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Article Open Access

Glycolytic reprogramming in host response to Borrelia burgdorferi: A gene signature revealed by integrative bioinformatics analysis and machine learning

  • Authors:
    • Yan Dong
    • Yantong Chen
    • Yanshuang Luo
    • Meng Liu
    • Chao Song
    • Xuesong Chen
    • Fusong Yang
    • Qingyi Luo
    • Guozhong Zhou
  • View Affiliations / Copyright

    Affiliations: Faculty of Life Science and Technology and The Affiliated Anning First People's Hospital, Kunming University of Science and Technology, Kunming, Yunnan 650302, P.R. China, Department of Pain Medicine, The Affiliated Anning First People's Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650302, P.R. China, Department of Ultrasound, Yanan Hospital of Kunming City, The Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650302, P.R. China, School of Basic Medical Sciences, Kunming University of Science and Technology, Kunming, Yunnan 650500, P.R. China
    Copyright: © Dong et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 187
    |
    Published online on: May 12, 2026
       https://doi.org/10.3892/etm.2026.13182
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Abstract

Lyme disease (LD), a multifaceted condition caused by Borrelia burgdorferi (Bb), remains poorly understood, particularly regarding metabolic pathways. This study aimed to evaluate the role of glycolysis‑related genes (GRGs) in LD pathogenesis and identify key genes and mechanisms relevant to diagnosis and therapy. Differentially expressed GRGs were identified and further analyzed by correlation analysis and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Key genes were screened using least absolute shrinkage and selection operator (LASSO) and support vector machine‑recursive feature elimination, followed by gene set enrichment analysis, gene set variation analysis and immune infiltration analysis using CIBERSORT. Diagnostic value was assessed by receiver operating characteristic and nomogram analyses, and a competing endogenous RNA network and protein‑drug interaction predictions were constructed. The expression of key genes was further validated by reverse transcription‑quantitative PCR (RT‑qPCR) in Bb‑infected THP‑1 cells, and glucose and lactate concentrations in the culture supernatants were measured using commercial colorimetric assay kits. A total of 63 differentially expressed GRGs were identified. Of note, two key genes [lactate dehydrogenase A (LDHA) and thioredoxin (TXN)] exhibited a strong diagnostic performance. Immune infiltration analysis revealed that these genes were associated with regulatory T cells (r=0.33, P=0.05), gamma delta T cells (r=‑0.34, P=0.042), CD4 memory resting T cells (r=‑0.4, P=0.016) and monocytes (r=‑0.36, P=0.03). The potential glycolysis‑targeting drugs were predicted. RT‑qPCR analysis revealed increased LDHA and TXN expression in Bb‑infected THP‑1 cells. In addition, Bb stimulation reduced extracellular glucose levels and increased lactate accumulation in culture supernatants. Overall, this integrative analysis revealed notable alterations in glycolytic genes during Bb infection, suggesting that dysregulation of glycolysis contributes to LD immunopathology. The identified key genes (LDHA and TXN) may serve as infection‑related transcriptional response markers, offering insights into LD mechanisms and potential precision‑based strategies.
View Figures

Figure 1

Flow chart for bioinformatics
analysis of GRGs analysis in LD. GRGs, glycolysis-related genes;
LD, Lyme disease; GEO, Gene Expression Omnibus; GO, Gene Ontology;
KEGG, Kyoto Encyclopedia of Genes and Genomes; LASSO, Least
Absolute Shrinkage and Selection Operator; SVM-RFE, Support Vector
Machine-Recursive Feature Elimination; GSEA, Gene Set Enrichment
Analysis; GSVA, Gene Set Variation Analysis; ceRNA, competing
endogenous RNA; ROC, receiver operating characteristic; RT-qPCR,
reverse transcription-quantitative PCR.

Figure 2

Analysis of expression levels of GRGs
in LD. (A) Heat-map showing the expression pattern of GRGs in
different samples. (B) Correlation heat map showing the correlation
coefficients of the 63 GRGs. Red and green represent positive and
negative correlations, respectively. *P<0.05,
**P<0.01, ***P<0.001. GRGs,
glycolysis-related genes; LD, lyme disease.

