Jul 07, 2023
水素栄養性のスルフリモナスは深部に世界的に豊富に存在する
Nature Microbiology volume 8、pages 651–665 (2023)この記事を引用 5048 アクセス数 2 引用数 401 Altmetric Metrics 詳細 細菌属スルフリモナス (カンピロバクター門) のメンバー
Nature Microbiology volume 8、pages 651–665 (2023)この記事を引用
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細菌属スルフリモナス (カンピロバクタータ門) のメンバーは、海洋レドックスクラインの微生物群集を支配しており、硫黄と窒素の循環に重要です。 今回我々は、メタゲノミクスと代謝解析を用いて、中央北極海のガッケル海嶺と南西インド海嶺のスルフリモナス属の特徴を明らかにし、この種が全世界の海洋中央海嶺の非浮力熱水プルームに遍在していることを示した。 スルフリモナス属の 1 種である USulfurimonas pullma は、世界的に豊富に存在し、低温 (<0-4 °C)、酸素飽和、水素豊富な熱水プルームで活動していることが判明しました。 他のスルフリモナス種との比較、米国。 プルマは、ゲノムが減少（>17%）しており、A2 型オキシダーゼの獲得や硝酸および亜硝酸レダクターゼの喪失など、エネルギー源として水素を使用する好気性化学岩石栄養代謝のゲノム特徴が見られます。 米国の優位性とユニークなニッチ。 熱水プルームのプルマは、深海におけるスルフリモナスの知られていない生物地球化学的役割を示唆しています。
スルフリモナス属は、カンピロバクテロータ門（旧イプシロンプロテオバクテリア綱）に属します。 これはもともと、深海の熱水噴出孔で収集された堆積物から Sulfurimonas autotrophica が分離された後に提案されました 1。 それ以来、12 の異なるスルフリモナス種が酸素欠乏環境から分離されました 2、3、4、5、6、7、8、9、10、11。 16S rRNA 遺伝子配列に基づくと、この中温性で化学合成独立栄養性の細菌属は遍在しており、深海の熱水噴出孔の硫化物環境を含むレドックスクリン環境 12 に生息する微生物群集の主要なメンバーです 13、14、15、16、17。 記載されているスルフリモナス属のメンバーは、他の熱水性カンピロバクタータ属 (つまり、スルフルバム 16) や海洋硫黄酸化剤 (つまり、 SUP0518,19)。 しかし、豊富なスルフリモナス 16S rRNA 遺伝子配列は、熱水プルームの非浮力段階でも報告されています 14,20,21,22,23,24。 熱水プルームは、海底から放出される高温の無酸素熱水が冷たい酸素を含んだ海水と混合する場所で発生します。 それらは海底から数百メートルまで上昇し、発生源から数千キロメートル離れた場所に拡散することもあります25。 非浮力段階では、熱水プルームは主に冷たく酸素が飽和した海水と、高度に希釈された熱水流体（<0.01%）の混合物から構成されます25,26。 このため、非浮力熱水プルームは、スルフリモナスの恒久的なニッチおよび生息地とは考えられていません。 このようなプルーム内でスルフリモナス配列が繰り返し検出されることは、海底および海底下の環境からの受動的輸送によって説明される26。 しかし、非浮力プルームがスルフリモナス属の特定のメンバーの成長に適した環境を提供するかどうかを直接テストした研究はありません。 熱水プルームには、大量の無機還元ガス (H2S、CH4、H2) と金属 (Fe、Mn、Cu、Zn、Co) が含まれており 27、海洋化学に大きな影響を与えます 28。 したがって、プルーム内で増殖する微生物の生理機能を同定し、解明することは、海洋の生物地球化学を理解するために極めて重要です。
本研究では、熱水プルームにおけるスルフリモナスの分布と機能を調査しました。 私たちはガッケル海嶺の 2 つの噴出プルームと南西インド海嶺 (SWIR) の 1 つの噴出プルームでそのリボタイプ、遺伝子型、代謝を研究し、これらを中部海洋海嶺の他の噴出プルームやスルフリモナス属が生息する他の環境からの公的に入手可能なデータと比較しました。 私たちの仮説は、非浮力熱水プルームがスルフリモナス属の特定のメンバーにとって適切な環境であるというものです。
99%) in the non-buoyant hydrothermal plumes of Gakkel Ridge and in seawater from a ridge valley of the SWIR belonged to the genus Sulfurimonas (Supplementary Table 1 and Extended Data Fig. 2). In addition, more than 97% of the Sulfurimonas sequences of these three remote sites on ultraslow spreading ridges belonged to two closely related operational taxonomic units (OTU1 and OTU2), with a similarity of 99.5%. Fluorescence in situ hybridization using both a Campylobacterota-specific rRNA probe and tailored highly specific probes for the two detected Sulfurimonas OTUs confirmed these results (Extended Data Fig. 1b–f)./p>99.5% identity) dominated hydrothermal plumes across the ridge systems of the Central Arctic, Atlantic and Indian/Southern Oceans (Fig. 1a). The same ribotype was also found in the plume and the surrounding water column of the Guaymas Basin in the Gulf of California34, but with low proportions to the total bacterial community (Fig. 1a)./p>40 kpb). These results excluded that the observed genome reduction was an artefact of assembly and binning procedures./p>13 to >500 times higher expressed than genes for sulfur oxidation suggests that hydrogen is a critical energy source to sustain the growth of US. pluma in the Aurora plume (Fig. 2), where it was most abundant and active (Supplementary Table 1 and Extended Data Fig. 2). Laboratory experiments with cultures of S. denitrificans also found that this species grows more efficiently when supplied with hydrogen than with thiosulfate as electron donor38, suggesting that hydrogen can be an important energy substrate for the genus Sulfurimonas./p>20%), the cbb3-type oxidase becomes inefficient, resulting in impaired growth9,12. In fact, the cultured Sulfurimonas strains grow optimally at an O2 concentration of 1–8%, and become inactive at O2 concentrations higher than 20%1,2,3,4,5,9,11. Moreover, previous studies found Sulfurimonas predominantly in environments subject to strong fluctuations in O2 concentrations (that is, benthic and pelagic redoxclines12; Supplementary Table 3). The cold polar waters studied here are oxygen-saturated and the diluted hydrothermal fluids do not substantially lower their oxygen contents. Hence, US. pluma is permanently exposed to high oxygen concentrations (ca. 300 µM; Supplementary Table 3). We hypothesize that the acquisition of caa3-type (A2-type) cytochrome c oxidase allows an efficient respiration