Biology subjects

Shoji, T.

Publications and source records attributed to Shoji, T..

13 recordsLinked to original sources

bioRxiv2026

A Pseudo-Longitudinal Methylome Projection Framework Defines a Buccal PACE-like Aging-Rate Score from Cross-Sectional DNA Methylation Data

BackgroundDNA methylation-based biomarkers have enabled robust estimation of biological age across tissues, and longitudinally trained measures such as DunedinPACE provide estimates of the pace of aging from blood methylomes. However, longitudinal methylation data are often unavailable, particularly for minimally invasive tissues such as buccal mucosa. Here, we developed a pseudo-longitudinal framework to estimate a buccal mucosa-derived PACE-like aging-rate score from cross-sectional methylome data. MethodsWe used a buccal biological age estimator as an internal pseudo-time axis. Methylation beta-values were transformed to M-values, and CpG-specific smooth functions of biological age were fitted in cross-validation. Local derivatives of these functions were used to project each individuals buccal methylome forward by a small time step. The projected methylome was converted back to beta-values, biological age was recalculated, and the change in biological age per unit time was defined as a pseudo-aging velocity. This raw velocity was transformed to a non-negative PACE-like score centered at 1.0. We then trained cross-fitted models to predict the derived score from buccal CpG methylation profiles. ResultsIn 151 individuals, the proposed score was reproducibly predicted from buccal methylomes in out-of-fold analysis, with a Pearson correlation of 0.706 and Spearman correlation of 0.710 between observed and predicted PACE-like scores. Sensitivity analyses across CpG selection size and regression models showed broadly consistent performance. In contrast, the proposed buccal PACE-like score showed only modest association with measured DunedinPACE, and alternative attempts to reconstruct DunedinPACE from buccal methylomes, including supervised proxy modeling and buccal-to-blood CpG imputation, showed limited sample-level performance. ConclusionsThese results support the feasibility of deriving a tissue-specific PACE-like aging-rate score from cross-sectional buccal methylome data by treating biological age as a pseudo-time axis. The proposed score should not be interpreted as a replacement for blood-derived DunedinPACE, but rather as an exploratory buccal methylome dynamics index that may capture tissue-specific aging-related variation.

bioinformatics↗

bioRxiv2026

Predicting Biological Age and Clinical Biomarkers from DNA Methylation Profiles of Cheek Mucosa

BackgroundDNA methylation-based biomarkers have been widely used to predict biological age; however, most blood-derived data have been used in most existing models, and whether cheek mucosa can serve as an alternative indicator for methylation-based estimation of aging-related and clinical phenotypes is unclear. MethodsDNA methylation profiles from cheek mucosa and whole blood of 186 Japanese adults were analyzed using Illumina Infinium Methylation Screening Array (MSA). Models were constructed to predict chronological age, phenotypic age, and clinical laboratory biomarkers from cheek mucosa- and blood-derived methylation data. In addition to applying the ordinary elastic net method, a two-stage residual learning method incorporating existing blood-based epigenetic clocks was applied for more accurate prediction of biological age. Sex-stratified analyses and comparisons of selected CpG features across sexes and tissues were performed. ResultsCheek mucosa-derived MSA methylation data enabled accurate prediction of chronological age (R = 0.965) and phenotypic age (R = 0.964) using the two-stage method. The performance gain achieved by the two-stage approach was greater for phenotypic age than for chronological age. Multiple clinical laboratory biomarkers could be predicted using cheek mucosa-derived methylation data, particularly after sex stratification, including inflammatory, metabolic, thyroid-related, and sex hormone-related markers. Most biomarkers that could be predicted using blood-derived methylation data were also predicted using cheek mucosa-derived methylation data. However, the CpG sites selected for prediction showed minimal overlap across sexes and tissues despite overlap in the corresponding predictable phenotypes. ConclusionsCheek mucosa-derived DNA methylation profiles measured using the MSA can predict chronological age, phenotypic age, and multiple clinically relevant laboratory biomarkers, supporting the utility of cheek mucosa as a less invasive alternative for methylation-based assessment of biological aging and systemic physiological state.

