The oxidative cost of losing low temperature viability protein 1 [Ltv1]: insights into cellular damage and homeostasis

Abstract

The Ltv1 protein has been characterized with roles in ribosome biogenesis, maintenance of rRNA stability, ATP export, osmotic stress response, and signaling activation of the Target of Rapamycin pathway. We screened ltv1Δ/ltv1Δ mutants for additional phenotypic manifestations due to loss of Ltv1. We observed growth differences consistent with Ltv1’s well characterized roles, alongside evidence supporting a less-established role in reactive oxygen species response. In further characterization of cells lacking Ltv1 we documented higher levels of endogenous ROS and a greater accumulation response of reactive oxygen species to exogenous stress. Utilizing RNA-Sequencing we then determined the gene expression differences underlying the ltv1-deficiency induced oxidative stress sensitivity. This work elucidates a new significant role of Ltv1 in cellular homeostasis and protection against damage.

Elucidation of Ltv1's roles in oxidative stress response with gene expression differences determined through RNA-Sequencing.

Introduction

Ltv1 is involved in numerous growth-related cellular activities, preeminently described for its role in ribosome 40S subunit biogenesis, maturation, and transportation (Seiser et al. 2006). Past studies have revealed that Ltv1 plays an essential chaperone role assisting correct protein folding at the ribosomal head (Collins et al. 2018). Loss of ltv1 not only results in protein misfolding at the ribosomal head, but also increases rRNA instability and mistakes in translation (Collins et al. 2018). Once assembly, export, and folding are complete, Ltv1 assists in the final step of ribosomal maturation in the cytoplasm. Ribosome maturation requires several other proteins for completion working through a series of steps including demethylation, pre-rRNA transcript cleavage and degradation, and processing (Fassio et al. 2010). The Ltv1p binds directly to small ribosomal subunit proteins, such as Rps3, Rps15, and Rps20 to initiate ribosomal maturation. To ensure the premature ribosome can’t begin translation of free mRNA and risk translational errors, Ltv1 remains bound effectively blocking translation initiation (Belhabich-Baumas et al. 2017, Blomqvist et al. 2023). Once all of the modifications for ribosome maturation are complete, Ltv1 is phosphorylated allowing release and launch of translation (Ghalei et al. 2015, Blomqvist et al. 2023). Ltv1 is linked to small ribosomal subunit export from the nucleus to then join the large ribosomal subunit to carry out translation (Seiser et al. 2006). It accomplishes this role by containing a functional nuclear export signal that allows it to shuttle 40S rRNA from the nucleus to the cytoplasm (Seiser et al. 2006). In fact, cells that lack ltv1 are ribosome-diminished, containing only about half the normal amount of ribosomes (Loar et al. 2004). Ltv1 leads 40S processing, chaperoning the proper folding of rRNA and recruitment of ribosomal proteins in preparation for export to the cytoplasm (Han et al. 2022, Blomqvist et al. 2023).

In other cellular functions, the Ltv1p is implicated in EGO/GSE complex signaling, rRNA stability maintenance via chaperoning, ATP export, and osmotic stress response (Fig. 1). As mentioned, Ltv1 has a role in EGO complex signaling, which has downstream effects on pathways governed by Target of Rapamycin (TOR) Complex 1 (TORC1) (Wullschleger et al. 2006, Woolford and Baserga 2013, Blomqvist et al. 2023). In S. cerevisiae, TOR signaling contributes broadly to cellular growth processes, including transcription, translation, ribosome biogenesis, nutrient uptake, and autophagy (Gonzalez and Rallis 2017, Dokládal et al. 2021). The EGO complex, composed of Meh1, Slm4, Ego2, Gtr1, and Gtr2, resides at the vacuolar membrane and relays amino acid availability (Binda et al. 2009, Powis et al. 2015). When Ltv1 is incorporated into this complex, it is then designated EGO/GSE and can mediate additional signaling capacities, such as regulation of the amino acid permease Gap1 (Gao and Kaiser 2006, Yang et al. 2013). Further, Ltv1 also assists in ATP export. Deletion of ltv1 results in lower extracellular ATP concentrations, indicating that Ltv1 likely plays a role in ATP efflux (Peters et al. 2015). Ltv1 was also studied from the perspective of genes linked to ribosome function whose expression is impacted by environmental stressors (Loar et al. 2004). This work elucidated details of Ltv1’s roles in ribosome biogenesis and also uncovered a link between Ltv1 and environmental stress conditions of temperature fluctuations, osmotic stress, and oxidative stress. However, for these later roles no mechanism for the increased sensitivity of ltv1 mutants was uncovered. Other proteins described by Loar et al. had similar phenotypes, such as Yar1, where it was defined that the stress-sensitivity was linked to its ribosome roles, however, the same did not hold true for Ltv1. In our present study we conduct additional experiments investigating Ltv1’s role in reactive-oxygen species response and use RNA-Sequencing to add to the broad functional landscape of Ltv1 and clarify its role in oxidative stress response.

