Evaluation of Antimicrobial Resistancein Clinical Isolates of Enterococcus spp. Obtained from Hospital Patients in Latvia.

Background and Objective: Enterococci are typically found in a healthy human gastrointestinal tract but can cause severe infections in immunocompromised patients. Such infections are treated with antibiotics. This study addresses the rising concern of antimicrobial resistance (AMR) in Enterococci, focusing on the prevalence of vancomycin-resistant enterococcus (VRE) strains. Materials and Methods: The pilot study involved 140 Enterococci isolates collected between 2021 and 2022 from two multidisciplinary hospitals (with and without local therapeutic drug monitoring protocol of vancomycin) in Latvia. Microbiological assays and whole genome sequencing were used. AMR gene prevalence with resistance profiles were determined and the genetic relationship and outbreak evaluation were made by applying core genome multi-locus sequence typing (cgMLST). Results: The acquired genes and mutations were responsible for resistance against 10 antimicrobial classes, including 25.0% of isolates expressing resistance to vancomycin, predominantly of the vanB type. Genetic diversity among E. faecalis and E. faecium isolates was observed and seven potential outbreak clusters were identified, three of them containing sequence types ST6, ST78 and ST80. The prevalence of vancomycin resistance was highest in the hospital without a therapeutic drug-monitoring protocol and in E. faecium. Notably, a case of linezolid resistance due to a mutation was documented. Conclusions: The study illustrates the concerning prevalence of multidrug-resistant Enterococci in Latvian hospitals, showcasing the rather widespread occurrence of vancomycin-resistant strains. This highlights the urgency of implementing efficient infection control mechanisms and the need for continuous VRE surveillance in Latvia to define the scope and pattern of the problem, influencing clinical decision making and planning further preventative measures.

Antimicrobial Resistance in Enterococcus spp. Isolates from Red Foxes (Vulpes vulpes) in Latvia.

Antimicrobial resistance (AMR) is an emerging public health threat and is one of the One Health priorities for humans, animals, and environmental health. Red foxes (Vulpes vulpes) are a widespread predator species with great ecological significance, and they may serve as a sentinel of antimicrobial resistance in the general environment. The present study was carried out to detect antimicrobial resistance, antimicrobial resistance genes, and genetic diversity in faecal isolates of red foxes (Vulpes vulpes). In total, 34 Enterococcus isolates, including E. faecium (n = 17), E. faecalis (n = 12), E. durans (n = 3), and E. hirae (n = 2), were isolated. Antimicrobial resistance to 12 antimicrobial agents was detected with EUVENC panels using the minimum inhibitory concentration (MIC). The presence of antimicrobial resistance genes (ARGs) was determined using whole-genome sequencing (WGS). Resistance to tetracycline (6/34), erythromycin (3/34), ciprofloxacin (2/34), tigecycline (2/34), and daptomycin (2/34) was identified in 44% (15/34) of Enterococcus isolates, while all the isolates were found to be susceptible to ampicillin, chloramphenicol, gentamicin, linezolid, teicoplanin, and vancomycin. No multi-resistant Enterococcus spp. were detected. A total of 12 ARGs were identified in Enterococcus spp., with the presence of at least 1 ARG in every isolate. The identified ARGs encoded resistance to aminoglycosides (aac(6')-I, ant(6)-Ia, aac(6')-Iih and spw), tetracyclines (tet(M), tet(L) and tet(S)), and macrolide-lincosamide-streptogramin AB (lnu(B,G), lsa(A,E), and msr(C)), and their presence was associated with phenotypical resistance. Core genome multilocus sequence typing (cgMLST) revealed the high diversity of E. faecalis and E. faecium isolates, even within the same geographical area. The distribution of resistant Enterococcus spp. in wild foxes in Latvia highlights the importance of a One Health approach in tackling AMR.

Evaluation of Genetic Diversity and Virulence Potential of Legionella pneumophila Isolated from Water Supply Systems of Residential Buildings in Latvia.

