Abstract
Biofilm (BF) formation is a considerable obstacle to the effective control of Listeria monocytogenes (LM). In this study, we used transcriptomics to analyze LM BF and planktonic bacteria at different stages of BF formation and growth to compare differential gene expression between the 2. We identified 1588, 1517, and 1462 differentially expressed genes (DEGs) when early formation BF and planktonic bacteria were compared at 12, 24, and 48 h, respectively. Among these, 1123 DEGs were shared across the 3 data pool. Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses demonstrated significant changes associated with the phosphotransferase system, the microbial metabolism in diverse environments, the flagella assembly, the bacterial chemotaxis, the bacterial secretion, the quorum sensing, and the 2-component system. The top 5 upregulated DEGs were lmo0024, lmo0374, lmo0544, hly, and lmo2434. The top 5 downregulated DEGs were lmo2192, lmo1211, cheY, lmo0689, and secY. After real-time quantitative polymerase chain reaction, the expression of these 10 DEGs were consistent with the results of the transcriptomic sequence. This research lays the foundation for further studies on mechanisms regulating BF formation and will help to identify BF inhibitors to reduce the risk of LM infection.
Résumé
La formation de biofilm (BF) est un obstacle considérable à la maîtrise efficace de Listeria monocytogenes (LM). Dans cette étude, nous avons utilisé la transcriptomique pour analyser le BF et les bactéries planctoniques de LM à différents stades de la formation et de la croissance du BF afin de comparer l’expression différentielle des gènes entre les deux. Nous avons identifié 1588, 1517 et 1462 gènes exprimés de manière différentielle (DEGs) lors de la formation précoce du BF et les bactéries planctoniques ont été comparées à 12, 24 et 48 h, respectivement. Parmi ceux-ci, 1123 DEGs ont été partagés entre les trois pools de données. L’enrichissement fonctionnel de l’ontologie génique et les analyses des voies de l’Encyclopédie des gènes et des génomes de Kyoto ont démontré des changements significatifs associés au système de phosphotransférase, au métabolisme microbien dans divers environnements, à l’assemblage des flagelles, à la chimiotaxie bactérienne, à la sécrétion bactérienne, à la détection du quorum et au système à deux composants. Les cinq principaux DEGs régulés à la hausse étaient lmo0024, lmo0374, lmo0544, hly et lmo2434. Les 5 principaux DEGs régulés à la baisse étaient lmo2192, lmo1211, cheY, lmo0689 et secY. Après réaction d’amplification en chaîne par la polymérase quantitative en temps réel, l’expression de ces dix DEGs était cohérente avec les résultats du séquence transcriptomique. Cette recherche jette les bases d’études ultérieures sur les mécanismes régulant la formation de BF et aidera à identifier les inhibiteurs de BF pour réduire le risque d’infection LM.
(Traduit par Docteur Serge Messier)
Introduction
Listeria monocytogenes (LM) can penetrate the mucous membrane and endothelia barriers of humans and animals to cause listeriosis, which can manifest as gastroenteritis, meningitis, miscarriage, and sepsis (1–3). The distribution of LM-associated diseases is global, with gradually increasing incidences. Listeria monocytogenes is also an important pathogen implicated in zoonotic and food-borne diseases (4,5). It can become associated with biofilms (BFs) that enhance persistence within food processing environments (6). The BF is formed by large numbers of enveloped extracellular polysaccharide matrices, fibrin, and lipid proteins, which are secreted by the bacteria adhering to biological or non-biological surfaces (7). Due to the 3-dimensional multi-layered structure of BFs, antimicrobials or disinfectants cannot adequately reach the inner bacterial layers. In addition, bacteria in BFs are in a dormant state (8). Thus, penetration times are longer and drug resistance is significantly enhanced compared with planktonic bacteria, making conventional sterilization methods largely ineffective (9). Also, BFs may increase the risk of infection with other pathogens, facilitating the co-existence of a variety of bacteria, further polluting food processing environments, endangering human health, and affecting public health (9,10).
Although a large number of studies on the genome and virulence genes of LM have been carried out, there have been few reports on the BF formation of LM at the transcriptome level. In order to better understand the mechanism of this process, this study used RNA-Seq analysis to characterize changes to the transcriptome of BF and planktonic bacteria of LM. This information will help to develop new intervention strategies aimed at controlling LM.
