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摘要: 铜是许多细胞酶所必需的微量元素, 然而过量的铜通常对细胞有毒。细菌已经进化出许多铜抗性策略, 但其分子机制尚不完全清楚。本研究在农田土壤中分离了一株高度抗铜(对铜的抗性达3.2 mmol∙L−1)的细菌菌株RCu6, 并利用全基因组和转录组学方法分析研究了其抗铜机制。RCu6菌株的全基因组测序显示该菌株在分类学上属于溶杆菌(Lysobacter soli); 与已发现的同种菌株相比, 基因组分析显示该菌株具有独特的由多个基因编码的抗铜系统。转录组分析表明在0.8 mmol∙L−1和1.6 mmol∙L−1胁迫下分别有315个和839个基因差异表达; 铜稳态、组氨酸代谢、硫代谢和铁硫簇组装途径与RCu6菌的铜抗性相关, 表明RCu6抗铜是一个细胞内多系统协同过程。本研究揭示了溶杆菌属菌株铜抗性分子水平的机制, 为重金属污染农田土壤修复和利用提供了菌种资源和理论依据。Abstract: Copper is a trace element that has essential functions in many cellular enzymes; however, excessive copper levels can be toxic. Bacteria have evolved several copper resistance strategies, but the underlying mechanisms are not yet fully understood. Elucidating the mechanisms of copper resistance in bacteria is important for developing microbe-based techniques for mitigation of heavy metal pollution. In this study, a highly copper-resistant (resistant to copper concentrations up to 3.2 mmol∙L−1) bacterial strain RCu6 was isolated. The genomic characteristics of RCu6 were studied using whole-genome sequencing, and copper resistance mechanisms were analyzed using transcriptome analysis. Whole-genome sequencing of strain RCu6 indicated that it belonged to Lysobacter soli. Compared to other strains in the same genus, this strain has a unique DNA fragment encompassing cop, cus, czc, and other homologous copper resistance genes. Transcriptome analysis showed that 315 (239 up-regulated and 76 down-regulated) and 839 (449 up-regulated and 390 down-regulated) genes were differentially expressed under 0.8 mmol∙L−1 and 1.6 mmol∙L−1 copper concentrations, respectively. The differential gene expression was mainly associated with copper homeostasis, histidine metabolism, sulfur metabolism, and iron-sulfur cluster assembly metabolism, indicating that these processes may play important roles in copper resistance of RCu6. The results of the transcriptome analysis were further verified using qPCR. The expression levels of 12 randomly selected genes associated with copper resistance showed significant correlations between qPCR and RNA-Seq data (R2=0.84 for GAPDH gene and R2=0.98 for 16S rRNA gene as internal reference genes). Therefore, the genomic and transcriptome results suggest that copper resistance in the strain Lysobacter soli RCu6 is an intracellular multi-system collaborative process. This study provides new information for understanding the complex regulatory network of copper homeostasis in prokaryotes. It also provides bacterial resources and a theoretical basis for the remediation of heavy metals in farmland soil.
