scholarly journals Divergent patterns of selection on metabolite levels and gene expression

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alexander F. Kern ◽  
Grace Xiaolu Yang ◽  
Neil M. Khosla ◽  
Roy Moh Lik Ang ◽  
Michael P. Snyder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Pathways ◽  
Antifungal Drugs ◽  
Ergosterol Biosynthesis ◽  
Expression Data ◽  
Yeast Strains ◽  
Genetic Cross ◽  
Polygenic Adaptation ◽  
Trait Locus ◽  
The Impact

Abstract Background Natural selection can act on multiple genes in the same pathway, leading to polygenic adaptation. For example, adaptive changes were found to down-regulate six genes involved in ergosterol biosynthesis—an essential pathway targeted by many antifungal drugs—in some strains of the yeast Saccharomyces cerevisiae. However, the impact of this polygenic adaptation on metabolite levels was unknown. Here, we performed targeted mass spectrometry to measure the levels of eight metabolites in this pathway in 74 yeast strains from a genetic cross. Results Through quantitative trait locus (QTL) mapping we identified 19 loci affecting ergosterol pathway metabolite levels, many of which overlap loci that also impact gene expression within the pathway. We then used the recently developed v-test, which identified selection acting upon three metabolite levels within the pathway, none of which were predictable from the gene expression adaptation. Conclusions These data showed that effects of selection on metabolite levels were complex and not predictable from gene expression data. This suggests that a deeper understanding of metabolism is necessary before we can understand the impacts of even relatively straightforward gene expression adaptations on metabolic pathways.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals Optimising expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Author(s):  
Anna S.E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

AbstractSingle-cell RNA-sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states, and promises to improve our understanding of genetic regulation across tissues in both health and disease. While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimise sc-eQTL mapping. Here, we evaluate the role of different normalisation and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches and provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Kayla A. Johnson ◽  
Arjun Krishnan
Keyword(s):  
Gene Expression ◽  
Expression Data ◽  
Rna Seq ◽  
Functional Relationships ◽  
Gene Coexpression ◽  
Transformation Methods ◽  
Network Transformation ◽  
Almost All ◽  
Coexpression Networks ◽  
The Impact

Abstract Background Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression. Results Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships. Conclusions Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

scholarly journals Tying Metabolic Branches With Histone Tails Using Systems Biology

2019 ◽  
Vol 12 ◽  
pp. 251686571986968
Author(s):  
Sriram Chandrasekaran
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
Histone Modifications ◽  
Metabolic Pathways ◽  
Cellular Mechanism ◽  
Histone Tails ◽  
Metabolic Fluxes ◽  
Adenosyl Methionine ◽  
The Impact ◽  
Influence Gene Expression

Histone modifications represent an innate cellular mechanism to link nutritional status to gene expression. Metabolites such as acetyl-CoA and S-adenosyl methionine influence gene expression by serving as substrates for modification of histones. Yet, we lack a predictive model for determining histone modification levels based on cellular metabolic state. The numerous metabolic pathways that intersect with histone marks makes it highly challenging to understand their interdependencies. Here, we highlight new systems biology tools to unravel the impact of nutritional cues and metabolic fluxes on histone modifications.

scholarly journals Slower development of lower canopy beans produces better coffee

2020 ◽  
Vol 71 (14) ◽  
pp. 4201-4214 ◽  
Author(s):  
Bing Cheng ◽  
Heather E Smyth ◽  
Agnelo Furtado ◽  
Robert J Henry
Keyword(s):  
Gene Expression ◽  
Metabolic Pathways ◽  
Global Gene Expression ◽  
Coffee Production ◽  
High Quality ◽  
Development Stages ◽  
Expression Of Genes ◽  
Coffee Beans ◽  
The Impact ◽  
Selection Of

