Evidence map›Paper›PMID 42465840›Full record

ArticleFrontiers in cellular and infection microbiology2026

Integrated clinical and PBMC transcriptomic profiling identifies lipid metabolism-related candidate signatures associated with chronic brucellosis.

Rong Wang, Bin Niu, Xin Zhang, Yinghan Wang, Haiyan Tian, Chenming Zhang, Liaoyun Zhang

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Article in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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7 authors.

Rong Wang *Department of Infectious Diseases, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Bin Niu *Department of Infectious Diseases, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Xin Zhang *Department of Infectious Diseases, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Yinghan WangDepartment of Infectious Diseases, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Haiyan TianDepartment of Infectious Diseases, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Chenming ZhangDepartment of Infectious Diseases, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Liaoyun ZhangDepartment of Infectious Diseases, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To investigate clinical and peripheral blood mononuclear cells (PBMCs) transcriptomic features associated with chronic brucellosis and to test the hypothesis that lipid metabolism-related transcriptomic alterations in PBMCs may be involved in chronic disease progression. Methods: We analyzed 463 brucellosis patients (350 acute, 113 chronic) to compare clinical and laboratory features and to construct a multivariable logistic regression model for chronicity risk. RNA sequencing of PBMCs from 15 acute and 15 chronic cases identified differentially expressed genes, which were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning methods (LASSO regression and random forest) were used to select feature genes and build a diagnostic model. Immune cell infiltration and drug-gene interactions were further explored. Results: Chronic cases showed higher high-density lipoprotein cholesterol (HDL-C) and lower triglycerides, whereas acute cases had higher D-dimer, procalcitonin, and erythrocyte sedimentation rate (ESR). The clinical model incorporating 11 variables achieved an area under the curve (AUC) of 0.825. In an exploratory RNA-seq cohort comprising 30 PBMC samples from 15 acute and 15 chronic brucellosis patients, 2,775 exploratory candidate differentially expressed genes were identified, and enrichment analysis highlighted pathways related to fatty acid metabolism and peroxisome proliferator-activated receptor (PPAR) signaling. Three lipid metabolism-related feature genes, BDH1, CERS6, and DPEP3, showed high discriminatory performance in internal analysis, with an apparent AUC of 0.964. However, given the limited RNA-seq sample size and lack of external validation, this result should be interpreted cautiously. These genes were also associated with distinct immune infiltration patterns. Drug-Gene Interaction database (DGIdb)-based exploratory analysis suggested hypothesis-generating associations between CERS6 and several approved immunomodulatory agents, including TNF-α inhibitors. Conclusions: Chronic brucellosis may be associated with lipid metabolism-related transcriptomic alterations and immune-cell composition changes. The identified clinical and molecular features may provide candidate signatures for chronicity risk assessment and hypothesis generation, but require further validation in independent cohorts.

Indexed as

BrucellosisGene Expression ProfilingLeukocytes, MononuclearLipid MetabolismTranscriptomeAdultChronic DiseaseFemaleHumansMaleMiddle Agedbrucellosislipid metabolismmachine learningperipheral blood mononuclear cellsPPAR signaling pathwaytranscriptome analysis

Identifiers

PMID42465840
PMCPMC13372603

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