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Keywords:
- Lysobacter soli /
- Genomics /
- Transcriptomics /
- Copper resistance
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铜是生物系统中必不可少的微量元素, 然而, 高浓度的铜通常对生物有害[1]。生物体已经进化出复杂的铜稳态系统, 以维持正常的细胞铜供应, 同时解毒过量的铜[2]。阐明细菌对铜的抗性机制对利用微生物技术治理重金属污染、实现污染耕地安全利用具有重要意义。
目前的研究已经鉴定了多种细菌对铜的抗性机制, 如丁香假单胞菌(Pseudomonas syringae)的cop机制, 该机制包含copABCDRS 基因, copRS 为调节基因, copA和copC为周质空间蛋白, copD为内膜蛋白, 其功能是负责铜的运输; 大肠杆菌(Escherichia coli)的铜抗性机制包括cut机制、cus机制和pco机制。cut机制包含 cutABCDEFRS基因, cutA和cutB参与铜的吸收, cutF参与铜的运输, cutC和cutD参与铜的排放; pco机制包含pcoABCDRSE基因, pcoA具有氧化活性, 可将Cu+氧化为Cu2+减弱对菌体的毒害, cus系统中的cusF蛋白首先在大肠杆菌中被鉴定, 细胞周质中的Cu+被cusF蛋白转运到cusABC通道进行离子外排[3]。铜绿假单胞菌(Pseudomonas aeruginosa PAO1)通过铁载体与重金属配合物结合增强其对铜的抗性[4]。具有重金属抗性细菌也可以通过结合金属硫蛋白(MT)和低分子量半胱氨酸蛋白来抑制有毒重金属的生物利用度[5], 如贪铜菌(Cupriavidus gilardii CR3)通过半胱氨酸和谷胱甘肽的生物合成产生重金属螯合分子, 促进其对铜的解毒作用[6]。这些机制在分子水平上是一个复杂的过程, 尽管各种铜抗性系统的功能已在许多细菌中被鉴定出来, 然而, 这些复杂过程的很多分子调控机制仍不清楚。
全基因组测序是研究微生物功能的重要方法, 从基因组层面研究细菌对铜的抗性机制能够全面了解细菌体内与重金属抗性及代谢途径相关的基因; 转录组学分析可以确定细菌如何对特定的非生物条件作出转录反应, 是阐明细菌对重金属抗性分子机制的重要手段; 利用基因组学和转录组学结合能够全面地从分子层面揭示细菌对生理毒害和环境的响应机制。溶杆菌 Lysobacter soli RCu6 (以下简称RCu6)是我们在土壤中分离到的具有铜抗性的菌株, 属于溶杆菌(Lysobacter soli), 该菌基因组携带有许多铜抗性基因簇, 如copLABMGA、cusABC、czcABC等。然而, 目前对溶杆菌属细菌铜抗性机制的研究仍鲜见报道。本研究旨在揭示菌株RCu6的基因组特性以及RCu6对铜胁迫的转录反应和可能参与铜抗性的代谢途径, 为微生物的重金属抗性机制提供新的认识, 为通过微生物技术实现耕地的安全利用提供依据。
1. 材料与方法
1.1 菌株RCu6的分离和最小抑制浓度(MIC)的确定
本研究中使用的RCu6菌株分离自中国科学院栾城农业生态系统试验站(114°41′E, 37°53′N)。将10 g小麦季土壤和10 mL无菌超纯水置于50 mL离心管中, 于25 ℃、150 rpm下振荡培养1 h, 然后将细菌培养物接种于选择琼脂培养基上(具体配方见电子版附表S1或扫描首页OSID码), 取单菌落接种至选择培养基中进行纯化。
本研究利用Wang等[7]使用的两倍稀释法来确定RCu6的MIC (MIC被定义为培养24 h后无细菌生长的最低浓度)范围, 将RCu6接种到Luria-Bertani (LB)培养基中, 于25 ℃、150 rpm下培养6 h, 将CuSO4溶液(使培养基中Cu2+的浓度为0~4 mmol∙L−1, 每0.2 mmol∙L−1为一个梯度)添加到相应的锥形瓶中培养24 h。使用分光光度计(UV-6100S, 上海元析)测量OD600值来监测微生物量[8], 每个Cu2+浓度梯度均设置3个重复。
在LB培养基中添加Cu2+, 使其浓度分别为0.4 mmol∙L−1 、 0.8 mmol∙L−1 、 1.2 mmol∙L−1 和 1.6 mmol∙L−1, 于25 ℃、150 rpm的速度振荡培养72 h, 每个Cu2+浓度梯度设置3个重复。使用分光光度计每8 h记录一次OD600值, 构建细菌生长曲线。
1.2 铜去除能力的测定
在含有不同铜浓度(0.4 mmol∙L−1、0.8 mmol∙L−1、1.2 mmol∙L−1和1.6 mmol∙L−1)的LB培养基中培养RCu6菌48 h后, 以10 000 rpm离心10 min, 去掉上清, 收集菌体, 用无菌超纯水洗涤3次, 然后加入10 mL 5 mmol∙L−1 EDTA, 于25 ℃、150 rpm振荡培养30 min; 用原子吸收光谱法(Shimadzu AA-6300, 日本京都)测量上清液中的铜浓度以得到细胞表面吸附量; 将细菌在85 ℃下干燥24 h, 使用65%的浓硝酸在150 ℃下消解细菌2 h后, 测量铜的质量以得到RCu6菌株细胞内铜吸附量。
