Carcass decay inhibits denitrification indirectly by regulating the microbiota and physicochemical properties in a model water system
Data files
Jul 19, 2024 version files 345.59 KB
-
denitrification.zip
343.02 KB
-
README.md
2.57 KB
Abstract
Animal carcass decomposition is involved in nitrogen (N) cycle in the aquatic ecosystems, e.g., denitrification, volatilization, and leaching. The denitrification process is a crucial part of the nitrogen cycle, helping to alleviate biological toxicity resulting from nitrate accumulation. However, it is still unknown whether and how cadaver decay affects the denitrifiers and their activity in river water.
In this study, six denitrifiers (encoding napA, narG, nirK, nirS, norB, and nosZ, respectively) and denitrifying activity were investigated in a model water system with nitrogen pollution from corpse decomposition using high-throughput sequencing, real-time quantitative PCR, and denitrifying enzyme activity (DEA) assay.
Cadaver decay increased the quantity and altered the composition of denitrifying microbiota. Multiple regression on distance matrices revealed that corpse decay had a significantly greater impact on the denitrification communities compared to temperature. To our surprise, the DEA decreased in the contaminated water by cadavers. The multiple regression models revealed that the abundance of denitrifying taxa and the gene copies may jointly predict changes in DEA, with the highest contribution coming from the norB-type Hydrogenophaga. Further, the partial least squares path model (PLS-PM) showed that corpse decay indirectly inhibited DEA via the denitrification community and water physicochemical properties.
Synthesis and applications. This study provides important insights into how cadaver decomposition can reduce denitrification activity in water systems. These findings can assist us in scientifically managing and remediating polluted water bodies.
https://doi.org/10.5061/dryad.0vt4b8h6z
Paper: Carcass decay inhibits denitrification indirectly by regulating the microbiota and physicochemical properties in a model water system
Authors: Jiawei Yang, Qiaoling Yu, Wanghong Su, Sijie Wang, Xiaochen Wang, Qian Han, Huan Li
Description:
This README file describes the data package accompanying the above publication.
Files:
- AIC.xlsx: Includes a y variable and the best 7 x variables filtered based on Akaike’s information criterion (AIC).
- ALPHA.xlsx: Includes the alpha diversity (Observed OTUs, Chao1, ACE, Shannon, Simpson, Coverage) of six microbial communities (i.e., napA, narG, nirK, nirS, norB, and nosZ). C, control groups; E, experimental groups; 1-4, 23℃; 5-8, 26℃; 9-12, 29℃; 13-16, 32℃; 17-20, 35℃.
- DEA.xlsx: Includes the denitrification enzyme activity (DEA). C, control groups; E, experimental groups; 1-4, 23℃; 5-8, 26℃; 9-12, 29℃; 13-16, 32℃; 17-20, 35℃.
- DCG.xlsx: Includes the absolute copy numbers (copies/L) of 16S rRNA, napA, narG, nirK, nirS, norB, and nosZ. C, control groups; E, experimental groups; T, temperature.
- dissimilarity.xlsx: Includes inter group dissimilarity (e.g., F1 vs. H1) and intra group dissimilarity (e.g., F1 vs. F2) in six microbial communities based on the Bray-Curtis distance matrix. H (C), control groups; F (E), experimental groups; 1-4, 23℃; 5-8, 26℃; 9-12, 29℃; 13-16, 32℃; 17-20, 35℃.
- genera.xlsx: Includes the genera abundance od six microbial communities. H (C), control groups; F (E), experimental groups; 1-4, 23℃; 5-8, 26℃; 9-12, 29℃; 13-16, 32℃; 17-20, 35℃.
- matirx.xlsx: Includes six microbial communities of Bray-Curtis distance matrix. H (C), control groups; F (E), experimental groups; 1-4, 23℃; 5-8, 26℃; 9-12, 29℃; 13-16, 32℃; 17-20, 35℃.
- PHYS.xlsx: Includes the physicochemical properties of the water. C, control groups; E, experimental groups; 1-4, 23℃; 5-8, 26℃; 9-12, 29℃; 13-16, 32℃; 17-20, 35℃; SALT, salinity; DO, dissolved oxygen; NH4-N, ammonium nitrogen; ORP, oxidation-reduction potential; CON, conductivity; TDS, total dissolved solids; TC, total carbon; TN, total nitrogen; NO3-N, nitrate-nitrogen.
- R_model.txt: The code for the R model, including AIC, MRM, PERMANOVA, PHEATMAP, and PLS-PM used in the article.
- Yang, Jiawei et al. (2024), Carcass decay inhibits denitrification indirectly by regulating the microbiota and physicochemical properties in a model water system, Journal of Applied Ecology, Journal-article, https://doi.org/10.1111/1365-2664.14748