Figure 1.

An overview of Ltv1 roles in S. cerevisiae. Ltv1 has been characterized as having roles in ribosome biogenesis, maintenance of rRNA stability, ATP export, environmental, oxidative, and osmotic stress responses, and signaling activation of the Target of Rapamycin pathway.

Results

Qualitative screening of functional differences due to loss of ltv1

To screen for phenotypic manifestations that occur due to loss of ltv1, we utilized 15 Phenotypic Microarray (PM) plates to characterize growth differences between our wildtype and ltv1Δ/ltv1Δ strains. These plates contained 1440 total wells, many of which were different concentrations of the same condition, resulting in 1005 different conditions tested. These plates were used to test growth differences of the ltv1Δ/ltv1Δ mutant by analyzing adaptation time, rate of growth, and efficiency of growth (Fernandez-Ricaud et al. 2007). We conducted these assays in comparison to our wildtype strain, which was published as part of a previous study (Remines et al. 2024). Due to the qualitative nature of this screening, decisions on what to report as observable phenotypes were based on, 1) observation of similar growth patterns across different concentrations of the same condition within the PM and 2) corroborating growth pattern perturbances through analysis of different PM agents that impact the same intracellular pathways. Overall, we observed growth differences due to loss of ltv1 in 119 total wells that encompass 91 different conditions. This represents altered growth in 9.05% of the conditions tested (Supplementary Table 1). Supplementary Table 1 shows all wells that contain significant growth differences. All PM growth curves are provided in Supplementary Table 2. A full list of all well contents is given in Supplementary Table 3.

To identify categories in which ltv1-deficiency had a disproportionate impact, we compared the fraction of growth-altered conditions within each category to that category’s overall representation on the PM plates. As seen in Table 1, growth differences were overrepresented in categories related to Ltv1’s known cellular roles including protein synthesis and osmotic stress. Additional roles for Ltv1 may be indicated by recognition of growth changes due to damage to cell membrane/wall stability, toxic ions, alterations in cellular respiration, ion chelation, DNA synthesis inhibitors, pH alterations, and oxidation/oxidative stress.

Table 1.

Overrepresented mechanisms of action of conditions causing altered growth of ltv1Δ/ltv1Δ strains.

Mechanism of action of PM condition	ltv1∆/ltv1∆ Growth defects category percentages	Percentage of PM wells by category
Osmolarity/osmotic sensitivity	23.7%	2.2%
Cell membrane/wall stability	16.1%	1.5%
Toxic ions	15.1%	1.4%
Protein synthesis	10.8%	1%
Cellular respiration	4.3%	0.4%
Ion chelation	4.3%	0.4%
tRNA synthesis inhibition	4.3%	0.4%
DNA synthesis inhibition	4.3%	0.4%
pH	2.2%	0.2%
Oxidation/oxidative stress	2.2%	0.2%

Conditions with a variety of mechanisms of action exhibited increased alteration of growth between the wildtype and ltv1-deficient strains.

Loss of LTV1 results in increased sensitivity to reactive oxygen species inducing conditions

Loar et al. previously showed that loss of ltv1 results in increased sensitivity to diamide (Loar et al. 2004). The Phenotypic Microarray growth experiments recapitulate the ltv1-deficiency induced sensitivity to diamide (PM 21D, H1-4) and further showed sensitivity of the ltv1Δ/ltv1Δ mutant to another Reactive Oxygen Species inducing condition sodium selenite (PM 21D, G1-4) (Fig. 2). Growth inhibition of the ltv1Δ/ltv1Δ mutant manifested as increased adaptation times or complete inhibition of growth compared to wildtype cells (Fig. 2A and B). Further, we validated the phenotype outside of the Phenotypic Microarray plates using independent growth-curve assays, which again replicated the sensitivity results (Fig. 2C). Sodium selenite and diamide impact the cell through independent mechanisms of ROS induced damage. Sodium selenite promotes oxidative phosphorylation by interacting with thiols, including the natural ROS-eliminating agent glutathione (GSH) (Peyroche et al. 2012). GSH is the primary antioxidant in a cell’s defense against ROS, and is oxidized to its GSSG form after neutralizing ROS species (Averill-Bates 2023). Diamide readily oxidizes GSH to GSSG, again lowering a cell’s ability to combat ROS (Luikenhuis et al. 1998).

Figure 2.

