2). Manual
curation was conducted to enhance the prediction accuracy. A false
positive was usually caused by incorrectly involving transport
reactions during automatic model construction; these reactions were
then removed from the draft model according to the literature. While a
false negative was likely caused by mis-annotation of gene functions;
extensive gap filling with homology search was conducted to add the
missing reactions. For example, predictions using the draft model
showed that AB5075 was unable to grow with glycyl-l-asparagine or
l-alanyl-l-histidine as the sole nitrogen sources; whereas Biolog
results indicated that it could. We then added the related transport
reactions (R_DIPEPabc8 and R_DIPEPabc5, respectively) and two
aminopeptidase reactions (R_AMPTGLYASN and R_AMPTALAHIS, ABUW_2837 and
ABUW_3646, with 57% and 58% identity of their E. coli homologs,
respectively) to enable in silico utilization of these two nutrients.
Further prediction under anaerobic conditions was undertaken using
iAB5075. The prediction results showed no growth under anaerobic
conditions and it is consistent with the fact that A. baumannii is an
obligate aerobe. The overall prediction accuracy of nutrient
utilization achieved was high (86.3%; Fisher’s exact test, P = 2.2 ×
10^−16) compared with Biolog assay in which AB5075 grew on 59 carbon
sources and 46 nitrogen sources ([99]Figure 2). Core reactome analysis
was conducted for all flux-carrying reactions in LB, MH, and M9 media
supplemented with 73 different carbon and nitrogen sources. A core
reactome containing 202 flux-carrying reactions was identified
([100]Figure S2a), with 224 and 236 reactions as core reactomes for
carbon and nitrogen sources, respectively. These core reactions are
involved in 13 COG groups, including energy production and conversion,
and transport and metabolism of lipid and amino acid ([101]Figure S2b).
Overall, the pan reactome consists of 1240 flux-carrying reactions,
with 1139 and 1068 flux-carrying reactions specifically detected for
growth on carbon and nitrogen sources, respectively.
Figure 2.
Figure 2
[102]Open in a new tab
Comparison of Biolog experimental measurements (left columns) and model
predictions before (middle columns) and after curation (right columns).
Purple indicates valid growth (i.e., growth predicted by our model or
based on the Biolog results) and yellow indicates no growth. Results
from 190 carbon sources (PM1 and 2) and 95 nitrogen (PM3) sources are
displayed.
3.3. Prediction of Essential Genes, Reactions, and Metabolites for Bacterial
Growth
In silico single gene deletion was conducted, followed by calculating
the specific growth rate after iteratively removing each individual
gene and its associated reactions. With the FBA approach, 102, 114,
134, and 134 genes were identified as essential for growth in LB, MH,
M9C, and M9S, respectively; 95, 110, 126, and 128 genes were predicted
to be essential for the respective media with the MOMA approach.
Overall, 99 and 94 core essential genes were discovered using FBA and
MOMA, respectively, for the 4 growth conditions ([103]Figure 3). These
genes were involved in energy production, biosynthesis of cofactors and
cell envelope, and metabolism of amino acids, nucleotides, and lipids.
When the prediction of gene essentiality was compared with the AB5075
three-allele transposon mutant library, iAB5075 showed a high accuracy
of 87.6% using FBA and 88.5% with MOMA. Similarly, 155/143, 168/159,
200/189, and 200/191 reactions and 245/251, 255/259, 284/287, and
284/287 metabolites were considered essential for bacterial growth on
LB, MH, M9C, and M9S media using FBA and MOMA, respectively
([104]Figure 3 and [105]Supplementary Materials Dataset 1). Comparison
of FBA and MOMA core essential components revealed 89 core essential
genes, 137 core essential reactions, and 237 core essential
metabolites, indicating a substantially high agreement between both
methods. Taken together, the prediction accuracies using draft iAB5075
obtained directly from CarveMe were only 72.8% and 74.3% for Biolog
nutrient utilization and gene essentiality, respectively; whereas after
curation, the corresponding prediction accuracies were significantly
improved and achieved 86.3% and 87.6%. Overall, iAB5075 exhibits
precise prediction of gene essentiality and thus can be utilized to
systematically interrogate genotype–phenotype relationships.
