Toxicogenomics - based tests for hazard assessment

Tiago André Vidal Cardoso Toxicogenomics - based tests for hazard assessment março de 2022 UMinho | 2022 Tiago Cardoso Toxicogenomics - based tests for hazard assessment Universidade do Minho Escola de Ciências Tiago André Vidal Cardoso Toxicogenomics - based tests for hazard assessment Dissertação de Mestrado Genética Molecular Trabalho efetuado sob a orientação da Doutora Susana Alexandra Rodrigues Chaves Professora Doutora Maria João Marques Ferreira Sousa Moreira Universidade do Minho Escola de Ciências março de 2022 II I. Direitos de autor e condições de utilização do trabalho por terceiros Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidade do Minho. Atribuição-NãoComercial-SemDerivações CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/ III II. Agradecimentos A realização desta tese contou com a contribuição de várias pessoas, não podendo deixar de agradecer a todos os que me ajudaram e apoiaram, aos quais estou eternamente grato. Em primeiro lugar, agradeço às minhas orientadoras, Doutora Susana Chaves e Doutora Maria João Sousa por todo o conhecimento partilhado, pelo apoio, disponibilidade, orientação, críticas, incentivo, conselhos e ajuda na solução de problemas que foram surgindo ao longo deste trabalho. Quero agradecer em especial à Filipa Mendes que sempre me ajudou desde os primeiros dias no laboratório, sempre se mostrando disponível para tudo o que precisasse, nunca me deixando desamparado. Queria também agradecer a todos os colegas da Micro I por estarem sempre disponíveis para ajudar, em especial à Leslie Amaral, pelo apoio, pela boa disposição e gargalhadas. Gostava de agradecer ao Departamento de Biologia, a todos os técnicos e funcionários, em especial ao Sr. Luís por nunca deixar que nada faltasse. Agradeço também aos meus amigos em especial à Sara Silva, Diana Sousa, Beatriz Dourado, Catarina e Márcia Pacheco por estarem sempre presentes e por terem apoiado em todos os momentos. Quero agradecer à minha família, particularmente aos meus pais e irmã, que apesar de dizerem sempre que não percebiam o que eu estava a fazer, sempre me apoiaram. Obrigado por acreditarem em mim e por todo o apoio, sem vocês nada disto teria sido possível. Por último agradeço à Fundação para a Ciência e a Tecnologia e ao Fundo Europeu de Desenvolvimento Regional pelo financiamento do presente projeto (através dos programas COMPETE2020 e PT2020) através do projeto de investigação FunG-Eye (POCI-01-0145-FEDER-029505) e os programas UID/BIA/04050/2019 e UIDB/04050/2020, sem o qual não teria sido possível a realização do presente trabalho. IV III. Statement of integrity I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho. V Testes baseados em toxicogenômica para avaliação de risco IV. Resumo Como resultado do crescimento populacional, houve a necessidade de aumentar a produção alimentar e, para isso, houve um aumento na utilização de agroquímicos. Adicionalmente, com os avanços na indústria farmacêutica e química, foram introduzidas no mercado novas classes de medicamentos e químicos, muitas das quais não possuem dados toxicológicos ou possuem uma classificação toxicológica imprecisa. A produção destes compostos tem vindo a aumentar ao longo das últimas décadas e a tendência é que esse aumento se prolongue para os próximos anos. No entanto, estes produtos podem contaminar o meio aquático e nos alimentos, tornando-os perigosos para o Homem e para o ambiente. De facto, existe pouca informação sobre o modo de ação de muitos contaminantes e os seus potenciais efeitos em organismos não alvo. Um excelente exemplo é o cimoxanil, um fungicida sistémico da família das amidas que é amplamente usado para controlar o míldio em vinhas. Foi já descrito que o cimoxanil inibe o crescimento, a produção de biomassa e a respiração das células de S. cerevisiae . No entanto, o modo específico de ação do cimoxanil permanece desconhecido. O presente trabalho teve como objetivo analisar as respostas celulares produzidas pelo cimoxanil na levedura S. cerevisiae e, então, desenvolver um rastreio de alto débito para tentar compreender o mecanismo de ação deste composto. Para já, verificamos que para além de inibir o crescimento, o cimoxanil induz morte celular de uma forma dose-dependente e origina alterações funcionais e estruturais em células de levedura. Os nossos dados sugerem que as mitocôndrias serão um dos alvos do cimoxanil, embora outros alvos não possam ser excluídos e mais ensaios sejam necessários. Palavras-chave: cimoxanil; modo de ação; S. cerevisiae ; screening ; toxicogenómica. VI Toxicogenomics-based testes for hazard assessment V. Abstract As a result of population growth, there was a need to increase food production. To accomplish this goal, there was an increase in the use of agrochemicals. Also, with advances in the pharmaceutical and chemical industries, new classes of drugs have been introduced in the market, many of which do not have toxicological data or have an inaccurate classification. The production of these compounds has been increasing over the years, and the trend is that increases will continue for the following years. However, these products can endanger the aquatic environment and food, making them dangerous for humanity and the environment. Indeed, there is little information on the mode of action of many contaminants and their potential off-target effects. One excellent example is cymoxanil, a systemic fungicide of the amide family widely used to control downy mildew in vineyards. It has been reported that cymoxanil inhibits the growth, biomass production, and respiration of S. cerevisiae cells. However, the specific mode of action of cymoxanil remains unknown. The present work aimed to analyze the cellular responses produced by cymoxanil in S. cerevisiae and then develop a genome-wide screen to uncover the mechanism of action of this compound. We found that it leads to both growth inhibition and cell death of yeast cells in a dose-dependent manner and induces cell functional and structural alterations. Our data so far suggest that mitochondria are one of the targets of cymoxanil, although other targets cannot be excluded. Keywords: cymoxanil; mechanism of action; S. cerevisiae ; screening; toxicogenomics. VII VI. Table of contents I. Direitos de autor e condições de utilização do trabalho por terceiros ................................... II II. Agradecimentos ................................................................................................................ III III. Statement of integrity ........................................................................................................ IV IV. Resumo ............................................................................................................................. V V. Abstract ............................................................................................................................ VI VI. Table of contents ............................................................................................................. VII VII. List of abbreviations and acronyms .................................................................................... X VIII. List of figures ................................................................................................................... XII IX. List of tables ................................................................................................................... XIV 1. Introduction ....................................................................................................................... 1 1.1. Human development and chemical substances used ..................................................... 1 1.1.1. Cymoxanil .............................................................................................................. 