Growth differences observed in response to oxidative stressors: A: sodium selenite, B: diamide results from phenotypic microarray: growth curves come from PM plate 21D wells G1 (42 μM, low, solid line), G3 (382 μM, mid, dashed line), G4 (1.145 mM, high, dotted line), H1 (47 μM, low, solid line), H3 (142 μM, mid, dashed line), and H4 (1.278 mM, high, dotted line). C: Sodium selenite effects replicated independent of the Phenotypic Microarray at 40 μM. In each instance, the ltv1∆/ltv1∆ mutant strains exhibited decreased growth, indicating an inability to combat ROS as efficiently as the wildtype strain.

Ltv1 deletion results in increased endogenous ROS levels.

Cells possess multiple pathways to mitigate ROS, and it is notable that both sodium selenite and diamide exert their effects through glutathione. Since the ltv1∆/ltv1∆ strain exhibits impaired growth under ROS-generating conditions, we hypothesized that its growth could be improved by supplementing with antioxidant compounds. Consistent with this hypothesis, the addition of exogenous glutathione (GSH) enhanced the growth of the ltv1∆/ltv1∆ mutant and resulted in faster proliferation and improved overall growth efficiency (Fig. 3A). Further, addition of sodium thiosulfate, known to decrease oxidative stress by donating electrons to the radical ROS species, led to improved growth of the ltv1-deficient strain only (Fig. 3B) (Bijarnia et al. 2015). Glutathione addition is shown during growth in rich media (YPD), while sodium thiosulfate supplementation is shown during growth in minimal media (SC: synthetic complete); in both cases improvements in growth are observed, while reduced growth of ltv1∆/ltv1∆ mutant strains compared to wildtype is most notable in SC media. The rescue of the growth deficiency phenotype under oxidative stress when supplemented with antioxidant agents, led us to hypothesize that Ltv1 has an important role in mediating ROS levels.

Figure 3.

Response to antioxidants and ROS levels in ltv1∆/ltv1∆ mutant strains. A-B. Growth of ltv1-deficient strains was improved by A: Glutathione (2.5 mM) and B: Sodium thiosulfate (300 μM) supplementation, while growth of wildtype cells was not. C. ROS were measured using H₂DCFDA in four conditions: wildtype with 100 mM HU and without HU and ltv1Δ/ltv1Δ with 100 mM HU and without HU. Wildtype cells exhibited consistently lower ROS levels compared to the ltv1Δ/ltv1Δ strain, including a reduced response to HU-induced ROS stress. ltv1Δ/ltv1Δ P-value < 0.1 (*), wildtype P-value < 0.05 (**).

To further explore this role of Ltv1 in oxidative stress regulation, we next utilized H₂DCFDA (2′,7′-dichlorodihydrofluorescein diacetate) to measure intracellular ROS levels. We found that the ltv1∆/ltv1∆ mutant strains have higher intrinsic ROS levels during normal logarithmic growth compared to wildtype (Fig. 3C). Additionally, we wanted to test strain responses to a compound that would specifically increase ROS rather than dampening the cell’s ROS-response pathways. We investigated growth of the wildtype and ltv1Δ/ltv1Δ strains due to treatment with Hydroxyurea (HU) (Fig. 3C) and found the ltv1Δ/ltv1Δ mutant has a heightened response to this additional ROS source.

RNA-sequencing reveals decreased expression of oxidative stress response genes due to loss of ltv1

We carried out RNA-Sequencing (RNA-Seq) to determine gene expression differences that result due to deletion of ltv1. Eighty-six differentially expressed genes (DEGs) were identified between wildtype and ltv1Δ/ltv1Δ strains that met cutoffs of adj P-value ≤ 0.05, −0.58 ≤ log2 Fold Change (log2FC) ≥ 0.58 (Supplementary Table 4). These DEGs were comprised of 34 upregulated genes and 52 downregulated genes. (RNA-Seq data has been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE310133 (Remines, 2025), all significant DEGs are also available in Supplementary Table 4 (Edgar et al. 2002).) Gene Ontology (GO) analysis of these DEGs revealed enrichment for several terms across the three domains (Table 2). GO identifies downregulated DEGs in the ltv1∆/ltv1∆ mutant having roles in response to toxic substances and oxidoreductase activity/combatting ROS, and glycogen metabolism and catabolism (Table 2). Upregulated DEGs broadly have roles in RNA processing, biogenesis and stabilization, and export (Table 2). Of the 34 upregulated DEGs, 23 are involved with RNA in some fashion (Supplementary Table 4, GSE310133). These results are consistent with Ltv1’s well documented roles in ribosomal small subunit biogenesis and export. Previous studies have found that deletion of ltv1 causes significant enough impacts to result in increased translation mistakes and defective rRNA. Therefore, the upregulation of genes involved in RNA biogenesis and processing could be a response to mitigate the effects of absence of ltv1.

Table 2.

GOConsortium gene ontology results of upregulated and downregulated DEGs.

