Abstract

In spite of increased complexity in eukaryotes compared to prokaryotes, several basic metabolic and regulatory processes are conserved. Here we explored analogies in the eubacteria Escherichia coli and the unicellular fission yeast Schizosaccharomyces pombe transcriptomes under two carbon sources: 2% glucose; or a mix of 2% glycerol and 0.2% sodium acetate using the same growth media and growth phase. Overall, twelve RNA-seq libraries were constructed. A total of 593 and 860 genes were detected as differentially expressed for E. coli and S. pombe, respectively, with a log2 of the Fold Change ≥ 1 and False Discovery Rate ≤ 0.05. In aerobic glycolysis, most of the expressed genes were associated with cell proliferation in both organisms, including amino acid metabolism and glycolysis. In contrast in glycerol/acetate condition, genes related to flagellar assembly and membrane proteins were differentially expressed such as the general transcription factors fliA, flhD, flhC, and flagellum assembly genes were detected in E. coli, whereas in S. pombe genes for hexose transporters, integral membrane proteins, galactose metabolism, and ncRNAs related to cellular stress were overexpressed. In general, our study shows that a conserved "foraging behavior" response is observed in these eukaryotic and eubacterial organisms in gluconeogenic carbon sources.

Highlights

  • During the past decade, the amount of biological data has dramatically increased due to the development of high throughput experimental biology such as massive parallel DNA sequencing technologies

  • We introduce our data of genes with Log2FC values for a Functional Enrichment Analysis in STRING database for subsequently assignment of enriched Gene Ontology (GO), KEGG pathways, and local network STRING (LNS) terms as they are designated in this database [31,32], and to cluster the differentially expressed genes (DEGs) into functional categories with statistical enrichment using a lenient False Discovery Rate (FDR) stringency < 25% as allowed by the software, since we are using two species from two different domains of life

  • The analysis showed that 1164 clusters, 916 orthologous clusters and 248 single-copy gene clusters (Fig 1) were formed based on the protein sequences of both species

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Summary

Introduction

The amount of biological data has dramatically increased due to the development of high throughput experimental biology such as massive parallel DNA sequencing technologies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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