Metagenomic and -Transcriptomic Analyses of Microbial Nitrogen Transformation Potential, and Gene Expression in Swiss Lake Sediments
ISME COMMUNICATIONS(2024)
Swiss Fed Inst Aquat Sci & Technol | Univ Basel | Swiss Fed Inst Technol | Eawag
Abstract
The global nitrogen (N) cycle has been strongly altered by anthropogenic activities, including increased input of bioavailable N into aquatic ecosystems. Freshwater sediments are hotspots with regards to the turnover and elimination of fixed N, yet the environmental controls on the microbial pathways involved in benthic N removal are not fully understood. Here, we analyze the abundance and expression of microbial genes involved in N transformations using metagenomics and -transcriptomics across sediments of 12 Swiss lakes that differ in sedimentation rates and trophic regimes. Our results indicate that microbial N loss in these sediments is primarily driven by nitrification coupled to denitrification. N-transformation gene compositions indicated three groups of lakes: agriculture-influenced lakes characterized by rapid depletion of oxidants in the sediment porewater, pristine-alpine lakes with relatively deep sedimentary penetration of oxygen and nitrate, and large, deep lakes with intermediate porewater hydrochemical properties. Sedimentary organic matter (OM) characteristics showed the strongest correlations with the community structure of microbial N-cycling communities. Most transformation pathways were expressed, but expression deviated from gene abundance and did not correlate with benthic geochemistry. Cryptic N-cycling may maintain transcriptional activity even when substrate levels are below detection. Sediments of large, deep lakes generally showed lower in-situ N gene expression than agriculture-influenced lakes, and half of the pristine-alpine lakes. This implies that prolonged OM mineralization in the water column can lead to the suppression of benthic N gene expression.
MoreTranslated text
Key words
denitrification,nitrification,anammox,DNRA,freshwater sediment,metatranscriptomics,metagenomics
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话