Abstract
Acetic acid and furfural (AF) are two major inhibitors of
microorganisms during lignocellulosic ethanol production. In our
previous study, we successfully engineered Zymomonas mobilis 532
(ZM532) strain by genome shuffling, but the molecular mechanisms of
tolerance to inhibitors were still unknown. Therefore, this study
investigated the responses of ZM532 and its wild-type Z. mobilis (ZM4)
to AF using multi-omics approaches (transcriptomics, genomics, and
label free quantitative proteomics). Based on RNA-Seq data, two
differentially expressed genes, ZMO_RS02740 (up-regulated) and
ZMO_RS06525 (down-regulated) were knocked out and over-expressed
through CRISPR-Cas technology to investigate their roles in AF
tolerance. Overall, we identified 1865 and 14 novel DEGs in ZM532 and
wild-type ZM4. In contrast, 1532 proteins were identified in ZM532 and
wild-type ZM4. Among these, we found 96 important genes in ZM532
involving acid resistance mechanisms and survival rates against
stressors. Furthermore, our knockout results demonstrated that growth
activity and glucose consumption of mutant strains ZM532∆ZMO_RS02740
and ZM4∆ZMO_RS02740 decreased with increased fermentation time from 42
to 55 h and ethanol production up to 58% in ZM532 than that in
ZM532∆ZMO_RS02740. Hence, these findings suggest ZMO_RS02740 as a
protective strategy for ZM ethanol production under stressful
conditions.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12934-023-02095-1.
Keywords: Acetic acid, Furfural, Zymomonas mobilis, Lignocellulosic
biomass, RNA-Seq, Proteomics
Introduction
The rising global population and climate change have been described as
major threats to life on our planet. According to an estimation, the
population will reach 10 billion by 2030, which would urge climate
change to a dangerous extent, mainly associated with human activities,
e.g., burning fossil fuels (coal, oil, and gas) [[53]1]. The desire for
the world to curb human-induced climate change and ensure sustainable
energy through environment friendly resources has gained much attention
in the last decade [[54]2]. Plant feedstocks fermentation is
alternative to fossil fuel for renewable and sustainable energy. It can
mitigate climate change and enhance energy security [[55]3]. Substrate
utilization and production capacity of microorganisms are critical to
the production process of bioethanol. The biosynthesis of
lignocellulosic biomass primarily through yeast strains is mainly
industrial biocatalysts due to its economic, social sustainability, and
environmental benefits [[56]4]. Interestingly, engineered Escherichia
coli (E. coli), Zymomonas mobilis (Z. mobilis), and Bacillus subtilis
have successfully been deployed for industrial biofuel catalysts
[[57]5]. The best-known alcohol fermenting microbes are Saccharomyces
cerevisiae and Z. mobilis, which can ferment hexose sugars and sucrose
into ethanol but are inhibited by end products [[58]6, [59]7]. End
products also inhibit pentose-fermenting species such as Pichia
stipitis, Candida shehatae, and Pachysolen tannophilus [[60]8]. Despite
their ability to withstand inhibitory compounds, filamentous fungi are
unsuitable candidates for biofuel development due to their long
generation times, low yields, and productivities [[61]9]. As a result,
a microorganism that is inhibited by end products and takes longer to
hydrolyze lignocellulosic biomass is not suitable for industrial-scale
biofuel production [[62]10]. To use significant quantities of
substrates, the optimal strain must possess certain characteristics,
including the ability to achieve high cell mass growth and biofuel
production rates in biomass-derived hydrolysates [[63]6, [64]11], the
ability to use a wide variety of pentose and hexose sugars, the ability
to withstand high temperatures and low pH [[65]12], and strong
tolerance to inhibitors and end products.
Zymomonas mobilis is famous for higher ethanol efficacy and
energy-generating potential at the industrial level. Its 1 mol of ATP
is produced by a glucose molecule using the Enter-Doudoroff (ED)
pathway [[66]13]. However, various inhibitors are naturally produced
during the hydrolysis process that inhibits cell growth and efficiency
of microbial fermentation, such as furfural, vanillin, acetic acid,
5-hydroxymethyl furfural, aldehydes, phenols, and other organic acids
[[67]14, [68]15]. These inhibitors are detrimental to Z. mobilis growth
and ethanol fermentation [[69]16]. Acetic acid and furfural (AF) are
major inhibitors that damage the stability of the membrane and
intracellular homeostasis, resulting in lower pH, osmotic stress, and
reduced carbohydrate metabolism [[70]17]. So, to avoid lower production
capacity and yield reduction, industries extricate inhibitors
chemically from the lignocellulosic biomass, but such process often
enhances the production cost. In regards, genome-resequencing analysis
has also been used to explore the key genetic variations that are
responsive to modified phenotypes in robust Z. mobilis mutants induced
by mutations or adaptation [[71]18–[72]20].
Transcriptomics, proteomics, and metabolomics are examples of omics
technologies used in reverse genetics methods that can enhance our
understanding of biological systems and have become the latest trend in
molecular research [[73]21]. Hence, the technological revolution can
assist us in evaluating how microorganisms react to various
environmental stresses and improving strategies to enhance or change
their genotype to perform efficiently in the presence of inhibitors.
Many genetic approaches, including forward and reverse genetics, have
been applied to develop inhibitor-resistant Z. mobilis strains [[74]22,
[75]23]. Moreover, several genes have also been cloned to study their
involvement in ethanol production by the mean of their expression
level. For example, hfq (ZMO0347) encoding RNA chaperone, and nhaA
(ZMO0119) encoding sodium proton antiporter protein are used for
enhancing the ability of Z. mobilis (AcR) to produce sodium acetate
[[76]24, [77]25]. Similarly, overexpression of ZMO1696, ZMO1116, and
ZMO1885 is used against phenolic aldehyde inhibitors in ZM4 [[78]26].
The recently developed acid tolerant mutant strains have single
nucleotide variants (SNVs) in glutamine-fructose-6-phosphate
aminotransferase (encoding ZMO0056) and DNA repair proteins. A strain
known as RadA (encoding ZMO0589) contributes to acid tolerance in
mutant strains [[79]27]. According to some studies, adaptive evolution
and forward genetics may develop mutants that overcome inhibition
caused by furfural, acetic acid, and other inhibitors in corn stover
hydrolysate [[80]22, [81]28, [82]29]. Also, several studies have
reported the role of key genes/transcriptional factors in improving
furfural resistance [[83]30–[84]36].
In our previous studies, we developed a resistant strain, i.e., F34,
that was found to be tolerant to 3.0 g/L furfural, AQ8-1, and 8.0 g/L
acetic acid by mARTP mutagenesis in Z. mobilis [[85]27]. Also, a mutant
strain, i.e., ZM532 (derived from genome shuffling), had higher
productivity (0.463 g/L/h) and shorter fermentation (30 h) than AQ8-1
and F34 [[86]16]. But we had not explored the difference in global
transcriptional profiling between mutant ZM532 and wild type ZM4,
especially in rich media (RM) and media containing acetic acid and
furfural, which is potentially important at the industrial level.
Therefore, in the current study, we first used sanger sequencing
technology to verify the mutations in strains. Afterward, we applied
transcriptomics and proteomics to unravel molecular mechanisms under AF
and RM conditions. Further, we knocked out and overexpressed
differentially expressed genes (DEGs) to study their modulating role.
Hence, our findings in ethanol production might play an important role
in genetic engineering and synthetic biology.
Material and methods
Bacterial strains and fermentation conditions and preparation of cell samples
for transcriptome and proteome
This study used Z. mobilis strains (ZM4 and its mutant ZM532). The
glycerol stocks of ZM4 and ZM532 were grown at 30 °C and maintained on
two agar rich medium (RM) containing plates (20 g/L glucose, 10 g/L
yeast extract, 2 g/L KH[2]PO[4], 1 g/L (NH[4])[2]SO[4], 2 g/L
MgSO[4]·7H[2]O, and 15 g/L agar) until colonies were grown and stored
at 4 °C. Then, both strains were cultured in RM at 30 °C without
shaking for 16 h. Then, a single colony from both strains was
sub-cultured to fresh inoculum 50 mL RM media (20 g/L glucose, 10 g/L
yeast extract, 2 g/L KH[2]PO[4], 1 g/L (NH[4])[2]SO[4], and 2 g/L
MgSO[4]·7H[2]O) for 16 h at 30 °C without shaking (Inoculation into
fermentation medium was conducted when the initial cell density of
optical density 600 (OD[600]) for ZM4 and ZM532 were between 0.1 and
0.2). Cell pellets were extracted by centrifugation at 3000×g for 4 min
at 4 °C and then inoculated 50.0 mL of RM in a 100 mL flask in groups
without inhibitors for 8 h and with inhibitors (AF) for 36 h
fermentation period without shaking at 30 °C. The groups of ZM4 and
ZM532 susceptible to acetic and furfural acids combination were named
as AFZM4 (AFZM4_1, AFZM4_2, and AFZM4_3), and AF532 (AF532_1, AF532_2,
and AF532_3), respectively, while other groups without inhibitors, the
cells grown in RM were considered as control groups designated as RMZM4
(RMZM4_1, RMZM4_2, and RMZM4_3) and RM532 (RM532_1, RM532_2, and
RM532_3). Both fermentations and culturing were performed in
triplicates. Based on the previous experiment, the concentrations of AF
combinations were set at 5.0 g/L (acetic acid) and 3.0 g/L (furfural)
to study the responses of ZM4 and mutant ZM532 [[87]16]. The cells at
the exponential growth phase of ZM532 and ZM4 were collected and stored
at − 80 °C. The collected cell pellets were used for subsequent
transcriptome, proteome, and qPCR experiments.
