ISME 커뮤니케이션 3권, 기사 번호: 84(2023) 이 기사 인용
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해양 미생물 군집에 대한 연구가 증가하고 있지만 해수 샘플링 프로토콜이 다양하기 때문에 연구를 비교하기가 어렵습니다. 연구원들이 다양한 해수 샘플링 방법론을 사용하는 연구를 상호 비교하고 향후 샘플링 캠페인을 설계하는 데 도움을 주기 위해 EMOSE(EuroMarine Open Science Exploration Initiative)를 개발했습니다. EMOSE 프레임워크 내에서 우리는 NW 지중해의 단일 관측소(Service d'Observation du Laboratoire Arago [SOLA], Banyuls-sur-Mer)에서 하루 동안 수천 리터의 해수를 샘플링했습니다. 결과 데이터 세트에는 다양한 재료 유형 종류의 필터(카트리지 멤브레인 및 평막), 세 가지 크기 분류(>0.22 µm, 0.22–3 µm, 3–20 µm 및 >20 µm)를 포함하는 다양한 해수 처리 접근 방식이 포함됩니다. 1L에서 1000L까지 다양한 해수의 양이 있습니다. 우리는 여과된 해수의 양이 시퀀싱 전략과 관계없이 원핵 생물과 원생 생물의 다양성에 큰 영향을 미치지 않는다는 것을 보여줍니다. 그러나 크기 비율 사이, 그리고 이들과 "전체 물"(사전 분류 없음) 사이에는 알파 및 베타 다양성에 명확한 차이가 있었습니다. 전반적으로, 서로 다른 기공 크기의 필터를 사용하는 데이터 세트의 데이터를 병합할 때 주의할 것을 권장하지만, 필터 유형과 부피가 테스트된 시퀀싱 전략에 대한 교란 변수로 작용해서는 안 된다는 점을 고려합니다. 우리가 아는 한, 공개적으로 이용 가능한 데이터 세트가 샘플링된 양의 대규모 변화를 포함하여 광범위한 프로토콜에 걸쳐 해양 미생물군집 방법론적 옵션의 영향을 효과적으로 설명할 수 있는 것은 이번이 처음입니다.
지구상 미생물의 특성은 학제간 관심의 주제가 되었습니다. 실제로, 이제 여러 생물 군계에 걸쳐 미생물 생태학에 대한 지식을 습득하는 것이 세포에서 생태계에 이르기까지 생명에 대한 더 깊은 이해를 발전시키는 데 중요하다는 것이 인식되고 있습니다. 그 결과, 대규모 국제 공동 연구 프로젝트가 인간[1, 2], 산호[3], 해초(https://seagrassmicrobiome.org/protocols/) 또는 해면[4, 5]과 관련된 미생물군집에 중점을 두었습니다. 또한 지난 20년 동안 글로벌 해양 샘플링(2003~2010) [6, 7], 국제 해양 미생물 조사(ICoMM) [8], Malaspina 2010 Circumnavigation Expedition [9] 및 Tara Ocean 탐험(2009-2012)[10], Earth Microbiome 프로그램[11, 12] 및 Micro B3 주도의 Ocean Sampling Day(OSD)와 같은 인구 조사 프로그램[13 ]. 전 세계 해양 미생물 생태학에 대한 발전과 전망, 이들의 관련성 및 미래 과제에 대한 세부 사항은 다른 곳에서 광범위하게 검토되었습니다[14].
현재 세계의 미생물군집을 연구하려는 엄청난 노력으로 인해 다양한 환경과 숙주 조직의 미생물군집을 샘플링하기 위한 공통 프로토콜의 사용과 공통 서열분석 절차를 포함한 여러 표준화 계획이 생겨났습니다. 방법론적 표준화 노력과 관련된 이니셔티브로는 OSD[13], Earth Microbiome[15], European Marine Omics Biodiversity Observatory Network(EMO BON) [16] 및 MetaHIT(MetaHIT) [17] 등이 있습니다.
