scholarly journals Minor Isozymes Tailor Yeast Metabolism to Carbon Availability

mSystems ◽  
2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Patrick H. Bradley ◽  
Patrick A. Gibney ◽  
David Botstein ◽  
Olga G. Troyanskaya ◽  
Joshua D. Rabinowitz
Keyword(s):  
Gene Expression ◽  
Pyruvate Kinase ◽  
Gene Expression Data ◽  
Protein Function ◽  
Metabolic Regulation ◽  
Computational Method ◽  
Expression Data ◽  
Duplicated Genes ◽  
Carbon Availability ◽  
Central Carbon

ABSTRACTIsozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time, suggesting that they contribute to fitness. We developed an unsupervised computational method for identifying environmental conditions under which isozymes are likely to make fitness contributions. This method analyzes published gene expression data to find specific experimental perturbations that induce differential isozyme expression. In yeast, we found that isozymes are strongly enriched in the pathways of central carbon metabolism and that many isozyme pairs show anticorrelated expression during the respirofermentative shift. Building on these observations, we assigned function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2).ACO2is expressed during fermentation and proves advantageous when glucose is limiting.PYK2is expressed during respiration and proves advantageous for growth on three-carbon substrates.PYK2’s deletion can be rescued by expressing the major pyruvate kinase only if that enzyme carries mutations mirroringPYK2’s allosteric regulation. Thus, central carbon isozymes help to optimize allosteric metabolic regulation under a broad range of potential nutrient conditions while requiring only a small number of transcriptional states.IMPORTANCEGene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

scholarly journals Minor isozymes tailor yeast metabolism to carbon availability

2018 ◽  
Author(s):  
Patrick H. Bradley ◽  
Patrick A. Gibney ◽  
David Botstein ◽  
Olga G. Troyanskaya ◽  
Joshua D. Rabinowitz
Keyword(s):  
Gene Expression ◽  
Pyruvate Kinase ◽  
Protein Function ◽  
Carbon Sources ◽  
Central Carbon Metabolism ◽  
Carbon Availability ◽  
Central Carbon ◽  
Genome Scale ◽  
Aconitase 2

AbstractIsozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time for reasons that can be unclear. We find that, in yeast, isozymes are strongly enriched in central carbon metabolism. Using a gene expression compendium, we find that many isozyme pairs show anticorrelated expression during the respirofermentative shift, suggesting roles in adapting to changing carbon availability. Building on this observation, we assign function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2).ACO2is expressed during fermentation and proves advantageous when glucose is limiting.PYK2is expressed during respiration and proves advantageous for growth on three-carbon substrates.PYK2’s deletion is rescued by expressing the major pyruvate kinase, but only if that enzyme carries mutations mirroringPYK2’s allosteric regulation. Thus, central carbon isozymes enable more precise tailoring of metabolism to changing nutrient availability.ImportanceGene duplication is one of the main evolutionary drivers of new protein function. However, some gene duplicates have nevertheless persisted long-term without apparent divergence in biochemical function. Further, under standard lab conditions, many isozymes have subtle or no knockout phenotypes. These factors make it hard to assess the unique contributions of individual isozymes to fitness. We therefore developed a method to identify experimental perturbations that could expose such contributions, and applied it to yeast gene expression data, revealing a potential role for a set of yeast isozymes in adapting to changing carbon sources. Our experimental confirmation of distinct roles for two “minor” yeast isozymes, including one with no previously described knockout phenotype, highlight that even apparently redundant paralogs can have important and unique functions, with implications for genome-scale metabolic modeling and systems-level studies of quantitative genetics.

scholarly journals Robust Lineage Reconstruction from High-Dimensional Single-Cell Data

2016 ◽  
Author(s):  
Gregory Giecold ◽  
Eugenio Marco ◽  
Lorenzo Trippa ◽  
Guo-Cheng Yuan
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Quantitative Estimate ◽  
Cell Lineage ◽  
Computational Method ◽  
Expression Data ◽  
Cell Gene Expression ◽  
Cell Data ◽  
Cell Gene

Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into account in current methods. We present ECLAIR, a novel computational method for the statistical inference of cell lineage relationships from single-cell gene expression data. ECLAIR uses an ensemble approach to improve the robustness of lineage predictions, and provides a quantitative estimate of the uncertainty of lineage branchings. We show that the application of ECLAIR to published datasets successfully reconstructs known lineage relationships and significantly improves the robustness of predictions. In conclusion, ECLAIR is a powerful bioinformatics tool for single-cell data analysis. It can be used for robust lineage reconstruction with quantitative estimate of prediction accuracy.

scholarly journals A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset

2021 ◽  
Vol 12 ◽  
Author(s):  
Ge Zhang ◽  
Zijing Xue ◽  
Chaokun Yan ◽  
Jianlin Wang ◽  
Huimin Luo
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Complex Disease ◽  
Biological Data ◽  
Computational Method ◽  
Superior Performance ◽  
Expression Data

As one type of complex disease, gastric cancer has high mortality rate, and there are few effective treatments for patients in advanced stage. With the development of biological technology, a large amount of multiple-omics data of gastric cancer are generated, which enables computational method to discover potential biomarkers of gastric cancer. That will be very important to detect gastric cancer at earlier stages and thus assist in providing timely treatment. However, most of biological data have the characteristics of high dimension and low sample size. It is hard to process directly without feature selection. Besides, only using some omic data, such as gene expression data, provides limited evidence to investigate gastric cancer associated biomarkers. In this research, gene expression data and DNA methylation data are integrated to analyze gastric cancer, and a feature selection approach is proposed to identify the possible biomarkers of gastric cancer. After the original data are pre-processed, the mutual information (MI) is applied to select some top genes. Then, fold change (FC) and T-test are adopted to identify differentially expressed genes (DEG). In particular, false discover rate (FDR) is introduced to revise p_value to further screen genes. For chosen genes, a deep neural network (DNN) model is utilized as the classifier to measure the quality of classification. The experimental results show that the approach can achieve superior performance in terms of accuracy and other metrics. Biological analysis for chosen genes further validates the effectiveness of the approach.

A computational method using differential gene expression to predict altered metabolism of multicellular organisms

2017 ◽  
Vol 13 (11) ◽  
pp. 2418-2427 ◽  
Author(s):  
Lvxing Zhu ◽  
Haoran Zheng ◽  
Xinying Hu ◽  
Yang Xu
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Gene Expression Data ◽  
Differential Method ◽  
Computational Approach ◽  
Computational Method ◽  
Expression Data ◽  
Differential Gene ◽  
Altered Metabolism ◽  
Multicellular Organisms

The differential method provides a computational approach to predict altered metabolism between pairs of conditions by integrating gene expression data.

Autoimmunity in Tic Disorders: Where do we Stand?

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
P. Hoekstra
Keyword(s):  
Gene Expression ◽  
Pyruvate Kinase ◽  
Gene Expression Data ◽  
Tic Disorders ◽  
Expression Data ◽  
Serum Antibodies ◽  
New Findings

New findings on serum antibodies against pyruvate kinase and lysogangliosides plus cytokine data in children with tic disorders compared to helathy control children are presented. Also blood-based gene expression data related to autoimmunity in children with tic disorders are discussed.

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