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Yajima, M.

Publications and source records attributed to Yajima, M..

13 recordsLinked to original sources

Defining mutational signatures of lung cancer-associated carcinogens through in vitro exposure of human airway epithelial cells

While distinct environmental exposures imprint unique mutational signatures on cancer genomes, the specific causal patterns for many known carcinogens remain uncharacterized in relevant human tissues. To address this gap, we developed a novel, physiologically relevant system that uses a combination of airway epithelial cells and whole genome sequencing to characterize mutational patterns induced by genotoxic carcinogens associated with lung cancer. After validating the platforms accuracy by successfully recapturing the known signature for Benzo(a)pyrene (BaP), we used this system to gain detailed insights into the types of mutations that occur with exposure to N-nitrosotris-(2-chloroethyl) urea (NTCU) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), genotoxic compounds that induce lung squamous cell carcinoma and lung adenocarcinoma in mouse models, respectively. Cells exposed to NTCU had significantly more somatic SNVs compared to control samples. An average of 82.3% of mutations in NTCU samples were attributed to a novel mutational signature distinct from those in the COSMIC database but highly correlated with recent in vivo mouse models. In contrast, NNK exposure did not demonstrate a distinct mutational pattern above background at both high and low concentrations. Ultimately, this in vitro system provides a robust platform to define causal links between environmental exposures and mutational patterns in lung cancer mutagenesis. Statement of SignificanceIn vitro exposure of N-nitrosotris-(2-chloroethyl) urea to airway epithelial cells revealed a distinct mutational signature.

bioinformatics↗

Phenological benchmarking with a land surface model: an in-silico experiment for temperate forests

O_LIPlant phenology affects biotic interactions, water, carbon and nutrient cycling, ultimately influencing global climate, and making understanding and quantifying phenological dynamics central for ecological forecasting. This however varies depending on data source, smoothing methodology or date-extraction method. The selection of an appropriate phenological processing pipeline for the system of study thus requires accurate benchmarking in a controlled environment. C_LIO_LIIn this study we use a Land Surface Model (LSM) to simulate ecosystem dynamics at four temperate forest sites under five lesser studied climate perturbations. We use the generated synthetic dataset to control for environmental conditions while testing commonly used phenological detection methods (relative thresholds at 10%, 20% and 50%, highest rate of change, detection of inflection points in the curvatures angle, detection of recovery and senescence points). C_LIO_LIAmongst tested methods, we find general agreement in terms of response to climate alterations. Inflection and low thresholds (10%) methods provide the closest match to the models predicted start of season, with a maximum discrepancy of 4 days between the two. Higher thresholds (20%, 50%) are closer to the models predicted end of season, although with discrepancies of up to 10 days. In addition, high thresholds and methods based on the rate of change of vegetation parameters successfully detect the peak of season. We also show how some methods can pick up on effects that are ignored by temperature-only type growing seasons, thereby supporting the need for increased realism of phenological representations in land surface models. C_LIO_LIThis is, to our knowledge, the first time an LSM is used to generate an in silico experiment. This study not only tests phenological extraction methodologies within a common framework, but demonstrates how land surface models can be used as tools for ecological testing beyond their current use as predictive tools. C_LI

ecology↗

An atlas of gene programs in non-small cell lung cancer delineates novel patterns of lineage vacillation, plasticity, and aggressiveness

