Participant characteristics
To explore the potential mechanism of disease progression and identify candidate blood transcriptome biomarkers, we performed RNA sequencing and microRNA sequencing on peripheral blood samples from 44 patients with subjective cognitive decline (SCD) in preclinical AD and 82 individuals with normal cognition (NC), and assessed the peripheral blood transcriptomic dysregulation for SCD (Fig. 1). To observe the dynamics of these changes as the disease progresses, 51 MCI, and 25 AD samples were also sequenced following the same procedures as the SCD samples. To reduce the impact of comorbidities on the transcriptome, individuals with a history of hematological diseases, tumor and brain diseases (e.g., Parkinson's disease) were removed. Table 1 showed the demographic and cognitive characteristics of the enrolled participants from the four groups. The characteristics of the RNA sequencing data for the participants can be found in Supplementary Fig. 1. Compared with the NC group, the SCD and MCI groups did not show any significant difference with respect to age, gender, or education, whereas the AD patients were much older and had a higher proportion of APOE4 carriers than other groups. Both MCI and AD groups were significantly different from the NC group according to their cognition assessments, i.e. MMSE (Mini-Mental State Examination), MoCA_B (Montreal cognitive assessment-basic), and ACEIII scores (Addenbrooke's Cognitive Examination – III). The matched confounding factors between SCD and NC ensured that the transcriptomic changes identified in SCD mainly resulted from the disease state.
Table 1
The characteristics of participants of each group.
| NC (n = 82) | | SCD (n = 44) | | MCI (n = 51) | | AD (n = 25) | P-valued |
Mean (SD) or % | | Mean (SD) or % | P-valuea | | Mean (SD) or % | P-valueb | | Mean (SD) or % | P-valuec |
Age | 64.37 (8) | | 64.73 (7.18) | 0.995 | | 66.06 (7.72) | 0.62 | | 71.8 (8.6) | 2.83e-4 | 0.00055 |
Gender (Male %) | 32.93% | | 31.82% | 1 | | 23.53% | 0.34 | | 36% | 0.97 | 0.62 |
Education | 11.67 (4.31) | | 12.07 (3.49) | 0.94 | | 10.16 (2.68) | 0.13 | | 10.08 (4.76) | 0.28 | 0.029 |
APOE4 + (%) | 23.17% | | 11.36% | 0.17 | | 25.49% | 0.92 | | 40% | 0.16 | 0.056 |
MMSE | 27.76 (2.22) | | 27.59 (1.63) | 0.99 | | 26.51 (2.05) | 0.032 | | 17.24 (4.79) | 4.11e-14 | 5.07e-44 |
MoCA_B | 25.45 (2.95) | | 24.43 (2.94) | 0.38 | | 22 (3.21) | 3.06e-07 | | 12.32 (5.46) | 4.12e-14 | 3.22e-39 |
ACEIII | 80.51 (11.14) | | 78.98 (7.54) | 0.88 | | 70.86 (9.36) | 9.04e-06 | | 51.08 (16.71) | 4.65e-14 | 1.89e-24 |
NC = normal cognition; SCD = subjective cognitive decline; MCI = mild cognitive impairment; AD = Alzheimer's disease; MMSE = Mini-Mental State Examination; MoCA_B = Montreal cognitive assessment-basic; ACEIII = Addenbrooke's Cognitive Examination - III; SD = standard deviation.
Results presented as mean ± SD or frequencies with proportions. Quantitative and categorical characteristics differences were assessed with ANOVA and chi-square test, respectively.
a P-value for comparison between SCD and NC.
b P-value for comparison between MCI and NC.
c P-value for comparison between AD and NC.
d P-value for comparison among NC, SCD, MCI and AD.
