Nov 08, 2023
Гидрогенотрофный Sulfurimonas широко распространен по всему миру в глубоких
Природная микробиология, том 8,
Природная микробиология, том 8, страницы 651–665 (2023 г.) Процитировать эту статью
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Представители бактериального рода Sulfurimonas (тип Campylobacterota) доминируют в микробных сообществах морских редоксклинов и играют важную роль в круговороте серы и азота. Здесь мы использовали метагеномику и метаболический анализ, чтобы охарактеризовать Sulfurimonas с хребта Гаккеля в центральной части Северного Ледовитого океана и юго-западной части Индийского хребта, показав, что этот вид повсеместно распространен в неплавучих гидротермальных шлейфах на срединно-океанических хребтах по всему Мировому океану. Было обнаружено, что один вид Sulfurimonas, USulfurimonas plama, распространен по всему миру и активен в холодных (<0–4 °C), насыщенных кислородом и богатых водородом гидротермальных шлейфах. По сравнению с другими видами Sulfurimonas, US. Pluma имеет редуцированный геном (> 17%) и геномные признаки аэробного хемолитотрофного метаболизма с использованием водорода в качестве источника энергии, включая приобретение оксидазы типа А2 и потерю нитрат- и нитритредуктазы. Доминирование и уникальная ниша США. Pluma в гидротермальных шлеймах указывает на недооцененную биогеохимическую роль Sulfurimonas в глубоководном океане.
Род Sulfurimonas принадлежит к типу Campylobacterota (бывший класс Epsilonproteobacteria). Первоначально это было предложено после выделения Sulfurimonas autotropica из отложений, собранных в глубоководных гидротермальных источниках1. С тех пор 12 различных видов Sulfurimonas были выделены из среды с дефицитом кислорода2,3,4,5,6,7,8,9,10,11. Судя по последовательностям генов 16S рРНК, этот мезофильный и хемолитоавтотрофный род бактерий является повсеместным и доминирующим членом микробных сообществ, населяющих редоксклинальные среды12, включая сульфидные среды глубоководных гидротермальных источников13,14,15,16,17. Описанные представители рода Sulfurimonas занимают местообитания с умеренными температурами, повышенными концентрациями сероводорода и низкими концентрациями кислорода (<40 мкМ) по сравнению со местообитаниями других гидротермальных представителей рода Campylobacterota (т.е. Sulfuruvum16) и морских окислителей серы (т.е. СУП0518,19). Тем не менее, многочисленные последовательности гена 16S рРНК Sulfurimonas также были обнаружены на неплавучей стадии гидротермальных шлейфов14,20,21,22,23,24. Гидротермальные шлейфы возникают там, где горячие бескислородные гидротермальные жидкости, выбрасываемые с морского дна, смешиваются с холодной насыщенной кислородом морской водой. Они могут подниматься на сотни метров над морским дном и рассеиваться на тысячи километров от своего источника25. На неплавучей стадии гидротермальные плюмы состоят в основном из холодной и насыщенной кислородом морской воды с сильно разбавленными примесями гидротермального флюида (<0,01%)25,26. По этой причине неплавучие гидротермальные шлейфы не считаются постоянной нишей и средой обитания Sulfurimonas. Повторное обнаружение последовательностей Sulfurimonas в таких шлеймах объяснялось пассивным переносом со дна моря и из подводной среды26. Однако ни одно исследование не проверяло напрямую, обеспечивают ли неплавучие шлейфы подходящую среду для роста конкретных представителей Sulfurimonas. Гидротермальные шлейфы содержат значительные количества неорганических восстановленных газов (H2S, CH4 и H2) и металлов (Fe, Mn, Cu, Zn и Co)27, которые оказывают значительное влияние на химию океана28. Таким образом, идентификация и выяснение физиологии микроорганизмов, растущих в шлейфе, имеют решающее значение для понимания биогеохимии океана.
В данном исследовании мы исследовали распределение и функцию Sulfurimonas в гидротермальных шлеймах. Мы изучили его риботипы, генотипы и метаболизм в двух жерловых плюмах хребта Гаккеля и в одном плюме Юго-Западного Индийского хребта (SWIR) и сравнили их с общедоступными данными из других жерловых плюмов Срединно-Океанских хребтов и других сред обитания Sulfurimonas sp. Наша гипотеза состоит в том, что неплавучие гидротермальные шлейфы являются подходящей средой для определенных представителей Sulfurimonas.
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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