Gene-gene functional relationships in Alzheimer's disease: CELF1 regulates KLC1 alternative splicing.

The causes of Alzheimer's disease (AD) are poorly understood, although many genes are known to be involved in this pathology. To gain insights into the underlying molecular mechanisms, it is essential to identify the relationships between individual AD genes. Previous work has shown that the splice variant E of KLC1 (KLC1_vE) promotes AD, and that the CELF1 gene, which encodes an RNA-binding protein involved in splicing regulation, is at a risk locus for AD. Here, we identified a functional link between CELF1 and KLC1 in AD pathogenesis. Transcriptomic data from human samples from different ethnic groups revealed that CELF1 mRNA levels are low in AD brains, and the splicing pattern of KLC1 is strongly correlated with CELF1 expression levels. Specifically, KLC1_vE is negatively correlated with CELF1. Depletion and overexpression experiments in cultured cells demonstrated that the CELF1 protein down-regulates KLC1_vE. In a cross-linking and immunoprecipitation sequencing (CLIP-seq) database, CELF1 directly binds to KLC1 RNA, following which it likely modulates terminal exon usage, hence KLC1_vE formation. These findings reveal a new pathogenic pathway where a risk allele of CELF1 is associated with reduced CELF1 expression, which up-regulates KLC1_vE to promote AD.

Synergistic role of retinoic acid signaling and Gata3 during primitive choanae formation.

Developmental defects of primitive choanae, an anatomical path to connect the embryonic nasal and oral cavity, result in disorders called choanal atresia (CA), which are associated with many congenital diseases and require immediate clinical intervention after birth. Previous studies revealed that reduced retinoid signaling underlies the etiology of CA. In the present study, by using multiple mouse models which conditionally deleted Rdh10 and Gata3 during embryogenesis, we showed that Gata3 expression is regulated by retinoid signaling during embryonic craniofacial development and plays crucial roles for development of the primitive choanae. Interestingly, Gata3 loss of function is known to cause hypoparathyroidism, sensorineural deafness and renal disease (HDR) syndrome, which exhibits CA as one of the phenotypes in humans. Our model partially phenocopies HDR syndrome with CA, and is thus a useful tool for investigating the molecular and cellular mechanisms of HDR syndrome. We further uncovered critical synergy of Gata3 and retinoid signaling during embryonic development, which will shed light on novel molecular and cellular etiology of congenital defects in primitive choanae formation.

Genetics of Alzheimer's disease: an East Asian perspective.

Alzheimer's disease (AD) is an age-related multifactorial neurodegenerative disorder. Advances in genome technology, including next generation sequencing have uncovered complex genetic effects in AD by analyzing both common and rare functional variants. Multiple lines of evidence suggest that the pathogenesis of AD is influenced by multiple genetic components rather than single genetic factor. Previous genetic studies on AD have predominantly included European ancestry cohorts; hence, the non-European population may be underrepresented, potentially leading to reduced diversity in AD genetic research. Additionally, ethnic diversity may result in dissimilar effects of genetic determinants in AD. APOE genotypes are a well-established genetic risk factor in AD, with the East Asian population having a higher risk of AD associated with the APOE ε4 allele. To date, seven genome-wide association studies (GWAS) have been conducted in East Asians, which report a total of 26 AD-associated loci. Several rare variants, including the p.H157Y variant in TREM2, and the p.G186R and p.R274W variants in SHARPIN are associated with risk of AD in East Asians. Extending genetic studies to diverse populations, including East Asians is necessary, which could yield more comprehensive insights into AD, and here we review the recent findings regarding the genetic determinants of AD from an East Asian perspective.

Disruption of a RAC1-centred network is associated with Alzheimer's disease pathology and causes age-dependent neurodegeneration.

