Organ-specific NLR resistance gene expression varies with plant symbiotic status

AbstractNucleotide-binding site leucine-rich repeat resistance genes (NLRs) allow plants to detect microbial effectors. We hypothesized that NLR expression patterns would reflect organ-specific differences in effector challenge and tested this by carrying out a meta-analysis of expression data for 1,235 NLRs from 9 plant species. We found stable NLR root/shoot expression ratios within species, suggesting organ-specific hardwiring of NLR expression patterns in anticipation of distinct challenges. Most monocot and dicot plant species preferentially expressed NLRs in roots. In contrast, Brassicaceae species, including oilseed rape and the model plant Arabidopsis thaliana, were unique in showing NLR expression skewed towards the shoot across multiple phylogenetically distinct groups of NLRs. The Brassicaceae NLR expression shift coincides with loss of the endomycorrhization pathway, which enables intracellular root infection by symbionts. We propose that its loss offer two likely explanations for the unusual Brassicaceae NLR expression pattern: loss of NLR-guarded symbiotic components and elimination of constraints on general root defences associated with exempting symbionts from targeting. This hypothesis is consistent with the existence of Brassicaceae-specific receptors for conserved microbial molecules and suggests that Brassicaceae species are rich sources of unique antimicrobial root defences.

Download Full-text

Gene Expression Studies to Identify Significant Genes in AR, MTOR, MAPK Pathways and their Overlapping Regulatory Role in Prostate Cancer

Journal of Integrative Bioinformatics ◽

10.1515/jib-2018-0080 ◽

2019 ◽

Vol 16 (3) ◽

Author(s):

Nimisha Asati ◽

Abhinav Mishra ◽

Ankita Shukla ◽

Tiratha Raj Singh

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Gene Expression Data ◽

Meta Analysis ◽

Expression Patterns ◽

Mitogen Activated Protein Kinase ◽

Expression Data ◽

Mapk Pathways ◽

Expression Studies ◽

Gene Expression Studies

AbstractGene expression studies revealed a large degree of variability in gene expression patterns particularly in tissues even in genetically identical individuals. It helps to reveal the components majorly fluctuating during the disease condition. With the advent of gene expression studies many microarray studies have been conducted in prostate cancer, but the results have varied across different studies. To better understand the genetic and biological regulatory mechanisms of prostate cancer, we conducted a meta-analysis of three major pathways i.e. androgen receptor (AR), mechanistic target of rapamycin (mTOR) and Mitogen-Activated Protein Kinase (MAPK) on prostate cancer. Meta-analysis has been performed for the gene expression data for the human species that are exposed to prostate cancer. Twelve datasets comprising AR, mTOR, and MAPK pathways were taken for analysis, out of which thirteen potential biomarkers were identified through meta-analysis. These findings were compiled based upon the quantitative data analysis by using different tools. Also, various interconnections were found amongst the pathways in study. Our study suggests that the microarray analysis of the gene expression data and their pathway level connections allows detection of the potential predictors that can prove to be putative therapeutic targets with biological and functional significance in progression of prostate cancer.

Download Full-text

Faculty Opinions recommendation of Meta-Analysis of Genome-Wide Association Studies and Network Analysis-Based Integration with Gene Expression Data Identify New Suggestive Loci and Unravel a Wnt-Centric Network Associated with Dupuytren's Disease.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726582766.793522025 ◽

2016 ◽

Author(s):

Rik Lories

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Gene Expression Data ◽

Association Studies ◽

Meta Analysis ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Expression Data ◽

Dupuytren's Disease ◽

Genome Wide

Download Full-text

Mapping global shifts in Saccharomyces cerevisiae gene expression across asynchronous time trajectories with diffusion maps

10.1101/2021.02.11.430862 ◽

2021 ◽

Author(s):

Taylor Reiter ◽

Rachel Montpetit ◽

Ron Runnebaum ◽

C. Titus Brown ◽

Ben Montpetit

Keyword(s):

