Spaceflight missions affect the behavior of microbes that are inevitably introduced into space environments and may impact astronauts’ health. Current studies have mainly focused on the biological characteristics and molecular mechanisms of microbes after short-term or long-term spaceflight, but few have compared the impact of various lengths of spaceflight missions on the characteristics of microbes. Researchers generally agree that microgravity (MG) is the most critical factor influencing microbial physiology in space capsules during flight missions. This study compared the growth behavior and transcriptome profile of Proteus mirabilis cells exposed to long-term simulated microgravity (SMG) with those exposed to short-term SMG. The results showed that long-term SMG decreased the growth rate, depressed biofilm formation ability, and affected several transcriptomic profiles, including stress response, membrane transportation, metal ion transportation, biological adhesion, carbohydrate metabolism, and lipid metabolism in contrast to short-term SMG. This study improved the understanding of long-term versus short-term SMG effects on P. mirabilis behavior and provided relevant references for analyzing the influence of P. mirabilis on astronaut health during spaceflights.
The field of space life science has become a new research frontier following the advent of space exploration and the development of space technology (Belobrajdic et al. 2021; Macaulay et al. 2021). During space exploration, microbes are inevitably introduced into space habitats by astronauts and may grow on surfaces in the space capsule (Singh et al. 2018; Checinska Sielaff et al. 2019). Due to extreme environmental factors, such as microgravity (MG), high vacuum, and intense radiation, microbes in space undergo a series of changes in gene expression at the transcriptional level and eventually perform global physiological alterations, including metabolism, growth rate, motility, virulence, and biofilm formation (Taylor 2015; Senatore et al. 2018). For instance, strains of Candida albicans exposed to a short-duration spaceflight showed enhanced cell aggregation and random budding under the differential expression of transcriptional regulators involved in oxidative stress resistance, aggregation, and random budding (Crabbé et al. 2013). Escherichia coli strains grew at higher antibiotic concentrations within 49 h of being cultured in a space environment compared to the Earth and differentially expressed genes (DEGs) related to starvation and oxidative stress responses (Aunins et al. 2018). Acinetobacter baumannii subjected to long-duration spaceflight showed decreased biofilm formation ability than the ground controls, coinciding with down-regulated type IV pili-related genes (Zhao et al. 2018). Overall, these data strongly suggest that microbes’ biological characteristics and molecular mechanisms were affected during or immediately following spaceflights, regardless of mission duration. However, much less is known regarding the precise impact of different mission durations on microbial growth behavior and transcriptome profiles.
MG is defined as a state of weightlessness that occurs due to decreased physical force exerted by gravity, where gravity ranges from 10–3 to 10–6g (Bijlani et al. 2020). There is general agreement that MG is a crucial factor influencing microbial physiology in the space capsule during flight missions (Tesei et al. 2021). Due to technological and logistical hurdles, the microbial experiments under true MG in space stations are minimal. Therefore, different types of ground-based bioreactors, such as clinostats, rotating wall vessels (RWVs), and random positioning machines (RPMs), have been designed to develop a simulated microgravity (SMG) environment to analyze microbial responses to space MG (Acres et al. 2021). A high aspect ratio vessel (HARV) is a typical RWV that creates a low-fluid shear and weightless environment by revolving around an axis perpendicular to the gravitational vector (Sheet et al. 2019). Specifically, it simulates the true MG environment of spaceflight and has been used extensively to analyze the physiological response of bacteria to SMG (Hauserman et al. 2021). Since the invention of the HARV, the effects of SMG have been studied in several microbial species including E. coli, Pseudomonas aeruginosa, and Staphylococcus aureus (Crabbé et al. 2010; Castro et al. 2011; Tirumalai et al. 2019).
Moreover, several studies have shown that results obtained from HARV experiments are similar to those obtained in a true MG. For example, Salmonella Typhimurium cultivated in a true MG environment showed increased virulence in mice and revealed a role for its global regulator Hfq (Wilson et al. 2007). This phenomenon was shown due to a prior ground-based experiment that utilized the HARV and had previously produced similar results (Nickerson et al. 2000).