1.3 菌种鉴定及全基因组测序、组装、注释
使用G-spin DNA提取试剂盒(iNtRON Bio-technology Inc., Sungnam, Korea), 按照说明提取RCu6菌的基因组DNA。使用27F (5′-AGAGTTTGATCMTGGCTCAG-3′)和1492R (5′-TACGGYTACCTTGTTACGACTT-3′)引物扩增16S rRNA基因[9], 扩增过程为95 ℃初始变性5 min; 然后进行25个循环(95 °C变性1 min, 55 ℃退火1 min, 72 ℃延伸2 min); 最后在72 ℃延伸10 min。16S rRNA基因和细菌基因组测序工作委托苏州金唯智生物科技有限公司完成。使用BLASTN (NCBI)和EzTaxon-e servers[10]对细菌16S rRNA基因进行比对分析。
使用FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)和Cutadapt软件[11]对基因组测序获得的原始序列进行质量控制和过滤, 以去除低质量序列和接头序列; 使用HGAP4[12]对过滤后的read进行基因组组装; 使用Prodigal[13]预测蛋白质编码基因; 使用默认参数设置的tRNAScan-SE[14]和RNAmmer[15]预测tRNA基因和rRNA基因; 使用Non-Redundant Protein Sequence Database (NRDB), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO)和Protein Orthologous Group (COG)对编码基因的功能进行注释。本部分研究产生的原始序列(raw reads)已提交到NCBI (PRJNA799173)。
1.4 RNA提取和转录组测序
选择两种浓度的铜胁迫(0.8 mmol∙L−1和1.6 mmol∙L−1)进行转录组研究, 不加铜的处理作为对照, 每种处理设置3个重复。将RCu6菌株按1%的体积比接种量接种到50 mL LB培养基中, 于25 ℃、150 rpm下培养至OD600为0.5。随后添加CuSO4溶液(使其Cu2+的浓度分别为0.8 mmol∙L−1和1.6 mmol∙L−1, 0 mmol∙L−1为对照), 并培养至OD600值为1.2, 在4 ℃下以8000×g离心2 min收集菌体。使用RNAeasy Mini试剂盒(Qiagen)提取细菌细胞中的总RNA; 使用NEBNext® UltraTM Directional RNA Library Prep Kit for Illumina构建RNA-seq文库, 在Illumina HiSeq X Ten (Illumina, San Diego, CA, USA)平台对文库进行2×150 bp测序(文库的构建和转录组测序工作委托苏州金唯智生物科技有限公司完成)。
1.5 转录组数据分析
使用FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)对Raw reads进行质量控制; 使用Cutadapt[11]去除低质量分数(Phred分数<30)的碱基以及接头序列; 使用Bowtie2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)构建参考基因组索引并与RCu6完整基因组进行比对, 使用HTSeq [16]计算基因表达count值; 使用Deseq2[17]分析铜胁迫下基因表达水平(无铜胁迫为对照); 根据校正后的P值(P-adj)<0.01筛选出显著差异表达基因(DEGs); 并将DEGs用于GO富集分析和KEGG通路分析。使用Annota-tionDbi (https://bioconductor.org/packages/AnnotationDbi)构建R package进行GO富集分析(P-adj<0.05), 使用KOBAS (http://kobas.cbi.pku.edu.cn)进行KEGG通路富集分析。本部分研究产生的原始序列已提交到NCBI (PRJNA799196)。
1.6 实时定量PCR