4 1.2. Toxicology ..................................................................................................................... 6 1.2.1. Toxicogenomics: a new tool for hazard assessment ................................................ 6 1.2.2. In vitro toxicity detection tests – The yeast S. cerevisiae as a tool for toxicogenomics studies…............................................................................................................................ 8 1.2.3. The Saccharomyces Genome Deletion Project ..................................................... 10 1.2.3.1. Functional toxicogenomics using gene deletions ............................................. 11 2. Aim ................................................................................................................................ 15 3. Materials and methods .................................................................................................... 16 3.1. Yeast strains .............................................................................................................. 16 3.2. Escherichia coli transformation and plasmid extraction ............................................... 16 3.3. Yeast pER.ISC1-mCherry transformation ..................................................................... 17 3.4. Growth conditions and treatment ................................................................................ 18 3.5. Viability assays ........................................................................................................... 18 XIV IX. List of tables Table 1 – Examples of functional toxicological screens performed in yeasts. ............................ 13 Table 2 – List of the BY4741 strains used in this work. ........................................................... 16 Table 3 – List of plasmids used in this study. .......................................................................... 17 Table 4 – Transformation mix. ................................................................................................ 17 1 1. Introduction 1.1. Human development and chemical substances used According to the United Nations, the world population increased from 2.5 billion in 1950 to 7.8 billion in 2020. For the year 2090, the prediction for the world population is about 11 billion (between 7.9 and 14.6 billion, depending on the estimate). Therefore, the number of people on Earth in the last 70 years has tripled and will continue to increase (Figure 1). With the successive population increase, there was a demand to maintain and even increase the production of essential goods, particularly food. Therefore, there has been industrialization of agriculture to enhance food production and preservation. As a result, there has been an increase in the use of agrochemicals, particularly two large groups of compounds, chemical fertilizers and pesticides (Balbus et al., 2013; Carvalho, 2006; Liu et al., 2015). Agrochemicals, namely pesticides, are mainly used in agriculture and urban green areas to protect plants from pests/diseases and human vector-borne diseases, such as malaria and dengue (Nicolopoulou-Stamati, Maipas, Kotampasi, Stamatis, & Hens, 2016). However, although these chemical substances have increased food production, their indiscriminate use has become a risk to the environment and humans (Mensah et al., 2014), since many of these pesticides can appear in food, which may have consequences for the final consumer. Pesticides used in agriculture can also be leached by rains, causing groundwater contamination (Alewu & Nosiri, 2011; Liu et al., 2015). Figure 1 - Estimated population growth until the year 2090. Adapted from United Nations, 2020. 2 Figure 2 shows the global use of pesticides over the years (insecticides, herbicides, fungicides, plant growth regulators, and rodenticides), particularly in Europe and Eastern Asia, according to FAO, 2019. Although the use of pesticides tends to be constant in Europe, it has been increasing since 1990 in Asia and the rest of the world, and the trend is that this increase will continue in the following years (Carvalho, 2006; Liu et al., 2015; Mensah et al., 2014). Apart from agrochemicals, pharmaceutical manufacturing facilities and sectors that use these products, for example, the livestock industry, can also be a significant source of contaminants in the environment (Phillips et al., 2010). One example is anticancer drugs, which have been increasingly used over the years due to increased cancer incidence (Ferrando-Climent et al., 2014). It has been shown that the effluents from hospitals possess a higher level of contamination. However, these drugs are released from domestic wastewater as well (Ferrando-Climent et al., 2013; Ferrando-Climent et al., 2014). The process of decontaminating them is not easy, being a challenge for the traditional water treatment methods (Zhang et al., 2013). It was also demonstrated that this type of substances has “cytotoxic, genotoxic, mutagenic, carcinogenic, endocrine disruptor and/or teratogenic effects in numerous organisms,” which would be expected as they are meant to disrupt and prevent cellular proliferation (Ferrando-Climent et al., 2014; Zhang et al., 2013; Zounkova et al., 2010). In another study, effluents from two wastewater treatment plants (WWTPs) that receive discharges from pharmaceutical manufacturing facilities displayed concentrations 10 to 1000 times higher of pharmaceutical products than the effluents from the WWTPs that do not receive such discharges. It was also found that the water Figure 2 - Use of the major pesticides’ groups (insecticides, herbicides, fungicides, plant growth regulators, and rodenticides) and relevant chemical families in Europe, Eastern Asia, and the world, from 1990 to 2017. Data report the quantities (in tonnes of active ingredients) of pesticides used in or sold to the agricultural sector for crops and seeds. Data from (FAO, 2019). 3 released from these two WWTPs was discharged into streams, where the measured pharmaceutical products could be tracked 30 km downstream from the emission source (Phillips et al., 2010). Legislative bodies regulate the use of many of the chemical substances; for example, the European Union (EU) has the most “comprehensive and protective regulations”, and it is up to the European Commission to oversee the “approval, restriction and cancellation of pesticides in the EU following Regulations 1107/2009 and 396/2005” (Donley, 2019; European Parliament Council of the European Union, 2009). The European Chemicals Agency (ECHA) works for the “safe use of chemicals, implementing EU chemicals legislation, benefiting human health and the environment” (Bjorn, 2007). Although there is control by governments, it has now been found that some substances already introduced into the market and considered as “safe” have a potential risk to the environment and human health because some of these products are recalcitrant in wastewater treatment procedures resulting in bioaccumulation, becoming a serious concern (Noutsopoulos et al., 2019; Rosales et al., 2018). The risks associated with chemical substances will depend on their type and concentration. Their adverse effects can occur at different levels, from interfering with DNA structure and function and leading to gene expression disruption (Figure 3) to inducing reproductive damage and inhibition of cell proliferation, among others (Phillips et al., 2010; Ueda, 2009). Figure 3 - Effects of chemicals on gene expression. Metals can catalyze oxidative and/or conformational damage and transcription disturbance. These actions on the structure and function of DNA disrupt the regulation of gene expression, leading to various diseases. Retrieved from (Ueda, 2009). 