Proteus mirabilis is a common opportunistic pathogen frequently isolated from patients with urinary tract infections (UTIs), particularly in the elderly population at nursing homes and patients undergoing longterm catheterization (Matthews and Lancaster 2011; Armbruster et al. 2018). This pathogen primarily differentiates into elongated swarm forms, migrates to the epithelial cells of the urinary tract, and finally induces UTIs (Armbruster et al. 2012). In addition, P. mirabilis expresses fimbriae to mediate catheter attachment and biofilm maturation, which protects bacteria from the host immune response and the antibiotic inhibitory effect (Fusco et al. 2017). P. mirabilis has already been found in samples from human space habitats or post-flight astronauts, and it is likely to induce infectious diseases in the space environment given that the immune system of astronauts is significantly impaired during spaceflight (Klaus and Howard 2006; Crucian et al. 2018). Therefore, it is necessary to study the biological characteristics and genetic features of P. mirabilis in MG. To date, multiomics analysis has already been developed to study the molecular mechanisms of microbial survival and adaption in MG and SMG environments (Milojevic and Weckwerth 2020). Although a recent study has analyzed the phenotype and transcriptome of P. mirabilis after short-term exposure to SMG, long-term SMG exposure and varying lengths of SMG exposure to P. mirabilis remain unclear (Wang et al. 2021). In this study, the growth behavior and transcriptome profile of P. mirabilis exposed to long-term SMG were compared with those exposed to short-term SMG to analyze the effect of MG exposure duration on its characteristics. This study is expected to improve the understanding of long-term versus short-term SMG on P. mirabilis and provide relevant references for analyzing the influence of P. mirabilis on astronaut health during spaceflights.
Experimental
Materials and Methods
Bacterial strain and culture conditions. The original P. mirabilis strain, used in this study, was obtained from the Space Biomedical Laboratory of the Second Medical Center of the Chinese PLA General Hospital and has already been described many times (Wang et al. 2021). The strain was cultivated at 37°C for 24 h under constant shaking (200 rpm) in Luria-Bertani (LB) medium.
HARV experiment. The bacterial culture was adjusted to a turbidity of 1.0 and inoculated at 1:100 in the HARV bioreactor chambers (Synthecon Inc., USA). The chambers were filled with the culture medium, and air bubbles in the chambers were carefully removed through valves. The bacterial cultures were grown in rotated chambers around the horizontal axis at 37°C with a rotation rate of 25 rpm (Fig. 1). After 24 h, the bacterial cultures were poured out from the chambers, diluted to a turbidity of 1.0, and added into the new chambers at a volume ratio of 1:100 in LB medium. The bacterial suspension was cultivated at 37°C and 25 rpm for another 24 h. Experimental operations of bacterial inoculation in the chambers of HARV bioreactors were successively repeated three times for three days (short-term exposure to SMG, designated as PMS) and 21 times for three weeks (long-term exposure to SMG, designated as PML).
Growth rate assay. The growth rates of PML and PMS were determined using the Bioscreen C system and BioLink software (Lab Systems, Finland). Approximately, 200 μl of PMS and PML was taken from each chamber at every 2 h interval on the 3rd day and 21st day, respectively. Then the samples were inoculated into a 96-well microtiter plate. The growth rates of PML and PMS were monitored by measuring the optical density at 600 nm (OD600) every 2 h. A blank well containing 200 μl of LB medium was used as a negative control. This experiment was performed in three replicates.
Fig. 1
Schematic diagram of mechanical principle of HARV. MG is simulated by rotating the samples around a plane perpendicular to the gravity vector.
