CD163-Mediated Small-Vessel Injury in Alzheimer’s Disease: An Exploration from Neuroimaging to Transcriptomics
<p>The study workflow chart. Abbreviations: WMH, white-matter hyperintensities; DTI_ROI, diffusion tensor imaging region of interest; CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease; ln_WMH_TCV, natural logarithm of standardized white-matter hyperintensities (WMH) to total cerebrum cranial volume (TCV); MCI_WMH+, mild cognitive impairment with severe white-matter hyperintensities, whose values of ‘ ln_WMH_TCV’ were above the median; MCI_WMH–, mild cognitive impairment with no or mild white-matter hyperintensities, whose values of ‘ ln_WMH_TCV’ were below the median.</p> "> Figure 2
<p>The difference among CN, MCI, and dementia in DTI indices and the prediction accuracy of ln_WMH_TCV and TCB_TCV in predicting AD progression: (<b>A</b>) The intersection of difference in FA, MD, RD, and AxD indices were considered the most fragile fiber bundles; (<b>B</b>) The predictive effectiveness of cerebral atrophy and small-vessel injury at different stages of AD diagnosis; (<b>C</b>) different cross-sectional views of the fragile fiber bundles. Red: bilateral splenium of the corpus callosum (SUMSCC, SCC_R, SCC_L), blue: bilateral fornix (SUMFX), violet: posterior thalamic radiation_left (PTR_L), yellow: fornix (cres)/stria terminalis_left (FX_ST_L, FX_L), cyan: tapetum (TAP_R, TAP_L). Abbreviations: FA, fractional anisotropy; MD, mean diffusivity; RD, radial diffusivity; AxD, axial diffusivity, TAP_R, tapetum right; TAP_L, tapetum left; SUMSCC, bilateral splenium of the corpus callosum; SUMFX, bilateral fornix; SCC_R, splenium of corpus callosum right; SCC_L, splenium of corpus callosum left; PTR_L, posterior thalamic radiation left; FX_ST_L, fornix (cres)/stria terminalis left; FX_L, fornix left; CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease; ROC, receiver operating characteristic; AUC, area under the curve; TCB_TCV, standardized total cerebrum brain volume (TCB) to total cerebrum cranial volume (TCV); ln_WMH_TCV, natural logarithm of standardized white-matter hyperintensities (WMH) to total cerebrum cranial volume (TCV).</p> "> Figure 3
<p>Identification of DEGs and functional enrichment analysis: (<b>A</b>) Volcano plot of DEGs constructed using the fold-change values (0.12) and <span class="html-italic">p</span>-value (0.05); red-orange color dots represent genes upregulated in MCI_WMH+, gray dots represent genes not differing significantly between MCI_WMH+ and MCI_WMH–, and cyan dots represent genes downregulated in MCI_WMH+. (<b>B</b>) Volcano plot of DEGs constructed using the fold-change values (0.12) and <span class="html-italic">p</span>-value (0.05); red-orange color dots represent genes upregulated in CN_WMH+ group, gray dots represent genes not differing significantly between CN_WMH+ and CN_WMH– groups, and cyan dots represent genes downregulated in CN_WMH+ group. (<b>C</b>) Venn plot. Only fifteen genes shared between WMH-related genes in CN patients and those with MCI. (<b>D</b>,<b>E</b>) The results of GSEA analysis of KEGG in MCI_WMH+ samples. Abbreviations: MCI_WMH+, mild cognitive impairment with severe white-matter hyperintensities; MCI_WMH–, mild cognitive impairment with no or mild white-matter hyperintensities; CN_WMH+, cognitively normal with severe white-matter hyperintensities; CN_WMH–, cognitively normal with no or mild white-matter hyperintensities; KEGG_GSEA, gene set enrichment analysis (GSEA) of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways; DEGs, differentially expressed genes.</p> "> Figure 4
<p>Screening of closely related genes and hub genes of WMH-related genes in MCI group using cytoHubba and MCODE plugins: (<b>A</b>) Macroscopic display of PPI networks for all DEGs of WMH-related genes in MCI group, with a redder color indicating a higher degree score; the gene nodes in the topological characteristics of this PPI network were ranked in descending order of degree value (<b>B</b>–<b>D</b>), with a deeper purple color indicating a higher score. The top three modules’ genes are filtered by MCODE; (<b>E</b>) A redder color indicates a higher score and a yellower color indicates a lower score. The hub genes are filtered by the MCC for the top 30 genes; (<b>F</b>) Venn plot the common genes are both filtered by MCODE and MCC. Abbreviations: MCODE, molecular complex detection; MCC, maximal clique centrality.</p> "> Figure 5