Gene Expression ◽

Saccharomyces Cerevisiae ◽

Expression Patterns ◽

Pinot Noir ◽

Wine Fermentation ◽

Gene Expression Patterns ◽

Expression Data ◽

Site Specific ◽

Time Series Gene Expression ◽

Specific Factors

AbstractGrapes grown in a particular geographic region often produce wines with consistent characteristics, suggesting there are site-specific factors driving recurrent fermentation outcomes. However, our understanding of the relationship between site-specific factors, microbial metabolism, and wine fermentation outcomes are not well understood. Here, we used differences in Saccharomyces cerevisiae gene expression as a biosensor for differences among Pinot noir fermentations from 15 vineyard sites. We profiled time series gene expression patterns of primary fermentations, but fermentations proceeded at different rates, making analyzes of these data with conventional differential expression tools difficult. This led us to develop a novel approach that combines diffusion mapping with continuous differential expression analysis. Using this method, we identified vineyard specific deviations in gene expression, including changes in gene expression correlated with the activity of the non-Saccharomyces yeast Hanseniaspora uvarum, as well as with initial nitrogen concentrations in grape musts. These results highlight novel relationships between site-specific variables and Saccharomyces cerevisiae gene expression that are linked to repeated wine fermentation outcomes. In addition, we demonstrate that our analysis approach can extract biologically relevant gene expression patterns in other contexts (e.g., hypoxic response of Saccharomyces cerevisiae), indicating that this approach offers a general method for investigating asynchronous time series gene expression data.ImportanceWhile it is generally accepted that foods, in particular wine, possess sensory characteristics associated with or derived from their place of origin, we lack knowledge of the biotic and abiotic factors central to this phenomenon. We have used Saccharomyces cerevisiae gene expression as a biosensor to capture differences in fermentations of Pinot noir grapes from 15 vineyards across two vintages. We find that gene expression by non-Saccharomyces yeasts and initial nitrogen content in the grape must correlates with differences in gene expression among fermentations from these vintages. These findings highlight important relationships between site-specific variables and gene expression that can be used to understand, or possibly modify, wine fermentation outcomes. Our work also provides a novel analysis method for investigating asynchronous gene expression data sets that is able to reveal both global shifts and subtle differences in gene expression due to varied cell – environment interactions.

Download Full-text

SOMDE: A scalable method for identifying spatially variable genes with self-organizing map

10.1101/2020.12.10.419549 ◽

2020 ◽

Author(s):

Minsheng Hao ◽

Kui Hua ◽

Xuegong Zhang

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Patterns ◽

Self Organizing Map ◽

Expression Data ◽

Spatial Expression ◽

Variable Expression ◽

Sequencing Technologies ◽

Physical Context ◽

Variable Genes

AbstractRecent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue micro-environments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data.We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses selforganizing map (SOM) to cluster neighboring cells into nodes, and then uses a Gaussian Process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5-50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in ~5 minutes in large datasets of more than 20,000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde.

Download Full-text

Arabidopsis sepals: A model system for the emergent process of morphogenesis

Quantitative Plant Biology ◽

10.1017/qpb.2021.12 ◽

2021 ◽

Vol 2 ◽

Author(s):

Adrienne H. K. Roeder

Keyword(s):

Gene Expression ◽

Arabidopsis Thaliana ◽

Cell Division ◽

Slow Growth ◽

Expression Patterns ◽

Giant Cells ◽

Model System ◽

Gene Expression Patterns ◽

Emergent Properties ◽

Correct Size

Abstract During development, Arabidopsis thaliana sepal primordium cells grow, divide and interact with their neighbours, giving rise to a sepal with the correct size, shape and form. Arabidopsis sepals have proven to be a good system for elucidating the emergent processes driving morphogenesis due to their simplicity, their accessibility for imaging and manipulation, and their reproducible development. Sepals undergo a basipetal gradient of growth, with cessation of cell division, slow growth and maturation starting at the tip of the sepal and progressing to the base. In this review, I discuss five recent examples of processes during sepal morphogenesis that yield emergent properties: robust size, tapered tip shape, laminar shape, scattered giant cells and complex gene expression patterns. In each case, experiments examining the dynamics of sepal development led to the hypotheses of local rules. In each example, a computational model was used to demonstrate that these local rules are sufficient to give rise to the emergent properties of morphogenesis.