Biofilm formation assay. About 200 μl of each sample was added to a 96-well honeycomb microtiter plate and statically cultivated at 37°C for 24 h. After removing unattached cells with PBS, the 96-well microtiter plate was cultured at a temperature of 80°C for 15 min to fix the biofilm. The adhered cells were stained with 200 μl of 0.1% crystal violet for 15 min. Subsequently, the 96-well microtiter plate was washed with PBS and dried for another 10 min. Finally, the stained wells were dissolved in 95% ethanol to digest the biofilm, and the optical density at 570 nm (OD570) for each well was measured using a Thermo Multiskan Ascent (Thermo, USA). This experiment was performed in three replicates.
Swarming motility assay. The swarming motility of P. mirabilis was analyzed using Petri dishes containing a swarming medium (0.5% agar, 0.5% glucose, and 0.8% nutrient broth). Bacterial cultures were adjusted to a 108 CFU/ml density and inoculated in the dishes using a sterile needle. After incubation at 37°C for 24 h, the swarming motility of the strains was determined by measuring the diameter of the circular turbid zone formed by the bacterial suspension migrating away from the point of inoculation. This experiment was performed in three replicates.
RNA extraction, cDNA synthesis, and sequencing. The exponential growth phase of samples was determined according to the growth curves, and cultures at the exponential phase were used for transcriptome analysis. Briefly, total RNAs of samples were extracted by TRIZOL and purified using RNeasy Mini Kit (Qiagen, German) following the manufacturer’s instructions. The quality and quantity of RNAs were checked by capillary electrophoresis (Agilent Bioanalyzer 2100, USA) and with a spectrophotometer (Nanodrop 2000, USA). The RIN/RQN values ranged from 8.2 to 9.5. Subsequently, the ribosomal RNA (rRNA) was removed from the total RNA using a Ribo-Zero RNA Removal Kit (Illumina, USA). The purified RNA samples were then subjected to cDNA library construction using TruSeqTM Stranded Total RNA Library Prep Kit (Illumina, USA). Fragmentation buffer was added to cleave the mRNA into ~ 200 bp fragments, and the fragments were reverse transcribed into the first strand of cDNA by random primer. The second strand of cDNA was synthesized using DNA polymerase I and RNase H. Next, the samples were resolved with the End Repair Mix for end repair and Poly (A) addition for adapter ligation. After adapter ligation, the second strand of cDNA was degraded by the UNG enzyme, and the suitable fragments were selected for PCR amplification. Finally, TBS380 (Picogreen) samples were quality-checked and sequenced using Illumina HiSeq System.
Read processing, alignment, and quantification. All raw reads for RNA-seq were filtered, and clean reads were obtained by removing reads with adaptors, unknown nucleotides ≥ 5%, and low-quality bases (< Q10) number ≥ 20%. The RNA-seq reads were then mapped to the reference genome (P. mirabilis HI4320) using HISAT and Bowtie tools. The gene expression levels of each sample were analyzed using RESM software. Gene expression values were calculated using FPKM. Differential gene expression was analyzed using the DESeq2 method, and genes with log2 (FoldChange) ≥ 1 and adjusted p‐value ≤ 0.05 were defined as differentially expressed genes (DEGs). The function of each DEG was analyzed using the Kyoto Encyclopedia of Gene and Genomes (KEGG) database.
Data and statistical analyses. Phenotypic experiments and RNA sequencing were conducted in triplicate and the values were represented as mean ± standard deviation. A statistical comparison of data was evaluated by the two-tailed Student’s t-test. Statistical significance between PML and PMS was defined as p < 0.05.
Results
The growth rate of PML versus PMS. The growth rates of PML and PMS were measured, and growth rate data are shown in Table I. When grown in LB medium, the maximum specific growth rates of PML and PMS were 0.167 h–1 and 0.196 h–1, respectively. In terms of growth ability, PML exhibited a lower OD600 value than PMS, suggesting that growth ability decreased with increasing SMG exposure time (Fig. 2).
Fig. 2
Growth curve of PML (red) and PMS (blue). The growth curves of PML and PMS were quantified by measuring the OD600 values every 2 h for 24 h.
OD600 value of PML and PMS.
