シアノバクテリアSynechocystis sp.PCC 6803が有するNADキナーゼの生理機能

学位記号番号 : 博理工甲第1151号博士の専攻分野の名称 : 博士(工学) 学位授与年月日 : 令和元年9月20日textapplication/pdfthesi

Feasibility of gel-like radiopaque embolic material using gelatin sponge and contrast agent for tract embolization after percutaneous treatment

PLoS ONE. 2023, 18 (2), e0281384journal articl

Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets

PLoS ONE. 2022, 17 (3), e0265751journal articl

Efficacy of rechallenge transcatheter arterial chemoembolization after lenvatinib treatment for advanced hepatocellular carcinoma

JGH Open. 2022, 6 (11), P.754-762journal articl

Observation of the DsJ(2317) and DsJ(2457) in B Decays

journal articl

Formation of Peer group in Which Children Enjoy Expression and Share it : Through Course Planning to Foster Children Who Enjoy Their Life (Education Practice in the Course of a Junior High Department of Tottori University school for Children with Special Needs)

departmental bulletin pape

LIE DERIVATIVES ON HOMOGENEOUS REAL HYPERSURFACES OF TYPE B IN A COMPLEX PROJECTIVE SPACE

application/pdfThe purpose of this paper is to give some characterizations of homogeneous real hypersurfaces of type B in a complex projective space P_{n}(C) in terms of Lie derivative.departmental bulletin pape

Table_2_Integrative Analysis Identifies Genetic Variants Associated With Autoimmune Diseases Affecting Putative MicroRNA Binding Sites.XLSX

Genome-wide and fine mapping studies have shown that more than 90% of genetic variants associated with autoimmune diseases (AID) are located in non-coding regions of the human genome and especially in regulatory sequences, including microRNAs (miRNA) target sites. MiRNAs are small endogenous noncoding RNAs that modulate gene expression at the post-transcriptional level. Single nucleotide polymorphisms (SNPs) located within the 3′ untranslated region of their target mRNAs (miRSNP) can alter miRNA binding sites. Yet, little is known about their effect on regulation by miRNA and the consequences for AID. Conversely, it is well known that two or more AID may share part of their genetic background. Here, we hypothesized that miRSNPs could be associated with more than one AID. To identify miRSNPs associated with AID, we integrated results from three different prediction tools (Polymirts, miRSNP, and miRSNPscore) using a naïve Bayes classifier approach to identify miRSNPs predicted to affect binding sites of miRNAs. Further, to detect miRSNPs associated with two or more AID, we integrated predictions with summary statistics from 12 AID studies. In addition, to prioritize miRSNPs, miRNAs and AID-associated target genes, we used public expression quantitative trait locus (eQTL) data and mRNA-seq and small RNA-seq data. We identified 34 miRNSPs associated with at least two AID. Furthermore, we found 86 miRNAs predicted to target 18 of the associated gene's mRNAs. Our integrative approach revealed new insights into miRNAs and AID associated target genes. Thus, it helped to prioritize AID noncoding risk SNPs that might be involved in the causal mechanisms, providing valuable information for further functional studies.</p

Experimental validation of potential SPEDF and FOXA1 modulators.

<p>Proliferation of ZR751 and HB2 cells was assayed following transfection with siRNA directed against modulators of SPDEF and FOXA1, identified by MINDy analysis. ZR751 cells are dependent on SPDEF and FOXA1 for proliferation whereas HB2 cells do not require these TFs for growth. The plot shows cell proliferation, represented as % cell confluence at the time-point when cell confluence plateaus in the non-targeting siRNA control treatment (NTC), measured using an IncuCyte imaging system (only a single time-point is represented in the plot). The proliferation screen, which was statistically analysed using the <b>compareGrowthCurves</b> command in the <i>statmod</i> package in <i>R</i> (<a href="http://CRAN.R-project.org/package=statmod" target="_blank">http://CRAN.R-project.org/package=statmod</a>), with multiple testing correction carried out using the Benjamini-Hochberg method (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168770#pone.0168770.s006" target="_blank">S3 Table</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168770#pone.0168770.s003" target="_blank">S3 Fig</a>), identified a number of modulators that have a consistent cell type-specific effect (55 out of 189 modulators), and these are highlighted in red on the x-axis. Control treatments are highlighted in blue on the x-axis. The dashed lines show maximum percent cell confluence achieved with the NTC treatment for ZR751 and HB2 cells. CTL: untransfected control; NTC: non-targeting siRNA control.</p

Table_1_Integrative Analysis Identifies Genetic Variants Associated With Autoimmune Diseases Affecting Putative MicroRNA Binding Sites.XLSX

Genome-wide and fine mapping studies have shown that more than 90% of genetic variants associated with autoimmune diseases (AID) are located in non-coding regions of the human genome and especially in regulatory sequences, including microRNAs (miRNA) target sites. MiRNAs are small endogenous noncoding RNAs that modulate gene expression at the post-transcriptional level. Single nucleotide polymorphisms (SNPs) located within the 3′ untranslated region of their target mRNAs (miRSNP) can alter miRNA binding sites. Yet, little is known about their effect on regulation by miRNA and the consequences for AID. Conversely, it is well known that two or more AID may share part of their genetic background. Here, we hypothesized that miRSNPs could be associated with more than one AID. To identify miRSNPs associated with AID, we integrated results from three different prediction tools (Polymirts, miRSNP, and miRSNPscore) using a naïve Bayes classifier approach to identify miRSNPs predicted to affect binding sites of miRNAs. Further, to detect miRSNPs associated with two or more AID, we integrated predictions with summary statistics from 12 AID studies. In addition, to prioritize miRSNPs, miRNAs and AID-associated target genes, we used public expression quantitative trait locus (eQTL) data and mRNA-seq and small RNA-seq data. We identified 34 miRNSPs associated with at least two AID. Furthermore, we found 86 miRNAs predicted to target 18 of the associated gene's mRNAs. Our integrative approach revealed new insights into miRNAs and AID associated target genes. Thus, it helped to prioritize AID noncoding risk SNPs that might be involved in the causal mechanisms, providing valuable information for further functional studies.</p