<p>Screening feature-selection genes from DEGs between mild and severe small-vessel injury in MCI group: (<b>A</b>) 15 feature-selection genes were screened by Boruta algorithm; (<b>B</b>) 22 feature-selected genes were screened by SVM-RFE algorithm; (<b>C</b>) Venn plot, seven variables including CD163, FOLR2, ALDH3B1, DTX2, ALDH2, ZNF23, and MIR22HG intersected by Boruta and SVM-RFE algorithms; (<b>D</b>) the ROC curve of seven machine learning models, and the AUC value represents the model predictive effectiveness in testing set; (<b>E</b>) the expression level of feature-selection genes CD163, FOLR2, ALDH3B1, DTX2, ALDH2, ZNF23, and MIR22HG in the MCI between mild and severe small-vessel injury. CD163, <span class="html-italic">p</span>-value = 1.2 × 10<sup>−4</sup>; FOLR2, <span class="html-italic">p</span>-value = 6.4 × 10<sup>−4</sup>; ALDH3B1, <span class="html-italic">p</span>-value = 4.7 × 10<sup>−4</sup>; DTX2, <span class="html-italic">p</span>-value = 1.8 × 10<sup>−3</sup>; ALDH2, <span class="html-italic">p</span>-value = 3 × 10<sup>−3</sup>; ZNF23, <span class="html-italic">p</span>-value = 5.1 × 10<sup>−4</sup>; MIR22HG, <span class="html-italic">p</span>-value = 8.6 × 10<sup>−5</sup>. ** <span class="html-italic">p</span> < 0.01; *** <span class="html-italic">p</span> < 0.001. Abbreviations: SVM-RFE, support vector machine recursive feature elimination; RMSE, root mean square error; AUC, area under the curve; gbm, gradient boosting machine; rf, random forest; KNN, k-nearest neighbors; SVM, support vector machine; GLM, generalized linear model; XGboost, extreme gradient boosting; rpart, recursive partition tree.</p> "> Figure 6
<p>Alterations in the abundance of immune cells in MCI group with severe small-vessel injury (MCI_WMH+) and correlation analysis between hub genes with the abundance of immune cell and FA values: (<b>A</b>) Estimated proportions of 28 immune cell types between two groups in MCI group; (<b>B</b>) Correlation analysis of hub genes with different immune cell types; (<b>C</b>) Correlation analysis of hub genes with differential brain-area FA values; (<b>D</b>) Venn plot, two genes (CD163 and FOLR2) intersected by Feature-selection algorithms and PPI. (<b>E</b>) The correlation between CD163, FOLR2, and ln_WMH_TCV. * <span class="html-italic">p</span> < 0.05; ** <span class="html-italic">p</span> < 0.01. Abbreviations: FA, fractional anisotropy; SUMFX, bilateral fornix; TAP_R, tapetum right; SCC_L, splenium of corpus callosum left; SCC_R, splenium of corpus callosum right; SUMSCC, bilateral splenium of the corpus callosum; PTR_L, posterior thalamic radiation left; FX_ST_L, fornix (cres)/stria terminalis left; FX_L, fornix left; TAP_L, tapetum left; ln_WMH_TCV, natural logarithm of standardized white-matter hyperintensities (WMH) to total cerebrum cranial volume (TCV).</p> "> Figure 7
<p>AD pathology leads to small-vessel injury and elevates CD163 expression. Immunofluorescence staining results of (<b>A</b>) 5×FAD cerebral cortex (left scale bar = 25 um; right scale bar = 10 um) and (<b>B</b>) WT cerebral cortex, green: CD31; red: AQP4; magenta: Aβ; blue: DAPI. (<b>C</b>–<b>E</b>) Calculation of the width of the perivascular spaces and the comparation between WT and 5×FAD. (<b>F</b>) rt-qPCR results from cortical tissues exhibited an upregulation of CD163 expression in 5×FAD mice. (<b>G</b>) Immunofluorescence staining: control vs. Aβ-treated rat primary microglia (scale bar = 50 um), green: CD163; red: iba-1; blue: DAPI. (<b>H</b>) Immunofluorescence staining results of CD163 showing the MFI and the percentage of cells in different MFIs. * <span class="html-italic">p</span> < 0.05; *** <span class="html-italic">p</span> < 0.001; **** <span class="html-italic">p</span> < 0.0001. Abbreviations: MFI, mean fluorescence intensity; Aβ, amyloid beta.</p> ">
