Identification of Potential Blood-Based Biomarkers for Frailty by Using an Integrative Approach.

INTRODUCTION: Although frailty is a geriatric syndrome that is associated with disability, hospitalization, and mortality, it can be reversible and preventable with the appropriate interventions. Additionally, as the current diagnostic criteria for frailty include only physical, psychological, cognitive, and social measurements, there is a need for promising blood-based molecular biomarkers to aid in the diagnosis of frailty. METHODS: To identify candidate blood-based biomarkers that can enhance current diagnosis of frailty, we conducted a comprehensive analysis of clinical data, messenger RNA-sequencing (RNA-seq), and aging-related factors using a total of 104 older adults aged 65-90 years (61 frail subjects and 43 robust subjects) in a cross-sectional case-control study. RESULTS: We identified two candidate biomarkers of frailty from the clinical data analysis, nine from the RNA-seq analysis, and six from the aging-related factors analysis. By using combinations of the candidate biomarkers and clinical information, we constructed risk prediction models. The best models used combinations that included skeletal muscle mass index measured by dual-energy X-ray absorptiometry (adjusted p = 0.026), GDF15 (adjusted p = 1.46E-03), adiponectin (adjusted p = 0.012), CXCL9 (adjusted p = 0.011), or apelin (adjusted p = 0.020) as the biomarker. These models achieved a high area under the curve of 0.95 in an independent validation cohort (95% confidence interval: 0.79-0.97). Our risk prediction models showed significantly higher areas under the curve than did models constructed using only basic clinical information (Welch's t test p < 0.001). CONCLUSION: All five biomarkers showed statistically significant correlations with components of the frailty diagnostic criteria. We discovered several potential biomarkers for the diagnosis of frailty. Further refinement may lead to their future clinical use.

Association between Circulating Levels of CXCL9 and CXCL10 and Physical Frailty in Older Adults.

INTRODUCTION: Dysregulation of pro-inflammatory chemokines is considered a potential mechanism for the development of age-related medical conditions such as frailty. However, evidence linking circulating chemokines with frailty remains lacking. MATERIALS AND METHODS: We performed a case-control study including 48 cases and 48 controls aged 65-90 years, using the National Center for Geriatrics and Gerontology outpatient registry data. Cases were outpatients with physical frailty and low habitual daily activity. Controls were robust outpatients who performed habitual daily activities. The Japanese version of the Cardiovascular Health Study criteria was used to diagnose physical frailty, and the modified Baecke questionnaire was used to evaluate habitual daily activities. Serum CXCL9 and CXCL10 levels were measured using enzyme-linked immunosorbent assay. RESULTS: The median age (interquartile range) in cases and controls was 78 (73-83) and 76 (72-80) years, with the proportions of men were 47.9% and 43.8%, respectively. In the logistic regression model with adjustment for age, sex, and other confounding factors, the multivariable odds ratios (95% confidence intervals) for the highest versus lowest tertile of CXCL9 and CXCL10 levels were 7.90 (1.61-49.80) and 1.61 (0.42-6.30), respectively. However, we did not observe a linear association between CXCL9 levels and physical frailty components. DISCUSSION/CONCLUSION: Our preliminary data exhibit that circulating CXCL9 levels were positively associated with the odds of physical frailty. However, these findings lack evidence of a dose-response relationship between CXCL9 levels and physical frailty components. Further research with a larger sample size is required to confirm these findings.

Whole-genome sequencing reveals novel ethnicity-specific rare variants associated with Alzheimer's disease.

Alzheimer's disease (AD) is the most common multifactorial neurodegenerative disease among elderly people. Genome-wide association studies (GWAS) have been highly successful in identifying genetic risk factors. However, GWAS investigate common variants, which tend to have small effect sizes, and rare variants with potentially larger phenotypic effects have not been sufficiently investigated. Whole-genome sequencing (WGS) enables us to detect those rare variants. Here, we performed rare-variant association studies by using WGS data from 140 individuals with probable AD and 798 cognitively normal elder controls (CN), as well as single-nucleotide polymorphism genotyping data from an independent large Japanese AD cohort of 1604 AD and 1235 CN subjects. We identified two rare variants as candidates for AD association: a missense variant in OR51G1 (rs146006146, c.815 G > A, p.R272H) and a stop-gain variant in MLKL (rs763812068, c.142 C > T, p.Q48X). Subsequent in vitro functional analysis revealed that the MLKL stop-gain variant can contribute to increases not only in abnormal cells that should die by programmed cell death but do not, but also in the ratio of Aß42 to Aß40. We further detected AD candidate genes through gene-based association tests of rare variants; a network-based meta-analysis using these candidates identified four functionally important hub genes (NCOR2, PLEC, DMD, and NEDD4). Our findings will contribute to the understanding of AD and provide novel insights into its pathogenic mechanisms that can be used in future studies.