Legionella is an opportunistic pathogen with a biphasic life cycle that occasionally infects humans. The aim of the study was to assess the distribution of virulence genes and genetic diversity among L. pneumophila isolated from water supply systems of residential buildings in Latvia. In total, 492 water samples from 200 residential buildings were collected. Identification of Legionella spp. was performed according to ISO 11731, and 58 isolates were subjected to whole-genome sequencing. At least one Legionella-positive sample was found in 112 out of 200 apartment buildings (56.0%). The study revealed extensive sequence-type diversity, where 58 L. pneumophila isolates fell into 36 different sequence types. A total of 420 virulence genes were identified, of which 260 genes were found in all sequenced L. pneumophila isolates. The virulence genes enhC, htpB, omp28, and mip were detected in all isolates, suggesting that adhesion, attachment, and entry into host cells are enabled for all isolates. The relative frequency of virulence genes among L. pneumophila isolates was high. The high prevalence, extensive genetic diversity, and the wide range of virulence genes indicated that the virulence potential of environmental Legionella is high, and proper risk management is of key importance to public health.

Wastewater-based prediction of COVID-19 cases using a random forest algorithm with strain prevalence data: A case study of five municipalities in Latvia.

Wastewater-based epidemiology (WBE) is a rapid and cost-effective method that can detect SARS-CoV-2 genomic components in wastewater and can provide an early warning for possible COVID-19 outbreaks up to one or two weeks in advance. However, the quantitative relationship between the intensity of the epidemic and the possible progression of the pandemic is still unclear, necessitating further research. This study investigates the use of WBE to rapidly monitor the SARS-CoV-2 virus from five municipal wastewater treatment plants in Latvia and forecast cumulative COVID-19 cases two weeks in advance. For this purpose, a real-time quantitative PCR approach was used to monitor the SARS-CoV-2 nucleocapsid 1 (N1), nucleocapsid 2 (N2), and E genes in municipal wastewater. The RNA signals in the wastewater were compared to the reported COVID-19 cases, and the strain prevalence data of the SARS-CoV-2 virus were identified by targeted sequencing of receptor binding domain (RBD) and furin cleavage site (FCS) regions employing next-generation sequencing technology. The model methodology for a linear model and a random forest was designed and carried out to ascertain the correlation between the cumulative cases, strain prevalence data, and RNA concentration in the wastewater to predict the COVID-19 outbreak and its scale. Additionally, the factors that impact the model prediction accuracy for COVID-19 were investigated and compared between linear and random forest models. The results of cross-validated model metrics showed that the random forest model is more effective in predicting the cumulative COVID-19 cases two weeks in advance when strain prevalence data are included. The results from this research help inform WBE and public health recommendations by providing valuable insights into the impact of environmental exposures on health outcomes.

Prevalence and Genetic Diversity of Legionella spp. in Hotel Water-Supply Systems in Latvia.

Legionella is one of the most important waterborne pathogens that can lead to both outbreaks and sporadic cases. The majority of travel-associated Legionnaires' disease (TALD) cases are contracted during hotel stays. The aim of this study was to evaluate the prevalence and genetic diversity of Legionella spp. in hotel water supply systems in Latvia. In total, 834 hot water samples were collected from the water systems of 80 hotels in Latvia. At least one Legionella spp. positive sample was detected in 47 out of 80 hotels (58.8%). Overall, 235 out of 834 samples (28.2%) were Legionella spp. positive. The average hot water temperature in Latvian hotels was 49.8 °C. The most predominant L. pneumophila serogroup (SG) was SG3 which was found in 113 (49.8%) positive samples from 27 hotels. For 79 sequenced L. pneumophila isolates, 21 different sequence types (ST) were obtained, including 3 new types-ST2582, ST2579, and ST2580. High Legionella contamination and high genetic diversity were found in the hotel water supply systems in Latvia, which, together with the insufficient hot water temperature, may indicate that the lack of regulation and control measures may promote the proliferation of Legionella.

Application of Baltic Pine (Pinus sylvestris) Needle Extract as a Gut Microbiota-Modulating Feed Supplement for Domestic Chickens (Gallus gallus).

The valorization of wood industry residues is very desirable from a circular economy perspective. Pine needle extracts are known for their health-promoting properties and therefore can be used as herbal remedies and nutritional supplements. Since the withdrawal of antibiotics as growth promoters in the European Union, natural feed additives that improve poultry health and production are needed. It was proposed that pine needle extract could be a good alternative to antibiotic usage at sub-therapeutic concentrations. The results relevant to our assumption could be obtained by using domestic chickens as an in vivo model for the evaluation of gut microbiota-altering properties of pine needle extract as an herbal supplement. We tested the antimicrobial effects of Baltic pine (Pinus sylvestris) needle extract. Then, we used chicken (Gallus gallus) that received feed supplemented with two different concentrations of the extract for 40 days to evaluate the changes in gut microbiota using 16S rRNA gene sequencing. This preliminary study demonstrated trends toward dose-dependent desirable changes in broiler microbiome, such as a reduction in the relative abundance of Campylobacter.