4 Because of the potential deleterious effects, it is necessary to carry out studies to identify the adverse effects that chemical substances can bring. Indeed, the mechanism of action (MoA) of many pesticides is not well described and sometimes there is no information on their toxicological profile (Hillebrand et al., 2019). Therefore, it is not just chemicals newly introduced in the market that are getting a second look, but also those that are already in use but which have a poor or even no characterization of their MoA (Beaman et al., 2008). A complete characterization of toxicological profiles and a better understanding of the MoAs is thus essential to improve the efficiency of hazard identification and risk assessment (Hamadeh et al., 2002), to carry out preventative and mitigation actions. An excellent example of a chemical compound lacking information on the MoA is cymoxanil. 1.1.1. Cymoxanil Cymoxanil (CYM) [2-cyanoN -[(ethylamino)carbonyl]-2-(methoxyimino)acetamide] (Figure 4) is the only fungicide that belongs to the chemical class of cyanohydroxyiminoacetamides (Genet & Vincent, 1999; Hillebrand et al., 2019). Its sale started in 1997 and it is used to control fungus-like pathogens in a wide variety of cultures like downy mildew diseases induced by Plasmopara viticola in grapes and late blight caused by Phytophthora infestans in tomatoes and potatoes (Fidente et al., 2005). This fungicide is applied as a foliar spray and designated as a foliar fungicide. The level of protection will depend on climate conditions after application and crop growth (European Commission, 2020). The preventive effect of CYM is short-lived (2 - 4 days), and this fungicide has been applied in mixtures with other contact and/or systemic fungicides, such as mancozeb, propamocarb, and famoxadone, which are known to have a longer-lasting protective effect (European Commission, 2020; Genet & Vincent, 1999; Hillebrand et al., 2012; Toffolatti et al., 2015). CYM has a rapid degradation in plants (Belasco et al., 1981), animals, and the environment and has acute oral toxicity in rats: 50% lethal dose (LD50) = 960 mg/kg/day. Because of that, it is classified by the World Health Organization (WHO) as slightly toxic (Belasco et al., 1981; Hillebrand et al., 2012). Although CYM has been used for the past 23 years, its MoA remains unknown. It has just been shown that CYM is metabolized to glycine as the Figure 4 - Chemical structure of cymoxanil. 5 major and last metabolite formed via the intermediates 2-cyano-2-methoxyimino-acetic acid and 2-cyano2-hydroxyimino-acetic acid formed in plants and animals (Belasco et al., 1981; Gisi & Sierotzki, 2008; Hillebrand et al., 2012). These two metabolites were tested in Botrytis cinerea strain Cya S1 ( B. cinerea ) to determine if they could be the active principle of CYM, however, they did not display any fungitoxic activity in the tested model, contrary to CYM. Ziogas & Davidse (1987) reported that CYM does not affect respiration and energy production of Phytophthora infestans ( P. infestans ) strain 85371 at concentrations above 100 µg/ml, at which the citric acid and glyoxylate cycles remain operative in zoospores. Instead, they demonstrate that CYM inhibits mycelial growth by 50% and germ tube formation by sporangia at a concentration below 1 µg/ml. On the other hand, up to 100 µg/ml of CYM did not affect the sporangia and zoospore release, demonstrating that the processes inhibited by CYM are not essential for zoospore release. The same study also showed that 100 μg/ml CYM did not inhibit the uptake of radiolabelled precursors of DNA [ metyl3H]thymidine, RNA [3H]uridine, or protein [14C]phenylalanine by the P. infestans mycelium. However, in P. infestans sporangia, thymidine incorporation was reduced, uridine incorporation was slightly affected, and phenylalanine incorporation was unaffected. This finding indicates that DNA synthesis inhibition might be a secondary effect, since 10 μg/ml CYM completely inhibited mycelial growth and germ tube formation but failed to inhibit thymidine incorporation as well as RNA synthesis, since CYM failed to inhibit endogenous RNA polymerase activity of isolated nuclei. In contrast to the previous report, Ribeiro et., al (2000) have shown that CYM inhibits growth and biomass production in S. cerevisiae IGC 3507 cells at concentrations from 5 - 100 µg/ml, and respiration in the concentration range from 5 – 25 μg/ml in a mineral medium with vitamins and glucose or acetic acid as the carbon source. These findings were corroborated by Estève et al (2009) that used S. cerevisiae var. bayanus wild type as a model organism. Additionally, Lum et al, (2004) demonstrated that S. cerevisiae strains with heterozygous deletions in LCB1, a gene involved in sphingolipid synthesis (Buede et al., 1991) and in FMP30 , a protein with a role in maintaining cardiolipin levels and mitochondrial morphology (Kuroda et al., 2011), were sensitive to CYM. Regarding the MoA of CYM in combination with other substances, Huang et al, (2020) demonstrated that cymoxanil plus famoxadone leads to decreased heart rate, pericardial edema, heart shape changes, and reduced body length in zebrafish embryos as the model organism. They also demonstrated that famoxadone-cymoxanil causes oxidative stress, affects cell proliferation, and decreases ATPase activity. Overall, CYM is an example of chemical substances in use that have a poorly characterized toxicological profile. 6 1.2. Toxicology Toxic substances can be classified into two different classes, exposure class or use class. In the first case, toxicants are classified as food, air, water, or soil occurrences. In the second case, drugs are classified as drugs of abuse, therapeutic drugs, agricultural chemicals, additives in food, pesticides, toxins from plants, and cosmetics (Parasuraman, 2011). The study of the adverse effects of these toxic substances belongs to toxicology (Alewu & Nosiri, 2011; Hamadeh et al., 2002). The tests currently available to assess the effectiveness, quality, and adverse effects of products such as drugs and biological compounds are based on testing animals in vivo . For instance, acute toxicity testing is carried out by determining the effect of a single dose on an animal species, and the use of two different animal species (one rodent and one nonrodent) is recommended (Parasuraman, 2011). The tested substance is administered to an animal at different dose levels. Acute toxicity testing can determine the LD50 of the assessed substance. It is recognized that there is a need to perform experiments on animals to make scientific and medical advances. However, it is necessary to use many animals to obtain this value, and the mortality ratio is high (Parasuraman, 2011). Besides, this type of experiment brings several limitations, such as being relatively expensive and time-consuming, taking 2 - 3 years to determine the toxicity of only one compound (Judson et al., 2009; Nuwaysir et al., 1999). With the new political guidelines, the indiscriminate use of animals for research purposes was considered “irrational, unacceptable and immoral” (Singh et al., 2018) and their use in laboratory assessments also has ethical implications, and it decreased through the implementation of the 3R's (Replace – animal studies with non-animal methods; Reduce – as few animal studies as required and necessary; and Refine – minimize the stress of study animals) (Webster et al., 2010). As mentioned above, tens of thousands of chemicals are used annually, most of which do not have toxicological data available (North & Vulpe, 2010). The trend is that this number will increase with population, industry, and scientific development (Judson et al., 2009). For instance, advances in nanotechnology have led to the introduction of new compounds for general use, bringing with it an additional challenge for hazard assessment (North & Vulpe, 2010). Given the growing number of possibly toxic substances, it is of great importance to develop new and faster approaches to characterize toxicological profiles of a high number of compounds. One of the solutions can be toxicogenomics. 