자유 생활 및 숙주 관련 미생물 군집에 대한 대규모 분석은 미생물-동물[3, 5, 18, 19] 및 미생물-식물[20] 상호 작용은 물론 구조, 기능 및 상호 작용을 이해하는 데 큰 진전을 이루었습니다. 다양한 지구 서식지에 있는 미생물 군집의 다양성 [21]. 그러나 미생물 다양성을 샘플링하고 기술하고 연구하기 위한 모범 사례와 전략을 더 잘 표준화하고 조화시키기 위해서는 격차를 메울 필요가 있습니다. 특히 해양 미생물군집 연구에서는 미생물 풍부도 추정이 메타바코딩에 사용되는 마커 유전자 및 프라이머[22], 다양한 DNA 추출 프로토콜[23, 24], 염기서열분석 깊이 및 게놈 등 여러 요인에 따라 달라지는 것으로 알려져 있습니다. 접근법(앰플리콘 시퀀싱 대 메타게놈 시퀀싱) 및 클러스터링 기준[25]. 샘플링 전략이 미생물 플랑크톤 다양성 추정에 영향을 미치는 것으로 인식되고 있지만[25], 해양 미생물군집 다양성과 분류학적 구성을 연구하기 위해 샘플링 절차에 대한 방법론적 변수의 영향을 체계적으로 테스트하기 위한 연구는 부족합니다. 이러한 연구는 해양 미생물 군집의 전체 크기 범위를 샘플링하기 위한 정확한 프로토콜을 설계하는 데 중요합니다[26].
0.22 µm) and of the 0.22 µm to 3 µm size fractions used 142 mm diameter polyethersulfone Express Plus membrane filters (Product Code GPWP14250, Millipore). For the 3 µm to 20 µm fractionations, 142 mm diameter polycarbonate membrane filters were used (Product Code TSTP14250, Millipore). As for the large size fractions (>20 µm), the 47 mm diameter nylon mesh filter was used instead (referred to as flat membrane from here on)./p>20 µm). Additionally, whole water cartridge membrane volumes from 1 L to 10 L were also compared for the metagenomes. Below, we consider the prokaryotes and protists results independently./p>0.22 µm) or size fractions (0.22–3 µm, 3–20 µm and >20 µm) in columns. Color distinguishes between flat and cartridge membrane filters. Within each grid unit, the prokaryotic species richness is plotted against volume, which ranges from 2.5 L to 1000 L./p>0.22 µm) and 0.22–3 µm size fraction samples presented a similar number of prokaryotic taxonomic lineages (Fig. 3a) and both presented fewer prokaryotic taxonomic lineages than the 3–20 µm size fraction (Fig. 3a). Accordingly, the statistical test indicated significant differences in the species richness obtained after > 0.22 µm, 0.22–3 µm and 3–20 µm (p < 0.05, Kruskal–Wallis), more specifically, between >0.22 µm and 3–20 µm size fractions (p < 0.05, post-hoc Dunn test). On the metagenomes side, for the same comparison, there were no appreciable differences in the number of prokaryotic taxonomic lineages (Fig. 3a) and they were not significant (p > 0.05, Kruskal–Wallis). Details on the above-mentioned statistical tests are available in Supplementary Table S7./p>0.22 µm), 0.22–3 µm and 3–20 µm size fractions for the same volume (10 L) and filter (flat membrane), for MetaB16SV4V5 (left) and metagenomes (right). Note that metagenomes didn’t include samples in 3–20 µm size fraction in (a). b Comparison for size fractions (0.22–3 µm, 3–20 µm and > 20 µm size fractions) for the same volume (100 L) and filter (flat membrane), for MetaB16SV4V5 (left) and metagenomes (right). Note that metagenomes didn’t include samples in >20 µm size fraction in (b). c Comparison for flat membrane vs cartridge membrane, for the same volume (10 L) and whole water (>0.22 µm), for MetaB16SV4V5 (left) and metagenomes (right). d Comparison between 2.5 L (single filter) and 10 L (four 2.5 L filters pooled together), using the same filter (cartridge membrane) and whole water (> 0.22 µm), for MetaB16SV4V5 (left) and metagenomes (right). All panels illustrate the species richness obtained for each sample (point). To help the reader compare the variables, we added boxplots on top of the points. Significance was determined using either Mann–Whitney test for two independent groups, or Kruskall–Wallis for more than two independent groups, followed by a post-hoc Dunn test, if needed. Significance was illustrated with the symbols: p > 0.05 (empty); p < 0.05 (*); p < 0.01 (**); and p < 0.001 (***)./p>20 µm, using the flat membrane filter, which revealed an increase in the prokaryotic species richness with increasing pore size, for both MetaB16SV4V5 and metagenomes (Fig. 3b). In fact, the median number of prokaryotic taxonomic lineages obtained by MetaB16SV4V5 increased significantly from 335 (0.22–3 µm) to 429 (3–20 µm) and 538 (>20 µm) (p < 0.05, Kruskal–Wallis, Fig. 3b), more specifically between 0.22–3 µm and > 20 µm size fractions (p < 0.05, post-hoc Dunn test). Similarly, metagenomes increased the median number of prokaryotic taxonomic lineages from 155 (0.22–3 µm) to 195 (3–20 µm) (Fig. 3b), which was also significant (p < 