The molecular biological mechanisms of Alzheimer's disease (AD) involve disease-associated crosstalk through many genes and include a loss of normal as well as a gain of abnormal interactions among genes. A protein domain network (PDN) is a collection of physical bindings that occur between protein domains, and the states of the PDNs in patients with AD are likely to be perturbed compared to those in normal healthy individuals. To identify PDN changes that cause neurodegeneration, we analysed the PDNs that occur among genes co-expressed in each of three brain regions at each stage of AD. Our analysis revealed that the PDNs collapsed with the progression of AD stage and identified five hub genes, including Rac1, as key players in PDN collapse. Using publicly available as well as our own gene expression data, we confirmed that the mRNA expression level of the RAC1 gene was downregulated in the entorhinal cortex (EC) of AD brains. To test the causality of these changes in neurodegeneration, we utilized Drosophila as a genetic model and found that modest knockdown of Rac1 in neurons was sufficient to cause age-dependent behavioural deficits and neurodegeneration. Finally, we identified a microRNA, hsa-miR-101-3p, as a potential regulator of RAC1 in AD brains. As the Braak neurofibrillary tangle (NFT) stage progressed, the expression levels of hsa-miR-101-3p were increased specifically in the EC. Furthermore, overexpression of hsa-miR-101-3p in the human neuronal cell line SH-SY5Y caused RAC1 downregulation. These results highlight the utility of our integrated network approach for identifying causal changes leading to neurodegeneration in AD.

Polygenic Architecture of Human Neuroanatomical Diversity.

We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured

Identification of core gene networks and hub genes associated with progression of non-alcoholic fatty liver disease by RNA sequencing.

AIM: Non-alcoholic fatty liver disease (NAFLD) progresses because of the interaction between numerous genes. Thus, we carried out a weighted gene coexpression network analysis to identify core gene networks and key genes associated with NAFLD progression. METHODS: We enrolled 39 patients with mild NAFLD (fibrosis stages 0-2) and 21 with advanced NAFLD (fibrosis stages 3-4). Total RNA was extracted from frozen liver biopsies, and sequenced to capture a large dynamic range of expression levels. RESULTS: A total of 1777 genes differentially expressed between mild and advanced NAFLD (q-value <0.05) clustered into four modules. One module was enriched for genes that encode cell surface or extracellular matrix proteins, and are involved in cell adhesion, proliferation, and signaling. This module formed a scale-free network containing four hub genes (PAPLN, LBH, DPYSL3, and JAG1) overexpressed in advanced NAFLD. PAPLN is a component of the extracellular matrix, LBH and DPYSL3 are reported to be tumor suppressors, and JAG1 is tumorigenic. Another module formed a random network, and was enriched for genes that accumulate in the mitochondria. These genes were downregulated in advanced NAFLD, reflecting impaired mitochondrial function. However, the other two modules did not form unambiguous networks. KEGG analysis indicated that 71 differentially expressed genes were involved in "pathways in cancer". Strikingly, expression of half of all differentially expressed genes was inversely correlated with methylation of CpG sites (q-value <0.05). Among clinical parameters, serum type IV collagen 7 s was most strongly associated with the epigenetic status in NAFLD. CONCLUSIONS: Newly identified core gene networks suggest that the NAFLD liver undergoes mitochondrial dysfunction and fibrosis, and acquires tumorigenic potential epigenetically. Our data provide novel insights into the pathology and etiology of NAFLD progression, and identify potential targets for diagnosis and treatment.

Effect of Clozapine on DNA Methylation in Peripheral Leukocytes from Patients with Treatment-Resistant Schizophrenia.

Clozapine is an atypical antipsychotic, that is established as the treatment of choice for treatment-resistant schizophrenia (SCZ). To date, no study investigating comprehensive DNA methylation changes in SCZ patients treated with chronic clozapine has been reported. The purpose of the present study is to reveal the effects of clozapine on DNA methylation in treatment-resistant SCZ. We conducted a genome-wide DNA methylation profiling in peripheral leukocytes (485,764 CpG dinucleotides) from treatment-resistant SCZ patients treated with clozapine (n = 21) in a longitudinal study. Significant changes in DNA methylation were observed at 29,134 sites after one year of treatment with clozapine, and these genes were enriched for "cell substrate adhesion" and "cell matrix adhesion" gene ontology (GO) terms. Furthermore, DNA methylation changes in the CREBBP (CREB binding protein) gene were significantly correlated with the clinical improvements. Our findings provide insights into the action of clozapine in treatment-resistant SCZ.