Abstract The production of high-quality coffee is being challenged by changing climates in coffee-growing regions. The coffee beans from the upper and lower canopy at different development stages of the same plants were analyzed to investigate the impact of the microenvironment on gene expression and coffee quality. Compared with coffee beans from the upper canopy, lower canopy beans displayed more intense aroma with higher caffeine, trigonelline, and sucrose contents, associated with greater gene expression in the representative metabolic pathways. Global gene expression indicated a longer ripening in the lower canopy, resulting from higher expression of genes relating to growth inhibition and suppression of chlorophyll degradation during early bean ripening. Selection of genotypes or environments that enhance expression of the genes slowing bean development may produce higher quality coffee beans, allowing coffee production in a broader range of available future environments.

scholarly journals Targeting Unconventional Pathways in Pursuit of Novel Antifungals

2021 ◽  
Vol 7 ◽  
Author(s):  
Stephanie Nguyen ◽  
Jia Q. Truong ◽  
John B. Bruning
Keyword(s):  
Small Molecules ◽  
Metabolic Pathways ◽  
Fungal Infections ◽  
Antifungal Drugs ◽  
Public Health Issue ◽  
Fungal Cell ◽  
Nucleotide Biosynthesis ◽  
Frequent Use ◽  
Drug Classes ◽  
The Impact

The impact of invasive fungal infections on human health is a serious, but largely overlooked, public health issue. Commonly affecting the immunocompromised community, fungal infections are predominantly caused by species of Candida, Cryptococcus, and Aspergillus. Treatments are reliant on the aggressive use of pre-existing antifungal drug classes that target the fungal cell wall and membrane. Despite their frequent use, these drugs are subject to unfavorable drug-drug interactions, can cause undesirable side-effects and have compromised efficacy due to the emergence of antifungal resistance. Hence, there is a clear need to develop novel classes of antifungal drugs. A promising approach involves exploiting the metabolic needs of fungi by targeted interruption of essential metabolic pathways. This review highlights potential antifungal targets including enolase, a component of the enolase-plasminogen complex, and enzymes from the mannitol biosynthesis and purine nucleotide biosynthesis pathways. There has been increased interest in the enzymes that comprise these particular pathways and further investigation into their merits as antifungal targets and roles in fungal survival and virulence are warranted. Disruption of these vital processes by targeting unconventional pathways with small molecules or antibodies may serve as a promising approach to discovering novel classes of antifungals.

scholarly journals EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways

2020 ◽  
Vol 61 (10) ◽  
pp. 1818-1827
Author(s):  
Kuan-Chieh Tseng ◽  
Guan-Zhen Li ◽  
Yu-Cheng Hung ◽  
Chi-Nga Chow ◽  
Nai-Yun Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Metabolic Pathways ◽  
Expression Profiles ◽  
Regulatory Mechanisms ◽  
Biological Processes ◽  
Expression Data ◽  
Rna Seq ◽  
Correlation Networks ◽  
Transcriptional Regulatory Mechanisms

Abstract Co-expressed genes tend to have regulatory relationships and participate in similar biological processes. Construction of gene correlation networks from microarray or RNA-seq expression data has been widely applied to study transcriptional regulatory mechanisms and metabolic pathways under specific conditions. Furthermore, since transcription factors (TFs) are critical regulators of gene expression, it is worth investigating TFs on the promoters of co-expressed genes. Although co-expressed genes and their related metabolic pathways can be easily identified from previous resources, such as EXPath and EXPath Tool, this information is not simultaneously available to identify their regulatory TFs. EXPath 2.0 is an updated database for the investigation of regulatory mechanisms in various plant metabolic pathways with 1,881 microarray and 978 RNA-seq samples. There are six significant improvements in EXPath 2.0: (i) the number of species has been extended from three to six to include Arabidopsis, rice, maize, Medicago, soybean and tomato; (ii) gene expression at various developmental stages have been added; (iii) construction of correlation networks according to a group of genes is available; (iv) hierarchical figures of the enriched Gene Ontology (GO) terms are accessible; (v) promoter analysis of genes in a metabolic pathway or correlation network is provided; and (vi) user’s gene expression data can be uploaded and analyzed. Thus, EXPath 2.0 is an updated platform for investigating gene expression profiles and metabolic pathways under specific conditions. It facilitates users to access the regulatory mechanisms of plant biological processes. The new version is available at http://EXPath.itps.ncku.edu.tw.

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