RNA extraction, library preparation, and sequencing
Total RNAs were extracted using the RNA isolation kit from the ZM4 and
ZM532 cells cultured in RM and AF medium. (Tiangen, China). The RNA
purity, concentration (ng/ul), and integrity were evaluated using the
Nanodrop spectrophotometer (Qubit 2.0, Agilent 2100). Briefly, the mRNA
was purified from a total amount of 3 μg RNA per sample using poly-T
oligo-attached magnetic beads and fragmented using divalent cations
under elevated temperature. Random hexamer primer and M-MuLV reverse
transcriptase were used for cDNA first-strand synthesis [[88]23,
[89]37]. Subsequently, second-strand cDNA synthesis was performed using
DNA polymerase I and RNase H. After adenylation of 3′ ends of cDNA
fragments, NEB Next Adaptor with hairpin loop structure were ligated to
prepare for hybridization. Then, the cDNA fragments were purified with
AMPure XP system (Beckman Coulter, Beverly, CA, USA) to select
fragments of 150–200 bp in length. PCR was then performed, products
were purified (AMPure XP system), and library quality was assessed on
the Agilent Bioanalyzer 2100 system. The sample clustering was
performed on a cBot Cluster Generation System using TruSeq PE Cluster
Kit v3-cBot- HS (Illumina, San Diego, CA, USA) according to the
manufacturer’s instructions. The high-throughput sequencing was
conducted by Illumina Hiseq 2000 platform after passing through some
screening phases. The transcript sequences of Z. mobilis used for the
study have been deposited in the Gene Expression Omnibus (GEO)
repository of the National Center for Biotechnology Information (NCBI)
GEO accession number: [90]GSE168900.
Reads mapping to the reference genome and quantification of gene expression
Raw data (raw reads) were interpreted via in-house perl scripts, and
clean data was extracted by eliminating reads comprising adapters.
Then, the clean data of Q20, Q30, and guanine-cytosine (GC) content
were computed. Complete genome annotation files downloaded from the
genome website Bowtie2-2.2.3
([91]ftp://ftp.ncbi.nlm.nih.gov/genomes/bacteria/Zymomonas_mobilis/)
were used to construct a reference genome index and match clean reads
to the reference genome [[92]38]. Novel genes, operons, and
transcription start sites were identified by Rockhopper [[93]34]. Then,
extracted the 5′UTR (3′UTR) sequences. Then, RBS finder [[94]39] and
TransTermHP [[95]40] were used to predict SD sequences and terminator
sequences, respectively. IntaRNA was used to predict the sRNA targets.
And then, we used RNAfold to predict RNA secondary structures [[96]41,
[97]42]. The mapping of clean reads to each gene was counted using
HTSeq v0.6.1. The fragments per kilobase of exon per million fragments
mapped (FPKM) reads of every single gene were determined as described
earlier [[98]43].
Differentially expression genes analyses
The edger software package modified the read counts for each sequenced
library via one standardized scaling factor. DEGs analyses of two
conditions were performed using the DESeq package in R (1.18.0)
[[99]44]. Then using Benjamini & Hochberg approach, the p-values were
adjusted. Genes with fold change (FC) > 1.5 and a false discovery rate
(FDR; < 0.05) were considered DEGs.
Validation of differentially expressed genes by quantitative PCR
The RNA from the AF and RM groups was extracted and used to construct
the cDNA library. First-strand synthesis was performed using MonScript
(Monad) according to the manufacturer’s instructions. With three
biological replicates to perform the expression of ZM4-AF and ZM532-AF
(resistance group) and ZM4-RM and ZM532-RM (control group) by qPCR
(BIO-RAD, Richmond, CA, USA). The reaction phase was denaturation for
15 min at 95 °C, followed by 40 amplification cycles for 10 s at 95 °C
and 30 s at 53 °C. Using the delta-delta- Ct (
[MATH: 2-ΔΔCt :MATH]
) method with 16S RNA as a reference control, relative gene expressions
were computed (Additional file [100]1: Table S2). The student t-test
(p < 0.05) was used for mean comparisons. Results were shown in a bar
chart with the means and their standard deviation (M ± SD).
Total protein extraction and protein quality test
ZM4 and ZM532 samples (cell pallets)were independently ground in liquid
nitrogen and lysed with a lysis buffer (consisting of 6 M Urea and 0.2%
SDS, 100 mM NH[4]HCO[3], pH 8.0), accompanied by 5 min of ice
ultrasound. The lysate was centrifuged at 12,000g for 15 min at 4 °C,
and the supernatants were transmitted to a clean tube. The extracts
from each sample were reduced to 10 mM DTT for 1 h at 56 °C and
alkylated with iodoacetamide under dark room temperature for 1 h.
Samples were thoroughly vortexed with 4 × the volume of precooled
acetone and incubated at − 20 °C for at least 2 h. Samples were then
centrifuged and precipitated. They were washed twice with cold acetone,
and pellets were dissolved with a dissolution buffer of 0.1 M
triethylammonium bicarbonate (TEAB, pH 8.5) and 6 M urea
[[101]45–[102]47].
Protein quality test
BSA standard protein solution was prepared according to the Bradford
protein quantitative kit's instructions, with gradient concentration
ranging from 0 to 0.5 g/L. BSA standard protein solutions and sample
solutions with different dilution multiples were added to a 96-well
plate to fill up the volume to 20 µL. Each gradient was repeated three
times. The plate was added 180 μL G250 dye solution quickly and placed
at room temperature for 5 min. The absorbance at 595 nm was detected.
The standard curve was drawn with the absorbance of the standard
protein solution, and the protein concentration of the sample was
calculated. 20 μg of the protein sample was loaded to 12% SDS-PAGE gel
electrophoresis, wherein the concentrated gel was performed at 80 V for
20 min, and the separation gel was performed at 120 V for 90 min. The
gel was stained by coomassie brilliant blue R-250 and decolored until
the bands were visualized clearly.
Trypsin treatment
120 μg of each protein sample was taken, and the volume was made up to
100 μL with lysis buffer, 3 μL of 1 μg/μL trypsin, and 500 μL of 100 mM
TEAB buffer was added; the sample was mixed and digested at 37 °C
overnight. An equal volume of 1% formic acid was mixed with the
digested sample and centrifuged at 12,000g for 5 min at room
temperature. The supernatant was slowly loaded to the C18 desalting
column, washed with 1 mL of washing solution (0.1% formic acid, 4%
acetonitrile) 3 times, then eluted twice by 0.4 mL of elution buffer
(0.1% formic acid, 75% acetonitrile). The eluents were combined and
lyophilized.
LC–MS/MS analysis
Mobile phase A (100% water, 0.1% formic acid) and B solution (80%
acetonitrile, 0.1% formic acid) were prepared. The lyophilized powder
was dissolved in 10 μL of solution A, centrifuged at 15,000 rpm for
20 min at 4 °C, and 1 μg of the supernatant was injected into a
home-made C18 Nano-Trap column (2 cm × 75 μm, 3 μm). Peptides were
separated in a home-made analytical column (15 cm × 150 μm, 1.9 μm),
using a linear gradient elution as listed in Table [103]1. The
separated peptides were analyzed by Q Exactive series mass spectrometer
(Thermo Fisher), with ion source of Nano spray Flex™ (ESI), spray
voltage of 2.3 kV and ion transport capillary temperature of 320 °C.
Full scan range from m/z350to 1500 with a resolution of 60,000 (at
m/z200), an automatic gain control (AGC) target value was 3 × 10^6 and
a maximum ion injection time was 20 ms. The top20 (40) precursors of
the highest abundant in the full scan were selected and fragmented by
higher energy collisional dissociation (HCD) and analyzed in MS/MS,
where a resolution was 15,000 (at m/z200), the automatic gain control
(AGC) target value was 5 × 10^4, the maximum ion injection period was
45 min, the intensity threshold was 2.2 × 10^4, the normalised
collision energy was 27 percent, and the dynamic exclusion parameter
was 20 s.
Table 1.