Systematic review and meta-analysis of Japanese familial Alzheimer's disease and FTDP-17.

Mutations in APP, PSEN1 and PSEN2 as the genetic causes of familial Alzheimer's disease (FAD) have been found in various ethnic populations. A substantial number of FAD pedigrees with mutations have been reported in the Japanese population; however, it remains unclear whether the genetic and clinical features of FAD in the Japanese population differ from those in other populations. To address this issue, we conducted a systematic review and meta-analysis of Japanese FAD and frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17) by literature search. Using this analysis, we identified 39 different PSEN1 mutations in 140 patients, 5 APP mutations in 35 patients and 16 MAPT mutations in 84 patients. There was no PSEN2 mutation among Japanese patients. The age at onset in Japanese FAD patients with PSEN1 mutations was significantly younger than that in patients with APP mutations. Kaplan-Meier analysis revealed that patients with MAPT mutations showed a shorter survival than patients with PSEN1 or APP mutations. Patients with mutations in different genes exhibit characteristic clinical presentations, suggesting that mutations in causative genes may modify the clinical presentations. By collecting and cataloging genetic and clinical information on Japanese FAD and FTDP-17, we developed an original database designated as Japanese Familial Alzheimer's Disease Database, which is accessible at http://alzdb.bri.niigata-u.ac.jp/.

Polygenic effects on the risk of Alzheimer's disease in the Japanese population.

BACKGROUND: Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer's disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans. METHODS: In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD. RESULTS: The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (ß estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results. CONCLUSIONS: We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.

The clinical application of optimized AT(N) classification in Alzheimer's clinical syndrome (ACS) and non-ACS conditions.

We aimed to assess the utility of AT(N) classification in clinical practice. We measured the cerebrospinal fluid levels of amyloid-ß (Aß) 42, Aß40, phosphorylated tau, total tau, and neurofilament light chain (NfL) in samples from 230 patients with Alzheimer's clinical syndrome (ACS) and 328 patients with non-ACS. The concordance of two A-markers (i.e., Aß42 alone and the Aß42/Aß40 ratio) was not significantly different between the ACS (87.4%) and non-ACS (74.1%) groups. However, the frequency of discordant cases with AAß42-alone+/AAß-ratio- was significantly higher in the non-ACS (23.8%) than in the ACS group (7.4%). The concordance of two N-markers (i.e., total tau and NfL) was 40.4% in the ACS group and 24.4% in the non-ACS group. In the ACS samples, the frequency of biological Alzheimer's disease (i.e., A+T+) in Ntau+ cases was 95% while that in NNfL+ cases was 65%. Reflecting Aß deposition and neurodegeneration more accurately, we recommend the use of AT(N) classification defined by cerebrospinal fluid AAß-ratioTNNfL in clinical practice.

Different AT(N) profiles and clinical progression classified by two different N markers using total tau and neurofilament light chain in cerebrospinal fluid.