0.05, Mann–Whitney). Details on the above mentioned statistical tests are available at Supplementary Table S7. Please note that for metagenomes in Fig. 3b there are no samples for the >20 µm size fraction because some samples were lost due to insufficient DNA for sequencing, while some samples that were successfully sequenced were later discarded due to low number of reads (below 10 000 reads, for a list of discarded samples in the rarefaction step see Supplementary Table S4). The overview of prokaryotic species richness was overall consistent and supported by the rarefaction curves because the different size fractions had similar levels of alpha diversity, while the same did not apply for volume (Supplementary Figs. S2 and S3)./p> 0.05, Mann–Whitney). Metagenomes provided an equivalent number of prokaryotic taxonomic lineages between either filter (Fig. 3c) and the differences were not significant (p > 0.05, Mann–Whitney). Although we compared cartridge and flat membrane filters under the same volume (10 L), the cartridge membrane filters reached 10 L by pooling together four cartridge membrane filters of 2.5 L. However, the single 2.5 L cartridge membrane filter and 10 L pooled from four cartridge membrane filters of 2.5 L obtained an equivalent number of prokaryotic taxonomic lineages, without significant differences (p > 0.05, Mann–Whitney) for either sequencing approach (Fig. 3d). Details on the above mentioned statistical tests are available at Supplementary Table S7./p>20 µm size fractions. Additionally, the volume did not follow any clear direction in the ordination figures (Fig. 4a, b). PERMANOVA tests were made to support the ordination figures, with similar results for MetaB16SV4V5 and metagenomes. Specifically, both volume and size fractions significantly changed the community composition (p < 0.05, PERMANOVA), but this result should be interpreted with caution, because if the same test considers the division of samples by size fraction, then community composition did not change significantly across volume (p > 0.05, PERMANOVA). Details on the PERMANOVA statistical tests for prokaryotes are available at Supplementary Table S8. The variation within size fractions, measured by distance to centroid, further supported the clustering of prokaryotic community composition by size fractions (Fig. 4c,d, Supplementary Table S9)./p>0.22 µm), 0.22–3 µm, 3–20 µm and >20 µm size fractions. Division by (a) MetaB16SV4V5 and (b) metagenomes. Additionally, boxplots represent the distance to centroids of samples within each size fraction, divided by (c) MetaB16SV4V5 and (d) metagenomes. Note that metagenomes didn’t include the >20 µm size fraction. For details on missing replicates, we refer the reader to Supplementary Table S1./p> 20 µm) using the same volume (100 L) and filter (flat membrane)./p>0.22 µm) for MetaB18SV9 showed any appreciable change in the protist species richness from 2.5 L (median = 343, IQR = 6.75, n = 4) to 10 L (median = 348, IQR = 34.8, n = 12) (Fig. 7). However, for either MetaB18SV9 and metagenomes, there was no appreciable difference in the protist species richness from 10 L to 1000 L, within any of the size fractions (Fig. 7). Comparing pore sizes, whole water (>0.22 µm), 3–20 µm and >20 µm size fractions identified more protist taxonomic lineages than 0.22–3 µm size fraction samples (Fig. 7). The number of protist taxonomic lineages obtained after each sample are available at Supplementary Table S10. The higher impact of size fraction, rather than volume, on protist species richness was further supported by rarefaction curves (Supplementary Figs. S2 and S3), even though the size fractions were not as distinct from one another as they were with the prokaryotic data./p>0.22 µm) or size fractions (0.22–3 µm, 3–20 µm and >20 µm) in columns. Color distinguishes between flat membrane and cartridge membrane filters. Within each grid unit, the protist species richness is plotted against volume. For details on missing replicates, we refer the reader to Supplementary Table S1./p>0.22 µm) or 3–20 µm size fraction (Fig. 8a). However, the range of the number of protist taxonomic lineages obtained for whole water included the range of values for both