Download Full-text

Global gene expression patterns in response to white patch syndrome: Disentangling symbiont loss from the thermal stress response in reef-building coral

10.1101/2019.12.13.875989 ◽

2019 ◽

Author(s):

Carly D. Kenkel ◽

Veronique J.L. Mocellin ◽

Line K. Bay

Keyword(s):

Gene Expression ◽

Abiotic Stress ◽

Thermal Stress ◽

Stress Response ◽

Meta Analysis ◽

Expression Patterns ◽

Global Gene Expression ◽

Host Protein ◽

Gene Expression Patterns ◽

White Patch

AbstractThe mechanisms resulting in the breakdown of the coral symbiosis once the process of bleaching has been initiated remain unclear. Distinguishing symbiont loss from the abiotic stress response may shed light on the cellular and molecular pathways involved in each process. This study examined physiological changes and global gene expression patterns associated with white patch syndrome (WPS) in P. lobata, which manifests in localized bleaching independent of thermal stress. In addition, a meta-analysis of global gene expression studies in other corals and anemones was used to contrast differential regulation as a result of abiotic stress from expression patterns correlated with symbiotic state. Symbiont density, chlorophyll a content, holobiont productivity, instant calcification rate, and total host protein content were uniformly reduced in WPS relative to healthy tissue. While expression patterns associated with WPS were secondary to fixed effects of source colony, specific functional enrichments suggest that the viral infection putatively giving rise to this condition affects symbiont rather than host cells. The meta-analysis revealed that expression patterns in WPS-affected tissues were significantly correlated with prior studies examining short-term thermal stress responses. This correlation was independent of symbiotic state, as the strongest correlations were found between WPS adults and both symbiotic adult and aposymbiotic coral larvae experiencing thermal stress, suggesting that the majority of expression changes reflect a non-specific stress response. Across studies, the magnitude and direction of expression change among particular functional enrichments suggests unique responses to stressor duration, and highlights unique responses to bleaching in an anemone model which engages in a non-obligate symbiosis.

Download Full-text

Building Gene Networks by Analyzing Gene Expression Profiles

Advanced Methodologies and Technologies in Medicine and Healthcare - Advances in Medical Diagnosis, Treatment, and Care ◽

10.4018/978-1-5225-7489-7.ch003 ◽

2019 ◽

pp. 27-44

Author(s):

Crescenzio Gallo

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Gene Networks ◽

Dna Microarrays ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Expression Data ◽

Gene Expressions ◽

Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

Download Full-text

Visual Comparison of Multiple Gene Expression Datasets in a Genomic Context

Journal of Integrative Bioinformatics ◽

10.1515/jib-2008-97 ◽

2008 ◽

Vol 5 (2) ◽

Author(s):

Krzysztof Borowski ◽

Jung Soh ◽

Christoph W. Sensen

Keyword(s):

Gene Expression ◽

Microarray Data ◽

Gene Function ◽

Graphical Representation ◽

Expression Profiles ◽

Expression Patterns ◽

Expression Data ◽

Genomic Context ◽

Multiple Gene ◽

Microarray Datasets

SummaryThe need for novel methods of visualizing microarray data is growing. New perspectives are beneficial to finding patterns in expression data. The Bluejay genome browser provides an integrative way of visualizing gene expression datasets in a genomic context. We have now developed the functionality to display multiple microarray datasets simultaneously in Bluejay, in order to provide researchers with a comprehensive view of their datasets linked to a graphical representation of gene function. This will enable biologists to obtain valuable insights on expression patterns, by allowing them to analyze the expression values in relation to the gene locations as well as to compare expression profiles of related genomes or of di erent experiments for the same genome.

Download Full-text