立位における上肢運動時の腰部多裂筋深層線維および浅層線維の筋反応時間の検討

Based on the current literature, it remains unclear whether electromyographic onset of the deep fibers of the multifidus (DM) is dependent on the direction of shoulder movement and the position of the center of foot pressure (CFP). In the present study, we re-examined the electromyographic onset of the DM during shoulder flexion and extension and investigated the influence of the CFP position before arm movement. Intramuscular and surface electrodes recorded the electromyographic onset of the DM, superficial fibers of the multifidus (SM), rectus abdominis, and anterior and posterior deltoid. Eleven healthy participants performed rapid, unilateral shoulder flexion and extension in response to audio stimuli at three CFP positions: quiet standing, extreme forward leaning, and extreme backward leaning. It was found that the electromyographic onset of the DM and SM relative to the deltoid was dependent on the direction of arm movement. Additionally, of all electromyographic onsets recorded, only that of the DM occurred earlier in the extreme forward leaning position than in the extreme backward leaning position during shoulder flexion. These results suggest that the electromyographic onset of DM was influenced by the biomechanical disturbance such as shoulder movement and CFP position.首都大学東京, 2015-09-30, 博士（理学療法学）, 甲第619号首都大学東

Integrated genetic and epigenetic analysis defines novel molecular subgroups in rhabdomyosarcoma.

横紋筋肉腫におけるゲノム・エピゲノム異常の全体図を解明 -横紋筋肉腫を4群に分類-. 京都大学プレスリリース. 2015-07-03.Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma in childhood. Here we studied 60 RMSs using whole-exome/-transcriptome sequencing, copy number (CN) and DNA methylome analyses to unravel the genetic/epigenetic basis of RMS. On the basis of methylation patterns, RMS is clustered into four distinct subtypes, which exhibits remarkable correlation with mutation/CN profiles, histological phenotypes and clinical behaviours. A1 and A2 subtypes, especially A1, largely correspond to alveolar histology with frequent PAX3/7 fusions and alterations in cell cycle regulators. In contrast, mostly showing embryonal histology, both E1 and E2 subtypes are characterized by high frequency of CN alterations and/or allelic imbalances, FGFR4/RAS/AKT pathway mutations and PTEN mutations/methylation and in E2, also by p53 inactivation. Despite the better prognosis of embryonal RMS, patients in the E2 are likely to have a poor prognosis. Our results highlight the close relationships of the methylation status and gene mutations with the biological behaviour in RMS

Recursive regularization for inferring gene networks from time-course gene expression profiles

<p>Abstract</p> <p>Background</p> <p>Inferring gene networks from time-course microarray experiments with vector autoregressive (VAR) model is the process of identifying functional associations between genes through multivariate time series. This problem can be cast as a variable selection problem in Statistics. One of the promising methods for variable selection is the elastic net proposed by Zou and Hastie (2005). However, VAR modeling with the elastic net succeeds in increasing the number of true positives while it also results in increasing the number of false positives.</p> <p>Results</p> <p>By incorporating relative importance of the VAR coefficients into the elastic net, we propose a new class of regularization, called recursive elastic net, to increase the capability of the elastic net and estimate gene networks based on the VAR model. The recursive elastic net can reduce the number of false positives gradually by updating the importance. Numerical simulations and comparisons demonstrate that the proposed method succeeds in reducing the number of false positives drastically while keeping the high number of true positives in the network inference and achieves two or more times higher true discovery rate (the proportion of true positives among the selected edges) than the competing methods even when the number of time points is small. We also compared our method with various reverse-engineering algorithms on experimental data of MCF-7 breast cancer cells stimulated with two ErbB ligands, EGF and HRG.</p> <p>Conclusion</p> <p>The recursive elastic net is a powerful tool for inferring gene networks from time-course gene expression profiles.</p

Elevated β-catenin pathway as a novel target for patients with resistance to EGF receptor targeting drugs