the 0.22–3 µm and 3–20 µm size fractions (Fig. 8a). More specifically, the number of protist taxonomic lineages obtained by MetaB18SV9 varied between 290 and 380 for the whole water, 289 and 338 for 0.22–3 µm size fraction, and 338 to 357 in 3–20 µm size fractions (Fig. 8a), which were not significantly different (p > 0.05, Kruskal–Wallis). The number of protist taxonomic lineages obtained by metagenomes varied between 88 and 129 for whole water, 91 and 97 for 0.22–3 µm, and 105 and 128 for 3–20 µm size fractions (Fig. 8a); these differences were also statistically non-significant (p > 0.05, Kruskal–Wallis). We note, however, the number of samples for the metagenome provide little support for the described differences in this specific comparison. Details on the above mentioned statistical tests are available in Supplementary Table S11./p>0.22 µm), 0.22–3 µm and 3–20 µm size fractions for the same volume (10 L) and filter (membrane), for MetaB18SV9 (left) and metagenomes (right). b Comparison for size fractions (0.22–3 µm, 3–20 µm and > 20 µm size fractions) for the same volume (100 L) and filter (membrane), for MetaB18SV9 (left) and metagenomes (right). c Comparison for flat membrane vs cartridge membrane, for the same volume (10 L) and whole water (>0.22 µm), for MetaB18SV9 (left) and metagenomes (right). d Comparison between 2.5 L (single filter) and 10 L (four 2.5 L filters pooled together), using the same filter (cartridge membrane) and whole water (> 0.22 µm), for MetaB18SV9 (left) and metagenomes (right). All panels illustrate the species richness obtained for each sample (point). To help the reader compare the variables, we added boxplots on top of the points. Significance was determined using either Mann–Whitney test for two independent groups, or Kruskall–Wallis for more than two independent groups, followed by a post-hoc Dunn test, if needed. Significance was illustrated with the symbols: p > 0.05 (empty); p < 0.05 (*); p < 0.01 (**); and p < 0.001 (***)./p>20 µm size fractions for the same filter (membrane) and volume (100 L), the 0.22–3 µm size fraction had fewer protist taxonomic lineages than the 3–20 µm and >20 µm size fractions (Fig. 8b), for either MetaB18SV9 and metagenomes. These differences were significant for the MetaB18SV9 (p < 0.05, Kruskal–Wallis), but not for the metagenomes (p > 0.05, Kruskal–Wallis). However, the significance of the test was not very strong and the post-hoc test for MetaB18SV9 was not significant for any combination of size fractions, after adjustment (p > 0.05, post-hoc Dunn test). Details on the above mentioned statistical tests are available in Supplementary Table S11./p>0.22 µm). The differences in the number of protist taxonomic lineages between cartridge and flat membrane filters were small (Fig. 8c) and not significant (p > 0.05, Mann–Whitney). However, the range of values was wider for the flat membrane filter than the cartridge membrane filter with the MetaB18SV9 approach (Fig. 8c). The number of protist taxonomic lineages within the replicates of flat membrane filters varied between 290 and 380 (difference of 90 taxonomic lineages), while in the cartridge membrane filters varied between 354 and 373 (difference of 19 taxonomic lineages) (Fig. 8c). For metagenomes, the values were equivalent between both types of filters (Fig. 8c). Please note that the cartridge membrane and flat membrane filters were compared at 10 L volume, but the cartridge membrane samples obtained 10 L by pooling together four cartridge membrane filters of 2.5 L together. For MetaB18SV9, the number of protist taxonomic lineages obtained after pooling four 2.5 L cartridge membrane filters was higher than using a single filter of 2.5 L (Fig. 8d), but not significant (p > 0.05, Mann–Whitney). However, this was not the same for the metagenomes, where the number of protist taxonomic lineages was equivalent and slightly higher for a single filter of 2.5 L (Fig. 8d), but also not significant (p > 0.05, Mann–Whitney). Details on the above mentioned statistical tests are available at Supplementary Table S11./p>20 µm size fractions were distant from the remaining, in either MetaB18SV9 and metagenomes (Fig. 9a, b). This was further