从1.6 mmol∙L−1铜胁迫的转录组数据中随机选择12个与铜抗性相关的DEGs (copZ, copA, 1_585, hisD, copG, hisG, copM, cueR, copB, sufC, copL, copB), 使用12种引物进行qPCR以验证RNA-seq数据的可靠性。使用Primer 3软件(http://bioinfo.ut.ee/primer3-0.4.0/)设计引物序列; 在qPCR之前, 使用RNase-free DNase I (Promega, 美国)从总RNA中去除基因组DNA; 并利用primeScriptTM RT reagent Kit with gDNA eraser (TaKaRa)进行反转录; qPCR体系包含10 μL TB Green® Premix Ex TaqTM II(Takara), 正、反向引物各0.5 μL(10 μmol∙L−1)、1 μL cDNA和 8 μL无菌蒸馏水, qPCR程序为95 ℃下变性3 min, 然后进行35个周期的扩增(即95 ℃下5 s, 60 ℃下30 s); 利用16S rRNA基因和GAPDH基因作为内参基因, 每个反应重复3次; 使用2–ΔΔCT方法计算相对表达量[18-19]。
2. 结果与分析
2.1 RCu6菌株的铜抗性和富集能力
在本研究中, Cu2+对菌株RCu6的最小抑制浓度为3.2 mmol∙L−1, RCu6菌在低铜浓度(0.4 mmol∙L−1、0.8 mmol∙L−1和1.2 mmol∙L−1)下的生长速度与对照组(0 mmol∙L−1)相似; 在1.6 mmol∙L−1的Cu浓度下, 与对照组相比, RCu6的生长被抑制(图1a)。随着Cu浓度的增加(从0.4 mmol∙L−1到1.6 mmol∙L−1), 细胞表面吸收量从0.16 mg∙g−1增加到1.59 mg∙g−1, 在细胞内富集量从0.28 mg∙g−1增加到0.92 mg∙g−1 (图1b)。
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图 1 Lysobacter soli RCu6在不同铜浓度培养基中的生长情况(a)及其细胞膜和细胞内铜吸附累积量(b)Figure 1. Growth curve (a) and copper extracellular adsorption and intracellular accumulation (b) of Lysobacter soli RCu6 in medium with different copper concentrations2.2 菌株RCu6的基因组学特性
利用NCBI数据库对16S rRNA基因序列进行BLAST分析, 结果显示菌株RCu6与溶杆菌Lysobacter soli DCY21的一致性最高(99.72%) (图2)。RCu6菌株的基因组为4.1 Mbp, GC含量为67.86%, 共有3681个编码序列, 21个rRNA基因和52个tRNA基因。基因组的功能注释显示, 蛋白质编码基因的比例为97%, 被注释到COG数据库的基因比例为67%。最丰富的3个类别是转录(transcription)、氨基酸运输和代谢(amino acid transport and metabolism)以及细胞壁/膜/包膜生物形成(cell wall/membrane/envelope biogenesis) (图3)。
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图 2 溶杆菌Lysobacter soli RCu6的系统发育树系统发育树采用MEGAX邻接法构建, Escherichia colicoli ATCC 11775T为外群。Figure 2. Phylogenetic position of Lysobacter soli RCu6Phylogenetic tree was constructed by the neighbor-joining method using MEGAX. Escherichia colicoli ATCC 11775T was used as an outgroup.![]()
图 3 Lysobacter soli RCu6基因组信息从内到外分别是GC偏移、GC含量、rRNAs (红色)、tRNAs (橙色)、编码基因负链、编码基因正链[编码基因使用clusters of orthologous groups (COG)功能注释, 不同的颜色表示不同功能]。Figure 3. Lysobacter soli RCu6 genome informationCircle diagram showing the nuclear genome. From the inside to the outside are GC-skew, GC content, rRNAs (red), tRNAs (orange), antisense coding genes, sense coding genes. The clusters of orthologous groups (COG) functional classification of coding genes are represented in different colors.RCu6与该属菌株的基因组比较, 在其他菌株基因组上显示了一些缺口(图4), 表明该菌株基因组的独特性。RCu6与L. soli XL170、L. soli KCTC 22011、L. ummosus 3.2.11和L. capsici 55之间的平均核苷酸一致性(ANI)值分别为96.53%、96.73%、78.76%和78.81%。