The type I interferon signaling pathway is down-regulated in SCD
Transcript isoform diversity and dysregulation show higher disease specificity and are being increasingly implicated in neurological and neurodegenerative diseases [17, 18], we therefore quantified gene expression at both gene and isoform-level. With the transcriptome data, we identified 101 protein-coding genes and 360 transcript isoforms that were differentially expressed (P < 0.05 & |log2(Foldchange, FC)| > log2(1.3)) in the SCD group as compared with the NC group (Fig. 2A and Supplementary Table 1). Notably, although there was a substantial overlap (P = 4.4e-155) between differentially expressed (DE) genes and isoforms, isoform-level alterations exhibited larger fold changes (Fig. 2A) than the gene-level and disease specificity (Supplementary Fig. 2B-C), highlighting the importance of splicing dysregulation in preclinical AD pathogenesis. In accordance with SCD as the earliest manifestation of AD, some previously reported AD-associated genes were identified here, such as NR3C1 and GSK3B [19, 20]. Consistent with previous results,[21], principal component analysis (PCA) based on the DE genes and isoforms (Fig. 2B, Supplementary Fig. 2D-E) revealed moderately separated clusters between disorders and normal controls. We also noted a scattered distribution of samples in each group. Both of which indicate a high heterogeneity of expression profile of peripheral blood.
As shown in the volcano plot (Fig. 2C), some interferon (IFN) stimulated genes and their isoforms were substantially downregulated in SCD, such as IFI27, OAS2, IFI44L, and RSAD2. Consistent with this finding, the interferon signaling pathway activity was significantly inhibited in SCD (Supplementary Fig. 3A), as confirmed by ingenuity pathway analysis (IPA) [22], especially for the type I interferon signaling pathway (Fig. 2D, Supplementary Table 2). Notably, STAT1, a key mediator of IFN signaling [23], was downregulated in SCD (Fig. 2E, Supplementary Fig. 3B). STAT1 mediates cellular response to interferons, cytokines, and other growth factors and activates the transcription of IFN-stimulated genes that target almost any step in a virus life cycle [24]. Therefore, the downregulation of STAT1 and IFN stimulated genes and type I IFN signaling in SCD would increase the replication of viral, like Herpesviridae. The bacterial and viral infections were reported to contribute to the pathophysiology of AD or to cognitive decline, most frequently implicating Herpesviridae [25].
Notably, the type I IFN signaling was activated in AD (Supplementary Fig. 3C-D, Supplementary Table 2), which may arise from the feedback regulation of antiviral response. Furthermore, gene set enrichment analysis (GSEA), takes all of the genes into consideration rather than only DE genes, which also gives validation to type I IFN signaling being down-regulated in SCD while up-regulated in MCI and AD compared with NC (Fig. 2F). The ISG (IFN-stimulated gene) score defining IFN signaling signature based on the mean of expression of six ISGs (IFI44L, IFI27, RSAD2, SIGLEC1, IFIT1, and IS15) in this pathway [26, 27], was significantly reduced in SCD compared with NC and gradually increased in MCI and AD (Fig. 2G and 2H). Together, these results demonstrated that in contrast to the MCI and AD stages, the SCD stage exhibited a down-regulation of type I interferon signaling pathways.
STAT1, a key transcription regulator of type I IFN signaling, is differentially spliced in SCD
Alternative splicing is an important post-transcriptional regulation mechanism, contributing to isoform diversity and protein complexity [28, 29]. Here, we identified 138 genes with differentially local splicing (DS, P < 0.001) in SCD compared with NC (Fig. 3A, Supplementary Table 3). DS genes in SCD overlapped significantly (P = 0.0032) with DE transcript isoforms, indicating that local splicing could partially explain isoform dysregulation. There were significant pairwise overlaps of DS genes between three stages of AD (Supplementary Fig. 4A). For example, differentially excised introns of NRF1, a transcription factor contributing to the pathogenesis of neurodegenerative diseases via perturbation of diverse mitochondrial and extra-mitochondrial functions [30], were found in SCD and AD (Supplementary Fig. 4D). However, DS genes in AD exhibited few overlaps with those previously identified in brain transcriptome data (Supplementary Fig. 4B-C) [31, 32], highlighting the tissue-specificity of splicing events.