There is a high death rate of lung cancer patients. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are effective in some lung adenocarcinoma patients with EGFR mutations. However, a significant number of patients show primary and acquire resistance to EGFR-TKIs. Although the Akt kinase is commonly activated due to various resistance mechanisms, the key targets of Akt remain unclear. Here, we show that the Akt-β-catenin pathway may be a common resistance mechanism. We analyzed gene expression profiles of gefitinib-resistant PC9M2 cells that were derived from gefitinib-sensitive lung cancer PC9 cells and do not have known resistance mechanisms including EGFR mutation T790M. We found increased expression of Axin, a β-catenin target gene, increased phosphorylation of Akt and GSK3, accumulation of β-catenin in the cytoplasm/nucleus in PC9M2 cells. Both knockdown of β-catenin and treatment with a β-catenin inhibitor at least partially restored gefitinib sensitivity to PC9M2 cells. Lung adenocarcinoma tissues derived from gefitinib-resistant patients displayed a tendency to accumulate β-catenin in the cytoplasm. We provide a rationale for combination therapy that includes targeting of the Akt-β-catenin pathway to improve the efficacy of EGFR-TKIs

FXYD3 functionally demarcates an ancestral breast cancer stem cell subpopulation with features of drug-tolerant persisters

乳がんの再発を起こす原因細胞を解明. 京都大学プレスリリース. 2023-11-16.The heterogeneity of cancer stem cells (CSCs) within tumors presents a challenge in therapeutic targeting. To decipher the cellular plasticity that fuels phenotypic heterogeneity, we undertook single-cell transcriptomics analysis in triple-negative breast cancer (TNBC) to identify subpopulations in CSCs. We found a subpopulation of CSCs with ancestral features that is marked by FXYD domain–containing ion transport regulator 3 (FXYD3), a component of the Na⁺/K⁺ pump. Accordingly, FXYD3⁺ CSCs evolve and proliferate, while displaying traits of alveolar progenitors that are normally induced during pregnancy. Clinically, FXYD3⁺ CSCs were persistent during neoadjuvant chemotherapy, hence linking them to drug-tolerant persisters (DTPs) and identifying them as crucial therapeutic targets. Importantly, FXYD3⁺ CSCs were sensitive to senolytic Na⁺/K⁺ pump inhibitors, such as cardiac glycosides. Together, our data indicate that FXYD3⁺ CSCs with ancestral features are drivers of plasticity and chemoresistance in TNBC. Targeting the Na⁺/K⁺ pump could be an effective strategy to eliminate CSCs with ancestral and DTP features that could improve TNBC prognosis

A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition

Patient-specific analysis of molecular networks is a promising strategy for making individual risk predictions and treatment decisions in cancer therapy. Although systems biology allows the gene network of a cell to be reconstructed from clinical gene expression data, traditional methods, such as Bayesian networks, only provide an averaged network for all samples. Therefore, these methods cannot reveal patient-specific differences in molecular networks during cancer progression. In this study, we developed a novel statistical method called NetworkProfiler, which infers patient-specific gene regulatory networks for a specific clinical characteristic, such as cancer progression, from gene expression data of cancer patients. We applied NetworkProfiler to microarray gene expression data from 762 cancer cell lines and extracted the system changes that were related to the epithelial-mesenchymal transition (EMT). Out of 1732 possible regulators of E-cadherin, a cell adhesion molecule that modulates the EMT, NetworkProfiler, identified 25 candidate regulators, of which about half have been experimentally verified in the literature. In addition, we used NetworkProfiler to predict EMT-dependent master regulators that enhanced cell adhesion, migration, invasion, and metastasis. In order to further evaluate the performance of NetworkProfiler, we selected Krueppel-like factor 5 (KLF5) from a list of the remaining candidate regulators of E-cadherin and conducted in vitro validation experiments. As a result, we found that knockdown of KLF5 by siRNA significantly decreased E-cadherin expression and induced morphological changes characteristic of EMT. In addition, in vitro experiments of a novel candidate EMT-related microRNA, miR-100, confirmed the involvement of miR-100 in several EMT-related aspects, which was consistent with the predictions obtained by NetworkProfiler

A plastidial sodium-dependent pyruvate transporter (vol 476, pg 472, 2011)

Furumoto T, Yamaguchi T, Ohshima-Ichie Y, et al. A plastidial sodium-dependent pyruvate transporter (vol 476, pg 472, 2011). Nature. 2011;478(7368):274

