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opencc-zeroMay 2022View details →

Unraveling the roles of genotype and environment in the expression of plant defense phenotypes

1. Phenotypic variability results from interactions between genotype and environment and is a major driver of ecological and evolutionary interactions. Measuring the relative contributions of genetic variation, the environment, and their interaction to phenotypic variation remains a fundamental goal of evolutionary ecology. 2. In this study, we assess the question: How do genetic variation and local environmental conditions interact to influence phenotype within a single population? We explored this question using seed from a single population of common milkweed, Asclepias syriaca, in northern Michigan. We first measured resistance and resistance traits of 14 maternal lines in two common garden experiments (field and greenhouse) to detect genetic variation within the population. We carried out a reciprocal transplant experiment with three of these maternal lines to assess effects of local environment on phenotype. Finally, we compared the phenotypic traits measured in our experiments with the phenotypic traits of the naturally-growing maternal genets to be able to compare relative effect of genetic and environmental variation on naturally-occurring phenotypic variation. We measured defoliation levels, arthropod abundances, foliar cardenolide concentrations, foliar latex exudation, foliar carbon and nitrogen concentrations, and plant growth. 3. We found a striking lack of correlation in trait expression of the maternal lines between the common gardens, or between the common gardens and the naturally-growing maternal genets, suggesting that environment plays a larger role in phenotypic trait variation of this population. We found evidence of significant genotype-by-environment interactions for all traits except foliar concentrations of nitrogen and cardenolide. Milkweed resistance to chewing herbivores was associated more strongly with the growing environment. We observed no variation in foliar cardenolide concentrations among maternal lines but did observe variation among maternal lines in foliar latex exudation. 4. Overall, our data reveal powerful genotype-by-environment interactions on the expression of most resistance traits in milkweed.

Data from: Phylogenetic distribution and expression pattern analyses identified a divergent basal body assembly protein involved in land plant spermatogenesis

<pre>Data from: Phylogenetic distribution and expression pattern analyses identified a divergent basal body assembly protein involved in land plant spermatogenesis Author information Shizuka Koshimizu1, Naoki Minamino2, Tomoaki Nishiyama3, Emiko Yoro4, Mayuko Sato5, Mayumi Wakazaki5, Kiminori Toyooka5, Kazuo Ebine2,6, Keiko Sakakibara4, Takashi Ueda2,6, and Kentaro Yano1 1 School of Agriculture, Meiji University, Kawasaki 214-8571, Japan 2 Division of Cellular Dynamics, National Institute for Basic Biology, Okazaki 444-8585, Japan 3 Research Center for Experimental Modeling of Human Disease, Kanazawa University, Kanazawa 920-0934, Japan 4 Department of Life Science, Rikkyo University, Tokyo 171‐8501, Japan 5 RIKEN Center for Sustainable Resource Science, Yokohama 230-0045, Japan 6 Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies), Okazaki 444-8585, Japan This directory contains 0_README (this file) 1_Marchantia_FL_data 2_Physcomitrella_FL_data 3_Physcomitrella_TEM_images 4_PAML 1_Marchantia_FL_data Raw image data using quantification of spermatid phenotypes in Marchantia. quantification.xlsx: Summary of quantification data. 211111 and 211210: Observation date. ∟Mpbld10-1, Mpbld10-2, and Tak-1: Observed lines. ∟raw data: *.lsm files are raw image data. ∟binary image: *.tif files are binarization images of the Hoechst33342 data. ∟DIC: *.tif files are maximum intensity projection images of the C2 channels (detection of DIC images) of the raw data. ∟Hoechst33342: *.tif files are maximum intensity projection images of the C1 channels (detection of Hoechst33342 signals) of the raw data. 2_Physcomitrella_FL_data Merged data of DIC and Hoechst33342 signal images using quantification of spermatid phenotypes in Physcomitrella. line22-*.png: The images of Ppbld10-22 mutant. line30-*.png: The images of Ppbld10-30 mutant. wt.png: The images of wild type. Number in the images 1: With flagella 2: Without flagella 3_Physcomitrella_TEM_images TEM images of spermatids in the Ppbld10-30 mutant, except for images shown in Supporting Information Fig. S11. 4_PAML Files using analysis by PALM. input.fasta: Input multi fasta file. species_tree.nwk: The gene tree file for the global clock model (rooted using chlorophytes as an outgroup). species_tree_marked.nwk: A gene tree file with marks specifying land plant stem and crown branches as category #1 (for a local clock model). clock1: A directory containing output files of a PAML run with the global clock model (clock = 1) clock2: A directory containing output files of a PAML run with a local clock model (clock = 2). The branches assumed to have a different rate (r1) than the default rate r0 are specified with #1 in species_tree_marked.nwk.</pre>