Differentially spliced type I IFN signaling genes in SCD significantly overlapped with those that were differentially expressed at the isoform level, including STAT1, STAT2, and MX1 (Fig. 3B and 3C), which may partially resolve the dysregulation of the type I IFN signaling pathway. Notably, a DS intron cluster (chr2:191874730–191878744) in STAT1 exhibited significantly increased exon skipping in SCD (P = 1.22e-05) and MCI (P = 7.4e-4) compared with NC (Fig. 3E). We identified splicing quantitative trait locus (sQTLs) driving or contributing to this DS using FastQTL [33], adjusting for known and inferred covariates (Supplementary Fig. 5 and Supplementary Table 4). The most significant SNP rs118149197 (P = 4.3e-20, Fig. 3F-G), located in the UTR5’ region of STAT1, with a higher mutation burden in SCD and MCI than NC, was predicted to affect RNA splicing by SPIDEX [34]. Individuals with this variant had a significantly higher PSI (percent spliced in) of this intron cluster (Fig. 3F). Consistent with a previous observation that DS events might predict the aberrant expression of isoforms [17], some transcripts of STAT1 showed significantly decreased expression and transcript usage in SCD, like STAT1-201 and STAT1-223 (Fig. 3H and Supplementary Fig. 6). As described above, STAT1 is one of the key transcription regulators of type I interferon signaling and activates the transcription of IFN-stimulated genes. Therefore, the dysregulated splicing and expression of STAT1 in SCD patients may reduce their response to interferons and the defense to viruses (e.g, herpes simplex).
NRIR and has-miR-146a-5p, upstream regulators of type I IFN signaling genes, are dysregulated in SCD
Long noncoding RNAs (lncRNAs) and miRNAs regulate gene expression at epigenetic, transcriptional, and post-transcriptional levels [35, 36]. They play a key role in neurogenesis, neuronal maturation, neuronal function, and neuronal survival, and thus are involved in many neurological diseases like epilepsy and AD [37–39]. Here, we sought to explore the regulation of ncRNAs for the IFN signaling genes in SCD.
Based on GENCODE annotations, we identified 8 lncRNAs exhibiting differential expression in SCD compared with NC (Fig. 4A, Supplementary Table 5). Notably, as shown in the volcano plot (Fig. 4B), NRIR was remarkably down-regulated (log2FC = -0.80; P = 1.9e-4) in SCD. The expression of this lncRNA was slightly increased in MCI (log2FC = 0.2), and significantly elevated in AD (log2FC = 0.6; P = 0.06) (Fig. 4C). Consistent with NRIR driving interferon response in human monocytes [40], NRIR exhibited significantly positive correlation with the ISG score (R = 0.85, P < 2.2e-16, Fig. 4D) for the type I IFN signaling and STAT1 (R = 0.85, P < 2.2e-16, Supplementary Fig. 7), indicating that decreased expression of NRIR might play a regulatory role in the impaired interferon activity of SCD.
To assess the potential role of miRNAs dysregulation in transcriptomic alteration, we performed genome-wide miRNA expression profiling in samples with mRNA sequencing. We identified differently expressed miRNAs (Fig. 4E, Supplementary Table 6) and predicted their target genes. We noted has-miR-125b-5p and hsa-miR-33a-5p, two blood-based miRNA biomarkers for diagnosis of AD [41], were dysregulated in both SCD and AD. Several miRNAs significantly targeted genes in the type I IFN signaling pathway (FDR < 0.05, Supplementary Table 7). Notably, has-miR-146a-5p showing the most significant enrichment (FDR = 6.3e-15), significantly targeted and negatively regulated down-expressed type I IFN genes in SCD, such as STAT1 (Fig. 4F), IFIT1, and ISG15. Compared with NC, It was slightly up-regulated in SCD and returned to normal in MCI and AD (Fig. 4G). The negative correlation (R = -0.38, P = 3.0e-8) between the PC1 of type I IFN signaling genes and the PC1 of miRNAs that targeted these genes (Fig. 4G) suggests that the accumulation of slightly upregulated miRNA negatively regulates the type I IFN signaling pathway.