Materials and methods
Bacterial strains
Listeria monocytogenes strain ATCC19111 (serotype 1/2a) was preserved in the Laboratory of Veterinary Public Health at Gansu Agricultural University, China. The strain was streaked onto Brain Heart Infusion (BHI) agar and incubated for 24 h at 37°C. A single colony was picked, inoculated into BHI liquid medium, and grown to an OD562 of 0.2. This volume was then diluted 100-fold to generate a final bacterial volume for downstream assays.
Biofilm adhesion assays
The 20 mL of diluted bacterial volume was transferred to each Petri dish containing a glass slide and incubated for 6, 12, 24, 48, and 72 h at 37°C. After the respective incubation period, the glass slide was removed, rinsed in normal sterile saline, fixed in methanol, stained with 1% crystal violet, rinsed again, dried, and observed at 1000× magnification using an optical microscope (Olympus, Tokyo Prefecture, Japan).
Biofilm time-course
When the OD562 was 0.2 under the condition of 220 RPM and at 37°C, 10 mL of the activated overnight bacterial volume was taken to expand the culture at a ratio of 1:50. The bacterial solution was transferred to cell culture dishes to reach about 20 mL in each dish and cultured for 12, 24, and 48 h at 37°C, respectively. The cultured liquid was poured out to discard the unattached bacteria. Two washes with 20 mL PBS washed away the unfirmly adhered bacteria. Phosphate-buffered solution (10 mL) was added and BF bacteria were carefully scraped off with a cell spatula. The bacterial solution in the dish was collected and centrifuged at 7104 × g at 4°C for 10 min. Finally, the supernatant was absorbed to collect sediment and stored at −80°C. Biofilm samples were marked as LM_1, LM_2, and LM_3 to denote the 12, 24, and 48 h culture times. Planktonic bacteria cultured under the same conditions were used as a control and marked as LM_0. Experiments were performed in triplicate.
RNA extraction and sequencing
Total RNA was extracted from BF and planktonic samples using the RNAprep Pure Kit (Tiangen, Beijing, China) following the manufacturer’s instructions. RNA quality and concentrations were determined by measuring absorbance and calculating OD260/OD280 ratios. RNA integrity was examined by agarose gel electrophoresis. In total, 5 μg of RNA per sample was sent to Novogene in Beijing for RNA sequencing.
RNA-seq and data analysis
TIANSeq Library Prep Kit (Tiangen) was used according to the instructions to construct the chain specific library for RNA samples. The library was sequenced using a sequencing platform (NovaSeq 6000; Illumina, California, USA). Raw data (i.e., raw reads) of FASTQ format were processed through in-house Perl scripts. For this step, clean data (i.e., clean reads) were obtained by removing reads containing adapter sequences, reads containing ploy-N, and low-quality reads from raw data. The LM reference genome (NC_003210) and associated gene annotation information were downloaded from the National Center for Biotechnology Information (NCBI) database. The sequence alignment software Bowtie 2 (version 2.3.4.3; Johns Hopkins University, Baltimore, Maryland, USA) was used for genome location analysis of clean reads.
RNA-seq expression was analyzed using fragments per kilobase of transcript per million mapped reads and was corrected for sequencing depth and gene length (11). The number of gene counts per sample was standardized using DESeq2 (version 1.20) software. We then performed hypothesis testing and set the threshold as | log2(fold change) > 1, P-value adjusted (Padj value) < 0.05, to screen out DEGs between BF and planktonic bacteria. To systematically analyze gene biological function and genome information, ClusterProfiler (version 3.8.1) software was used for Gene Ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for DEGs. P-values were calculated and selected Padj values were set to < 0.05 to determine significant gene enrichment (12). All raw sequences and analyzed data for this study were deposited in NCBI’s GEO database under the accession number PRJNA780092.
Gene expression verification
The first 5 upregulated and downregulated DEGs were selected for real-time quantitative polymerase chain reaction (RT-qPCR) to verify RNA-seq results. Reactions were performed using a LightCycler 96 Instrument (Roche, Basel, Switzerland) in 20 μL reactions, including 10 μL of 2 × SuperReal PreMix Plus, 0.6 μL of 10 μM forward primers, 0.6 μL of 10 μM reverse primers, 100 ng cDNA template, and enough RNase-Free ddH2O to reach 20 μL. The reaction program pre-denatured at 95°C for 15 min, denatured at 95°C for 10 s, annealed and extended at 60°C for 30 s, and cycled the last 2 steps 40 times. The melting curve analysis was 15 s at 95°C, 60 s at 65°C, and 1 s at 97°C to verify the specific formation of the targeted product. Primer sequences were designed and synthesized at GENEWIZ (Suzhou, China) (Table I). The 16S rRNA gene was used as an mRNA control and the 2−ΔΔCt method was used to calculate expression differences of DEGs between BF and planktonic samples. Three replicates were performed for all groups and data were statistically analyzed.