Combinations of molecular programs can be altered in cancer cells and contribute to tumor initiation and aggressiveness. We developed a novel framework called a Gene Program Association Study (GPAS) to create an atlas of gene co-expression modules in cancer cells from 127 primary non-small-cell-lung cancers (NSCLCs) and determine their association with clinical and histopathological features. Lung adenocarcinoma (LUAD) displayed "lineage vacillation" defined by extensive heterogeneity of modules from different alveolar cell types. Modules associated with late-stage LUAD were found in cells from early-stage tumors suggesting that aggressive phenotypes can be observed before clinical progression. Basal- and squamous-like intermediate cells were observed in the transition to invasive mucinous LUAD. In lung squamous cell carcinoma, a novel subtype was identified with lower levels of canonical squamous modules and higher levels of fibrinogen. Overall, this atlas elucidated the combinations of molecular programs in cancer cells that contribute to heterogeneity and aggressiveness in NSCLC. Statement of SignificanceWe performed a Gene Program Association Study (GPAS) to create an atlas of gene modules in cancer cells from NSCLCs. This atlas revealed extensive lineage vacillation, combinations of modules that defined late-stage disease, novel routes of lineage plasticity, and novel subtypes in lung adenocarcinoma or lung squamous cell carcinoma.

bioinformatics↗

The evolutionary modifications of a GoLoco motif in the AGS protein facilitate micromere formation in the sea urchin embryo

The evolutionary introduction of asymmetric cell division (ACD) into the developmental program facilitates the formation of a new cell type, contributing to developmental diversity and, eventually, to species diversification. The micromere of the sea urchin embryo may serve as one of those examples: An ACD at the 16-cell stage forms micromeres unique to echinoids among echinoderms. We previously reported that a polarity factor, Activator of G-protein Signaling (AGS), plays a crucial role in micromere formation. However, AGS and its associated ACD factors are present in all echinoderms and across most metazoans. This raises the question of what evolutionary modifications of AGS protein or its surrounding molecular environment contributed to the evolutionary acquisition of micromeres only in echinoids. In this study, we learned that the GoLoco motifs at the AGS C-terminus play critical roles in regulating micromere formation in sea urchin embryos. Further, other echinoderms AGS or chimeric AGS that contain the C-terminus of AGS orthologs from various organisms showed varied localization and function in micromere formation. In contrast, the sea star or the pencil urchin orthologs of other ACD factors were consistently localized at the vegetal cortex in the sea urchin embryo, suggesting that AGS may be a unique variable factor that facilitates ACD diversity among echinoderms. Consistently, sea urchin AGS appears to facilitate micromere-like cell formation and accelerate the enrichment timing of the germline factor Vasa during early embryogenesis of the pencil urchin, an ancestral type of sea urchin. Based on these observations, we propose that the molecular evolution of a single polarity factor facilitates ACD diversity while preserving the core ACD machinery among echinoderms and beyond during evolution. HighlightsO_LIEvolutionary modifications of GoLoco motifs are critical for AGS function in micromere formation in the sea urchin embryo. C_LIO_LIThe chimeric AGS, which contains the C-terminus of AGS orthologs from various organisms, suggests that human LGN, pencil urchin AGS, and Drosophila Pins compensate for the activity of sea urchin AGS. C_LIO_LISea urchin AGS (SpAGS) regulates the localization of the conserved asymmetric cell division (ACD) machinery members at the vegetal cortex. C_LIO_LISpAGS is a variable factor facilitating ACD diversity during species diversification. C_LI

developmental biology↗

Decontamination of ambient and margin noise in droplet-based single cell protein expression data with DecontPro

Assays such as CITE-seq can measure the abundance of cell surface proteins on individual cells using antibody derived tags (ADTs). However, many ADTs have high levels of background noise that can obfuscate down-stream analyses. Using an exploratory analysis of PBMC datasets, we find that some droplets that were originally called "empty" due to low levels of RNA contained high levels of ADTs and likely corresponded to neutrophils. We identified a novel type of artifact in the empty droplets called a "spongelet" which has medium levels of ADT expression and is distinct from ambient noise. ADT expression levels in the spongelets correlate to ADT expression levels in the background peak of true cells in several datasets suggesting that they can contribute to background noise along with ambient ADTs. We then developed DecontPro, a novel Bayesian hierarchical model that can decontaminate ADT data by estimating and removing contamination from these sources. DecontPro outperforms other decontamination tools in removing aberrantly expressed ADTs while retaining native ADTs and in improving clustering specificity. Overall, these results suggest that identification of empty drops should be performed separately for RNA and ADT data and that DecontPro can be incorporated into CITE-seq workflows to improve the quality of downstream analyses.