	GO Category	GO:ID	Description	Genes in GO term	DEGs in GO term	±	FDR
Upregulated genes	Biological process	GO:0 000 055	Ribosomal large subunit export from nucleus	33	MRT4, RPF1, ECM1, RRS1	+	5.19E-03
		GO:0 000 466	Maturation of 5.8S rRNA from tricistronic rRNA transcript (SSU-rRNA, 5.8S rRNA, LSU-rRNA)	92	YTM1, UTP23, RPF1, MTR4, RRS1	+	1.57E-02
	Molecular function	GO:0 003 724	RNA helicase activity	42	MTR4, HCA4, DBP2, FAL1	+	6.90E-02
	Cellular component	GO:0 030 687	Preribosome, large subunit precursor	62	YTM1, MRT4, ECM1, RRS1, RLP24	+	4.35E-05
		GO:0 005 730	Nucleolus	300	YTM1, UTP23, MRT4, RPF1, FAF1, MTR4, HCA4, ECM1, RRS1, POP3, FAL1, NSR1, RLP24	+	4.79E-08
Downregulated genes	Biological process	GO:0 005 980	Glycogen catabolic process	3	GDB1, GPH1	+	3.99E-02
		GO:1 990 961	Xenobiotic detoxification by transmembrane export across the plasma membrane	9	FLR1, YHK8, SNQ2	+	1.94E-02
		GO:0 005 978	Glycogen biosynthetic process	13	GLC3, GSY2, GDB1	+	3.24E-02
		GO:0 006 749	Glutathione metabolic process	19	ECM4, TRX2, GTT1, GTT2	+	6.74E-03
		GO:0 098 869	Cellular oxidant detoxification	31	GPX2, ECM4, GTT1, SOD2	+	2.57E-02
		GO:0 006 081	Cellular aldehyde metabolic process	41	AAD3, AAD6, HSP31, AAD4	+	4.95E-02
		GO:0 065 007	Biological regulation	1695	AIT1, DCS2	−	4.21E-02
	Molecular function	GO:0 004 364	Glutathione transferase activity	8	ECM4, GTT1, GTT2	+	1.26E-02
		GO:0 004 029	Aldehyde dehydrogenase (NAD+) activity	12	YNL134C, ALD3, OSI1	+	1.94E-02
		GO:0 016 209	Antioxidant activity	30	GPX2, ECM4, GTT1, SOD2	+	1.87E-02
		GO:0 016 651	Oxidoreductase activity, acting on NAD(P)H	30	YNL134C, ZTA1, OYE3, YLR460C	+	1.66E-02
		GO:0 016 616	Oxidoreductase activity, acting on CH-OH group of donors, NAD or NADP as acceptor	84	GCY1, IMD2, AAD3, ADH6, BDH2, AAD4	+	1.42E-02
	Cellular component	GO:0 032 991	Protein-containing complex	2169	IMD2, APE1	−	1.62E-03

GOConsortium was utilized to determine over- and under-represented Gene Ontology (GO) categories from separate upregulated and downregulated DEG lists. Parent categories are shown without child categories. Enrichment in Biological Process, Molecular Function, and Cellular Component was analyzed with genes at an adj P-value < 0.05 (Mi et al. 2019). Overrepresented terms are indicated with “+” whereas underrepresented terms are indicated with “−.”

Of great interest is the finding that loss of Ltv1 results in downregulation of genes involved in response to oxidative stress. This finding provides for a new mechanistic understanding of the increased endogenous levels and sensitivity to ROS in these strains and supports the previous finding that this phenotype is unrelated to Ltv1’s roles in ribosome biogenesis (Loar et al. 2004). Differentially expressed genes involved in ROS response pathways were mapped to visualize the potential overall impact of reduction of expression of this category of genes, Fig. 4. Seven significantly downregulated DEGs with roles in reducing ROS include: OYE3 (log2FC = −2.91, adj P-value = 0.00415), HSP31 (log2FC = −2.24, adj P-value = 0.00049), GPX2 (log2FC = −2.07, adj P-value = 0.01467), GTT1 (log2FC = −1.69, adj P-value = 0.02434), GTT2 (log2FC = −3.71, adj P-value = 1.63E-07), TRX2 (log2FC = −1.86, adj P-value = 0.04626), and SOD2 (log2FC = −1.63, P-value = 0.02426).

Figure 4.

Glutathione and ROS response connected pathways. Production of glutathione in S. cerevisiae is used to effectively combat ROS that are generated from sources such as metabolic pathways. Gene names are capitlized in italics, differentially expressed genes, all lowered in expression, due to loss of Ltv1 are indicated by filled circles next to the gene name. GSH is the reduced version of glutathione, used in neutralizing ROS, at which time it is oxidized to GSSG (Henderson et al. 2014, Diaz and Shi 2022). GSSG can then be reduced back to GSH using an NADPH molecule, to once again be used to eliminate ROS. This redox cycle is repeated as ROS are encountered. The decreased expression of seven genes impacting multiple cellular pathways developed to combat ROS hinders these key protective mechanisms.

