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. 2023 Feb 25;13(1):3276.
doi: 10.1038/s41598-022-27276-7.

Differential expression of interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) in Alzheimer's disease and HIV-1 associated neurocognitive disorders

Armando Garces  1 , Bryan Martinez  1 , Roberto De La Garza  1 , Deepa Roy  1 , Kaylie-Anna Vallee  2 , Jerel Adam Fields  2 , David J Moore  2 , Hansapani Rodrigo  3 , Upal Roy  4
Affiliations

Differential expression of interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) in Alzheimer's disease and HIV-1 associated neurocognitive disorders

Armando Garces et al. Sci Rep. .

Abstract

The United Nations projects that one in every six people will be over the age of 65 by the year 2050. With a rapidly aging population, the risk of Alzheimer's disease (AD) becomes a major concern. AD is a multifactorial disease that involves neurodegeneration in the brain with mild dementia and deficits in memory and other cognitive domains. Additionally, it has been established that individuals with Human Immunodeficiency Virus-1 (HIV-1) experience a 5 to 10-year accelerated aging and an increased risk of developing HIV-associated neurocognitive disorders (HAND). Despite a significant amount of clinical evidence pointing towards a potential overlap between neuropathogenic processes in HAND and AD, the underlying epigenetic link between these two diseases is mostly unknown. This study is focused on identifying differentially expressed genes observed in both AD and HAND using linear regression models and a more robust significance analysis of microarray. The results established that the dysregulated type 1 and 2 interferon pathways observed in both AD and HAND contribute to the similar pathologies of these diseases within the brain. The current study identifies the important roles of interferon pathways in AD and HAND, a relationship that may be useful for earlier detection in the future.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Heatmap of mRNA expression in HAND patients. This figure represents the expression value of mRNA from HAND and controls patients’ brains in GSE35864. The data were normalized using the GCRMA Bioconductor package. The mRNA expression of IFIT3, STAT1, ISG15, and GBP1 was chosen by comparing independent SAM results and identifying common markers that were shared between pathologies. The color key represents the Z score the blue represents underexpression and red represents overexpression. These mRNA transcripts were found to be under-expressed in patients with HAD compared to HIV-1 patients.
Figure 2
Figure 2
Heatmap of mRNA expression in the white matter of HAND patients. This figure represents the expression value of mRNA from the white matter of untreated HAND and control patients’ brains in GSE28160. The data were normalized using the GCRMA Bioconductor package. The mRNA expression of PLSCR1, DTNA, BMP2K, IFIT3, DDR2, ITRP2, TLR7, ANXA1, GBP1, SNAP25, NRXN1, DLGAP1, GABRB3, STAT1, ISG15, C3AR1, and C3 was chosen by comparing independent SAM results and identifying common markers that were shared between pathologies. The color key represents the Z score, the blue represents underexpression and the red represents overexpression. The larger sample size makes it easier to see trends compared to the other HAND dataset. We can observe overexpression of mRNA transcripts from untreated HAND including PLSCR1, DTNA, BMP2K, IFIT3, DDR2, ITRP2, TLR7, ANXA1, GBP1, STAT1, ISG15, C3AR1, and C3. The mRNA expression of SNAP25, NRXN1, DLGAP1, and GABRB3 was under-expressed compared to controls.
Figure 3
Figure 3
Heatmap of mRNA expression in nucleus accumbens and amygdala AD patients. This figure represents the expression value of mRNA from the Nucleus accumbens and Amygdala of AD and controls patients’ brains in GSE84422. The data were normalized using the GCRMA Bioconductor package. The mRNA expression of PLSCR1, DTNA, BMP2K, IFIT3, DDR2, ITRP2, TLR7, ANXA1, GBP1, SNAP25, NRXN1, DLGAP1, GABRB3, STAT1, ISG15, C3AR1, and C3 were chosen by comparing independent SAM results and identifying common markers that were shared between pathologies. The color key represents the Z score the blue represents underexpression and red represents overexpression. SAM results demonstrate that mRNA expression of DTNA was overexpressed while transcripts ANXA1, DDR2, ITRP2, BMP2K, C3, C3AR1, and TLR7 were under-expressed.
Figure 4
Figure 4
Heatmap of mRNA expression in the frontal cortex AD patients. This figure represents the expression value of mRNA from the frontal cortex regions of AD and control patients’ brains in GSE84422. The data were normalized using the GCRMA Bioconductor package. The mRNA expression of PLSCR1, DTNA, BMP2K, IFIT3, DDR2, ITRP2, TLR7, ANXA1, GBP1, SNAP25, NRXN1, DLGAP1, GABRB3, STAT1, ISG15, C3AR1, and C3 were chosen by comparing independent SAM results and identifying common markers that were shared between pathologies. The color key represents the Z score the blue represents underexpression and red represents overexpression. SAM results demonstrate the overexpression of transcripts ISG15, GBP1, IFIT3, DLGAP1 FCGR1B, NRXN1, SNAP25, and GABRB3.
Figure 5