A functional variant of SHARPIN confers increased risk of late-onset Alzheimer's disease.

Late-onset Alzheimer's disease (LOAD) is the most common form of dementia, and its pathogenesis is multifactorial. We previously reported a rare functional variant of SHARPIN (rs572750141, NP_112236.3:p.Gly186Arg) that was significantly associated with LOAD. In addition, several recent studies have suggested the potential role of SHARPIN in AD pathogenesis. In this study, we sought to identify additional functional variants of SHARPIN in Japanese population. Six highly deleterious variants of SHARPIN, comprising four missense variants, one frameshift variant, and one stop-gain variant were detected from whole-genome sequencing data for 180 patients with LOAD and 184 with mild cognitive impairment. One of these candidate variants (rs77359862, NP_112236.3:p.Arg274Trp) was significantly associated with an increased risk of LOAD in 5043 LOAD cases and 11984 controls (P = 0.0016, odds ratio = 1.43). Furthermore, this variant SHARPIN showed aberrant cellular localization and reduced the activation of NF-κB, a central mediator of inflammatory and immune responses. Further investigation of the physiologic role of SHARPIN may reveal the mechanism of onset of LOAD.

Network-based meta-analysis and the candidate gene association studies reveal novel ethnicity-specific variants in MFSD3 and MRPL43 associated with dementia with Lewy bodies.

Dementia with Lewy bodies (DLB) is the second most common form of neurodegenerative dementia in elderly people, following Alzheimer's disease. Only three genes, SNCA (α-synuclein), APOE (apolipoprotein E), and GBA (glucosylceramidase), have been convincingly demonstrated to be associated with DLB. Here, we applied whole-genome sequencing to blood samples from 61 DLB patients and 45 cognitively normal controls. We used accumulation of candidate mutations to detect novel DLB-associated genes. Subsequent single nucleotide polymorphism (SNP) genotyping and association studies in a large number of samples from Japanese individuals revealed novel heterozygous variants in MFSD3 (rs143475431, c.888T>A:p.C296*; n = 5,421, p = 0.00063) and MRPL43 (chr10:102746730, c.241A>C:p.N81H; n = 4,782, p = 0.0029). We further found that the MFSD3 variant increased plasma levels of butyrylcholinesterase (n = 1,206, p = 0.029). We believe that our findings will contribute to the understanding of DLB and provide insight into its pathogenic mechanism for future studies.

GlyStruct: glycation prediction using structural properties of amino acid residues.

BACKGROUND: Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs. RESULTS: We developed a classifier, GlyStruct based on support vector machine, to predict glycated and non-glycated lysine residues using structural properties of amino acid residues. The features used were secondary structure, accessible surface area and the local backbone torsion angles. For this work, a benchmark dataset was extracted containing 235 glycated and 303 non-glycated lysine residues. GlyStruct demonstrated improved performance of approximately 10% in comparison to benchmark method of Gly-PseAAC. The performance for GlyStruct on the metrics, sensitivity, specificity, accuracy and Mathew's correlation coefficient were 0.7013, 0.7989, 0.7562, and 0.5065, respectively for 10-fold cross-validation. CONCLUSION: Glycation has emerged to be one of the clinically important PTM of proteins in recent times. Therefore, the development of computational tools become necessary to predict glycation, which could help medical professionals administer drugs and manage patients more effectively. The proposed predictor manages to classify glycated and non-glycated lysine residues with promising results consistently on various cross-validation schemes and outperforms other state of the art methods.

A rare functional variant of SHARPIN attenuates the inflammatory response and associates with increased risk of late-onset Alzheimer's disease.