1.2.1. Toxicogenomics: a new tool for hazard assessment Toxicogenomics has emerged as a new transdisciplinary field that complements traditional toxicological studies with the global analysis, omics, of modifications induced by a toxicant among any 7 biomolecules (genome, transcriptome, metabolome, and proteome) (Hamadeh et al., 2002; Vachon et al., 2018). As shown in Figure 5, the number of publications in the field of toxicogenomics has been increasing over the years, with a total of 2528 articles on PubMed. Toxicogenomics can be used to analyze several thousand genes to identify changes associated with drug-induced toxicity (Chavan-Gautam et al., 2017). Through omics tools, it is possible to identify response patterns to a specific toxicant at the genomic, transcriptomic, proteomic, and/or metabolomic level, leading to the characterization of the metabolic pathways involved in this response (dos Santos & Sá-Correia, 2015; Hamadeh et al., 2002; Harrill, 2008). Transcriptomics can be defined as studying the complete set of RNA transcripts produced by the genome. Quantification of the transcriptome can be carried out using methods such as qRT-PCR, microarrays, and RNA-seq. Subsequently, comparisons between different expression results can be made to identify different types of gene expression in response to various compounds (Alexander-Dann et al., 2018; Vatakuti, 2016a). Proteomics aims to characterize the expression of all proteins in the cell, tissue, or organism. This omic thus allows understanding the function of proteins in response to toxic stress (Vatakuti, 2016b). Metabolomics, in turn, aims to characterize the metabolic profile in the cell, tissue, or organism under a given condition (Vatakuti, 2016b). The toxicogenomics approach can focus on multiple levels of the molecular cascade that can be impacted by a toxicological challenge. When changes occur in one of the levels, it can lead to changes in other levels, being necessary study the effects on these. For instance, the use of gene expression alone is not adequate to understand the action of a toxicant in the cell since abnormalities in the production and/or function of proteins are also predictable to occur, and Figure 5 – Number of publications in the field of toxicogenomics, updated on the PubMed platform from 1980 to 2021. Data from (PubChem Compound Summary for CID 5361250, 2021). 8 thus the integration of the different omics is recommended to understand these changes (dos Santos et al., 2012; Martins et al., 2019). 1.2.2. In vitro toxicity detection tests – The yeast S. cerevisiae as a tool for toxicogenomics studies Along with omics tools, high-throughput screening based on cells is one of the methods that can accelerate toxicity tests. The assessment of the stress response through these tests can lead to the identification of toxic pathways, that is, “cellular response pathways that, when sufficiently perturbed in an intact animal, are expected to result in adverse health effects” (North & Vulpe, 2010; Singh et al., 2018). The use of yeast S. cerevisiae as an experimental model has several advantages such as: (1) it is a unicellular non-pathogenic microorganism with (2) rapid and inexpensive growth; (3) it is amenable to genetic manipulation; (4) it possesses a strikingly high-level of functional conservation within the human genome and other higher eukaryotes; (5) it has the unique advantage of possessing functional information available for most genes (dos Santos et al., 2012) and genome-wide analyses are easily implemented due to the collection of S. cerevisiae strains, in which each known or suspected open reading frame (ORF) is deleted and replaced with the marker KanMX (e.g., EUROSCARF)) (Giaever et al., 2002; Sousa et al., 2013). Although many cytotoxic compounds act on their target organisms through physiological mechanisms that do not exist in yeasts, many basic mechanisms underlying toxicity, adaptation, and resistance to chemical and environmental stresses are conserved between yeasts and higher organism cells (North & Vulpe, 2010). 9 Identifying variations in gene expression and proteins in S. cerevisiae exposed to toxics allows the identification of pathways and cellular components that are important and are involved in the toxicological response (dos Santos et al., 2012). S. cerevisiae provides an integrated assessment and a comprehensive view of the mechanisms of toxicity throughout the genome, with the combination of omics (Figure 6). Figure 6 – Application of different omics to understand and predict the mechanisms and profiles of toxicity in yeasts. Transcriptomics and proteomics provide an assessment of changes that occur in response to toxicants. Metabolomics provides the metabolic profile of small-cell molecules in response to toxicity, and chemogenomics identifies molecular targets for cellular toxicity. Retrieved from (dos Santos et al., , 2012). 16 3. Materials and methods 3.1. Yeast strains The S. cerevisiae strains used in this work are listed below in Table 2. BY4741 was used as the WT strain. BY4741 WT, BY4741 Δ erg6 , BY4741 rho0 were used in viability assays. For fluorescence microscopy assays, the strains BY4741 pUG35-Nhp6A-GFP, BY4741 pRS413-Pep4-mCherry, BY4741 pYX242-mt-GFP, and BY4741 pER.Isc1-mCherry were used. 3.2. Escherichia coli transformation and plasmid extraction pER.Isc1-mCherry was transformed and amplified in Escherichia coli (E. coli) . 100 ng of plasmid DNA were added to 100 µL of competent E. coli XL-1 Blue cells and incubated on ice for 30 minutes, followed by heat shock at 42 °C for 45 seconds. Then, the cells were incubated on ice for 10 minutes and 900 µL of SOC [2 % (w/v) tryptone, 0.5 % (w/v) yeast extract, 10 mM NaCl, 2.5 mM KCl, 10 mM MgCl2 and 20 mM glucose] medium was added. Cells were then incubated for 1 hour at 37 °C with agitation at 200 rpm. Afterwards, cells were collected, centrifuged at 3000 rpm for 3 minutes. 800 µl of supernatant was discarded, and the pellet was resuspended in the remaining supernatant. At the end, the cells were plated on solid LB medium [1% (w/v) tryptone, 0.5% (w/v) yeast extract, 1% (w/v) NaCl, 2% (w/v) agar] containing 100 µg/ml ampicillin, overnight at 37 °C. The next day, one colony was Yeast Strains Genotype Source BY4741 MATa; his3 Δ 1 ; leu2 Δ 0 ; met15 Δ 0 ; ura3 Δ 0 EUROSCARF BY4741 pER.ISC1-mCherry MATa; his3 Δ 1 ; leu2 Δ 0 ; met15 Δ 0 ; ura3 Δ 0, pER.ISC1-mCherry (LEU2) This study BY4741 pRS413Pep4-mCherry MATa; ura3-52; leu2-3, 112; his3-Δ1, pRS413-Pep4-mCherry (HIS3) (Terra-Matos et al., 2022) BY4741 pUG35-nhp6a-GFP MATa, his3 Δ 1 , leu2 Δ 0 , met15 Δ 0 , ura3 Δ 0 , pUG35-nhp6a-GFP (URA3) (Canossa, 2017) BY4741 pYX-mt-GFP. MATa, his3 Δ 1 ; leu2 Δ 0 ; met15 Δ 0 ; ura3 Δ 0 , pYX-mt-GFP (URA3) This study BY4741 rho0 MATa, his31 , leu20, met150, ura30 [rho0] (Carvalho, 2018) BY4741 Δ erg6 MATa; his3 Δ 1 ; leu2 Δ 0 ; met15 Δ 0 ; ura3 Δ 0; YML008C::kanMX4 EUROSCARF Table 2 – List of the BY4741 strains used in this work. 17 selected and incubated in liquid SOC medium containing 100 µg/ml ampicillin for plasmid extraction. Plasmids were extracted from cultures grown overnight using the GenElute Plasmid Miniprep Kit according to the manufacturer's instructions (Sigma Aldrich). Extracted plasmid DNA was quantified in a NanoDrop spectrophotometer (Nanodrop ND1000) (pER.Isc1-mCherry = 221.4 µg/µL). 3.3. Yeast pER.ISC1-mCherry transformation In the Table 3 are listed the plasmid used in this study that was first extracted from E. coli using GenElute Plasmid Miniprep Kit (Sigma Aldrich). Plasmid Description Source p ER.ISC1 -mCherry CEN/ARS, LEU2 , PtetO-CYC1, Endoplasmatic reticulum targeted ISC1-mCherry (Rego, 2017) Strain BY4741 was cultivated overnight in YPD medium (1% (v/w) yeast extract, 2% (v/w) bactopeptone and 2% (v/w) glucose). The next day, cells were diluted in YPD containing 4% (v/w) glucose to an OD640nm = 0.2 and incubated at 30 °C with agitation at 200 rpm until reaching an OD640nm = 0.6-0.8. Then, cells were collected, centrifuged at 5000 rpm for 3 minutes, the supernatant was discarded, and cells were washed with deionized sterile water, centrifuged again at maximum speed for 1 minute, and the supernatant was discarded. For each transformation, 100 µL of deionized sterile water was added to resuspend the pellet. 