Table I.
Summary of primer sequences.
| Primer name | Nucleotide sequence |
|---|---|
| lmo2192-F | AAAACTCATGTGGGTAACGACC |
| lmo2192-R | AAACGCCTGAAGAAACGAATAA |
| lmo1211-F | AAAATAACGCCTAATGTTCCAC |
| lmo1211-R | AACGGCCATCAATACACGACTT |
| lmo0691(cheY)-F | TGACACTGGATATTACGATGCC |
| lmo0691(cheY)-R | TTACGATAAAATCTTTCGCACC |
| lmo0689-F | AAAGAAGATGAAATTGGTCGTG |
| lmo0689-R | TATTCGGCTTACTTACTTGTGC |
| lmo2612(secY)-F | CTGAAGACATGCGGTTAAATCC |
| lmo2612(secY)-R | AAATTGACTGAGTGGTCGAAGC |
| lmo0024-F | ATGAAGATGGCGAATACGAAGA |
| lmo0024-R | ACAGATTTGGCTGGCAAACAAC |
| lmo0374-F | ATCTGGGCTGTATCTGACGCTG |
| lmo0374-R | TTTCCGCCAACTACCTTTTCTG |
| lmo0544-F | CATACGGAAATAATCCTGTGA |
| lmo0544-R | TGGCTTATACATTCGGTCGTT |
| lmo0202(hly)-F | TCACTCTGGAGGATACGTTGCT |
| lmo0202(hly)-R | AGATGGACGATGTGAAATGAGC |
| lmo2434-F | AGTTGATAAGCAATGCGAGGTT |
| lmo2434-R | TAGTGAAGACGACAAGCGAAAA |
| 16S rRNA-F | CACTGGGACTGAGACACGG |
| 16S rRNA-R | GGACAACGCTTGCCACCTA |
Statistical analyses
Each experiment was repeated twice and 3 replicates were used for each treatment. Significance was established through Student’s t-tests using a P-value < 0.05.
Results
Biofilm adhesion assays
Low bacterial levels adhered to glass slides after 6 h, whereas numbers were increased with some microcolony formation after 12 h. Mature and dense BFs appeared and microcolonies were connected to a network structure after 24 h. Biofilms became denser and formed a multilayered structure after 48 h, whereas BFs were aggregated and superimposed to form complex cluster structures after 72 h (Figure 1).
Figure 1.
The biofilm formation ability (1000×) at different incubation times, with a scale of 20 μm. A — Blank control. B — 6 h. C — 12 h. D — 24 h. E — 48 h. F — 72 h.
Differentially expressed genes analysis
Differentially expressed genes were screened by comparing BF and planktonic LM at different periods. At 12 h, 1588 DEGs were identified, including 797 upregulated and 791 downregulated genes. At 24 h, 1517 DEGs were identified, including 758 upregulated and 759 downregulated genes. At 48 h, 1462 DEGs were identified, including 736 upregulated and 726 downregulated genes (Figure 2). However, we observed that 1123 DEGs were shared across the 3 time periods (Figure 3). Cluster analyses were performed on these 1123 DEGs to decide if they had similar biological functions or participated in common metabolic and signaling pathways (Figure 4).
Figure 2.
Volcano map of differentially expressed genes. The abscissa indicates the fold change of gene expression and the ordinate indicates the significance of the gene difference. The red dots indicate the upregulated genes, the green dots indicate the downregulated genes, and the blue dots indicate the genes that are not significantly different. LM_0 represents the control and LM_1, LM_2, and LM_3 represent 12, 24, and 48 h in culture.
Figure 3.
Venn diagram of the number of differentially expressed genes (DEGs). A total of 1123 DEGs were expressed in different periods. The number of specific DEGs at 12, 24, and 48 h are 190, 94, and 162, respectively. LM_0 represents the control and LM_1, LM_2, and LM_3 represent 12, 24, and 48 h in culture.
