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Dongwei Liu

Publications and source records attributed to Dongwei Liu.

2 recordsLinked to original sources

Long-Term Warming Reduces Bacterial Diversity and Functional Potential in Temperate Forest Soil.

Soil microbes are key regulators of forest carbon cycling, yet how their diversity and functional potential respond to long-term warming remains poorly understood. Here, we report a five-year in situ warming experiment in a temperate forest, combining ten repeated measurements of microbial diversity and functional gene potential, as well as continuous monitoring of soil CO2 flux. We found that warming progressively reduced bacterial diversity and induced phylogenetically conserved community reorganization. Under warming, community composition shifted in a phylogenetically conserved manner. Warming generally reduced the abundance of microbial functional genes across most carbon-, nitrogen-, and phosphorus-cycling gene categories, except for genes associated with starch decomposition. Warming also altered the factors associated with soil CO2 flux: microbial diversity showed a stronger association with soil CO2 flux under long-term warming, whereas soil moisture was the dominant predictor in the control treatment. This warming-enhanced biodiversity control over soil CO2 flux was associated with shifts in microbial functional potential, particularly increases in starch-degrading genes and microbial biomass production potential. Together, our results suggest that warming can restructure microbial communities in ways that strengthen biodiversity-dependent regulation of soil carbon cycling, with implications for climate-carbon feedbacks.

Soil Microbiology

Proteomics uncovers ICAM2 (CD102) as a novel serum biomarker of proliferative lupus nephritis.

OBJECTIVES: This study aimed to identify novel, non-invasive biomarkers for lupus nephritis (LN) through serum proteomics. METHODS: Serum proteins were detected in patients with LN and healthy control (HC) groups through liquid chromatography-tandem mass spectrometry. The key networks associated with LN were screened out using Cytoscape software, followed by pathway enrichment analysis. The best candidate biomarkers were selected by machine learning models, further validated in a larger independent cohort. Finally, the expression of these candidate markers was verified in kidney tissue samples, and the mechanism was explored by knocking down the expression of intercellular adhesion molecule 2 (ICAM2) through in vitro cell transfection with siRNA. RESULTS: Following the serum proteomic screening of LN, a key network of 20 proteins was identified. Machine learning models were used to select ICAM2 (CD102), metalloproteinase inhibitor 1 (TIMP1) and thrombospondin 1 (THSB1) for validation in independent cohorts. ICAM2 exhibited the highest area under the curve (AUC) value in distinguishing LN from HC (AUC=0.92) and was significantly correlated with activity index, proteinuria, albumin and anti-dsDNA antibody levels. Particularly, ICAM2 was significantly elevated in proliferative LN and was associated with specific pathological attributes, outperforming conventional parameters in distinguishing proliferative LN from non-proliferative LN. ICAM2 expression was also elevated in renal tissue samples from patients with proliferative LN. In vitro, knockdown of ICAM2 expression can inhibit the activation of the PI3K/Akt pathway and alleviate the injury of glomerular endothelial cells. CONCLUSION: ICAM2 (CD102) may serve as a potential serum biomarker for proliferative LN that reflects renal pathology activity, potentially contributing to the progression of LN through the PI3K/Akt pathway.

Humans