BACKGROUND: Late-onset Alzheimer's disease (LOAD), the most common form of dementia, results from complicated interactions among multiple environmental and genetic factors. Despite recent advances in genetic analysis of LOAD, more than half of the heritability for the disease remains unclear. Although genetic studies in Caucasians found rare risk variants for LOAD with large effect sizes, these variants are hardly detectable in the Japanese population. METHODS: To identify rare variants possibly explaining part of the genetic architecture for LOAD in Japanese people, we performed whole-exome sequencing analyses of 202 LOAD individuals without the APOE ε4 risk allele, a major genetic factor for LOAD susceptibility. We also implemented in vitro functional analyses of the variant(s) to reveal possible functions associated with LOAD risk. RESULTS: Via step-by-step selection of whole-exome variants, we found seven candidate risk variants. We then conducted a case-control association study in a large Japanese cohort consisting of 4563 cases and 16,459 controls. We finally identified a rare nonsynonymous variant, rs572750141 (NM_030974.3:p.Gly186Arg), in SHARPIN that was potentially associated with increased risk of LOAD (corrected P = 8.05 × 10- 5, odds ratio = 6.1). The amino acid change in SHARPIN resulted in aberrant cellular localization of the variant protein and attenuated the activation of NF-κB, a central mediator of inflammatory and immune responses. CONCLUSIONS: Our work identified a rare functional SHARPIN variant as a previously unknown genetic risk factor for LOAD. The functional alteration in SHARPIN induced by the rare coding variant is associated with an attenuated inflammatory/immune response that may promote LOAD development.

Integrated analysis of human genetic association study and mouse transcriptome suggests LBH and SHF genes as novel susceptible genes for amyloid-ß accumulation in Alzheimer's disease.

Alzheimer's disease (AD) is a common neurological disease that causes dementia in humans. Although the reports of associated pathological genes have been increasing, the molecular mechanism leading to the accumulation of amyloid-ß (Aß) in human brain is still not well understood. To identify novel genes that cause accumulation of Aß in AD patients, we conducted an integrative analysis by combining a human genetic association study and transcriptome analysis in mouse brain. First, we examined genome-wide gene expression levels in the hippocampus, comparing them to amyloid Aß level in mice with mixed genetic backgrounds. Next, based on a GWAS statistics obtained by a previous study with human AD subjects, we obtained gene-based statistics from the SNP-based statistics. We combined p values from the two types of analysis across orthologous gene pairs in human and mouse into one p value for each gene to evaluate AD susceptibility. As a result, we found five genes with significant p values in this integrated analysis among the 373 genes analyzed. We also examined the gene expression level of these five genes in the hippocampus of independent human AD cases and control subjects. Two genes, LBH and SHF, showed lower expression levels in AD cases than control subjects. This is consistent with the gene expression levels of both the genes in mouse which were negatively correlated with Aß accumulation. These results, obtained from the integrative approach, suggest that LBH and SHF are associated with the AD pathogenesis.

Stepwise iterative maximum likelihood clustering approach.

BACKGROUND: Biological/genetic data is a complex mix of various forms or topologies which makes it quite difficult to analyze. An abundance of such data in this modern era requires the development of sophisticated statistical methods to analyze it in a reasonable amount of time. In many biological/genetic analyses, such as genome-wide association study (GWAS) analysis or multi-omics data analysis, it is required to cluster the plethora of data into sub-categories to understand the subtypes of populations, cancers or any other diseases. Traditionally, the k-means clustering algorithm is a dominant clustering method. This is due to its simplicity and reasonable level of accuracy. Many other clustering methods, including support vector clustering, have been developed in the past, but do not perform well with the biological data, either due to computational reasons or failure to identify clusters. RESULTS: The proposed SIML clustering algorithm has been tested on microarray datasets and SNP datasets. It has been compared with a number of clustering algorithms. On MLL datasets, SIML achieved highest clustering accuracy and rand score on 4/9 cases; similarly on SRBCT dataset, it got for 3/5 cases; on ALL subtype it got highest clustering accuracy for 5/7 cases and highest rand score for 4/7 cases. In addition, SIML overall clustering accuracy on a 3 cluster problem using SNP data were 97.3, 94.7 and 100 %, respectively, for each of the clusters. CONCLUSIONS: In this paper, considering the nature of biological data, we proposed a maximum likelihood clustering approach using a stepwise iterative procedure. The advantage of this proposed method is that it not only uses the distance information, but also incorporate variance information for clustering. This method is able to cluster when data appeared in overlapping and complex forms. The experimental results illustrate its performance and usefulness over other clustering methods. A Matlab package of this method (SIML) is provided at the web-link http://www.riken.jp/en/research/labs/ims/med_sci_math/ .

Phenotypic Variability of ANK2 Mutations in Patients With Inherited Primary Arrhythmia Syndromes.