Time (h)
PML
PMS
0
0.548 ± 0.005
0.547 ± 0.006
2
0.581 ± 0.008
0.591 ± 0.005
4
0.674 ± 0.010
0.726 ± 0.005
6
0.863 ± 0.026
0.955 ± 0.012
8
1.206 ± 0.018
1.411 ± 0.026
10
1.560 ± 0.022
1.636 ± 0.013
12
1.709 ± 0.019
1.806 ± 0.027
14
1.776 ± 0.016
1.872 ± 0.024
16
1.826 ± 0.021
1.938 ± 0.025
18
1.876 ± 0.018
1.983 ± 0.015
20
1.927 ± 0.018
1.955 ± 0.014
22
1.900 ± 0.019
1.957 ± 0.028
24
1.889 ± 0.018
1.916 ± 0.014
Biofilm formation ability of PML versus PMS. The biofilm formation ability of PML and PMS was assessed by the crystal violet staining, and optical density at 570 nm (OD570) was measured for each strain
to determine the quantity of the biofilm. The results showed that PML decreased the OD570 value in contrast to PMS (0.096 ± 0.006 vs. 0.118 ± 0.011, p = 0.032), suggesting that the biofilm formation ability of the strain decreased as SMG exposure time increased (Fig. 3).
Fig. 3
Biofilm formation ability of PML (red) and PMS (blue). Biofilm formation ability of PML and PMS was examined by measuring the OD570 values in microtiter plates.
Swarming motility of PML versus PMS. The swarming motility of PML and PMS was determined by measuring the diameter of the circular turbid zone formed by the strains on the Petri dishes. The results showed that a similar circular zone developed in PML and PMS (33.667 ± 3.215 mm vs. 37.333 ± 0.058 mm, p = 0.124), suggesting that swarming motility of the strain was unchanged with increasing exposure time to SMG (Fig. 4).
Fig. 4
Swarming motility of PML (red) and PMS (blue). Swarming motility of PML and PMS was determined by measuring the translucent zone diameters in swarming agar.
Transcriptome profile analysis of PML versus PMS. Comparative transcriptomic analysis of PML and PMS was performed to reveal the impact of different time lengths of SMG on the molecular characteristics of the strain. The accession number of transcriptomic data for PML is SAMN23475021 and SAMN23509825, and for PMS is SAMN23498564. The percentage of total reads for PML and PMS mapped to the reference strain P. mirabilis HI4320 were 94.31% and 95.36%, respectively. The correlation of gene expression levels between PML and PMS was evaluated using the Pearson correlation coefficient. In this study, the R2 between samples was 0.987, suggesting that the sample selection was reasonable and that the findings were reliable. A Venn diagram of common and specific genes in PML and PMS is shown in Fig. 5. A total of 3,085 common genes were identified in both PML and PMS, while 16 and 12 specific genes were detected in PML and PMS, respectively.
Fig. 5
The Venn diagram of common and specific genes in PML and PMS.
DEGs were determined using the criteria of abs log2 (FoldChange) ≥ 1 and adjusted p-value ≤ 0.05. As shown in Table II, a total of 50 genes were differentially expressed between PML and PMS, including 19 upregulated genes and 31 downregulated genes. A scatter plot of PML versus PMS is shown in Fig. 6.
Fig. 6
Scatter plot of DEGs. The x-axis and y-axis represent the logarithm of the gene expression for PML and PMS, respectively. Red spots and blue spots represent upregulated and downregulated genes, respectively.
Statistics of differentially expressed genes (DEGs).