Genetic diversity and known virulence genes in Listeria innocua strains isolated from cattle abortions and farm environment.

Listeria innocua is considered as non-pathogenic bacteria living in an environment although several cases of immunocompromised humans and ruminant listeriosis infections have been reported. Previously, L. innocua was identified as a potential pathogen and virulence in association with L. monocytogenes PrfA dependent virulence (LIPI-1) gene cluster was demonstrated in hemolytic L. innocua. L. innocua usually considered non-pathogenic versus pathogenic L. monocytogenes and L. ivanovii because of the main virulence gene loss. There are limited studies and reports available about L. innocua-caused illness in cattle. A total of 18 STs were identified in cattle abortions while 17 STs in the farm environment with majority of STs were present in both abortions and environmental samples. Genome sequencing showed that in one farm identical L. innocua clones were represented in water, feed, soil, and faeces sample groups, suggesting that animals most likely through the faecal shedding may remain as the main source of L. innocua in a farm environment. Out of all L. innocua isolates PrfA-dependent virulence genes were not found in aborted foetuses isolates and environmental L. innocua isolate groups; however, in 20% of isolates a complete LIPI-3 pathogenicity island encoding listeriolysin S was identified. In this study, we demonstrated that genetically diverse L. innocua clones were widely distributed in cattle farm environment and certain isolates had a significant pathogenicity potential for cattle, thus causing adverse health effects, including abortions.

The Gut Microbiome among Postmenopausal Latvian Women in Relation to Dietary Habits.

In recent years, many studies have been initiated to characterise the human gut microbiome in relation to different factors like age, lifestyle, and dietary habits. This study aimed to evaluate the impact of yoghurt intake on the gut microbiome among postmenopausal women and how overall dietary habits modulate the gut microbiome. In total, 52 participants were included in the study and two groups-a control (n = 26) and experimental group (n = 26)-were established. The study was eight weeks long. Both study groups were allowed to consume a self-selected diet, but the experimental group had to additionally consume 175 g of plain organic milk yoghurt on a daily basis for eight weeks. In addition, a series of questionnaires were completed, including a questionnaire on the subject's sociodemographic background, health status, and lifestyle factors, as well as a food frequency questionnaire. Stool samples were collected for the analysis of the gut microbiome (both prior to and after the eight weeks of the study). Sequencing of V4-V5 regions of the 16S rRNA gene was used to determine the bacterial composition of stool samples. The dominant phylum from the gut microbiome was Firmicutes (~70% to 73%), followed by Bacteroidota (~20% to 23%). Although no significant changes in the gut microbiome were related to daily consumption of yoghurt, we report that consumption of food products like grains, grain-based products, milk and milk products, and beverages (tea, coffee) is associated with differences in the composition of the gut microbiome. Establishing nutritional strategies to shape the gut microbiome could contribute to improved health status in postmenopausal women, but further research is needed.

Prevalence, virulence determinants, and genetic diversity in Yersinia enterocolitica isolated from slaughtered pigs and pig carcasses.