100 µL of cell suspension were transferred to new microtubes, centrifuged again, the supernatant discarded and resuspended in 369 µL of transformation mix, as presented in Table 4, and incubated at 42 °C for 40 minutes. Cells were then centrifuged at maximum speed, the supernatant discarded, and the pellet resuspended in 50 µL of deionized sterile water. In the end, cells were plated on a medium lacking leucine and incubated at 30 °C for 2 days. Table 4 - Transformation mix. Reagents Negative control (µL) Transformation (µL) PEG3350 (50%) 250 250 Lithium acetate (LiAc) (1M) 36 36 Boiled ssDNA (carrier 10 mg/ml) 50 50 H2O 33 32 Plasmid DNA - 1 Table 3 - List of plasmids used in this study. 18 3.4. Growth conditions and treatment Cells of the S. cerevisiae WT strain were grown overnight on Synthetic Complete (SC) medium [0.17% (w/v) YNB, 0.14% (w/v) DROP-out, 2% (w/v) galactose, 0.04% (w/v) leucine, 0.008% (w/v) histidine, 0.008% uracil] at 30 °C, 200 rpm. The yeast strains expressing plasmids were grown in SC medium lacking histidine for Pep4-mcherry [0.17% (w/v) YNB, 0.14% (w/v) DROP-out, 2% (w/v) galactose, 0.04% (w/v) leucine, 0.008% uracil], uracil for Nhp6A-GFP and mt-GFP [0.17% (w/v) YNB, 0.14% (w/v) DROP-out, 2% (w/v) galactose, 0.04% (w/v) leucine, 0.008% (w/v) histidine] and leucine for Isc1-mCherry [0.17% (w/v) YNB, 0.14% (w/v) DROP-out, 2% (w/v) galactose, 0.008% (w/v) histidine, 0.008% uracil]. Then, cells were centrifuged and resuspended diluted in fresh medium to an OD640nm = 0.1. After, cells were treated with the desired concentrations of CYM during different time periods for the different assays performed throughout this work. A 38000 µg/ml stock solution of CYM (Sigma Aldrich) was prepared every week by diluting the compound in dimethyl sulfoxide (DMSO). The treatment was carried out by adding CYM to the diluted cells to a final concentration of 12.5 µg/ml, 25 µg/ml, 50 µg/ml, or 100 µg/ml. The same volume of DMSO (≈ 0.13%) was added to another tube as a negative control. After that, cells were incubated at 30 °C with agitation at 200 rpm for 24 hours. Cells were collected after 4, 8, and 24 hours of treatment for the different assays. At 0 hours, cells were collected before adding the compound. When used, 100 µg/ml cycloheximide (CHX) was added to the cell suspensions at the same time as CYM. 3.5. Viability assays 50 µL of culture samples were washed in sterile deionized water and diluted 10-4 in sterile deionized water. 5 µL of each condition and time point were spotted directly from the cell culture and cell suspension at four different dilutions (10-1, 10-2, 10-3, and 10-4) and plated on YPD plates for the spot assay. Five drops of 40 µL from the 10-4 dilution were plated on YPD plates to assess cell viability by counting colony formation units (CFUs). The plates were incubated for 2 days at 30 °C. 3.6. Oxygen consumption quantification To estimate oxygen consumption, a Clark electrode connected to a recorder (Kipp & Zonen) was used. The electrode was immersed in a water chamber with magnetic stirring. 4.5 ml of deionized water and 0.5 ml of yeast suspension (concentrated to an OD640nm = 50) were added to the chamber, and a baseline was obtained. Subsequently, 100 µl of 5 M galactose was added, and the oxygen consumption 19 was followed in the recorder. The oxygen consumption values were calculated based on O2 saturation at 25 °C as 23.6 mmol/ml. 3.7. Epifluorescence microscopy analyses The yeast strains expressing Pep4-mcherry, Nhp6A-GFP, Isc1-mCherry, and pYX-mt-GFP were grown in SC medium lacking the appropriate components and treated with CYM or with DMSO (negative control). Cells were then collected for visualization by epifluorescence microscopy to evaluate the localization of the proteins. In the case of the strain expressing Pep4-mcherry, cells were stained with Celltracker™ Blue CMAC (Molecular Probes Eugene, OR) at a final concentration of 2 μM and incubated for 20 minutes at room temperature, to assess vacuole membrane permeabilization. In the case of the strain expressing Nhp6A-GFP, cells were also incubated with 2 μg/ml of propidium iodide (PI) to assess plasma membrane integrity. Cells were visualized with a Leica Microsystems DM-5000B microscope with appropriate filter settings (red, green, blue, and Differential Interference Contrast (DIC)) with a 100x oil immersion objective. Images were obtained with a Leica DFC350 FX Digital Camera and processed with LAS X Microsystems software. 3.8. Evaluation of mitochondrial protein degradation by SDS gel electrophoresis/Western Blot S. cerevisiae BY4741 pYX-mt-GFP cells were treated with CYM or with DMSO (negative control), and after 4, 8, and 24 hours of treatment, 1 ml of cells were collected to prepare the cell extracts. 3.8.1. Cell extracts preparation Cells were resuspended in 500 μl of water, followed by the addition of 50 μl of 7.4% (v/v) βmercaptoethanol in 2 M NaOH. Then, cells were vortexed and incubated on ice for 15 minutes. After that, 50 μl of 50% (w/v) TCA were added, followed by a 15-minute incubation on ice. The samples were then centrifuged at 14800 rpm for 5 minutes at 4 °C and the pellets were resuspended in 30 μl of 1x Laemmli buffer (0.0625 M Tris-HCl, 2.3% (w/v) SDS, 10% (w/v) glycerol, 1.25% (w/v) β-mercaptoethanol, 0.125% (w/v) bromophenol blue). Then, the extracts were denatured at 100 °C for 5 minutes and stored at -20 °C until SDS-PAGE analysis. 20 3.8.2. SDS gel electrophoresis/Western Blot The protein lysates obtained as described above were separated by SDS gel electrophoresis on a 12.5% SDS-poly-acrylamide gel, in a Mini-Protean III electrophoresis system (Bio-Rad) at 25 mA per gel, using 1x running buffer (0.025 M Tris base, 0.192 M Glycine, 46.1% SDS). The separated proteins were then transferred to a nitrocellulose membrane (Hybond-ECL, GE Healthcare) at 60 mA for 90 minutes in a semi-dry transfer unit (TE77X Hoefer) using transfer buffer (0.025 M Tris-Base, 0.192 M glycine). Then, the membranes were blocked in 5% (w/v) non-fat milk in 1x PBS containing 0.05% (v/v) Tween 20 for 30 minutes at room temperature with agitation. Then, the membranes were incubated with the primary antibodies overnight at 4 °C. For phosphoglycerate kinase (Pgk1p) detection, a mouse monoclonal antibody anti-yeast phosphoglycerate kinase (1:5000, Molecular Probes) was used. For mitochondrial porin (Por1p) detection, a mouse monoclonal anti-yeast porin (1:10000, Molecular Probes) was used. Finally, the membranes were incubated with an anti-mouse peroxidase-coupled secondary antibody (1:5000) for 60 minutes at room temperature. Chemiluminescence detection was performed using the ECL detection system in an automatic reveal machine (Agfa, Curix 60). 3.9. Flow cytometry analysis Flow cytometry analysis was performed in a CytoFlex System B4-R2-V0 (Beckman Coulter) flow cytometer. Ten thousand cells were analyzed per sample at a low-medium flow rate. Flow cytometry analyses were performed with CytExpert software version 2.4.0.28 (Beckman Coulter, Inc.). 3.9.1. Assessment of membrane potential and plasma membrane integrity Cells were collected, washed, and resuspended in resuspension buffer (0,1 mM MgCl2, 10 mM MES (2(N-Morpholino)ethanesulfonic acid), and 2% (w/v) glucose, pH 6.0 set with NaOH). Then, to assess mitochondrial membrane potential, they were incubated with DiOC6(3) (Molecular Probes Eugene, OR) at a final concentration of 1 nM for 30 minutes at 30 °C in the dark, in duplicate. One of the duplicates was incubated with 2 μg/ml of PI to assess plasma membrane integrity. 