Gene ID
Expression (up/down)
Log2 FoldChang (PML/PMS)
Adjusted p-value
Gene products
csdA
up
1.308
< 0.001
cysteine sulfinate desulfinase
csdE
up
1.156
< 0.001
cysteine desulfuration protein
cutC
up
1.323
0.004
choline trimethylamine-lyase
phoA
up
1.686
0.002
alkaline phosphatase
pspA
up
1.077
0.003
phage shock protein A
pspB
up
1.107
0.002
phage shock protein B
pstB
up
1.423
0.049
phosphate transport system ATP-binding protein
rplT
up
1.060
0.023
large subunit ribosomal protein
ugpA
up
1.439
0.002
phosphate transport system ATP-binding protein
ugpB
up
1.377
< 0.001
phosphate transport system ATP-binding protein
ugpC
up
1.228
0.010
phosphate transport system ATP-binding protein
ugpE
up
1.575
0.002
phosphate transport system ATP-binding protein
PMI_RS02645
up
1.148
< 0.001
fimbria/pilus periplasmic chaperon
PMI_RS09180
up
1.031
0.022
cysteine desulfurase
PMI_RS09250
up
1.064
0.009
sensor histidine kinase
PMI_RS13360
up
2.720
< 0.001
small multidrug resistance pump
PMI_RS13370
up
1.424
0.017
putative phosphotransacetylase
PMI_RS14325
up
1.473
0.023
amino acid transport system substrate-binding protein
PMI_RS17915
up
1.520
< 0.001
Cd2+/Zn2+-exporting ATPase
artP
down
–1.009
< 0.001
arginine transport system ATP-binding protein
cyoB
down
–1.290
0.022
cytochrome o ubiquinol oxidase subunit I
epd
down
–1.011
0.007
D-erythrose 4-phosphate dehydrogenase
fadB
down
–1.330
0.016
3-hydroxyacyl-CoA dehydrogenase
fadE
down
–1.045
0.047
acyl-CoA dehydrogenase
feoA
down
–1.277
< 0.001
ferrous iron transport protein A
glpA
down
–1.465
0.005
glycerol-3-phosphate dehydrogenase
glpB
down
–1.348
0.023
glycerol-3-phosphate dehydrogenase
glpT
down
–1.221
0.003
glycerol-3-phosphate transporter
ilvN
down
–1.080
0.018
acetolactate synthase I/III small subunit
metI
down
–1.178
0.003
D-methionine transport system permease protein
msrB
down
–1.021
< 0.001
peptide-methionine oxide reductase
phsA
down
–1.181
0.046
thiosulfate reductase
ptsG
down
–1.099
< 0.001
PTS system, glucose-specific IIB component
PMI_RS00340
down
–1.105
< 0.001
protein NrfC
PMI_RS00505
down
–1.210
0.048
cytochrome ubiquinol oxidase subunit III
PMI_RS00510
down
–1.283
0.036
cytochrome ubiquinol oxidase subunit IV
PMI_RS07940
down
–1.255
0.028
–
PMI_RS09930
down
–1.008
0.048
toxin CptA
PMI_RS10955
down
–2.137
< 0.001
major pilin subunit PapA
PMI_RS13425
down
–1.001
< 0.001
phosphorelay signal transduction system
PMI_RS13430
down
–1.190
< 0.001
TetR/AcrR family transcriptional regulator
PMI_RS14090
down
–1.029
< 0.001
putative transport protein
PMI_RS14825
down
–1.315
0.017
Mat/Ecp fimbriae adhesion
PMI_RS14830
down
–1.430
0.012
Mat/Ecp fimbriae outer membrane usher protein
PMI_RS14835
down
–1.349
0.037
Mat/Ecp fimbriae periplasmic chaperone
PMI_RS14840
down
–2.151
0.048
Mat/Ecp fimbriae adhesion
PMI_RS15035
down
–1.003
0.027
cation/acetate symporter
PMI_RS15880
down
–1.250
< 0.001
glycerol uptake facilitator protein
PMI_RS17135
down
–1.018
0.020
minor fimbrial subunit
PMI_RS18425
down
– 1.060
0.023
putative oxidoreductase
Fourteen categories, including 50 DEGs (identical DEGs may fall into different categories), were identified between PML and PMS according to the KEGG pathway classification (Fig. 7). Compared with PMS, PML is characterized by regulating genes related to cellular community, membrane transport, energy metabolism, carbohydrate metabolism, lipid metabolism, and amino acid metabolism.