Abstract
:1. Introduction
2. Results
2.1. WMH and Cerebral Atrophy Both Contributed to Early Stage of Alzheimer’s Disease
2.2. Differentially Expressed Genes (DEGs) and Functional Enrichment Analysis of WMH-Related Genes in MCI
2.3. PPI Network Construction of WMH-Related Genes in MCI
2.4. Feature-Selection Genes from WMH-Related Genes in MCI
2.5. Immune Landscape and Feature-Selection-Genes Correlation Analysis
2.6. AD Pathology Resulted in Small-Vessel Injury and Elevated CD163 Expression
3. Discussion
4. Materials and Methods
4.1. Description of ADNI Subjects in the Study, Dataset Acquisition, and Data Preprocessing
4.2. MRI Analysis
4.3. Differential Gene Expression Analysis
4.4. Enrichment Analysis
4.5. Protein–Protein Interactions (PPIs)
4.6. Identification and Validation of Feature-Selection Genes Using Machine Learning
4.7. Immune Cell Infiltration
4.8. The Correlation Analyses of Feature-Selection Genes
4.9. Animal and Cell Experiments
4.9.1. Animal and Cell Model of Alzheimer’s Disease
4.9.2. IF Staining
4.9.3. RNA Extraction and RT-qPCR
4.10. Statistical Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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CN (n = 149) | MCI (n = 304) | Dementia (n = 44) | p | |
---|---|---|---|---|
Demographic characteristics | ||||
Male, n (%) | 68 (45.60%) | 163 (53.60%) | 25 (56.80%) | 0.090 |
Age, mean (SD) | 73.45 (5.99) b | 71.46 (7.42) ac | 75.23 (9.20) b | 0.001 |
Education, mean (SD) | 16.68 (2.53) c | 16.10 (2.64) | 15.50 (2.71) a | 0.013 |
APOEε4 carriers, n (%) | 105/38/6 (70.50%/25.50%/4.00%) bc | 175/106/23 (57.60%/34.90%/7.60%) ac | 11/26/7 (25.00%/59.10%/15.90%) ab | <0.001 |
Neuropsychological tests | ||||
MMSE, mean (SD) | 29.03 (1.21) bc | 28.11 (1.62) ac | 22.52 (3.04) ab | <0.001 |
FAQ, mean (SD) | 0.16 (0.60) bc | 2.44 (3.56) ac | 13.57 (7.20) ab | <0.001 |
MoCA, mean (SD) | 25.72 (2.16) bc | 23.52 (3.07) ac | 17.19 (4.93) ab | <0.001 |
CDRSB, mean (SD) | 0.03 (0.14) bc | 1.38 (0.86) ac | 4.64 (1.77) ab | <0.001 |
Neuroimaging | ||||
ln_WMH_TCV, mean (SD) | −1.34 (1.16) | ×1.23 (1.20) | −0.99 (1.10) | 0.209 |
TCB_TCV, mean (SD) | 77.71 (2.52) c | 77.56 (3.02) c | 74.26 (2.39) ab | <0.001 |
T_hippo_TCV, mean (SD) | 0.56 (0.05) bc | 0.54 (0.07) ac | 0.46 (0.07) ab | <0.001 |
Odds Ratio (95% CI) | p | C-Statistics | |
---|---|---|---|
CN vs. MCI [1] | |||
ln_WMH_TCV | 1.262 (1.041–1.537) | 0.019 | 0.6614 |
CN vs. MCI [2] | |||
TCB_TCV | 0.887 (0.809–0.970) | 0.010 | 0.671 |
CN vs. MCI [3] | |||
ln_WMH_TCV | 1.278 (1.053–1.559) | 0.014 | 0.682 |
TCB_TCV | 0.882 (0.804–0.966) | 0.007 | |
CN vs. Dementia [4] | |||
ln_WMH_TCV | 1.322 (0.874–2.083) | 0.202 | 0.918 |
TCB_TCV | 0.505 (0.385–0.634) | <0.001 | |
MCI vs. Dementia [5] | |||
ln_WMH_TCV | 1.081 (0.759–1.548) | 0.668 | 0.845 |
TCB_TCV | 0.657 (0.554–0.768) | <0.001 |
Gene | Primer |
---|---|
CD163 (mouse) | CD163-F AATCACATCATGGCACAGGTCACC CD163-R TCGTCGCTTCAGAGTCCACAGG |
GADPH (mouse) | GAPDH-F GGCAAATTCAACGGCACAGTCAAG GAPDH-R TCGCTCCTGGAAGATGGTGATGG |
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Chen, Y.; Lu, P.; Wu, S.; Yang, J.; Liu, W., for the Alzheimer’s Disease Neuroimaging Initiative; Zhang, Z.; Xu, Q. CD163-Mediated Small-Vessel Injury in Alzheimer’s Disease: An Exploration from Neuroimaging to Transcriptomics. Int. J. Mol. Sci. 2024, 25, 2293. https://doi.org/10.3390/ijms25042293
Chen Y, Lu P, Wu S, Yang J, Liu W for the Alzheimer’s Disease Neuroimaging Initiative, Zhang Z, Xu Q. CD163-Mediated Small-Vessel Injury in Alzheimer’s Disease: An Exploration from Neuroimaging to Transcriptomics. International Journal of Molecular Sciences. 2024; 25(4):2293. https://doi.org/10.3390/ijms25042293
Chicago/Turabian StyleChen, Yuewei, Peiwen Lu, Shengju Wu, Jie Yang, Wanwan Liu for the Alzheimer’s Disease Neuroimaging Initiative, Zhijun Zhang, and Qun Xu. 2024. "CD163-Mediated Small-Vessel Injury in Alzheimer’s Disease: An Exploration from Neuroimaging to Transcriptomics" International Journal of Molecular Sciences 25, no. 4: 2293. https://doi.org/10.3390/ijms25042293