bioinformatics↗

bioRxiv2026

Dynamical instability measured by temporal entropy improves psychiatric classification across cohorts

Psychiatric disorders, such as attention-deficit/hyperactivity disorder, autism spectrum disorder, and schizophrenia, are clinically heterogeneous and lack objective biomarkers for reliable diagnosis. Although blood transcriptomic data have been proposed as a potential source of diagnostic information, their generalizability across independent cohorts remains unclear. This study aimed to assess whether biologically informed measures of dynamic instability enhance the reproducibility and generalizability of psychiatric classifications based on peripheral blood data by integrating publicly available blood transcriptomic datasets from multiple cohorts and evaluating classification performance using individual-level cross-validation and study-level holdout validation. To investigate the underlying biological structure, we applied a dynamic systems framework, including pseudotime-based vector field inference and attractor analysis. Additionally, we introduced temporal entropy as a measure of dynamic instability in the inferred transcriptomic trajectories. High classification performance was observed in individual-level cross-validation (area under the receiver operating characteristic [AUROC] > 0.8 across several comparisons); however, performance decreased substantially in study-level validation (AUROC {approx} 0.5-0.7), indicating limited generalizability. Attractor analysis revealed that transcriptomic states formed continuous and overlapping structures rather than distinct diagnostic clusters. Stratification based on temporal entropy identified a subset of individuals with unstable transcriptomic dynamics, and excluding these individuals improved the classification performance across most diagnostic pairs (AUROC > 0.7). These findings suggest that transcriptomic variability and dynamic instability contribute to the limited reproducibility of psychiatric classifications. Incorporating temporal entropy as a measure of system-level instability may enhance the robustness and interpretability of biomarker-based models and provide a new perspective on psychiatric disorders as dynamic systems.

bioinformatics↗

bioRxiv2026

Prediction of Biological Age and Blood Biomarkers from DNA Methylation Profiles Measured by the Methylation Screening Array: Development and Validation of Models on Japanese Data

BackgroundEpigenetic clocks based on DNA methylation (DNAm) are widely used indicators of biological aging; however, most established models have been developed using EPIC arrays and non-Japanese populations. The Methylation Screening Array (MSA), a cost-efficient platform with reduced CpG content, has not been evaluated for its capacity to support biological age estimation and biomarker prediction in Japanese cohorts. MethodsDNAm profiles and clinical laboratory measurements were obtained from 166 Japanese participants for model development; an independent cohort of 48 individuals processed at a separate institute was used for validation. A linear regression model was trained using the Elastic Net method to predict phenotypic age from MSA-derived methylation data, and a two-stage modeling (residual learning) framework integrating EPIC-based clock predictions with MSA-specific residual predictions was evaluated. Additional models were constructed to examine the predictability of 59 clinical biomarkers and their log-transformed variants, including sex-stratified analyses. ResultsThe MSA-based model accurately predicted phenotypic age in the validation dataset; prediction performance improved when the EPIC-based estimates were incorporated through the residual learning framework. Several clinical biomarkers, particularly those related to leukocyte composition and sex hormone regulation, were also predicted from the MSA data, although some markers were strongly affected by sex. Some of the nine constituent phenotypic age biomarkers were not individually predicted. ConclusionsMSA methylation profiles contain sufficient biological information for reliable prediction of epigenetic aging markers in Japanese individuals. These findings demonstrate the feasibility of applying cost-efficient MSA-based DNAm profiling for biological age prediction and provide a methodological foundation for expanding epigenetic biomarker applications in Japan.

bioinformatics↗

bioRxiv2025

Horizontal acquisition of nicotine catabolism gene cluster drives the assembly of tobacco root microbiota community

