Additional file 1 of Interaction of high-fat diet and brain trauma alters adipose tissue macrophages and brain microglia associated with exacerbated cognitive dysfunction
Creators: name: Henry, Rebecca J.
name: Barrett, James P.
name: Vaida, Maria
name: Khan, Niaz Z.
name: Makarevich, Oleg
name: Ritzel, Rodney M.
name: Faden, Alan I.
name: Stoica, Bogdan A.
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Neuroscience
subject: Physiology
subject: Pharmacology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Computational Biology

description: Additional file 1: Figure S1. Gating strategy for CD45+/CD11b+ and CD11b+ Red Bead+ ATM/myeloid cell populations. A Gating strategy for CD45+/CD11b+ ATM/myeloid cell populations in the adipose tissue at 28 dpi. B Gating strategy for CD11b+ Red Bead+ ATM/myeloid cell population in the adipose tissue at 28 dpi. Although there was a factor effect of diet on % Red Bead+ of CD11b+ ATM/myeloid cells, there was no reported differences between groups in post-hoc analysis (C). Post-hoc analysis revealed that TBI-HFD had a significant increase in MFI of Red Bead+ of CD11b+ ATM/myeloid cells when compared to TBI-SD counterparts (D). Figure S2. Morpheus unsupervised hierarchical clustering heat map on VAT cellular transcriptome. Only genes that showed evidence of significant changes (p &lt; 0.05 in at least one of the two-way ANOVA outcomes) were included (369 genes). The heat map demonstrated the same ordered clustering across the groups observed Fig. 9A. Figure S3. IPA detects Upstream Regulator-Genes upregulated and downregulated genes in VAT. IPA determined the activation of pathways based on the inclusion of genes with p &lt; 0.05; all pathways with z-score &gt; 2.8 or &lt; − 2.8 in at least one group are presented. Analysis of the Z-score of adipose upstream upregulated genes pathways including multiple inflammatory-related, identify significant TBI, HFD, and TBI-HFD interaction effects. Notably, TBI-HFD significantly increases upregulated pathways compared to TBI-SD. Only diet was a significant factor for downregulated genes. Figure S4. IPA detects activated and inhibited Upstream Regulator-Drugs pathways in VAT. IPA determined the activation of pathways based on the inclusion of genes with p &lt; 0.05; all pathways with z-score &gt; 2.8 or &lt; − 2.8 in at least one group are presented. Analysis of the Z-score of adipose upstream upregulated and downregulated drugs pathways, including multiple inflammatory-related, identify significant TBI, HFD, and TBI-HFD interaction effects. Notably, TBI-HFD significantly increases upregulated pathways and decreases downregulated pathways compared to TBI-SD. Figure S5. IPA detects upregulated and downregulated Diseases and Bio Functions pathways in VAT. IPA determined the activation of pathways based on the inclusion of genes with p &lt; 0.05; all pathways with z-score &gt; 3.5 or &lt; − 3.5 in at least one group are presented. Analysis of the Z-score of adipose disease pathways, including multiple inflammatory-related, identify significant TBI, HFD, and TBI-HFD interaction effects (except lack of significant injury effect in downregulated pathways). Notably, TBI-HFD significantly increases upregulated pathways and decreases downregulated pathways compared to TBI-SD. Figure S6. HFD selectively exacerbates TBI-induced increases in the expression of hippocampal, but not cortical neuroinflammatory genes. A. TBI-SD mice display a significant increase in hippocampal expression of IL-1β, NOX2, and p22phox when compared to Sham-SD counterparts, at 28 dpi. TBI-HFD mice exhibit a significant increase in hippocampal expression of p22phox when compared to Sham-HFD and TBI-SD. In addition, TBI-HFD displays a significant increase in hippocampal expression of CD11b, when compared to Sham-HFD counterparts. B. TBI-SD mice display an increase in cortical expression of TNF-α, NOX-2, and p22phox when compared to Sham-SD counterparts. *p &lt; 0.05, **p &lt; 0.01 vs Sham-SD; +  +  + p &lt; 0.001 vs TBI-SD; ###p &lt; 0.001 vs Sham-HFD. N = 6–7 per