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Effects of sodium houttuyfonate on transcriptome of Pseudomonas aeruginosa
BMC Research Notes volume 12, Article number: 685 (2019)
The purpose of this experiment is to analyze the changes of transcriptome in Pseudomonas aeruginosa under the action of sodium houttuyfonate (SH) to reveal the possible mechanism of SH inhibiting P. aeruginosa. We analyzed these data in order to compare the transcriptomic differences of P. aeruginosa in SH treatment and blank control groups.
In this project, RNA-seq of BGISEQ-500 platform was used to sequence the transcriptome of P. aeruginosa, and sequencing data of 8 samples of P. aeruginosa are generated as follows: SH treatment (SH1, SH2, SH3, SH4), negative control (Control 1, Control 2, Control 3, Control 4). Quality control is carried out on raw reads to determine whether the sequencing data is suitable for subsequent analysis. Totally 170.53 MB of transcriptome sequencing data is obtained. Then the filtered clean reads are aligned and compared to the reference genome to proceed second quality control. After completion, 5938 genes are assembled from sequencing data. Further quantitative analysis of genes and screening of differentially expressed genes based on gene expression level reveals that there are 2047 significantly differentially expressed genes under SH treatment, including 368 up-regulated genes and 1679 down-regulated genes.
Pseudomonas aeruginosa is a gram-negative bacterium, which can produce endotoxin, exotoxin, proteolytic enzyme and other substances and infect human and other organisms [1, 2]. At present, macrolide and aminoglycoside antibiotics are commonly used for curing the clinical infection of P. aeruginosa. However, with the emergence of drug resistance, P. aeruginosa are difficult to treat by common antibiotics. Thus, we are seeking for effective antimicrobial agents from traditional Chinese medicine to treat infection of P. aeruginosa. Previously, our research group have proved that sodium houttuyfonate (SH) can effectively inhibit the P. aeruginosa [3, 4]. Here, our aim is to investigate possible antimicrobial mechanism of SH by comparing the transcriptomic differences between SH drug and blank control groups.
The assemble transcriptome contains thousands of transcripts. Thus this study provides transcriptomic comparison between SH medication group and blank control group rather than comparisons of expression of several certain genes such as algD, algR, lasI, phzM, lasA and bdlA, in previous studies [4,5,6]. The difference of these transcriptome can be used as the basis for studying gene expression changes in SH treatment and control groups.
We cultured P. aeruginosa under two conditions, with 4 biological replications which were cultured independently under each condition:
ATCC 27853 was inoculated into LB liquid medium and cultured overnight at 37 °C. The culture were centrifuged for 1 min at 12,000 r/min, and pour out the supernatant, diluted with sterile water to 0.5 Maxwell colorimetric tube, and diluted to 107 times for later use. The SH was prepared according to our previous research . The prepared SH samples in LB medium with 1 MIC (minimum inhibitory concentration) SH of 512 μg/ml were cultured for 24 h at 37 °C until OD600 was 0.6–0.8, and collected by centrifugation of 1 min at 12,000 r/min, and then rinsed with sterile water for 3 times. Then we placed the collected bacterial samples in the centrifuge tube, sealed it with sealing film, and sent the sample for RNA-seq with dry ice.
The samples of blank control were collected similarly as SH treatment samples except without drug treatment.
Totally 170.53 MB of transcriptome sequencing data is obtained after RNA-seq applying BGISEQ-500 platform. The original data of sequencing includes reads with low quality, linker contamination and high content of unknown base N are removed before data analysis to ensure the reliability of the results. This project used SOAPnuke , a filtering software independently developed by Huada Corporation, to make statistics and trimmomatic  to filter. Firstly, readers including connectors are removed. Then the reads with unknown base N content more than 5% are wiped off. Finally, the low-quality reads are removed (we define reads with a mass value of less than 10 and a proportion of more than 20% of the total number of bases in the reads as low-quality reads). The filtered “Clean Reads” are saved in FASTQ format. The file format corresponding to each sample is FASTQ format (Table 1).
The original sequencing sequence data (fastq file), including reference genome information, can be obtained on NCBI. After the quality control of the original data, we used Bowtie2  to compare clean reads to the reference gene sequence (Table S1), and then RSEM  was used to calculate the expression levels of genes and transcripts. After completion, totally 5938 genes are assembled from sequencing data.
In order to reflect the correlation of gene expression between samples, Pearson correlation coefficients of all gene expression amounts between every samples are calculated, and expression amount distribution analysis are performed. The obtained results are shown in Fig. S1A . According to the gene expression level of each sample, a total of 2047 differentially expressed genes are detected by the threshold of fold changes > 2, Q value < 0.001, including 368 up-regulated genes and 1679 down-regulated genes. The results are shown in volcanic map of Fig. S1B .