BackgroundPlant roots are hotspots for interactions with soil microbes, where a characteristic bacterial community structure is formed. Plant specialized metabolites often play pivotal roles in this assembly process. However, the molecular basis underlying root microbiota responses to these bioactive compounds, and how such metabolic interactions shape the assembly of host-specific root microbiota, remain largely unknown. Nicotine is a toxic alkaloid predominantly produced by the genus Nicotiana, and the genus Arthrobacter is known as one of the nicotine-degrading bacteria in the tobacco root microbiota. In this study, we used the tobacco-Arthrobacter interaction system as a model and integrated comparative genomics and experimental genetic manipulation assays to uncover the role of bacterial catabolism capacity for host specialized metabolites in shaping host-specific root microbiota. ResultsNicotine catabolism genes are uniquely found in the Arthrobacter strains derived from nicotine-containing environments, and this restricted gene distribution is driven by a plasmid-mediated horizontal gene transfer. To assess the ecological consequences of this genomic adaptation in Arthrobacter fitness in tobacco roots, we conducted adaptation assays under both in vitro and in planta conditions using genetically manipulated Arthrobacter and tobacco mutants, which are impaired in nicotine catabolism and biosynthesis, respectively. Nicotine improves Arthrobacter colonization to the tobacco roots through both catabolism-dependent and catabolism-independent mechanisms. Bacterial community analysis using a synthetic community approach further demonstrated that these metabolic interactions, mediated by tobacco nicotine biosynthesis and its catabolism by Arthrobacter, jointly affect root microbiota composition. ConclusionsOur findings illustrated that bacterial catabolic capacity toward host-derived plant specialized metabolites is key for successful root colonization. This metabolic adaptation is driven by plasmid-mediated horizontal gene transfer and ultimately shapes the structure of the overall root microbiota community.

microbiology↗

bioRxiv2025

Cross-Platform Calibration of Epigenetic Age Between the EPICv2 and MSA Arrays

Epigenetic clocks are regression models that predict biological age based on the DNA methylation patterns. They are developed using DNA methylation array data; however, systematic differences in the predicted values between the Infinium Methylation Screening Array (MSA) and Infinium MethylationEPIC v2.0 BeadChip (EPICv2) platforms have not been evaluated. We quantified the systematic differences between the MSA and EPICv2 arrays for six major epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge, GrimAge v2, and DunedinPACE) using 166 human blood samples measured on both platforms. For the five clocks expressed in years, the mean differences (MSA - EPICv2) ranged from -7.67 years for the Horvath clock to -0.634 years for GrimAge v2, despite strong correlations between platforms (r = 0.946-0.991). For DunedinPACE, which predicts the pace of aging, the mean difference was -0.117, with a correlation coefficient of 0.855 between the platforms. Based on these training data, we developed three linear regression models to map the MSA-based epigenetic ages and pace of aging to the EPICv2-based values, which were used as a reference: Model 1 (offset location calibration), Model 2 (slope and location calibration), and Model 3 (slope and location calibration with covariates). Validation using 48 independent samples measured at separate institutions showed that the calibration models reduced cross-platform differences for Horvath, Hannum, PhenoAge, GrimAge, and DunedinPACE. Although Model 3 yielded mean differences closest to zero for the Horvath clock (-0.227 years) and PhenoAge (-0.510 years), Model 1, the simplest specification, also reduced the mean cross-platform differences to -1.03 years for the Horvath clock and -1.37 years for PhenoAge. For DunedinPACE, Model 1 reduced the mean difference from -0.092 to 0.025 and the RMSE from 0.103 to 0.052. These methods are expected to enable reliable epigenetic age comparisons across platforms and facilitate large-scale epigenetic studies.

bioinformatics↗

bioRxiv2024

Comparative genomic analysis of multidrug-resistant clinical isolates of Mycobacterium abscessus revealed worldwide horizontal plasmid transfer

Genetic epidemiological analysis of mobile genetic elements, such as plasmids, has rarely been carried out in Mycobacterium abscessus, regardless of its usefulness in speculating the past contact between bacteria. In this study, through whole genome sequencing analysis of clinical isolates of M. abscessus sequentially collected from the same patient, we identified a single nucleotide variant that could cause antibiotic resistance, and three plasmids (pMAB625-1, pMAB625-2 and pMAB625-3) not found in the type strain. We investigated the distribution of plasmids previously identified in M. abscessus, including pMAB625 plasmids, in 462 clinical isolates worldwide in silico. pMAB625-3 was detected in the largest number of isolates. Furthermore, phylogenetic tree analysis revealed that these plasmids transferred beyond regions and subspecies and acquired unique mutations. These results indicate that transmission of plasmids increases the genomic diversity in M. abscessus, and plasmid epidemiology is useful for estimating the past contact between bacteria.