bioinformatics↗

Interactive analysis of single-cell data using flexible workflows with SCTK2.0

Analysis of single-cell RNA-seq (scRNA-seq) data can reveal novel insights into heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analysis. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single Cell Toolkit 2.0 (SCTK2.0), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/499900v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@2a3964org.highwire.dtl.DTLVardef@1debe9org.highwire.dtl.DTLVardef@6b3a2aorg.highwire.dtl.DTLVardef@1b7bf84_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIntuitive graphical user interface for interactive analysis of scRNA-seq data C_LIO_LIAllows non-computational users to analyze scRNA-seq data with end-to-end workflows C_LIO_LIProvides interoperability between tools across different programming environments C_LIO_LIProduces HTML reports for reproducibility and easy sharing of results C_LI

bioinformatics↗

The germline factor DDX4 contributes to the chemoresistance of small cell lung cancer cells

Human cancers often re-express germline factors, yet their mechanistic role in oncogenesis and cancer progression remains unknown. Here we demonstrate that DDX4, a germline factor and RNA helicase conserved in all multicellular organisms, contributes to epithelial mesenchyme transition (EMT)-like features and cisplatin resistance in small cell lung cancer (SCLC) cells. DDX4 depletion in H69AR and SHP77 cell lines decreased motility and resistance to cisplatin, whereas its overexpression increased these features. Proteomic analysis suggests that DDX4 upregulates metabolic protein expression related to DNA repair and immune/inflammatory response, suggesting its fundamental function may be in regulating cellular metabolism. Consistent with these trends in cell lines, DDX4 depletion compromised in vivo tumor development while its overexpression enhanced tumor growth even after cisplatin treatment in nude mice. Although the DDX4 expression level in somatic tumors is generally low compared to that in the germline, the relatively higher DDX4 expression in SCLC patients correlates with decreased survival and shows increased expression of EMT and cisplatin resistance markers. Taken together, we conclude that DDX4 influences the survival of SCLC patients by altering cellular metabolism in response to environmental cues such as drug treatments. This fundamental function of DDX4 as a germline factor might be applicable in other cancer types that express DDX4 and may serve as a key to combat specific tumors that are highly resistant to treatments. HighlightsO_LIDDX4 contributes to cellular motility and drug resistance in SCLC cells. C_LIO_LIDDX4-overexpression globally alters the proteome and suppresses cytokine production. C_LIO_LIDDX4 promotes tumorigenesis and drug resistance in vitro and in vivo. C_LIO_LIDDX4 expression correlates with survival in SCLC patients and with immune/inflammatory response both in cell lines and patient samples. C_LI

cancer biology↗

The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the discovery, prediction, and exploration of mutational signatures

Mutational signatures are patterns of somatic alterations in the genome caused by carcinogenic exposures or aberrant cellular processes. To provide a comprehensive workflow for preprocessing, analysis, and visualization of mutational signatures we created the Mutational Signature Comprehensive Analysis Toolkit (musicatk) package. musicatk enables users to select different schemas for counting mutation types and easily combine count tables from different schemas. Multiple distinct methods are available to deconvolute signatures and exposures or to predict exposures in individual samples given a pre-existing set of signatures. Additional exploratory features include the ability to compare signatures to the COSMIC database, embed tumors in two dimensions with UMAP, cluster tumors into subgroups based on exposure frequencies, identify differentially active exposures between tumor subgroups and plot exposure distributions across user-defined annotations such as tumor type. Overall, musicatk will enable users to gain novel insights into the patterns of mutational signature observed in cancer cohorts.

bioinformatics↗

Celda: A Bayesian model to perform bi-clustering of genes into modules and cells into subpopulations using single-cell RNA-seq data

Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform simultaneous co-clustering of genes into transcriptional modules and cells into subpopulations. Celda can quantify the probabilistic contribution of each gene to each module, each module to each cell population, and each cell population to each sample. We used Celda to identify transcriptional modules and cell subpopulations in a publicly available peripheral blood mononuclear cell (PBMC) dataset. Celda identified a population of proliferating T cells and a single plasma cell which were missed by two other clustering methods. Celda identified transcriptional modules that highlighted unique and shared biological programs across cell types. Celda also outperformed a PCA-based approach for gene clustering on simulated data. Overall, Celda presents a novel statistically principled approach towards characterizing transcriptional programs and cellular heterogeneity in single-cell RNA-seq data.

bioinformatics↗

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, a variety of technical artifacts can be present in scRNA-seq data and need to be assessed before downstream analyses can be performed. While several algorithms and tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Comprehensive pipelines to streamline the process of generating and visualizing QC metrics are lacking. To address this need, we built the SCTK-QC pipeline within the singleCellTK R package (https://github.com/compbiomed/singleCellTK). Features in this pipeline include the ability to import data from 11 different preprocessing tools or file formats, perform empty droplet detection with 2 different algorithms, generate standard quality control metrics such as number of UMIs per cell or the percentage of mitochondrial counts, predict doublets using 6 different algorithms, and estimate ambient RNA. QC data can be exported to R and/or Python objects used in popular down-stream workflows. Results are visualized in an easy-to-read HTML report. This pipeline can also be used by non-computational users with an interactive graphical user interface developed with R/Shiny. Overall, the SCTK-QC pipeline will streamline and standardize QC analysis for scRNA-seq data across a variety of different single-cell transcriptomic platforms and preprocessing tools.

bioinformatics↗

Decontamination of ambient RNA in single-cell RNA-seq with DecontX

Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq) and discover novel cellular heterogeneity in complex biological systems. However, ambient RNA present in the cell suspension can be incorporated into these droplets and aberrantly counted along with a cells native mRNA. This results in cross-contamination of transcripts between different cell populations and can potentially decrease the precision of downstream analyses. We developed a novel hierarchical Bayesian method called DecontX to estimate and remove contamination in individual cells from scRNA-seq data. DecontX accurately predicted the proportion of contaminated counts in a mixture of mouse and human cells. Decontamination of PBMC datasets removed aberrant expression of cell type specific marker genes from other cell types and improved overall separation of cell clusters. In general, DecontX can be incorporated into scRNA-seq workflows to assess quality of dissociation protocols and improve downstream analyses.

bioinformatics↗

Protein kinase C is a key target for attenuation of Leigh syndrome by rapamycin

Leigh syndrome is a fatal neurometabolic disorder caused by defects in mitochondrial function. mTOR inhibition with rapamycin attenuates disease progression in a mouse model of Leigh syndrome (Ndufs4 KO mouse); however, the mechanism of rescue is unknown. Here we assessed the impact of rapamycin on the brain proteome and phosphoproteome of Ndufs4 KO mice. We report that rapamycin remodels the brain proteome to alter mitochondrial structure, inhibits signaling through both mTOR complexes, and inhibits multiple protein kinase C (PKC) isoforms. Administration of PKC inhibitors was sufficient to increase survival, delay neurological deficits, and prevent hair loss in Ndufs4 KO mice. Thus, PKC may be a viable therapeutic target for treating severe mitochondrial disease. One Sentence SummaryProteomic and phosphoproteomic analysis of mouse brain identifies PKC as a key target to treat mitochondrial disease

neuroscience↗

Interactive single cell RNA-Seq analysis with the Single Cell Toolkit (SCTK)

Single cell RNA-sequencing (scRNA-Seq) allows researchers to profile transcriptional activity in individual cells. However, the complex nature of these data and variability in study design and data generation requires sophisticated computational tools and informed analytical decisions. Here, we present the Single Cell Toolkit (SCTK), an interactive scRNA-Seq analysis package that enables users to perform scRNA-Seq analysis interactively using a command-line workflow or a graphical user interface (GUI) written in R/Shiny.

bioinformatics↗