Figure 5
Heatmap of mRNA expression in the basal ganglia of AD patients. This figure represents the expression value of mRNA from the basal ganglia regions of AD and control patients’ brains in GSE84422. The data were normalized using the GCRMA Bioconductor package. The mRNA expression of PLSCR1, DTNA, BMP2K, IFIT3, DDR2, ITRP2, TLR7, ANXA1, GBP1, SNAP25, NRXN1, DLGAP1, GABRB3, STAT1, ISG15, C3AR1, and C3 were chosen by comparing independent SAM results and identifying common markers that were shared between pathologies. The color key represents the Z score the blue represents underexpression and red represents overexpression. Based on SAM results ISG15, GBP1, STAT1, PLSCR1, and DTNA were overexpressed in the basal ganglia of AD subject brains.
Figure 6
Figure 6
Principal component analysis on HAND patients data. Principal Component Analysis of the controls and other 3 HIV-1 patient groups within the three brain regions, basal ganglia, frontal cortex, and white matter. (A–C) showed no clear groupings among the different subject groups from data derived from GSE35864. (D) represents untreated HAND data from GSE28160.
Figure 7
Figure 7
Principal component analysis on AD patients data. Principal Component Analysis of the controls and the three platforms that make up dataset GSE84422. Details of the platforms and patient data can be found in table 1. (A) Represents the data derived from the nucleus accumbens and amygdala. This shows some clustering in the negative direction of the PC1 axis in which both the control and AD subject group seem to cluster together within the same area on the axis. AD patients data, however, spread out more and some subject data can be seen on the positive end of the PC1 axis meaning there is more variability in the subject data. (B, C) represents the frontal cortex data both plots represent clusters of data on opposite ends of the PC1 axis but both contain subject data that is clustered together and show some variability among control groups on the PC2 axis. Figures 7D,E represents the basal ganglia subject data which displays more variability in Fig. 7D with no clear clusters. However, (E) depicts clusters on the positive end of the PC1 plot and towards the negative end of the PC2 axis.
Figure 8
Figure 8
SAM results in comparison of untreated HAND and AD patients (nucleus accumbens/amygdala). This Venn diagram represents the SAM results that were considered significant (q-value =  < .05). Both SAM analyses were done independently and then compared. The white matter of untreated HAND patients had a total of 397 upregulated genes (blue) and 52 downregulated genes (red). The nucleus accumbens and amygdala of AD patients resulted in 62 upregulated genes (blue) and 385 downregulated genes (red).
Figure 9
Figure 9
SAM results in comparison of untreated HAND and AD patients (frontal cortex). This Venn diagram represents the SAM results that were considered significant (q-value =  < .05). Both SAM analyses were done independently and then compared. The frontal cortex regions of AD patients had 17 upregulated genes (blue) and a total of 197 downregulated genes (red). The frontal cortex of HAD patients showed a total of 6 downregulated genes (red). The white matter of untreated HAND patients had a total of 397 upregulated genes (blue) and 52 downregulated genes (red). Untreated HAND patients and AD patients shared 13 common genes. However, the only common differentially expressed gene based on mRNA expression was IFIT3.
Figure 10
Figure 10
SAM results in comparison of untreated HAND and AD patients (basal ganglia). This Venn diagram represents the SAM results that were considered significant (q-value =  < .05). Both SAM analyses were done independently and then compared. The basal ganglia regions of AD patients had 40 downregulated genes (red). While the HIV-1 patients only had 14 genes up-regulated (blue) based on mRNA expression. The white matter region of untreated HAND patients had a total of 397 upregulated genes (blue) and 52 downregulated genes (red). In comparison, untreated HAND and AD shared 7 genes in common seen in purple. While HIV-1 patients only had PLSCR1 and GBP1 in common with AD and untreated HAND patients.
Figure 11
Figure 11
Dysregulated RNA in AD and HAND. This graph represents the dysregulation of RNA expression in HAND and AD. The results were derived from the SAM analysis. Two unpaired analysis was done independently “Control vs AD” and “Control vs HAND”. The Score (D) represented negative values which represent down-regulation of RNA and positive values represent upregulation in RNA expression. The dark yellow represents “AD” data and the grey represents “HAND”. The common genes that were derived from the SAM Analysis results and submitted through DAVID for the gene ontology analysis.
Figure 12
Figure 12
Chord diagram of mRNA and respective functions. This chord diagram represents the common genes that were derived from the SAM Analysis results and submitted through DAVID for the Gene ontology analysis. Each “chord” connects a gene to its respective function or process. In this graph, genes may be involved in multiple processes.
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