ISME Journal volume 17、pages 916–930 (2023)この記事を引用する
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細菌による有機物の分解は陸上生態系における栄養循環に主要な役割を果たしていますが、ウイルスの重要性は依然として十分に理解されていません。 今回我々は、メタゲノミクスとメタトランスクリプトミクスを時間的サンプリングと組み合わせて、工業規模の超好熱性堆肥化（HTC）中の栄養循環における中温菌と好熱性細菌とそのウイルスの重要性を研究した。 我々の結果は、ウイルスと細菌の密度動態と活動が密接に結びついており、中温菌と好熱菌に特有のウイルスがその宿主密度を追跡し、HTC中のトップダウン制御を介して微生物群集の継承を引き起こすことを示している。 さらに、中温菌に特異的なウイルスは、炭素循環に関連するいくつかの代謝補助遺伝子（AMG）をコードして発現し、細菌とともに栄養素の代謝回転に影響を与えました。 栄養素の代謝回転はウイルスと宿主の比率と正の相関があり、生態系の機能、ウイルスの存在量、ウイルスの活動の間に正の関係があることを示しています。 検出された RNA ウイルスのほとんどは真核生物に関連しており、堆肥化の好熱期中の栄養循環には関連していなかったため、これらの影響は主に DNA ウイルスによって引き起こされました。 私たちの発見は、DNAウイルスが細胞溶解を通じて細菌バイオマスをリサイクルし、主要なAMGを発現することによって、HTC中に栄養循環を促進する可能性があることを示唆しています。 したがって、ウイルスは、バイオテクノロジーおよび農業システムの生産性を最適化するために機能する微生物生態系の指標として使用できる可能性があります。
有機物の分解は重要な生態系プロセスであり、陸上生態系全体の栄養循環と生産性に影響を与えます[1]。 細菌群集と真菌群集の両方が「微生物ループ」を介した栄養素のリサイクルにおいて重要な役割を果たしていることが知られています[1]が、細菌ウイルス、つまりバクテリオファージ（ファージ）の役割はまだ十分に理解されていません[2]。 地球上で最も豊富な生物学的実体であるウイルスは、細胞溶解を介して微生物の死亡率を高める上で重要な役割を果たしており[3、4]、栄養素の放出を介して元素循環に重大な影響を与え、微生物群集の構成、多様性、微生物壊死量に影響を与えています[5、6、 7、8、9、10]。 海洋における栄養循環におけるウイルスの重要性は十分に確立されている[11]が、ウイルスが土壌中の栄養素の代謝回転と有機物の無機化をどのように調節するのかは理解し始めたばかりである[9、10、12]。 グリコシド加水分解酵素とメタン代謝に関連するウイルスにコードされた補助代謝遺伝子 (AMG) は、土壌生態系における炭素循環に寄与していることが実証されています [4、6、13]。 例えば、機能活性が確認されたエンドマンナナーゼなど、9 つのグリコシドヒドロラーゼファミリーを含む 14 の AMG は、ウイルスが複雑な炭素分解に関与する潜在的な能力を持っていることを示しています [4]。 土壌中の細菌細胞の溶解を介して栄養循環を促進することに加えて、ウイルスは最近、細菌宿主の代謝能力を高める補助代謝遺伝子（AMG）をコードすることによって、環境ストレス下での宿主細菌の生存を改善することが示されている[14]。 これらの最近の進歩にもかかわらず、ウイルスと細菌がどのようにして陸域生態系全体で栄養循環と有機物の分解を促進するかについては、まだ限られた理解しかありません[15]。
今回我々は、超好熱性堆肥化（HTC）をモデルシステムとして使用し、有機物の分解における中温菌および好熱菌とそのウイルスの役割を研究しました。 HTC は都市廃棄物または農業固形廃棄物の有機画分の分解に使用される廃棄物処理技術であり、好熱性細菌群集の活動による外因性加熱なしで極度の高温 (最大 90 °C) に到達します [16、17、18]。 HTC には、超好熱性 (>80 °C)、好熱性 (>50 °C)、および成熟段階 (周囲温度) の 3 つの主要な温度段階が含まれます。 プロセス中、炭素と窒素が豊富なポリマー物質 (リグノセルロース、タンパク質、多糖類、脂質) は堆肥化の好熱段階で分解され、ゆっくりと分解するフミン質が豊富な化合物は成熟段階で分解されます。 有機物の分解を促進する微生物群集の組成は、HTC 中の堆肥化温度に応じて動的に変化します [19]。好熱性で耐熱性の分類群 (Firmicutes、Bacillus および Deinococcota、Thermus) は、HTC 中の有機物の分解に重要です。好熱性相 [16]。 温度、原材料、堆肥化の物理化学的性質の影響は、微生物群集の集合と有機物の分解に関連して広く研究されている[20、21]が、HTC中のウイルスの役割についてはほとんど知られていない。
90 °C), thermophilic phase (from day 10 to 26: >55 °C) and maturation phase (from day 27 to 45: <45 °C). To cover changes during the whole composting process, eight samples from five compost piles were collected at different phases of hyperthermophilic composting on days 0 (D0), 4 (D4), 7 (D7), 9 (D9), 15 (D15), 21 (D21), 27 (D27), 33 (D33) and 45 (D45). To obtain well-distributed and homogenized samples, each pile was diagonally divided into five domains, and each domain was sampled from the same location at a depth of 40–50 cm at different sampling time points. Within each pile, five subsamples (5000 g each) per domain were collected, and then mixed into a single composite sample, which was further divided into two aliquots. One replicate aliquot was stored in liquid nitrogen for biological analyses and the other was kept at 4 °C for physicochemical analyses. An automatic temperature controller was used to determine temperature changes during the composting./p>30 and length >36 bases) [35]. All high-quality sequences were co-assembled using SPAdes v3.13.1 with the parameters “-k 33, 55, 77, 99, 111,127 --meta” [36]. We also assembled reads generated at each thermal phase of composting separately (composting phase-specific assemblies) using SPAdes with the same parameters. All assembled scaffolds longer than 2.0 kb were binned using metawrap [37] based on MetaBAT2 [38], MaxBin2 [39], and Concoct [40] with default parameters. Bins were further manually curated to obtain high-quality genomes using Bin_refinement module in Metawrap [37]. The completeness and contamination of genome bins were assessed using CheckM v1.0.13 [41], and metagenome-assembled genomes (MAGs) with more than 50% completeness and less than 10% contamination level were retained for further analyses. Bins from different samples were dereplicated to produce medium to high quality genomes using dRep v.2.3.2 [42] and assigned to taxonomic classifications based on the Genome Taxonomy Database (GTDB; release 03-RS86) using the GTDB-Tk toolkit (v.0.3.2) with the classify workflow [43]. To construct bacterial MAGs, genes were called using Prodigal with parameters “-p meta” [44] and annotated against the KEGG and Pfam databases using the Diamond tool [45]. The predicted proteins were screened for candidate CAZymes using hmmscan module from HMMER v3.2.1 and dbCAN database (cutoffs: coverage fraction: 0.40; e-value:1e-18) [46]. Genes encoding proteases and peptidases were identified using Diamond against the MEROPS database release 12.0 (cutoffs: e-value 1e-20 -accel 0.8). Ribosomal RNAs were predicted using RNAmmer v1.2 [47]. The optimal growth temperature (OGT) of MAGs was predicted by the machine learning method using the Tome v1.1 [48]. Thermophilic MAGs were defined as ones with OGT ≥ 50 °C, while MAGs were assigned as mesophilic when their OGT < 50 °C. To build phylogenetic MAG trees, the “classify” workflow in GTDB-Tk (v.0.3.2; default settings) was used to identify 120 bacterial marker genes, which were used for tree construction based on multiple sequence alignment. The resulting FASTA files containing multiple sequence alignments of the submitted genomes were used for maximum likelihood phylogenetic tree inference using FastTree v.2.1.10 with the default parameters [49]. Newick tree output files were visualized with iTOL v.5 [50]./p>1000 bp, composting phase-specific assemblies) were compared against the database containing all available viral RdRp gene sequences in NCBI/GenBank (37, 441 genes, downloaded on February 2023) and previous published studies [77, 78] using Diamond BLASTx (coverage ≥ 70%, E-value≤1e-10 and score ≥ 70). Sequences that had hits in the RdRp database with the RdRp core domain were considered as the potential RNA viruses [80]. This analysis identified 109 contigs with RdRp gene. These potential RNA virus contigs were clustered with CD-HIT using 95% average nucleotide identity across 85% alignment fraction, resulting in a total of 83 potential RNA viruses./p>5 kb) were obtained from the metatranscriptomic assemblies. After clustering (95% nucleotide similarity and over 85% coverage), a total of 41 dsDNA viral operational taxonomic units (dsvOTUs) were retained. By comparing the viruses’ contigs derived from transcriptomic data and metagenomic data, only 7 of 68 dsvOTUs could be assembled from the metagenomic data. This is not surprising as the DNA was removed during RNA library preparation and very few DNA sequences was retained in transcriptome./p>80 °C for 9 days (“hyperthermophilic phase”), after gradually declining to 55 °C (“thermophilic phase”) and ambient temperature by day 27 (“maturation phase”, Fig. 1a). The organic matter (OM) decomposition, carbon, and nitrogen turnover followed closely different phases of HTC (Fig. 1a). Compared to the initial composting raw materials, total carbon (TC, F3,23 = 33.6, p < 0.0001) and nitrogen (TN, F3,23 = 19.8, p < 0.0001) contents significantly decreased by 32% and 28% by the end of HTC, respectively (Fig. S1a). Similarly, the OM content that showed the highest degradation rate at the hyperthermophilic phase declined from 51.3% to 38.7% (F3,23 = 68.3, p < 0.0001), while the concentration of water-soluble carbon (WSC, F3,23 = 19.8, p < 0.0001) and water-soluble nitrogen (WSN, F3,23 = 26.4, p < 0.0001) increased during HCT, reaching peak concentrations at the hyperthermophilic phase (Fig. 1a). The degradation rate of OM correlated positively with temperature, WSC, and WSN (Fig. S1b), indicative