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박테리아에 의한 유기물의 분해는 육상 생태계의 영양분 순환에 중요한 역할을 하는 반면, 바이러스의 중요성은 여전히 잘 알려져 있지 않습니다. 여기에서 우리는 산업 규모의 고열성 퇴비화(HTC) 동안 영양 순환에 대한 중온성 및 호열성 박테리아와 바이러스의 중요성을 연구하기 위해 메타게노믹스와 메타전사체학을 시간적 샘플링과 결합했습니다. 우리의 결과는 중온성 및 호열성 박테리아에 특정한 바이러스가 숙주 밀도를 추적하여 HTC 동안 하향식 제어를 통해 미생물 군집 계승을 촉발하는 바이러스-박테리아 밀도 역학 및 활동이 밀접하게 결합되어 있음을 보여줍니다. 더욱이, 중온성 박테리아에 특이적인 바이러스는 탄소 순환과 관련된 여러 보조 대사 유전자(AMG)를 암호화하고 발현하여 박테리아와 함께 영양분 전환에 영향을 줍니다. 영양분 회전율은 바이러스-숙주 비율과 양의 상관관계가 있었으며, 이는 생태계 기능, 바이러스 풍부도 및 바이러스 활동 사이의 양의 관계를 나타냅니다. 대부분의 검출된 RNA 바이러스는 진핵생물과 연관되어 있고 퇴비화의 호열성 단계 동안 영양분 순환과 연관되지 않았기 때문에 이러한 효과는 주로 DNA 바이러스에 의해 주도되었습니다. 우리의 연구 결과는 DNA 바이러스가 세포 용해를 통해 박테리아 바이오매스를 재활용하고 주요 AMG를 발현함으로써 HTC 동안 영양 순환을 촉진할 수 있음을 시사합니다. 따라서 바이러스는 생명공학 및 농업 시스템의 생산성을 최적화하기 위해 기능하는 미생물 생태계의 지표로 잠재적으로 사용될 수 있습니다.
유기물의 분해는 육상 생태계 전반의 영양분 순환과 생산성에 영향을 미치는 핵심 생태계 과정입니다[1]. 박테리아와 곰팡이 군집 모두 "미생물 루프"를 통해 영양분을 재활용하는 데 중요한 역할을 하는 것으로 알려져 있지만[1], 박테리아 바이러스, 즉 박테리오파지(파지)의 역할은 아직 잘 알려져 있지 않습니다[2]. 지구상에서 가장 풍부한 생물학적 존재인 바이러스는 세포 용해를 통해 미생물 사망률을 높이는 데 중요한 역할을 하며[3, 4], 영양분 방출을 통해 요소 순환에 큰 영향을 미치고 미생물 군집 구성, 다양성 및 미생물 괴사량에 영향을 줍니다[5,6, 7,8,9,10]. 해양의 영양 순환에 대한 바이러스의 중요성은 잘 알려져 있지만[11], 우리는 바이러스가 토양에서 영양분의 전환과 유기물의 광물화를 어떻게 조절하는지 이해하기 시작했을 뿐입니다[9, 10, 12]. 글리코시드 가수분해효소 및 메탄 대사와 관련된 바이러스 암호화 보조 대사 유전자(AMG)는 토양 생태계의 탄소 순환에 기여하는 것으로 입증되었습니다[4, 6, 13]. 예를 들어, 기능적 활성이 확인된 엔도만나나제와 같은 9개의 글리코시드 가수분해효소 계열을 포함하는 14개의 AMG는 바이러스가 복잡한 탄소 분해에 참여할 수 있는 잠재적인 능력을 가지고 있음을 나타냅니다[4]. 토양에서 박테리아 세포의 용해를 통해 영양 순환을 촉진하는 것 외에도 바이러스는 최근 박테리아 숙주의 대사 능력을 향상시키는 보조 대사 유전자(AMG)를 암호화하여 환경 스트레스 하에서 숙주 박테리아의 생존을 향상시키는 것으로 나타났습니다 [14]. 이러한 최근의 발전에도 불구하고 우리는 바이러스와 박테리아가 어떻게 함께 육상 생태계 전반에 걸쳐 영양분 순환과 유기물 분해를 주도하는지에 대해 여전히 제한된 이해를 갖고 있습니다[15].
여기서 우리는 유기물 분해에서 중온성 및 호열성 박테리아와 그 바이러스의 역할을 연구하기 위한 모델 시스템으로 고열성 퇴비화(HTC)를 사용했습니다. HTC는 도시 또는 농업 고형 폐기물의 유기물 분해에 사용되는 폐기물 처리 기술로 호열성 박테리아 군집 활동으로 인해 외부 가열 없이 극도로 높은 온도(최대 90°C)에 도달합니다[16,17,18]. HTC에는 초호열성(>80°C), 호열성(>50°C) 및 성숙 단계(주변 온도)의 세 가지 주요 온도 단계가 포함되어 있습니다. 이 과정에서 탄소 및 질소가 풍부한 고분자 물질(리그노셀룰로오스, 단백질, 다당류 및 지질)은 퇴비화의 호열성 단계에서 분해되는 반면, 천천히 분해되는 부식질이 풍부한 화합물은 숙성 단계에서 분해됩니다. HTC [19] 동안 퇴비화 온도에 따라 유기물 분해를 동적으로 변화시키는 미생물 군집의 구성과 고온성, 내열성 분류군(Firmicutes, Bacillus 및 Deinococcota, Thermus)은 퇴비화 동안 유기물의 분해에 중요합니다. 호열성 단계 [16]. 미생물 군집 조립 및 유기물의 분해와 관련하여 온도, 원료 및 물리화학적 퇴비화 특성의 영향이 광범위하게 연구되었지만[20, 21], HTC 동안 바이러스의 역할에 대해서는 알려진 바가 거의 없습니다.
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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