Figure 3

Enrichment analysis and machine
learning approach reveals 2 genes in 63 GRGs were identified as key
genes for LD. (A) KEGG term analysis of differentially expressed
GRGs. (B) GO term analysis of differentially expressed GRGs. (C)
ten-fold cross-validation curve for the LASSO logistic regression
model used to determine the optimal penalty parameter. (D)
coefficient profiles of candidate genes in the LASSO model as a
function of log(λ). (E) Classification accuracy of the SVM-RFE
model with different numbers of selected features. (F)
Cross-validation error of the SVM-RFE model with different numbers
of selected features. Finally, 14 genes (maximum precision=0.957,
minimum root mean square error=0.0429) were identified as the best
key genes; (G) The vein plot revealed two key gene
regulation-related genes (LDHA and TXN) identified through LASSO
and SVM-RFE methods. GRGs, glycolysis-related genes; LD, Lyme
disease; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and
Genomes; LASSO, Least Absolute Shrinkage and Selection Operator;
SVM-RFE, Support Vector Machine-Recursive Feature Elimination; CV,
cross validation.

Figure 4

Single-gene GSEA-KEGG pathway
analysis. GSEA-KEGG pathway analysis of (A) LDHA and (B) TXN. GSEA,
Gene Set Enrichment Analysis; KEGG, Kyoto Encyclopedia of Genes and
Genomes.

Figure 5

GSVA enrichment results based on
KEGG. (A) GSVA results of the KEGG set in the LDHA high expressed
group; (B) GSVA results of the KEGG set in the TXN high expressed
group. GSVA, Gene Set Variation Analysis; KEGG, Kyoto Encyclopedia
of Genes and Genomes.

Figure 6

Results of the CIBERSORT analysis of
the immune infiltration analysis. (A) PC analysis was performed
between LD group and control group. Red points and ellipses
indicate LD samples and blue points and ellipses indicate normal
samples. (B) Box plot showing the significant difference in the
infiltration of 22 immune cell types between the LD group and
control group. (C) Correlation landscape of immune cell
infiltration and its association with the key genes LDHA and TXN.
The heatmap displays the Pearson correlation coefficients between
the expression levels of 2 hub glycolysis-related differential
genes (columns) and the infiltration levels of 22 immune cell types
(rows) as determined by CIBERSORT analysis with the LM22 signature
matrix. Red colors indicate positive correlations, while blue
colors indicate negative correlations. Color intensity reflects the
strength of correlation. (D) Representative scatter plots showing
the correlations between the expression levels of LDHA/TXN and
selected immune cell subsets, including resting memory CD4 T cells,
gamma delta T cells, regulatory T cells and monocytes. Correlation
coefficients and P-values are shown in each panel.
*P<0.05, **P<0.01,
***P<0.001. PC, principal component; LD, Lyme
disease.

Figure 7

The candidate drug-gene interactions
and ceRNA regulatory network construction. (A) Drug-gene
interaction network constructed based on the Drug Gene Interaction
Database. Red ellipse points represent genes and blue rectangle
points represent drugs. (B) Predicted drug's molecular formula,
gene and adjusted P-value. (C) A ceRNA regulatory network centered
on 2 hub GRGs in Lyme disease. This focused sub-network illustrates
the post-transcriptional regulation of 2 key GRGs through
lncRNA-miRNA-mRNA interactions. Red ellipse represent mRNAs (LDHA
and TXN), green triangles represent miRNAs (n=26) and blue diamonds
represent lncRNAs (n=34). A total of 59 nodes and 60 edges are
shown. Edge width represents the prediction confidence score from
integrated analysis of the miRanda, miRDB and TargetScan databases.
Network visualization was performed using Cytoscape v3.10.2 with a
force-directed layout algorithm. crRNA, competing endogenous RNA;
lncNRA, long non-coding RNA; miRNA, microRNAs; GRGs,
glycolysis-related genes.

Figure 8

ROC curve, calibration plot and
decision curve analysis of the LDHA-TXN model. (A) ROC curves of
the two individual key genes, LDHA and TXN, in the GSE42606 cohort.
The corresponding AUC values are shown in the panel. (B) ROC curve
of the combined two-gene diagnostic model constructed using LDHA
and TXN in the GSE42606 cohort. The AUC and 95% CI are indicated in
the figure. (C) ROC curves of the individual genes LDHA and TXN in
the independent validation cohort GSE63085, showing their
diagnostic performance in external validation. (D) ROC curve of the
combined two-gene diagnostic model in the independent validation
cohort GSE63085. The AUC and 95% CI are shown in the panel. (E)
Calibration curve of the nomogram/model. (F) DCA of the two-gene
model. The red line represents the net benefit of the model, while
the gray and black lines represent the strategies of treating all
patients or none, respectively. (G) Nomogram constructed based on
LDHA and TXN for individualized prediction of Lyme disease risk. In
the nomogram, each variable corresponds to a score and the total
score can be calculated by summing the scores of all variables. The
ROC curve, calibration plot, and DCA shown were generated from
pooled out-of-fold predictions obtained during outer 5-fold
cross-validation, whereas the nomogram was fitted on the full
dataset after validation for visualization only. ROC, receiver
operating characteristic; AUC, area under curve; CV,
cross-validation; DCA, decision curve analysis; OOF,
out-of-fold.