Discussion

Ltv1’s roles in TOR activation, ribosome biogenesis, and rRNA stability have been the most comprehensively studied to date. While previous work identified a role for the protein in environmental stress response and found it to be unlinked to its ribosome functions, no further mechanistic details were brought forth (Loar et al. 2004). Using complementary, high throughput experimentation, we have been able to capture a wider spectrum of responses due to loss of ltv1 and gain deeper insight into the link to oxidative stress.

We show here that loss of Ltv1 results in increased levels of ROS and greater sensitivity to oxidative stressors. Cells produce ROS during daily metabolic activity, such as during mitochondrial oxidative metabolism, making these defects in ltv1 mutants an obstacle to cellular homeostasis. The increased sensitivity of ltv1-deficient strains to ROS appears to be caused, at least in part, due to reduced gene expression of multiple genes implicated in oxidative stress response. These include Gpx2, Gtt1, and Gtt2 which play direct roles in the elimination of ROS. Gpx2 reduces ROS by catalyzing a reduction-oxidation reaction between GSH and GSSG (Canizal-García et al. 2021) (Fig. 4). Similarly, Gtt1 and Gtt2 reduce ROS by catalyzing a reduction-oxidation reaction, through conjugating GSH to receive a sulfide group (Mariani et al. 2008) (Fig. 4). Previous research has found that when GTT1 and GTT2 are deleted, cells under oxidative stress, particularly hydrogen peroxide, exhibited increased protein carbonylation, an irreversible modification of proteins that are exposed to ROS, and therefore higher cell death rates (Mariani et al. 2008). Therefore, their downregulation would reduce the cellular capacity to combat these reactive species. Similarly, reduced expression of OYE3 can lower a cell’s ability to neutralize ROS compounds (Odat et al. 2007). Oye2 and Oye3 dimerize to renew reduced GSH for reuse in reduction-oxidation reactions with ROS species (Liu et al. 2020) (Fig. 4). Oye2/Oye3 also help to regulate and induce programmed cell death in scenarios of high-ROS, serving as a tunable redox module determining cell fate (Odat et al. 2007). Hsp31 is a crucial player in homeostatic balance of GSH levels for combatting ROS, with deletion of hsp31 lowering GSH concentrations within a cell (Bankapalli et al. 2015). HSP31 encodes a methylglyoxalase involved in pyruvate metabolism which also impacts oxidative stress response (Bankapalli et al. 2015). Methylglyoxal is a highly reactive α-oxoaldehyde detoxified via the glyoxalase system directly dependent on GSH availability (Pavin et al. 2021) (Fig. 4). Hsp31 has been hypothesized to regulate GSH/GSSG ratios by controlling the available amount of NADPH present, thereby regulating the essential electron donor needed to form GSH (Bankapalli et al. 2015). Further studies have found that the presence of hydrogen peroxide and other common ROS promote transcription of HSP31 (Aslam and Hazbun 2016). Reduced HSP31 expression in ltv1Δ/ltv1Δ mutants, which also have higher intrinsic ROS levels, likely contributes to the diminished growth observed in further ROS-inducing conditions. In the thioredoxin pathway, Trx2 contributes a NADPH molecule used as an electron donor to form GSH from GSSG, while in the superoxide radical degradation path a redox reaction with NADPH allows Trx2 to eliminate hydrogen peroxide reactivity (González-Siso et al. 2009). Loss of Trx2 results in significantly increased levels of GSSG (Muller 1996). Decreased expression in the ltv1Δ/ltv1Δ mutants therefore contributes to lowering intracellular GSH available to combat multiple types of ROS. Sod2 protects against oxidative stress by converting superoxides into oxygen and hydrogen peroxide, which are then converted into oxygen and water within the superoxide radical degradation pathway (Palma et al. 2020). Loss of sod2 in S. cerevisiae leads to increased genome instability during oxidative stress (Gupta et al. 2023). When SOD2 is mutated in human cells, oxidative damage and degradation of DNA leads to several forms of cancer (Lebovitz et al. 1996). Complete deletion of SOD2 is lethal in mice as well, as the cells are unable to maintain mitochondrial integrity due to ROS accumulation leading to organ damage (Li et al. 1995).

