Identification of CTHRC1 as a novel candidate for neurodevelopmental disorders

Background: Cognitive dysfunction affects over 50 million individuals worldwide, with Alzheimer’s disease (AD) representing two-thirds of cases. We identified CTHRC1 (Collagen Triple Helix Repeat Containing 1) as a novel candidate associated with cognitive function and neurodegeneration.

Methods: Human proteomic analysis revealed CTHRC1 as highly upregulated in AD patients (~5-fold increase, adj. p = 0.05), with corresponding elevation in 5xFAD mice. Single-cell RNA sequencing showed predominant astrocyte and oligodendrocyte progenitor expression. Using BXD mice, systems genetics analysis revealed associations between hippocampal CTHRC1 expression and 22 cognition-related phenotypes. PheWAS, ePheWAS, and GWAS analyses confirmed links to nervous system and AD-related traits.

Results: eQTL mapping identified CTHRC1 as cis-regulated in hippocampus, and correlating with protein transport, transcription, and neurodegeneration pathways. Network analysis revealed 17 direct interactors, including key neurodegeneration genes (BACE1, NEFL, IRS1, VDAC1, SNCAIP) connecting CTHRC1 to core AD pathways (APP, MAPT, APOE, PSEN1/2). CTHRC1 overexpression in SH-SY5Y cells promoted tau degradation and modulated network partner expression.

Conclusion: CTHRC1 represents a central hub in cognitive function networks, suggesting therapeutic potential for neurodegenerative disorders.

1 Introduction

Cognitive dysfunction, ranging from mild cognitive impairment to dementia, is associated with higher disability risk and increased health expenditures. Dementia is the most devastating condition affecting today’s elderly population and the leading cause of disability worldwide (Calia et al., 2019; Qin et al., 2022). Over 50 million individuals suffered from dementia in 2019, projected to increase three-fold to 150 million by 2050 (Nichols et al., 2022). Dementia incidence increases exponentially with age, particularly between 65–90 years, doubling approximately every 5 years (Corrada et al., 2010; Jorm and Jolley, 1998). Alzheimer’s disease (AD) accounts for at least two-thirds of dementia cases in people ≥65 years and is the sixth leading cause of death in the United States (Kumar et al., 2025). Hippocampal damage produces inflexible behavior, affecting memory, navigation, exploration, imagination, creativity, and decision-making, as the hippocampus and its interconnected systems support flexible information use (Rubin et al., 2014). Age-related hippocampal alterations, including neuroinflammation from oxidative stress, reduced neurogenesis, and decreased synaptic plasticity, contribute to cognitive decline (Bettio et al., 2017). Several studies have investigated genes associated with cognition and AD (Liu et al., 2021; Hill and Gammie, 2022; Fan et al., 2019), identifying dozens, including APOE (Gibbons et al., 2011; Lefterov et al., 2019), DLGAP2 (Ouellette et al., 2020), APP, PSEN1, PSEN2 (Cruchaga et al., 2012), and STAT3 (Hong et al., 2020). However, due to cognitive dysfunction’s complexity, pathogenic mechanisms remain incompletely understood. Therefore, identifying novel causative agents and drug targets is needed to provide new approaches for diagnosis, treatment, prevention, and management of cognitive impairment.

Systems genetics is an approach that uses a range of experimental and statistical methods to examine intermediate phenotypes, such as transcript, protein, or metabolite levels, to bridge DNA variations with the traits of interest. Furthermore, this approach considers gene-by-gene and gene-by-environment interactions to understand complex traits (Civelek and Lusis, 2014; Moreno-Moral and Petretto, 2016). Cognitive-related disorders are challenging to study directly in humans due to their complexity, environmental uncontrollability, unavailability of samples, and ethical issues. This limitation is overcome by genetic reference populations (GRPs) with controllable environmental factors that provide a suitable platform for linking genetic, environmental, and gene-by-environmental factors to understand such complex traits (Civelek and Lusis, 2014). The fully inbred BXD mouse panel, generated from the crosses between C57BL/6J (B6) and DBA/2J (D2) strains for more than 20 consecutive generations, serves as a suitable animal model for discovering complex mechanisms underlying various physiological conditions (Ashbrook et al., 2021; Andreux et al., 2012). Currently, this family contains over 200 strains with whole-genome sequence data available for ~150, hundreds of omics datasets generated from various tissues including different brain regions, and thousands of phenome datasets (Ashbrook et al., 2021). This panel has been used to show that pattern separation is heritable in the mouse and to identify mechanisms underlying variation in pattern separation (Dickson and Mittleman, 2022). Additionally, a significant difference in operant performance and learning, including faster reversal learning in D2 compared to B6 mice, has been established (Graybeal et al., 2014). The founder strains differ in several types of complex learning, including contextual fear conditioning (Wehner et al., 1997). Furthermore, B6 shows higher myelin transcript expression compared with D2 and accompanying differences in myelin protein composition and content and white matter conduction velocity. The study indicates that genetic variation in myelin gene expression translates to differences observed in axon conduction speed and possibly in hippocampus-related memory and learning tasks (Goudriaan et al., 2020).

Collagen triple helix repeat containing 1 (CTHRC1) encodes a protein that may play a role in the cellular response to arterial injury through vascular remodeling. Mutations in CTHRC1 have been identified to be associated with Barrett esophagus and esophageal adenocarcinoma (Orloff et al., 2011). Furthermore, this gene has been reported to promote tumor progression by regulating the immunosuppression of the tumor microenvironment (Hu et al., 2023). However, a few recent studies, particularly proteomic analyses, have shown CTHRC1 protein to be deregulated in AD either in human samples or mouse models (Wang et al., 2020; Bai et al., 2020). In a study by Wang et al., the comparison of cortex and serum samples led to the identification of an AD-correlated protein panel of CTHRC1, GFAP, and OLFM3 (Wang et al., 2020). The study by Bai et al. found a consistent increase in the expression of CTHRC1 protein in 5xFAD animals and a high correlation with amyloid-β (Aβ) in AD cases (Bai et al., 2020). Another study by Carlyle et al.¹ found CTHRC1 protein to be more abundant in the high pathology dementia-AD group than in the normal group. CTHRC1 is increasingly emerging as a candidate gene in AD because it is repeatedly detected in unbiased proteomic screens, yet its role in the brain remains essentially unexplored. Its known functions—including ECM remodeling (Duan et al., 2022), regulation of cell migration (Li et al., 2022), modulation of inflammatory pathways (Guo et al., 2021), and interactions with Wnt signaling (Kelley, 2008) —are all processes central to hippocampal plasticity and cognitive function. Its secretion and correlation with amyloid burden further highlight its potential relevance both as a mechanistic contributor and as a biomarker. These properties make CTHRC1 a biologically plausible and underinvestigated candidate for understanding genetic influences on cognition and AD pathology.

In this study, we used proteomic expression data to identify CTHRC1 as one of the top differentially expressed proteins in AD patients versus the control group and further validated its expression in 6-month-old 5xFAD mice models. Human genome-wide association data was investigated to link CTHRC1 mutations with cognition. The hippocampal gene expression data from BXD mice was used for systems genetics analysis to unfold the functional significance of Cthrc1 and identify its interacting partners. Further, the correlation analysis with learning and memory phenotypes in BXDs was performed to understand the impact of Cthrc1 expression and that of its interacting partners on cognitive properties. Figure 1 shows a summarized workflow of our study.

Figure 1

Figure 1. Experimental workflow identifying CTHRC1 as a candidate gene for neurodegeneration and cognition.

2 Methodology

2.1 Proteomic data analysis

In a recent study, Bai et al. performed deep multilayer proteomics of postmortem brain samples of AD patients and 5xFAD mice models at 3, 6, and 12 months of age using mass spectrometry to identify the molecular network and biomarkers underlying Alzheimer’s disease (Bai et al., 2020). We used the human AD proteomic datasets to identify differentially expressed proteins in the disease state compared to the control samples. The human dataset contained 18 post-mortem samples each for the control and AD group. Each group had two independent pools (9 case per pool). The mean age of the control group was 83 years, whereas that of AD group was 76 years. The 5xFAD mice model (n = 4) and wild-type (n = 4) samples were then used to confirm the expression difference of the selected genes. Additional details on the samples and their proteomic profiling can be found in the original article (Bai et al., 2020). The differential expression of the proteomics data was performed through the amica tool² (Didusch et al., 2022) using the raw intensity values. The amica web-tool has been specifically designed for proteomic analysis and uses limma R package (Ritchie et al., 2015) to assess the differential expression of proteins.

2.2 Hippocampus gene expression dataset

The hippocampus mRNA expression dataset in the current study was generated in our group. The standardized dataset can be accessed through our GeneNetwork portal³ by selecting the group as “BXD Family,” type as “Hippocampus mRNA,” and the dataset name as “Hippocampus Consortium M430v2 (June 6) RMA” (GEO Series: GSE84767). A brief description of the experimental protocols used for generating this dataset is provided below, and additional details can be found on our GeneNetwork portal.

2.3 Mice and tissue harvesting

The hippocampus data used in the current study was from 71 genetically diverse strains of mice, including 67 BXD recombinant inbred strains, their parental strains (C57BL/6 J, DBA2/J), and two reciprocal F1 hybrids. The majority of the animals were between 45 and 90 days old (mean 66 days, ranging from 41 to 196 days). The mice were obtained from the University of Tennessee Health Science Center (UTHSC), the University of Alabama (UAB), or directly from the Jackson Laboratory. The animals were housed and maintained at UTHSC prior to execution. Animals were deeply anesthetized with 4–5% isoflurane in oxygen until loss of pedal and corneal reflexes, followed by cervical dislocation. Brains were then removed and placed in an RNAlater solution prior to dissection. Cerebella and olfactory bulbs were removed; brains were hemisected, and both hippocampi were dissected whole. All procedures involving mouse tissue were approved by the Institutional Animal Care and Use Committee at the University of Tennessee Health Science Center.

2.4 RNA extraction and generation of hippocampus expression data

A pool of dissected tissue, typically from six hippocampi and three naive adults of the same strain, sex, and age, was collected and used to generate cRNA samples. A total of 215 RNA samples were extracted at UTHSC. Briefly, the RNA STAT-60 protocol was used according to the manufacturer’s instructions, which included tissue homogenization, RNA extraction, RNA pretreatment, RNA washing, and RNA purification. Finally, RNA purity was evaluated using the 260/280 nm absorbance ratio, and the samples with values greater than 1.8 were considered. The RNA integrity of the samples was assessed by the Agilent Bioanalyzer 2,100, and those with an RNA integrity number (RIN) greater than 8 were considered for microarray hybridization.