BACKGROUND: Mutations inANK2have been reported to cause various arrhythmia phenotypes. The prevalence ofANK2mutation carriers in inherited primary arrhythmia syndrome (IPAS), however, remains unknown in Japanese. Using a next-generation sequencer, we aimed to identifyANK2mutations in our cohort of IPAS patients, in whom conventional Sanger sequencing failed to identify pathogenic mutations in major causative genes, and to assess the clinical characteristics ofANK2mutation carriers.Methods and Results:We screened 535 probands with IPAS and analyzed 46 genes including wholeANK2exons using a bench-top NGS (MiSeq, Illumina) or performed whole-exome-sequencing using HiSeq2000 (Illumina). As a result, 12 of 535 probands (2.2%, aged 0-61 years, 5 males) were found to carry 7 different heterozygousANK2mutations.ANK2-W1535R was identified in 5 LQTS patients and 1 symptomatic BrS and was predicted as damaging by multiple prediction software. In total, as to phenotype, there were 8 LQTS, 2 BrS, 1 IVF, and 1 SSS/AF. Surprisingly, 4/8 LQTS patients had the acquired type of LQTS (aLQTS) and suffered torsades de pointes. A total of 7 of 12 patients had documented malignant ventricular tachyarrhythmias. CONCLUSIONS: VariousANK2mutations are associated with a wide range of phenotypes, including aLQTS, especially with ventricular fibrillation, representing "ankyrin-B" syndrome. (Circ J 2016; 80: 2435-2442).

IRX4 at 5p15 suppresses prostate cancer growth through the interaction with vitamin D receptor, conferring prostate cancer susceptibility.

Recent genome-wide association studies (GWAS) identified a number of prostate cancer (PC) susceptibility loci, but most of their functional significances are not elucidated. Through our previous GWAS for PC in a Japanese population and subsequent resequencing and fine mapping, we here identified that IRX4 (Iroquois homeobox 4), coding Iroquois homeobox 4, is a causative gene of the PC susceptibility locus (rs12653946) at chromosome 5p15. IRX4 is expressed specifically in the prostate and heart, and quantitative expression analysis revealed a significant association between the genotype of rs12653946 and IRX4 expression in normal prostate tissues. Knockdown of IRX4 in PC cells enhanced their growth and IRX4 overexpression in PC cells suppressed their growth, indicating the functional association of IRX4 with PC and its tumor suppressive effect. Immunoprecipitation confirmed its protein-protein interaction to vitamin D receptor (VDR), and we found a significant interaction between IRX4 and VDR in their reciprocal transcriptional regulation. These findings indicate that the PC-susceptibility locus represented by rs12653946 at 5p15 is likely to regulate IRX4 expression in prostate which could suppress PC growth by interacting with the VDR pathway, conferring to PC susceptibility.

The HLA-DRB1*09:01-DQB1*03:03 haplotype is associated with the risk for late-onset Alzheimer's disease in APOE [Formula: see text]4-negative Japanese adults.

Late-onset Alzheimer's disease (LOAD) is the most common cause of dementia among those older than 65 years. The onset of LOAD is influenced by neuroinflammation. The human leukocyte antigen (HLA) system is involved in regulating inflammatory responses. Numerous HLA alleles and their haplotypes have shown varying associations with LOAD in diverse populations, yet their impact on the Japanese population remains to be elucidated. Here, we conducted a comprehensive investigation into the associations between LOAD and HLA alleles within the Japanese population. Using whole-genome sequencing (WGS) data from 303 LOAD patients and 1717 cognitively normal (CN) controls, we identified four-digit HLA class I alleles (A, B, and C) and class II alleles (DRB1, DQB1, and DPB1). We found a significant association between the HLA-DRB1*09:01-DQB1*03:03 haplotype and LOAD risk in APOE [Formula: see text]4-negative samples (odds ratio = 1.81, 95% confidence interval = 1.38-2.38, P = 2.03[Formula: see text]). These alleles not only showed distinctive frequencies specific to East Asians but demonstrated a high degree of linkage disequilibrium in APOE [Formula: see text]4-negative samples (r2 = 0.88). Because HLA class II molecules interact with T-cell receptors (TCRs), we explored potential disparities in the diversities of TCR α chain (TRA) and ß chain (TRB) repertoires between APOE [Formula: see text]4-negative LOAD and CN samples. Lower diversity of TRA repertoires was associated with LOAD in APOE [Formula: see text]4-negative samples, irrespective of the HLA DRB1*09:01-DQB1*03:03 haplotype. Our study enhances the understanding of the etiology of LOAD in the Japanese population and provides new insights into the underlying mechanisms of its pathogenesis.