Yersinia enterocolitica is an important zoonotic foodborne pathogen that could be transferred from infected pigs to their carcasses at slaughter, with subsequent introduction of the pathogen into the food chain. The aim of the present study was to study the prevalence, virulence characteristics, and genetic diversity of Y. enterocolitica isolates present in slaughtered pig tonsils and carcasses by using the WGS approach. A total of 200 slaughtered pig tonsils from 11 pig farms were collected in 2020-2021 at six slaughterhouses located in Latvia. Out of these samples, n = 190 were obtained from slaughtered pigs raised on Latvian farms while n = 10 were of Lithuanian origin, with the number of farms sampled being 10 and 1, respectively. Additionally, 30 pig carcasses were sampled at five slaughterhouses from pigs originating from five farms in 2021. Samples were investigated microbiologically, Y. enterocolitica isolates were biotyped and serotyped. Y. enterocolitica 4/O:3 was screened for antimicrobial resistance with the EUVSEC test panels. Whole genome sequence analysis (WGS) was performed in order to detect virulence genes and to assess the genetic diversity of Y. enterocolitica isolates. A total of 139 isolates, including one to three isolates from 84 Y. enterocolitica positive slaughtered pig tonsils and 13 pig carcass samples, were subjected to WGS analysis. The prevalence of Y. enterocolitica 4/O:3 in slaughtered pig tonsils and carcasses was 35% (70/200) and 13% (4/30), respectively. Antimicrobial resistance to ampicillin and tetracycline was detected in 97% (72/74) and 1% (1/74) of Y. enterocolitica 4/O:3 isolates. Y. enterocolitica 4/O:3 was represented only by ST18, while Y. enterocolitica 1A by ST3, ST147, ST304, ST307, and ST473. The ST18 isolates harbored the same main chromosomal (ail, inv, myfA, ystA) and majority shared plasmid-borne virulence genes (virF, yadA, yop virulon). The main virulence genes were not identified within the STs of Y. enterocolitica 1A and only minor differences were found between ST3, ST147, ST304, ST307, and ST473. Among ST18 isolates, cgMLST analysis revealed 43 cgMLST genotypes while 16 cgMLST genotypes were found among Y. enterocolitica 1A STs. The present study has shown the distribution of genetically distant cgMLST genotypes in slaughtered pigs from pig farms located in different geographical regions of Latvia, with one to 11 cgMLSTs identified within each sampled farm. The presence of undistinguishable cgMLST genotypes in slaughtered pig tonsils and the respective carcasses supported the link between the slaughter of Y. enterocolitica - positive pigs and carcass contamination with Y. enterocolitica 4/O:3.

Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease.

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the "one-size-fits-all" approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.

Characterization and Genetic Diversity of Listeria monocytogenes Isolated from Cattle Abortions in Latvia, 2013-2018.

Listeria monocytogenes can cause disease in humans and in a wide range of animal species, especially in farm ruminants. The aim of the study was to determine the prevalence and genetic diversity of L. monocytogenes related to 1185 cattle abortion cases in Latvia during 2013-2018. The prevalence of L. monocytogenes among cattle abortions was 16.1% (191/1185). The seasonality of L. monocytogenes abortions was observed with significantly higher occurrence (p < 0.01) in spring (March-May). In 61.0% of the cases, the affected cattle were under four years of age. L. monocytogenes abortions were observed during the third (64.6%) and second (33.3%) trimesters of gestation. Overall, 27 different sequence types (ST) were detected, and four of them, ST29 (clonal complex, CC29), ST37 (CC37), ST451 (CC11) and ST7 (CC7), covered more than half of the L. monocytogenes isolates. Key virulence factors like the prfA-dependent virulence cluster and inlA, inlB were observed in all the analyzed isolates, but lntA, inlF, inlJ, vip were associated with individual sequence types. Our results confirmed that L. monocytogenes is the most important causative agent of cattle abortions in Latvia and more than 20 different STs were observed in L. monocytogenes abortions in cattle.

Prevalence, Genetic Diversity and Factors Associated with Distribution of Listeria monocytogenes and Other Listeria spp. in Cattle Farms in Latvia.

Listeria spp. is a diverse genus of Gram-positive bacteria commonly present in the environment while L. monocytogenes and L. ivanovii are well known human and ruminant pathogens. The aim of the present study was to reveal the prevalence and genetic diversity of L. monocytogenes and other Listeria spp. and to identify the factors related to the abundance of pathogen at cattle farms. A total of 521 animal and environmental samples from 27 meat and dairy cattle farms were investigated and the genetic diversity of L. monocytogenes isolates was studied with WGS. The prevalence of Listeria was 58.9%, while of L. monocytogenes it was -11%. The highest prevalence of L. monocytogenes was found in the environment-soil samples near to manure storage (93%), mixed feed from the feeding trough and hay (29%), water samples from farms drinking trough (28%) and cattle feces (28%). Clonal complexes (CC) of CC37 (30%), CC11 (20%) and CC18 (17%) (all IIa serogroup) were predominant L. monocytogenes clones. CC18, CC37 and CC8 were isolated from case farms and CC37, CC11 and CC18 from farms without listeriosis history. Only one hypervirulent CC4 (1%) was isolated from the case farm. Sequence types (STs) were not associated with the isolation source, except for ST7, which was significantly associated with soil (p < 0.05). The contamination of soil, feeding tables and troughs with L. monocytogenes was associated with an increased prevalence of L. monocytogenes at farms. Our study indicates the importance of hygienic practice in the prevention of the dissemination of L. monocytogenes in the cattle farm environment.