of efficient nutrient cycling during HTC./p>5 kb) were obtained from the metagenomic assemblies. After clustering (95% nucleotide similarity and over 85% coverage), a total of 1297 viral operational taxonomic units (vOTUs) were retained (Table S1), which mainly belonged to double-stranded DNA viruses (97%) and were predicted to be mostly lytic (66.2%). The genome quality of vOTUs consisted of 0.7% of high-quality, 2.4% of medium-quality, and 85.6% low-quality vOTUs, while the quality of remaining 11.3% of vOTUs could not be determined. Overall, 78.6% of vOTUs were detected during non-thermophilic phases (D0 and D27), while 21.3% occurred during thermophilic phases (D4 and D15, Table S1). Only 7.7% of vOTUs could be clustered with taxonomically known viruses in RefSeq database (v216, released in February 2023), while only 35 vOTUs (2.6%) clustered with known viruses in IMG/VR (v3) database, suggesting that most of the composting viruses were novel. Primarily, they belonged to Dividoviricota (88%) and Uroviricota (2%) phyla and Mesyanzhinovviridae (27.2%), Herelleviridae (18.2%), Salasmaviridae (16.4%), Autographiviridae (5.4%), Vilmaviridae (5.4%) and Matshushitaviridae (3.6%) families (Table S1 and Fig. S4). Similar to bacteria, the richness (F3.8 = 4.7, p = 0.0359) and composition (R2 = 0.78, p < 0.001, PERMANOVA test) of viral communities followed different phases of HTC (Fig. 1d). While Vilmaviridae (37.8%) and Autographiviridae (14.5%) were dominant viruses in the composting raw material (D0), Vilmaviridae abundances significantly decreased to 1.5% by the maturation phase (D27; F3,8 = 9.7, p = 0.0047, Fig. S4). In contrast, relative abundance of Matshushitaviridae family under Dividoviricota phylum (consisting mainly of thermophile-associated Thermus phages) increased from 1.4% at D0 to 66.3% at D15 (F3,8 = 5.7, p = 0.0245, Fig. S4). As most of viruses could not be classified, viral abundances were also investigated based on their predicted host taxonomy (see Methods). The viral taxa abundances followed bacterial taxa abundances (Fig. 1d), and for example, the abundance of viruses infecting Deinococcota clearly increased with rising composting temperature by D15. Moreover, Matshushitaviridae viral abundances correlated positively with their Firmicute (R2 = 0.34, p = 0.028) and Deinococcota (R2 = 0.53, p = 0.0042, Fig. S5) host abundances. Overall, changes in viral community richness (R2 = 0.50, p = 0.0058) and composition (beta-dissimilarity, R2 = 0.71, p < 0.0001) correlated positively with changes in bacterial community richness and composition (Fig. 2a, b)./p> 50 °C) bacteria (upper panel) and 180 mesophilic and 47 thermophilic MAGs (lower panel) during HTC. b Box plots and heatmap representing changes in the transcriptional activity of viruses associated with mesophilic and thermophilic bacteria (upper panel) and individual vOTUs (lower panel) during HTC. Box plots and heatmaps representing changes in the transcriptional activity of mesophilic (OGT < 50 °C) and thermophilic (OGT > 50 °C) bacteria during HTC based on mean (upper panel) and individual (lower panel) MAGs (including 180 mesophilic and 47 thermophilic MAGs) in association to carbon (CAZyme) (c) and nitrogen metabolism genes (d). e Box plot and heatmap representing changes in the transcriptional activity of virus-associated carbon (CAZyme) metabolism genes linked with mesophilic MAGs (OGT < 50 °C). In all (a–e), the mean transcriptional activity (MAGs and vOTUs) shown in boxplots is based on transcript abundances (transcripts per million, TPM) normalized by MAG and vOTU abundances. Box plots encompass 25–75th percentiles, whiskers show the minimum and maximum values, and the midline shows the median (dots present the biologically independent samples, asterisks denote for significant