scholarly journals Characterizing Xenopus tropicalis endurance capacities with multilevel transcriptomics

2016 ◽  
Author(s):  
Adam J. Richards ◽  
Anthony Herrel ◽  
Mathieu Videlier ◽  
Konrad Paszkiewicz ◽  
Nicolas Pollet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Xenopus Tropicalis ◽  
Endurance Capacity ◽  
Functional Modules ◽  
Gene Set ◽  
Individual Level ◽  
Gene Level ◽  
Genetic Mechanisms ◽  
Ideal Tool ◽  
The Individual

AbstractVertebrate endurance capacity is a phenotype with considerable genetic heterogeneity. RNA-Seq technologies are an ideal tool to investigate the involved genes and processes, but several challenges exist when the phenotype of interest has a complex genetic background. Difficulties manifest at the level of results interpretation because commonly used statistical methods are designed to identify strongly associated genes. If an observed phenotype can be achieved though multiple distinct genetic mechanisms then typical gene-centric methods come with the attached risk that signal may be lost or misconstrued.Gene set analysis (GSA) methods are now widely accepted as a means to address some of the shortcomings of gene-by-gene analysis methods. We carry out both gene level and gene set level analyses on Xenopus tropicalis to identify the genetic factors that contribute to endurance heterogeneity. A typical workflow might consider gene level and pathway level analyses, but in this work we propose an additional focus at the intermediate level of functional modules. We generate functional modules for GSA testing in order to be explicit in how ontology information is used with respect to the functional genomics of Xenopus. Additionally, we make use of multiple assemblies to corroborate implicated genes and processes.We identified 42 core genes, 10 functional modules, and 14 pathways based on gene expression differences between endurant and non-endurant frogs. The majority of the genes and processes are readily associated with muscle contraction or catabolism. A substantial number of these genes are involved in lipid metabolic processes, suggesting an important role in frog endurance heterogeneity. Unsurprisingly, many of the gene expression differences between endurant and non-endurant frogs can be distilled down to the capacity to utilize substrate for energy, but at the individual level frogs appear to make use of diverse machinery to achieve these differences.

scholarly journals Technical considerations when designing a gene expression panel for renal transplant diagnosis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
F. Toulza ◽  
K. Dominy ◽  
T. Cook ◽  
J. Galliford ◽  
J. Beadle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Transplant ◽  
Biological Significance ◽  
Tissue Sampling ◽  
Individual Gene ◽  
Antibody Mediated Rejection ◽  
Gene Level ◽  
Diagnostic Panel ◽  
The Individual ◽  
Gene Panels

Abstract Gene expression analysis is emerging as a new diagnostic tool in transplant pathology, in particular for the diagnosis of antibody-mediated rejection. Diagnostic gene expression panels are defined on the basis of their pathophysiological relevance, but also need to be tested for their robustness across different preservatives and analysis platforms. The aim of this study is the investigate the effect of tissue sampling and preservation on candidate genes included in a renal transplant diagnostic panel. Using the NanoString platform, we compared the expression of 219 genes in 51 samples, split for formalin-fixation and paraffin-embedding (FFPE) and RNAlater preservation (RNAlater). We found that overall, gene expression significantly correlated between FFPE and RNAlater samples. However, at the individual gene level, 46 of the 219 genes did not correlate across the 51 matched FFPE and RNAlater samples. Comparing gene expression results using NanoString and qRT-PCR for 18 genes in the same pool of RNA (RNAlater), we found a significant correlation in 17/18 genes. Our study indicates that, in samples from the same routine diagnostic renal transplant biopsy procedure split for FFPE and RNAlater, 21% of 219 genes of potential biological significance do not correlate in expression. Whether this is due to fixatives or tissue sampling, selection of gene panels for routine diagnosis should take this information into consideration.

scholarly journals Analysing the meta-interaction between pathways by gene set topological impact analysis

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Shen Yan ◽  
Xu Chi ◽  
Xiao Chang ◽  
Mengliang Tian
Keyword(s):  
Pathway Analysis ◽  
Impact Analysis ◽  
Functional Modules ◽  
Gene Set ◽  
Module Analysis ◽  
Pathway Module ◽  
Biological Insight ◽  
The Individual ◽  
Transcriptomic Changes ◽  
Current Pathway