Figure 3.
[106]Figure 3
[107]Open in a new tab
Essential genes, metabolites, and reactions predicted under four
nutrient conditions using flux balance analysis (FBA) and minimization
of metabolic adjustment (MOMA). The four nutrient conditions were LB
media, MH media, M9 media supplemented with citrate (M9C), and M9 media
supplemented with succinate (M9S). Results for FBA are shown at top and
those for MOMA at bottom. (a) Essential genes, (b) essential
metabolites, and (c) essential reactions. The circles represent
different media (green, LB; orange, MH; blue, M9+citrate; red,
M9+succinate). The vertical black bars represent the numbers of
essential genes (a), metabolites (b), or reactions (c) uniquely or
commonly identified for different media. The horizontal bars indicate
the numbers of essential genes (a), metabolites (b), or reactions (c)
totally identified for differential media.
3.4. Modeling Metabolic Changes of Strain AB5075 in a Murine Bacteremia Model
with Transcriptomic Constraints
A. baumannii may alter its metabolism to adapt to the host environment
during infection [[108]37]. However, our understanding of such complex
metabolic responses is very limited. With a combination of fold change
> 2 and false discovery rate (FDR)-adjusted p value < 0.05, a total of
1408 genes in A. baumannii AB5075 were differentially expressed at 4 h
post-infection compared to 2 h, and 396 genes were mapped to iAB5075
([109]Table S1). Based on pathway enrichment analysis (Fisher’s exact
test, FDR-adjusted p-value < 0.05), the differentially expressed genes
were mainly enriched in the following pathways: intracellular
trafficking, secretion and vesicular transport; secondary metabolites
biosynthesis, transport, and catabolism; inorganic ion transport and
metabolism; and translation, ribosomal structure, and biogenesis. Among
those genes mapped to iAB5075, 283 were identified as being involved
with transport and metabolism of carbohydrates, nucleotides, and amino
acids; energy production and conversion; and cell envelope metabolism,
indicating their critical roles in adaption to the host environment.
The transcriptomic data were then incorporated in the model as flux
constraints to accurately predict metabolic fluxes using the INIT
algorithm. In addition, constraints on bacterial growth rate and
protein crowding were also imposed on the model. Specifically, 478
flux-carrying reactions were detected at 2 h post infection, while 502
reactions had non-zero fluxes at 4 h post infection. Compared to 2 h
post infection, 198 significantly changed fluxes (fold change > 2, FDR
< 0.05) were identified at 4 h post infection, including 146 increased
and 52 decreased fluxes ([110]Table S2). The increased fluxes were
mainly detected in a broad range of metabolic pathways, including the
tricarboxylic acid (TCA) cycle, gluconeogenesis, amino acid metabolism,
and biosynthesis of peptidoglycan, lipopolysaccharide (LPS),
nucleotides, and fatty acids.
In the TCA cycle, six of eight metabolic fluxes were significantly
increased at 4 h post-infection compared to 2 h post-infection, while
the flux through fumarate hydratase was significantly decreased
([111]Figure 4a). Notably, the production flux through malate
dehydrogenase was 1.11 mmol·gDW·h^−1 from malate to oxaloacetate 4 h
post infection, whereas the flux from oxaloacetate to malate was 3.25
mmol·gDW·h^−1; the two metabolites (malate and oxaloacetate) were
utilized by the gluconeogenesis pathway, resulting in significantly
increased fluxes through gluconeogenesis ([112]Figure 4b). At the same
time, most fluxes through the pentose phosphate pathway (PPP) were
significantly increased, whereas the flux from d-ribose 5-phospahte
towards d-sedoheptulose 7-phosphate was significantly decreased
([113]Figure 4b). Remarkably, the flux thorough 5-phospho-α-d-ribose
1-diphosphate (PRPP) synthase was dramatically increased (0
mmol·gDW·h^−1 at 2 h post infection versus 0.24 mmol·gDW·h^−1 at 4 h
post infection), and the increased PRPP was then utilized in the
biosynthesis of purine and pyrimidine nucleotides.
