LMO 16S rRNA metabarcoding dataset

資料紀錄

此資源出現紀錄的資料已發佈為達爾文核心集檔案（DwC-A），其以一或多組資料表構成分享生物多樣性資料的標準格式。 核心資料表包含 405,592 筆紀錄。

亦存在 2 筆延伸集的資料表。延伸集中的紀錄補充核心集中紀錄的額外資訊。 每個延伸集資料表中資料筆數顯示如下。

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版本

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如何引用

研究者應依照以下指示引用此資源。:

Bunse C, Pinhassi J, Lundin D, Farnelid H, Lindehoff E (2026). LMO 16S rRNA metabarcoding dataset. Version 1.0. Linnaeus University. Occurrence dataset. https://www.gbif.se/ipt/resource?r=lmo_2011-2023_16s&v=1.0

權利

研究者應尊重以下權利聲明。:

此資料的發布者及權利單位為 Linnaeus University。 This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.

GBIF 註冊

此資源已向GBIF註冊，並指定以下之GBIF UUID: 604b1b36-37c1-498f-9e49-439831bff7a5。  Linnaeus University 發佈此資源，並經由GBIF Sweden同意向GBIF註冊成為資料發佈者。

關鍵字

Occurrence; Observation

聯絡資訊

地理涵蓋範圍

Lat: 56.9309 Long: 17.0607

分類群涵蓋範圍

Archaea and Bacteria

時間涵蓋範圍

計畫資料

Marine microbiomes exhibit seasonal dynamics in many ocean regions. While we can characterize the biodiversity and composition of ocean microbiomes, we lack a systematic understanding of how environmental drivers shape the seasonal succession of marine microbes. To leverage the pronounced temporal changes in growth conditions in the temperate waters of the Baltic Sea, we here report on dynamics in the prokaryoplankton community over 13 years at the Linnaeus Microbial Observatory (LMO), focusing on both free-living (FL; 0.2-3 µm fraction) and particle-associated (PA; >3 µm fraction) prokaryotes. As expected, our analysis showed a higher diversity in the PA compared to the FL fraction and that the grand majority of 16S rRNA gene amplicon sequence variants (ASVs) were consistently rare. Yet, to an unexpected degree also a majority of the abundant ASVs transitioned into the rare biosphere over extended periods of the year. Prokaryotes in both the PA and FL fractions showed pronounced seasonal dynamics, with notable differences between the fractions from phylum down to the ASV level. Abundant ASVs were strongly correlated to several environmental drivers, including nutrient concentrations, salinity and temperature. Intriguingly, at comparable temperatures in spring and autumn (e.g. at 12°C), the community composition was strikingly different. This highlighted that specific temperature values per se are informative, but that the trajectories of change in temperature and other environmental drivers (low to high, high to low) merit attention in ecological research. Our data suggest that the community composition of prokaryoplankton in temperate regions strongly depend on factors beyond temperature and photoperiod values, including the history of the community, the direction of environmental change, and qualitative and quantitative levels of (in)organic nutrients, as well as biotic interactions.

參與計畫的人員:

取樣方法

Seawater was collected at the Linnaeus Microbial Observatory (LMO, N 56° 55.8540', E 17° 3.6420'), situated in the Western Baltic Proper during 2011 to 2023. Seawater was sampled at 2 m depth using a Ruttner sampler and was transported back to the laboratory in Kalmar (Linnaeus University, Sweden) (~1 hour) where it was processed for various abiotic and biotic parameters as described in detail in (Lindh et al., 2015; Bunse et al., 2019; Fridolfsson et al., 2023). During 2011-2013, samples were collected approximately twice a week during the productive season, in 2014 samples were collected monthly, and from 2015 samples were collected bi-weekly when weather permitted sampling. Environmental data were gathered following the procedures described in (Lindh et al., 2015; Bunse et al., 2019; Fridolfsson et al., 2023). Prokaryotic cell counts via flow cytometry were analysed using Partec Cube8 (2013–2018) and CytoFlex, Beckman Coulter (2019-2024) instruments equipped with blue lasers (488nm) with protocols adapted from (Gasol and Morán, 2015). Raw data were subsequently analyzed using the software FCSalyser 0.9.22 and CytExpert and gates in SSC/FL1 and SSC/B525 were drawn to count prokaryotic cells across all years. DNA extraction and 16S rRNA gene processing For prokaryotic community composition estimates, 3-8 L seawater was filtered through 3.0 µm pore size, 47 mm diameter, polycarbonate filters (Pall life sciences), referred to as particle-associated (PA). Bacterioplankton biomass was subsequently collected on 0.2 µm Sterivex™ cartridge filters (Millipore) referred to as the 0.2-3 µm "free-living" fraction (FL), and filters were stored frozen at -80°C in TE-buffer. DNA was extracted using the phenol-chloroform protocol described by (Boström et al., 2004) and modified after (Bunse et al., 2016). We amplified the V3V4 region of the 16S rRNA gene using the primer pair 341f-805r (Herlemann et al., 2011) as described and validated in (Hugerth et al., 2014). The 16S rRNA gene and Illumina adapters were amplified using the Phusion Mastermix (ThermoScientific) in 20 cycles (98°C 30 sec, (98°C 10 s, 58°C 30 s, 72°C 15 s), 72°C 2 min). After cleaning the PCR1 product using AmpPureXP following the manufacturer’s instructions, the Miseq Step two PCR with standard Illumina handles and index primers included 12 cycles (98°C 30 s, (98°C 10 s, 62 °C 30 s, 72 °C 5 s), 72°C 2 min). PCR products were quantified using Qubit 2.0 Fluorometer (Invitrogen) and subsequent gel electrophoresis confirmed amplicon specificity. Sequencing was carried out at the Science for Life Laboratory, Sweden on the MiSeq platform (Illumina), producing 2 × 300 bp paired-end reads. At the time of analysis, ASV data were available until end of 2022 for PA and until end of 2023 for FL.

方法步驟描述:

1. The samples from the LMO timeseries are continuously processed and were sequenced over the course of 13 years in different sequencing batches. Therefore, raw reads were processed with the nf-core/ampliseq pipeline (v2.11.0-g0473e15, Nextflow: 24.04.4 (Straub et al., 2020)) for each Illumina run separately. The nf-core/ampliseq pipeline is based on DADA2 (v. 1.30.0; (Callahan et al., 2016)) which uses an error correction algorithm for Illumina amplicon reads to produce amplicon sequence variants (ASVs). The pipeline was run with default settings except for forward and reverse trimming lengths which were set to 259 and 199 respectively. We taxonomically annotated the full dataset using SBDI-GTDB (v. R09-RS220; (Parks et al., 2018; Lundin and Andersson, 2024)) and SILVA (v. 138.2) (Quast et al., 2012) respectively.

額外的詮釋資料