Abstract Background Pathway analysis is widely applied in transcriptome analysis. Given certain transcriptomic changes, current pathway analysis tools tend to search for the most impacted pathways, which provides insight into underlying biological mechanisms. Further refining of the enriched pathways and extracting functional modules by “crosstalk” analysis have been proposed. However, the upstream/downstream relationships between the modules, which may provide extra biological insights such as the coordination of different functional modules and the signal transduction flow have been ignored. Results To quantitatively analyse the upstream/downstream relationships between functional modules, we developed a novel GEne Set Topological Impact Analysis (GESTIA), which could be used to assemble the enriched pathways and functional modules into a super-module with a topological structure. We showed the advantages of this analysis in the exploration of extra biological insight in addition to the individual enriched pathways and functional modules. Conclusions GESTIA can be applied to a broad range of pathway/module analysis result. We hope that GESTIA may help researchers to get one additional step closer to understanding the molecular mechanism from the pathway/module analysis results.

scholarly journals The molecular basis of socially-mediated phenotypic plasticity in a eusocial paper wasp

2020 ◽  
Author(s):  
Benjamin A. Taylor ◽  
Alessandro Cini ◽  
Christopher D. R. Wyatt ◽  
Max Reuter ◽  
Seirian Sumner
Keyword(s):  
Gene Expression ◽  
Phenotypic Plasticity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Paper Wasp ◽  
Strong Response ◽  
Individual Level ◽  
Multiple Phenotypes ◽  
The Individual ◽  
The Relationship

AbstractPhenotypic plasticity, the ability to produce multiple phenotypes from a single genotype, represents an excellent model with which to examine the relationship between gene expression and phenotypes. Despite this, analyses of the molecular bases of plasticity have been limited by the challenges of linking individual phenotypes with individual-level gene expression profiles, especially in the case of complex social phenotypes. Here, we tackle this challenge by analysing the individual-level gene expression profiles of Polistes dominula paper wasps following the loss of a queen, a perturbation that induces some individuals to undergo a significant phenotypic shift and become replacement reproductives. Using a machine learning approach, we find a strong response of caste-associated gene expression to queen loss, wherein individuals’ expression profiles become intermediate between queen and worker states. Importantly, this change occurs even in individuals that appear phenotypically unaffected. Part of this response is explained by individual attributes, most prominently age. These results demonstrate that large changes in gene expression may occur in the absence of detectable phenotypic changes, resulting here in a socially mediated de-differentiation of individuals at the transcriptomic but not the phenotypic level. Our findings also highlight the complexity of the relationship between gene expression and phenotype, where transcriptomes are neither a direct reflection of the genotype nor a proxy for the molecular underpinnings of the external phenotype.

scholarly journals The central region of CNOT1 and CNOT9 stimulates deadenylation by the Ccr4–Not nuclease module

2018 ◽  
Vol 475 (21) ◽  
pp. 3437-3450 ◽  
Author(s):  
Lorenzo Pavanello ◽  
Benjamin Hall ◽  
Blessing Airhihen ◽  
Gerlof Sebastiaan Winkler
Keyword(s):  
Gene Expression ◽  
Central Region ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Large Subunit ◽  
Eukaryotic Cells ◽  
Functional Modules ◽  
Wild Type ◽  
Catalytic Subunits ◽  
The Individual

Regulated degradation of cytoplasmic mRNA is important for the accurate execution of gene expression programmes in eukaryotic cells. A key step in this process is the shortening and removal of the mRNA poly(A) tail, which can be achieved by the recruitment of the multi-subunit Ccr4–Not nuclease complex via sequence-specific RNA-binding proteins or the microRNA machinery. The Ccr4–Not complex contains several modules that are attached to its large subunit CNOT1. Modules include the nuclease module, which associates with the MIF4G domain of CNOT1 and contains the catalytic subunits Caf1 and Ccr4, as well as the module containing the non-catalytic CNOT9 subunit, which binds to the DUF3819 domain of CNOT1. To understand the contributions of the individual modules to the activity of the complex, we have started to reconstitute sub-complexes of the human Ccr4–Not complex containing one or several functional modules. Here, we report the reconstitution of a pentameric complex including a BTG2–Caf1–Ccr4 nuclease module, CNOT9 and the central region of CNOT1 encompassing the MIF4G and DUF3819 domains. By comparing the biochemical activities of the pentameric complex and the nuclease module, we conclude that the CNOT1–CNOT9 components stimulate deadenylation by the nuclease module. In addition, we show that a pentameric complex containing the melanoma-associated CNOT9 P131L variant is able to support deadenylation similar to a complex containing the wild-type CNOT9 protein.

scholarly journals Alternative splicing in normal and pathological human placentas is correlated to genetic variants

2021 ◽  
Author(s):  
Camino S. M. Ruano ◽  
Clara Apicella ◽  
Sébastien Jacques ◽  
Géraldine Gascoin ◽  
Cassandra Gaspar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Genetic Variants ◽  
Gene Expression Regulation ◽  
Essential Feature ◽  
Normal Growth ◽  
Brain Diseases ◽  
Individual Level ◽  
The Individual ◽  
Placental Diseases

