Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (21): 4585-4600.doi: 10.3864/j.issn.0578-1752.2021.21.009

• PLANT PROTECTION • Previous Articles     Next Articles

Transcriptome and Proteome Analysis of Bacillus subtilis NCD-2 Response to L-proline from Cotton Root Exudates

ZHAO WeiSong(),GUO QingGang,DONG LiHong,WANG PeiPei,SU ZhenHe,ZHANG XiaoYun,LU XiuYun,LI SheZeng,MA Ping()   

  1. Plant Protection Institute of Hebei Academy of Agricultural and Forestry Sciences/IPM Centre of Hebei Province/Key Laboratory of IPM on Crops in Northern Region of North China, Ministry of Agriculture and Rural Affairs, Baoding 071000, Hebei
  • Received:2021-03-21 Accepted:2021-05-31 Online:2021-11-01 Published:2021-11-09
  • Contact: Ping MA E-mail:zhaoweisong1985@163.com;pingma88@126.com

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Abstract:

【Objective】L-proline in root exudates of cotton is one of the key factors affecting the colonization of biocontrol microorganisms. Previous studies showed that L-proline could improve the ability of Bacillus subtilis NCD-2 biofilm formation. The objective of this study is to explore the regulatory genes related to the biofilm formation and biocontrol potential of strain NCD-2 through high-throughput sequencing technology, and to lay a foundation for further understanding the molecular interaction between cotton root exudates and biocontrol strain.【Method】Strain NCD-2 was co-cultured with L-proline at the concentration of 10 mg·mL -1 for 24 h, and it was analyzed by transcriptome (RNA-seq) and isotope labeled relative quantitative proteomics (iTRAQ). The expression of some differential genes in different metabolic pathways was verified by RT-qPCR.【Result】Transcriptome analysis showed that 1 071 differentially expressed genes (DEGs) were obtained after L-proline and NCD-2 co-culture, of which 602 genes were up-regulated and 469 genes were down-regulated. GO analysis showed that 49, 14 and 30 functional items were significantly enriched in biological process, cell component and molecular function, respectively. KEGG pathways were mainly enriched in compound metabolism, flagellum assembly, bacterial motility or chemotaxis. Proteomic analysis showed that a total of 211 differentially expressed proteins (DEPs) were detected compared to the control, of which 118 proteins were up-regulated and 93 proteins were down-regulated. GO analysis showed that 13 and 8 functional items were significantly enriched in biological process and molecular function, respectively. KEGG pathways were mainly enriched in amino acid metabolism, carbohydrate metabolism, flagellum assembly and ABC transporter. Further transcriptional-proteomics analysis revealed that 112 DEGs (or DEPs), including 38 down-regulated genes (or proteins) and 74 up-regulated genes (or proteins), were detected. GO functional items were significantly enriched in nine aspects, including nutrient reservoir activity, catalytic activity, cell membrane, localization, cellular lipid metabolic process, oxidation-reduction process, sigma factor activity, transport activity and spore formation. KEGG pathways were mainly enriched in energy metabolism, ABC transporter, antibiotic biosynthesis, flagellum assembly, motility or chemotaxis and two-component system. Twenty-six DEGs were verified by RT-qPCR. The results showed that there were some differences in the expression level, but the expression trend was basically consistent with that of RNA-seq and iTRAQ.【Conclusion】The interaction between L-proline in cotton root exudates and B. subtilis NCD-2 is a complex biological process, which depends on multiple genes in different metabolic pathway networks. It is cleared that DEGs (or DEPs) of the two-component system, antibiotic biosynthesis, energy metabolism, motility or chemotaxis, flagellum assembly and ABC transporter pathway may play an important role in the interaction between cotton root exudates and B. subtilis.