Samples were processed in the INIA Bioanalytical Core at the W. Harry Feinstone Center for Genomic Research, The University of Memphis. RNA samples from 2 to 3 animals of the same age, strain, and sex were hybridized to a single Affymetrix GeneChip Mouse Expression 430 2.0 short oligomer array. The Affymetrix 430v2 arrays consist of ~993,000 25-mer nucleotide probes that can profile the expression of approximately 39,000 transcripts and most known genes and expressed sequence tags. The raw microarray data were normalized using the robust multi-array average (RMA) method (Irizarry et al., 2003) and further converted to an improved z score (2z + 8) (Irizarry et al., 2003). Briefly, in Z-score normalization, instead of leaving the mean at 0 and the standard deviation at 1 unit, we shift up to a mean of 8 units and increase the spread by having a standard deviation of 2 units (2Z + 8 normalized data). This removes the negative values from the tables. Additional details on RNA extraction and hybridization protocols can be found in NCBI-GEO under the accession GSE84767.

2.5 Gene–gene and gene-phenotype correlation analyses

Pearson correlation analysis was used to identify Cthrc1 co-expressed genes in the hippocampus or learning- and memory-related traits associated with the genes being explored. The R values with a p < 0.05 were considered statistically significant. The mRNA expression values from the “Hippocampus Consortium M430v2 (June 6) RMA” dataset were used for the correlation analysis. The hippocampal gene expression dataset and cognitive phenotype traits can be accessed through our GeneNetwork portal (Mulligan et al., 2017) (see Text footnote 3). The correlation analysis was performed using the corrplot v0.92 package⁴ in R.

2.6 Phenome-wide association study (PheWAS) and expression-based PheWAS (ePheWAS) analyses

Phenome-wide association studies (PheWAS) have emerged as a viable reverse genetic strategy in humans (Li et al., 2018). Recently this approach has been applied to the BXDs, enabling the discovery of novel gene-phenotype associations. This has been possible owing to the availability of a large amount of genotypic data and thousands of phenotypic traits for BXDs, the largest mouse genetic reference population. Genes that contain high-impact variants, including missense, splice site, and frameshift mutations, as well as genes that have significant cis-e(p) QTLs in the BXD transcriptome and proteome datasets, were included in the PheWAS analysis. The association between the genetic variants of each gene, represented by the SNPs, as well as their cis-QTLs were associated with about 5,000 clinical phenotypes. Here, we used a multi-locus mixed-model approach (MLMM) to estimate the associations between each gene and cognitive traits.

In addition, associations between transcript/protein expression and phenotypic traits were estimated using the mixed model regression analysis through ePheWAS. Transcript-trait pairs with fewer than 15 overlapping lines were removed from the analysis. Bonferroni correction was used to perform phenotype-wide significance analysis. The PheWAS and ePheWAS analyses were performed on Systems Genetics at EPFL⁵ (Li et al., 2018), and those with a –log10(p) ≥ 2 were considered significant.

2.7 Enrichment analysis

The functional enrichment analysis of Cthrc1-correlated genes was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID)⁶ (Sherman et al., 2022) to identify significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology biological processes (GO-BP). Mammalian Phenotype Ontology (MPO) analysis was performed using the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt)⁷ (Liao et al., 2019). For this enrichment analysis, “protein coding genes” was selected as the reference set, while ‘minimum number of genes per category’ was kept as 5. The p-values were corrected using the Benjamini-Hochberg method for multiple testing, and annotations with an FDR-adjusted p < 0.05 were considered statistically significant. Furthermore, the genes were submitted to the Metascape tool⁸ (Zhou et al., 2019) to explore their relationships based on GO-BP terms in the form of a clustered network. The Metascape tool employs a heuristic algorithm to select the most informative terms from the GO clusters. It samples 20 top-score clusters, selects up to the 10 best-scoring terms (lowest p-values) within each cluster, then connects all term pairs with Kappa similarity above 0.3.

2.8 Functional association network of genes

The Cthrc1-coexpressed genes that were involved in the neurodevelopmental pathways were analyzed to predict their functional association network using the GeneMANIA tool⁹ (Warde-Farley et al., 2010). It connects the genes using a very large set of functional association data, including protein and genetic interactions, pathways, co-expression, co-localization, and protein domain similarity. The GeneMANIA cytoscape app¹⁰ was used for further analysis and visualization of the network.

2.9 Collection of genes associated with cognition and neurodevelopmental disorders

The genes important in cognition and AD were collected from multiple gene-function resources using the keywords “cognition” or “Alzheimer.” These keywords were searched in the following databases/repositories: Mouse Genome Informatics (MGI) phenotype/disease association (Human-Mouse: Disease Connection)¹¹ (Smith et al., 2018), the NHGRI-EBI GWAS Catalog¹² (Buniello et al., 2019), and the International Mouse Phenotyping Consortium (IMPC)¹³ (Groza et al., 2023). Translation of human gene lists to mouse orthologs was done using the bioDBnet tool¹⁴ (Mudunuri et al., 2009).

2.10 Brain cell type-specific expression

The brain cell type expression of the genes, including that of Cthrc1, was obtained from the Mouse Cell Atlas (MCA)¹⁵ database (Fei et al., 2022) using the tissue type as “Adult Brain.” The MCA database uses single-cell RNA sequencing to determine the cell type composition of major mouse organs.

2.11 CTHRC1 knockdown and overexpression

The SH-SY5Y human neuroblastoma cell line was obtained from ATCC (CRL-2266). These cells were cultured in DMEM/F12 (1:1) containing 10% fetal bovine serum and maintained at 37 °C in a humidified 5% CO₂ incubator.

CTHRC1 CRISPR RNA (crRNA) (AUGGCAUUCCGGGU ACACCUGUUUUAGAGCUAUGCU) was synthesized by Azenta. crRNA annealed with tracrRNA (IDT), then incubated with Alt-R^® S.p. Cas9 Nuclease V3 (IDT). Generation of a CTHRC1 knockdown SH-SY5Y cell line was achieved with the Cas9/guide RNA ribonucleoprotein complex (Cas9/RNP) delivered directly to the cells by electroporation. All kits and equipment were provided by Thermo Fisher Scientific. Programmed condition (1,500 V, 20 ms pulse width, 2 pulses) was chosen for electroporation.

A CTHRC1 wild-type cDNA was cloned in the pLVX-TetOne-Puro vector. SH-SY5Y cells were then infected by the lentivirus encoding full-length CTHRC1 or empty virus.

2.12 Quantitative real-time PCR

The total cellular RNA was extracted by RNeasy^® Mini Kit (QIAGEN), and RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific) was used to reverse transcribe into cDNA. Then real-time quantitative PCR was performed using SYBR^™ Green PCR Master Mix (Applied Biosystems). Data were analyzed using the 2^-ΔΔCT method after standardization with β-actin expression levels in each sample. The primers are listed in Supplementary Table 1.

2.13 Western blotting

Whole-cell lysates were prepared using RIPA buffer (Sigma), and protein concentrations were quantified with the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific). Equal amounts of protein were resolved on 4–12% Bis-Tris Midi Protein Gels (Thermo Fisher Scientific) and transferred onto membranes using the iBlot^™ 2 system (Thermo Fisher Scientific). Membranes were blocked with 5% milk for 1 h at room temperature and subsequently incubated with primary antibodies followed by HRP-conjugated secondary antibodies. The following primary antibodies were used: SNCAIP polyclonal antibody (Proteintech, #17818-1-AP; 1:300), Tau (Tau 46) monoclonal antibody (Santa Cruz Biotechnology, #sc-32274; 1:200), and Actin antibody (Cell Signaling Technology, #4967S; 1:1000). Secondary detection was performed using HRP-conjugated anti-rabbit IgG (Cell Signaling Technology, #7074; 1:2000) and HRP-conjugated anti-mouse IgG (Cell Signaling Technology, #7076; 1:2000).

3 Results

3.1 CTHRC1 is associated with cognition-related functions and phenotypes

The CTHRC1 gene encodes for collagen triple helix repeat containing 1. Mutations at this locus have been associated with Barrett esophagus and esophageal adenocarcinoma. The differential expression analysis at the protein level between AD and LPC identified CTHRC1 as the second most upregulated protein based on fold-change. It was found to be upregulated with approximately 5-fold (adj. p = 0.05) in AD patients compared to the controls (Figure 2A). This protein also showed a significant increase in its expression in 6-month-old 5xFAD mice compared to their wild-type counterparts (Figure 2B). Furthermore, the cell-type-specific expression of Cthrc1 based on single-cell RNA sequencing of mouse brain demonstrated its expression predominantly in astrocytes and oligodendrocyte progenitor cells. Cthrc1 was also found to be expressed to some extent in GABAergic neurons (Figure 2C).

Figure 2

Figure 2. Association of Cthrc1 with cognition-related properties. (A) Top 50 differentially expressed proteins between human AD and control samples. (B) Expression of CTHRC1 protein between 6-month-old 5xFAD and WT mice. (C) Expression of Cthrc1 mRNA in mouse brain cell types based on single-cell RNA sequencing. The scRNA-seq data was collected from the Mouse Cell Atlas (MCA) database (https://bis.zju.edu.cn/MCA/). (D) mRNA expression of Cthrc1 in the hippocampus of 69 BXD strains and two parental strains (B6 and D2). The expression data was obtained from our GeneNetwork database (https://genenetwork.org/). (E,F) PheWAS and ePheWAS analysis of Cthrc1 in BXD mice. Each dot indicates a nervous system phenotype (x-axis). The y-axis indicates the minus log10(p) value. The red dotted line indicates the -log10(p) threshold of 2. (G) Correlation between Cthrc1 mRNA levels and learning and memory phenotypes in BXD mice. Both expression data and phenotypes can be accessed from the GeneNetwork database (https://genenetwork.org/) using the phenotype identifier. (H) Genomic variants in the human CTHRC1 locus are associated with different phenotypic traits, including those related to AD. The data is based on Genome-Wide Association Analysis (GWAS, https://www.ebi.ac.uk/gwas/).