Sodium selenite and diamide promote oxidative phosphorylation, oxidative stress, and DNA strand breakage. These compounds interact with GSH, using up its reductive potential, forming GSSG (Luikenhuis et al. 1998, Peyroche et al. 2012). The work presented here expands our knowledge on the impacts of Ltv1 on ROS pathways through downregulation of multiple ROS-combatting genes. The identification of seven genes involved in oxidative stress elimination, whose expression is reduced due to the deletion of Ltv1, provides a link to ROS sensitivity of these cells. Further, we have shown these cells have increased baseline ROS and increased levels of oxidative stress in response to external stimuli. Additionally, we show partial rescue of a slow growth phenotype by addition of glutathione, indicating the ROS burden has a role in the impaired growth of these cells (Loar et al. 2004). These data contribute to our understanding of Ltv1 impacts on controlling intracellular oxidative stress.

Beyond expanding our current understanding of Ltv1’s role in oxidative stress, this work provides additional evidence to fully understanding its already defined roles. By identifying additional protein synthesis and osmotic stress conditions that alter cellular growth, we expand the contexts in which these effects can be investigated further. Our work also identifies potential novel, or perhaps interconnected, roles of Ltv1 in cell membrane/wall stability, toxic ion response, ion chelation, and pH homeostasis for continued examination. Additionally, the RNA-Seq dataset reveals genome-wide transcriptional changes that likely reflect both known and novel effects of ltv1 loss beyond its canonical roles impacting ribosomes and translation.

The human homolog of Ltv1, also named LTV1, has conserved ribosomal roles, acting as a 40S ribosomal subunit assembly factor consistent with those identified in S. cerevisiae (Ghalei et al. 2015, Han et al. 2022, Blomqvist et al. 2023). Mutations in the human homolog have been associated with disease development. Prior studies have implicated LTV1 deficiency in human glioma and breast malignancy cell lines (Collins et al. 2018). Additionally, LTV1 mutation is implicated in LIPHAK syndrome, a recessive dermatological disorder characterized by poikiloderma, hair abnormalities, and acral keratoses, with pathophysiology thought to be driven by deficient ribosome biogenesis and development (Han et al. 2022). The additional insights we have provided into yeast Ltv1 function in oxidation homeostasis create a need for further studies to elucidate the mechanism by which LTV1 maintains oxidative homeostasis, which may have implications for understanding its role in human disease. Given the highly conserved nature of yeast to human LTV1, future human homolog studies should explore the impact that LTV1 has on oxidative pathways, as oxidative dysfunction is known to be aberrant in many neoplasms (Jomova et al. 2025).

Materials and methods

Homozygous knockout strain creation

Deletion of ltv1 was created in S288C strains through PCR generation of a gene cassette containing 200–400 bp homology arms and an internal G418 resistant selectable marker, followed by transformation into haploid strains and mating to generate the diploid homozygous deletant. This ltv1Δ/ltv1Δ strain has the genotype: MAT a/α, ltv1::KANMX/ltv1::KANMX, leu2-3/leu2-3, his3-∆200/his3-∆200, trp1-∆1/trp1-∆1, lys2-801/LYS2, ura3-52/ura3-52, can1-100/CAN1, ade2-101/ade2-101, 2x [CF:(ura3::TRP1, SUP11, CEN4, D8B)], (matched to the wildtype strain genotype minus the ltv1-deficiency).

Phenotypic Microarray

Phenotypic Microarray plates were obtained from BiOLOG. 15 PM plates were chosen based on conditions to test: PM1 and PM2A test carbon sources, PM3B tests nitrogen sources, PM4A tests phosphorous and sulfur sources, PM5 tests nutrient supplements, PM6-8 test peptide nitrogen sources, PM9 tests osmolytes, PM10 tests pH, and PM21D, PM22D, PM23A, PM24C, and PM25D test for chemical sensitivities. Substrate contents of each well are listed in Supplementary Table 3; concentrations are proprietary to BiOLOG. Growth impacts were tested in duplicate on wildtype cells, however experimental constraints only allowed testing of ltv1Δ/ltv1Δ mutants once (Remines et al. 2024). To ensure reproducibility of results we focused on treatments where similar growth impacts were seen across multiple concentrations of the same condition and those where comparable growth impacts were seen across multiple conditions with similar mechanisms of action. Plates were inoculated per manufacturer instructions and according to the genotypes of our strains. Each yeast strain was struck on separate BUY agar plates from BiOLOG and grown for 24 h at 30°C, subcultured to new BUY plates for an additional 24 h, then added into previously prepared stock solutions to obtain uniform suspensions with a turbidity of 62% transmittance. A BioTek ELx800 microplate reader was used to obtain OD₆₀₀ absorbance readings of each plate at 0, 24, 48, and 72 h. Prior to each OD₆₀₀ reading cells were resuspended via pipetting. All plates were sealed with parafilm and incubated at 30°C with agitation between measurements. OD₆₀₀ readings from each time point were plotted to generate growth curves using excel for first analysis, Supplementary Table 2. Growth curves from the Phenotypic Microarray were generated using Prism with the third order polynomial (cubic) equation for Fig. 2A and B.

