scholarly journals Systems analyses of key metabolic modules of floral and extrafloral nectaries of cotton

2019 ◽  
Author(s):  
Elizabeth C. Chatt ◽  
Siti-Nabilla Mahalim ◽  
Nur-Aziatull Mohd-Fadzil ◽  
Rahul Roy ◽  
Peter M. Klinkenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gossypium Hirsutum ◽  
Gene Expression Data ◽  
Plasma Membranes ◽  
De Novo ◽  
Starch Synthesis ◽  
Extrafloral Nectaries ◽  
Starch Degradation ◽  
Expression Data ◽  
Ideal System

AbstractNectar is a primary reward mediating plant-animal mutualisms to improve plant fitness and reproductive success. In Gossypium hirsutum (cotton), four distinct trichomatic nectaries develop, one floral and three extrafloral. The secreted floral and extrafloral nectars serve different purposes, with the floral nectar attracting bees to promote pollination and the extrafloral nectar attracting predatory insects as a means of indirect resistance from herbivores. Cotton therefore provides an ideal system to contrast mechanisms of nectar production and nectar composition between floral and extrafloral nectaries. Here, we report the transcriptome, ultrastructure, and metabolite spatial distribution using mass spectrometric imaging of the four cotton nectary types throughout development. Additionally, the secreted nectar metabolomes were defined and were jointly composed of 197 analytes, 60 of which were identified. Integration of theses datasets support the coordination of merocrine-based and eccrine-based models of nectar synthesis. The nectary ultrastructure supports the merocrine-based model due to the abundance of rough endoplasmic reticulum positioned parallel to the cell walls and profusion of vesicles fusing to the plasma membranes. The eccrine-based model which consist of a progression from starch synthesis to starch degradation and to sucrose biosynthesis was supported by gene expression data. This demonstrates conservation of the eccrine-based model for the first time in both trichomatic and extrafloral nectaries. Lastly, nectary gene expression data provided evidence to support de novo synthesis of amino acids detected in the secreted nectars.One sentence summaryThe eccrine-based model of nectar synthesis and secretion is conserved in both trichomatic and extrafloral nectaries determined by a system-based comparison of cotton (Gossypium hirsutum) nectaries.

scholarly journals Nectar biosynthesis is conserved among floral and extrafloral nectaries

2021 ◽  
Author(s):  
Elizabeth C Chatt ◽  
Siti-Nabilla Mahalim ◽  
Nur-Aziatull Mohd-Fadzil ◽  
Rahul Roy ◽  
Peter M Klinkenberg ◽  
...  
Keyword(s):  
Plasma Membranes ◽  
De Novo ◽  
Extrafloral Nectaries ◽  
Starch Degradation ◽  
Floral Nectar ◽  
Nectar Production ◽  
Ideal System ◽  
Transcriptomics Data ◽  
Predatory Insects ◽  
Sucrose Biosynthesis

Abstract Nectar is a primary reward mediating plant-animal mutualisms to improve plant fitness and reproductive success. Four distinct trichomatic nectaries develop in cotton (Gossypium hirsutum), one floral and three extrafloral, and the nectars they secrete serve different purposes. Floral nectar attracts bees for promoting pollination, while extrafloral nectar attracts predatory insects as a means of indirect protection from herbivores. Cotton therefore provides an ideal system for contrasting mechanisms of nectar production and nectar composition between different nectary types. Here, we report the transcriptome and ultrastructure of the four cotton nectary types throughout development and compare these with the metabolomes of secreted nectars. Integration of these datasets supports specialization among nectary types to fulfill their ecological niche, while conserving parallel coordination of the merocrine-based and eccrine-based models of nectar biosynthesis. Nectary ultrastructures indicate an abundance of rough endoplasmic reticulum positioned parallel to the cell walls and a profusion of vesicles fusing to the plasma membranes, supporting the merocrine model of nectar biosynthesis. The eccrine-based model of nectar biosynthesis is supported by global transcriptomics data, which indicate a progression from starch biosynthesis to starch degradation and sucrose biosynthesis and secretion. Moreover, our nectary global transcriptomics data provide evidence for novel metabolic processes supporting de novo biosynthesis of amino acids secreted in trace quantities in nectars. Collectively, these data demonstrate the conservation of nectar-producing models among trichomatic and extrafloral nectaries.