Additional data for "Tissue-specific impacts of aging and genetics on gene expression patterns in humans"

Additional files for paper "Tissue-specific impacts of aging and genetics on gene expression patterns in humans" by Yamamoto and Chung et al. 2021. Contains file of results from joint age and genetics model.

Robustness of organ morphology is associated with modules of co-expressed genes related to plant cell wall

Reproducibility in organ size and shape is a fundamental trait of living organisms. The mechanisms underlying such robustness remain, however, to be elucidated. In the manuscript <a href="https://www.biorxiv.org/content/10.1101/2022.04.26.489498v1">"Robustness of organ morphology is associated with modules of co-expressed genes related to plant cell wall", doi: https://doi.org/10.1101/2022.04.26.489498</a>, we took the sepal of Arabidopsis as a model, and we investigated whether variability of gene expression plays a role in variation of organ morphology. To address this question, we produced a dataset composed of both transcriptomic and morphological information obtained from 27 individual sepals from wild-type plants. This repository contains the raw confocal image of 30 sepals used as starting point for the analysis, as well as their extracted contours as binary images. These images were used to recover the 3D shape of the sepals. The 30 abaxial sepals were collected at early stage 11, from three different Col-0 wild-type plants, labeled D, E and F, grown simultaneously in experimentally controlled standard conditions. Each sepal was imaged under a confocal microscope using autofluorescence. Immediately following imaging, the sepal was frozen in liquid nitrogen for RNA extraction, on which an RNA-seq analysis was performed. Related informations : <ul> <li>The repository of the numerical tools used for 3D shape extraction as well as the results of geometrical measurements is <a href="http://forge.cbp.ens-lyon.fr/redmine/projects/florivar">here</a>.</li> <li>The repository of RNA-Seq analysis results of these same sepals is here.</li> <li>And the analysis tools used to relate geometrical measurements to RNA-seq data are here.</li> </ul>

Differential expression analysis to aluminum toxicity in Citrus x limonia Osbeck

Here we deliver the Differential gene expression  on genes response to aluminum toxicity in Citrus x limonia. Root apices of ‘Mandarin’ lime plants grown for 60 days in nutrient solutions either with 1480 mM Al3+ or 0 mM Al3+ were analyzed by RNA-seq. Clean reads were mapped to the sweet orange (Citrus sinensis) genome (Xu et al. 2013). Gene expression levels were calculated by CPM (Counts per million) reads. We used HTSeq ver. 0.6.1 (Anders et al. 2015) CPM estimation. The differentially expressed genes (DEGs) here reported by NOIseq ver. 2.16.0 (Tarazona et al. 2016).    Results: Number of differentially expressed (DE) features (Probability > 0.7): 3,351 Up-regulated (M > 0): 1,664 Down-regulated (M < 0): 1,687 All software were run on OmicsBox interface. References: Anders S., Pyl PT. and Huber W. (2015). HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics (Oxford, England), 31(2), 166-9. OmicsBox - Bioinformatics made easy. BioBam Bioinformatics (Version 2.0.36). March 3, 2019. www.biobam.com/omicsbox. Tarazona S., Furio-Tari P., Turra D., Pietro AD., Nueda MJ., Ferrer A. and Conesa A. (2015). Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic acids research, 43(21), e140. Xu Q, Chen L-L, Ruan X, et al (2013) The draft genome of sweet orange (Citrus sinensis). Nat Genet 45:59–66.   Legend:  Regulation - UP or DOWN = differentially expressed genes, UPregulated or DOWNregulated Citrus_40_Al_2 -  Normalized CPM for root apexes under  1480 mM Al3+  Citrus_0_Al_1 - Normalized CPM for root apexes under  0 mM Al3+