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图 4 基于比较基因组圈图的Lysobacter soli RCu6和密切相关菌株的BLAST比较基因组分析最内圈表示GC偏移, 第2圈表示GC含量, 第3圈表示RCu6, 第4~8圈分别表示 Lysobacter soli XL170、L. soli KCTC 22011、L. gummosus 3.2.11、L. capsici 55和L. antibioticus 76的BLAST结果。Figure 4. Genome comparison between Lysobacter soli RCu6 and closely related strains based on BLAST comparative genomic analysisThe innermost ring indicates GC offset, the second ring shows GC content, and the third ring shows RCu6. The fourth to eighth rings show BLAST results for Lysobacter soli XL 170, L. soli KCTC 22011, L. gummosus 3.2.11, L. capsici 55 and L. antibioticus 76, respectively.2.3 转录组数据的分析和验证
转录组测序后, 经过质量控制, 从9个测序样本中共产生了3.51亿条序列, 99%以上的RNA测序序列映射到了RCu6基因组上(见电子版附表S2, 或扫描首页OSID码)。与对照组相比, 0.8 mmol∙L−1和1.6 mmol∙L−1铜处理组表现出强烈的响应, 分别共有315个(占检测基因的8.6%, 239个上调和76个下调)和839个(占检测基因的22.8%, 449个上调和390个下调) DEGs。
在qPCR中, 选定的上调和下调基因的表达量也得到了证实(见电子版附表S3或扫描首页OSID码), qPCR和RNA-seq数据之间显示出显著的相关性, GAPDH基因作内参基因时的相关性R2=0.84; 16S rRNA基因作内参基因时R2=0.98 (P<0.001) (图5; 引物见电子版附表S4或扫描首页OSID码)。这些结果证实了RNA-Seq数据的可靠性。
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图 5 铜浓度为1.6 mmol∙L−1下Lysobacter soli RCu6基因表达qPCR和RNA-Seq间的相关性Figure 5. Correlation of gene expression data between qPCR and RNA-Seq of strain Lysobacter soli RCu6 at a copper concentration of 1.6 mmol∙L−12.4 响应铜胁迫的基因和代谢途径
为深入了解RCu6铜胁迫有关的代谢途径, 采用超几何检验(Hypergeometric tests)来确定显著富集的GO terms和KEGG代谢途径。结果显示在0.8 mmol∙L−1铜胁迫组中富集的GO terms包括: 离子结合(GO: 0043169、0005507、0043167和0046872)、对氧化胁迫的反应(GO: 0006979)和氧化还原酶活性(GO: 0016491); 在1.6 mmol∙L−1铜胁迫组中富集的GO terms包括: 运输(GO: 0051181和1901678)、氨基酸代谢过程(GO: 0009072、0006547和0052803)、金属离子结合(GO: 0005507和0046872)、氧化还原过程(GO: 0055114)、氧化还原酶活性(GO: 0016491)以及电子传输链(GO: 0022900) (图6)。KEGG富集分析显示, 在0.8 mmol∙L−1和1.6 mmol∙L−1铜胁迫时, 显著富集的途径有(详见电子版附表S5或扫描首页OSID码): 柠檬酸循环(TCA cycle)、氨基酸的生物合成(biosynthesis of amino acids)、碳代谢(carbon metabolism)、组氨酸代谢(histidine metabolism)和硫代谢(sulfur metabolism)。这一结果表明, 这些过程可能在铜抗性机制中发挥了重要作用。