SNP in re-sequence ZM532 by comparing previous published ten
genome-shuffled mutant strain and Z. mobilis ZM4 (GenBank:
[104]AE008692.2)
Locus Ref^a Previous^b Current^c Status Ten genome shuffled^d
Gene/Product
CDS
51,967 C T T Confirmed + ZMO_RS00235/glutamine-fructose-6-phosphate
aminotransferase
590,452 G A A Confirmed + ZMO_RS02620/DNA repair protein Rad A
849,208 C T T Confirmed + ZMO_RS03765/arginine-tRNA ligase
849,311 C A A Confirmed +
971,308 A G – No SNP found − ZMO_RS09165/1S5/1S1182 family transposase
971,369 A G – No SNP found −
Intergenic regions
971,059 T A A Confirmed + ZMO_RS09160–ZMO_RS09165
971,715 C – T New SNP +
971,717 T – G New SNP +
975,503 T G G Confirmed + ZMO_RS04290–ZMO_RS04295
975,506 G A A Confirmed + Monofunctional biosynthetic peptidoglycan
975,509 C T T Confirmed + Transglycosylase/cytochrome c
975,523 C T T Confirmed +
975,525 A T T Confirmed +
975,528 T G G Confirmed +
975,532 A T T Confirmed +
975,537 A C C Confirmed +
975,540 G T T Confirmed +
975,544 A – G New SNP +
975,545 G – A New SNP +
975,547 T G G Confirmed +
975,899 T C New SNP + ZMO_RS04295
1,612,575 G A – No SNP found − ZMO_RS07065–ZMO_RS07070
1,612,744 G – A New SNP + Alpha/betahydrolase/Trna-Met
2,055,763 T C C Confirmed + ZMO_RS09095-END
2,055,333 G – A New SNP + Uroporphyrinogen decarboxylase/END
[105]Open in a new tab
^aReference genome ZM4
^bWang et al. [[106]16]
^cCurrent study with ZM532 strain
^d ± indicate the presence/absence of variation in the genome,
respectively
The raw data of MS detection was named as “Raw”.
Label-free quantitative protein analysis
The result of each fraction was searched separately by the search
engines for Z.-mobilis-NCBI databases: Proteome Discoverer 2.2. (PD
2.2, Thermo). The search parameters were set as follows: the tolerance
of precursor ion mass was 10 ppm, and the tolerance of product ion mass
was 0.02 Da. Carbamidomethyl was mentioned in PD 2.2 as a fixed
amendment. The oxidation of methionine (M) and acetylation of
N-terminus were identified in PD 2.2 as variable modifications. A
maximum of 2 missing cleavage sites were allowed. At least 1 distinct
peptide with no more than 1.0% false discovery rate (FDR) contains the
protein identified. Related peptides that could not be identified by an
MS/MS analysis were categorized in the same category of proteins. Based
on the intensity used for label-free quantification, precursor ions
were quantified using a label-free method. The Mann–Whitney Test for
proteins whose quantitation differs significantly between experimental
and control groups (p < 0.05 and log2FC > 1.5) was evaluated as the
differentially expressed proteins. GO analysis was performed using an
interproscan-5 program against a non-redundant protein database (such
as Pfam, PRINTS, ProDom, SMART, ProSiteProfiles, and PANTHER)
[[107]48]. Criteria for analysis of GO and Kyoto Encyclopedia of Genes
and Genomes (KEGG) were followed as illustrated by [[108]49]. All
sequencing phases were performed by Novogene Sequencing Company
(Chengdu, China). The mass spectrometry proteomics data have been
deposited to the ProteomeXchange Consortium via the PRIDE [[109]50]
partner repository with the dataset identifier PXD030417.
Genomic DNA isolation and re-confirmation of previously identified 19 SNPs in
mutant strain ZM532
ZM532 and ZM4, 5 mL of cells were harvested from overnight culture by
centrifugation at 13,500 rpm for genomic DNA (gDNAs) extraction via
Bacterial DNA Kit (Omega, Bio-tek, USA). The quality and concentration
of gDNAs were estimated by Qubit 3 Fluorometer and gel electrophoresis
(0.25% agarose, 120 V/cm, 40 min), respectively. To ensure that the
ZM532 is associated with Z. mobilis, ZM532 genes were amplified by PCR.
From 5.0 μL overnight culture, fresh cells were harvested, washed, and
re-suspended in 10 μL of ddH[2]O. PCR conditions and reactions were set
and performed following the manufacturer’s instructions with minor
modifications (Toyobo, Japan) with primers (Additional file [110]1:
Table S1). Amplicons were sent to GENEWIZ Inc. (Chengdu, China) for
sequencing. After sequencing, BioEdit 7.0 software [[111]51] was used
to analyze the data against the reference genome of strain ZM4
([112]NC_006526.2) to identify single nucleotide polymorphism
(SNP)/insertion-deletion (indel) in ZM532.
Construction of plasmids, strains, and culture conditions
ZMO_RS02740 and ZMO_RS06525 in RNAseq were selected for verification
through Type I-F clustered regularly interspaced short palindromic
repeats (CRISPR)-CRISPR associated protein (Cas) (CRISPR–Cas) system
technology following recommended procedure (Additional file [113]1:
Fig. S1) [[114]52], encoding chemotaxis protein Mot A and major
facilitator superfamily (MFS) proteins) in stress tolerance by knock
out. pEZ15Asp was used as a backbone vector. sgRNA fragments were
ligated with a linear vector pEZ15Asp linearized through Gibson
assembly method, yielding plasmids Pmini-T-ZMO-RS02740, carrying an
artificial mini-CRISPR array. Donor DNA fragments containing up stream
(ZMO-RS02740, 500 bp) gfp marker and its promoter pdc (1020 bp)
downstream (ZMO-RS02740, 500 bp) regions were obtained by overlap
extension PCR amplification using primers. The PCR products were linked
with Pmini-T-ZMO-RS02740 vector through Gibson assembly after
generating the genome editing plasmid, Pmini-T-ZMO-RS02740. The correct
plasmids were electroporated into ZM4 and ZM532 competent cells by
using the previously described method [[115]53]. Transformants were
cultured on RM agar plates with spectinomycin (100 μg/mL). After
4–5 days of incubation at 30 °C, positive clones were detected by
colony PCR with check primer and DNA sequencing (Tsingke, Chengdu,
China). Similarly, the Pmini-T-ZMO-RS06525 plasmid was constructed
using the same approach (Additional file [116]1: Table S3). All DNA
manipulation, such as the transformation of E. coli, plasmid
preparation from E. coli, ligation, digestion of restriction enzyme,
and agarose gel electrophoresis, were conducted according to standard
protocols [[117]54]. Cell growth, ethanol production, and glucose
consumption by recombinant strains were calculated under furfural
(3.0 g/L), and acetic acid (5.0 g/L) stress conditions.
Analytical methods
Concentrations of ethanol production and glucose consumption were
determined using the High-performance liquid chromatography (HPLC,
Agilent 1200) with column (HPX-87H), while UV Spectrophotometer was
used to estimate the cell density at OD600. Fresh cultures were
incubated at 30 ͦ. At specific periods, 1uL of culture was harvested by
centrifugation at 4500×g for 2 min, and the extracts were collected and
diluted 10 times. HPLC (Agilent 1200) was used to estimate ethanol
production and glucose consumption at 0.6 mL/min flow rate with 5 mM
H[2]SO[4], and 35 °C column temperature with 20.0 μL volume of
injection, respectively. The following formulas were used to calculate
ethanol productivity and yield.
[MATH: Ethanol
productivity=Ethanol titer/fermentation
time. :MATH]
1
[MATH: Ethanol yield=Ethanol
titer/glucose
consumed. :MATH]
2
The theoretical ethanol yield is 0.51 g/g of sugars consumed.
Evaluation of candidate operons under AF conditions
We selected ZMO-RS02740 and ZMO-RS06525 amplified from mutant strain
ZM532 and ZM4 gDNAs using specific primers (Additional file [118]1:
Table S4). With Ptet promoter, PCR products were cloned into shutter
vector pEZ15Asp [[119]55] via Gibson assembly process [[120]56] using
overlapping primers consisting of 18–20 nucleotides. With right plasmid
constructions, recombinant strains were detected by colony PCR with
primer checks and confirmed by Sanger sequencing (Tsingke, Chengdu,
China). These control plasmids were pEZ15Asp-ZM402740,
pEZ15Asp-ZM53202740, pEZ15Asp-ZM406525, and pEZ15Asp-ZM53206525,
respectively. While treatment plasmids were named ZM4-02740,
ZM532-02740, ZM4-06525, and ZM532-06525, respectively. The right
plasmids were then transformed into competent ZM532∆ZMO_RS02740,
ZM4∆ZMO_RS02740, ZM532∆ZMO_RS06525, and ZM4∆ZMO_RS06525 mutant cells
via electroporation using previously described method [[121]53]. After
getting mutants, cell growth was calculated under furfural (3.0 g/L),
and acetic acid (5.0 g/L) stress conditions.
Statistical analysis
According to student t-test statistics, the data was significant as the
value obtained was p < 0.05, and the data expression is mean ± SD
(Mean ± SD).
Results
Re-sequencing of previously identified 19 mutations for confirmation
We identified 23 single nucleotide polymorphisms (SNPs) (4 CDS and 19
within the intergenic region) via Sanger sequencing. Six SNPs are novel
in this study (Table [122]1). Wang et al. [[123]16] identified 19
identical SNPs in wild type ZM4 in the CDS (6) and 13 in the intergenic
regions, respectively (Table [124]1).