scholarly journals ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

2019 ◽  
Author(s):  
Samuel A Danziger ◽  
David L Gibbs ◽  
Ilya Shmulevich ◽  
Mark McConnell ◽  
Matthew WB Trotter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Immune Cell ◽  
De Novo ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Data Set ◽  
Rnaseq Data

AbstractImmune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are hard to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

scholarly journals Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0230164
Author(s):  
Md Nazmul Haque ◽  
Sadia Sharmin ◽  
Amin Ahsan Ali ◽  
Abu Ashfaqur Sajib ◽  
Mohammad Shoyaib
Keyword(s):  
Gene Expression ◽  
Mutual Information ◽  
Gene Expression Data ◽  
Gene Selection ◽  
De Novo ◽  
Expression Profiles ◽  
Biological Data ◽  
Expression Data ◽  
Finite Sample ◽  
Key Genes

With the advent of high-throughput technologies, life sciences are generating a huge amount of varied biomolecular data. Global gene expression profiles provide a snapshot of all the genes that are transcribed in a cell or in a tissue under a particular condition. The high-dimensionality of such gene expression data (i.e., very large number of features/genes analyzed with relatively much less number of samples) makes it difficult to identify the key genes (biomarkers) that are truly attributing to a particular phenotype or condition, (such as cancer), de novo. For identifying the key genes from gene expression data, among the existing literature, mutual information (MI) is one of the most successful criteria. However, the correction of MI for finite sample is not taken into account in this regard. It is also important to incorporate dynamic discretization of genes for more relevant gene selection, although this is not considered in the available methods. Besides, it is usually suggested in current studies to remove redundant genes which is particularly inappropriate for biological data, as a group of genes may connect to each other for downstreaming proteins. Thus, despite being redundant, it is needed to add the genes which provide additional useful information for the disease. Addressing these issues, we proposed Mutual information based Gene Selection method (MGS) for selecting informative genes. Moreover, to rank these selected genes, we extended MGS and propose two ranking methods on the selected genes, such as MGSf—based on frequency and MGSrf—based on Random Forest. The proposed method not only obtained better classification rates on gene expression datasets derived from different gene expression studies compared to recently reported methods but also detected the key genes relevant to pathways with a causal relationship to the disease, which indicate that it will also able to find the responsible genes for an unknown disease data.

scholarly journals Gene expression data support the hypothesis that Isoetes rootlets are true roots and not modified leaves

2019 ◽  
Author(s):  
Alexander J. Hetherington ◽  
David M. Emms ◽  
Steven Kelly ◽  
Liam Dolan
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
De Novo ◽  
Land Plant ◽  
Expression Data ◽  
Plant Group ◽  
Extant Species ◽  
Gene Expression Signatures ◽  
Selaginella Moellendorffii ◽  
Respective Species

AbstractRhizomorphic lycopsids are the land plant group that includes the first giant trees to grow on Earth and extant species in the genus Isoetes. Two mutually exclusive hypotheses account for the evolution of terminal rooting axes called rootlets among the rhizomorphic lycopsids. One hypothesis states that rootlets are true roots, like roots in other lycopsids. The other states that rootlets are modified leaves. Here we test predictions of each hypothesis by investigating gene expression in the leaves and rootlets of Isoetes echinospora. We assembled the de-novo transcriptome of axenically cultured I. echinospora. Gene expression signatures of I. echinospora rootlets and leaves were different. Furthermore, gene expression signatures of I. echinospora rootlets were similar to gene expression signatures of true roots of Selaginella moellendorffii and Arabidopsis thaliana. RSL genes which positively regulate cell differentiation in roots were either exclusively or preferentially expressed in the I. echinospora rootlets, S. moellendorffii roots and A. thaliana roots compared to the leaves of each respective species. Taken together, gene expression data from the de-novo transcriptome of I. echinospora are consistent with the hypothesis that Isoetes rootlets are true roots and not modified leaves.

scholarly journals BICORN: An R package for integrative inference of de novo cis-regulatory modules

2019 ◽  
Author(s):  
Xi Chen
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Target Genes ◽  
De Novo ◽  
R Package ◽  
Expression Data ◽  
Regulatory Modules ◽  
Regulatory Module