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图 6 0.8 mmol∙L−1和1.6 mmol∙L−1铜胁迫下Lysobacter soli RCu6细菌差异表达基因的GO功能富集图GeneRatio是差异表达基因中与功能相关的基因数与整个差异表达基因总数的比率, Y轴列出了富集到的GO功能名称。Count是差异表达基因中与该功能相关的基因数, P. adjust越小表示富集程度更高。Figure 6. GO function enrichment plots of differentially expressed genes in Lysobacter soli RCu6 bacteria under 0.8- and 1.6-mmol∙L−1 Cu stressGeneRatio is the ratio of the number of genes associated with this Term in the differentially expressed genes to the total number of all differentially expressed genes, and the Y-axis lists the names of the GO functions enriched to. Count is the number of genes associated with this Term in the differentially expressed genes. P. adjust close to 0 indicates a greater enrichment.表S6和S7 (见电子版附表或扫描首页OSID码)分别显示上调和下调程度最高的基因, 这些基因功能分类主要包括金属离子运输和代谢、氧化还原酶家族以及部分未知功能基因; 在0.8 mmol∙L−1铜胁迫下, 两个上调最高的基因分别是铜伴侣基因copZ (1_1472)和重金属转运P型ATP酶copA(1_2614)(log2foldchange分别为7.68和7.46), 而下调最大的是编码外膜受体蛋白基因1_143 (log2foldchange为−4.17); 在1.6 mmol∙L−1铜胁迫时, 两个上调最高的基因分别是血红素吸收蛋白基因hmuP (1_1268)和外膜受体基因(1_2539) (log2foldchange为9.88和9.07), 下调最大的是编码与外膜受体蛋白相互作用的基因(1_143) (log2foldchange为−6.66)。
与铜抗性相关的基因对两种浓度的铜胁迫表现出较大的响应(见电子版附表S8或扫描首页OSID码), 大多数基因(0.8 mmol∙L−1浓度下有23个, 1.6 mmol∙L−1浓度下有21个)在铜胁迫下显著上调, DEGs包括3个铜抗性基因簇: copLABMGA_cusABC_soxR_copZ_merT、copLABA_cueR和czcABCD。转录组结果表明这些基因可能与菌株RCu6的转录调控(copL)、铜结合(copZ、copG)、铜运输(copA1、copB)、多铜氧化酶(copA2、copG)和铜外排系统(cusAB、czcC)相关。此外, 在0.8 mmol∙L−1和1.6 mmol∙L−1铜胁迫下与氧化应激和氧化还原酶活性有关的基因分别有28个和74个显著差异表达。有趣的是除多铜氧化酶外, 脱氢酶还原酶(fabg)、铁依赖性加氧酶(piuC)、细胞色素C氧化酶(copM)、脱氢酶(qor)和催化酶(KatG, AHPC)显著上调, 表明这些基因可能在铜胁迫下的氧化应激和维持细胞氧化还原平衡中发挥作用。
3. 讨论与结论
本文揭示了菌株RCu6对铜的耐受性及其对铜胁迫响应的分子机制。我们进行了菌株的全基因组测序, 并利用转录组研究了在铜胁迫条件下的转录水平响应机制, 揭示了铜抗性基因表达、组氨酸代谢途径、硫代谢和Fe-S簇组装系统对铜胁迫有明显的响应。
在本研究中, RCu6携带许多与铜抗性有关的基因, 这些基因对铜胁迫表现出强烈的转录响应。RCu6菌的铜抗性的实现可能与copL基因(转录调控)[20]相关, 这与多种细菌(如丁香假单胞菌[21]和贪铜菌)[3]中描述的双组分调控系统(copRS)不同。铜结合蛋白(copZ、copG 编码)可以与细胞内的游离Cu+或Cu2+结合以降低铜的毒性[3,22-23], 铜转运蛋白(copA1、copB)可以跨细胞膜转运铜离子[24-25], 多铜氧化酶(copA2、copG)可以将Cu+氧化为Cu2+以降低铜离子的细胞毒性[26-28], 铜离子外排系统(cusAB、czcC)形成的外膜通道可以跨膜转运铜[2-4,29-30]。本研究中，CopA1和copZ是铜抗性基因中上调幅度最大的基因(表S8), 这表明铜的转运和结合可能是RCu6抵抗铜胁迫的重要途径。