The four SNPs in the CDS regions caused amino acid (AA) variation,
resulting in synonymous and non-synonymous mutations (Table [125]1). In
ZMO_RS03765 (arginine-tRNA ligase), one non-synonymous and synonymous
AA change was identified at the same time. In contrast, in ZMO_RS00235
and ZMO_RS02620, two non-synonymous AA changes were detected, which are
linked to glutamine-fructose-6-phosphate aminotransferase and DNA
repair protein RadA, respectively (Table [126]1). As synonymous
(silent) mutations are largely invisible to natural selection
[[127]57], while nonsynonymous (amino-acid-replacing) mutations may be
under strong selective pressure, comparison of the rates of fixation of
those two types of mutations provides a powerful tool for understanding
the mechanisms of DNA sequence evolution. For example, variable
nonsynonymous/synonymous rate ratios among lineages may indicate
adaptive evolution [[128]58] or relaxed selective constraints along
certain lineages [[129]59]. Likewise, models of variable
nonsynonymous/synonymous rate ratios among sites may provide important
insights into functional constraints at different amino acid sites and
may be used to detect sites under positive selection [[130]60].
Moreover, if SNPs change either the function of a gene or its
expression, and the change provides greater fitness for a population
(i.e., a higher capacity to survive and/or reproduce in a given
environment), the change will be favored by natural selection
[[131]61]. Sometimes nonsynonymous mutations are actually positive
changes. Natural selection may favor this new expression of the gene,
and the individual may have developed a favorable adaptation from the
mutation. While the gene ZMO_RS02620 encodes a DNA repair protein
called RadA, which is necessary for cellular survival when cells are
exposed to acid stress [[132]62]. Jeong et al. [[133]63] reported that
under acid stress in E. coli O157: H7 strand disintegrates and DNA
integrity was retained by Dps and RecA-mediated repairs, indicating
that DNA repair can play an important role in acid tolerance. Wang et
al. [[134]16] identified two non-synonymous AA changes in ZMO_RS09165
(IS5/IS1128 family transposase), but these were absent in the present
study. Conversely, we found SNPs in the intergenic regions of the pairs
of genes: ZMO_RS09160 and ZMO_RS09165; ZMO_RS04290 and ZMO_RS04295; and
ZMO_RS07065 and ZMO_RS07070 in ZM532 mutant. There was also a frame
shift mutation in ZMO_RS04405, which codes for ABC transporter
substrate-binding protein because of two single nucleotide deletions in
the CDS region (Additional file [135]1: Table S5). According to Ask et
al. [[136]64], ABC transporters PDR5 and YOR1 operate in the efflux of
ions and are activated by xenobiotics under the transcriptional control
of Pdr1p and Pdr3p in S. cerevisiae, and can likely function in
transporting furfural out of the cell, thus removing the stress caused
by this agent. We also detected a 21 bp deletion in the ZMO_RS05590
(hypothetical protein-coding gene), while the previous studies
identified two distinct deletions (21 bp and 28 bp) in different
locations of the same gene. Additionally, there was a7 bp deletion in
ZMO_RS07255 (carbamoyl phosphate synthase large subunit) and 1 bp InDel
in the intergenic region of ZMO_RS06410-ZMO_RS06415 (Additional file
[137]1: Table S5). In short, genes involved in the same mutations on
the parental and mutant strains have been reported previously and may
play a critical role against acids stressors.
Overview of transcriptome under inhibitors (AF)
The RNA-seq yielded a total of 32.79 Gb clean data, averaging 2.73 Gb
for each sample with 91% of bases recording Q30 and above, with a
q ≥ 20 (an error probability of 0.03%) (Additional file [138]1: Table
S6). The GC-contents in the four distinct groups ranged from
48.26–49.15. A gene was considered DEG after comparing the gene
expression profiles between RM and AF treatments with FC > 1.5 and FDR
corrected p < 0.05. A total of 1865 and 14 novel DEGs were identified.
Differentially expressed genes in ZM532 and ZM4 and their expression profiles
We identified 745 and 905 DEGs in ZM532 and ZM4, respectively
(Fig. [139]1A). Among these, 352 DEGs were up-regulated; while 393 were
down-regulated in AF_ZM532_vs_RM_532, respectively. However, 442 DEGs
were up-regulated, and 463 were down-regulated in AF_ZM4_vs_RM_ZM4
(Fig. [140]1A, B). In addition, 2 up and 8 down-regulated genes in
AF_ZM532_vs_AF_ZM4; while 7 up and 1 down-regulated gene in
RM_ZM532_vs_RM_ZM (Fig. [141]1C, D). The higher number of DEGs detected
in the strain ZM4 suggests an intense transcriptional alteration in
response to the inhibitors due to ZM4 relative sensitivity. We
performed hierarchical cluster analysis based on the log2FC and FPKM
values to validate the DEGs from ZM4 and ZM532 strains (Fig. [142]1E).
The analysis clustered the DEGs into two main groups with the two
strains clustering together regardless of the treatments. This implies
that very few DEGs were distinguishable between these two strains in
their response to AF treatments.
Fig. 1.
[143]Fig. 1
[144]Open in a new tab
A Identification of the differentially expressed genes (DEG). Volcano
plot depicting the up and down-regulated genes between RM and AF in A
AF_ZM532_vs_RM_532, B AF_ZM4_vs_RM_ZM4, C AF_ZM532_vs_AF_ZM4, D
RM_ZM532_vs_RM_ZM and E Heatmap clustering analysis of the samples
based on log2 fold change obtained from FPKM data of the DEGs
Subsequently, we searched for candidate genes involved in the tolerance
mechanism against the inhibitors by comparing the DEGs between the two
strains. We detected 98 DEGs exclusively involved in the AF resistance
in ZM532, including 42 up-regulated DEGs in response to the inhibitors
and associated with oxido-reductase activity (Additional file [145]1:
Fig. S2A). Additional 647 DEGs were mutually detected in both samples.
While in AF_ZM532_vs_AF_ZM4 (purple) and RM_ZM532_vs_RM_ZM4, only 1
DEGs was co-detected in both strains. We identified 7 DEGs to
RM_ZM532_vs_RM_ZM4 and 9 to AF_ZM532_vs_AF_ZM4 (Additional file [146]1:
Fig. S2B). The most up- and down-regulated DEGs were ZMO_RS02740
(log[2]FC = 6.05) and ZMO_RS06525 (log[2]FC = − 2.373) in the ZM532
strain. These candidate DEGs represent important resources for further
functional validation in AF resistance in Z. mobilis.
Label free data and functional annotation of the proteins under AF
The proteomes of the two Z. mobilis strains (ZM4 and its mutant ZM532)
were generated under control and treatment conditions to elucidate the
molecular response and tolerance to AF inhibitors. We successfully
identified 1532 proteins in both samples (Table [147]2).
Table 2.
Statistics on the Label free data
Total spectra Matched spectrum Peptides Identified proteins
1,647,059 364,149 23,673 1532
[148]Open in a new tab
The mutant strain ZM532 was more resistant to the inhibitors than ZM4.
The number of proteins detected in the AF samples was lower than in the
RM samples (Additional file [149]1: Fig. S3A), suggesting that the
treatment inhibited protein synthesis in Z. mobilis. Most of the
identified proteins' mass distribution and protein length were between
10 and 60 kDa and 6–20 amino acids, respectively (Additional file
[150]1: Fig. S3B-C). There was less variability in the majority of the
detected proteins (CV < 0.2) (Additional file [151]1: Fig. S3D). The
proteins were functionally annotated by BLASTP (E value ≤ 1e−4) using
the Clusters of Orthologous Groups (COG), GO, KEGG, and Interpro (IPR)
databases. We successfully annotated 99% of the total proteins in at
least one database (Fig. [152]2A), and 976 proteins (76%) were
annotated in all four databases. A protein's function is usually
associated with its subcellular localization; the capability to predict
subcellular localization directly from protein sequences benefits
inferred protein functions. The statistical analysis of the proportion
of subcellular location (Cell-mPLOC 2.0 website) of the differential
protein is shown in Additional file [153]1: Fig. S4. The identified
proteins demonstrated that 52.17% of proteins were found in resemblance
with cytoplasmic proteins; 32.61% proteins significant hits for cell
inner membrane proteins, followed by periplasm proteins (5.34%),
extracellular proteins (2.57%), flagellum proteins (0.59%) and 0.20%
proteins with nucleoid protein. Furthermore, principal component
analysis (PCA) was performed based on the protein expression profile.
The first two PCs accounted for more than half of the total variations,
indicating that AF treatment significantly altered the proteome in both
strains (Fig. [154]2B).
Fig. 2.
[155]Fig. 2
[156]Open in a new tab
A Venn diagram depicting the shared and specific proteins functionally
annotated in various databases; Identification of the DEPs; B PCA of
the samples; Volcano plot depicting the up and down-regulated proteins
between RM and AF in C ZM532_AF_vs_ZM532_RM; D ZM4_AF_vs_ZM4_RM; E
ZM4_AF_vs_ZM532_AF; F ZM4_RM_vs_ZM532_RM
Differentially expressed proteins in response to the inhibitors (AF)
The protein expression data were compared between ZM4 and ZM532 groups
to detect the differentially expressed proteins (DEPs) based on the
FC > 1.5 and p < 0.05. A total of 107 up and /204 down-regulated
proteins out of 1477 were detected in ZM4_AF_vs_ZM4_RM; while 123
up-regulated and 205 down-regulated proteins out of 1474 were
identified in ZM532_AF_vs_ZM532_RM, respectively (Fig. [157]2C, D). In
addition, 16 up-regulated and 5 down-regulated proteins were identified
out of 1462 in ZM4_AF_vs_ZM532_AF, while 8 up and 5 down-regulated
proteins were observed out of 1491 in ZM4_RM_vs_ZM532_RM (Fig. [158]2E,
F). Comparative analysis of the DEPs between ZM4 and ZM532 revealed 186
DEPs share the same pattern of the regulation (Additional file [159]1:
Fig. S5). This suggests they represent the core proteome response to
the inhibitors, regardless of the tolerance level of the Z. mobilis
strains.