group. Data expressed as mean ± SEM. Figure S7. Morpheus unsupervised hierarchical clustering heat map on isolated microglia (CD11b+) cells. Although only the genes that showed evidence of significant changes (p &lt; 0.05 in at least one of the two-way ANOVA outcomes) were included (as for Fig. 8) the pattern remained to Fig. 5A. Figure S8. IPA detects activated and inhibited Upstream Regulator-Genes pathways in microglia. IPA determined the activation of pathways based on the inclusion of genes with p &lt; 0.05; all pathways with z-score &gt; 2.5 or &lt; -2.5 in at least one group are presented. TBI, but not HFD (except for a modest significant factor effect in upregulated) had strong significant factor effects in both upregulated (including several inflammatory-related pathways) and downregulated pathways; no significant TBI-HFD interaction was observed. Figure S9. IPA detects activated and inhibited Upstream Regulator-Drugs pathways in microglia. IPA determined the activation of pathways based on the inclusion of genes showing changes in each group vs Sham-SD with p &lt; 0.05; all pathways with z-score &gt; 2.5 or &lt;− 2.5 in at least one group are presented. There was a significant effect of TBI, HFD, and TBI-HFD interaction in upregulated upstream drug pathways which included several inflammatory pathways. Notably, TBI-HFD showed increased effects compared to TBI-SD. However, only TBI effects were reported on downregulated upstream drugs in microglia. Figure S10. IPA detects upregulated and downregulated Diseases and Bio Functions pathways in microglia. IPA determined the activation of pathways based on the inclusion of genes with p &lt; 0.05; all pathways with z-score &gt; 2.5 or &lt; − 2.5 in at least one group are presented. TBI, but not HFD, or TBI-HFD effects are reported on upregulated and downregulated diseases in microglia. Figure S11. Morpheus unsupervised hierarchical clustering heat map on microglia vs. flowthrough cellular transcriptome. The ratio between global microglia and flowthrough normalized expression data (560 genes out of a total of 769 genes had average expression &gt; 50) was used to determine the top 50 overexpressed and under-expressed genes in microglia vs flowthrough. Figure S12. Morpheus unsupervised hierarchical clustering heat map on flowthrough cellular transcriptome. Morpheus heat map on all 560 genes shows no ordered clustering across the groups indicative of the smaller number of genes with consistent and significant changes. Figure S13. Morpheus unsupervised hierarchical clustering heat map on flowthrough cellular transcriptome. When only genes showing significant changes (p &lt; 0.05 in at least one of the two-way ANOVA outcomes) were selected the heat map demonstrated the same ordered clustering across the groups observed in microglia. Genes downregulated included neurogenesis and neuronal signaling markers while genes upregulated included oligodendrocyte phenotype markers. Figure S14. IPA detects Upstream Regulator-Genes upregulated in flowthrough cellular transcriptome. IPA determined the activation of pathways based on the inclusion of genes with p &lt; 0.05. TBI, but no HFD or TBI-HFD showed significant effects on upstream genes upregulated in flowthrough cellular transcriptome. Figure S15. IPA detects upregulated and downregulated Diseases and Bio Functions pathways in flowthrough cellular transcriptome. IPA determined the activation of pathways based on the inclusion of genes with p &lt; 0.05; all pathways with z-score &gt; 2.0 or &lt; − 2.0 in at least one group are presented. Significant TBI, but no HFD or TBI-HFD interaction effects are observed in diseases upregulated and downregulated pathways in flowthrough cellular transcriptome. Notably, TBI-HFD showed increased effects compared to TBI-SD in upregulated pathways. Figure S16. Statistical analysis tables for the main figures. We present the statistical tables for all the transcriptomic (Nanostring) and pathways analysis (IPA) data included in the main figures. The corresponding figure numbers are indicated.