硫是生物体内必不可少的元素, 硫化合物具有重金属解毒的作用[31], 多种硫代谢产物参与氧化应激条件下的细胞反应[32-33]。在本研究中, 与硫代谢途径有关基因的差异表达(表S2)表明它们可能与铜抗性的发挥有重要关联, 高铜胁迫高度诱导了编码亚硫酸盐蛋白合成的基因(cysH、cysI)、编码半胱氨酸合成的基因(cysK)以及编码谷胱甘肽合成酶基因(gshB)的表达。前期研究已经明确了硫参与微生物对重金属的解毒作用[34], 例如Huang等[6]利用转录组学技术确定了铜胁迫下Cupriavidus gilardii CR3中硫同化基因的上调; Liu等[35]利用同化硫代谢菌来增强对重金属的耐受性。此外, 硫同化途径还为谷胱甘肽和氨基酸的合成以及S-Fe合成提供了硫酸盐。重金属胁迫和氧化环境下增加了谷胱甘肽的合成[36-38], 谷胱甘肽已被证明在革兰氏细菌中能增强细菌对铜的抗性[39-40]; 它可以还原Cu2+形成Cu+-GSH复合物, 以稳定体内游离铜离子[41]; 而谷胱甘肽在螯合过程中可以螯合金属离子[42], 从而增加细胞对铜的抵抗力。本研究中, 编码谷胱甘肽合成酶基因(gshB)的上调表明菌株RCu6抵抗铜毒性可能与谷胱甘肽合成相关。
L-组氨酸的生物合成存在于古细菌、细菌、真核生物和植物中。以前的研究已经揭示了L-组氨酸在细菌中的基本调节过程[43], 以及L-组氨酸生物合成在真菌金属平衡和毒力中的作用[44]。然而, 关于L-组氨酸在结合铜和增强细菌铜抗性方面的机制仍然不清楚。本研究发现, 编码组氨酸生物合成途径的基因在铜胁迫条件下明显高表达(图6, 表S5)。在RCu6基因组中, 我们发现了8个组氨酸生物合成酶基因(HisG、HisI、HisA、HisF、HisD、HisC、HisH和HisB), 所有8个组氨酸生物合成基因都位于同一个操纵子上, 研究表明这些基因的转录可能受到严格调控[43], 过量的铜可能是诱导其转录的原因。无论是作为游离氨基酸还是作为蛋白质中的金属结合残基, 组氨酸对结合的金属有很大的亲和力[42,44-47], 因此组氨酸代谢可能对铜胁迫条件下的蛋白质生物合成以及细菌金属稳态至关重要。
本研究中发现硫铁簇的组装可能与菌株RCu6铜解毒相关, 但在不同的生物体中Fe-S簇的组装系统有所不同, 例如RCu6可能是由SUF系统介导, 而Cupriavidus gilardii CR3是由ISC系统介导[6]。我们的转录组数据表明, 参与硫铁簇生物生成的基因(sufS、sufC、sufD、sufB)在铜胁迫下上调(表S8, 见电子版附表或扫描首页OSID码), 这一结果也与Giner-Lamia等[48]使用微阵列测序数据结果一致。SUF系统被认为可以对氧化应激和重金属应激的调节作出反应[49-50], SufE和SufS向SufB提供硫, SufB蛋白可以形成硫铁原子簇, 硫铁团簇在组装过程中可以参与氧化还原过程[51-52], 而铜离子通过替换硫铁簇中的铁, 减少了细胞中游离的铜离子[6], 从而增强对铜的抗性能力。
本研究结果揭示了溶杆菌Lysobacter soli RCu6对铜的抗性能力和抗性机制。RCu6对铜的抗性MIC值为3.2 mmol∙L−1; 该菌基因组中携带有许多铜抗性同源基因(copLABMGA、cusABC、czcABC等); 结合转录组学分析表明, RCu6的铜抗性可能是一个细胞内多系统协同过程, 包括铜稳态系统、硫代谢和硫铁簇合成途径以及组氨酸代谢途径。这些结果为揭示溶杆菌的铜抗性分子机制提供了新的见解, 为开发农田土壤重金属污染的微生物修复技术提供了依据。
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图 1 Lysobacter soli RCu6在不同铜浓度培养基中的生长情况(a)及其细胞膜和细胞内铜吸附累积量(b)
Figure 1. Growth curve (a) and copper extracellular adsorption and intracellular accumulation (b) of Lysobacter soli RCu6 in medium with different copper concentrations
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图 2 溶杆菌Lysobacter soli RCu6的系统发育树
系统发育树采用MEGAX邻接法构建, Escherichia colicoli ATCC 11775T为外群。
Figure 2. Phylogenetic position of Lysobacter soli RCu6
Phylogenetic tree was constructed by the neighbor-joining method using MEGAX. Escherichia colicoli ATCC 11775T was used as an outgroup.
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图 3 Lysobacter soli RCu6基因组信息
