Recognition of galactose by a scaffold protein recruits a transcriptional activator for the GAL regulon induction in Candida albicans
Abstract
The GAL pathway of yeasts has long served as a model system for understanding of how regulatory mode of eukaryotic metabolic pathways evolves. While Gal4 mode has been well-characterized in Saccharomycetaceae clade, little is known about the regulation of the GAL pathway in other yeasts. Here, we find that Rep1, a Ndt80-like family transcription factor, serves as a galactose sensor in the commensal-pathogenic fungus Candida albicans. It is presented at the GAL gene promoters independent of the presence of galactose. Rep1 recognizes galactose via a direct physical interaction. The net result of this interaction is the recruitment of a transcriptional activator Cga1 (Candida galactose gene activator, orf19.4959) and transcription of the GAL genes proceeds. Rep1 and Cga1 are conserved across the CTG species. Rep1 itself does not possess transcriptional activity. Instead, it provides a scaffold to recruit different factors for transcriptional regulation. Rep1-Cga1 mode of regulation represents a new example of network rewiring in fungi, which provides insight into how C. albicans evolves transcriptional programs to colonize diverse host niches.
Data availability
The mass spectrometry proteomics data are deposited to the ProteomeXchange Consortium with the dataset identifier PXD037522. The ChIP-Seq data are deposited to Dryad https://doi.org/10.5061/dryad.tqjq2bw35. Source Data files have been provided in Figure 1-Source data, Figure 1-figure supplement 2-Source data, Figure 1-figure supplement 3-Source data, Figure 2-Source Data 1&2, Figure 2-figure supplement 4-Source data, Figure 3-Source Data 1&2, Figure 3-figure supplement 5-Source data 1&2, Figure 4-Source Data 1&2, and Figure 4-figure supplement 6-Source data.
-
Candida albicans transcription factor Rep1 ChIP-SeqDryad Digital Repository, doi:10.5061/dryad.tqjq2bw35.
Article and author information
Author details
Funding
National Natural Science Foundation of China (32070074)
- Yang Lu
National Natural Science Foundation of China (32170089)
- Chang Su
National Natural Science Foundation of China (81973370)
- Chang Su
Natural Science Foundation of Hubei Province (2022CFB103)
- Chang Su
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Sun et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 791
- views
-
- 162
- downloads
-
- 4
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Genetics and Genomics
An unprecedented amount of SARS-CoV-2 data has been accumulated compared with previous infectious diseases, enabling insights into its evolutionary process and more thorough analyses. This study investigates SARS-CoV-2 features as it evolved to evaluate its infectivity. We examined viral sequences and identified the polarity of amino acids in the receptor binding motif (RBM) region. We detected an increased frequency of amino acid substitutions to lysine (K) and arginine (R) in variants of concern (VOCs). As the virus evolved to Omicron, commonly occurring mutations became fixed components of the new viral sequence. Furthermore, at specific positions of VOCs, only one type of amino acid substitution and a notable absence of mutations at D467 were detected. We found that the binding affinity of SARS-CoV-2 lineages to the ACE2 receptor was impacted by amino acid substitutions. Based on our discoveries, we developed APESS, an evaluation model evaluating infectivity from biochemical and mutational properties. In silico evaluation using real-world sequences and in vitro viral entry assays validated the accuracy of APESS and our discoveries. Using Machine Learning, we predicted mutations that had the potential to become more prominent. We created AIVE, a web-based system, accessible at https://ai-ve.org to provide infectivity measurements of mutations entered by users. Ultimately, we established a clear link between specific viral properties and increased infectivity, enhancing our understanding of SARS-CoV-2 and enabling more accurate predictions of the virus.
-
- Cell Biology
- Genetics and Genomics
Cells react to stress by triggering response pathways, leading to extensive alterations in the transcriptome to restore cellular homeostasis. The role of RNA metabolism in shaping the cellular response to stress is vital, yet the global changes in RNA stability under these conditions remain unclear. In this work, we employ direct RNA sequencing with nanopores, enhanced by 5ʹ end adapter ligation, to comprehensively interrogate the human transcriptome at single-molecule and -nucleotide resolution. By developing a statistical framework to identify robust RNA length variations in nanopore data, we find that cellular stress induces prevalent 5ʹ end RNA decay that is coupled to translation and ribosome occupancy. Unlike typical RNA decay models in normal conditions, we show that stress-induced RNA decay is dependent on XRN1 but does not depend on deadenylation or decapping. We observed that RNAs undergoing decay are predominantly enriched in the stress granule transcriptome while inhibition of stress granule formation via genetic ablation of G3BP1 and G3BP2 rescues RNA length. Our findings reveal RNA decay as a key component of RNA metabolism upon cellular stress that is dependent on stress granule formation.