of US. pluma in this fully oxic environments. This cytochrome c oxidase is present in many aerobic bacteria and it has strong homology to the mitochondrial cytochrome oxidase (A1-type)43. Of note, within the phylum of Campylobacterota, we found all four subunits of caa3-type oxidase in the genome of Sulfurovum sp. AR derived from aerobic Arctic sediments44. This oxidase has an amino acid identity of 70% to that of US. pluma. However, this caa3-type oxidase cannot be misassembled in the US. pluma MAGs because Sulfurovum sequences are rare in the Gakkel seawater (Supplementary Table 1), and the synteny analysis of contigs encoding for this enzyme points toward an acquisition by horizontal gene transfer (Supplementary Fig. 2)./p>99.5% 16S rRNA gene sequence similarity) in hydrothermal plumes across the globe (Fig. 1) suggests that the Sulfurimonas cluster, including US. pluma, is part of the ocean microbial seed bank, and therefore that background seawater might be the source of US. pluma. On the other hand, it may be that US. pluma enters into the hydrothermal plumes from populations living on seafloor vent-associated environments, which due to oxygen tolerance have a higher dispersal potential than benthic Sulfurimonas species, resulting in higher global connectivity17. Future studies on uncultivated Sulfurimonas species described here will be needed to verify these hypotheses, and to shed light on environmental and ecological forces that shape the connections and composition of microbial communities between different environments such as subsurface aquifers, diffusive flow and hydrothermal plumes./p>99%), representing the Sulfurimonas OTU1 and OTU2 identified by the analysis of 16S rRNA gene amplicon sequences (described in the section ‘Illumina 16S rRNA gene sequencing’). We designed specific probes for OTU1 (SLFM-A484 5’–GCTTATTCATAGGCTACC–3’; 15% formamide) and OTU2 (SLFM-B484 5’–GCTTATTCATATGCTACC–3’; 20% formamide), both synthetized by Biomers. Due to the high similarity between these two oligonucleotides (one mismatch for G and T), each probe was used in a mix together with the other (non-labelled) probe as competitor oligonucleotide. To check the coverage and specificity of US. pluma’s probes in the environmental samples, double CARD-FISH hybridizations were carried out using the Campylobacterota probe (EPSY914) as a positive control./p>50,000 reads per sample (CeBiTec), following the standard instructions of the 16S metagenomic sequencing library preparation protocol (Illumina). The workflow and scripts used in this study for the quality cleaning, merging, clustering and annotation of the sequences can be found in ref. 67. Briefly, only reverse and forward reads with quality score higher than 20 (applying a sliding window of 4) were merged, clustering of sequences into OTUs was done using the programme swarm (v2.2.2)68, and the taxonomic classification was based on the SILVA rRNA reference database (release 132)65./p>7 were used for sequencing. The TruSeq Stranded Total RNA kit (Illumina) was used for RNA library preparation. The rRNA depletion step was omitted. Of the total RNA, 80 ng (in 5 μl volume) was combined with 13 μl of ‘Fragment, Prime and Finish mix’ for the RNA fragmentation step according to the Illumina TruSeq stranded mRNA sample preparation guide. Subsequent steps were performed as described in the sample preparation guide. The library was sequenced on a HiSeq1500 platform (Illumina) in a 1 × 150 bp single-end run generating >20 million reads per sample. The resulting reads were pre-processed, including removal of adaptors and quality trimming (slidingwindow:4:21 minlen:100) using bbduk v34 from the BBMAP package69 and Trimmomatic software v0.3570, respectively. The trimmed reads were sorted into ribosomal RNA (rRNA) and non-ribosomal RNA (non-rRNA) reads using SortMeRNA software v2.071. A random subset of 1 million rRNA reads per sample was taxonomically classified with phyloFlash software v3.0 beta 172 based on the SILVA database (release 132)65./p>50 kpb), completeness (>75%) and redundancy (<25%) filtering, and a total of 19 de-replicated bins (ANI > 99%) were obtained. Sulfurimonas bins were identified and refined using Anvi’o interactive interface (v6.2)84 after the Anvi’o contig database was built to calculate k-mer frequencies