从内到外分别是GC偏移、GC含量、rRNAs (红色)、tRNAs (橙色)、编码基因负链、编码基因正链[编码基因使用clusters of orthologous groups (COG)功能注释, 不同的颜色表示不同功能]。
Figure 3. Lysobacter soli RCu6 genome information
Circle diagram showing the nuclear genome. From the inside to the outside are GC-skew, GC content, rRNAs (red), tRNAs (orange), antisense coding genes, sense coding genes. The clusters of orthologous groups (COG) functional classification of coding genes are represented in different colors.
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图 4 基于比较基因组圈图的Lysobacter soli RCu6和密切相关菌株的BLAST比较基因组分析
最内圈表示GC偏移, 第2圈表示GC含量, 第3圈表示RCu6, 第4~8圈分别表示 Lysobacter soli XL170、L. soli KCTC 22011、L. gummosus 3.2.11、L. capsici 55和L. antibioticus 76的BLAST结果。
Figure 4. Genome comparison between Lysobacter soli RCu6 and closely related strains based on BLAST comparative genomic analysis
The innermost ring indicates GC offset, the second ring shows GC content, and the third ring shows RCu6. The fourth to eighth rings show BLAST results for Lysobacter soli XL 170, L. soli KCTC 22011, L. gummosus 3.2.11, L. capsici 55 and L. antibioticus 76, respectively.
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图 5 铜浓度为1.6 mmol∙L−1下Lysobacter soli RCu6基因表达qPCR和RNA-Seq间的相关性
Figure 5. Correlation of gene expression data between qPCR and RNA-Seq of strain Lysobacter soli RCu6 at a copper concentration of 1.6 mmol∙L−1
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图 6 0.8 mmol∙L−1和1.6 mmol∙L−1铜胁迫下Lysobacter soli RCu6细菌差异表达基因的GO功能富集图
GeneRatio是差异表达基因中与功能相关的基因数与整个差异表达基因总数的比率, Y轴列出了富集到的GO功能名称。Count是差异表达基因中与该功能相关的基因数, P. adjust越小表示富集程度更高。
Figure 6. GO function enrichment plots of differentially expressed genes in Lysobacter soli RCu6 bacteria under 0.8- and 1.6-mmol∙L−1 Cu stress
GeneRatio is the ratio of the number of genes associated with this Term in the differentially expressed genes to the total number of all differentially expressed genes, and the Y-axis lists the names of the GO functions enriched to. Count is the number of genes associated with this Term in the differentially expressed genes. P. adjust close to 0 indicates a greater enrichment.
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