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. 2024 Feb 29;23(1):71.
doi: 10.1186/s12934-024-02309-0.

Shared and more specific genetic determinants and pathways underlying yeast tolerance to acetic, butyric, and octanoic acids

Affiliations

Shared and more specific genetic determinants and pathways underlying yeast tolerance to acetic, butyric, and octanoic acids

Marta N Mota et al. Microb Cell Fact. .

Erratum in

Abstract

Background: The improvement of yeast tolerance to acetic, butyric, and octanoic acids is an important step for the implementation of economically and technologically sustainable bioprocesses for the bioconversion of renewable biomass resources and wastes. To guide genome engineering of promising yeast cell factories toward highly robust superior strains, it is instrumental to identify molecular targets and understand the mechanisms underlying tolerance to those monocarboxylic fatty acids. A chemogenomic analysis was performed, complemented with physiological studies, to unveil genetic tolerance determinants in the model yeast and cell factory Saccharomyces cerevisiae exposed to equivalent moderate inhibitory concentrations of acetic, butyric, or octanoic acids.

Results: Results indicate the existence of multiple shared genetic determinants and pathways underlying tolerance to these short- and medium-chain fatty acids, such as vacuolar acidification, intracellular trafficking, autophagy, and protein synthesis. The number of tolerance genes identified increased with the linear chain length and the datasets for butyric and octanoic acids include the highest number of genes in common suggesting the existence of more similar toxicity and tolerance mechanisms. Results of this analysis, at the systems level, point to a more marked deleterious effect of an equivalent inhibitory concentration of the more lipophilic octanoic acid, followed by butyric acid, on the cell envelope and on cellular membranes function and lipid remodeling. The importance of mitochondrial genome maintenance and functional mitochondria to obtain ATP for energy-dependent detoxification processes also emerged from this chemogenomic analysis, especially for octanoic acid.

Conclusions: This study provides new biological knowledge of interest to gain further mechanistic insights into toxicity and tolerance to linear-chain monocarboxylic acids of increasing liposolubility and reports the first lists of tolerance genes, at the genome scale, for butyric and octanoic acids. These genes and biological functions are potential targets for synthetic biology approaches applied to promising yeast cell factories, toward more robust superior strains, a highly desirable phenotype to increase the economic viability of bioprocesses based on mixtures of volatiles/medium-chain fatty acids derived from low-cost biodegradable substrates or lignocellulose hydrolysates.

Keywords: Chemogenomic analysis; Genome engineering; Superior yeasts; Toxicity mechanisms; Volatile fatty acids; Weak acids; Yeast tolerance.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Time-course effect of acetic, butyric and octanoic acids in yeast physiology during the growth curve. The progress of S. cerevisiae BY4741pHl growth in YPD (pH 4.5) in the absence (a) or presence of 62 mM (3.78 g/L) acetic acid (b), 15.04 mM (1.33 g/L) butyric acid (c) or 0.47 mM (0.07 g/L) octanoic acid (d), at 30 ℃, with orbital agitation, were followed based on culture OD600nm (dark blue circles), CFU/mL (light blue triangles), and glucose consumption (black triangles). The variation of cell permeability (RFU, green squares) and the percentage of PI-Positive cells (red diamonds) during cultivation are also shown. Results are representative of at least three independent experiments carried out
Fig. 2
Fig. 2
Time-course effect of acetic, butyric and octanoic Acids in yeast intracellular pH. Growth curve and intracellular pH (pHi, black and red crosses, and grey line) variation during S. cerevisiae BY4741pHl growth in MM (pH 4.5) in the absence (a) or presence of 53 mM (3.23 g/L) acetic acid (b), 11 mM (0.97 g/L) butyric acid (c) or 0.43 mM (0.06 g/L) octanoic acid (d) at 30 ℃ with orbital agitation. Cell growth was based on OD600nm (dark blue circles). Results from two representative experiments performed to obtain the pHi profiles are shown as the values of each replicate (black and red crosses) and as the calculated average of the two values (grey line)
Fig. 3
Fig. 3
Equivalent growth inhibitory concentrations of the weak acids used for the chemogenomic analysis. Spot growth of S. cerevisiae BY4741 cultivated in YPD solid medium (pH 4.5) either or not supplemented with acetic acid (75 mM C2), butyric acid (14 mM C4), or octanoic acid (0.3 mM C8). Exponentially-growing cell suspensions of BY4741 (OD600nm of 1 ± 0.05) were diluted to an OD600nm of 0.5 ± 0.005 (a) and this suspension was used to prepare 1:2 (b), 1:4 (c), 1:20 (d), 1:100 (e), 1:500 (f), 1:2500 (g) and 1:12,500 (h) diluted suspensions. Spot growth was registered after 48 h of incubation at 30 ℃. This picture is a representative example of several independent growth experiments
Fig. 4
Fig. 4
Diagram representing the number of specific or shared tolerance genes. The chemogenomic analysis was performed for equivalent concentrations of acetic acid (C2) (blue), butyric acid (C4) (red), and octanoic acid (C8) (green). Panel (a) refers to all the tolerance genes obtained for the three weak acids while panel (b) refers to the genes whose deletion led to the +  + phenotype
Fig. 5
Fig. 5
Biological functions enriched in the datasets obtained from the chemogenomic analysis performed. The datasets include genes found to be required for maximum yeast tolerance to 75 mM acetic acid (C2), 14 mM butyric acid (C4), or 0.3 mM octanoic acid (C8) at pH 4.5. Genes listed in, Additional file 1: Table S1, Additional file 2: Table S2, and Additional file 3: Table S3, were clustered according to the corresponding biological process GO assignments using the PANTHER Classification System (http://pantherdb.org), and functional categories were considered to be over-represented if the p-value < 0.05. The fold enrichment is calculated by dividing the number of genes present in the input dataset by the total number of genes of the yeast genome expected to belong to a specific functional class

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