3.9.2. Assessment of mitochondrial mass and ROS accumulation Cells were collected, washed, and resuspended in 1x PBS. For mitochondrial mass assessment, cells were incubated with MitoTracker™ Green FM (Invitrogen™) at a final concentration of 0.4 µM for 30 minutes at 37 °C in the dark. For ROS accumulation assessment cells were incubated with MitoTracker™ Red CM-H2Xros (Invitrogen™) at a final concentration of 0.8 µg/ml for 30 minutes at 37 °C in the dark. 21 3.9.3. Assessment of pH alterations For pH alteration assessment, BCECF, AM (2',7'-Bis-(2-Carboxyethyl)-5-(and-6)-Carboxyfluorescein, Acetoxymethyl Ester) (Invitrogen™) was used. The treated cells were collected, washed, and resuspended in SC-Gal medium. Then, they were incubated with the probe at a final concentration of 18 µM for 20 minutes at 30 °C in the dark. 3.9.4. Statistical analysis of the results The results obtained are represented by mean and standard deviation (SD) values of at least two independent experiments. Statistical analyses were carried out using GraphPad Prism Software v8.00 (GraphPad Software, California, USA). 22 4. Results and discussion As the goal of this project was to optimize a phenotypic genome-wide screen of the response to cymoxanil, first we set out to identify conditions where the cells displayed a higher sensitivity to this compound, as well cellular alterations that could be used as a readout. In this way, it would be possible to proceed with the screening of the mutant collection in order to find resistance and sensitive mutants in response to CYM and, thus, to characterize the MoA of the CYM. 4.1. Cymoxanil inhibits cell growth and oxygen consumption To select the conditions where the cells displayed more sensitivity to CYM, we tested different media and growth conditions. We first inoculated cells in YPD medium and, after overnight growth, diluted the culture in SC-Gal media containing 0.5% proline as a nitrogen source (SCpro-Glu). The addition of proline as the only source of nitrogen and SDS was already described as a membrane permeabilizer for toxic compounds (McCusker & Davis, 1991; Pannunzio et al., 2004). Half the culture was exposed to CYM immediately, and another was allowed to grow in SCpro-Glu plus 0.003% SDS for another 3 hours before exposure (Figure 10). CYM treatment inhibited growth (Figure 11 A) and decreased the viability of cells (Figure 11 B) regardless of the treatment medium. However, cell viability loss in response to CYM Figure 10 – Schematic representation of the assay in the presence of SDS. An inoculum was made in YPD medium, then diluted in SCpro – Glu medium. Half of the culture was immediately exposed to 100 µg/ml CYM (without refreshment), and the other grew in the presence of 0.003% SDS, and 100 µg/ml CYM was only added after 3 hours of growth (with refreshment). 23 was higher without refreshment, even though cells grew more in these culture conditions, both with and without CYM treatment (Figure 11 A and B). Next, we assessed the effect of CYM when the inoculum was performed in YPD or SCpro-Glu and then the cells treated in YPD or SCpro-Glu. Regardless of the inoculum medium, CYM led to a higher inhibition of cell growth and decrease of viability when cells were exposed to CYM in SCpro-Glu medium (Figure 12 A and B). Therefore, we proceeded with assays with both inoculum and treatment in SCpro-Glu medium. As previously referred, it was already described that CYM leads to an inhibition of biomass production and oxygen consumption in yeast, but in a different strain background (Ribeiro et al., 2000). To confirm Figure 11 - Sensitivity of S. cerevisiae BY4741 WT cells to 100 µg/ml of cymoxanil without and with culture refreshment. A) Cells were grown overnight in YPD medium. The following day cells were diluted to an OD640nm = 0.1, and 0,003% SDS was added to medium SCproGlu (with refreshment) and let it grow for 3 hours. After, 100 µg/ml CYM was added and incubated for 24 hours in YPD or SCpro-Glu media with or without refreshment of 3 hours of cells. OD640nm was read at different time points until 24 hours of treatment. B) Viability of cells treated as in A) assessed at different time points by spot assay. 24 that phenotype, we assessed the effect of the compound on growth, viability, and oxygen consumption in BY4741 WT cells. We observed that CYM leads to inhibition of cell growth (Figure 13 A) and loss of viability (Figure 13 B) in a dose-dependent manner, as well as to a decrease in oxygen consumption (Figure 13 C) under our experimental conditions. Figure 12 - Sensitivity of S. cerevisiae BY4741 WT cells to 100 µg/ml of cymoxanil with different inoculum/media combinations. A) Cells were grown overnight in YPD or SCpro-Glu medium. The following day cells were diluted to an OD640nm = 0.1, exposed to 100 µg/ml CYM for 24 hours in YPD or SCpro-Glu media. OD640nm was read at different time points until 24 hours of treatment. B) Viability of cells treated as in A) assessed at different time points by spot assay. Results from one experiment. 25 Figure 13 - Response of S. cerevisiae BY4741 WT cells to different concentrations of cymoxanil. A) Cells were diluted to an OD640nm = 0.1, exposed to 12.5, 25, or 50 µg/ml CYM for 24 hours in SCpro-Glu media. OD640nm was read at different time points until 24 hours of treatment. Values are mean ± SD of two independent experiments. B) Viability of cells treated as in A) assessed at different time points by spot assay. A representative image is shown from two independent experiments. C) O2 consumption measured by Clark electrode. Cells were treated as in A) a B). Results from one experiment. 32 comparison with the control (Figure 17 C), indicative of the effect of CYM on mitochondria. We also found that inhibition of oxygen consumption does not occur immediately. In fact, inhibition occurs when cells are incubated with the compound for at least 3 hours (see 7. Annexes Figure A1). 4.5. Assessment of mitochondria fragmentation in medium containing galactose Since we altered the carbon source, we also assessed the effect of CYM on cells exposed to 50 µg/ml of CYM in SC-Gal for 24 hours. In this case, we observed that, after 4 hours of treatment, some cells display a fragmented mitochondrial network. This effect on mitochondria is more evident after 8 hours. It was possible to observe mitochondrial network fragmentation in a higher number of cells after 24 hours of treatment, where we saw multiple spots and smaller mitochondria, in contrast with the intact mitochondrial network of the control (Figure 18). Figure 18 - Effect of CYM on mitochondrial network. Epifluorescence microscopy images of BY4741 WT pYX-mt-GFP cells after treatment in the presence or absence of CYM in SC-Gal medium. Samples were collected at different time points, before (time 0) and after 4, 8 and 24 hours of treatment, and then visualized by epifluorescence microscopy with a 100x oil immersion objective. 