Figure 4.
Figure 4
[114]Open in a new tab
Differentially regulated metabolic fluxes in multiple pathways at 4 h
post infection compared to 2 h post infection. Differentially regulated
metabolic fluxes through (a) the TCA cycle, (b) the gluconeogenesis and
pentose phosphate pathways, (c) nucleotide metabolism, and (d) cell
structure biosynthesis. Significantly regulated fluxes (fold change > 2
and FDR < 0.05) are shown in color with red (produced fluxes) or blue
(consumed fluxes). AcCoA, acetyl-CoA; CIT, citrate; Isocit, isocitrate;
2-OG, α-ketoglutarate; SuccCoA, succinyl-CoA; Succ, succinate; Fum,
fumarate; MAL, (S)-malate; OAA, oxaloacetate; Pyr, pyruvate; PEP,
phosphoenolpyruvate; 2PG, 2-phosphoglycerate; 3PG, 3-phosphoglycerate;
1,3-BPG, 1,3-bisphosphoglycerate; GA3P, glyceraldehyde 3-phosphate;
DHAP, dihydroxyacetone phosphate; F6P, fructose 6-phosphate; FBP,
fructose 1,6-biphosphate; G6P, glucose 6-phosphate; R5P, ribose
5-phosphate; Ru5P, ribulose 5-phosphate; Xu5P, xylulose 5-phosphate;
S7P, sedoheptulose 7-phosphate; PRPP, phosphoribosyl pyrophosphate;
PRA, 5-phospho-β-d-ribosylamine; Gly, glycine; GAR,
N^1-(5-phospho-β-d-ribosyl)glycinamide; FGAR,
N^2-formyl-N^1-(5-phospho-β-d-ribosyl)glycinamide; FGAM,
2-(formamido)-N^1-(5-phospho-β-d-ribosyl)acetamidine; AIR,
5-amino-1-(5-phospho-β-d-ribosyl)imidazole; CAIR,
5-amino-1-(5-phospho-d-ribosyl)imidazole-4-carboxylate; SAICAR,
5’-phosphoribosyl-4-(N-succinocarboxamide)-5-aminoimidazole; AICAR,
5-amino-1-(5-phospho-d-ribosyl)imidazole-4-carboxamide; FAICAR,
5-formamido-1-(5-phospho-d-ribosyl)-imidazole-4-carboxamide; Asp,
l-aspartate; SucAMP, adenylo-succinate; Gln, L-glutamine; CP, carbamoyl
phosphate; NCD, N-carbamoyl-l-aspartate; (S)-DHODH, (S)-dihydroorotate;
UDP-GlcNAc, UDP-N-acetyl-α-d-glucosamine; UNAGEP,
UDP-N-acetyl-α-d-glucosamine-enolpyruvate; UDP-MurNAc,
UDP-N-acetyl-α-d-muramate; UDP-MurNAc-l-Ala,
UDP-N-acetyl-α-d-muramoyl-l-alanine; UDP-MurNAc-l-Ala-d-Glu,
UDP-N-acetyl-α-d-muramoyl-l-alanyl-d-glutamate; meso-DAP,
meso-diaminopimelate; UDP-MurNAc-l-Ala-d-Glu-d,l-DAP,
UDP-N-acetyl-α-d-muramoyl-l-alanyl-γ-d-glutamyl-meso-2,6-diaminopimelat
e; UDP-MurNAc-l-Ala-d-Glu-meso-DAP-d-Ala-d-Ala,
UDP-N-acetyl-α-d-muramoyl-l-alanyl-γ-d-glutamyl-meso-2,6-diaminopimeloy
l-d-alanyl-d-alanine; Und-P, di-trans,octa-cis-undecaprenyl phosphate;
(3R)-3-HTA-[acp], (3R)-3-hydroxytetradecanoyl-[acp]; UDP-3-hmaglc,
UDP-3-O-[(3R)-3-hydroxydecanoyl]-N-acetyl-α-d-glucosamine;
UDP-3-O-(3-hydroxymyristoyl)-α-d-GlcN,
UDP-3-O-(3-hydroxymyristoyl)-N-acetyl-α-d-glucosamine;
UDP-2-N-[(3R)-3-HDA]-3-O-[(3R)-3-HDA]-GlcN,
UDP-2-N-[(3R)-3-hydroxydodecanoyl]-3-O-[(3R)-3-hydroxydecanoyl]-α-d-glu
cosamine.