Background: The AT(N) classification was proposed for categorising individuals according to biomarkers. However, AT(N) profiles may vary depending on the markers chosen and the target population. Methods: We stratified 177 individuals who participated in the Japanese Alzheimer's Disease Neuroimaging Initiative by AT(N) classification according to cerebrospinal fluid (CSF) biomarkers. We compared the frequency of AT(N) profiles between the classification using total tau and neurofilament light chain (NfL) as N markers (AT(N)tau and AT(N)NfL). Baseline characteristics, and longitudinal biological and clinical changes were examined between AT(N) profiles. Results: We found that 9% of cognitively unimpaired subjects, 49% of subjects with mild cognitive impairment, and 61% of patients with Alzheimer's disease (AD) dementia had the biological AD profile (ie, A+T+) in the cohort. The frequency of AT(N) profiles substantially differed between the AT(N)tau and AT(N)NfL classifications. When we used t-tau as the N marker (AT(N)tau), those who had T- were more frequently assigned to (N)-, whereas those who had T+were more frequently assigned to (N)+ than when we used NfL as the N marker (AT(N)NfL). During a follow-up, the AD continuum group progressed clinically and biologically compared with the normal biomarker group in both the AT(N)tau and AT(N)NfL classifications. More frequent conversion to dementia was observed in the non-AD pathological change group in the AT(N)tau classification, but not in the AT(N)NfL classification. Conclusions: AT(N)tau and AT(N)NfL in CSF may capture different aspects of neurodegeneration and provide a different prognostic value. The AT(N) classification aids in understanding the AD continuum biology in various populations.

Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer's disease using multimodal data.

Mild cognitive impairment (MCI) is a high-risk condition for conversion to Alzheimer's disease (AD) dementia. However, individuals with MCI show heterogeneous patterns of pathology and conversion to AD dementia. Thus, detailed subtyping of MCI subjects and accurate prediction of the patients in whom MCI will convert to AD dementia is critical for identifying at-risk populations and the underlying biological features. To this end, we developed a model that simultaneously subtypes MCI subjects and predicts conversion to AD and performed an analysis of the underlying biological characteristics of each subtype. In particular, a heterogeneous mixture learning (HML) method was used to build a decision tree-based model based on multimodal data, including cerebrospinal fluid (CSF) biomarker data, structural magnetic resonance imaging (MRI) data, APOE genotype data, and age at examination. The HML model showed an average F1 score of 0.721, which was comparable to the random forest method and had significantly more predictive accuracy than the CART method. The HML-generated decision tree was also used to classify-five subtypes of MCI. Each MCI subtype was characterized in terms of the degree of abnormality in CSF biomarkers, brain atrophy, and cognitive decline. The five subtypes of MCI were further categorized into three groups: one subtype with low conversion rates (similar to cognitively normal subjects); three subtypes with moderate conversion rates; and one subtype with high conversion rates (similar to AD dementia patients). The subtypes with moderate conversion rates were subsequently separated into a group with CSF biomarker abnormalities and a group with brain atrophy. The subtypes identified in this study exhibited varying MCI-to-AD conversion rates and differing biological profiles.

Construction of prediction models for growth traits of soybean cultivars based on phenotyping in diverse genotype and environment combinations.

As soybean cultivars are adapted to a relatively narrow range of latitude, the effects of climate changes are estimated to be severe. To address this issue, it is important to improve our understanding of the effects of climate change by applying the simulation model including both genetic and environmental factors with their interactions (G×E). To achieve this goal, we conducted the field experiments for soybean core collections using multiple sowing times in multi-latitudinal fields. Sowing time shifts altered the flowering time (FT) and growth phenotypes, and resulted in increasing the combinations of genotypes and environments. Genome-wide association studies for the obtained phenotypes revealed the effects of field and sowing time to the significance of detected alleles, indicating the presence of G×E. By using accumulated phenotypic and environmental data in 2018 and 2019, we constructed multiple regression models for FT and growth pattern. Applicability of the constructed models was evaluated by the field experiments in 2020 including a novel field, and high correlation between the predicted and measured values was observed, suggesting the robustness of the models. The models presented here would allow us to predict the phenotype of the core collections in a given environment.

Genome-wide copy number variation analysis of hepatitis B infection in a Japanese population.

Genome-wide association studies have been performed to identify common genetic variants associated with hepatitis B (HB). However, little is known about copy number variations (CNVs) in HB. In this study, we performed a genome-wide CNV analysis between 1830 healthy controls and 1031 patients with HB infection after quality control. Using signal calling by the Axiom Analysis Suite and CNV detection by PennCNV software, we obtained a total of 4494 CNVs across all individuals. The genes with CNVs that were found only in the HB patients were associated with the immune system, such as antigen processing. A gene-level CNV association test revealed statistically significant CNVs in the contactin 6 (CNTN6) gene. Moreover, we also performed gene-level CNV association tests in disease subgroups, including hepatocellular carcinoma patients, liver cirrhosis patients, and HBV carriers, including asymptomatic carriers and patients with HBV-derived chronic hepatitis. Our findings from germline cells suggested that patient-specific CNVs may be inherent genetic risk factors for HB.