openmit-licenseFeb 2022View details →

Single-cell datasets for temporal gene expression integration

Contains loom files and preprocessed adata objects to compare methods for temporal gene expression integration. Loom files can be accessed using the 'read' function in Scvelo. Preprocessed adata objects can be accessed using the 'read_h5ad' function in Scanpy. The raw single-cell RNA sequencing datasets can be found under the following accession codes. <ul> <li> Mouse embryonic cell cycle dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/nbt.3102">10.1038/nbt.3102</a>) was originally downloaded from ArrayExpress with the accession code E-MTAB-2805 </li> <li> Hematopoiesis differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/10.1182/blood-2016-05-716480</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE81682 </li> <li> NKT cell differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/ni.3437">10.1038/ni.3437</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE74596. </li> <li> Hematopoiesis differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org</a>/<a href="https://doi.org/10.1038/nature19348">10.1038/nature19348</a>) was originally downloaded from the Gene Expression Omnibus with the accession codes GSE70236, GSE70240, GSE70244 </li> <li> LPS stimulation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1016/j.cels.2017.03.010">10.1016/j.cels.2017.03.010</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE94383. </li> <li> INF-gamma stimulation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41587-020-00803-5">10.1038/s41587-020-00803-5</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE161465. </li> <li> AML chemotherapy dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41591-018-0233-1">10.1038/s41591-018-0233-1</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE116481. </li> <li> AML diagnosis/relapse dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41375-021-01338-7">10.1038/s41375-021-01338-7</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE126068. </li> <li> MS case control PBMC and CSF datasets from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41467-019-14118-w">10.1038/s41467-019-14118-w</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE138266. </li> </ul>

Quantification of transgene expression in GSH AAVS1 with a novel CRISPR/Cas9 based approach reveals high transcriptional variation

Inderbitzin, Loosli and colleagues employ a novel method to construct and extensively characterize DNA barcode libraries and apply CRISPR/Cas9 technology for targeted insertion into the safe harbor gene AAVS1 in Jurkat cells. This technique revealed high fluctuations in gene expression in AAVS1, spanning over two logs.

opencc-by-4.0Jun 2022View details →

Dataset from Microbial bile salt hydrolase activity influences gene expression profiles and gastrointestinal maturation in infant mice

Files from Intestinal Alkaline Phosphatase, Ki-67, and apoptosis (TUNEL) from colon samples. RT-qPCR dCT values of genes analyzed in colon, small intestine, liver and colon organoids

opencc-by-4.0Jul 2022View details →

Soil ecotoxicology needs robust biomarkers – a meta-analysis approach to test the robustness of gene expression-based biomarkers for measuring chemical exposure effects in soil invertebrates

Gene expression-based biomarkers are regularly proposed  as rapid, sensitive and mechanistically informative tools to identify whether soil invertebrates are experiencing adverse effects due to chemical exposure. However, before biomarkers could be deployed within diagnostic studies, systematic evidence of the robustness of such biomarkers to detect effects  is needed. Here, we present an approach for conducting a systematic meta-analysis of the robustness of gene expression-based biomarkers in soil invertebrates. The approach was developed and trialled for two measurements of gene expression commonly proposed as biomarkers in soil ecotoxicology: metallothionein (MT) gene expression in earthworms for metals and heat shock protein 70 (HSP70) gene expression in earthworms for organic chemicals. From a systematic analysis of the published literature, we collected 294 unique gene expression data points and used linear mixed-effect models to assess concentration, exposure duration and species effects on the quantified response. This database provided contains gene-expression data from publications that have used gene expression-based biomakers to study effects of chemical pollutants on soil invertebrates. R scripts are provided that were used to study the patterns of gene expression as reported in accompanying publication.  We encourage colleagues in the field to apply this approach to other biomarkers, as such quantitative assessment is a prerequisite to ensuring that the suitability and limitations of proposed biomarkers are known and stated.

opencc-zeroJun 2022View details →

The expression of demographic costs of reproduction varies among coexisting plants with different life history traits

