Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 396

The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.

Long-term and high-concentration heavy-metal contamination strongly influences the microbiome and functional genes in Yellow River sediments

Open access
Chen, Yong, Jiang, Yiming, Huang, Haiying, Mou, Lichao, Ru, Jinlong, Zhao, Jianhua, Xiao, Shan·2018·Science of The Total Environment

Abstract

The world is facing a hard battle against soil pollution such as heavy metals. Metagenome sequencing, 16S rRNA sequencing, and quantitative polymerase chain reaction (qPCR) were used to examine microbial adaptation mechanism to contaminated sediments under natural conditions. Results showed that sediment from a tributary of the Yellow River, which was named Dongdagou River (DDG) supported less bacterial biomass and owned lower richness than sediment from Maqu (MQ), an uncontaminated site in the upper reaches of the Yellow River. Additionally, microbiome structures in these two sites were different. Metagenome sequencing and functional gene annotations revealed that sediment from DDG contains a larger number of genes related to DNA recombination, DNA damage repair, and heavy-metal resistance. KEGG pathway analysis indicated that the sediment of DDG contains a greater number of enzymes associated with heavy-metal resistance and reduction. Additionally, the bacterial phyla Proteobacteria, Bacteroidetes, and Firmicutes, which harbored a larger suite of metal-resistance genes, were found to be the core functional phyla in the contaminated sediments. Furthermore, sediment in DDG owned higher viral abundance, indicating virus-mediated heavy-metal resistance gene transfer might be an adaptation mechanism. In conclusion, microbiome of sediment from DDG has evolved into an integrated system resistant to long-term heavy-metal pollution.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.scitotenv.2018.05.109

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 64 references

Metadata

Authors
Chen, Y., Jiang, Y., Huang, H., Mou, L., Ru, J., Zhao, J., & Xiao, S.
Venue
Science of The Total Environment
Publisher
Elsevier BV
Published
2018-10
Volume
637-638
Pages
1400-1412
ISSN
0048-9697 (ISSN-L)
OpenAlex
W2804060790
Cited by
396
References
64
FWCI
10.37
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Long-term and high-concentration heavy-metal contamination strongly influences the microbiome and functional genes in Yellow River sediments. https://app.cochat.ai/papers/10.1016/j.scitotenv.2018.05.109
The paper
Chen, Y., Jiang, Y., Huang, H., Mou, L., Ru, J., Zhao, J., & Xiao, S. (2018). Long-term and high-concentration heavy-metal contamination strongly influences the microbiome and functional genes in Yellow River sediments. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2018.05.109

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.