Wildtype and ltv1Δ/ltv1Δ strains were tested for reproducibility of the response observed in the PM plates in sodium selenite. Cells were diluted to 0.1 OD₆₀₀ in Synthetic Complete (SC) or SC plus 40 uM sodium selenite, in duplicate, and incubated in a 96-well plate. A kinetic program was set-up on an Agilent BioTek Synergy H1M2 microplate plate reader to maintain 30°C with an orbital shake path across 72 h with OD₆₀₀ measurements every 30 min. Data was plotted to generate growth curves of an average of the biological replicates.

Antioxidant response

Wildtype and ltv1Δ/ltv1Δ strains were tested for response to glutathione and sodium thiosulfate via growth curve analysis. Cells were diluted to 0.1 OD₆₀₀ in Yeast Peptone Dextrose (YPD) or YPD plus 2.5 mM glutathione, or Synthetic Complete (SC) or SC plus 300 uM sodium thiosulfate, in duplicate, and incubated in a 96-well plate. A kinetic program was set-up on an Agilent BioTek Synergy H1M2 microplate plate reader to maintain 30°C with an orbital shake path across 48 h with OD₆₀₀ measurements every 30 min. Data was plotted to generate growth curves of an average of the biological replicates.

ROS measurement

Quantification of ROS levels was performed as in James et al. (2015). Briefly, independent cultures were diluted to an OD₆₀₀ of 0.3 and then grown for 2 to 3 h to reach an OD₆₀₀ of 0.5–0.9 of logarithmic phase cells, then 1.5 × 10⁷ cells were collected from each of three biological replicates per strain. For HydroxyUrea (HU) tests, at the time of dilution to 0.3 OD₆₀₀, HU was added to a final concentration of 100 mM and cells were grown in the presence of the drug for the 2–3 h period before harvest. Cells were washed twice with 0.1 M phosphate buffer saline (PBS) pH 7.4. Pelleted cells were then resuspended in 1 ml of 0.1 M PBS containing 10 μM of 2′,7′‐ dichlorodihydrofluorescein diacetate (H2DCF‐DA). Samples were incubated in the dark for 30 min. Then cells were washed twice with 0.1 M PBS followed by resuspension in 1 ml of 2 M lithium acetate. Tubes were then gently vortexed for 2 min and centrifuged at 18 400 × g for 1 min. The same conditions were used to wash the cell pellets with PBS. Cell pellets were then resuspended in 1 ml of 0.01% SDS and 10 μl of chloroform, by vigorous vortexing, then centrifuged at 18 400 × g for 2 min. Finally, 100 μl of each sample was transferred into a black transparent‐bottom 96‐well plate. Excitation and emission wavelengths used for H2DCF‐DA, 504 nm and 524 nm, respectively were measured on an Agilent BioTek Synergy H1M2 microplate plate reader.

RNA extraction

Yeast cells were grown in 8 ml of YPD overnight with orbital agitation at 200 rpm at 30°C and harvested at mid-exponential phase (OD₆₀₀ 0.5–0.6, ∼1.1 × 10⁷ cells/ml). Cells were pelleted at 2500 × g for 5 min, medium was removed, and pellets were washed with ddH₂O and re-pelleted, twice. Pellets were transferred to a 1.5 ml screw cap microcentrifuge tube and stored at −80°C. RNA extraction by hot phenol was used to retrieve high quality purified RNA to be used for RNA sequencing analysis. To prepare the phenol buffer, equal volumes of melted crystalline phenol was mixed with a buffer containing 50 mM sodium acetate, 10 mM EDTA, and 1% SDS, and brought to a 5.2 pH with glacial acetic acid. 0.1 g 8-hydroxyquinolone solution was then added per 100 ml phenol and stirred. The upper non-phenol layer was removed twice. Twenty-five milliliters of buffer was left on top of the phenol layer, and gradually heated and stirred, and placed in a 65°C water bath. The microcentrifuge tubes were placed on ice for about 5 min to thaw. Total of 600 µl of the hot phenol was pipetted into the microcentrifuge tubes, vortexed, then incubated in a 65°C water bath for 5 min and inverted once every ∼1 min during the incubation period. Afterward, the tubes were spun at 15 000 × g for 5 min. A 200 µl-micropipette was used to remove the lower phase of the solution, repeated twice. Total of 600 µl of the mixed, hot phenol was pipetted to the leftover cell pellet and heated at 65°C for about 1 min, then vortexed. Fifty microliters of 3 M sodium acetate at pH 5.5 was added to each of two screw cap 1.5 ml microcentrifuge tubes and a 200 µl-micropipette was utilized to transfer the upper aqueous phase to a tube with the 3 M sodium acetate. Five hundred microliters of refrigerated Tris buffered phenol:chloroform (1:1) was then added and vortexed, mixed for 3 min, then spun for 3 min at 15 000 × g. One ml of 100% ethanol was added to two tubes, and a 200 µl-micropipette was used to transfer the upper aqueous layer to the tube with ethanol. The tubes were inverted and placed in a −80°C freezer for 20 min. Next, the tubes were spun for 5 min at 15 000 × g at 4°C, and any supernatant was removed. Three hundred microliters of ddH₂O was then added and vortexed for 30 s; and then mixed for 3 min to dissolve RNA. Thirty microliters 3 M sodium acetate at pH 5.5 and 900 µl 100% ethanol were then added to the tubes and mixed. The tubes were again placed in a −80°C freezer for 20 min, spun, and supernatant removed. Each pellet was rinsed with 70% (v/v) ethanol and spun for 2 min, and supernatant removed. The sample was then air dried. Each pellet was suspended in ddH₂O and vortexed and stored at −80°C. OD_260/280 readings were taken to calculate the RNA concentration and determine purity. Afterward, a 1% (w/v) agarose gel was run by loading 2–8 µl of sample into each well, and a 1 kb ladder was used for reference (Kushner and Tiede 2005).