Co-expression network module negatively associated with SCD is enriched for IFN signaling pathway
To further gain a systematic understanding of the relationship between expression changes and disease status and regulatory interactions among molecules, we performed integrated weighted gene co-expression network analysis (WGCNA) for protein-coding mRNAs and lncRNAs at gene and isoform levels, and miRNA to assign individual RNAs into network modules [42].
We identified 18 gene modules (Fig. 5A) summarized by eigengenes (i.e., PC1) in SCD, and assessed the association between them and disease status and covariates. The module M5 was significantly negatively correlated with disease status (Pearson’s correlation, R = -0.23, P = 0.01) but not with any confounding factors such as age or gender, suggesting that this module was primarily driven by the SCD status. It showed a remarkably positive correlation with the ISG score (Pearson’s correlation, R = 0.93, P = 3e-55) and was enriched for the interferon signaling pathway (Fig. 5B). WGCNA permits screening for the hub genes that may be promising biomarkers for diagnosis and prediction of outcomes of disease [43]. We evaluated the module membership (MM, correlation of gene expression with the module eigengene) and gene significance (GS, mediated P-value of each gene (GS = -log10P) in the linear regression between gene expression and the clinical traits) for each gene in the identified modules [43]. Several hub genes with both higher correlation with diagnosis (GS > 0.2) and higher module connectivity (MM > 0.80) were identified in module M5 (Fig. 5C, Supplementary Fig. 8), including the transcription factor STAT1 and lncRNA regulator NRIR of IFN signaling identified above. Besides, STAT2, another key mediator of the JAK-STAT pathway, had the highest within-module connectivity (MM = 0.94) and a strong correlation with SCD (GS = -0.22), suggesting an association with SCD and a key role in the regulation of the interferon signaling. The isoform-level network of SCD (Supplementary Fig. 9) captured the generally equivalent interferon module (i.e., module M5), as well as a module M12 related to neutrophil degranulation, mRNA metabolism, and mRNA splicing, demonstrating the importance of splicing dysregulation. In contrast to what we found in SCD, modules of virus infection (module M11) and interferon signaling (module M19) in the AD co-expression network were significantly upregulated (Supplementary Fig. 10).
STAT1 and TRIM22 may serve as candidate biomarkers for conversion to MCI
We further sought to determine whether the hub genes could be candidate biomarkers for disease conversion in a longitudinal dataset (i.e., Alzheimer's Disease Neuroimaging Initiative, ADNI). A total of 743 participants (245 objectively NC, 382 MCI, and 116 AD) underwent Affymetrix array sequencing for blood transcriptome. Objectively NC participants were divided into two groups based on the median expression level of each hub gene. For 16 out of 23 hub genes (Supplementary Table 8), individuals of the lower-expression group carried a higher risk of conversion to MCI/AD compared with the higher-expression group, particularly for TRIM22 (P = 0.0022, Fig. 5C) and STAT1 (P = 0.0031, Fig. 5D). In the two groups with lower expression of STAT1 or TRIM22, 26 of 122 samples converted to MCI and 1 converted to AD, which was more than twice the conversion rate of the higher-expression group. STAT1 mediates the actions of IFNs and cytokines and upregulates genes causing pathogen response [44]. TRIM22 is an IFN-inducible TRIM family protein that restricts the replication of viruses via either regulating innate signaling pathways or serving as a viral restriction factor [45]. Thus, the down regulation of STAT1, TRIM22, and interferon signaling would weaken the cellular antiviral ability, increase virus replication (like replication of Herpseviridae) [46], and increase the risk of disease conversion. As expected, samples with low interferon activity had a high conversion rate when taking the first quartile of the ISG score as the threshold (Fig. 5E). Overall, this indicates that STAT1 and TRIM22, hub genes of the interferon signaling module, could be candidate biomarkers for disease conversion.