Next, we investigated the mRNA expression of Cthrc1 in the hippocampus (the brain region primarily associated with learning and memory) of 69 BXD mice and two parental strains (C57BL/6 J or B6, and DBA/2 J or D2) using our GeneNetwork database. The mean expression of Cthrc1 (probe-set 1452968_at) across BXD strains was found to be 8.7 ± 0.19 SD. BXD31 had the least expression (value = 8.3), whereas BXD73a showed the highest expression (value = 9.2) for Cthrc1 mRNA with a fold difference of ~1.9 across the BXD strains. Furthermore, we observed a difference of ~1.3 between both the parental strains, with D2 exhibiting higher levels (value = 9.0) of Cthrc1 than that of B6 (value = 8.7) (Figure 2D). PheWAS analysis using thousands of genomic variants and >5,000 phenotypes from BXD mice suggested significant association of Cthrc1 SNPs with 10 nervous system-related phenotypes. The phenotypes that were included in the analysis belonged to locomotor activity, depression assay, fear conditioning, and other traits (Figure 2E). Similarly, ePheWAS analysis indicated that Cthrc1 expression in the BXD hippocampus modulates multiple nervous system-related traits, including phenotypes, such as hippocampus dentate gyrus cells and motor coordination (Figure 2F). When we correlated the hippocampal Cthrc1 mRNA levels with learning and memory phenotypes (n = 341) in BXD mice, the results indicated a significant (p < 0.05) correlation of 22 cognition-related phenotypes with Cthrc1 expression (Figure 2G), suggesting that Cthrc1 mRNA levels in the BXD hippocampus modulate the phenotypic traits related to cognition. The GWAS results indicated a significant association of variants located in the genomic locus of CTHRC1 with AD and other brain-related traits, highlighting a strong association of this gene with neurodevelopmental disorders (Figure 2H).

3.2 Cthrc1 expression in the BXD hippocampus is strongly cis-regulated

We performed genome-wide eQTL mapping using the GEMMA mapping method to investigate the genomic regulation of Cthrc1 mRNA expression. A -log10(p-value) of 4 was used as the significance threshold to determine whether Cthrc1 expression is genomically regulated by epistatic loci. Our results identified mapping of a significant locus for Cthrc1 on Chr 15 at 38.99 Mb (rs3660608) (Figures 3A,B). The mapped locus was very close to the genomic position of the Cthrc1 gene (Chr 15 @ 39.08 Mb), indicating that Cthrc1 mRNA expression in the hippocampus is cis-regulated. Thus, Cthrc1 may genetically regulate other downstream genes in the hippocampus. Statistical analysis of Cthrc1 expression between the two parental genotypes (B6 and D2) according to the eQTL peak position (rs3660608) showed that BXD mice with the D2 allele exhibit significantly higher expression than those carrying the B6 allele (p = 3.48E-05, Figure 3C).

Figure 3

Figure 3. Expression quantitative trait locus (eQTL) mapping for Cthrc1 expression in the hippocampus of BXD mice. Manhattan plots showing the eQTL (A) across the mouse genome and (B) on chromosome 15 for Cthrc1, identified by the GEMMA mapping method. The x-axis indicates the position on the mouse genome in megabases (Mb), whereas the y-axis indicates the −log₁₀(p) score, a value measuring the linkage between gene expression and genomic region. The blue triangle on the x-axis indicates the genomic position of the gene. The red horizontal line indicates the significant −log₁₀(p) score threshold of 4. (C) Cthrc1 is significantly different (p = 3.48E-05) between the B and D alleles at 38.99 Mb on Chr 15 (rs3660608).

3.3 Cthrc1-correlated genes in hippocampus modulate neurodevelopmental pathways

To gain better insights into the pathways and biological functions of Cthrc1 involved in the hippocampus, we performed a Pearson genetic correlation analysis between Cthrc1 mRNA expression and the entire BXD hippocampal transcriptome data [Hippocampus Consortium M430v2 (June 6) RMA]. At an uncorrected p-value of <0.05, 12,475 probes corresponding to 9,305 genes were found to be correlated with Cthrc1 expression. To further shortlist the genes that are correlated with high confidence, we selected those that had a mean expression of >7 in the BXD hippocampus. This resulted in a total of 9,728 probes corresponding to 7,705 genes. Large-scale transcriptomic datasets commonly yield thousands of correlated genes because hippocampal gene expression is highly modular and dominated by co-regulated networks and shared pathway activity (Oldham et al., 2008). In systems genetics resources such as the BXD panel, the large sample size further increases power to detect broad correlation structures, many of which reflect coordinated biological processes rather than direct regulation (Chesler et al., 2005). Thus, the correlation of Cthrc1 with such a large number of genes in the hippocampus is implicative of its key role in hippocampal biology and the associated functions. To validate this hypothesis, we performed functional analysis, including KEGG pathway, GO, and MPO enrichment. The results indicated significant representation of cognition-related annotations, further pointing towards the importance of Cthrc1 in neurodevelopmental disorders. With an FDR P cutoff of <0.05, we identified a total of 319 GO-BPs, 300 MPOs, and 170 KEGG pathways. As shown in Figure 4A, “protein transport” was found to be the most enriched GO-BP with 300 genes (FDR p = 2.67E-29), followed by “positive regulation of transcription from RNA polymerase II promoter” with 483 genes (FDR p = 9.14E-24). The top 50 GO-BPs included several nervous system-related annotations, such as “axon guidance,” “neuron projection development,” “axonogenesis,” and “hippocampus development” (Supplementary File 1). The network of GO-BP terms constructed using the Metascape tool revealed sharing of genes among the annotations related to axon development and hippocampus development. Furthermore, interactions among the terms related to synapse assembly, autophagy, and catabolic processes were also observed. Interestingly, the cluster associated with “cytoskeletal-dependent intracellular transport” formed an independent group and included terms related to synaptic vesicle transport and localization (Figure 4B). Furthermore, the results from the MPO enrichment analysis were found to be in similar lines with that of GO-BPs. Around 15 of the top 20 MPO terms enriched were related to the nervous system (Figure 4C). Some of the important enriched terms included “abnormal nervous system physiology” (n genes = 726; FDR p = 2.56E-16), “abnormal brain morphology” (n genes = 639; FDR p = 2.56E-14), “abnormal cognition” (n genes = 300; FDR p = 4.45E-11), “abnormal hippocampus morphology” (n genes = 145; FDR p = 1.44E-10), “abnormal learning/memory/conditioning” (n genes = 300; FDR p = 3.88E-11), and “abnormal neuron morphology” (n genes = 533; FDR p = 2.56E-11). KEGG pathway analysis demonstrated enrichment of multiple nervous system-related pathways. As shown in Figure 4D, “pathways of neurodegeneration” and “Alzheimer disease” were among the top 5 pathways, based on the percentage of genes involved. While “pathways of neurodegeneration” involved 197 Cthrc1-correlated genes, the AD pathway involved 155 genes. Together, both these pathways included 229 Cthrc1-correlated genes. The pathway analysis results thus implicate that Cthrc1 may be linked to the neurodegeneration pathways through these 229 genes. The complete list of significant GO-BPs, MPOs, and KEGG pathways is provided as Supplementary File 1.

Figure 4

Figure 4. Functional enrichment analysis of Cthrc1-correlated genes. (A) Top 20 Gene Ontology biological processes (GO-BPs) significantly enriched by Cthrc1-correlated genes. (B) Top 2000 Cthrc1-correlated genes were analyzed using the Metascape tool (https://metascape.org/) to explore the relationships between the GO-BP terms. The nodes represent enriched GO-BP terms and are colored by their respective cluster IDs, whereas the edges link similar terms. The most significant term of the cluster is displayed as a label to represent that cluster. The Metascape tool employs a heuristic algorithm to select the most informative terms from the GO clusters. It samples 20 top-score clusters, selects up to the 10 best-scoring terms (lowest p-values) within each cluster, then connects all term pairs with Kappa similarity above 0.3. (C) Top 20 mammalian phenotype ontologies and (D) Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways significantly enriched by the Cthrc1-correlated genes.

3.4 Functional gene association network links Cthrc1 to neurodevelopmental disorder pathways

Together, “pathways of neurodegeneration” and “Alzheimer disease” pathways include 229 Cthrc1-correlated genes. Hence, we explored the interaction of these genes with Cthrc1 using functional association data from GeneMANIA to identify the primary partners of Cthrc1 and eventually link Cthrc1 to these pathways. The interaction network (Supplementary Figure 1A) contained 227 query genes (including Cthrc1) that were connected with 8,648 edges. The large number of interactions among these genes was expected owing to their involvement in the same pathway. We extracted the Cthrc1 subnetwork from the large primary network. The subnetwork contained 18 genes (including Cthrc1) connected with 97 edges (Supplementary Figure 1B). The subnetwork further revealed that Cthrc1 physically interacts with Wnt3a and Fzd6 while functionally interacting with the other 15 partners. The nodes BACE1, CASP9, SLC39A6, and GRIN2C were found to be differentially regulated at the protein level in 6-month-old 5xFAD mice compared to their wild-type counterparts. Furthermore, Fzd1, Nefm, Fzd6, Bace1, and Irs1 have been implicated in cognition-related properties or disorders based on collected data from MGI/IMPC/GWAS Catalog. Interestingly, several of these genes in the Cthrc1 subnetwork were negatively correlated with Cthrc1 mRNA expression in the BXD hippocampus (Table 1). Next, we wondered if Cthrc1 is linked to the well-known AD/neurodevelopmental disorder genes (ND genes) through its primary interactors. To this end, we explored the interaction of Cthrc1-primary interactors with the well-known ND genes, including App, Mapt, Apoe, Psen1, and Psen2. Our analysis revealed that several of the Cthrc1-primary interactors were directly connected to multiple ND genes. In particular, Tubb2b, Vdac1, and Nefm physically interact with one of the five ND genes. Furthermore, all five ND genes were found to be upregulated in AD compared to the control, either in the 5xFAD mouse model or in human samples at the protein level (Supplementary Figure 1C). Thus, the network analysis results demonstrated that although Cthrc1 does not directly interact with the ND genes, it affects their function through its primary interacting partners in the same pathway. Furthermore, it is noteworthy that all the ND genes shown in the network were significantly negatively correlated with Cthrc1 in the BXD hippocampus (Table 1), implying a protective role of Cthrc1 on neurodevelopmental disorders, such as AD.

Table 1

Table 1. Summary of Cthrc1-subnetwork genes.