Pathway enrichment analysis of the differentially expressed proteins
GO, and KEGG enrichment analyses were performed to understand the
biological pathways activated in response to the inhibitors. For GO
enrichment analysis, we mainly focused on molecular function and
cellular component classes. In the two strains, DEPs related to
‘non-membrane-bounded organelle’, ‘large ribosomal subunit’, and
‘ribosome’ were the enriched terms in the GO cellular component class,
indicating major alterations in the ribosome induced by the inhibitors.
These proteins mainly contributed to ‘structural molecule activity,
‘electron carrier activity, and structural constituent of ribosome’
(molecular function terms), indicating that the inhibitors affect the
structural integrity and normal ribosome activity (Fig. [160]3A, B).
While in ZM4_AF_vs_ZM532_AF, DEPs mainly related to hydrolase activity,
endonuclease activity, and damage DNA binding were more enriched
molecular functions terms. Moreover, hydrolase activity is important
against inhibitors, which involve several critical functions such as
maturation, turnover, recycling, and autolysis. In addition, DNA damage
binding activity has a direct relationship to decreased nucleotide
excision repair, and endonucleases play a role in DNA repair in
resistance strain ZM532 (Fig. [161]3C). But in the case of
ZM4_RM_vs_ZM532_RM, most of DEPs linked with the plasma membrane at the
cellular level, while at molecular level DEPs were involved in
N-acetyletransferase activity (Fig. [162]3D).
Fig. 3.
[163]Fig. 3
[164]Open in a new tab
GO enrichment analysis of DEPs in GO enrichment of the DEPs with
p < 0.05 in A ZM532_AF.vs.ZM532_RM; B ZM4_AF.vs.ZM4_RM; C
ZM4_AF.vs.ZM532_AF; D ZM4_RM.vs.ZM532_RM
A similar result was obtained concerning the KEGG enrichment analysis
in the two strains, highlighting ‘ribosome’ as the most affected
pathway (Additional file [165]1: Fig. S6A, B). In addition, flagellar
assembly, peptidoglycan biosynthesis, and ribosome were enriched KEEG
pathways in ZM4_AF_vs_ZM532_AF, while folate biosynthesis, an amino
sugar, and nucleotide sugar metabolism and ABC transporter were the
most enriched term in ZM4_RM_vs_ZM532_RM (Additional file [166]1: Fig.
S6C, D).
The identified DEPs were mapped to the reference pathways in the KEGG
database, and 21 different biological pathways were obtained in 4 major
categories (Additional file [167]1: Fig. S6E). The KEGG pathways (which
include 914 proteins) were members of a major group, metabolism, 153
were linked to genetic information processing, 59 were related to
cellular processes, and 87 were related to environmental information
(Additional file [168]1: Fig. S7). KEGG enrichment analysis in the two
strains also affirms ‘ribosome’ as the most affected pathway in both
strains.
Analysis of expression fold changes of the proteins involved in
selected pathways revealed that the resistant strain (ZM532) strongly
delayed the activity in the ribosome by reducing the synthesis of all
ribosome-assembly proteins under AF treatment (Table [169]3), a
mechanism known as hypometabolism [[170]65]. In contrast, several
ribosome-assembly proteins were either up-regulated or unaffected
following AF treatment in ZM4 (Table [171]3). We speculate that the
ability to limit ribosome activity is an effective adaptation mechanism
against AF.
Table 3.
Log2FC expression of the proteins involved in ribosome assembly and
functioning under inhibitor (AF) as compared to control (RM) treatment
[172]graphic file with name 12934_2023_2095_Tab3a_HTML.jpg
[173]graphic file with name 12934_2023_2095_Tab3b_HTML.jpg
[174]graphic file with name 12934_2023_2095_Tab3c_HTML.jpg
[175]Open in a new tab
Blue and red cells are up-regulated and down-regulated proteins,
respectively (p < 0.05)
Candidate genes and proteins involved Z. mobilis tolerance mechanism
Transcriptional and proteomic levels of ZMO_RS02930 and ZMO_RS08760
encode GroEL protein for adaptation to acidic stress. This protein
(GroEL protein) is expressed higher in ZM532 than in ZM4. Two novel
genes (Novel00013 and Novel00014), which encode GroEL protein, were
up-regulated exclusively in the mutant strain ZM532, which may account
for the robustness of our mutant strain against AF stresses and could
lead to high ethanol production (Additional file [176]1: Table S7, S8,
S9, S10). Our transcriptome and proteomics data showed that molecular
chaperone complexes ZMO_RS06375 (ClpB), and ZMO_RS01740 (ClpA) were
up-regulated in both strains (Additional file [177]1: Tables S7, S8,
S9, S10). Other chaperone proteins, ZMO_RS04435 (Hsp20 family protein)
and ZMO_RS03810 (peptidylprolyl isomerase), were up-regulated, and both
proteins were exclusively found only in mutant strain ZM532 (Additional
file [178]1: Tables S7, S10). Up-regulation of these genes and proteins
may have contributed to the improved tolerance of Z. mobilis to acetic
acid and furfural stressors.
Our transcriptomics and proteomics results demonstrated that sulfur
encoding genes ZMO_RS06540 and ZMO_RS03345 were up-regulated in ZM532
and ZM4, but their expressions were higher in ZM532 compared to ZM4
(Additional file [179]1: Table S7, S8, S9, S10). As furfural and acetic
acid could inhibit sulfur amino acid biosynthesis either by restricting
the availability of reduced sulfur (H[2]S) from sulfate or by
inhibiting the incorporation of reduced sulfur into cysteine.
Up-regulation of these genes may have contributed to the improved
tolerance of Z. mobilis to acetic acid and furfural stressors.
Up-regulated proteins, ZMO_RS00760 (Pgk), ZMO_RS05570 (gpmA), and
ZMO_RS07905 (glucokinase) were found only in mutant strain ZM532 in
both transcriptomics and proteomics data, while ZMO_RS06615 (pgl) was
up-regulated in both strains (Additional file [180]1: Tables S7, S8,
S9, S10). Moreover, alcohol dehydrogenase encoded ZMO_RS05560, which
was up-regulated in both strains, but these genes were doubly expressed
in ZM532 compared to ZM4. ZMO_RS03395 (Hydroxyacylglutathione
hydrolase), ZMO_RS05445 (glucose-6-phosphate isomerase) and ZMO_RS03970
(galactose-1-epimerase) were up-regulated in ZM532 (Additional file
[181]1: Tables S7, S10). These genes may partly account for the
robustness of our mutant strain ZM532 against acetic acid and furfural
stresses. DNA repair genes and proteins such as ZMO_RS07115 (RecF) and
ZMO_RS01515 (DNA mismatch repair enzyme mutL) were up-regulated with
higher expression levels in ZM532 than ZM4 (Additional file [182]1:
Tables S7, S8, S9, S10). These indicate that Z. mobilis and ZM532
(mutant) could reduce DNA damage caused by furfural and acetic acid by
activating relevant genes or DNA replication, base repair, and
recombination. We also found the ZMO_RS03175 (ppk) gene, which encodes
RNA degradosome polyphosphate kinase in ZM532 (Additional file [183]1:
Tables S7, S10).
In the current omics (transcriptome and proteome) study,
transcriptional response regulatory proteins (YebC/PmpR family
DNA-binding transcriptional regulator), ZMO_RS00645, ZMO_RS05270,
ZMO_RS06920 (TetR family transcriptional regulator), ZMO_RS05215
(phosphate regulon transcriptional regulatory protein PhoB),
ZMO_RS06945 (transcription anti-termination factor NusB) and
ZMO_RS05270 transcriptional response regulator were up-regulated
exclusively in ZM532 (Additional file [184]1: Tables S7, S10).
Proteins and genes associated with translation, ribosomal structure,
and biogenesis ZMO_RS00305 (ybeY) and ZMO_RS04930 (tsaE) were
up-regulated only in ZM532 (Additional file [185]1: Tables S7, S10).
However, several proteins such as ZMO_RS07450 (tilS), ZMO_RS03355,
ZMO_RS00625 (trmB), and ZMO_RS06475 were down-regulated under the
stress conditions (Additional file [186]1: Tables S7, S8, S9, S10).