AbstractTwo major obstetric diseases, preeclampsia (PE), a pregnancy-induced endothelial dysfunction leading to hypertension and proteinuria, and intra-uterine growth-restriction (IUGR), a failure of the fetus to acquire its normal growth, are generally triggered by placental dysfunction. Many studies have evaluated gene expression deregulations in these diseases, but none has tackled systematically the role of alternative splicing. In the present study, we show that alternative splicing is an essential feature of placental diseases, affecting 1060 and 1409 genes in PE vs controls and IUGR vs controls, respectively, many of those involved in placental function. While in IUGR placentas, alternative splicing affects genes specifically related to pregnancy, in preeclamptic placentas, it impacts a mix of genes related to pregnancy and brain diseases. Also, alternative splicing variations can be detected at the individual level as sharp splicing differences between different placentas. We correlate these variations with genetic variants to define splicing Quantitative Trait Loci (sQTL) in the subset of the 48 genes the most strongly alternatively spliced in placental diseases. We show that alternative splicing is at least partly piloted by genetic variants located either in cis (52 QTL identified) or in trans (52 QTL identified). In particular, we found four chromosomal regions that impact the splicing of genes in the placenta. The present work provides a new vision of placental gene expression regulation that warrants further studies.

scholarly journals Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia

PLoS Genetics ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. e1009482
Author(s):  
Shizhen Tang ◽  
Aron S. Buchman ◽  
Philip L. De Jager ◽  
David A. Bennett ◽  
Michael P. Epstein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Linear Function ◽  
Variance Component ◽  
Significant Risk ◽  
Gwas Data ◽  
Risk Genes ◽  
Reference Transcriptome ◽  
Individual Level ◽  
Level Data ◽  
The Individual

Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL’s estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer’s dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.

scholarly journals The molecular basis of socially-mediated phenotypic plasticity in a eusocial paper wasp

2020 ◽  
Author(s):  
Benjamin Taylor ◽  
Alessandro Cini ◽  
Christopher Wyatt ◽  
Max Reuter ◽  
Seirian Sumner
Keyword(s):  
Gene Expression ◽  
Phenotypic Plasticity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Paper Wasp ◽  
Strong Response ◽  
Individual Level ◽  
Multiple Phenotypes ◽  
The Individual ◽  
The Relationship

Abstract Phenotypic plasticity, the ability to produce multiple phenotypes from a single genotype, represents an excellent model with which to examine the relationship between gene expression and phenotypes. Despite this, analyses of the molecular bases of plasticity have been limited by the challenges of linking individual phenotypes with individual-level gene expression profiles, especially in the case of complex social phenotypes. Here, we tackle this challenge by analysing the individual-level gene expression profiles of Polistes dominula paper wasps following the loss of a queen, a perturbation that induces some individuals to undergo a significant phenotypic shift and become replacement reproductives. Using a machine learning approach, we find a strong response of caste-associated gene expression to queen loss, wherein individuals’ expression profiles become intermediate between queen and worker states. Importantly, this change occurs even in individuals that appear phenotypically unaffected. Part of this response is explained by individual attributes, most prominently age. These results demonstrate that large changes in gene expression may occur in the absence of detectable phenotypic changes, resulting here in a socially mediated de-differentiation of individuals at the transcriptomic but not the phenotypic level. Our findings also highlight the complexity of the relationship between gene expression and phenotype, where transcriptomes are neither a direct reflection of the genotype nor a proxy for the molecular underpinnings of the external phenotype.

scholarly journals Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection

2018 ◽  
Author(s):  
Alvaro N. Barbeira ◽  
Milton D. Pividori ◽  
Jiamao Zheng ◽  
Heather E. Wheeler ◽  
Dan L. Nicolae ◽  
...  
Keyword(s):  
Complex Traits ◽  
Target Genes ◽  
Association Studies ◽  
Test Methods ◽  
Genome Wide Association Studies ◽  
Individual Level ◽  
Genome Wide ◽  
Gene Level ◽  
The Individual ◽  
The Uk

AbstractIntegration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association test methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restricts our ability to detect associations. Here we propose an efficient statistical method that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes: MulTiXcan. MulTiXcan integrates evidence across multiple panels while taking into account their correlation. We apply our method to a broad set of complex traits available from the UK Biobank and show that we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed an extension to work on summary statistics: S-MulTiXcan, which we show yields highly concordant results with the individual level version. Results from our analysis as well as software and necessary resources to apply our method are publicly available.

Sign in / Sign up

Export Citation Format

Share Document