3.5 Cthrc1-subnetwork genes are significantly correlated with cognitive phenotypes

While Cthrc1 is significantly correlated with multiple learning and memory phenotypes from BXD mice (Figure 2G), we intended to explore whether its primary interactors also correlate with the cognitive phenotypes. For the correlation analysis, a total of 341 learning and memory phenotypes belonging to different groups (e.g., Y-maze test, Morris water maze test, and contextual fear conditioning) were retrieved from our GeneNetwork portal. These were then correlated with the hippocampal mRNA expression of the 17 genes that directly interact with Cthrc1 in the functional association network (Supplementary Figure 1B). Of the total 341 phenotypes (Figure 5A), 190 learning- and memory-related traits were correlated with the hippocampal expression of at least one of the 17 genes (Figure 5B). Among these 190, 36% (n = 68) belonged to contextual fear conditioning, whereas 29.5% (n = 56) belonged to the y-maze test (Figure 5B). Furthermore, among the 17 genes, Nefl correlated with the most number of phenotypes, followed by Casp9 with 30 and 29 significant correlations, respectively. Grin2c correlated with the least number of learning and memory phenotypes (Figure 5C). The Y-maze test is a behavioral task that is used to study spatial learning and memory, which is underlined by the hippocampus (Kraeuter et al., 2019). We (and our collaborators) have collected several traits related to the Y-maze in BXD mice that can be accessed through our GeneNetwork portal. The correlation of the 17 Cthrc1-primary interactors with Y-maze phenotype traits indicated significant correlation with 56 of the total 96 traits phenotypes (Figures 5B,D). Of the 17 genes, Wnt3a was found to be correlated with the most number of Y-maze-related traits (n = 14), followed by Vdac1 (n = 13). Interestingly, Wnt3a was found to be physically interacting with Cthrc1 in the functional association network and was connected to all five ND genes. On the contrary, Vdac1 was functionally connected to Cthrc1; however, it was physically interacting with microtubule-associated protein tau (Mapt) in the network (Supplementary Figure 1C). The two other genes that correlated with a higher number of Y-maze traits were Casp9 and Bace1 (n = 12 and 11, respectively). There were seven Y-maze traits, each of which was significantly correlated with 4 Cthrc1-primary interactors (Figure 5D; Supplementary File 2).

Figure 5

Figure 5. Correlation of learning and memory phenotypes with Cthrc1-direct interactors. The hippocampal expression values for the genes interacting directly with Cthrc1 (n = 17) and phenotype trait data (n = 341) were obtained from the GeneNetwork portal. The gene-trait analysis was performed using the corrplot R package. Number of (A) all learning and memory phenotypes and (B) significant phenotypes divided into major subgroups. Arm visit: 8-arm radial maze test; CPP: Conditioned place preference; CFC: Contextual fear conditioning; Y-maze: performance on the Y-maze; WMZ: Morris water maze test. The number next to the phenotype group name indicates the number of traits in that group. (C) Barplot showing the number of learning and memory phenotypes correlated with each of the 17 genes. (D) Correlation plot showing the significance between BXD hippocampal mRNA expression of the 17 genes and Y-maze phenotypes. The R values significant with p < 0.05 are marked with an asterisk. Blue squares indicate positive correlation, whereas red squares indicate negative correlation. The size and color intensity of the squares are proportional to the correlation coefficient (R) values.

3.6 Genes interacting with Cthrc1 are expressed in various brain cell types

To explore which brain cell types express Cthrc1-interactors, we analyzed the single-cell sequencing data of adult mouse brains from MCA. Our results revealed that many of the genes were predominantly expressed in four brain cell types, viz., astrocyte, microglia, oligodendrocyte, and GABAergic neuron, among the 15 brain cell types investigated. Furthermore, Bace1, Chuk, Vdac1, and Tubb2b were found to be expressed in the greatest number of cell types. While the first three genes, Bace1, Chuk, and Vdac1, were expressed in 12 of the 15 brain cell types, Tubb2b was found to be expressed in 11 of the 15 brain cell types investigated (Supplementary Figure 2). None of the genes were expressed in neutrophils. The expression of Tubb2b was found to be highest in GABAergic neurons, which was also highest among all cell types checked. In addition, it showed high expression in a few other cell types as well, including astrocyte and oligodendrocyte progenitor cells. The expression of Bace1 was found to be the highest in oligodendrocytes, whereas Chuk was found to be highly expressed in neurons. The expression of Vdac1 was found to be high in ependymal cells and granulocytes. Surprisingly, none of the investigated brain cells expressed Wnt3a or Wnt2 (Supplementary Figure 2).

3.7 GWAS analysis indicates association between Cthrc1-interactors and neurodevelopmental traits in humans

We used GWASCatalog¹⁶ to explore the genetic linkage of Cthrc1-interacting genes with the neurodevelopmental or cognition related traits in humans. The mutations in 10 of the 17 genes explored were significantly associated with one or more brain related traits in the human population. The following genes were associated with significant cognition or neurodevelopmental related traits: BACE1, FZD1, INS, IRS1, NEFL, NEFM, PIK3R3, SNCAIP, and WNT3A. SNCAIP (synuclein alpha interacting protein) showed association with the maximum number of traits (n = 12). The traits were mainly related to cognitive performance, Parkinson’s disease, white matter, and neurofibrillary tangles (Supplementary Table 2). The next best gene in terms of the number of associations was NEFL, which was correlated with five traits. Both FZD6 and CTHRC1 were associated with AD related traits, indicating a close association between both these molecules, which was also evident from the functional association network analysis.

3.8 Gene expression changes following CTHRC1 knockdown or overexpression

We analyzed the changes in the expression of key candidate genes following CTHRC1 knockdown or overexpression in the SH-SY5Y human neuroblastoma cell line, which is often used as an in vitro model for studying neuronal function and differentiation.

Firstly, we used sgRNA technology to knockdown the expression of CTHRC1 in SH-SY5Y cells using different sgRNA constructs. The preliminary data showed that CTHRC1-sgRNA3 could achieve 67% knockdown efficiency; hence, it was used for further knockdown experiments. Following CTHRC1 knockdown, the expression of SLC39A6 was found to be reduced, while that of GRIN2C was elevated, which was consistent with our in silico analysis. However, no significant changes in the expression levels of BACE1 and CASP9 were observed (Figure 6A).

Figure 6

Figure 6. Gene expression changes following CTHRC1 manipulation in SH-SY5Y human neuroblastoma cells. Relative gene expression changes following CTHRC1 (A) siRNA knockdown or (B) overexpression compared to control. Data are presented as mean ± SEM. Statistical significance was determined by t-test (*p < 0.05, **p < 0.01, ***p < 0.001; ns, not significant). Green bars represent control, whereas orange bars represent CTHRC1 knockdown or overexpression. (C) Western blotting analysis following CTHRC1 overexpression in SH-SY5Y cells. β-actin was used as a control. EV: empty vector.

Further, we overexpressed CTHRC1 in SH-SY5Y cells by using Dox during passages. Interestingly, overexpression of CTHRC1 led to downregulation of several genes, including SNCAIP, NEF1, FZD1, TUBB2B, VDAC1, PIK3R3, NEFM, CHUK, FZD6, WNT3A, and PSEN1, and upregulation of WNT2 (Figure 6B). Our western blotting data indicated that overexpression of CTHRC1 in SH-SY5Y cells led to tau degradation. Furthermore, a slight decrease in SNCAIP protein was observed following CTHRC1 overexpression, pointing out a potential protective role of this gene in AD (Figure 6C).

We constructed an integrated molecular network to visualize how CTHRC1 may interface with established Alzheimer’s disease pathways (Supplementary Figure 3). The resulting map highlights multiple points of convergence between CTHRC1-associated genes and core AD risk factors, including APP, PSEN1, PSEN2, and APOE. Notably, CTHRC1-linked signaling modules—including Wnt/Fzd1 activation, MAPT regulation, VDAC1-associated mitochondrial pathways, and SNCAIP/NEFM synaptic components—feed into APP processing and presenilin-dependent cleavage steps. These interactions converge on downstream processes such as amyloidogenic APP processing, tau-related mechanisms, mitochondrial stress, and caspase-mediated injury, collectively pointing toward a multilayered contribution of the CTHRC1 subnetwork to neurodegenerative outcomes.

4 Discussion

In this study, we demonstrate that Cthrc1 plays an important role in cognitive function. Human proteomic data showed significant upregulation in AD patients compared to controls, which was confirmed at the protein level in 5xFAD mice. PheWAS and ePheWAS analyses linked Cthrc1 with nervous system phenotypes in BXD mice, while hippocampal Cthrc1 expression correlated with >20 learning and memory traits. GWAS results indicated CTHRC1 variants are significantly associated with AD. Using systems genetics and the BXD panel, we established the importance of Cthrc1 in cognitive function and related disorders.

The hippocampus is central to flexible cognition and social behavior (Rubin et al., 2014; Sweatt, 2004; Anacker and Hen, 2017). Our eQTL mapping revealed that Cthrc1 is cis-regulated in the BXD hippocampus. Genetic variants located near a gene (cis-eQTLs) typically exert consistent and direct control over that gene’s expression, producing reproducible changes in transcript levels across different tissues, conditions, and populations. This local genetic regulation reflects hardwired genetic control rather than temporary or context-dependent effects increasing the likelihood that cis-regulated genes will have reliable downstream functional consequences (Albert and Kruglyak, 2015; Consortium G.T. et al., 2017; Cookson et al., 2009). The strong cis-eQTL identified for Cthrc1 indicates that its expression is under stable local genetic control, suggesting that Cthrc1 may consistently influence downstream regulatory pathways. Pearson correlation analysis identified thousands of genes significantly co-expressed with Cthrc1, highlighting its importance in hippocampal biology and cognitive function. Such co-expression analyses are widely used to infer gene function and identify biomarkers, including in neurodevelopmental disorders (Hu et al., 2020; Zang et al., 2023; He et al., 2022).

Functional analysis of Cthrc1-correlated genes revealed enrichment in nervous system, cognitive function, and neurodevelopmental disorder related annotations. “Protein transport” was the most significant biological process, involving ~300 genes. Regulated protein transport is essential for synaptic function, requiring dynamic synaptic proteome alterations (Schieweck and Kiebler, 2019). Similar enrichment for protein transport and synthesis has been reported in cognition and neurodevelopmental disorders (Zhang et al., 2018; Rosenberg et al., 2014). Other enriched processes included transcription, protein phosphorylation, and chromatin organization. Altered protein phosphorylation occurs in AD brain (Perluigi et al., 2016), where phosphorylated tau disrupts microtubule stability and cognitive function (Chen et al., 2023; Ferrer et al., 2021). MPO analysis further confirmed strong associations with brain and nervous system terms. KEGG analysis showed “Metabolic pathways” contained the most genes, followed by “Pathways in cancer.” Metabolic dysfunction contributes to neurodevelopmental disorders and AD (Trushina et al., 2013; Yan et al., 2020), as the brain requires substantial energy for neuronal function (Navarro and Boveris, 2010). Age-related glucose utilization decline contributes to cognitive impairment (Petit-Taboue et al., 1998). Cancer pathway enrichment likely reflects Cthrc1’s involvement in various cancers (Zhang et al., 2021; Zhao et al., 2022; Mei et al., 2020). The top 5 pathways included “Alzheimer disease” and “pathways of neurodegeneration,” containing 229 genes total, which we explored for interactions with Cthrc1.