The increased fluxes in the PPP toward nucleotide metabolism were
significantly increased 4 h post infection ([115]Figure 4b). As the
downstream utilization of PRPP, the overall fluxes via enzymes (e.g.,
amido phosphoribosyl transferase, phosphoribosyl amine-glycine ligase,
and phosphoribosyl glycinamide formyltransferase) in
inosine-5’-phosphate biosynthesis were increased, resulting in the
increased production of dGTP and dATP from guanosine and adenosine
ribonucleotide de novo biosynthesis ([116]Figure 4c). Additionally,
increased fluxes involving pyrimidine deoxyribonucleotide biosynthesis
were also identified, which enhanced the production of dCTP and dTTP
([117]Figure 4c).
At 4 h post-infection, there were remarkable increases in fluxes
involving the biosynthesis of cell envelope components (e.g.,
peptidoglycan and LPS biosynthesis). At this time, most fluxes within
peptidoglycan biosynthesis from UDP-N-acetyl-α-d-glucosamine to
meso-diaminopimelate containing lipid II were increased 266.6% to
300.0%, whereas the flux over glutamate racemase was significantly
decreased ([118]Figure 4d). In lipid A biosynthesis, fluxes through
acyl-ACP-UDP-N-acetylglucosamine O-acyltransferase,
UDP-3-O-acyl-N-acetylglucosamine deacetylase,
UDP-3-O-(3-hydroxymyristoyl) glucosamine N-acyltransferase,
UDP-2,3-diacylglucosamine diphosphatase, lipid-A-disaccharide synthase,
and tetraacyldisaccharide 4’-kinase were upregulated, resulting in a
dramatic increase in the production of lipid A ([119]Figure 4d).
In addition to the increased fluxes through the above metabolic
pathways, fluxes through fatty acid biosynthesis and amino acid
metabolism were also significantly impacted at both 2 and 4 h post
infection. In fatty acid biosynthesis, most fluxes were significantly
increased at 4 h compared to 2 h ([120]Table S3a), whereas 71 key
fluxes in amino acid metabolism were significantly affected at each
time point ([121]Table S3b).
4. Discussion
A. baumannii is a Gram-negative opportunistic pathogen which has
imposed a heavy burden on the global health care system [[122]38].
There is an urgent need to understand how A. baumannii responds to the
host immune system during infection. GSMM has been increasingly
employed to predict key genetic targets in order to guide therapeutic
interventions and decipher the mechanism(s) of antibiotic killing and
resistance. Unfortunately, few studies have previously examined
bacterial metabolic responses in vivo using GSMMs, and none of these
studies examined A. baumannii. We report here, for the first time, the
development, validation, and application of a GSMM named iAB5075 for
the MDR clinical isolate A. baumannii AB5075.
To the best of our knowledge, the only GSMMs previously developed for
A. baumannii are for strains ATCC 19606 and AYE
[[123]14,[124]16,[125]39,[126]40]. However, ATCC 19606 and AYE were
isolated in the 1950s and 2003, respectively, and have significant
differences in their genomic content and virulence phenotypes compared
to more recent clinical isolates [[127]41]. In addition, of both
isolates, only AYE is MDR. Importantly, AB5075, the focus of the
present study was a well-characterized modern clinical isolate that has
been employed as a type strain of A. baumannii to investigate virulence
and multidrug resistance [[128]41,[129]42,[130]43]. Given this
situation, we developed and validated an AB5075-specific GSMM, iAB5075
([131]Figure 2 and [132]Figure 3). Compared to the most recent GSMM in
AYE (iCN718), iAB5075 contains a larger number of unique genes,
metabolites, and reactions, and has a much higher prediction accuracy
(i.e., 87.6% for iAB5075 compared to 80.2% for iCN718). These
differences are mainly attributable to the different compartment
settings, transport and exchange reactions contained within the two
models ([133]Table 1), and indicate that iAB5075 represents the most
comprehensive GSMM thus far developed for A. baumannii.
