Biology subjects

Iwai, S.

Publications and source records attributed to Iwai, S..

4 recordsLinked to original sources

bioRxiv2026

Translesion DNA synthesis on pyrimidine dimers by Plant organellar DNA polymerases is metal-dependent

Ultraviolet (UV) radiation generates crosslinked DNA lesions--primarily cyclobutane pyrimidine dimers (CPDs) and [6-4] photoproducts ([6-4] PPs)--that block the progression of replicative DNA polymerases. In plants, these lesions are efficiently removed from nuclear DNA by dedicated repair pathways; however, comparable repair mechanisms are absent in plastids and mitochondria. Consequently, how plant organellar DNA polymerases (POPs) tolerate or bypass UV-induced damage has remained unclear. Here, we show that the two Arabidopsis thaliana organellar polymerases, AtPolIs, possess robust translesion synthesis (TLS) activity across CPDs. Although wild-type enzymes display only limited extension across [6-4] PPs, removal of their exonuclease function dramatically enhances bypass, yielding an efficiency of replication across the [6-4] PP that closely resembles that observed on an undamaged template. This establishes AtPolI as the first known replicative DNA polymerase capable of efficiently bypassing a [6-4] PP. We further demonstrate that TLS across UV photoproducts relies on three unique amino acid insertions within the AtPolI polymerase domain, as deletion of any single insertion abolishes TLS. Notably, Mn{superscript 2} can restore TLS activity in these variants, but only for CPD lesions. Together, these findings identify AtPolIs as the first plant organellar replicases with intrinsic [6-4] PP bypass capability and define the structural features that enable this function. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/701875v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@f497bcorg.highwire.dtl.DTLVardef@15ba2eorg.highwire.dtl.DTLVardef@724e29org.highwire.dtl.DTLVardef@697c8b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2022

Topic modeling for multi-omic integration in the human gut microbiome and implications for autism

While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial pro-cesses, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed "cross-omic topics", which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.

bioinformatics↗

bioRxiv2021

Eukaryotic stress induced mutagenesis is limited by a local control of Translesion Synthesis

The DNA Damage Response (DDR) preserves the genetic integrity of the cell by sensing and repairing damages after a genotoxic stress. Translesion Synthesis (TLS), an error-prone DNA damage tolerance pathway, is controlled by PCNA ubiquitination. In this report, we raise the question whether TLS is controlled locally, or globally. Using a recently developed method that allows to follow the bypass of a single lesion inserted into the yeast genome, we show that: i) TLS is controlled locally at each individual lesion by PCNA ubiquitination, ii) a single lesion is enough to induce PCNA ubiquitination, and iii) PCNA ubiquitination is an imperative requirement for TLS to occur. More importantly, we show that global PCNA ubiquitination that follows a genotoxic stress does not increase TLS at individual lesions. We conclude that unlike the SOS response in bacteria, the eukaryotic DDR does not promote TLS and mutagenesis.

molecular biology↗

bioRxiv2018

Prediction of prostate cancer by deep learning with multilayer artificial neural network.

ObjectivesTo predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning utilizing a multilayer artificial neural network was investigated.\n\nMaterials and methodsA total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography-guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis were input into the constructed multilayer artificial neural network (ANN) programs. 232 patients were used as training cases of ANN programs, and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model.\n\nResultsWith any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and 9 explanatory variables from 22, respectively. In common between them, age at biopsy, findings on digital rectal examination, findings in the peripheral zone on MRI diffusion-weighted imaging, and body mass index were positively influential variables, while numbers of previous prostatic biopsy and prostate volume were negatively influential. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5-10% higher compared with that with logistic regression analysis (LR). The AUCs with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise logistic regression compared with the AUC with LR. The ANN had a higher net-benefit than LR between prostate cancer probability cut-off values of 0.38 and 0.6.\n\nConclusionANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.

bioinformatics↗