Fig. 7
KEGG pathway analysis of DEGs. The x-axis and y-axis represent the numbers of DEGs and KEGG pathway category, respectively.
Notably, some DEGs were found to belong to diverse functional categories including stress response (pspAB, msrB, PMI_RS13425), membrane transportation (artP, metI, pstB, ugpABCE, PMI_RS14325), metal ion transportation (feoA, PMI_RS14090, PMI_RS15035, PMI_RS17915), biological adhesion (PMI_RS02645, PMI_RS10955, PMI_RS14825, PMI_RS14830, PMI_ RS14835, PMI_RS14840, PMI_RS17135), carbohydrate metabolism (fadB, ilvN, ptsG, PMI_RS13370), and lipid metabolism (fadBE, glpAB) and selected for further analysis between PML and PMS. The variation between PML and PMS at the transcriptomic level might lead to physiological changes in P. mirabilis and help bacteria survive better in a prolonged SMG environment.
Discussion
It is the first study to analyze the growth behavior and transcriptome profile of P. mirabilis exposed to long-term versus short-term SMG. The results showed that P. mirabilis exhibited a decreased growth rate and impaired biofilm formation ability under long-term SMG compared to short-term SMG. Further transcriptomic profiling demonstrated that the relevant molecular mechanisms of physiological alteration might be attributed to the differential expression of genes associated with membrane transportation, metal ion transportation, carbohydrate metabolism, lipid metabolism, and biological adhesion. This study improved the understanding of the various lengths of MG exposure on P. mirabilis behavior and provided relevant references for analyzing the influence of P. mirabilis on astronaut health during spaceflights.
The effects of MG on the growth rate and final cell density of microorganisms are controversial. Previous studies have shown that microorganisms have an increased growth rate, with a growth curve characterized by a shorter lag phase and a more prolonged exponential phase under MG or SMG. For example, C. albicans has been reported to have a slightly shorter generation time and higher growth propensity in spaceflights than those cultivated under NG (Nielsen et al. 2021). Similarly, another experiment demonstrated that Lactobacillus acidophilus showed a shorter lag phase and faster growth rate in the early stage of SMG in HARVs (Shao et al. 2017). Another study showed that the final cell population of Vibrio natriegens (Garschagen et al. 2019) increased significantly under SMG using a clinostat. A common explanation is that the lack of sedimentation occurring in MG led to a uniform distribution of cells with abundant utilization of nutrients, enabling denser microbial growth (Senatore et al. 2018). However, a contrasting study reported that S. aureus exhibited a reduced growth rate under SMG compared to NG (Hauserman et al. 2021). A recent experiment also showed that E. coli showed a decreased growth profile under SMG compared with that under NG, and the growth difference might be due to several factors involved in cell fitness (Yim et al. 2020). Following the above, a flight mutant strain of Bacillus cereus was found to have a decreased growth rate compared with the ground control, which may have been due to downregulated genes involved in lipid biosynthesis and energy transportation (Su et al. 2014). In addition, some experiments demonstrated that microbes grown in SMG or space conditions did not differ in growth rate cell size and shape from the controls. Researchers reported no significant differences in final OD600 for Rhodospirillum rubrum between the SMG and the control samples (Mastroleo et al. 2013). An experiment focused on Lactobacillus reuteri showed no difference in the growth rate and cell size between two SMG systems and normal gravity controls (Senatore et al. 2020). Recently, an investigation conducted on the ISS focused on Cupriavidus metallidurans, Bacillus subtilis, and Sphingomonas desiccabilis. The experiment verified the effect of different gravity regimens on the final cell density of all bacteria on the ISS, and no differences in final cell count and optical density were identified within any of the three strains despite different sedimentation rates (Santomartino et al. 2020). The most plausible explanation was that all species had reached the stationary phase, leading to a similar cell count for each bacterial species. In this study, the OD600 value of cell cultures was used to determine the growth rate of P. mirabilis under different SMG exposure durations, and the strain grew slower in long-term versus short-term SMG environments. The results suggested that P. mirabilis decreased its growth ability with increasing exposure time to SMG. The overlap of several factors caused the growth variation, and the downregulated genes involved in lipid metabolism, carbohydrate metabolism, and metal ion transportation might take an essential role in the decreased growth ability of the long-term SMG exposure strain.