Transcriptomics is increasingly employed to examine the mechanisms
underpinning bacterial pathogenesis and antibacterial resistance in
Gram-negative pathogens in vitro and in vivo
[[134]13,[135]44,[136]45,[137]46]. However, no study to date has
integrated in vivo transcriptomic data with GSMM in A. baumannii. It is
important to note that the nutrient complex composition varies from in
vivo to in vitro based on nutrient conditions [[138]47]. In the present
study, the integration and simulation with in vivo transcriptomic data
from the murine bloodstream infection model provide an important
expansion of genes involved with adaption to the host environment.
This, in turn, enabled a global view of metabolic responses to the host
at the network level. Compared to 2 h post infection, 4 h post
infection induced significant metabolic changes in biosynthesis of
nucleotides, peptidoglycan, lipopolysaccharide, and fatty acids; and
central carbon and amino acid metabolism.
When INIT algorithm was applied to the RNA-seq data, 198 fluxes were
identified as critical for AB5075 to cause serious bloodstream
infections in mice at 4 h post infection ([139]Figure 4). These fluxes
represent a novel set of metabolic functions, which are integral to the
establishment of A. baumannii infections. Within central metabolism,
the upregulated fluxes were associated with TCA cycle, gluconeogenesis,
and pentose phosphate pathway ([140]Figure 4a,b). The increased
production flux in TCA cycle via malate dehydrogenase at 4 h resulted
in enhanced fluxes in gluconeogenesis ([141]Figure 4b). The downstream
metabolites from glucogenesis were subsequently utilized by PPP
pathway, with most of the identified PPP fluxes being dramatically
increased ([142]Figure 4b). It has previously been shown that the TCA
cycle and gluconeogenesis play key roles in the virulence of Salmonella
enterica during infection [[143]48]. A number of intermediates in the
TCA cycle and gluconeogenesis have been identified to contribute to the
virulence in macrophages in vivo [[144]48]. Our observations suggest
that the increased fluxes through central metabolic pathways might have
important consequences for bloodstream infections, given that A.
baumannii relies on these pathways to provide adequate energy
metabolism and to contribute to the establishment and enhancement of
infection.
A. baumannii can evade the host innate immune response using multiple
virulence factors, such as surface glycoconjugates [[145]49]. An
important barrier that protects A. baumannii against host immune
responses during infection is the outer membrane. In A. baumannii, the
primary component of the outer leaflet of outer membrane is LPS, which
consists of lipid A, a core oligosaccharide, and a polysaccharide
O-antigen. Model simulation showed that fluxes through lipid A
biosynthesis were significantly increased (up to 267%) 4 h post
infection compared to 2 h post infection. In addition to lipid A
biosynthesis, increased fluxes were also detected in peptidoglycan
biosynthesis via the conversion of UDP-N-acetyl-α-d-glucosamine to meso
diaminopimelate containing lipid II. It has previously been shown that
genes involved in the biosynthesis and maintenance of LPS and
peptidoglycan contribute to the fitness of A. baumannii during
bacteremia [[146]9], and that both LPS and peptidoglycan contribute to
bacterial cell stability and resistance to lysozyme in the host
environment [[147]50,[148]51]. During infection, the significantly
increased fluxes in LPS and peptidoglycan biosynthesis are in line with
these previous findings, suggesting that both metabolic pathways
contribute to bacterial fitness during infection in the murine host.