Moreover, we identified four key mutations ZMO_RS00235, ZMO_RS03765
ZMO_RS06410 and ZMO_RS04295 in our transcriptome and proteomic data
(Additional file [187]1: Tables S7, S8, S9, S10), which encode
glutamine-fructose-6-phosphate aminotransferase, arginine-tRNA ligase,
FUSC family protein and cytochrome c (Table [188]1) as described
earlier and our current sanger re-sequencing data. The gene ZMO_RS00235
was up-regulated in the mutant strain, which may contribute to AF
stress tolerance. Moreover, ZMO_RS03765 is associated with arginine
biosynthesis, which is crucial for acid stress. However, no concrete
evidence has been adduced for the role of arginine in acid resistance;
the cell wall/membrane itself may be important to maintain cell
integrity. As previously reported by Ryan et al., ADI genes allow
Listeria monocytogenes to survive under acidic conditions; with
arginine, their expression is higher at low pH. Based on research
conducted by Huang et al. (2015) L-arginine used to suppress the
biofilm formation of Streptococcus mutants but there is no clear
evidence that biofilm contributes to acid tolerance, cell wall/membrane
is necessary to maintain cell integrity. In addition, ZMO_RS06410 might
improve fusidic acid resistance and methicillin resistance. It may also
be useful for Z. mobilis to survive acid stress. One of the genes, ZMO
RS04295 encodes Monofunctional biosynthetic peptidoglycan
transglycosylase (MBPT) and cytochrome c to promote glycan chain
synthesis in bacterial cell walls, and its function is identical to
that of DNA polymerases (Baker et al., 2010) (Additional file [189]1:
Tables S7, S8, S9, S10). This could be important to preserve the
integrity and tolerance of the cells to the inhibitors. These mutations
have roles in acids tolerance as cytochrome C may provide some
protective layer sheet against AF stresses. In addition, ZMO_RS04890
encoded TatD family hydrolase were up-regulated in the mutant strain
ZM532 and was suppressed on wild-type strain ZM4 under acidic condition
may be crucial against acids resistance. Follow-up studies further
showed that TatD bears 3′–5′ exonuclease activity that processes
single-stranded DNA in DNA repair (Additional file [190]1: Tables S7,
S8, S9, S10). Since TatD is an evolutionarily conserved protein, it
should have an important cellular role. However, our understanding of
this protein is largely hampered due to a lack of knowledge of its
biological functions and structure-to-function relationship, this
warrant future study to provide evidence for TatD in DNA repair.
ZMO_RS01205, OstA encoded organic solvent tolerance protein was
up-regulated gene in mutant strain ZM532 and suppressed in wild-type
strain ZM4 under acidic conditions (Additional file [191]1: Tables S7,
S8, S10). In the future, it will be critical to investigate genes'
single and combined effects on the increase in Ost activity in response
to salt and acid stress. Moreover, it will also assist in identifying
the transcriptional regulator proteins which are important in the Ost
mechanisms in ZM4. We identified up-regulated gene ZMO_RS08390,
encoding carbohydrate porin compared to resistance strain with
wild-type (AF_ZM532_vs_AF_ZM4) (Additional file [192]1: Tables S7,
S10). Porins are proteins on the outer membrane of the bacteria cell
wall that regulate cellular permeability and drug resistance; a
systematic approach to porin roles in ZM4 physiology and acid
resistance (AF) does not exist yet.
Correlations between transcriptomic and proteome
Integrative molecular approaches such as genome, transcriptome, and
proteome may help us understand toxicant's effects at multiple levels
of the biological organization while also facilitating risk assessment.
The transcriptome data was combined with the proteome data to identify
corresponding relationships. A total of 662, 578, 1379 IDs were
identified in ZM532_AF_vs_ZM532_RM_AF_532_vs_RM_532,
ZM4_Af_vs_ZM4_RM_AF_ZM4_vs_RM_ZM4 and
ZM532_AF_vs_ZM4_AF_AF_532_vs_AF_ZM4 by both RNA-seq and proteomics
(Fig. [193]4A–C). In the three groups, 111, 138, and 1 unique protein
related to transcriptome DEGs were identified, respectively.
Correlation analysis was performed between the multiple genes
(proteins) identified by transcriptome and proteome study in the three
groups (Fig. [194]4A–C). Among mRNA and the corresponding protein, the
Pearson correlation coefficient was positive (Pearson = 0.233, 0.217,
and 0.014) for all groups (Fig. [195]4D–F). As a result, we suggest
that it is critical to assess protein expression to understand
phenotypic changes and not rely solely on the transcriptional level.
Fig. 4.
[196]Fig. 4
[197]Open in a new tab
Correlation analysis between transcriptome and proteome. A-C The Venn
diagram is determined by transcriptome and proteome expression., all
the transcripts in our transcriptome data; different_trancripts,
differentially expressed transcripts between
ZM532_AF_vs_ZM532_RM_AF_532_vs_RM_532,
ZM4_AF_vs_ZM4_RM_AF_ZM4_vs_RM_ZM4 and
ZM532_AF_vs_ZM4_AF_AF_532_vs_AF_ZM4; different_proteins, distinct
expressed proteins between ZM4 and ZM532; all_prot, all the proteins in
our proteome data; D–F Analysis of correlations between transcriptome
and proteome expression levels
Verification of RNAseq candidate genes involved in Z. mobilis tolerance
mechanism
CRISPR–Cas Type I-F edited Z. mobilis revealed that the
protospacer-bearing plasmids had significant interference activity. For
self-targeting and genome engineering, we transferred the DNA cleavage
of interest to an adjacent protospacer motif (PAM)-flanking sequence on
the chromosome. The ZMO_RS02740 (204 bp) and ZMO_RS06525 (1275 bp) were
selected as engineering targets. Plasmids were primarily constructed to
import a leader-repeat-spacer-repeat cassette of an artificial CRISPR
expression individually (Fig. [198]5A). A donor DNA comprising of two
homology arms for supporting homologous recombination engineered to
carry expected mutations to improve the reliability of selected
genotypes by self-targeting (Fig. [199]5B). By using genome engineering
plasmids pKO-ZMO_RS02740 and pKO-ZMO_RS06525 (Fig. [200]5B), both
target genes were successfully deleted in ZM4 and ZM532 (Fig. [201]5C,
D). The genotypes of randomly selected transformants in ZM532 and ZM4
were analyzed by colony PCR and Sanger sequencing, confirming the
deletion of both genes (Fig. [202]5C, D).
Fig. 5.
[203]Fig. 5
[204]Open in a new tab
Establishment of the Type I-F CRISPR-based genome engineering system
for Z. mobilis. A A self-targeting plasmid contained an artificial
CRISPR locus. B Design of the self-targeting CRISPR and the donor DNA
in knockout plasmids; C deletion of mutants by screening of colony PCR.
D Confirmation by Sanger sequencing
Cell growth, glucose consumption, and ethanol production of mutant strains
∆ZMO_RS02740 and ∆ZMO_RS06525 under AF conditions
Four mutant strains (ZM532∆ZMO_RS02740, ZM4∆ZMO_RS02740,
ZM532∆ZMO_RS06525, and ZM4∆ZMO_RS06525) were investigated under RM and
AF conditions, respectively. AF affects glucose consumption, cell
growth, and ethanol production (Fig. [205]6A–D). With the same initial
OD600, when strains were cultivated for 36 h, the ZM4∆ZMO_RS06525 OD600
values were increased by 5.6% compared with the wild-type strain ZM4
under the same initial OD600. This OD600 value decreased when
ZMO-RS02740 was knocked out in ZM532 and ZM4. The growth activity and
glucose consumption of mutant strains ZM532∆ZMO_RS02740 and
ZM4∆ZMO_RS02740 were decreased and thus, increasing fermentation time
from 42 h in ZM532 to 55 h. Ethanol production was 58% higher in ZM532
than that in ZM532∆ZMO_RS02740. However, in ZMO_RS06525 knockout in
ZM4, the time of fermentation was significantly decreased from 60 h for
ZM4 to 42 h for ZM4∆ZMO_RS06525, which contributed to a 45.54% increase
in ethanol production (Table [206]4). These results highlight that the
mutant, ZM532 has more ability to convert sugar to ethanol and
withstand toxic conditions. These observations are consistent with our
transcriptome results.
Fig. 6.
[207]Fig. 6
[208]Open in a new tab
A Cell growth of Control ZM532, ZM4; and Knockout Mutants
ZM532∆ZMO_RS02740; ZM4∆ZMO_RS02740; ZM532∆ZMO_RS06525; and
ZM4∆ZMO_RS06525 under RM; B cell growth; C glucose consumption and D
ethanol production; wild type ZM4; ZM532 and Knockout Mutants
ZM532∆ZMO_RS02740; ZM4∆ZMO_RS02740; ZM532∆ZMO_RS0652 and
ZM4∆ZMO_RS06525 under AF stress conditions; E cell growth of Control
strains with empty vector such as pEZ15Asp-ZM402740;
pEZ15Asp-ZM53202740; pEZ15Asp-ZM406525 and pEZ15Asp-ZM53206525; and
overexpress mutants with ptet promoter such as ZM4-02740; ZM532-02740;
ZM4-06525 and ZM532-06525 under RM; F Cell growth of Control strains
with empty vector such as pEZ15Asp-ZM402740; pEZ15Asp-ZM53202740;
pEZ15Asp-ZM406525 and pEZ15Asp-ZM53206525; and overexpress mutants with
ptet promoter such as ZM4-02740; ZM532-02740; ZM4-06525 and ZM532-06525
under AF stress conditions. The results are demonstrated in arbitrary
(means ± SD). The error bars represent the standard deviation. Three
replicates were performed for each strain
Table 4.