33 4.6. Functional characterization Considering the previous observations regarding the effect of CYM on mitochondrial network fragmentation, it seems that CYM in some way is affecting the mitochondria. To assess if this effect is not only morphologic but also functional, we evaluated ROS production, changes in mitochondrial membrane potential, and mitochondrial mass. 4.6.1. Assessment of mitochondrial membrane potential and mass The mitochondrial membrane potential (ΔΨm) is an indicator of cell health. To assess changes in mitochondrial membrane potential, we used the dye DiOC6(3), which accumulates in mitochondria depending on its membrane potential (Pringle et al., 1989). Cells were stained with DiOC6(3) and analyzed by flow cytometry. Cells were also stained with PI in order to certify that the analysis of ΔΨm is performed in cells that still did not suffer any alteration in the integrity of the plasma membrane. After 4 hours of treatment, it is possible to observe a hyperpolarization of the mitochondrial membrane, followed by a depolarization at 8 hours of treatment (Figure 19 A and B). However, to be sure Figure 19 – Mitochondrial membrane potential assessment. Cells were treated in the presence or absence of CYM in the SC-Gal medium. Samples were collected at different time points, before (time 0) and after 4, 8, and 24 hours of treatment, and analyzed by flow cytometry. A) Representative image of three independent experiments of the DiOC6(3) quantification. B) Percentage of a median of DiOC6(3). The percentage was calculated considering the control, in all time points, as 100% (red line). C) Loss of plasma membrane integrity assessed by PI staining. Statistical analysis was performed by two-way ANOVA. *P<0.05, ****P<0.0001. 34 of the results, it is necessary repeat this assay or even utilize DiOC5(3), JC-1, or TMRM, which can also be used for mitochondrial membrane potential assessments. The 24h time point was not analyzed due to the high number of cells that had lost their plasma membrane integrity (Figure 19 C). Figure 20 shows the mean signal of Mitrotracker Green fluorescence intensity representing the mitochondrial content during the 24 hours of treatment. The mitochondrial content slightly increased after 4 and 8 hours of treatment, but not significantly. After 24 hours of treatment, there was a more evident increase in the fluorescence, but this may be derived from the loss of plasma membrane integrity of cells thus unspecific staining of cells. To test this hypothesis, co-staining with PI was performed, but due to the high contamination of Mitrotracker Green on the PI channel (red), these results were not considered (not shown). 4.6.2. Quantification of ROS production Next, we assessed if the compound leads to ROS accumulation. ROS accumulation has been shown to cause oxidative damage on nucleic acids, proteins, and lipids. Untreated cells and cells treated with CYM were labeled with MitoTracker Red CM-H2XRos and Dihydroethidium (DHE). The reduced form of MitoTracker Red CM-H2XRos only fluoresces when oxidized by ROS, emitting red fluorescence (CottetRousselle et al., 2011). DHE reacts with superoxide to form 2-hydroxyethidium and has been used to assess superoxide levels (Carmona-Gutierrez et al., 2010; Liao et al., 2020). Figure 20 - Mitochondrial mass assessment through flow cytometry using the Mitrotracker Green probe. Cells were treated in the presence or absence of CYM in SC-Gal medium. Samples were collected at different time points, before (time 0) and after 4, 8, and 24 hours of treatment, and then analyzed by flow cytometry. 35 When MitoTracker Red CM-H2XRos was used as a probe, the percentage of stained cells after 4 and 8 hours of CYM treatment was very low. However, after 24 hours of treatment, there was a higher percentage of ROS-positive cells (Figure 21 A). When DHE was used as a probe, staining was also low after 4 and 8 hours of treatment with CYM compared with the control (Figure 21 B). DHE-positive staining was only observed at 24 hours of treatment. However, the percentage of DHE-positive cells was very similar to that of PI-positive cells. It is, therefore, possible that the production of ROS accessed by DHE is not accurate, being affected by unspecific staining. However, since the percentage of MitoTracker Red CM-H2XRos -positive cells was higher, it seems there is at least some increase in ROS production after 24 hours of exposure to CYM. 4.6.3. Assessment of pH alterations Several biological processes are dependent on the regulation of intracellular pH. For instance, protein structures and enzyme activity depend on this factor. In the secretory pathway, the pH is more acidic starting from the ER to secretory vesicles. This acidification is essential to activate some enzymes involved in post-translational modifications and degradation processes, as some proteases are activated in the acidic vacuole (Deschamps et., al 2013). In contrast, mitochondrial pH is higher, as a membrane potential and pH gradient are required to drive production of ATP (Brand & Lehningert, 1977). Given the importance of maintaining pH homeostasis, it is relevant to assess pH alterations that can be caused by CYM. Figure 21 - Levels of mitochondrial ROS and superoxide anion in S. cerevisiae strains. Cells were treated in the presence or absence of 50 µg/ml CYM in SC-Gal medium. Samples were collected at different time points, before (time 0) and after 24 hours of treatment and stained with MitoTracker Red CM-H2XRos (A) and DHE (B) and then analyzed by flow cytometry. Values are mean ± SD of at least three independent experiments. Cells were treated in the presence or absence of CYM in the SC-Gal medium. Statistical analysis was performed by two-way ANOVA. *P<0.05, ****P<0.0001. 36 To analyze intracellular pH changes, we used BCECF, AM, a fluorescein derivative that has been used to measure the pH. BCECF, AM accumulates in the vacuole where hydrolytic enzymes cleave the acetoxy methyl ester from BCECF-AM and lead to vacuolar retention (Plant et al., 1999). Changes in the pH were monitored by assessing changes in the green/red fluorescence intensity ratio. This ratio value is independent of the probe concentration and only dependent on pH, where the ratio increases as the pH decreases (Johnson et al., 2010; Ozkan & Mutharasan, 2002). After 4 and 8 hours, no alteration was observed in the green/red ratio on untreated cells, while it decreased after 24h under both conditions. However, after 4 and 8 hours of treatment, we observed that CYM led to a slight increase in the ratio, indicative of acidification, while control cells were not affected (Figure 22). To confirm whether this increase is significant, it will be necessary to use a positive control in subsequent experiments. 4.7. Optimizing a genome-wide screen The results obtained show that CYM leads to cell death, and likely targets the mitochondria, though more evidence of the involvement of the mitochondria in the response to CYM is needed. However, the molecular target remains unknown. To uncover which specific cellular processes can be affected by CYM, we proceed with optimization of a genome-wide screen to find strains that demonstrate sensitivity and/or resistance to the compound. First, it was necessary to optimize the exposure conditions, since the effect of any drug on microplates can be different from assays performed in tubes, due to low agitation and aeration. We first inoculated SC-Gal medium in 96-well plates with BY4741 WT cells using a pin replicator. The next day, we diluted the cells in fresh medium and added different concentrations of CYM, and Figure 22 – Cymoxanil induces intracellular acidification in BY4741 WT cells. Representative histograms of the BCECF-AM cells displaying intracellular acidification were assessed through the decrease of FITC-A/PE-A fluorescence intensity ratio compared to negative control cells (DMSO). 