As people's average life expectancy has increased due to the further improvement of modern medicine and science, the number of patients with AD will be increased as well [38]. Genome-wide association studies have identified numerous genomic loci associated with AD, while the causal genes and variants were still being continuously identified [39]. Most of the conventional methods of differential gene analysis focus on the differential expression of a single gene, i.e., the greater the difference of a single gene expression, the more important the role the single gene plays [40]. In our study, we identified the genes with the highest changes in the expression in the hippocampus of AD patients. We further investigated these genes with functional enrichment analysis. Studies have shown that LTF increased the α-Secretase-Dependent amyloid precursor protein processing via the ERK1/2-CREB and HIF-1α pathways in a mouse model of AD [41]. Furthermore, loss of SLC17A6 was correlated with cognitive decline in AD [42]. Although cilia and flagella associated protein (CFAP) was essentially important for sperm flagellum biogenesis [43], no association was revealed between CFAP126 and AD. In the APP/PS1 mouse model of AD, showing anxiety-like behavior, the photo stimulating the pBLA-vCA1 circuit ameliorated the anxiety in a Calb1-dependent manner [44]. Similar to these genes, many of the DEGs identified in our study were related to AD with only one gene related to cilia. The KEGG functional enrichment analysis revealed that these DEGs were involved in pathways of nicotine addiction, neuroactive ligand-receptor interaction, vesicle-mediated transport in synapse, synaptic vesicle cycle, neurotransmitter transport, and synapse organization. The KEGG pathway of nicotine addiction is associated with neurological diseases [45], and other pathways were related to synapse, whereas no association with cilia was identified.
GSEA used the pre-defined gene set to rank the genes based on the degree of differential expression. This analysis has been used to identify key transcriptome biomarkers in AD [46]. The results of our GSEA analysis showed that the genes have implications for disease were enriched in four gene sets, i.e., CHR2Q13, CHR19P12, CHR4P14, and KRAS.PROSTATE_UP. V1_DN. The gene TUBAL3 related to cilia was identified only in KRAS.PROSTATE_UP. V1_DN.
With the development of bioinformatics technology, more and more advanced and adequate methods have been developed and applied in life sciences [47]. At present, WGCNA is a well established method and applied in various studies of human diseases [29]. In our study, the results of the WGCNA analysis showed that 12 modules were related to AD, and the lightcyan module showing the highest correlation with AD contained a total of 160 genes. Based on the PPI network, 20 hub genes were identified to play important roles in the network associated with PCD [48–51]. We ranked the hub genes following the Score obtained in the Disgenet database, and the results revealed that the top four genes included DNAI1, DNAAF3, CCDC114, and CCDC65. Studies have shown that DNAI1 was strongly linked to the ciliary beat pattern variations [48], while the DNAAF3 variation in respiratory cilia was found uniformly immotile due to their defected dynein arms [49]. CCDC114 was located at the basal body of a cilium and the knockdown of CCDC114 could affect the formation of cilia in hRPE1 cells [50]. CCDC65 was a central hub gene for assembly of the nexin-dynein regulatory complex and other regulators of ciliary and flagellar motility [51]. The results of both the GO and KEGG analyses showed that these hub genes were not only related to the regulation of axonemal dynein complex assembly and cilium movement, but also played an important role in Huntington's disease. It was worth noting that both the axonemal dynamic complex and cilium movement were relevant to neurological diseases [52].
Gene interaction heatmap showed that the 19 hub genes were associated with AD related key genes. Gene interaction is used to predict the function of the unknown gene [53]. Our results indicated a relationship between PCD and AD.
The role of A-β in AD development has been widely recognized with its deposition as one of the main symptoms of AD. Studies showed that the A-β inhibition of mitochondrial axonemal transport was associated with the early pathophysiology of AD [54–56]. Furthermore, it was reported that the A-β was transmitted through neuronal connections on the axon membrane [57]. PCD was a multiple inherited disorder caused by ciliary structural defects [58]. Evidently, PCD was directly related to ciliary movement. Moreover, studies have shown that the motor protein of axons was related to PCD [59, 60]. These results suggest that AD and PCD are linked by the functions of cilia and axons.
For the first time, we have applied not only the conventional methods of differential gene analysis, but also the GSEA and WGCNA to analyze the experiment data and to identify the gene sets related to PCD in the hippocampus of AD patients. We have withdrawn the following conclusions. First, most genes obtained by conventional differential gene analysis have been confirmed by our results. The GO and KEGG analyses further verified that these genes were related to AD. However, both the enrichment and GSEA analyses failed to identify any association between AD and PCD. Second, PCD related modules in hippocampus of AD patients were discovered by WGCNA. Third, the gene interaction heatmap showed that hub genes were bound to AD related key genes. These results indicated that the hub genes were involved in the regulation of axonemal dynein complex assembly and the regulation of cilium movement, both of which played important roles in AD as well. These results strongly suggest that both AD and PCD were related in the functions of cilia and axons.