The balance of LPS and fatty acid biosynthesis plays an important role
in maintaining cell envelope function and integrity in A. baumannii
[[149]52]. Compared to 2 h post infection, most fluxes through fatty
acids biosynthesis were significantly increased at 4 h post infection,
consistent with the increased fluxes in LPS biosynthesis. These
increased fluxes are crucial for rebalancing fatty acids and LPS to
maintain membrane integrity.
In addition to surface glycoconjugates, other strategies also
contribute to bacterial defense against host immune systems, including
secreted proteins and multiple other regulators and metabolic pathways
(e.g., OmpA porin, BfmR global regulator, phenylacetic acid catabolism
pathways). Nucleotide second messengers (e.g., cAMP, cyclic di-GMP, and
penta/tetra-guanosine phosphate) are key bacterial regulators involved
in adaptations to conditions of limiting or non-optimal carbon and
energy resources [[150]53,[151]54]. Cyclic di-GMP and other nucleotide
second messengers control a variety of processes (e.g., production of
exopolysaccharides, protein adhesins, pili and flagella, and cell
differentiation) and have a central role in modulating virulence and
persistence [[152]55]. Increased fluxes were detected in
inosine-5’-phosphate biosynthesis I, inosine-5’-phosphate biosynthesis
II, and pyrimidine ribonucleotide biosynthesis ([153]Figure 4c),
indicating that AB5075 increased production of nucleotide to adapt to
the host environment. As the downstream production in nucleotide
metabolism, second messengers most likely contributed to the regulation
of surface adaption and virulence factors that assisted with initiation
of infections in the mice. Finally, fluxes via amino acid metabolism
were significantly impacted in AB5075 during bacteremia. These effects
might be attributed to the adaption of metabolic changes in multiple
pathways.
Overall, fluxes were significantly changed through multiple pathways in
AB5075 at 4 h post infection compared with 2 h post infection. Marked
fluxes through central metabolism, nucleotide metabolism, and fatty
acid and cell envelope biosynthesis might contribute to adaptations to
the host environment and enhanced infection during bacteremia. These
impacted pathways may be potential therapeutic targets for future drug
development and therapy optimization.
5. Conclusions
In summary, we constructed and validated the first GSMM, iAB5075, for a
modern model strain of MDR A. baumannii, namely AB5075. This is the
first study to integrate in vivo transcriptomic data with GSMM, and
importantly, the modeling provides a precise understanding of metabolic
changes in A. baumannii in response to a host immune system during
bacteremia. Model iAB5075 provides a unique >in silico platform for
predicting bacterial metabolic responses to different treatments at the
network level, which may assist in the optimization of antibiotic
treatment in patients.
Supplementary Materials
The following are available online at
[154]https://www.mdpi.com/2076-2607/8/11/1793/s1.
[155]Click here for additional data file.^ (840.6KB, zip)
Author Contributions
Conceptualization, J.Z.; Methodology, J.Z. and Y.Z.; Software, J.Z. and
J.H.; Validation, J.Z., M.A. and F.S.; Formal Analysis, J.Z.;
Investigation, J.Z. and Y.Z.; Resources, J.W. and K.C.; Data Curation,
J.Z. and Y.-W.L.; Writing—Original Draft Preparation, J.Z.;
Writing—Review and Editing, J.Z., Y.Z., T.V., F.S. and J.L.;
Visualization, J.Z. and M.A.; Supervision, Y.Z. and J.L.; Project
Administration, Y.Z. and J.L.; Funding Acquisition, J.L. All authors
have read and agreed to the published version of the manuscript.
Funding
This study was supported by a research grant from the National
Institute of Allergy and Infectious Diseases of the National Institutes
of Health (R01 AI132681). J.L. is an Australian National Health and
Medical Research Council (NHMRC) Principal Research Fellow and T.V. is
an Australian NHMRC Industry Career Development Level 2 Research
Fellow. The content is solely the responsibility of the authors and
does not necessarily represent the official views of the National
Institute of Allergy and Infectious Diseases or the National Institutes
of Health.
Conflicts of Interest
The authors declare no conflict of interest.
Footnotes
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References