37 assessed growth and loss of plasma membrane integrity by PI staining, which resulted in the clearest phenotype from the initial characterization. As shown in Figure 23 A, 100 µg/ml, and 200 ug/ml CYM partially inhibited growth of BY4741 cells, already after 8 hours of exposure. However, an increase in PI-positive cells was evident mostly after 24 hours (Figure 23 B). We, therefore, proceed with an optimization of the assay with two mutants that could be used as controls with increased sensitivity: rho0, a strain characterized by the absence of mitochondrial DNA (mtDNA) (Dirick et al., 2014) and Δ erg6, deleted in the gene encoding for a C-24 sterol methyltransferase, an enzyme that acts in the late steps of ergosterol biosynthesis, which has been characterized as hypersensitive to many drugs, since the cells are more permeable to the diffusion of drugs across the membrane, increasing drug uptake (Bard et al., 1978; Emter et al., 2002; Welihinda et al., 1994). After 8 hours of treatment, we found that CYM-treated cells do not significantly lose plasma membrane integrity compared with the control. However, after 24 hours of treatment, there was a significant increase of PI-positive cells and the rho0 strain was slightly more sensitive when compared with the WT strain. Still, it was the Δ erg6 mutant that was most sensitive to the compound, with approximately 80 % of the cells losing their plasma membrane integrity (Figure 24). Figure 23 – Conditions optimization of the screening. A) Optical density of the BY4741 WT cells and B) Loss of plasma membrane integrity of the cells assessed by PI staining. Cells were treated in the presence or absence of 100 µg/ml, or 200 µg/ml CYM in the SC-Gal medium. Samples were collected at different time points, before (time 0) and after 8 and 24 hours of treatment, and then analyzed by flow cytometry. Values are mean ± SD of at least three independent experiments. Statistical analysis was performed by two-way ANOVA. *P<0.05, ****P<0.0001. 38 Figure 24 – Loss of plasma membrane integrity of selected mutants assessed by PI staining. BY4741 WT, Δ erg6 , and rho0 were treated in the presence or absence of 200 µg/ml CYM in the SC-Gal medium. Samples were collected at different time points, before (time 0) and after 8 and 24 hours of treatment, and then analyzed by flow cytometry. Values are mean ± SD of at least three independent experiments. Cells were treated in the presence or absence of CYM in the SC-Gal medium. Statistical analysis was performed by two-way ANOVA. ****P<0.0001. . 39 5. Conclusions and future perspectives Cymoxanil is a fungicide that has been used for the past 24 years. However, its mechanism of action remains largely unknown (Belasco et al., 1981; Fidente et al., 2005; Gisi & Sierotzki, 2008; Hillebrand et al., 2012). There are only a few reports regarding the effect of cymoxanil in the inhibition of mycelial growth, germ tube formation by sporangia as well the inhibition of DNA and RNA synthesis as a secondary effect in the P. infestans model, the causal organism of late blight in potato and tomato cultures (Ziogas & Davidse, 1987). It was also demonstrated that cymoxanil inhibits growth and respiration in the model organism S. cerevisiae (Estève et al., 2009; Ribeiro et al., 2000). Like other compounds that are widely used, it is necessary to understand its mode of action, to better understand the target and potential of effects, and carry out preventative and mitigation actions. One method that can increase our understanding on the cymoxanil mechanism of action in a comprehensive way is to screen the phenotype of a S. cerevisiae mutant collection, in which each strain has been deleted in one of the non-essential yeast genes. In the past, our lab had already attempted to optimize a screen with cymoxanil based on growth and optical density, but no reproducible results were obtained (Carvalho, 2018). Therefore, it was necessary to perform more optimizations and uncover another read-out for a cellular phenotype. First, we proceeded with a structural and functional characterization to determine how cymoxanil affects cells. We found that cymoxanil leads to an inhibition of both cell growth and respiration, as observed by Ribeiro et., al (2000). The cymoxanil effect on respiration may be explained by the presence and hypothetic release of the cyan group. We didn’t find information about the release of this group in yeast, but it was already described that low concentrations of cyanide, about 100 and 300 µM, lead to respiratory inhibition in S. cerevisiae (Peña et., al 2015), and thus this hypothesis should be tested. For instance, we could treat the cells with 50 µg/ml of cymoxanil for four hours (time where oxygen consumption displayed the highest inhibition), and then lyse the cells, filtrate the suspension and perform HPLC analyses. Our results also suggest that cymoxanil does not affect organelles like the vacuole and endoplasmic reticulum, but affects mitochondria, agreeing with previous indications that the mechanism of action of cymoxanil may be related to the mitochondria (Estève et., al (2009) and Ribeiro et., al (2000)). As we found that cymoxanil leads to cell death, we sought to characterize this process. The functional characterization shows that cymoxanil leads to hyperpolarization of the mitochondrial membrane after 4 hours of treatment, followed by a depolarization after 8 hours of treatment, without alteration in mitochondrial mass. However, it will be necessary to repeat the assay or even utilize another 40 probe to confirm the results obtained for mitochondria membrane potential alterations. Cymoxanil also leads to loss of plasma membrane integrity and ROS accumulation after 24 hours of treatment. We also assessed the nuclear release of the Nhp6Ap, a well-known necrotic marker. However, we observed a different release pattern from the one described for necrotic processes. Instead of a uniformly dispersed localization of Nhp6Ap, cells that lose plasma membrane integrity also lose the green fluorescence of Nhp6A-GFP. So, the results obtained were inconclusive. We were also not able to conclude whether cycloheximide, a protein synthesis inhibitor, reverts cymoxanil-induced cell death, which would indicate if this process is not dependent on protein synthesis. We therefore could not definitively conclude if cymoxanil leads to a necrotic or an apoptotic-like regulated process. For this reason, it would be interesting to analyze other cell death markers like cytochrome c release from mitochondria, since this release is considered a critical initial step in the apoptotic process (Ott et al., 2002). Another marker would be the co-staining of Annexin V (AnnV) and propidium iodide (PI), where it will be possible to discriminate between early apoptotic cells (AnnV+, PI-), primary necrotic (AnnV-, PI+), and late apoptotic/secondary necrotic cells (AnnV+, PI+). The identification of genes, in a genome-wide screen, that confer resistance or sensitivity induced by cymoxanil could be a powerful method to uncover the mechanism of action of this compound. For this reason, a screening was optimized to assess the effect of cymoxanil on S. cerevisiae cells. To understand the mechanism of action of cymoxanil, we took advantage of the reproducible increase in PI staining of cells treated with this compound to proceed with the optimization of a genome-wide screen. We selected two mutants as controls that could show increased sensitivity to cymoxanil: Δ erg6 , more permeable to small molecules, and rho0, defective in mitochondrial respiration. We found that the Δ erg6 mutant was much more sensitive to the compound, likely because the cells were more permeable to small molecules. These findings reinforce the idea that the target of cymoxanil is intracellular. In contrast, rho0 cells do not display a large difference in the percentage of cells that lose their plasma membrane integrity when compared with the control, so it’s possible that cymoxanil has other molecular targets inside the cells or its mode of action can be related with respiratory processes that are not compromised in rho0 cells. In this way, other targets cannot be excluded, and more assays need to be carried out. In a first optimization, we were able to find conditions that may be used in a larger genome-wide screen in S. cerevisiae , as well as a strain sensitive to the compound that can be a promising control. 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The inhibition is not a process that occurs immediately. In our conditions, the respiration inhibition occurs when cells are incubated with the compound for 3 hours, as we can see when we compare the straight slope (straight gray line) of control whit the treatment.