Figure 4.
Cluster heat map of differentially expressed genes. Columns represent samples collected under different time treatments. The color scale on the right represents the log-transformed fragments per kilobase million. LM_0 represents the control and LM_1, LM_2, and LM_3 represent 12, 24, and 48 h in culture.
Gene ontology function enrichment
All functional terms were listed from GO function enrichment analyses (Table II) and the most significant 30 terms were selected (Figure 5). Three biological processes: metabolism, biosynthesis, and translation; 3 cellular components: organelles, protein complexes, and cytoplasm; and 4 molecular functions: structural molecules, ligases, catalysis, and transport activity were altered among the 1123 DEGs, suggesting genes participated in metabolism and structural composition and were possibly related to BF formation.
Table II.
Gene ontology (GO) function term.
| GO ID | GO term | Enrichment rate | P-value adjusted |
|---|---|---|---|
| Biological process | |||
| GO:0006412 | Translation | 60/556, 10.7% | 1.09E-10 |
| GO:0006518 | Peptide metabolic process | 60/556, 10.7% | 1.44E-10 |
| GO:0043043 | Peptide biosynthetic process | 60/556, 10.7% | 1.44E-10 |
| Cellular component | |||
| GO:0043228 | Non-membrane-bounded organelle | 45/211, 21.3% | 2.15E-07 |
| GO:0044444 | Cytoplasmic part | 41/211, 19.4% | 2.70E-07 |
| GO:1990904 | Ribonucleoprotein complex | 39/211, 18.5% | 2.70E-07 |
| Molecular function | |||
| GO:0005198 | Structural molecule activity | 42/602, 7.0% | 2.43E-07 |
| GO:0016875 | Ligase activity, forming carbon-oxygen bonds | 18/602, 3.0% | 6.72E-05 |
| GO:0140101 | Catalytic activity, acting on a tRNA | 23/602, 3.8% | 0.000138 |
Figure 5.
Gene ontology (GO) function enrichment graph. The differentially expressed genes are summarized in 3 main categories: Biological process (BP), cellular component (CC), and molecular function (MF). The Y-axis indicates the number of genes and the X-axis indicates the GO terms.
Kyoto Encyclopedia of Genes and Genomes pathway enrichment
Seven pathways were significantly enriched using KEGG analysis (Table III), including the phosphotransferase system and flagella assembly pathway in 12 h BF samples, the phosphotransferase system and microbial metabolism pathways in different environments in 24 h BF samples, and flagella assembly, bacterial chemotaxis pathways, bacterial secretion, quorum sensing (QS), and 2-component system pathways in 48 h BF samples.
Table III.
Part of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway.
| KEGG ID | KEGG pathway | Enrichment rate | P-value adjusted |
|---|---|---|---|
| lmog02060 | Phosphotransferase system | 8.01% | 1.0E-08 |
| lmog01120 | Microbial metabolism in diverse environments | 8.13% | 0.01 |
| lmog02040 | Flagellar assembly | 4.25% | 0.000236 |
| lmog02030 | Bacterial chemotaxis | 1.95% | 0.018951 |
| lmog03070 | Bacterial secretion system | 3.89% | 0.0395072 |
| lmog02024 | Quorum sensing | 0.88% | 0.0429352 |
| lmog02020 | 2-component system | 2.63% | 0.0459352 |
Gene expression verification by qRT-PCR
The functions of the 10 selected DEGs are shown in Table IV. After we compared RT-qPCR and RNA-seq results, the expression levels of lmo2192, lmo1211, cheY, lmo0689, and secY were downregulated, whereas lmo0024, lmo0374, lmo0544, hly, and lmo2434 were upregulated. The change trend of the fold change was consistent with sequencing data (Figure 6). In general, for these DEGs, the expression levels of RNA-seq were higher than qRT-PCR, except for lmo0689.
Table IV.