Figure 9

Validation of LDHA and TXN expression
and glycolysis-related metabolic alterations in Lyme disease.
Normalized expression levels of (A) LDHA and (B) TXN in the
GSE42606 cohort between the control and Bb groups. Normalized
expression levels of (C) LDHA and (D) TXN in the GSE63085 cohort
between the control and Bb groups. Reverse
transcription-quantitative PCR validation of (E) LDHA and (F) TXN
mRNA expression in THP-1 cells after Bb stimulation compared with
the PBS control group. (G) LAC and (H) GLU levels in culture
supernatants of THP-1 cells after Bb stimulation and in the PBS
control group. *P<0.05, ***P<0.001.
LAC, lactate; GLU, extracellular glucose; Bb, Borrelia
burgdorferi.
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Copy and paste a formatted citation
Spandidos Publications style
Dong Y, Chen Y, Luo Y, Liu M, Song C, Chen X, Yang F, Luo Q and Zhou G: Glycolytic reprogramming in host response to <em>Borrelia burgdorferi</em>: A gene signature revealed by integrative bioinformatics analysis and machine learning. Exp Ther Med 32: 187, 2026.
APA
Dong, Y., Chen, Y., Luo, Y., Liu, M., Song, C., Chen, X. ... Zhou, G. (2026). Glycolytic reprogramming in host response to <em>Borrelia burgdorferi</em>: A gene signature revealed by integrative bioinformatics analysis and machine learning. Experimental and Therapeutic Medicine, 32, 187. https://doi.org/10.3892/etm.2026.13182
MLA
Dong, Y., Chen, Y., Luo, Y., Liu, M., Song, C., Chen, X., Yang, F., Luo, Q., Zhou, G."Glycolytic reprogramming in host response to <em>Borrelia burgdorferi</em>: A gene signature revealed by integrative bioinformatics analysis and machine learning". Experimental and Therapeutic Medicine 32.1 (2026): 187.
Chicago
Dong, Y., Chen, Y., Luo, Y., Liu, M., Song, C., Chen, X., Yang, F., Luo, Q., Zhou, G."Glycolytic reprogramming in host response to <em>Borrelia burgdorferi</em>: A gene signature revealed by integrative bioinformatics analysis and machine learning". Experimental and Therapeutic Medicine 32, no. 1 (2026): 187. https://doi.org/10.3892/etm.2026.13182
Copy and paste a formatted citation
x
Spandidos Publications style
Dong Y, Chen Y, Luo Y, Liu M, Song C, Chen X, Yang F, Luo Q and Zhou G: Glycolytic reprogramming in host response to <em>Borrelia burgdorferi</em>: A gene signature revealed by integrative bioinformatics analysis and machine learning. Exp Ther Med 32: 187, 2026.
APA
Dong, Y., Chen, Y., Luo, Y., Liu, M., Song, C., Chen, X. ... Zhou, G. (2026). Glycolytic reprogramming in host response to <em>Borrelia burgdorferi</em>: A gene signature revealed by integrative bioinformatics analysis and machine learning. Experimental and Therapeutic Medicine, 32, 187. https://doi.org/10.3892/etm.2026.13182
MLA
Dong, Y., Chen, Y., Luo, Y., Liu, M., Song, C., Chen, X., Yang, F., Luo, Q., Zhou, G."Glycolytic reprogramming in host response to <em>Borrelia burgdorferi</em>: A gene signature revealed by integrative bioinformatics analysis and machine learning". Experimental and Therapeutic Medicine 32.1 (2026): 187.
Chicago
Dong, Y., Chen, Y., Luo, Y., Liu, M., Song, C., Chen, X., Yang, F., Luo, Q., Zhou, G."Glycolytic reprogramming in host response to <em>Borrelia burgdorferi</em>: A gene signature revealed by integrative bioinformatics analysis and machine learning". Experimental and Therapeutic Medicine 32, no. 1 (2026): 187. https://doi.org/10.3892/etm.2026.13182
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