1. Demographic costs of reproduction in flowering plants should depend on life history and reproductive effort, but how the expression of costs varies with life history traits is poorly understood. 2. We experimentally increased and reduced reproductive effort (fruit production) to quantify demographic costs of reproduction in four coexisting species with contrasting growth forms (clonal vs. nonclonal) and flower production (single- vs. multi-flowered). We repeated the experiment in three years, and measured demographic rates the year after treatment. In two years, we also quantified costs of flower maintenance by contrasting the performance of nonfruiting plants with intact flowers and plants with their flowers removed. 3. Costs varied among species, in both magnitude and demographic rate affected. Costs of natural reproduction were expressed as reductions in size and fecundity next year, whereas increased reproduction additionally reduced sprouting probability. The magnitude of demographic costs of both reproduction and flower maintenance was highest in the nonclonal, multi-flowered species, and costs were more frequently detected in the two multi-flowered species than in the single-flowered ones. This may be explained by higher biomass allocation to reproductive parts and a longer flowering period in the former. Demographic costs of reproduction did not depend on clone size. 4. These results document that demographic costs vary among coexisting species sharing similar niches, and are associated with divergence in life history traits. Such trait-dependent variation in costs may reduce competition among coexisting species and facilitate diversity.

opencc-zeroJun 2022View details →

Evolutionary targets of gene expression divergence in a complex of closely related pine species

The environment is a powerful selective pressure for sessile organisms, such as plants, and adaptation to the environment is particularly important for long-lived species, like trees. Despite the importance of adaptive trait variation to the survival and success of trees, the molecular basis of adaptation is still poorly understood. Gene expression patterns in three closely related, but phenotypically and ecologically divergent, pine species were analyzed to detect differentiation that may be associated with their adaptation to distinct environments. Total RNA of Pinus mugo, P. uncinata and P. sylvestris samples grown under common garden conditions was used for de novo transcriptome assembly, providing a new reference dataset that includes species from the taxonomically challenging Pinus mugo complex. Gene expression profiles were found to be very similar with only 121 genes significantly diverged in any of the pairwise species comparisons. Functional annotation of these genes revealed major categories of distinctly expressed transcripts including: wood trait properties, oxidative stress response, and response to abiotic factors such as salinity, drought and temperature. We discuss putative associations between gene expression profiles and adaptation to different environments, for example: upregulation of genes involved in lignin biosynthesis in the species which have adapted to mountainous regions characterized by strong winds and thick snow cover. Our study provides valid candidates for verification of the importance of the gene expression role, in addition to evidence for selection within genomic regions, in the process of ecological divergence and adaptation to higher altitudes in pine taxa.

opencc-zeroJul 2022View details →

Single-cell expression and TCR data from CD19-specific CAR T cells in a phase I/II clinical trial

By leveraging single-cell transcriptome and T cell receptor (TCR) sequencing, we aimed to track the transcriptional signatures of CAR T cell clonotypes throughout the course of treatment and furthermore identify molecular patterns leading to potent CAR T cell cytotoxicity. The data presented in this study encompass blood and bone marrow samples from patients ≤ 21 years of age with relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL) participating in the SJCAR19 phase I/II clinical trial (<a href="https://clinicaltrials.gov/ct2/show/NCT03573700">NCT03573700</a>). In brief, patients enrolled in the clinical trial received either 1 x 10^6 (dose level 1) or 3 x 10^6 (dose level 2) per kilogram of body weight following successful generation of autologous CAR T cell products and lymphodepleting chemotherapy. Peripheral blood was drawn from each participant every week until week 4 post-infusion, at week 6 or 8, and month 3 or 6 if feasible. At week 4 post-infusion, blood marrow was also collected from participants. Total T cells (CD3+) were sorted from each post-infusion sample, as well as the pre-infusion CAR T cell products, and processed through 10x Genomics' single-cell gene expression and V(D)J sequencing platform using the standard protocol. We identified a unique and unexpected transcriptional signature in a subset of pre-infusion CAR T cells that shared TCRs with post-infusion cytotoxic effector CAR T cells. Functional validation of cells with even a subset of these pre-effector markers demonstrated their immediate cytotoxic potential and resistance to exhaustion.