Summary of gene function description.
| Gene ID | Differentially expressed genes | Gene description |
|---|---|---|
| lmo0024 | Upregulated | PTS mannose transporter subunit IID |
| lmo0374 | Upregulated | PTS beta-glucoside transporter subunit IIB |
| lmo0544 | Upregulated | PTS sorbitol transporter subunit IIC |
| lmo0202(hly) | Upregulated | Listeriolysin O precursor |
| lmo2434 | Upregulated | Glutamate decarboxylase |
| lmo2192 | Downregulated | Peptide ABC transporter ATP-binding protein |
| lmo1211 | Downregulated | Hypothetical protein |
| lmo0691(cheY) | Downregulated | Chemotaxis response regulator CheY |
| lmo0689 | Downregulated | Chemotaxis protein CheV |
| lmo2612(secY) | Downregulated | Preprotein translocase subunit SecY |
PTS — Phosphotransferase system; ABC — ATP-binding cassette; ATP — Adenosine triphosphate.
Figure 6.
Verification of sequencing results by real-time quantitative PCR (RT-qPCR). A — 5 upregulated genes. B — 5 downregulated genes. Blue histograms represent expression levels determined by RNA-seq in reads per kilobase million and red columns represent gene expression levels determined by RT-qPCR. Bars represent the mean (± standard error) of experiments.
Discussion
We identified 1123 DEGs by comparing BF and planktonic bacteria at different periods. These genes accounted for 36.98% of the 3037 genes in the LM genome, of which 567 genes were downregulated and accounted for 50.5% of total DEGs. Most of these genes were related to protein metabolism, biosynthesis, protein complexes, structural molecular activity, transport activity, and catalytic activity. In previous work, metabolism, biosynthesis, macromolecular complexes, and transferase activity changed significantly between Salmonella Enteritidis planktonic and BF states (13), suggesting that these terms may be related to BF formation, which is consistent with our results. In addition, it was previously observed that intl1 mRNA expression levels in Acinetobacter baumannii BF were upregulated more than 4-fold when compared with planktonic bacteria and the drug resistance of intl1-positive strains was significantly increased (14). In this study, the expression levels of lmo1955, which encodes integrase, were significantly upregulated in BF, suggesting that integrase has important roles in BF formation and bacterial resistance.
Bacteria adjust their metabolic state [e.g., amino acid metabolism, pentose phosphate pathway, tricarboxylic acid cycle (TCA)] and other metabolic processes based on environmental changes, such as high salinity, low pH, low temperature, and carbon control (15,16). A previous study reported that when compared with planktonic bacteria, succinate dehydrogenase in Staphylococcus aureus BF was upregulated (17). Another study indicated that the LM BF was downregulated for DEG expression related to glycolysis and gluconeogenesis-related pathways, but upregulated for the pentose phosphate pathway (18). Our data showed that lmo0265 expression in the TCA cycle, lmo0342 expression in the pentose phosphate pathway, and lmo2783 and lmo0374 expression in the phosphotransferase system were all significantly upregulated. Therefore, upregulated gene expression in metabolic pathways could possibly promote BF formation.
Flagella play important roles in bacterial movement, with flagella-related proteins protecting bacteria from stress damage (19). Chemotaxis not only helps bacteria find and migrate to nutrient environments but is a vital process during transmission and host infection (20). Way et al (21) reported that bacterial ability to form BFs was significantly reduced after the LM flagellar gene flaA was deleted. A similar study showed that Vibrio parahaemolyticus could not form mature BFs after the flgD and flgE flagellar genes were deleted (22). In our study, the related flagellar genes, flaA, flgC, flgD, motB, fliF, and fliR were significantly enriched and downregulated. Therefore, these changes probably affect LM adherence to surfaces and proliferation, concomitant with increasing tolerance.
Quorum-sensing systems cooperate to form BFs by sensing bacterial cell density during proliferation in order to adapt to external environments. This strategy adjusts bacterial biological characteristics, such as motility, adhesion, BF formation ability, and virulence factor secretion (23). In our study, when compared with planktonic bacteria, genes related to QS and bacterial secretion were differentially expressed in BFs. Among these, hly, sigL, lmo2434, lmo0048, and lmo0512 were upregulated, whereas lmo1210, lmo1211, lmo2192, secY, and lmo1803 were downregulated. The QS pathway continuously readjusts by sensing bacterial density (24). As the BF increases, intrinsic bacterial drug resistance becomes gradually enhanced, imposing regulatory effects on efflux pumps mediating drug resistance (25). A previous study showed that hly, which encodes the LM precursor, hemolysin O, significantly reduced BF formation and aggregation when deleted (26). Lmo2192 encodes the peptide ABC transporter ATP binding protein, which transports macromolecules and other substrates via the binding and hydrolysis of ATP on cell membranes. It was downregulated in LM BF and was previously shown to have important roles in bacterial QS and secretion systems (27,28).