AbstractBICORN is an R package developed to integrate prior transcription factor binding information and gene expression data for cis-regulatory module (CRM) inference. BICORN searches for a list of candidate CRMs from binary bindings on potential target genes. Applying Gibbs sampling, BICORN samples CRMs for each gene using the fitting performance of transcription factor activities and regulation strengths of TFs in each CRM on gene expression. Consequently, sparse regulatory networks are inferred as functional CRMs regulating target genes. The BICORN package is implemented in R and is available at https://cran.r-project.org/web/packages/BICORN/index.html.

scholarly journals SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing

2019 ◽  
Vol 35 (20) ◽  
pp. 3944-3952 ◽  
Author(s):  
Dennis C Wylie ◽  
Hans A Hofmann ◽  
Boris V Zemelman
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Gene Expression Data ◽  
Kernel Smoothing ◽  
De Novo ◽  
Supplementary Information ◽  
Spatial Proximity ◽  
P Value ◽  
Regulatory Sequences ◽  
Expression Data

Abstract Motivation We set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score—fold-change, test-statistic, P-value—comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies non-parametric kernel smoothing to uncover promoter motif sites that correlate with elevated differential expression scores. SArKS detects motif k-mers by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motif sites can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing. Results We applied SArKS to published gene expression data representing distinct neocortical neuron classes in Mus musculus and interneuron developmental states in Homo sapiens. When benchmarked against several existing algorithms using a cross-validation procedure, SArKS identified larger motif sets that formed the basis for regression models with higher correlative power. Availability and implementation https://github.com/denniscwylie/sarks. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SArKS: de novo discovery of gene expression regulatory motifs and domains by suffix array kernel smoothing

2017 ◽  
Author(s):  
Dennis Wylie ◽  
Hans A. Hofmann ◽  
Boris V. Zemelman
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Gene Expression Data ◽  
Motif Discovery ◽  
Kernel Smoothing ◽  
De Novo ◽  
Supplementary Information ◽  
Spatial Proximity ◽  
Regulatory Sequences ◽  
Expression Data

AbstractMotivationWe set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score—fold-change, test-statistic, p-value—comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies nonparametric kernel smoothing to uncover promoter motifs that correlate with elevated differential expression scores. SArKS detects motifs by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.ResultsWe applied SArKS to published gene expression data representing distinct neocortical neuron classes in M. musculus and interneuron developmental states in H. sapiens. When benchmarked against several existing algorithms for correlative motif discovery using a cross-validation procedure, SArKS identified larger motif sets that formed the basis for regression models with higher correlative power.Availabilityhttps://github.com/denniscwylie/[email protected] informationappended to document.

scholarly journals Gene expression data support the hypothesis that Isoetes rootlets are true roots and not modified leaves

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander J. Hetherington ◽  
David M. Emms ◽  
Steven Kelly ◽  
Liam Dolan
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
De Novo ◽  
Land Plant ◽  
Expression Data ◽  
Plant Group ◽  
Extant Species ◽  
Gene Expression Signatures ◽  
Selaginella Moellendorffii ◽  
Respective Species

AbstractRhizomorphic lycopsids are the land plant group that includes the first giant trees to grow on Earth and extant species in the genus Isoetes. Two mutually exclusive hypotheses account for the evolution of terminal rooting axes called rootlets among the rhizomorphic lycopsids. One hypothesis states that rootlets are true roots, like roots in other lycopsids. The other states that rootlets are modified leaves. Here we test predictions of each hypothesis by investigating gene expression in the leaves and rootlets of Isoetes echinospora. We assembled the de novo transcriptome of axenically cultured I. echinospora. Gene expression signatures of I. echinospora rootlets and leaves were different. Furthermore, gene expression signatures of I. echinospora rootlets were similar to gene expression signatures of true roots of Selaginella moellendorffii and Arabidopsis thaliana. RSL genes which positively regulate cell differentiation in roots were either exclusively or preferentially expressed in the I. echinospora rootlets, S. moellendorffii roots and A. thaliana roots compared to the leaves of each respective species. Taken together, gene expression data from the de-novo transcriptome of I. echinospora are consistent with the hypothesis that Isoetes rootlets are true roots and not modified leaves.

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