Key words: L-proline, Bacillus subtilis, transcriptome, proteome, interaction relationship

Table 1

Primers and their base sequences"

基因 Gene 序列Sequence (5′ to 3′) 基因 Gene 序列Sequence (5′ to 3′)
fliF F ACCGAAAGCGGGAACTAC opuCC F GCAAGGAAGCGGAGAAAG
R CATCAGGCGGCTCTACCA R AGCCGTAGGAATCAAACCA
gInQ F TATCGGGATGGTGTTTCA gcvPB F TTATTTCCCGCTTAATGTTG
R GTCAGCCTTGTCTGGGAT R TCTTCCGCCTCGTATCTG
cheY F GGAGCACAAGCGGTAGAG putC F AATCGTTTCAATCAACCCAG
R CTGAATGGCATCAATAAC R TATCGGCATCCGCCTCGT
fliY F CAGACCGTATTCCTGATG rocA F TAGTTGAGCATCCGAAGAC
R CACCGTTTCTTCTTCCTC R CAATTACCCGTTTGAGCC
opuCA F CCAGCAGAACATCTCACTC yodQ F GGCAATACAGCCCTTCTT
R GCGGATAACGGTCTAAATAC R GCCGCACTCTTCATCTAC
znuA F GGGCTTTCACCTGACCAA ald F CCTCTTCTGACGCCAATG
R CGAGCGTATCTGCGACCT R CAACGCCTCCTCCGATAA
gapB F AAGAGGTTGTGGCTGGTG yerA F GAGAAGGCTGGAACTGGG
R TTGCTTCGACGACTATGT R ATTGTAGGCGTCGATTGC
mmgD F AATGGAAACGCTGGAACG asnO F TGTCGGATGTGCCTGTTT
R TGTTTGCGGAAGGAGACC R CATTTGTGGTGCGTTGTG
acoC F TGGACCAGGCGGACGAAT yisZ F AGGATCGGGACATGGTTA
R TCGGGCAGCGATGACCTT R ATGAAATCGGGTGAAAGC
thrD F ACTGATCGCCGCTTACTT ggt F ACACTGTCCAGGATTTCG
R ACCGCTCCGTGAGAATGT R GGAGGAGGAGTAGTAGCG
yjmD F CAGCGGAGGTGAAGAAGC yoaD F CCCGAACTTTCATTTGTC
R GAGGGTAGCGAGCGGATT R CTCCATCCTTCAGCCACT
epsA F TCGAATCTCAGTGACATCCA epsC F AATCCAGAAGAGGCGGTCAA
R AGATAGGTGCAATTCCGC R GCCGAAGCGAACAGCAAC
ssuB F ATTATCAATCGCACCAGG scoB F TTGTCGCAAATGAGATACCC
R CAACAGTCAGCCAAGGAA R CGTCTTCCGTTCCTTCCA
gyrB F GAAGCACGGACAATCACC
R TCCAAAGCACTCTTACGG

Table 2

Sequencing data quality statistics"

处理
Treatment		 原始序列
Raw reads		 过滤序列
Clean reads		 过滤序列大小
Clean reads bases (G)		 Q20 比例Q20 percentage (%) Q30比例Q30 percentage (%) G和C占总碱基数量百分比
GC content (%)
CK 11104298±1620312 10870077±1617999 1.63±0.24 97.46±0.11 92.90±0.32 41.81±0.40
T 8978376±1390913 8783987±1431917 1.32±0.22 97.99±0.11 93.79±0.27 43.30±0.05

Table 3

Statistics on the number and proportion of genes in different expression levels"

项目
Item		 处理
Treatment		 FPKM值 FPKM value
0-1 1-3 3-15 15-60 >60
基因数量
Number of genes		 CK 616.33±7.64 130.33±10.41 493.67±31.13 984.67±33.01 2305.00±54.44
T 690.00±22.27 165.67±4.73 569.33±18.58 1073.67±36.23 2031.33±74.22
基因表达比例
Gene expression ratio (%)		 CK 13.60±0.17 2.87±0.23 10.90±0.69 21.74±0.73 50.88±1.20
T 15.23±0.49 3.66±0.10 12.57±0.41 23.70±0.80 44.84±1.64

