2018bioRxiv (Cold Spring Harbor Laboratory)Open access

Gene function contributes to gene expression levels in S. cerevisiae

Mark J. Hickman, Andrea Jackson, Abigail J. Smith, Julianne M. Thornton, Amanda Tursi

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Abstract

ABSTRACT It is not understood what evolutionary factors drive some genes to be expressed at a higher level than others. Here, we hypothesized that a gene’s function plays an important role in setting expression level. First, we established that each S. cerevisiae gene is maintained at a specific expression level by analyzing RNA-seq data from multiple studies. Next, we found that mRNA and protein levels were maintained for the orthologous genes in S. pombe , showing that gene function, conserved in orthologs, is important in setting expression level. To further explore the role of gene function in setting expression level, we analyzed mRNA and protein levels of S. cerevisiae genes within gene ontology (GO) categories. The GO framework systematically defines gene function based on experimental evidence. We found that several GO categories contain genes with statistically significant expression extremes; for example, genes involved in translation or energy production are highly expressed while genes involved in chromosomal activities, such as replication and transcription, are weakly expressed. Finally, we were able to predict expression levels using GO information alone. We created and optimized a linear equation that predicted a gene’s expression based on the gene’s membership in 161 GO categories. The greater number of GO categories with which a gene is associated, the more accurately expression could be predicted. Taken together, our analysis systematically demonstrates that gene function is an important determinant of expression level.

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ABSTRACT It is not understood what evolutionary factors drive some genes to be expressed at a higher level than others. Here, we hypothesized that a gene’s function plays an important role in setting expression level. First, we established that each S. cerevisiae gene is maintained at a specific expression level by analyzing RNA-seq data from multiple studies. Next, we found that mRNA and protein levels were maintained for the orthologous genes in S. pombe , showing that gene function, conserved in orthologs, is important in setting expression level. To further explore the role of gene function in setting expression level, we analyzed mRNA and protein levels of S. cerevisiae genes within gene ontology (GO) categories. The GO framework systematically defines gene function based on experimental evidence. We found that several GO categories contain genes with statistically significant expression extremes; for example, genes involved in translation or energy production are highly expressed while genes involved in chromosomal activities, such as replication and transcription, are weakly expressed. Finally, we were able to predict expression levels using GO information alone. We created and optimized a linear equation that predicted a gene’s expression based on the gene’s membership in 161 GO categories. The greater number of GO categories with which a gene is associated, the more accurately expression could be predicted. Taken together, our analysis systematically demonstrates that gene function is an important determinant of expression level.

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Available abstract

ABSTRACT It is not understood what evolutionary factors drive some genes to be expressed at a higher level than others. Here, we hypothesized that a gene’s function plays an important role in setting expression level. First, we established that each S. cerevisiae gene is maintained at a specific expression level by analyzing RNA-seq data from multiple studies. Next, we found that mRNA and protein levels were maintained for the orthologous genes in S. pombe , showing that gene function, conserved in orthologs, is important in setting expression level. To further explore the role of gene function in setting expression level, we analyzed mRNA and protein levels of S. cerevisiae genes within gene ontology (GO) categories. The GO framework systematically defines gene function based on experimental evidence. We found that several GO categories contain genes with statistically significant expression extremes; for example, genes involved in translation or energy production are highly expressed while genes involved in chromosomal activities, such as replication and transcription, are weakly expressed. Finally, we were able to predict expression levels using GO information alone. We created and optimized a linear equation that predicted a gene’s expression based on the gene’s membership in 161 GO categories. The greater number of GO categories with which a gene is associated, the more accurately expression could be predicted. Taken together, our analysis systematically demonstrates that gene function is an important determinant of expression level.

Key concepts: Gene, Gene expression, Biology, Genetics, Pair-rule gene, Function (biology), Regulation of gene expression, Transcription (linguistics)

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