genomics↗

bioRxiv2024

A PI(3,5)P2/ESCRT-III axis terminates STING signalling by facilitating TSG101-mediated lysosomal microautophagy

Stimulator of interferon genes (STING) is critical for the type I interferon response to pathogen- or self-derived cytosolic DNA. STING is degraded by the endosomal sorting complexes required for transport (ESCRT)-driven lysosomal microautophagy (LMA), the impairment of which leads to sustained inflammatory responses. It has been unknown how ESCRT targets STING directly to lysosomes. Here, through kinase inhibitor screening and knockdown experiments of all the individual components of ESCRT, we show that STING degradation requires PIKfyve (a lipid kinase that generates PI(3,5)P2) and CHMP4B/C (components of ESCRT-III subcomplex). Knockdown of Pikfyve or Chmp4b/c results in the accumulation of STING vesicles of a recycling endosomal origin in the cytosol, leading to sustained type I interferon response. CHMP4B/C localize at lysosomes and their lysosomal localization is abolished by interference with PIKfyve activity. Our results identify lysosomal ESCRT-III as a PI(3,5)P2 effector, reveal a role of the less characterized phosphoinositide PI(3,5)P2 in lysosomal biology, and provide insights into the molecular framework that distinguishes LMA from other cellular processes engaged with ESCRT.

cell biology↗

bioRxiv2022

A jasmonate-responsive ERF transcription factor regulates steroidal glycoalkaloid biosynthesis genes in eggplant

Steroidal glycoalkaloids (SGAs) are a class of cholesterol-derived anti-nutritional defense compounds that are produced in species of the genus Solanum, such as tomato (S. lycopersicum), potato (S. tuberosum), and eggplant (S. melongena). However, the regulation of defense-related metabolites in eggplant remains underexplored. In tomato and potato, the JASMONATE-RESPONSIVE ETHYLENE RESPONSE FACTOR 4 (JRE4) transcription factor positively regulates a large number of genes involved in SGA biosynthesis. Here, we report that overexpression of eggplant JRE4 (SmJRE4) induces numerous metabolic genes involved in SGA biosynthesis in leaves. We demonstrate jasmonate-dependent induction of SmJRE4 and its downstream metabolic genes and show that ethylene treatment attenuates this induction. Our findings thus provide molecular insights into SGA biosynthesis and its regulation in this major crop.

plant biology↗

bioRxiv2021

q-mer analysis: a generalized method for analyzing RNA-Seq data.

RNA-Seq data are usually summarized by counting the number of transcript reads aligned to each gene. However, count-based methods do not take alignment information, where and how each read was mapped in the gene, into account. This information is essential to characterize samples accurately. In this study, we developed a method to summarize RNA-Seq data without losing alignment information. To include alignment information, we introduce "q-mer analysis," which summarizes RNA-Seq data with 4q kinds of q-length oligomers. Using publicly available RNA-Seq datasets, we demonstrate that at least q [greater double equals] 9 is required for capturing alignment information in Homo sapiens. Furthermore, principal component analysis showed that q-mer analysis with q = 14 linearly distinguished samples from controls, while a count-based method failed. These results indicate that alignment information is essential to characterize transcriptomics samples. In conclusion, we introduce q-mer analysis to include alignment information in RNA-Seq analysis and demonstrate the superiority of q-mer analysis over count-based methods in that q-mer analysis can distinguish case samples from controls. Combining RNA-Seq research with q-mer analysis could be useful for identifying distinguishing transcriptomic features that could provide hypotheses for disease mechanisms.

bioinformatics↗

bioRxiv2021

Methylation of 23S rRNA G748 and the ribosomal protein L22 Lys-94 are critical factors for maintaining the association between ribosome stalling and proteome composition in Streptococcus pneumoniae