The interaction network among the 229 ND pathway genes along with Cthrc1 showed ~8,500 interactions. Cthrc1-primary subnetwork contained a total of 17 partners (Bace1, Casp9, Chuk, Fzd1, Fzd6, Grin2c, Ins2, Irs1, Nefl, Nefm, Pik3r3, Slc39a6, Sncaip, Tubb2b, Vdac1, Wnt2, and Wnt3a), among which Wnt3a and Fzd6 physically interact with Cthrc1. Wnt3a hippocampal expression correlated with the maximum number of Y-maze phenotypes in BXD mice, while GWAS revealed its association with brain weight and cortical thickness. The role of Wnt signaling in the development and maintenance of brain structures is widely accepted (Lee et al., 2000; Zhou et al., 2004). Wnt3a acts locally to regulate the expansion of the caudomedial cortex, which eventually forms the hippocampus (Lee et al., 2000). Zhou et al. (2004) examined the hippocampal phenotype of single Lrp6 (a Wnt signaling co-receptor) mutant mice and found that these mice had decreased production of dentate granule neurons and abnormalities of the radial glial scaffolding in the forming dentate gyrus. We could not detect the expression of Wnt3a in the adult mouse or human brain, probably because Wnt3a and Wnt2 are predominantly expressed during neurodevelopmental stages and may reflect developmental interactions with CTHRC1 rather than adult hippocampal expression. Fzd6 correlated with 18 learning/memory phenotypes and acts as a negative regulator of Wnt/β-catenin signaling (Pascual-Vargas and Salinas, 2021). Magdesian et al. (2008) reported that Aβ binds to the extracellular cysteine-rich domain of the Frizzled family of proteins and inhibits the Wnt/beta-catenin signaling pathway; blocking of this interaction might prevent neuronal dysfunction in AD. Interestingly, based on our analysis, both Wnt3a and Fzd6 functionally interact with all five ND genes. The other genes that were connected with all five ND genes were Bace1, Chuk, Nefl, Irs1, Grin2c, Ins2, and Casp9. Bace1 and Grin2c were found to be upregulated in 6-month-old 5xFAD mice compared to their wild-type counterparts, whereas Casp9 was found to be downregulated. In addition, Bace1 (beta secretase 1) and Irs1 were found to be associated with neurodegenerative disorders or cognition based on data collected by us. GWAS analysis identified Bace1 variants to be linked with traits, such as apolipoprotein levels and total PHF-tau. Beta-secretase has been reported to participate in the cleavage of the APP protein and promote the cerebral deposition of Aβ, an early and critical feature of AD (Vassar et al., 1999; Hampel et al., 2021). Furthermore, germline and conditional knockout mice for BACE1 show complex neurological phenotypes (Hampel et al., 2021). These reports were in agreement with the brain cell type expression of Bace1 in mice, where we detected its expression in multiple cell types, particularly in neurons, oligodendrocytes, and ependymal cells. The ependymal cells are known to be involved in cerebrospinal fluid homeostasis, brain metabolism, and removal of brain waste, and have been implicated in various diseases, including neurodegenerative disorders (MacDonald et al., 2021). Insulin receptor substrate 1 (IRS1), which encodes a protein that is phosphorylated by insulin receptor tyrosine kinase, was another interesting molecule that not only interacted with Cthrc1 but also all five ND genes. Brain insulin resistance is characteristic of AD and depends on IRS1 phosphorylation (Talbot et al., 2012; Mullins et al., 2017). Our GWAS analysis also linked IRS1 to sTREM-2 and P-tau181p levels in cerebrospinal fluid. Yarchoan et al. demonstrated that abnormal serine phosphorylation of IRS1 is associated with tau pathology in AD and tauopathies (Yarchoan et al., 2014). Nefl (neurofilament light chain) comprises the exoskeleton that functionally maintains the neuronal caliber. The network showed its physical interaction with APP, in addition to functional association with the other four ND genes. A recent meta-analysis study suggested that increased plasma levels of Nefl in AD patients and in patients with mild cognitive impairment (MCI) were associated with cognitive decline (Fan et al., 2023). Another neurofilament protein, NEFM, coding for the medium chain, was found to physically interact with two ND genes, MAPT and APOE, in the functional association network. This gene has been found to be linked to brain morphology traits in humans. Vdac1 and Tubb2b were also physically connected to the ND genes in the functional association network. Vdac1 encodes a voltage-dependent anion channel, a major component of the outer mitochondrial membrane. It is expressed in most of the brain cell types, with the highest expression in ependymal cells, which are known to be involved in brain metabolism and removal of brain waste. High levels of VDAC1 have been reported in the brains of post-mortem AD patients and in APP-transgenic mice (Shoshan-Barmatz et al., 2018). Smilansky et al. (2015) demonstrated the involvement of VDAC1 and a VDAC1 N-terminal peptide (VDAC1-N-Ter) in Aβ cell penetration and cell death induction. The neuronal role of VDAC1 through maintaining normal mitochondrial homeostasis has been demonstrated by other studies as well (Manczak and Reddy, 2012; Fernandez-Echevarria et al., 2014). In the current study, Vdac1 significantly correlated with 13 y-maze traits in BXD mice. Tubb2b (tubulin beta 2B class IIb), a major component of microtubules, physically interacts with MAPT in the functional association network. GABAergic neurons, followed by oligodendrocyte progenitor cells, showed the highest expression for this gene. Using an APP knock-in mouse model, Rice et al. (2020) showed that endogenous APP is highly expressed in GABAergic interneurons throughout the hippocampus, suggesting that these cells may have a central role in AD plaque formation, because of which the GABAergic system is considered as a potential therapeutic target for AD (Calvo-Flores Guzman et al., 2018). Mutations in TUBB2B are associated with abnormal development of the brain, microcephaly, and axon guidance defects (Jaglin et al., 2009; Romaniello et al., 2012). Another CTHRC1 interactor, SNCAIP (synuclein alpha interacting protein), interacted with APP and MAPT. It was of particular interest because of its association with a number of cognition- and neurodevelopmental-disorder-related human traits. There is mounting evidence of this gene’s involvement in the second most common type of dementia, the Lewy body diseases (Beyer et al., 2008; Humbert et al., 2007).

Although CTHRC1 has not yet been identified as a major genome-wide significant AD risk gene, several observations suggest that genetic variation in CTHRC1 could influence susceptibility to AD or other neurodegenerative diseases. First, CTHRC1 shows a strong cis-eQTL, indicating that its expression is under stable genetic control, and such regulatory variants frequently contribute to complex trait risk. Second, our integrative analyses show that CTHRC1 is tightly connected to core AD pathways, including APP/BACE1 processing, PSEN signaling, tau-related mechanisms, mitochondrial function (VDAC1), and inflammatory or synaptic processes. Genes positioned at such network convergence points are often sensitive to regulatory variation that modulates disease outcomes. Third, several CTHRC1-interacting partners (APP, PSEN1/2, APOE, MAPT, BACE1, VDAC1) are established AD genes, and variants in upstream modulators of these pathways frequently affect neurodegenerative phenotypes. Together, these findings support the hypothesis that regulatory or coding variants in CTHRC1 could influence AD-related pathways, thereby modifying risk or progression of AD and potentially other neurodegenerative disorders. Furthermore, the cross-species convergence in our study provides a strong evidence for conserved molecular mechanisms. By observing consistent CTHRC1-associated patterns in both human AD datasets and mouse models, our study reduces the likelihood that these findings arise from species-specific artifacts; instead suggests that CTHRC1 participates in fundamental processes relevant to neurodegeneration and cognition, thereby strengthening the translational value of our results.

We performed experimental validations to verify the results obtained by our in silico analysis. CTHRC1 was knocked down or overexpressed in human neuroblastoma cells, followed by expression profiling of key candidate genes. CTHRC1 knockdown reduced the expression of SLC39A6, while elevated the expression of GRIN2C. Furthermore, overexpression of CTHRC1 reduced the expression of several key candidates, including those involved in neurodegeneration, such as PSEN1, SNCAIP, VDAC1, and TUBB2B, and increased the expression of WNT2 (De Strooper and Chavez Gutierrez, 2015; Engelender et al., 1999; Jimenez et al., 2019; Tapia-Rojas and Inestrosa, 2018). Additionally, reduction in tau protein was observed along with a slight decrease in SNCAIP protein. The tau proteins form a group of six highly soluble protein isoforms produced by alternative splicing from the gene MAPT. They have roles primarily in maintaining the stability of microtubules in axons and are abundant in the neurons of the central nervous system (CNS), where the cerebral cortex has the highest abundance. Pathologies and dementias of the nervous system, such as Alzheimer’s disease and Parkinson’s disease, are associated with tau proteins (Chang et al., 2018). The tau hypothesis states that excessive or abnormal phosphorylation of tau results in the transformation of normal adult tau into paired-helical-filament (PHF) tau and neurofibrillary tangles (NFTs). SNCAIP contains several protein–protein interaction domains, including ankyrin-like repeats, a coiled-coil domain, and an ATP/GTP-binding motif. It interacts with alpha-synuclein in neuronal tissue and may play a role in the formation of cytoplasmic inclusions and neurodegeneration (Engelender et al., 1999). These data confirm the protective role of CTHRC1 in neurodegenerative diseases.

While our study provides evidence supporting the potential role of CTHRC1 in neurodegenerative processes and cognitive decline, a few limitations should be acknowledged. To clarify the molecular mechanisms underlying CTHRC1 function, dedicated knockout or overexpression mouse models will be essential. Additionally, further experimental validation is required to define the specific neurodegeneration- and cognition-related pathways influenced by CTHRC1. Future studies integrating functional assays, cellular models, and longitudinal in vivo analyses will be important for establishing a causal role and delineating its mechanistic contributions of CTHRC1.

5 Conclusion

Using human genomic/proteomic data and BXD systems genetics analysis, we demonstrated that Cthrc1 is associated with brain-related phenotypic traits. Functional network analysis identified primary Cthrc1 interactors, including Bace1, Nefl, Nefm, Irs1, Vdac1, Tubb2b, and Sncaip, which are involved in cognitive functions and neurodegenerative disorders. These molecules connect Cthrc1 with core neurodegeneration genes (APP, MAPT, APOE, PSEN1/2) in the functional network. Experimental validations using knockdown and overexpression studies confirm the neuroprotective role of Cthrc1. Our findings demonstrate for the first time the importance of hippocampal Cthrc1 in cognitive function and warrant further investigation of its mechanisms in neurodegenerative disorders.

Data availability statement

The original contributions presented in the study are publicly available. The data has been deposited in the GEO repository, accession number GSE84767, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84767.