Using functional signatures to identify repositioned drugs for breast, myelogenous leukemia and prostate cancer.

The cost and time to develop a drug continues to be a major barrier to widespread distribution of medication. Although the genomic revolution appears to have had little impact on this problem, and might even have exacerbated it because of the flood of additional and usually ineffective leads, the emergence of high throughput resources promises the possibility of rapid, reliable and systematic identification of approved drugs for originally unintended uses. In this paper we develop and apply a method for identifying such repositioned drug candidates against breast cancer, myelogenous leukemia and prostate cancer by looking for inverse correlations between the most perturbed gene expression levels in human cancer tissue and the most perturbed expression levels induced by bioactive compounds. The method uses variable gene signatures to identify bioactive compounds that modulate a given disease. This is in contrast to previous methods that use small and fixed signatures. This strategy is based on the observation that diseases stem from failed/modified cellular functions, irrespective of the particular genes that contribute to the function, i.e., this strategy targets the functional signatures for a given cancer. This function-based strategy broadens the search space for the effective drugs with an impressive hit rate. Among the 79, 94 and 88 candidate drugs for breast cancer, myelogenous leukemia and prostate cancer, 32%, 13% and 17% respectively are either FDA-approved/in-clinical-trial drugs, or drugs with suggestive literature evidences, with an FDR of 0.01. These findings indicate that the method presented here could lead to a substantial increase in efficiency in drug discovery and development, and has potential application for the personalized medicine.

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.

Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk factors for LOAD but not for LOAD subtypes. Here, we examined the genetic architecture of LOAD based on Japanese GWAS data from 1947 patients and 2192 cognitively normal controls in a discovery cohort and 847 patients and 2298 controls in an independent validation cohort. Two distinct groups of LOAD patients were identified. One was characterized by major risk genes for developing LOAD (APOC1 and APOC1P1) and immune-related genes (RELB and CBLC). The other was characterized by genes associated with kidney disorders (AXDND1, FBP1, and MIR2278). Subsequent analysis of albumin and hemoglobin values from routine blood test results suggested that impaired kidney function could lead to LOAD pathogenesis. We developed a prediction model for LOAD subtypes using a deep neural network, which achieved an accuracy of 0.694 (2870/4137) in the discovery cohort and 0.687 (2162/3145) in the validation cohort. These findings provide new insights into the pathogenic mechanisms of LOAD.

RNA-Sequencing Analysis Identification of Potential Biomarkers for Diagnosis of Sarcopenia.

Sarcopenia is a geriatric disease associated with increased mortality and disability. Early diagnosis and intervention are required to prevent it. This study investigated biomarkers for sarcopenia by using a combination of comprehensive clinical data and messenger RNA-sequencing (RNA-seq) analysis obtained from peripheral blood mononuclear cells. We enrolled a total of 114 older adults aged 66-94 years (52 sarcopenia diagnosed according to the Asian Working Group for Sarcopenia 2019 consensus and 62 normal older people). We used clinical data which were not included diagnosis criteria of sarcopenia, and stride length showed significance by logistic regression analysis (Bonferroni corrected p = .012, odds ratio = 0.14, 95% confidence interval [CI]: 0.05-0.40). RNA-seq analysis detected 6 differential expressed genes (FAR1, GNL2, HERC5, MRPL47, NUBP2, and S100A11). We also performed gene-set enrichment analysis and detected 2 functional modules (ie, hub genes, MYH9, and FLNA). By using any combination of the 9 candidates and basic information (age and sex), risk-prediction models were constructed. The best model by using a combination of stride length, HERC5, S100A11, and FLNA, achieved a high area under the curve (AUC) of 0.91 in a validation cohort (95% CI: 0.78-0.95). The quantitative PCR results of the 3 genes were consistent with the trend observed in the RNA-seq results. When BMI was added, the model achieved a high AUC of 0.95 (95% CI: 0.84-0.99). We have discovered potential biomarkers for the diagnosis of sarcopenia. Further refinement may lead to their future practical use in clinical use.

PathPred: an enzyme-catalyzed metabolic pathway prediction server.