A latent allocation model for the analysis of microbial composition and disease

Abstract Background Establishing the relationship between microbiota and specific diseases is important but requires appropriate statistical methodology. A specialized feature of microbiome count data is the presence of a large number of zeros, which makes it difficult to analyze in case-control studies. Most existing approaches either add a small number called a pseudo-count or use probability models such as the multinomial and Dirichlet-multinomial distributions to explain the excess zero counts, which may produce unnecessary biases and impose a correlation structure taht is unsuitable for microbiome data. Results The purpose of this article is to develop a new probabilistic model, called BERnoulli and MUltinomial Distribution-based latent Allocation (BERMUDA), to address these problems. BERMUDA enables us to describe the differences in bacteria composition and a certain disease among samples. We also provide a simple and efficient learning procedure for the proposed model using an annealing EM algorithm. Conclusion We illustrate the performance of the proposed method both through both the simulation and real data analysis. BERMUDA is implemented with R and is available from GitHub (https://github.com/abikoushi/Bermuda)

Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks.

[Background]As a result of recent advances in biotechnology, many findings related to intracellular systems have been published, e.g., transcription factor (TF) information. Although we can reproduce biological systems by incorporating such findings and describing their dynamics as mathematical equations, simulation results can be inconsistent with data from biological observations if there are inaccurate or unknown parts in the constructed system. For the completion of such systems, relationships among genes have been inferred through several computational approaches, which typically apply several abstractions, e.g., linearization, to handle the heavy computational cost in evaluating biological systems. However, since these approximations can generate false regulations, computational methods that can infer regulatory relationships based on less abstract models incorporating existing knowledge have been strongly required. [Results]We propose a new data assimilation algorithm that utilizes a simple nonlinear regulatory model and a state space representation to infer gene regulatory networks (GRNs) using time-course observation data. For the estimation of the hidden state variables and the parameter values, we developed a novel method termed a higher moment ensemble particle filter (HMEnPF) that can retain first four moments of the conditional distributions through filtering steps. Starting from the original model, e.g., derived from the literature, the proposed algorithm can sequentially evaluate candidate models, which are generated by partially changing the current best model, to find the model that can best predict the data. For the performance evaluation, we generated six synthetic data based on two real biological networks and evaluated effectiveness of the proposed algorithm by improving the networks inferred by previous methods. We then applied time-course observation data of rat skeletal muscle stimulated with corticosteroid. Since a corticosteroid pharmacogenomic pathway, its kinetic/dynamics and TF candidate genes have been partially elucidated, we incorporated these findings and inferred an extended pathway of rat pharmacogenomics. [Conclusions]Through the simulation study, the proposed algorithm outperformed previous methods and successfully improved the regulatory structure inferred by the previous methods. Furthermore, the proposed algorithm could extend a corticosteroid related pathway, which has been partially elucidated, with incorporating several information sources

Theoretical Computational Analysis Predicts Interaction Changes Due to Differences of a Single Molecule in DNA

Theoretical methods, such as molecular mechanics and molecular dynamics, are very useful in understanding differences in interactions at the single molecule level. In the life sciences, small conformational changes, including substituent modifications, often have a significant impact on function in vivo. Changes in binding interactions between nucleic acid molecules and binding proteins are a prime example. In this study, we propose a strategy to predict the complex structure of DNA-binding proteins with arbitrary DNA and analyze the differences in their interactions. We tested the utility of our strategy using the anticancer drug trifluoro-thymidine (FTD), which exerts its pharmacological effect by incorporation into DNA, and confirmed that the binding affinity of the BCL-2-associated X sequence to the p53 tetramer is increased by FTD incorporation. On the contrary, in p53-binding sequences extracted from FTD-resistant cells, the binding affinity of DNA containing FTD was found to be greatly reduced compared to normal DNA. This suggests that thymidine randomly substituted for FTD in resistant cells may acquire resistance by entering a position that inhibits binding to DNA-binding proteins. We believe that this is a versatile procedure that can also take energetics into account and will increase the importance of computational science in the life sciences