Ethics statement

Ethical approval was not required for the studies on humans in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used. The animal study was approved by Ethics Committee of Central South University. The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

JX: Writing – review & editing, Methodology, Formal analysis, Investigation, Validation, Data curation. YH: Writing – review & editing, Investigation, Data curation. ZL: Data curation, Writing – review & editing, Investigation. WZ: Data curation, Writing – review & editing, Investigation. CH: Writing – original draft, Formal analysis. LL: Methodology, Data curation, Writing – review & editing, Formal analysis. AB: Conceptualization, Formal analysis, Methodology, Writing – original draft. ML: Formal analysis, Resources, Methodology, Project administration, Conceptualization, Writing – review & editing, Supervision.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work is supported in part by the National Natural Science Foundation of China (No. 62225209) and the Key Program of the Hunan Provincial Natural Science Foundation (No. 2025JJ30025).

Conflict of interest

JX and CH were employed by CIR Biotech (Shanghai) Co., Ltd. YH, ZL, and WZ were employed by WuXi AppTec (Shanghai) Co., Ltd.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author ML declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2026.1737003/full#supplementary-material

Footnotes

1 ^https://www.biorxiv.org/content/10.1101/2020.07.31.230680v1.full

2 ^https://bioapps.maxperutzlabs.ac.at/app/amica

3 ^https://genenetwork.org/

4 ^https://github.com/taiyun/corrplot

5 ^https://systems-genetics.org

6 ^https://david.ncifcrf.gov/

7 ^http://bioinfo.vanderbilt.edu/webgestalt/

8 ^http://metascape.org

9 ^http://www.genemania.org/

10 ^https://pages.genemania.org/cytoscape-app/

11 ^https://www.informatics.jax.org/

12 ^https://www.ebi.ac.uk/gwas/

13 ^https://www.mousephenotype.org/

14 ^https://biodbnet-abcc.ncifcrf.gov/db/dbOrtho.php

15 ^https://bis.zju.edu.cn/MCA/

16 ^https://www.ebi.ac.uk/gwas/

References

Albert, F. W., and Kruglyak, L. (2015). The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212. doi: 10.1038/nrg3891,

PubMed Abstract | Crossref Full Text | Google Scholar

Anacker, C., and Hen, R. (2017). Adult hippocampal neurogenesis and cognitive flexibility—linking memory and mood. Nat. Rev. Neurosci. 18, 335–346. doi: 10.1038/nrn.2017.45,

PubMed Abstract | Crossref Full Text | Google Scholar

Andreux, P. A., Williams, E. G., Koutnikova, H., Houtkooper, R. H., Champy, M. F., Henry, H., et al. (2012). Systems genetics of metabolism: the use of the BXD murine reference panel for multiscalar integration of traits. Cell 150, 1287–1299. doi: 10.1016/j.cell.2012.08.012,

PubMed Abstract | Crossref Full Text | Google Scholar

Ashbrook, D. G., Arends, D., Prins, P., Mulligan, M. K., Roy, S., Williams, E. G., et al. (2021). A platform for experimental precision medicine: the extended BXD mouse family. Cell Syst 12, 235–247.e9. doi: 10.1016/j.cels.2020.12.002,

PubMed Abstract | Crossref Full Text | Google Scholar

Bai, B., Wang, X., Li, Y., Chen, P. C., Yu, K., Dey, K. K., et al. (2020). Deep multilayer brain proteomics identifies molecular networks in Alzheimer's disease progression. Neuron 105, 975–991.e7. doi: 10.1016/j.neuron.2019.12.015,

PubMed Abstract | Crossref Full Text | Google Scholar

Bettio, L. E. B., Rajendran, L., and Gil-Mohapel, J. (2017). The effects of aging in the hippocampus and cognitive decline. Neurosci. Biobehav. Rev. 79, 66–86. doi: 10.1016/j.neubiorev.2017.04.030

Crossref Full Text | Google Scholar

Beyer, K., Domingo-Sabat, M., Humbert, J., Carrato, C., Ferrer, I., and Ariza, A. (2008). Differential expression of alpha-synuclein, parkin, and synphilin-1 isoforms in Lewy body disease. Neurogenetics 9, 163–172.

Google Scholar

Buniello, A., MacArthur, J., Cerezo, M., Harris, L. W., Hayhurst, J., Malangone, C., et al. (2019). The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012. doi: 10.1093/nar/gky1120,

PubMed Abstract | Crossref Full Text | Google Scholar

Calia, C., Johnson, H., and Cristea, M. (2019). Cross-cultural representations of dementia: an exploratory study. J. Glob. Health 9:011001. doi: 10.7189/jogh.09.01101,

PubMed Abstract | Crossref Full Text | Google Scholar

Calvo-Flores Guzman, B., Vinnakota, C., Govindpani, K., Waldvogel, H. J., Faull, R. L. M., and Kwakowsky, A. (2018). The GABAergic system as a therapeutic target for Alzheimer's disease. J. Neurochem. 146, 649–669. doi: 10.1111/jnc.14345,

PubMed Abstract | Crossref Full Text | Google Scholar

Chang, H. Y., Sang, T. K., and Chiang, A. S. (2018). Untangling the tauopathy for Alzheimer's disease and parkinsonism. J. Biomed. Sci. 25:54. doi: 10.1186/s12929-018-0457-x,

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, Z., Wang, S., Meng, Z., Ye, Y., Shan, G., Wang, X., et al. (2023). Tau protein plays a role in the mechanism of cognitive disorders induced by anesthetic drugs. Front. Neurosci. 17:1145318. doi: 10.3389/fnins2023.1145318

Crossref Full Text | Google Scholar

Chesler, E. J., Lu, L., Shou, S., Qu, Y., Gu, J., Wang, J., et al. (2005). Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat. Genet. 37, 233–242. doi: 10.1038/ng1518,

PubMed Abstract | Crossref Full Text | Google Scholar

Civelek, M., and Lusis, A. J. (2014). Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 15, 34–48. doi: 10.1038/nrg3575,

PubMed Abstract | Crossref Full Text | Google Scholar

Consortium G.T. (2017). Genetic effects on gene expression across human tissues. Nature 550, 204–213. doi: 10.1038/nature24277

Crossref Full Text | Google Scholar

Cookson, W., Liang, L., Abecasis, G., Moffatt, M., and Lathrop, M. (2009). Mapping complex disease traits with global gene expression. Nat. Rev. Genet. 10, 184–194. doi: 10.1038/nrg2537

Crossref Full Text | Google Scholar

Corrada, M. M., Brookmeyer, R., Paganini-Hill, A., Berlau, D., and Kawas, C. H. (2010). Dementia incidence continues to increase with age in the oldest old: the 90+ study. Ann. Neurol. 67, 114–121. doi: 10.1002/ana.21915,

PubMed Abstract | Crossref Full Text | Google Scholar

Cruchaga, C., Haller, G., Chakraverty, S., Mayo, K., Vallania, F. L., Mitra, R. D., et al. (2012). Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer's disease families. PLoS One 7:e31039. doi: 10.1371/journal.pone.0031039,

PubMed Abstract | Crossref Full Text | Google Scholar

De Strooper, B., and Chavez Gutierrez, L. (2015). Learning by failing: ideas and concepts to tackle gamma-secretases in Alzheimer's disease and beyond. Annu. Rev. Pharmacol. Toxicol. 55, 419–437. doi: 10.1146/annurev-pharmtox-010814-124309,

PubMed Abstract | Crossref Full Text | Google Scholar

Dickson, P. E., and Mittleman, G. (2022). Working memory and pattern separation in founder strains of the BXD recombinant inbred mouse panel. Sci. Rep. 12:69. doi: 10.1038/s41598-021-03850-3,

PubMed Abstract | Crossref Full Text | Google Scholar

Didusch, S., Madern, M., Hartl, M., and Baccarini, M. (2022). Amica: an interactive and user-friendly web-platform for the analysis of proteomics data. BMC Genomics 23:817. doi: 10.1186/s12864-022-09058-7,

PubMed Abstract | Crossref Full Text | Google Scholar

Duan, X., Yuan, X., Yao, B., Song, W., Li, Z., Enhejirigala,, et al. (2022). The role of CTHRC1 in promotion of cutaneous wound healing. Signal Transduct. Target. Ther. 7:183. doi: 10.1038/s41392-022-01008-9,

PubMed Abstract | Crossref Full Text | Google Scholar

Engelender, S., Kaminsky, Z., Guo, X., Sharp, A. H., Amaravi, R. K., Kleiderlein, J. J., et al. (1999). Synphilin-1 associates with alpha-synuclein and promotes the formation of cytosolic inclusions. Nat. Genet. 22, 110–114. doi: 10.1038/8820,

PubMed Abstract | Crossref Full Text | Google Scholar

Fan, Z., Liu, X., Liu, J., Chen, C., and Zhou, M. (2023). Neurofilament light chain as a potential biomarker in plasma for Alzheimer's disease and mild cognitive impairment: a systematic review and a Meta-analysis. J. Integr. Neurosci. 22:85. doi: 10.31083/j.jin2204085,

PubMed Abstract | Crossref Full Text | Google Scholar

Fan, J., Tao, W., Li, X., Li, H., Zhang, J., Wei, D., et al. (2019). The contribution of genetic factors to cognitive impairment and dementia: apolipoprotein E gene, gene interactions, and polygenic risk. Int. J. Mol. Sci. 20:1177. doi: 10.3390/ijms20051177

Crossref Full Text | Google Scholar

Fei, L., Chen, H., Ma, L., E, W., Wang, R., Fang, X., et al. (2022). Systematic identification of cell-fate regulatory programs using a single-cell atlas of mouse development. Nat. Genet. 54, 1051–1061. doi: 10.1038/s41588-022-01118-8,

PubMed Abstract | Crossref Full Text | Google Scholar

Fernandez-Echevarria, C., Díaz, M., Ferrer, I., Canerina-Amaro, A., and Marin, R. (2014). Abeta promotes VDAC1 channel dephosphorylation in neuronal lipid rafts. Relevance to the mechanisms of neurotoxicity in Alzheimer's disease. Neuroscience 278, 354–366. doi: 10.1016/j.neuroscience.2014.07.079,

PubMed Abstract | Crossref Full Text | Google Scholar

Ferrer, I., Andrés-Benito, P., Ausín, K., Pamplona, R., del Rio, J. A., Fernández-Irigoyen, J., et al. (2021). Dysregulated protein phosphorylation: a determining condition in the continuum of brain aging and Alzheimer's disease. Brain Pathol. 31:e12996. doi: 10.1111/bpa.12996

Crossref Full Text | Google Scholar

Gibbons, A. S., Udawela, M., Jeon, W. J., Seo, M. S., Brooks, L., and Dean, B. (2011). The neurobiology of APOE in schizophrenia and mood disorders. Front. Biosci. (Landmark Ed) 16, 962–979. doi: 10.2741/3729,