Biofilms produced by microbes in the water system and device surface of the spacecraft cabin represent a significant phenotype concerning virulence and antibiotic resistance and induce corrosion of metals and structural materials (Novikova et al. 2006; Liu 2017). Therefore, it is necessary to analyze the biofilm formation ability of microbes under real and simulated MG. A representative investigation demonstrated that the biofilm of P. aeruginosa exhibited an atypical structure composed of columns overlapped with a canopy during spaceflights, which has not been observed on Earth (Kim et al. 2013). During short-term spaceflight, enhanced biofilm formation was also observed in Klebsiella pneumoniae (Li et al. 2014). The SMG environment also influences the biofilm formation ability of microbes by providing low shear forces of cellular fluid (Yamaguchi et al. 2014). For example, K. pneumoniae grown under SMG in HARVs exhibited increased biofilm formation compared to those grown under NG after two weeks of subculture, and the overexpressed genes related to type 3 fimbriae (mrkABCDF)-mediated biofilm formation on abiotic surfaces (Wang et al. 2016). Similarly, in a more recent experiment, Stenotrophomonas maltophilia cultivated under SMG in HARVs also produced more biofilm biomass than those under NG condition after two weeks of subculture, and upregulated genes responsible for type 4 fimbriae (pilAG) participated in this process (Su et al. 2021). In this study, the biofilm formation ability of the two groups was quantified using the OD570 values of crystal violet staining. The results showed that P. mirabilis under long-term SMG exposure decreased its biofilm formation ability compared with the identical strain under short-term SMG exposure, suggesting that P. mirabilis impaired its biofilm formation ability with increasing exposure time to SMG. Further transcriptomic profiling revealed that some genes related to fimbriae adhesion were downregulated under long-term versus short-term SMG. P. mirabilis encodes 17 putative chaperone-usher fimbrial operons and the role of five fimbriae, including urothelial cell adhesin (UCA), P. mirabilis fimbria (PMF), mannose resistant Proteus-like (MR/P), ambient temperature fimbria (ATF), and P. mirabilis-like pili (PMP), has been clearly identified (Pearson et al. 2008). It has been reported that P. mirabilis fimbriae have different roles in biofilm generation, and the UCA and PMF mutants improved biofilm formation ability compared to the wild-type strain, whereas the MR/P and ATF mutants formed significantly less biofilm than the wild-type strain (Scavone et al. 2016). Therefore, we preliminarily concluded that the downregulated genes encoded by other chaperone-usher fimbrial operons first decreased the activity of Mat/Ecp fimbriae protein, subsequently impairing biological adhesion and finally reducing the biofilm formation ability of P. mirabilis with increasing exposure time to SMG.
Motility is a critical microbial behavior that plays a crucial role in nutrient absorption, tissue localization and invasion, biofilm formation, virulence, and survival (Acres et al. 2021). Alterations in microbial motility often originate from changes in the gene expression of the flagella-related system (Morgenstein et al. 2010). Although few studies have focused explicitly on the effects of spaceflight and SMG on the phenotype of microbial motility, genetic and transcriptomic analyses have provided a general understanding of gene expression that can predict microbial motility according to the genes involved in flagella function (Milojevic and Weckwerth 2020). E. coli cultured in minimal MOPS medium upregulated its motility genes (fliCDZ and flgBDEK) in HARVs compared with NG rotation (Tucker et al. 2007). Consistent with this, P. aeruginosa cultured in LB medium also overexpressed motility genes, including fliACDGS, fleLNP, and flgM in HARVs in contrast to NG (Crabbé et al. 2010). In opposite, Salmonella Typhimurium grown in LB medium exhibited downregulated expression of flagellar genes, including fimA and fliB, in HARVs compared with NG rotation (Wilson et al. 2002). In this study, no significant difference in the flagella-related genes at the transcriptomic level was detected between long-term and short-term SMG exposure, and swarming motility of P. mirabilis was unchanged with increasing exposure time to SMG.