We observed changes in gene expression between LM BFs and planktonic bacteria using transcriptomics to help characterize the regulatory mechanisms underpinning BF formation. Gene ontology functional enrichment and KEGG pathway analyses demonstrated significant changes in metabolism, biosynthesis, transport activity, flagella assembly, microbial metabolism in different environments, bacterial chemotaxis, bacterial secretion, QS, and 2-component systems. These data provide valuable insights on the identification of BF inhibitors that could reduce LM infection risks in humans and animals.
Acknowledgments
The study was supported by grants from the National Nature Science Foundation of China (No. 32060822, 31960726 and 31560700), Key R&D Program of Gansu Province (No. 20YF8FA136), Gansu Agricultural University Youth Tutor Support Fund (No. GAU-QDFC-2020-10), and The National Key Research and Development Program of China (No. 2019YFC1605705). We are indebted to International Science Editing for the manuscript editing.
References
- 1.Dreyer M, Thomann A, Böttcher S, Frey J, Oevermann A. Outbreak investigation identifies a single Listeria monocytogenes strain in sheep with different clinical manifestations, soil and water. Vet Microbiol. 2015;179:69–75. doi: 10.1016/j.vetmic.2015.01.025. [DOI] [PubMed] [Google Scholar]
- 2.Lechowicz J, Krawczyk-Balska A. An update on the transport and metabolism of iron in Listeria monocytogenes: The role of proteins involved in pathogenicity. Biometals. 2015;28:587–603. doi: 10.1007/s10534-015-9849-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Zhang J, Cao G, Xu X, et al. Evolution and diversity of Listeria monocytogenes from clinical and food samples in Shanghai, China. Front Microbiol. 2016;7:1138. doi: 10.3389/fmicb.2016.01138. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Reda WW, Abdel-Moein K, Hegazi A, Mohamed Y, Abdel-Razik K. Listeria monocytogenes: An emerging food-borne pathogen and its public health implications. J Infect Dev Ctries. 2016;10:149–154. doi: 10.3855/jidc.6616. [DOI] [PubMed] [Google Scholar]
- 5.Rothrock MJ, Jr, Davis ML, Locatelli A, et al. Listeria occurrence in poultry flocks: Detection and potential implications. Front Vet Sci. 2017;4:125. doi: 10.3389/fvets.2017.00125. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Chlebicz A, Śliżewska K. Campylobacteriosis, salmonellosis, yersiniosis, and listeriosis as zoonotic foodborne diseases: A review. Int J Environ Res Public Health. 2018;15:863. doi: 10.3390/ijerph15050863. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Flemming HC, Wingender J, Szewzyk U, Steinberg P, Rice SA, Kjelleberg S. Biofilms: An emergent form of bacterial life. Nat Rev Microbiol. 2016;14:563–575. doi: 10.1038/nrmicro.2016.94. [DOI] [PubMed] [Google Scholar]
- 8.Wolska KI, Grudniak AM, Rudnicka Z, Markowska K. Genetic control of bacterial biofilms. J Appl Genet. 2016;57:225–238. doi: 10.1007/s13353-015-0309-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Ashbaugh AG, Jiang X, Zheng J, et al. Polymeric nanofiber coating with tunable combinatorial antibiotic delivery prevents biofilm-associated infection in vivo. Proc Natl Acad Sci U S A. 2016;113:E6919–E6928. doi: 10.1073/pnas.1613722113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Nobile CJ, Fox EP, Hartooni N, et al. A histone deacetylase complex mediates biofilm dispersal and drug resistance in Candida albicans. mBio. 2014;5:e01201–e01214. doi: 10.1128/mBio.01201-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Trapnell C, Williams BA, Pertea G, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–515. doi: 10.1038/nbt.1621. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019;47:W199–W205. doi: 10.1093/nar/gkz401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Jia K, Wang G, Liang L, Wang M, Wang H, Xu X. Preliminary transcriptome analysis of mature biofilm and planktonic cells of Salmonella enteritidis exposure to acid stress. Front Microbiol. 2017;8:1861. doi: 10.3389/fmicb.2017.01861. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lin L, Ling BD, Li XZ. Distribution of the multidrug efflux pump genes, adeABC, adeDE and adeIJK, and class 1 integron genes in multiple-antimicrobial-resistant clinical isolates of Acinetobacter baumannii-Acinetobacter calcoaceticus complex. Int J Antimicrob Agents. 2009;33:27–32. doi: 10.1016/j.ijantimicag.2008.06.027. [DOI] [PubMed] [Google Scholar]