differences (*p < 0.05, **p < 0.01. n.s, no significant differences). Heatmaps show the transcriptional activity (MAGs or vOTUs) based on non-normalized transcripts abundances (transcripts per million, TPM). In (c and d), selected CAZymes include GHs, GTs, PLs, CEs, CBMs, and AAs. Nitrogen metabolic pathways include assimilatory nitrate reduction, dissimilatory nitrate reduction, nitrification, and nitrogen fixation pathways. More detail about the functional genes included can be found in Supplementary Data 6 and 7, respectively./p>5 kb) were obtained from the metatranscriptomic assemblies. After clustering (95% nucleotide similarity and over 85% coverage), a total of 41 dsDNA viruses were retained. By comparing the dsDNA viruses contigs derived from transcriptomic data and metagenomic data, 89% viruses (61 of 68) could be assembled from both transcriptomic and metagenomic data, suggesting that very few dsDNA viruses exclusively exist in metatranscriptomic dataset. As a result, the RNA viral community abundance (based on beta-dissimilarity of abundance matrix) did not correlate with changes in composting properties (Mantel statistic r = 0.0173, p = 0.35), which suggests that they did not contribute to the nutrient cycling during composting./p>90 °C) and consistently surpassed bacterial abundances in terms of virus-host abundance ratio. Mesophilic and thermophilic bacteria and their viruses showed clear microbial community succession, where the initial phase of composting was dominated by mesophiles, which were subsequently replaced by thermophiles and subsequently by mesophiles towards the end of the HTC. Although similar compositional succession of bacterial and fungal communities have been observed in previous composting experiments [19, 90], this is the first evidence demonstrating that viruses can also drive ecological succession in microbial communities during HTC. These findings are also indicative of “Kill-the-Winner” hypothesis, where viruses target and regulate the most abundant group of host bacteria, reducing the dominance effects and evening out competition between different bacterial taxa [4, 89]. Such dynamics could explain the observed community shift between thermophilic and maturation phases of HTC, where thermophilic viruses likely drove down the abundances of thermophilic bacteria, giving rise to mesophilic bacteria and their phages. For example, Thermus and Planifilum bacterial genera play important role in heat production during HTC [17] and several lytic phages that infected Thermus thermophilus (T_bin.227) and Planifilum fulgidum (T_bin.201) were identified, including five potentially novel Thermus viruses that had genome sizes about 5 kbp similar to hyperthermophilic phage φOH3 isolated from Obama hot spring [91]. While 61% of detected phages were predicted to be lytic, it is possible that some of the correlations between bacterial and viral taxa were also driven by lysogenic phages or prophages because unfiltered DNA samples were used for metagenomics. As a result, our dataset likely underestimates phage diversity, and phage enrichment [92] should be used in future studies. Moreover, future work should also consider the potential role of RNA viruses for HTC, which we did not explore in detail as compost-associated bacteria are most often associated with DNA viruses [19]. Nevertheless, our results suggest that a small portion of thermophilic viruses played a key role in microbial activity during the thermophilic phase of HTC, indicating that compost ecosystem functioning was at least temporally driven by low-diversity microbial communities. Terrestrial phages could hence be important drivers of biogeochemical cycling in soil ecosystems via “viral shunt” akin to marine phages [11, 93]./p>
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