Fermentation time of glucose consumption (Time), ethanol titer, yield,
and productivity of wild-type ZM4, ZM532 and other mutant strains
Strain Glucose consumed g/L Time (h) Ethanol Theoretical value ratio
(%)
Titer (g/L) Yield (g/g glucose) Productivity (g/L/h)
50 g/L glucose + 5 g/L acetic acid + 3 g/L furfural
ZM532 (Control) 50.40 ± 0.86 42 21.39 ± 0.570 0.424 ± 0.005
0.509 ± 0.010 83
ZM532∆ZM0-RS02740 50.06 ± 0.17 55 17.71 ± 0.001 0.353 ± 0.010
0.322 ± 0.010 69
ZM532∆ZM0-RS06525 50.56 ± 0.69 55 17.50 ± 0.057 0.346 ± 0.001
0.318 ± 0.001 67
ZM4 (Control) 50.05 ± 0.23 60 17.57 ± 0.050 0.349 ± 0.010 0.290 ± 0.001
68
ZM4∆ZM0-RS02740 50.24 ± 0.63 55 17.37 ± 0.100 0.345 ± 0.010
0.320 ± 0.001 67
ZM4∆ZM0-RS06525 50.03 ± 0.55 42 17.87 ± 0.100 0.357 ± 0.010
0.430 ± 0.010 69
[209]Open in a new tab
Values are the means and standard deviations of representative
experiment with three technical replicates
Evaluation of candidate resistance genes under AF tolerance by a
complementary study
Further, four plasmids bearing candidate operons were constructed based
on a shuttle vector pEZ15Asp with Ptet as the promoter to investigate
the impact of these genetic variants on combined AF resistance. These
plasmid constructs were then separately transferred into competent
cells of ZM532 and ZM4, including the empty vector pEZ15Asp as the
control. Besides, recombinant strains' expression profiles were
examined without stress and with stress (AF) conditions to analyze
their effect on cell growth. Hence, these results suggested that the
ZM406525 encoding an MFS containing recombinant strain failed to
contribute to the resistance of acids in ZM4 and ZM532, which is
consistent with our RNA-Seq outcome (Fig. [210]6F); while all strains
had approximately similar growth rates under normal conditions
(Fig. [211]6E). In addition, the up-regulated expression of ZMORS02740
(Chemotaxis protein Mot A) was similar to our RNA-seq results
(Fig. [212]6F).
qPCR validation of differentially expressed genes under inhibitory (AF)
conditions
The results of qPCR showed three DEGs (ZMO_RS02740, ZMO-RS00080, and
ZMO-RS08110) were up-regulated in ZM532, while ZMO-RS03395,
ZMO-RS08600, and ZMO_RS06525 were down-regulated in the same strain
ZM532. Conversely, among the selected DEGs in ZM4, ZMO-RS00065 and
ZMO-RS02800 were up-regulated, while ZMO-RS01385 and ZMO-RS03775 were
down-regulated, which are in consonance with the transcriptome results
(Additional file [213]1: Fig. S8; Table S7, S8). These genes had high
expression either as up or down-regulated in RNA-seq results, giving a
clue for their potential for functional validation in our subsequent
experiments.
Discussion
Lignocellulose inhibitors are composed of aldehydes such as
Hydroxymethylfurfural, furfural, and weak acids, particularly acetic
acid [[214]17]. Ethanol and the toxicity of these inhibitors are
influenced by bacterial cells, lipid structure and fluidity, membrane
permeability, and physiological processes, including intake of
nutrients, electron transport chain, and absorption and energy
transduction [[215]66]. Resistance to these inhibitors is a complex
phenotype controlled by mysterious regulatory mechanisms. One of the
main challenges of cost-competitive bioethanol production from
lignocellulosic biomass is the development of resistant strains toward
stresses. Exploiting the global regulatory landscape may show different
impacts on bacterial metabolism leading to the overlap of cell stress
responses. Synthesis of resistant strains by functional and
evolutionary engineering is a valuable way to distinguish genetic
elements important to the resistance of inhibitors [[216]67]. In our
previous study, we constructed a mutant ZM532 by genome shuffling,
which is superior to the parental strain and Z. mobilis. However, the
molecular mechanisms underlying the enhanced tolerance and shortened
fermentation time were largely unknown. Therefore, genetic changes,
proteins, and gene expression profiles under AF stress or without
stress conditions were investigated using transcriptomics and
proteomics to unravel the molecular mechanisms in the wild type ZM4 and
mutant strain ZM532. We also identified 1865 and 14 novel DEGs in ZM532
and wild-type ZM4, while 1532 proteins were identified in ZM532 and
wild-type ZM4 by label free proteome using the genome of parental
strain ZM4 (ATCC 31821) as cited in [[217]68]. We identified one of the
most important up-regulated genes, ZMO_RS08390, encoding carbohydrate
porin compared to resistance strain with wild-type. Porins are proteins
on the outer membrane of the bacteria's cell wall that regulate
cellular permeability and drug resistance [[218]69]. However, a number
of studies on porin resistance to antibiotics are available [[219]70,
[220]71], but a systematic approach to porin roles in ZM4 physiology
and AF resistance does not exist yet. Porins primary natural function
is to transport polar nutrients, such as amino acids, carbohydrates,
and other ions [[221]72]. Moreover, porins play an important role in
Gram-negative bacterial envelope integrity by facilitating the passive
transport of various chemicals. For example, non-specific porins, such
as OmpA, found in outer membrane proteins, promote the passive
transport of many small molecules [[222]73, [223]74]. Additionally,
this protein is related to peptidoglycan via a flexible periplasmic
motif that interacts non-covalently with peptidoglycans [[224]75].
Because porins are linked to antibiotic resistance in Gram-negative
bacteria, which enables them to passively diffuse drugs throughout the
outer membrane. Although prior research suggested that porins regulate
antibiotic resistance, the contribution of porin in resistance to acids
(AF) is largely unknown and has not been studied yet. In addition,
ZMO_RS04890 encoded TatD family hydrolase, was up-regulated gene found
only in mutant strain ZM532 and participates in DNA fragmentation
during apoptosis in S. cerevisiae [[225]76] and Trypanosoma brucei
[[226]77]. The previous study showed that TatD-knockout cells are less
resistant to the DNA damaging agent hydrogen peroxide [[227]78].
Hydrogen peroxide may induce various DNA lesions, double-strand breaks,
oxidation, deaminated bases and sugar modifications [[228]79, [229]80].
TatD has ability to remove deaminated nucleotide from DNA chain,
inferring that it may be involved in H[2]O[2]-induced-DNA repair
[[230]78]. We also found ZMO_RS01205, OstA encoded organic solvent
tolerance protein was up-regulated in mutant strain ZM532 in our omics
data (transcriptome and proteome). An earlier studies reported gene
ostA is one of the genes contributing to the level of organic solvent
tolerance [[231]81, [232]82].
Macromolecule recombination, replication, and repair are central
molecular mechanisms for regulating and maintaining genetic information
in microbes. Bacterial proteins, cell membranes, and DNAs are usually
damaged under acidic conditions. Repair and resistance genes and
proteins such as RecF, Recj, and DNA mismatch repair enzyme mutL and
PPK could be improved to overcome these acidic destructions of
macromolecules. We found DNA repair genes and proteins such as
ZMO_RS07115 (RecF), ZMO_RS05530 (Recj), and DNA mismatch repair enzyme
ZMO_RS01515 (mutL) were up-regulated with higher expression levels in
ZM532 than ZM4. This indicates ZM4 and ZM532 (mutant) could reduce DNA
damage caused by AF by activating relevant genes or DNA replication,
base repair, and recombination. Our results are in line with those
previously reported [[233]21, [234]32, [235]83]. We also found
ZMO_RS03175 (ppk) and was up-regulated in ZM532 (mutant) and ZM4 under
AF stress which is consistent with findings of previous studies
[[236]31, [237]32, [238]83]. Up-regulation of these proteins is
important for cell recovery from DNA damage caused by these inhibitors.
In the current study, transcriptional response regulatory proteins and
genes ZMO_RS05270, ZMO_RS00645 (YebC/PmpR family DNA-binding
transcriptional regulator), ZMO_RS06920 (TetR family transcriptional
regulator), ZMO_RS05215 (phosphate regulon transcriptional regulatory
protein PhoB), transcription anti-termination factor ZMO_RS06945 (NusB)
and ZMO_RS05270 (transcriptional response regulator) were up-regulated
exclusively in ZM532. Proteins associated with translation, ribosomal
structure, and biogenesis, such as ZMO_RS00305 (ybeY) and ZMO_RS04930
(tsaE) were up-regulated only in ZM532. However, several proteins such
as ZMO_RS07450 (tilS), ZMO_RS03355, ZMO_RS00625 (trmB), and ZMO_RS06475
were down-regulated under the stress conditions (Additional file
[239]1: Tables S7, S10). This agrees with the transcriptomic results of
furfural and acetate-challenged Z. mobilis [[240]21, [241]31, [242]84].
Down-regulation of these proteins suggests the overall synthesis of
proteins to minimize cell growth [[243]31]. This may be partly
attributed to external stress that causes mRNA degradation and inhibits
translation [[244]85].