Conclusion
This study presents the only data available to compare long-term versus short-term SMG exposure effects on P. mirabilis cultured in HARVs. The results indicate that P. mirabilis modified its growth behavior to better adapt to the SMG environment with increasing exposure time. The transcriptomic analysis provides a practical and comprehensive strategy for identifying the effects of different lengths of SMG exposure on biological processes and pathways. This phenomenon may help understand how microbes adapt to spaceflight missions over different durations.
China’s space station will be completed in the early 2020s, and spaceflight missions will require astronauts to remain on the space station for long periods. With the reality of long-duration missions lasting years, avoiding infectious diseases induced by microbes and maintaining the safety of the space station are of great significance in the future. Due to the challenges of sending microbial samples into the space environment, most research still needs to be done in laboratories using ground-based bioreactors under SMG. Considering that one of the new challenges facing astronauts is reaching increasingly long-duration spaceflight targets, studying the influences of long-term versus short-time SMG exposure on the growth behavior of P. mirabilis is necessary to predict possible pathogenesis mechanisms and develop treatments for infectious diseases under spaceflight conditions. In the future, more studies are needed to establish a space microbiological safety assessment system to protect crew members and ensure the safe operation of space stations.
Schematic diagram of mechanical principle of HARV. MG is simulated by rotating the samples around a plane perpendicular to the gravity vector.
Fig. 2
Growth curve of PML (red) and PMS (blue). The growth curves of PML and PMS were quantified by measuring the OD600 values every 2 h for 24 h.
Fig. 3
Biofilm formation ability of PML (red) and PMS (blue). Biofilm formation ability of PML and PMS was examined by measuring the OD570 values in microtiter plates.
Fig. 4
Swarming motility of PML (red) and PMS (blue). Swarming motility of PML and PMS was determined by measuring the translucent zone diameters in swarming agar.
Fig. 5
The Venn diagram of common and specific genes in PML and PMS.
Fig. 6
Scatter plot of DEGs. The x-axis and y-axis represent the logarithm of the gene expression for PML and PMS, respectively. Red spots and blue spots represent upregulated and downregulated genes, respectively.
Fig. 7
KEGG pathway analysis of DEGs. The x-axis and y-axis represent the numbers of DEGs and KEGG pathway category, respectively.
Statistics of differentially expressed genes (DEGs).
Gene ID
Expression (up/down)
Log2 FoldChang (PML/PMS)
Adjusted p-value
Gene products
csdA
up
1.308
< 0.001
cysteine sulfinate desulfinase
csdE
up
1.156
< 0.001
cysteine desulfuration protein
cutC
up
1.323
0.004
choline trimethylamine-lyase
phoA
up
1.686
0.002
alkaline phosphatase
pspA
up
1.077
0.003
phage shock protein A
pspB
up
1.107
0.002
phage shock protein B
pstB
up
1.423
0.049
phosphate transport system ATP-binding protein
rplT
up
1.060
0.023
large subunit ribosomal protein
ugpA
up
1.439
0.002
phosphate transport system ATP-binding protein
ugpB
up
1.377
< 0.001
phosphate transport system ATP-binding protein
ugpC
up
1.228
0.010
phosphate transport system ATP-binding protein
ugpE
up
1.575
0.002
phosphate transport system ATP-binding protein
PMI_RS02645
up
1.148
< 0.001
fimbria/pilus periplasmic chaperon
PMI_RS09180
up
1.031
0.022
cysteine desulfurase
PMI_RS09250
up
1.064
0.009
sensor histidine kinase
PMI_RS13360
up
2.720
< 0.001
small multidrug resistance pump
PMI_RS13370
up
1.424
0.017
putative phosphotransacetylase
PMI_RS14325
up
1.473
0.023
amino acid transport system substrate-binding protein
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