PubMed Abstract | Crossref Full Text | Google Scholar

Goudriaan, A., Loos, M., Spijker, S., Smit, A. B., and Verheijen, M. H. G. (2020). Genetic variation in CNS myelination and functional brain connectivity in recombinant inbred mice. Cells 9:2119. doi: 10.3390/cells9092119

Crossref Full Text | Google Scholar

Graybeal, C., Bachu, M., Mozhui, K., Saksida, L. M., Bussey, T. J., Sagalyn, E., et al. (2014). Strains and stressors: an analysis of touchscreen learning in genetically diverse mouse strains. PLoS One 9:e87745. doi: 10.1371/journal.pone.0087745,

PubMed Abstract | Crossref Full Text | Google Scholar

Groza, T., Gomez, F. L., Mashhadi, H. H., Muñoz-Fuentes, V., Gunes, O., Wilson, R., et al. (2023). The international mouse phenotyping consortium: comprehensive knockout phenotyping underpinning the study of human disease. Nucleic Acids Res. 51, D1038–D1045. doi: 10.1093/nar/gkac972

Crossref Full Text | Google Scholar

Guo, Y., Jiang, C., Yao, S., Ma, L., Zhang, H., Wang, X., et al. (2021). CTHRC1 knockdown promotes inflammatory responses partially by p38 MAPK activation in human periodontal ligament cells. Inflammation 44, 1831–1842. doi: 10.1007/s10753-021-01461-8,

PubMed Abstract | Crossref Full Text | Google Scholar

Hampel, H., Vassar, R., de Strooper, B., Hardy, J., Willem, M., Singh, N., et al. (2021). The beta-secretase BACE1 in Alzheimer's disease. Biol. Psychiatry 89, 745–756. doi: 10.1016/j.biopsych.2020.02.001,

PubMed Abstract | Crossref Full Text | Google Scholar

He, B., Gorijala, P., Xie, L., Cao, S., and Yan, J. (2022). Gene co-expression changes underlying the functional connectomic alterations in Alzheimer's disease. BMC Med. Genet. 15:92. doi: 10.1186/s12920-022-01244-6,

PubMed Abstract | Crossref Full Text | Google Scholar

Hill, M. A., and Gammie, S. C. (2022). Alzheimer's disease large-scale gene expression portrait identifies exercise as the top theoretical treatment. Sci. Rep. 12:17189. doi: 10.1038/s41598-022-22179-z,

PubMed Abstract | Crossref Full Text | Google Scholar

Hong, X. Y., Wan, H. L., Li, T., Zhang, B. G., Li, X. G., Wang, X., et al. (2020). STAT3 ameliorates cognitive deficits by positively regulating the expression of NMDARs in a mouse model of FTDP-17. Signal Transduct. Target. Ther. 5:295. doi: 10.1038/s41392-020-00290-9,

PubMed Abstract | Crossref Full Text | Google Scholar

Hu, Y., Huang, L., Zhao, K., Li, Y., Givens, N. T., Heslin, A. J., et al. (2023). CTHRC1 is associated with immune escape and poor prognosis in gastric Cancer. Anticancer Res. 43, 115–126. doi: 10.21873/anticanres.16140,

PubMed Abstract | Crossref Full Text | Google Scholar

Hu, R. T., Yu, Q., Zhou, S. D., Yin, Y. X., Hu, R. G., Lu, H. P., et al. (2020). Co-expression network analysis reveals novel genes underlying Alzheimer's disease pathogenesis. Front. Aging Neurosci. 12:605961. doi: 10.3389/fnagi.2020.605961

Crossref Full Text | Google Scholar

Humbert, J., Beyer, K., Carrato, C., Mate, J. L., Ferrer, I., and Ariza, A. (2007). Parkin and synphilin-1 isoform expression changes in Lewy body diseases. Neurobiol. Dis. 26, 681–687. doi: 10.1016/j.nbd.2007.03.007,

PubMed Abstract | Crossref Full Text | Google Scholar

Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., et al. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264. doi: 10.1093/biostatistics/4.2.249,

PubMed Abstract | Crossref Full Text | Google Scholar

Jaglin, X. H., Poirier, K., Saillour, Y., Buhler, E., Tian, G., Bahi-Buisson, N., et al. (2009). Mutations in the beta-tubulin gene TUBB2B result in asymmetrical polymicrogyria. Nat. Genet. 41, 746–752. doi: 10.1038/ng.380,

PubMed Abstract | Crossref Full Text | Google Scholar

Jimenez, J., Herrera, D. A., Vargas, S. A., Guest, P. C., and Castillo, D. A. (2019). beta-Tubulinopathy caused by a mutation of the TUBB2B gene: magnetic resonance imaging findings of the brain. Neuroradiol. J. 32, 148–150. doi: 10.1177/1971400919828142

Crossref Full Text | Google Scholar

Jorm, A. F., and Jolley, D. (1998). The incidence of dementia: a meta-analysis. Neurology 51, 728–733. doi: 10.1212/wnl.51.3.728,

PubMed Abstract | Crossref Full Text | Google Scholar

Kelley, M. W. (2008). Leading Wnt down a PCP path: Cthrc1 acts as a coreceptor in the Wnt-PCP pathway. Dev. Cell 15, 7–8. doi: 10.1016/j.devcel.2008.06.008,

PubMed Abstract | Crossref Full Text | Google Scholar

Kraeuter, A. K., Guest, P. C., and Sarnyai, Z. (2019). The Y-maze for assessment of spatial working and reference memory in mice. Methods Mol. Biol. 1916, 105–111. doi: 10.1007/978-1-4939-8994-2_10,

PubMed Abstract | Crossref Full Text | Google Scholar

Kumar, A., Sidhu, J., Lui, F., and Tsao, J. W. (2025). Alzheimer disease StatPearls. Treasure Island (FL): StatPearls Publishing.

Google Scholar

Lee, S. M., Tole, S., Grove, E., and McMahon, A. (2000). A local Wnt-3a signal is required for development of the mammalian hippocampus. Development 127, 457–467. doi: 10.1242/dev.127.3.457,

PubMed Abstract | Crossref Full Text | Google Scholar

Lefterov, I., Wolfe, C. M., Fitz, N. F., Nam, K. N., Letronne, F., Biedrzycki, R. J., et al. (2019). APOE2 orchestrated differences in transcriptomic and lipidomic profiles of postmortem AD brain. Alzheimer's Res Ther 11:113. doi: 10.1186/s13195-019-0558-0,

PubMed Abstract | Crossref Full Text | Google Scholar

Li, H., Wang, X., Rukina, D., Huang, Q., Lin, T., Sorrentino, V., et al. (2018). An integrated systems genetics and omics toolkit to probe gene function. Cell Syst 6, 90–102.e4. doi: 10.1016/j.cels.2017.10.016,

PubMed Abstract | Crossref Full Text | Google Scholar

Li, B. X., Wang, Y. H., Yu, S., Zhao, H., and Lv, Q. Q. (2022). CTHRC1 promotes growth, migration and invasion of trophoblasts via reciprocal Wnt/beta-catenin regulation. J. Cell Commun. Signal 16, 63–74. doi: 10.1007/s12079-021-00625-3

Crossref Full Text | Google Scholar

Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z., and Zhang, B. (2019). WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 47, W199–W205. doi: 10.1093/nar/gkz401,

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, Y. J., Liu, T. T., Jiang, L. H., Liu, Q., Ma, Z. L., Xia, T. J., et al. (2021). Identification of hub genes associated with cognition in the hippocampus of Alzheimer's disease. Bioengineered 12, 9598–9609. doi: 10.1080/21655979.2021.1999549,

PubMed Abstract | Crossref Full Text | Google Scholar

MacDonald, A., Lu, B., Caron, M., Caporicci-Dinucci, N., Hatrock, D., Petrecca, K., et al. (2021). Single cell transcriptomics of ependymal cells across age, region and species reveals cilia-related and metal ion regulatory roles as major conserved ependymal cell functions. Front. Cell. Neurosci. 15:703951. doi: 10.3389/fncel.2021.703951

Crossref Full Text | Google Scholar

Magdesian, M. H., Carvalho, M. M., Mendes, F. A., Saraiva, L. M., Juliano, M. A., Juliano, L., et al. (2008). Amyloid-beta binds to the extracellular cysteine-rich domain of frizzled and inhibits Wnt/beta-catenin signaling. J. Biol. Chem. 283, 9359–9368. doi: 10.1074/jbc.M707108200,

PubMed Abstract | Crossref Full Text | Google Scholar

Manczak, M., and Reddy, P. H. (2012). Abnormal interaction of VDAC1 with amyloid beta and phosphorylated tau causes mitochondrial dysfunction in Alzheimer's disease. Hum. Mol. Genet. 21, 5131–5146. doi: 10.1093/hmg/dds360

Crossref Full Text | Google Scholar

Mei, D., Zhu, Y., Zhang, L., and Wei, W. (2020). The role of CTHRC1 in regulation of multiple Signaling and tumor progression and metastasis. Mediat. Inflamm. 2020:9578701. doi: 10.1155/2020/

Crossref Full Text | Google Scholar

Moreno-Moral, A., and Petretto, E. (2016). From integrative genomics to systems genetics in the rat to link genotypes to phenotypes. Dis. Model. Mech. 9, 1097–1110. doi: 10.1242/dmm.026104,

PubMed Abstract | Crossref Full Text | Google Scholar

Mudunuri, U., Che, A., Yi, M., and Stephens, R. M. (2009). bioDBnet: the biological database network. Bioinformatics 25, 555–556. doi: 10.1093/bioinformatics/btn654,

PubMed Abstract | Crossref Full Text | Google Scholar

Mulligan, M. K., Mozhui, K., Prins, P., and Williams, R. W. (2017). GeneNetwork: a toolbox for systems genetics. Methods Mol. Biol. 1488, 75–120. doi: 10.1007/978-1-4939-6427-7_,

PubMed Abstract | Crossref Full Text | Google Scholar

Mullins, R. J., Mustapic, M., Goetzl, E. J., and Kapogiannis, D. (2017). Exosomal biomarkers of brain insulin resistance associated with regional atrophy in Alzheimer's disease. Hum. Brain Mapp. 38, 1933–1940. doi: 10.1002/hbm.23494

Crossref Full Text | Google Scholar

Navarro, A., and Boveris, A. (2010). Brain mitochondrial dysfunction in aging, neurodegeneration, and Parkinson's disease. Front. Aging Neurosci. 2:34. doi: 10.3389/fnagi.2010.00034