Batch-Tag-Sequencing

Batch-Tag-Sequencing (Batch-Taq-Seq) was conducted at the UC Davis Genome Center. Total RNA from three biological replicates of the ltv1Δ/ltv1Δ and wildtype strains was sent on dry ice and accessed for concentration, purity, and RNA integrity by the DNA Technologies Core. Batch-Tag-Seq libraries were constructed from total RNA in a high-throughput workflow with inline unique molecular identifiers (UMIs) embedded. Libraries were prepared in batches and assigned sample indexes for multiplexed sequencing. Indexed libraries were pooled and sequenced together on Illumina instruments operated by the core. Runs used single-end 84- or 90-cycle chemistries on HiSeq 4000 or NextSeq 500 as specified by the Batch-Tag-Seq program, generating ∼3–6 million reads per sample. Primary data processing used Illumina software to convert base calls to gzip-compressed FASTQ files and to demultiplex samples by index. The core’s standard service returned only reads passing the Illumina quality filter and provided per-run demultiplexing metrics and file checksums. For downstream handling of the Batch-Tag-Seq read structure, the core documented the UMI and low-complexity leader sequence positions so that users could, if desired, transfer UMIs to read headers and trim the first 22 bases prior to alignment; the core delivered the full-length reads without trimming.

Bioinformatic analysis

The FASTQ files were run through the nf-core/rnaseq pipeline (v3.11.1) ( Di Tommaso et al. 2017, Ewels et al. 2020, Patel et al. 2025) to perform quality control (FastQC), adapter trimming (Trim Galore), alignment (STAR), and quantification (Salmon). Salmon transcript abundance estimates were summarized at the gene level using tximport, and differential expression analysis was performed using DESeq2 (v1.34.0) in R (v4.1.3), comparing expression values between ltv1Δ/ltv1Δ and wildtype strain data. Genes were considered to be differentially expressed if the adjusted P-value (Benjamini-Hochberg FDR) was less than 0.05 and the absolute fold change was greater than 1.5.

Conflicts of interest

The authors declare no conflicts of interest.

Funding

This work was supported by National Institutes of Health grant 1R15GM109269 (Erin Strome, PI) from the National Institute of General Medical Sciences, and an Institutional Development Award (IDeA) from P20GM103436 (Martha Bickford, PI). Sequencing support for this work was provided by National Science Foundation RCN-UBE: Yeast Orphan Gene Project, Grant 1624174 (Jill Keeney, PI). The sequencing was carried out by the DNA Technologies and Expression Analysis Core at the UC Davis Genome Center, supported by NIH Shared Instrumentation Grant 1S10OD010786-01. The contents of this work are solely the responsibility of the authors and do not represent the official views of the NSF or NIH.

Data availability

Strains are available upon request. The authors affirm that all data necessary for confirming the conclusions of the article are present within the article, figures, tables, supplemental files, and data in GEO. The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (Edgar et al. 2002) and are accessible through GEO Series accession number GSE310133. RNA-Seq data is also available in Supplementary Table 5, which contain raw and processed counts, test statistic and p-values for the ltv1Δ/ltv1Δ vs wildtype comparison. Supplementary material files: Supplementary Table 1 contains the full list of all PM wells where growth differences were observed between the ltv1-deficient and wildtype strains. Supplementary Table 2 provides all growth curves for all PM wells. Supplementary Table 3 provides the contents of every PM well. Supplementary Table 4 contains the lists of significant DEGs. Supplementary Table 5 contains expression data for all genes in the comparison between ltv1Δ/ltv1Δ and wildtype strains.