to identify open reading frames using Prodigal (v2.6.3)85 and single-copy genes using HMMER (v3.2.1)86, and to classify the bins on the basis of single-copy gene taxonomy of GTDB87 using DIAMOND (v0.9.14)88. Sequences of 16S rRNA genes were extracted with RNAmmer (v1.2)89. Refined Sulfurimonas bins were repeatedly re-assembled using BBmap (99% similarity) and SPAdes, removing contigs smaller than 1 kb after each re-assembly step to extend contigs and reduce the size of genome gaps. Completeness and redundancy of the final Sulfurimonas MAGs were evaluated using CheckM (v1.2.1; based on 104 bacterial single-copy genes)90, CheckM2 (v0.1.3; based on machine learning algorithm)91 and BUSCO (v5.2.2; based on 628 Campylobacterales single-copy genes)92. The number of transfer RNAs was identified using ARAGORN (v1.2.36)93. We obtained two almost complete Sulfurimonas MAGs, named MAG-1 and MAG-2 (Supplementary Table 2). These two MAGs represent consensus MAGs, which are based on 16 individual bins produced from different environmental samples. Proteins from the final Sulfurimonas MAGs were predicted and annotated using Prokka (v1.11)94. The Prokka-predicted proteins were additionally annotated with Pfam (release 30)95 and TIGRFAM (release 14)96 profiles using HMMER searches (v3.1b2)86 and by the identification of KEGG Orthology numbers with the GhostKOALA webserver97. The proteins were also assigned to clusters of orthologous groups (COGs)98 using the software COGsoft (v4.19.2012)99 and transmembrane motifs were identified using TMHMM (v2.0)100. On the basis of the various annotation tools, the annotation of proteins of specific interest was manually refined. The sequences of hydrogenases were classified using HydDB101. Iron-related genes were identified using FeGenie’s tool and database102. RedoxyBase103 and SORGOds104 were used to identify classes of peroxidase and types of superoxide reductase, respectively./p>98 and coverage >97%: JN874148.1 and JN874176.1; GeneBank nucleotide; accessed May 2020). The sequences of Sulfuricurvum kujiense from SILVA SSU r138 RefNR (n = 3) were used as outgroup. Sequences were aligned with MAFFT using the L-INS-i method with default settings114, and the alignment was cleaned with BMGE with default setting115. Both programmes were used on the Galaxy platform116. A maximum-likelihood-based tree was constructed using W-IQ-TREE117, first searching for the best substitution model118 before evaluating branch support using 1,000 ultrafast boostrap (UFBoot) and SH-aLRT branch test replicates. Evolutionary placement algorithm (EPA) in RAxML (v8.2.4)119 was applied to add 253 partial Sulfurimonas 16S rRNA gene sequences (250−1,400 bp retrieved from GenBank nucleotide database; data accessed May 2020) to the tree without changing its topology. Further partial 16S rRNA gene sequences of Sulfurimonas sp. obtained from previous next-generation sequencing studies conducted in deep-sea hydrothermal fluids (JAH_MCR_Bv6_MCR_CTD03_08; JAH_AXV_Bv6v4_FS788; downloaded from vamps.mbl.edu) and plumes (PRJEB36848; SRP016119; PRJNA638507) were likewise added to the tree./p>85% and redundancy <5%) from GenBank (accessed January 2020). Supplementary Table 9a reports information for each isolate genome and MAG. DNA and amino acid sequences of the genomes, including US. pluma MAG-1 and MAG-2, were stored in an Anvi’o’s genome database (programme ‘anvi-gen-genomes-storage’). From the genome database, we computed the pangenome to identify the gene clusters (programme ‘anvi-pan-genome’) representing sequences of one or more predicted open reading frames (Prodigal v2.6.3)85 grouped together on the basis of their homology at the translated DNA sequence level. For multiple sequences alignments, Anvi’o used MUSCLE (v3.8.1551)121, the MCL algorithm to identify clusters in amino acid sequence similarity122 and the programme ‘anvi-run-ncbi-cogs’ to annotate genes with functions by searching them against the COG database (October 2019 release)98 using blastp v2.9.0+123. ANI was computed for all Sulfurimonas species and MAGs representative for different environments (that is, hydrothermal vent and plume, marine pelagic, marine oxic aquifer, costal and terrestrial) with the anvi’o programme ‘anvi-compute-genome-similarity’./p>
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