supported by the significant results of PERMANOVA for the volume and size fractions independently (p < 0.05, PERMANOVA), but once they were considered together the effect on community composition was no longer significant (p > 0.05, PERMANOVA). Note that the variable for size fractions did not meet the homogeneity of variance pre-requisite of PERMANOVA (p > 0.05, betadisper). Details on the PERMANOVA statistical tests for protists are available in Supplementary Table S12. Additionally, a more detailed look into the betadisper results, i.e., a measure of distance to the centroid of samples within each size fraction, revealed that samples were very consistent within size fractions (Fig. 9c,d and Supplementary Table S13)./p>0.22 µm), 0.22–3 µm, 3–20 µm and >20 µm size fractions divided by (a) MetaB18SV9 and (b) metagenomes. Additionally, boxplots represent the distance to centroids of samples within each size fraction, divided by (c) MetaB16SV4V5 and (d) metagenomes./p>20 µm size fraction consistently identified more taxonomic lineages, independently of the volume, for example, Dinophyceae, Bacillariophyceae and Foraminifera (Fig. 10). In contrast, other groups were more prevalent in the 3–20 µm size fraction, like Cercozoa, Hacrobia and Haptophyta (Fig. 10). Several groups did not seem to favor any specific size fraction, like Excavata or Syndinales (Fig. 10). In the metagenomes, from 10 L to 1000 L, some groups had more protist taxonomic lineages in the >20 µm size fraction, like Bacillariophyceae, or fewer, like Hacrobia (Supplementary Fig. S5). Additionally, the metagenomes did not reveal any specific taxonomic group that increased the number of protist taxonomic lineages with increasing volume (Supplementary Fig. S5)./p>20 µm size fraction consistently had more taxonomic lineages, indicating that several taxonomic lineages were specifically found in that size fraction. One possible explanation for the identification of taxonomic lineages specific to the >20 µm size fraction is that those prokaryotes were attached to particles, or to the filter material itself. Considering that the turbidity of the water was very low, the only particles plausible for the prokaryotes to attach to would be the protists or other cell debris, including aggregates. Thus, we suggest that the prokaryotic taxonomic lineages specific to the large size fraction could be prokaryotes associated with microeukaryotes, colonial bacteria and/or specialized in colonizing larger particles. Given the presence of prokaryotes on > 20 µm size fractions and protists on 0.22–3 µm size fractions, we cannot rule out the possibility that extracellular DNA, besides actual cells, is retained in the filters, for example, by sorption [64]. However, the general picture is that free-living prokaryotes are identically identified in whole water (> 0.22 µm) and 0.22–3 µm size fraction, while particle-attached prokaryotes can be retained within larger pore size fractions (3–20 µm and >20 µm). This is consistent with previous studies that account for the effect of pre-filtration on prokaryotic diversity with 16 S rRNA gene sequencing [65]. Protists also follow the same general picture described in previous studies [40], with contamination between smaller size fractions, for example, because of cell fragments. In this study, either biological group was most unique in composition at >20 µm size fraction. Notwithstanding, we highlight that it was unexpected to find more prokaryotic and protist taxonomic lineages in the > 20 µm size fraction than in whole water, which cannot be fully explained by our experimental design and should be addressed in future work./p>0.22 µm) was generally equivalent to the 0.22–3 µm size fraction. This metabarcoding and metagenomic comparison of sampling protocols can help researchers to design their own sampling campaigns and to compare studies using different protocols. Even though we did a tremendous effort to address many different variables in protocols used by different campaigns, there is more to be tested and compared for the purpose of standardization of protocols in the future, for example, DNA extraction protocols./p>
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