Crossref Full Text | Google Scholar

Nichols, E., Steinmetz, J. D., Vollset, S. E., Fukutaki, K., Chalek, J., Abd-Allah, F., et al. (2022). Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019. Lancet Public Health 7, e105–e125. doi: 10.1016/S2468-2667(21)00249-8

Crossref Full Text | Google Scholar

Oldham, M. C., Konopka, G., Iwamoto, K., Langfelder, P., Kato, T., Horvath, S., et al. (2008). Functional organization of the transcriptome in human brain. Nat. Neurosci. 11, 1271–1282. doi: 10.1038/nn.2207

Crossref Full Text | Google Scholar

Orloff, M., Peterson, C., He, X., Ganapathi, S., Heald, B., Yang, Y. R., et al. (2011). Germline mutations in MSR1, ASCC1, and CTHRC1 in patients with Barrett esophagus and esophageal adenocarcinoma. JAMA 306, 410–419. doi: 10.1001/jama.2011.1029,

PubMed Abstract | Crossref Full Text | Google Scholar

Ouellette, A. R., Neuner, S. M., Dumitrescu, L., Anderson, L. C., Gatti, D. M., Mahoney, E. R., et al. (2020). Cross-species analyses identify Dlgap2 as a regulator of age-related cognitive decline and Alzheimer's dementia. Cell Rep. 32:108091. doi: 10.1016/j.celrep.2020.108091,

PubMed Abstract | Crossref Full Text | Google Scholar

Pascual-Vargas, P., and Salinas, P. C. (2021). A role for frizzled and their post-translational modifications in the mammalian central nervous system. Front. Cell Dev. Biol. 9:692888. doi: 10.3389/fcell.2021.692888

Crossref Full Text | Google Scholar

Perluigi, M., Barone, E., di Domenico, F., and Butterfield, D. A. (2016). Aberrant protein phosphorylation in Alzheimer disease brain disturbs pro-survival and cell death pathways. Biochim. Biophys. Acta 1862, 1871–1882. doi: 10.1016/j.bbadis.2016.07.005,

PubMed Abstract | Crossref Full Text | Google Scholar

Petit-Taboue, M. C., et al. (1998). Effects of healthy aging on the regional cerebral metabolic rate of glucose assessed with statistical parametric mapping. NeuroImage 7, 176–184.

Google Scholar

Qin, F., Luo, M., Xiong, Y., Zhang, N., Dai, Y., Kuang, W., et al. (2022). Prevalence and associated factors of cognitive impairment among the elderly population: a nationwide cross-sectional study in China. Front. Public Health 10:1032666. doi: 10.3389/fpubh.2022.1032666

Crossref Full Text | Google Scholar

Rice, H. C., Marcassa, G., Chrysidou, I., Horré, K., Young-Pearse, T. L., Müller, U. C., et al. (2020). Contribution of GABAergic interneurons to amyloid-beta plaque pathology in an APP knock-in mouse model. Mol. Neurodegener. 15:3. doi: 10.1186/s13024-019-0356-y,

PubMed Abstract | Crossref Full Text | Google Scholar

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., et al. (2015). Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e47. doi: 10.1093/nar/gkv007,

PubMed Abstract | Crossref Full Text | Google Scholar

Romaniello, F., Tonelli, A., Arrigoni, F., Baschirotto, C., Triulzi, F., Bresolin, N., et al. (2012). A novel mutation in the beta-tubulin gene TUBB2B associated with complex malformation of cortical development and deficits in axonal guidance. Dev. Med. Child Neurol. 54, 765–769. doi: 10.1111/j.1469-8749.2012.04316.x

Crossref Full Text | Google Scholar

Rosenberg, T., Gal-Ben-Ari, S., Dieterich, D. C., Kreutz, M. R., Ziv, N. E., Gundelfinger, E. D., et al. (2014). The roles of protein expression in synaptic plasticity and memory consolidation. Front. Mol. Neurosci. 7:86. doi: 10.3389/fnmol.2014.00086

Crossref Full Text | Google Scholar

Rubin, R. D., Watson, P. D., Duff, J., and Cohen, N. J. (2014). The role of the hippocampus in flexible cognition and social behavior. Front. Hum. Neurosci. 8:742. doi: 10.3389/fnhum.2014.00742

Crossref Full Text | Google Scholar

Schieweck, R., and Kiebler, M. A. (2019). Posttranscriptional gene regulation of the GABA receptor to control neuronal inhibition. Front. Mol. Neurosci. 12:152. doi: 10.3389/fnmol.2019.00152

Crossref Full Text | Google Scholar

Sherman, B. T., Hao, M., Qiu, J., Jiao, X., Baseler, M. W., Lane, H. C., et al. (2022). DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50, W216–W221. doi: 10.1093/nar/gkac194,

PubMed Abstract | Crossref Full Text | Google Scholar

Shoshan-Barmatz, V., Nahon-Crystal, E., Shteinfer-Kuzmine, A., and Gupta, R. (2018). VDAC1, mitochondrial dysfunction, and Alzheimer's disease. Pharmacol. Res. 131, 87–101. doi: 10.1016/j.phrs.2018.03.010,

PubMed Abstract | Crossref Full Text | Google Scholar

Smilansky, A., Dangoor, L., Nakdimon, I., Ben-Hail, D., Mizrachi, D., and Shoshan-Barmatz, V. (2015). The voltage-dependent Anion Channel 1 mediates amyloid beta toxicity and represents a potential target for Alzheimer disease therapy. J. Biol. Chem. 290, 30670–30683. doi: 10.1074/jbc.M115.691493,

PubMed Abstract | Crossref Full Text | Google Scholar

Smith, C. L., Blake, J. A., Kadin, J. A., Richardson, J. E., and Bult, C. J.Mouse Genome Database Group (2018). Mouse genome database (MGD)-2018: knowledgebase for the laboratory mouse. Nucleic Acids Res. 46, D836–D842. doi: 10.1093/nar/gkx1006,

PubMed Abstract | Crossref Full Text | Google Scholar

Sweatt, J. D. (2004). Hippocampal function in cognition. Psychopharmacology 174, 99–110. doi: 10.1007/s00213-004-1795-9,

PubMed Abstract | Crossref Full Text | Google Scholar

Talbot, K., Wang, H. Y., Kazi, H., Han, L. Y., Bakshi, K. P., Stucky, A., et al. (2012). Demonstrated brain insulin resistance in Alzheimer's disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline. J. Clin. Invest. 122, 1316–1338. doi: 10.1172/JCI59903,

PubMed Abstract | Crossref Full Text | Google Scholar

Tapia-Rojas, C., and Inestrosa, N. C. (2018). Loss of canonical Wnt signaling is involved in the pathogenesis of Alzheimer's disease. Neural Regen. Res. 13, 1705–1710. doi: 10.4103/1673-5374.238606,

PubMed Abstract | Crossref Full Text | Google Scholar

Trushina, E., Dutta, T., Persson, X. M. T., Mielke, M. M., and Petersen, R. C. (2013). Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer's disease using metabolomics. PLoS One 8:e63644. doi: 10.1371/journal.pone.0063644

Crossref Full Text | Google Scholar

Vassar, R., Bennett, B. D., Babu-Khan, S., Kahn, S., Mendiaz, E. A., Denis, P., et al. (1999). Beta-secretase cleavage of Alzheimer's amyloid precursor protein by the transmembrane aspartic protease BACE. Science 286, 735–741. doi: 10.1126/science.286.5440.735,

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, H., Dey, K. K., Chen, P. C., Li, Y., Niu, M., Cho, J. H., et al. (2020). Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer's disease. Mol. Neurodegener. 15:43. doi: 10.1186/s13024-020-00384-6,

PubMed Abstract | Crossref Full Text | Google Scholar

Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., et al. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38, W214–W220. doi: 10.1093/nar/gkq537,

PubMed Abstract | Crossref Full Text | Google Scholar

Wehner, J. M., Radcliffe, R. A., Rosmann, S. T., Christensen, S. C., Rasmussen, D. L., Fulker, D. W., et al. (1997). Quantitative trait locus analysis of contextual fear conditioning in mice. Nat. Genet. 17, 331–334. doi: 10.1038/ng1197-331,

PubMed Abstract | Crossref Full Text | Google Scholar

Yan, X., Hu, Y., Wang, B., Wang, S., and Zhang, X. (2020). Metabolic dysregulation contributes to the progression of Alzheimer's disease. Front. Neurosci. 14:530219. doi: 10.3389/fnins.2020.530219

Crossref Full Text | Google Scholar

Yarchoan, M., Toledo, J. B., Lee, E. B., Arvanitakis, Z., Kazi, H., Han, L. Y., et al. (2014). Abnormal serine phosphorylation of insulin receptor substrate 1 is associated with tau pathology in Alzheimer's disease and tauopathies. Acta Neuropathol. 128, 679–689. doi: 10.1007/s00401-014-1328-5,

PubMed Abstract | Crossref Full Text | Google Scholar

Zang, J. C. S, Hohoff, C., Van Assche, E., Lange, P., Kraft, M., Sandmann, S., et al. (2023). Immune gene co-expression signatures implicated in occurence and persistence of cognitive dysfunction in depression. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 127:110826. doi: 10.1016/j.pnpbp.2023.110826

Crossref Full Text | Google Scholar

Zhang, X. L., Hu, L. P., Yang, Q., Qin, W. T., Wang, X., Xu, C. J., et al. (2021). CTHRC1 promotes liver metastasis by reshaping infiltrated macrophages through physical interactions with TGF-beta receptors in colorectal cancer. Oncogene 40, 3959–3973. doi: 10.1038/s41388-021-01827-0,

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, Q., Ma, C., Gearing, M., Wang, P. G., Chin, L. S., and Li, L. (2018). Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimer's disease. Acta Neuropathol. Commun. 6:19. doi: 10.1186/s40478-018-0524-2

Crossref Full Text | Google Scholar

Zhao, L., Wang, W., Niu, P., Luan, X., Zhao, D., and Chen, Y. (2022). The molecular mechanisms of CTHRC1 in gastric cancer by integrating TCGA, GEO and GSA datasets. Front. Genet. 13:900124. doi: 10.3389/fgene.2022.900124

Crossref Full Text | Google Scholar

Zhou, C. J., Zhao, C., and Pleasure, S. J. (2004). Wnt signaling mutants have decreased dentate granule cell production and radial glial scaffolding abnormalities. J. Neurosci. 24, 121–126. doi: 10.1523/JNEUROSCI.4071-03.2004,

PubMed Abstract | Crossref Full Text | Google Scholar

Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., et al. (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10:1523. doi: 10.1038/s41467-019-09234-6,

PubMed Abstract | Crossref Full Text | Google Scholar