Background23S rRNA modification located at the nascent peptides exit tunnel plays an important role in both translation processes and the binding of the antibiotics. Methylation of the guanine at position 748 (m1G748) in 23S rRNA in Streptococcus pneumoniae is involved in the ribosome stalling and the binding of the antibiotic telithromycin (TEL). The disruption of the gene encoding RlmAII which methylates 23S rRNA G748 results in the increased resistance of TEL in S. pneumoniae. However, an isolated high-level TEL-resistant S. pneumoniae strain indicated that additional undescribed factors were involved in TEL resistance in S. pneumoniae. ResultsWe successfully isolated a high-level TEL-resistant S. pneumoniae RlmAII mutant and determined the whole-genome sequence. The lysine residue at the position 94 in ribosomal protein 22 (L22 K94) was critical in binding of TEL to the ribosome. A growth competition assay showed that L22 K94 was required for the function of m1G748. Ribosome profiling revealed that m1G748 and L22 K94 were both essential to maintain the relationship between the ribosome stalling and proteome composition. ConclusionIn S. pneumoniae, the combination of methylation status of G748 and the residue at position 94 in L22 are essential for both the distribution of ribosome stalling and the binding of TEL to ribosomes.

microbiology↗

bioRxiv2020

Ribosome profiling in Streptococcus pneumoniae reveals the role of methylation of 23S rRNA nucleotide G748 on ribosome stalling.

2.(1) BackgroundMany nucleotides in 23S rRNA are methylated post-transcriptionally by methyltransferases and cluster around the peptidyltransferase center (PTC) and the nascent peptidyl exit tunnel (NPET) located in 50S subunit of 70S ribosome. Biochemical interactions between a nascent peptide and the tunnel may stall ribosome movement and affect expression levels of the protein. However, no studies have shown a role for NPET on ribosome stalling using an NPET mutant. (2) ResultsA ribosome profiling assay in Streptococcus pneumoniae demonstrates for the first time that an NPET mutant exhibits completely different ribosome occupancy compared to wild-type. We demonstrate, using RNA footprinting, that changes in ribosome occupancy correlate with changes in ribosome stalling. Further, statistical analysis shows that short peptide sequences that cause ribosome stalling are species-specific and evolutionarily selected. NPET structure is required to realize these specie-specific ribosome stalling. (3) ConclusionsResults support the role of NPET on ribosome stalling. NPET structure is required to realize the species-specific and evolutionary conserved ribosome stalling. These findings clarify the role of NPET structure on the translation process.

genomics↗

bioRxiv2017

Propofol induces metabolic reprogramming and cell death in a mitochondrial electron transport chain-dependent manner

The intravenous anesthetic propofol (2,6-diisopropylphenol) has been used for the induction and maintenance of anesthesia in operating rooms and for sedation in intensive care units. Although there is no widely accepted definition of propofol infusion syndrome (PRIS), PRIS is defined as the development of metabolic acidosis, rhabdomyolysis, hyperkalemia, hepatomegaly, renal failure, arrhythmia, and progressive cardiac failure. In vitro evidence suggests that PRIS is related to the impaired mitochondrial function. There are indications that preexisting mitochondrial disorders predispose to PRIS. However, the precise molecular mechanisms, including mitochondrial defects and a metabolic conversion by propofol, are largely unknown as yet. To elucidate the underlying cellular and molecular mechanisms of PRIS, we investigated the effects of propofol on the cellular metabolic mode and cell death. We demonstrated that clinically relevant concentrations of propofol, used within a clinically relevant exposure time, suppressed the mitochondrial function, caused the generation of reactive oxygen species, and induced a metabolic switch, from oxidative phosphorylation to glycolysis, by targeting complexes I and III of mitochondria. The data also indicated that a predisposition to mitochondrial dysfunction, caused by a genetic mutation or pharmacological suppression of the electron transport chain by biguanides such as metformin and phenformin, promoted the cell death and caspase activation induced by propofol.

pharmacology and toxicology↗