Fig. 1

Overall distribution of DEGs"

Fig. 2

GO analysis of DEGs"

Table 4

KEGG pathway analysis"

KEGG代谢途径
KEGG pathway		 差异基因数目
Number of DEGs		 所有基因数目
Number of all genes		 差异表达基因比例
DEGs ratio (%)		 P值
P-value		 表达调控
Regulated
不同环境的微生物代谢
Microbial metabolism in diverse environments		 40 169 23.67 0.0076 Up
链霉素生物合成 Streptomycin biosynthesis 6 9 66.67 0.0088 Up
碳代谢Carbon metabolism 25 94 26.60 0.0103 Up
硫代谢Sulfur metabolism 8 17 47.06 0.0118 Up
乙醛酸和二羧酸代谢
Glyoxylate and dicarboxylate metabolism		 9 25 36.00 0.0276 Up
鞭毛组装 Flagellar assembly 26 33 78.79 0 Down
细菌趋化性 Bacterial chemotaxis 13 24 54.17 0 Down
核糖体 Ribosome 18 88 20.45 0.0248 Down
萜类化合物生物合成 Terpenoid backbone biosynthesis 5 14 35.71 0.0413 Down
错配修复Mismatch repair 6 20 30.00 0.0475 Down

Fig. 3

Cluster analysis of DEPs"

Fig. 4

GO function enrichment of DEPs"

Table 5

KEGG pathways analysis of DEPs"

KEGG代谢途径
KEGG pathway		 注释代谢途径的差异表达蛋白数量
Number of DEPs with pathway annotation		 注释代谢途径的蛋白数量
Number of proteins with pathway annotation		 差异蛋白比例
DEPs ratio
(%)		 P值
P-value		 上调数量Up-regulated
number		 下调数量Down-regulated
number
酮体的合成与降解
Synthesis and degradation of ketone bodies		 4 5 80.00 0.0004 4 0
乙醛酸和二羧酸代谢
Glyoxylate and dicarboxylate metabolism		 9 29 31.03 0.0009 7 2
牛磺酸和亚牛磺酸代谢
Taurine and hypotaurine metabolism		 3 7 42.86 0.0224 2 1
丙氨酸、天冬氨酸和谷氨酸代谢
Alanine, aspartate and glutamate metabolism		 7 31 22.58 0.0226 4 3
鞭毛组装Flagellar assembly 2 3 66.67 0.0255 0 2
戊糖和葡萄糖醛酸的相互转化
Pentose and glucuronate interconversions		 5 19 26.32 0.0284 4 1
精氨酸和脯氨酸代谢
Arginine and proline metabolism		 5 19 26.32 0.0284 3 2
丁酸代谢Butanoate metabolism 5 19 26.32 0.0284 5 0
不同环境的微生物代谢
Microbial metabolism in diverse environments		 22 151 14.57 0.0335 13 9
ABC转运蛋白ABC transporter 13 79 16.46 0.0486 7 6

Fig. 5

Venn diagram of expression regulation of transcriptome and proteome all_tran represents all the genes identified by transcriptome, diff_tran represents the differentially expressed genes identified by transcriptome, all_prot represents all the proteins identified by proteome, diff_prot represents the differential expressed proteins identified by proteome"

Fig. 6

RT-qPCR validation of DEGs 1-13 are genes related to energy metabolism (amino acids, sugars, etc.), representing putC, rocA, ald, gapB, yerA, mmgD, asnO, acoC, yisZ, thrD, ggt, yjmD and yoaD, respectively. 14-19 are genes related to ABC transporters, representing scoB, glnQ, opuCC, opuCA, znuA and ssuB, respectively. 20-21 are genes related to flagellar assembly, representing fliF and fliY, respectively. 22-23 are genes related to biosynthesis of antibiotics, representing gcvPB and yodQ, respectively. 24-25 are genes related to biofilm formation, representing epsA and epsC, respectively. 26 represents chemotaxis related gene cheY"

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