The KEGG RPAIR database is a collection of biochemical structure transformation patterns, called RDM patterns, and chemical structure alignments of substrate-product pairs (reactant pairs) in all known enzyme-catalyzed reactions taken from the Enzyme Nomenclature and the KEGG PATHWAY database. Here, we present PathPred (http://www.genome.jp/tools/pathpred/), a web-based server to predict plausible pathways of muti-step reactions starting from a query compound, based on the local RDM pattern match and the global chemical structure alignment against the reactant pair library. In this server, we focus on predicting pathways for microbial biodegradation of environmental compounds and biosynthesis of plant secondary metabolites, which correspond to characteristic RDM patterns in 947 and 1397 reactant pairs, respectively. The server provides transformed compounds and reference transformation patterns in each predicted reaction, and displays all predicted multi-step reaction pathways in a tree-shaped graph.

Identification of potential blood biomarkers for early diagnosis of Alzheimer's disease through immune landscape analysis.

Mild cognitive impairment (MCI) is a clinical precursor of Alzheimer's disease (AD). Recent genetic studies have reported on associations between AD risk genes and immunity. Here, we obtained samples and data from 317 AD, 432 MCI, and 107 cognitively normal (CN) subjects and investigated immune-cell type composition and immune clonal diversity of T-cell receptor (TRA, TRB, TRG, and TRD) and B-cell receptor (IGH, IGK, and IGL) repertoires through bulk RNA sequencing. We found the proportions of plasma cells, γδ T cells, neutrophils, and B cells were significantly different and the diversities of IGH, IGK, and TRA were significantly small with AD progression. We then identified a differentially expressed gene, WDR37, in terms of risk of MCI-to-AD conversion. Our prognosis prediction model using the potential blood-based biomarkers for early AD diagnosis, which combined two immune repertoires (IGK and TRA), WDR37, and clinical information, successfully classified MCI patients into two groups, low and high, in terms of risk of MCI-to-AD conversion (log-rank test P = 2.57e-3). It achieved a concordance index of 0.694 in a discovery cohort and of 0.643 in an independent validation cohort. We believe that further investigation, using larger sample sizes, will lead to practical clinical use in the near future.

Network-based analysis and characterization of adverse drug-drug interactions.

Co-administration of multiple drugs may cause adverse effects, which are usually known but sometimes unknown. Package inserts of prescription drugs are supposed to contain contraindications and warnings on adverse interactions, but such information is not necessarily complete. Therefore, it is becoming more important to provide health professionals with a comprehensive view on drug-drug interactions among all the drugs in use as well as a computational method to identify potential interactions, which may also be of practical value in society. Here we extracted 1,306,565 known drug-drug interactions from all the package inserts of prescription drugs marketed in Japan. They were reduced to 45,180 interactions involving 1352 drugs (active ingredients) identified by the D numbers in the KEGG DRUG database, of which 14,441 interactions involving 735 drugs were linked to the same drug-metabolizing enzymes and/or overlapping drug targets. The interactions with overlapping targets were further classified into three types: acting on the same target, acting on different but similar targets in the same protein family, and acting on different targets belonging to the same pathway. For the rest of the extracted interaction data, we attempted to characterize interaction patterns in terms of the drug groups defined by the Anatomical Therapeutic Chemical (ATC) classification system, where the high-resolution network at the D number level is progressively reduced to a low-resolution global network. Based on this study we have developed a drug-drug interaction retrieval system in the KEGG DRUG database, which may be used for both searching against known drug-drug interactions and predicting potential interactions.

Dementia subtype prediction models constructed by penalized regression methods for multiclass classification using serum microRNA expression data.

There are many subtypes of dementia, and identification of diagnostic biomarkers that are minimally-invasive, low-cost, and efficient is desired. Circulating microRNAs (miRNAs) have recently gained attention as easily accessible and non-invasive biomarkers. We conducted a comprehensive miRNA expression analysis of serum samples from 1348 Japanese dementia patients, composed of four subtypes-Alzheimer's disease (AD), vascular dementia, dementia with Lewy bodies (DLB), and normal pressure hydrocephalus-and 246 control subjects. We used this data to construct dementia subtype prediction models based on penalized regression models with the multiclass classification. We constructed a final prediction model using 46 miRNAs, which classified dementia patients from an independent validation set into four subtypes of dementia. Network analysis of miRNA target genes revealed important hub genes, SRC and CHD3, associated with the AD pathogenesis. Moreover, MCU and CASP3, which are known to be associated with DLB pathogenesis, were identified from our DLB-specific target genes. Our study demonstrates the potential of blood-based biomarkers for use in dementia-subtype prediction models. We believe that further investigation using larger sample sizes will contribute to the accurate classification of subtypes of dementia.