The Transcriptional and proteomic levels of ZMO_RS02930 and ZMO_RS08760
encode GroEL protein to adapt to acidic stress [[245]83]. However, this
protein (GroEL protein) was more highly expressed in ZM532 than in ZM4.
An earlier study revealed that dank is critical for microbe survival in
environmental stress conditions [[246]86]. Besides, dank play a
significant role in refolding of damaged proteins. Two novel genes
(Novel00013 and Novel00014), which encode GroEL protein, were
up-regulated exclusively in the mutant strain ZM53, which may account
for the robustness of our mutant strain against AF stresses and could
lead to high ethanol production (Additional file [247]1: Tables S7, S8,
S9, S10). Previous studies have confirmed that these proteins are
necessary for the normal growth of E. coli under toxic antibiotics
[[248]17, [249]31] and temperature stress conditions [[250]87]. Our
transcriptomics and the proteomic result showed that the expression
level of Clp protease complex, like ZMO_RS01740 (clpA) and ZMO_RS06375
(clpB) were up-regulated in both ZM4 and mutant ZM532, but the
expression level of Clp protease was higher in mutant ZM532 compared
with ZM4 (Additional file [251]1: Tables S7, S8, S9, S10). These may be
involved in protein remodeling and reactivation [[252]83,
[253]88–[254]90] to enhance the expression of these proteins to protect
DNA and protein from damage in acidic cytoplasm. However, our
transcriptomics results demonstrated that sulfur encoding genes
(ZMO_RS06540 and ZMO_RS03345) were up-regulated in ZM532 and ZM4 but
their expressions were higher in ZM532 compared to ZM4 (Additional file
[255]1: Tables S7, S8). As AF could inhibit sulfhur amino acid
biosynthesis either by restricting the availability of reduced sulfur
(H2S) from sulfate or by inhibiting the incorporation of reduced sulfur
into cysteine. The inhibition of sulfate reduction is unlikely to
represent the initial action of furfural that inhibits growth
[[256]91]. Up-regulation of these genes may have contributed to the
improved tolerance of Z. mobilis to AF stressors.
Our omics data (transcriptomic and proteomic) showed that molecular
chaperone ZMO_RS04435 (Hsp20 family protein), which regulates bacteria
growth and survival under different stresses, was up-regulated in the
mutant ZM532 (Additional file [257]1: Tables S7, S10). Hsp20 stabilizes
archaea and bacterial membrane lipids and small HSPs in microbial
pathogenesis [[258]92–[259]95]. However, chaperone ZMO_RS03810
(peptidylprolyl isomerase), which can maintain the overall reduction in
the level and folding of OMPs and the induction of the periplasmic and
ZMO_RS07675 (tetratricopeptide repeat protein) involves sensing and
treatment of defective or incomplete protein structures under stress
responses as previously discussed [[260]92, [261]94] both proteins
exclusively found only in mutant strain ZM532 (Additional file [262]1:
Tables S7, S10). For inhibitor tolerance of Z. mobilis cells, control
of these stress response molecular chaperones may be helpful.
The most critical part of living organisms is carbon metabolism.
Up-regulated proteins are involved in the central carbon metabolism
pathway's ED and TCA cycle routes. Although only one mole of ATP per
single mole of glucose is provided by the ED route, the ED pathway in
Z. Mobilis is almost twice the thermodynamically favorable pathway of
Embden-Meyerhof-Parnas (EMP) in E. coli or S. cerevisiae [[263]96].
Up-regulated proteins, Pgk, gpmA, and ZMO_RS07905 (glucokinase) were
found only in mutant strain ZM532 in our omics data, while ZMO_RS06615
(pgl) was up-regulated in both strains (Additional file [264]1: Tables
S7, 8, S9, S10). ZMO_RS03395 (hydroxyacylglutathione hydrolase),
ZMO_RS05565 (2-hydroxy acid dehydrogenase), ZMO_RS05445
(glucose-6-phosphate isomerase), ZMO_RS03970 (galactose-1-epimerase),
and ZMO_RS05445 (glucose-6-phosphate isomerase) were up-regulated in
ZM532 (Additional file [265]1: Tables S7, S10). These genes may partly
account for the robustness of our mutant strain ZM532 against AF
stresses. The up-regulation of these genes stimulates more ATPs for
acidic tolerance, as established by previous reports [[266]21,
[267]83].
Besides, recombinant strains' expression and knockout profile were
examined in without and with stress (AF) conditions to analyze their
effect on cell growth. Since the production of ethanol in Z. mobilis is
closely linked to cell growth and substantially reduced by the
inhibitory effects of toxic compounds [[268]97]. Hence, these results
suggested that the ZM406525 encoding an MFS containing recombinant
strain failed to contribute to the resistance of acids in ZM4 and ZM532
when overexpressed; while after knockout of this gene, growth activity
and glucose consumption increased, which is consistent with our RNA-Seq
outcome. Many MFS transporters are essential for microorganisms to grow
under stress conditions. Several superfamily transporters of major
facilitators are important for microorganisms to develop under
conditions of stress [[269]98]. Gram-negative bacteria can reduce their
entry by establishing a low permeability barrier to restrict the
intracellular concentration of toxic inhibitors [[270]99]. This
non-specific phenomenon, such as the down-regulation of ZMO06525, which
encodes an MFS transporter protein, was present in ZM4, while all
strains had approximately similar growth rates under normal conditions.
In addition, the up-regulated expression of ZMORS02740 (Chemotaxis
protein, Mot A) was similar to our RNA-seq results. But for the Ptet
promoter, the fermentation time of ZMORS02740 was reduced compared to
mutant strain ZM532, which may be ZMORS02740 coordinating with some
other genes and linker genes for acids resistance. When we combined
this gene with Ptet promoter, their balance was disturbed, resulting in
reduced fermentation time.
Our results revealed that the strain ZM532 is more capable of
converting biomass to ethanol, and enhanced fitness in the
toxicant-containing environment will benefit from this. Thus, ZM532 can
enhance bioethanol production under AF conditions with ZM4 as a
biocatalyst within a shorter fermentation period and greater
productivity than ZM4. Overall, the Z. mobilis AF tolerance molecular
mechanism presented in this study may be useful to synthetic biology
focused on enhancing biological processes involved in ethanol
production.
Supplementary Information
[271]12934_2023_2095_MOESM1_ESM.docx^ (1.3MB, docx)
Additional file 1. Figure S1 Schematic procedure used in the knock-out
of ZM532, ZMO_RS02740 and ZMO_RS06525 in ZM4 and ZM532. Figure S2 Venn
diagram depicting the unique and shared differentially expressed genes
between the two Z. mobilis, ZM532 strains, (A) AF_ZM532vsRM_532
(yellow) and its wild type AF_ZM4 vsRM_ZM4 (purple); (B)
AF_ZM532vsAF_ZM4 (purple) and RM_ZM532vsRM_ZM4 in response to acetic
acid and furfural combine treatments. Figure S3 Overview of the
quantitative mass spectrometry results (A) Number of proteins
identified in each sample; (B) Distribution of peptide length range;
(C) Protein molecular weight distribution; (D) Reproducibility between
biological replicates. Figure S4 Represented DEPs subcellular
localization analysis. Figure S5 Venn diagram showing the shared and
specific (DEP) between ZM4 and ZM532 in response to acetic acid and
furfural treatments. Figure 6 KEEG enrichment analysis of the DEPs p
< 0.05 in (A) ZM532_AF_vsZM532_RM and (B) ZM4_AF_vs_ZM4_RM; (C)
AF_ZM532_vs_AF_ZM4; (D) RM_ZM532_vs_RM_ZM; (E) COG functional
classification of the DE proteins. The proteins with significant
homologies in the COG database were classified into 21 COG categories.
Capital letters on the x-axis indicate COG categories on the right side
of the histogram. Figure S7 KEGG pathway annotation for Z. Mobilis. The
abscissa represents the number of proteins; the pathway categories are
shown on the y-axis. Figure S8 Transcript abundance of 10 selected
differentially expressed genes (DEGs) in the two samples (brown bar
represents either ZM4 or ZM532 which gave similar expression; gray bar
represents expression in the wild type, ZM4; ash bar represents
expression in the mutant strain, ZM532. The error bar represents
standard error of the three technical repeats. Table S1. List of Primer
pairs used in study. Table S2. List of primers used for qPCR
experiment. Table S3. List of primers, strains and plasmids. Table S4.
List of primers, strains and plasmids. Table S5. INDEL in re-sequence
ZM532 by comparing with previous published ten genome-shuffled mutant
strain and Z. mobilis ZM4 (GenBank: AE008692.2). Table S6. Overview of
the transcriptome sequencing dataset and quality check. Table S7
Differentially Expressed Genes of ZM532 strain in rich media and media
with acetic + furfural treatments. Table S8 Differentially Expressed
Genes of wild type ZM4 in rich media and media with acetic + furfural
treatment. Table S9 Differentially Expressed proteins of ZM4 strain in
rich media and media with acetic + furfural treatments. Table S10
Differentially Expressed proteins of ZM532 strain in rich media and
media with acetic + furfural treatments.
Acknowledgements