- 15.He L, Deng QL, Chen MT, Wu QP, Lu YJ. Proteomics analysis of Listeria monocytogenes ATCC 19115 in response to simultaneous triple stresses. Arch Microbiol. 2015;197:833–841. doi: 10.1007/s00203-015-1116-1. [DOI] [PubMed] [Google Scholar]
- 16.Zhou Q, Feng X, Zhang Q, et al. Carbon catabolite control is important for Listeria monocytogenes biofilm formation in response to nutrient availability. Curr Microbiol. 2012;65:35–43. doi: 10.1007/s00284-012-0125-4. [DOI] [PubMed] [Google Scholar]
- 17.Gaupp R, Schlag S, Liebeke M, Lalk M, Götz F. Advantage of upregulation of succinate dehydrogenase in Staphylococcus aureus biofilms. J Bacteriol. 2010;192:2385–2394. doi: 10.1128/JB.01472-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Qian JY, Zhang M, Dai C, Huo S, Ma H. Transcriptomic analysis of Listeria monocytogenes under pulsed magnetic field treatment. Food Res Intl. 2020;133:109195. doi: 10.1016/j.foodres.2020.109195. [DOI] [PubMed] [Google Scholar]
- 19.Duan Q, Zhou M, Zhu L, Zhu G. Flagella and bacterial pathogenicity. J Basic Microbiol. 2013;53:1–8. doi: 10.1002/jobm.201100335. [DOI] [PubMed] [Google Scholar]
- 20.Bi SY, Pollard AM, Yang YL, Jin F, Sourjik V. Engineering hybrid chemotaxis receptors in bacteria. ACS Synth Biol. 2016;5:989–1001. doi: 10.1021/acssynbio.6b00053. [DOI] [PubMed] [Google Scholar]
- 21.Way SS, Thompson LJ, Lopes JE, et al. Characterization of flagellin expression and its role in Listeria monocytogenes infection and immunity. Cell Microbiol. 2004;6:235–242. doi: 10.1046/j.1462-5822.2004.00360.x. [DOI] [PubMed] [Google Scholar]
- 22.Enos-Berlage JL, Guvener ZT, Keenan CE, McCarter LL. Genetic determinants of biofilm development of opaque and translucent Vibrio parahaemolyticus. Mol Microbiol. 2005;55:1160–1182. doi: 10.1111/j.1365-2958.2004.04453.x. [DOI] [PubMed] [Google Scholar]
- 23.Worthington RJ, Blackledge MS, Melander C. Small-molecule inhibition of bacterial two-component systems to combat antibiotic resistance and virulence. Future Med Chem. 2013;5:1265–1284. doi: 10.4155/fmc.13.58. [DOI] [PubMed] [Google Scholar]
- 24.Hawver LA, Jung SA, Ng WL. Specificity and complexity in bacterial quorum-sensing systems. FEMS Microbiol Rev. 2016;40:738–752. doi: 10.1093/femsre/fuw014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Zhao X, Yu Z, Ding T. Quorum-sensing regulation of antimicrobial resistance in bacteria. Microorganisms. 2020;8:425. doi: 10.3390/microorganisms8030425. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Price R, Jayeola V, Niedermeyer J, Parsons C, Kathariou S. The Listeria monocytogenes key virulence determinants hly and prfA are involved in biofilm formation and aggregation but not colonization of fresh produce. Pathogens. 2018;7:18. doi: 10.3390/pathogens7010018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Davidson AL, Chen J. ATP-binding cassette transporters in bacteria. Annu Rev Biochem. 2004;73:241–268. doi: 10.1146/annurev.biochem.73.011303.073626. [DOI] [PubMed] [Google Scholar]
- 28.Xie Z, Jian H, Jin Z, Xiao X. Enhancing the adaptability of the deep-sea bacterium Shewanella piezotolerans WP3 to high pressure and low temperature by experimental evolution under H2O2 stress. Appl Environ Microbiol. 2018;84:e02342–17. doi: 10.1128/AEM.02342-17. [DOI] [PMC free article] [PubMed] [Google Scholar]