Methylation Analysis in Monozygotic Twins With Treatment-Resistant Schizophrenia and Discordant Responses to Clozapine.

Schizophrenia is a mental illness that involves both genetic and environmental factors. Clozapine, an atypical antipsychotic, is a well-established therapy for treatment-resistant schizophrenia. In this study, we focused on a set of monozygotic twins with treatment-resistant schizophrenia in which one twin effectively responded to clozapine treatment and the other did not. Our previous study generated neurons from induced pluripotent stem (iPS) cells derived from these patients and compared the transcriptome profiles between mock- and clozapine-treated neurons. In this study, we performed genome-wide DNA methylation profiling to investigate the mechanisms underlying gene expression changes. First, we extracted the differentially methylated sites from each twin based on statistical analysis. Then, we combined the DNA methylation profiling with transcriptome profiling from our previous RNA-seq data. Among the genes with altered methylation and expression, we found the different proportions of the genes related to neuronal and synaptic functions between the clozapine responder and non-responder (35.7 and 6.7%, respectively). This trend was observed even when the basal differences between the responder and non-responder was excluded. These results suggest that effective clozapine action may correct the abnormalities of neuronal and synapse functions in schizophrenia via changes in methylation.

Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease.

BACKGROUND: Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. METHODS: In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. RESULTS: We applied our computational framework to prioritize novel putative target genes for Alzheimer's disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). CONCLUSIONS: Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

Upregulated expression of a subset of genes in APP;ob/ob mice: Evidence of an interaction between diabetes-linked obesity and Alzheimer's disease.

Clinical studies have indicated that obesity and diabetes are associated with Alzheimer's disease (AD) and neurodegeneration. However, the mechanism by which obesity/diabetes and AD interact with each other and contribute to dementia remains elusive. To obtain insights into their interaction at molecular levels, we performed gene expression analysis of APP;ob/ob mice, which were generated by crossing transgenic AD model mice (APP23 mice) with ob/ob mice, which are obese and mildly diabetic. The Aß level in these mice was reduced compared with that in pure APP mice. However, we identified a cluster of genes (cluster 10) upregulated in APP;ob/ob mice but not in either APP or ob/ob mice. Interestingly, genes upregulated in the human AD brain were enriched in cluster 10. Moreover, genes in cluster 10 formed a network and shared upregulated genes with a cell model of neurodegeneration and other models of neurological disorders such as ischemia and epilepsy. In silico analyses showed that serum response factor (SRF), recently identified in a single-cell analysis of human brains as a transcription factor that can control the conversion from healthy cells to AD cells, might be a common transcriptional regulator for a subset of cluster 10 genes. These data suggest that upregulation of genes uniquely associated with APP;ob/ob mice is an evidence of the interaction between obesity/diabetes and AD.

[Apolipoprotein E4, a Genetic Risk Factor for Alzheimer's Disease].

Apolipoprotein E (APOE) is reported to be a strong genetic risk factor for Alzheimer's disease (AD), broadly contributing to the AD pathologies observed in most populations. However, it is difficult to explicate these AD pathologies based only on the known functions of APOE. In this review article, we revisited the histories and functions of APOE and also reviewed its recently elucidated the pleiotropic roles in the brain.

Enhancer variants associated with Alzheimer's disease affect gene expression via chromatin looping.

BACKGROUND: Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer's disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops. METHODS: To characterize AD SNPs within non-coding regions, we extracted 406 AD SNPs with GWAS p-values of less than 1.00 × 10- 6 from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments. RESULTS: We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes. CONCLUSION: Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.