Evidence map›Paper›PMID 42765955›Full record

ArticleThe ISME journal2026

Microbial physiological trait shifts link heavy metal remediation to enhanced soil carbon storage potential.

Rui Xue, Jian Li, Shilin Hu, Jiazhi Ding, Cheng Wang, Wenshun Ke, Chaoran Li, Philippe Corvini, Li Cui

Abstract read
In one paragraph

Article in The ISME journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Rui XueState Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, China.
Jian LiMinistry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China.
Shilin HuState Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, China.
Jiazhi DingState Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, China.
Cheng WangEnvironmental Microbiomics Research Center, School of Environmental Science and Engineering, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, Guangdong 510006, China.
Wenshun KeCollege of Environment and Ecology, Hunan Agricultural University, Changsha, Hunan 410128, China.
Chaoran LiState Key Laboratory of Crop Stress Adaptation and Improvement, Henan University, Kaifeng, Henan 475004, China.
Philippe CorviniInstitute for Chemistry and Bioanalytics, FHNW University of Applied Sciences and Arts Northwestern Switzerland, 4132 Muttenz, Switzerland.
Li CuiState Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, China.ORCID 0000-0002-0708-8899

Funding

Fujian Province 2024J011016National Key Research and Development Program of China 2019YFC1803600National Key Research and Development Program of China 2022YFF0713100Natural Science Foundation of China 22176186
6 · The paper itself

Abstract

Widespread and chronic heavy metal pollution resulting from industrial activities has compromised the sustainability of soil ecosystems. Increasing and stabilizing soil carbon storage is central to soil development, but how remediation reshapes soil carbon cycling processes during the mitigation of heavy-metal contamination remains unclear. Here, we tracked genome-scale microbial metabolism, community turnover, and phenotype-level physiological responses during a 120-day remediation of heavy metal-contaminated soils with several decades of pollution histories and resolved their dynamic interplay with microbial carbon use efficiency (CUE) and CO2 emissions. We found that heavy-metal stress accelerated microbial respiratory carbon loss from soils, with contaminated soils exhibiting significantly higher cumulative CO₂ emissions than both nearby uncontaminated and remediated soils. Metagenomic profiles were enriched in oxidative-stress defense and metal-detoxification functions, consistent with elevated maintenance costs that may contribute to enhanced respiration. In contrast, remediation significantly reduced soil CO₂ emissions while increasing microbial growth rate and CUE, indicating a shift toward greater soil carbon storage potential. Raman-based in situ monitoring further showed that biomolecules associated with microbial growth, including phospholipids, nucleic acids, and proteins, increased progressively throughout the remediation process. Structural equation modelling further revealed that microbial physiological traits, particularly metabolic activity and intracellular biomolecular content, exerted stronger direct effects on CUE than community traits, including community stability and life-history strategy. These results identify microbial physiological traits as a key link between environmental stress and soil carbon cycling. Together, these findings suggest that remediating heavy metal-contaminated soils may represent an underappreciated pathway for enhancing terrestrial carbon sequestration.

Indexed as

BacteriaCarbonEnvironmental Restoration and RemediationMetals, HeavySoilSoil MicrobiologySoil PollutantsBiodegradation, EnvironmentalCarbon CycleCarbon DioxideCarbonCarbon DioxideMetals, HeavySoilSoil Pollutantsheavy metalmicrobial ecologysingle-cell Raman spectroscopysoil carbon cyclesoil remediation

Identifiers

PMID42765955
PMCPMC13657045

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.