opencc-by-4.0Jul 2022View details →

Liu_et_al_DMG_In Situ Sequencing_expression_maps

This repository includes the In Situ Sequencing datasets (HybISS) generated for Liu et al. 2022. Both gene expression maps (reads.zip) as well as cell type maps (this is, after segmenting individual cells) are included. Malignant cell types were assigned based on probabilistic cell typing by ISS (Qian et al. 2020) and can be found at celltyping_malignant.zip. Healthy cells, in addition to the described malignant ones, were included in celltyping_malignant_and_healthy.zip. In addition annotated data objects (anndata) were generated for all 16 samples (anndata_objects)

opencc-by-4.0Jul 2022View details →

A compendium of expression profiles for Corynebacterium glutamicum

Corynebacterium glutamicum is the major host for the industrial production of amino acids and has become one of the best studied model organisms in microbial biotechnology. Rational strain construction has led to an improvement of producer strains and to a variety of novel producer strains with a broad substrate and product spectrum. A key factor for the success of these approaches is detailed knowledge of transcriptional regulation in C. glutamicum. Here, we present a large compendium of 927 manually curated microarray-based transcriptional profiles for wild-type and engineered strains detecting genome-wide expression changes of the 3,047 annotated genes in response to various environmental conditions or in response to genetic modifications. The replicates within the 927 experiments were combined to 304 microarray sets ordered into six categories that were used for differential gene expression analysis. Hierarchical clustering confirmed that no outliers were present in the sets. The compendium provides a valuable resource for future fundamental and applied research with C. glutamicum and contributes to a systemic understanding of this microbial cell factory.

opencc-zeroJul 2022View details →

Antheridiogen controls spatial dynamics of sex‐expression in naturally occurring gametophytes of the tree fern Cyathea multiflora

Background and aims: Antheridiogen systems are an important and widespread mechanism by which sex expression is controlled and genetic diversity maintained in fern gametophyte populations. However, antheridiogens have rarely been studied outside of the laboratory and little is known about their function in natural populations. Combining predictions based on field and laboratory study, we document the sexual structure of tree fern gametophyte populations and test the effects of antheridiogen in situ. Key results: In all populations, sex ratios indirectly indicate antheridiogen activity. No hermaphroditic gametophytes were identified in any population. Female gametophytes are randomly distributed in each population while male gametophytes tend to cluster. In two of the populations, male sex expression is spatially dependent on females, providing direct evidence of antheridiogen function in situ. Conclusions: This study provides the first documentation of spatial sex expression in natural gametophyte populations of an antheridiogen-producing tree fern species. The profound impact of antheridiogen on gametophyte sex expression in field settings suggests this system is intimately tied to mating systems, fitness, and genetic diversity in Cyathea multiflora.

opencc-by-4.0Sep 2021View details →

Gene Expression (RNA seq)

Fastq file and count matrix used for the bulk RNA-seq analyses in "Disruption of Epithelial Integrity Drives Mesendoderm Differentiation in Human Induced Pluripotent Stem Cells by Enabling BMP and ACTIVIN Sensing", Rattier et al. Preparation and sequencing of RNA was performed by GATC Biotech.

opencc-by-4.0Aug 2022View details →

RBBP4 regulates the expression of Mre11-Rad50-NBS1(MRN)complex and promotes DNA double-strand breaks repair to mediate glioblastoma Chemoradiotherapy resistance

Article <a>related</a> SUPPLEMENTARY data.

opencc-by-4.0Apr 2022View details →

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

SNV of 23 parental barley inbreds of the double round robin population (DRR) (<a href="https://doi.org/10.1111/pbi.13746">https://doi.org/10.1111/pbi.13746</a>) used in the publication "Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation". SV, INDELs, and additional data are available via figshare (https://doi.org/10.6084/m9.figshare.16802473).

opencc-zeroAug 2022View details →

Genetic and transcriptomic datasets characterizing gene drive mosquitoes expressing antimicrobial peptides that retard Plasmodium sporogonic development

Gene drives hold promise for the genetic control of malaria vectors. The development of vector population modification strategies hinges on the availability of effector mechanisms impeding parasite development in transgenic mosquitoes. We augmented a midgut gene of the malaria mosquito Anopheles gambiae to secrete two exogenous antimicrobial peptides, Magainin 2 and Melittin. This small genetic modification, capable of efficient non-autonomous gene drive, hampers oocyst development in both Plasmodium falciparum and Plasmodium berghei. It delays the release of infectious sporozoites while it simultaneously reduces the lifespan of homozygous female transgenic mosquitoes. Modeling the spread of this modification using a large-scale agent-based model of malaria epidemiology reveals that it can break the cycle of disease transmission across a range of transmission intensities.