Nov 08, 2023
수소영양성 설푸리모나스는 전 세계적으로 심해에 풍부합니다.
자연미생물학 8권,
자연 미생물학 8권, 651~665페이지(2023)이 기사 인용
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측정항목 세부정보
박테리아 속 Sulfurimonas(Campylobacterota 문)의 구성원은 해양 산화환원선의 미생물 군집을 지배하며 황과 질소 순환에 중요합니다. 여기에서 우리는 중앙 북극해와 남서부 인도 해령의 Gakkel Ridge에서 나온 Sulfurimonas를 특성화하기 위해 메타게노믹스와 대사 분석을 사용하여 이 종이 전 세계 해양을 가로지르는 Mid Ocean Ridges의 비부력 열수 기둥에 어디에나 존재한다는 것을 보여주었습니다. 설푸리모나스 종 중 하나인 US설푸리모나스 플루마(USulfurimonas Pluma)는 전 세계적으로 풍부하고 추운(<0-4°C), 산소 포화 및 수소가 풍부한 열수 기둥에서 활동하는 것으로 밝혀졌습니다. 미국의 다른 Sulfurimonas 종과 비교. Pluma는 A2형 산화효소의 획득과 질산염 및 아질산염 환원효소의 손실을 포함하여 수소를 에너지원으로 사용하는 호기성 화학무기영양 대사의 감소된 게놈(>17%)과 게놈 특징을 가지고 있습니다. 미국의 지배력과 독특한 틈새 시장. 열수 기둥의 깃털은 심해에서 Sulfurimonas의 생지화학적 역할이 제대로 평가되지 않았음을 암시합니다.
Sulfurimonas 속은 Campylobacterota 문(이전 Epsilonproteobacteria 강)에 속합니다. 이는 원래 심해 열수 분출구에서 수집된 퇴적물로부터 Sulfurimonas autotrophica를 분리한 후에 제안되었습니다1. 그 이후로 12종의 서로 다른 설푸리모나스(Sulfurimonas) 종이 산소 결핍 환경에서 분리되었습니다2,3,4,5,6,7,8,9,10,11. 16S rRNA 유전자 서열을 기반으로 하는 이 중온성 및 화학석독립 영양 박테리아 속은 어디에나 존재하며 심해 열수 분출구의 황화 환경을 포함하여 산화환원선 환경에 서식하는 미생물 군집의 지배적인 구성원입니다. Sulfurimonas 속의 설명된 구성원은 다른 열수 Campylobacterota 구성원(즉, Sulfuruvum16) 및 해양 황 산화제(즉, SUP0518,19). 그러나 풍부한 Sulfurimonas 16S rRNA 유전자 서열은 열수 기둥의 비부력 단계에서도 보고되었습니다. 열수 기둥은 해저에서 방출되는 뜨거운 무산소 열수 유체가 차가운 산소가 함유된 해수와 혼합되는 곳에서 발생합니다. 그들은 해저에서 수백 미터까지 올라갈 수 있으며 근원지로부터 수천 킬로미터 떨어진 곳으로 흩어질 수 있습니다25. 부력이 없는 단계에서 열수 기둥은 대부분 차갑고 산소가 포화된 바닷물과 매우 묽은 열수 혼합물(<0.01%)로 구성됩니다25,26. 이러한 이유로 부력이 없는 열수 기둥은 설퍼리모나스의 영구적인 서식지 및 서식지로 간주되지 않습니다. 이러한 깃털에서 Sulfurimonas 서열이 반복적으로 검출되는 것은 해저 및 해저 환경으로부터의 수동적 이동에 의해 설명되었습니다26. 그러나 부력이 없는 기둥이 설푸리모나스의 특정 구성원의 성장에 적합한 환경을 제공하는지 여부를 직접적으로 테스트한 연구는 없습니다. 열수 기둥에는 상당량의 무기 환원 가스(H2S, CH4 및 H2)와 금속(Fe, Mn, Cu, Zn 및 Co)27이 포함되어 있으며 이는 해양 화학에 상당한 영향을 미칩니다28. 따라서 기둥에서 자라는 미생물의 생리학을 식별하고 해명하는 것은 해양의 생지화학을 이해하는 데 매우 중요합니다.
본 연구에서는 열수기둥 내 Sulfurimonas의 분포와 기능을 조사했습니다. 우리는 Gakkel Ridge의 두 개의 통풍구 기둥과 SWIR(Southwest Indian Ridge)의 한 기둥에서 리보타입, 유전자형 및 대사를 연구하고 이를 Mid Ocean Ridges의 다른 통풍구 기둥 및 Sulfurimonas sp.를 호스팅하는 기타 환경에서 공개적으로 사용 가능한 데이터와 비교했습니다. 우리의 가설은 부력이 없는 열수 기둥이 설푸리모나스의 특정 구성원에게 적합한 환경이라는 것입니다.
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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