The limitations of this data is that there is not a gradient comparison under multiple different concentrations of SH, and the transcriptome expression of P. aeruginosa under 24 h of culture is selected in this study, which may make the results inconsistent with other research results. In addition, in our previous studies [4, 5], the qRT-PCR results showed that genes of lasA, algD, algR are down regulated by SH treatment in P. aeruginosa. However, these genes are found to be below the detection threshold in this study. This may be due to the different technologies used in determination of the gene expression.
Availability of data materials
The data described in this data description can be freely and publicly accessed on the sequence reading file (SRA) with the item number of http://identifiers.org/ncbi/insdc.sra:SRP197195  and the gene expression omnibus (GEO) with the item number of https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133428 . We have used these data to conduct a large amount of additional analysis, and these results can be obtained from https://figshare.com/articles/SH_transcriptome-S1_xlsx/8241410  and the corresponding authors according to reasonable requirements.
ribonucleic acid sequencing
quantitative reverse transcription polymerase chain reaction
Denning GM, Iyer SS, Reszka KJ, et al. Phenazine-1-carboxylic acid, a secondary metabolite of Pseudomonas aeruginosa, alters expression of immunomodulatory proteins by human airway epithelial cells. Am J Physiol Lung Cell Mol Physiol. 2003;285(3):584–92. https://doi.org/10.1152/ajplung.00086.2003.
Cui Q, Li F, Xing W, et al. Differential expression of two phenazine-producing loci mediated by deficiency of the global regulator rsmA in Psedomonas aeruginosa PAO1. Wei Sheng Wu Xue Bao. 2012;52:1326–34.
Cheng HJ, Wang CZ, Lu WB, et al. Effects of houttuyfonate sodium on eliminating adhesion of Psedomonas aeruginosa and forming biofilms. Zhongguo Zhong Yao Za Zhi. 2012;37:3297–301.
Wu DQ, Cheng H, Huang W, Duan Q. Sodium houttuyfonate in vitro inhibits the biofilm formation and alginate biosynthesis gene expression of Pseudomonas aeruginosa clinical strain. Exp Ther Med. 2015;10:753–8. https://doi.org/10.3892/etm.2015.2562.
Wu D, Huang W, Duan Q, Li F, Cheng H. Sodium houttuyfonate affects production of N-acyl homoserine lactone and quorum sensing-regulated genes expression in Pseudomonas aeruginosa. Front microbiol. 2014;5:635. https://doi.org/10.3389/fmicb.2014.00635.
Wang Tianming, Huang Weifeng, Duan Qiangjun, Wang Jian, Cheng Huijuan, Shao Jing, Li Fang, Daqiang Wu. Sodium houttuyfonate in vitro inhibits biofilm dispersion and expression of bdlA in Pseudomonas aeruginosa. Mol Biol Rep. 2019;46(1):471–7. https://doi.org/10.1007/s11033-018-4497-9.
Chen Y, Chen Y, Shi C, et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience. 2018;7(1):1–6. https://doi.org/10.1093/gigascience/gix120.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics. 2014;30(15):2114–20. https://doi.org/10.1093/bioinformatics/btu170.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9. https://doi.org/10.1038/nmeth.1923.
Dewey CN, Bo L. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011;12(1):323. https://doi.org/10.1186/1471-2105-12-323.
Wu D, Zhao Y. RNA-Seq of Pseudomonas aeruginosa. Sequence Read Archive. 2019. http://identifiers.org/ncbi/insdc.sra:SRP197195. Accessed 11 Jun 2019.
Wu D, Zhao Y. Effect of sodium houttuyfonate on transcriptome of Pseudomonas aeruginosa. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133428. Accessed 28 Jun 2019.
Wu D, Zhao Y. SH transcriptome-S1.xlsx. Figshare. 2019. https://doi.org/10.6084/m9.figshare.8241410.v1.
We thank Professor Huang Xianqing of Shanghai Jiaotong University for providing P. aeruginosa strain and the sequencing facilities of Huada Gene Cloud Platform for the preparation and sequencing of the library.
The research is funded by National Natural Science Foundation of China (Grant No. 81503115) and the Discipline Construction Key Program of Anhui University of Chinese Medicine (DC18100042). The Funding is used to cover laboratory expenses, sample preparation and sequencing. These funding agencies have no role in research design, data collection, analysis of results, or manuscript writing.
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Zhao, Y., Si, Y., Mei, L. et al. Effects of sodium houttuyfonate on transcriptome of Pseudomonas aeruginosa. BMC Res Notes 12, 685 (2019) doi:10.1186/s13104-019-4721-2
- Pseudomonas aeruginosa
- Sodium houttuyfonate