Virulence Determinants and Genetic Diversity of Yersinia Species Isolated from Retail Meat.

Yersinia enterocolitica is an important foodborne pathogen, and the determination of its virulence factors and genetic diversity within the food chain could help understand the epidemiology of yersiniosis. The aim of the present study was to detect the prevalence, and characterize the virulence determinants and genetic diversity, of Yersinia species isolated from meat. A total of 330 samples of retailed beef (n = 150) and pork (n = 180) in Latvia were investigated with culture and molecular methods. Whole genome sequencing (WGS) was applied for the detection of virulence and genetic diversity. The antimicrobial resistance of pathogenic Y. enterocolitica isolates was detected in accordance with EUCAST. Yersinia species were isolated from 24% (79/330) of meats, and the prevalence of Y. enterocolitica in pork (24%, 44/180) was significantly higher (p < 0.05) than in beef (13%, 19/150). Y. enterocolitica pathogenic bioserovars 2/O:9 and 4/O:3 were isolated from pork samples (3%, 6/180). Only resistance to ampicillin was confirmed in Y. enterocolitica 4/O:3 and 2/O:9 isolates, but not in other antimicrobials. Major virulence determinants, including ail, inv, virF, ystA and myfA, were confirmed with WGS in Y. enterocolitica 2/O:9 and 4/O:3. MLST typing revealed 15 STs (sequence types) of Y. enterocolitica with ST12 and ST18, which were associated with pathogenic bioserovars. For Y. enterocolitica 1A, Y. kristensenii, Y. intermedia and Y. frederiksenii, novel STs were registered (ST680-688). The presence of virulence genes and genetic characteristics of certain Y. enterocolitica STs confirm the common knowledge that pork could be an important source of pathogenic Yersinia.

Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks.

Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)] and several viruses, but also parasites and some fungi. Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable knowledge in diverse ranges of sectors including epidemiological investigations of FBD outbreaks and antimicrobial resistance (AMR). As genotyping using whole-genome sequencing (WGS) is becoming more accessible and affordable, it is increasingly used as a routine tool for the detection of pathogens, and has the potential to differentiate between outbreak strains that are closely related, identify virulence/resistance genes and provide improved understanding of transmission events within hours to days. In most cases, the computational pipeline of WGS data analysis can be divided into four (though, not necessarily consecutive) major steps: de novo genome assembly, genome characterization, comparative genomics, and inference of phylogeny or phylogenomics. In each step, ML could be used to increase the speed and potentially the accuracy (provided increasing amounts of high-quality input data) of identification of the source of ongoing outbreaks, leading to more efficient treatment and prevention of additional cases. In this review, we explore whether ML or any other form of AI algorithms have already been proposed for the respective tasks and compare those with mechanistic model-based approaches.

Campylobacter species prevalence, characterisation of antimicrobial resistance and analysis of whole-genome sequence of isolates from livestock and humans, Latvia, 2008 to 2016.

BackgroundCampylobacter is the main cause of bacterial gastroenteritis worldwide. The main transmission route is through consumption of food contaminated with Campylobacter species or contact with infected animals. In Latvia, the prevalence of campylobacteriosis is reported to be low (4.6 cases per 100,000 population in 2016).AimTo determine prevalence, species spectrum and antimicrobial resistance (AMR) of Campylobacter spp. in Latvia, using data from various livestock and human clinical samples.MethodsWe analysed data of Campylobacter microbiological monitoring and AMR (2008 and 2014-16) in Latvia. Data from broilers, poultry, pigs, calves and humans were used to determine prevalence of Campylobacter. Additionally, 45 different origin isolates (22 human) were sequenced on the Illumina MiSeq platform; for each isolate core genome multilocus sequence typing was used and relevant antimicrobial resistance mechanisms were identified.ResultsOverall, Campylobacter prevalence in was 83.3% in pigs, 50.2% in broilers, 16.1% in calves and 5.3% in humans; C. jejuni was the predominant species in all sources except pigs where C. coli was main species. High level of resistance in Campylobacter were observed against fluoroquinolones, tetracycline and streptomycin, in most of sequenced isolates genetic determinants of relevant AMR profiles were identified.ConclusionsIn Latvia, prevalence of Campylobacter in livestock is high, especially in pigs and broilers; prevalence in poultry and humans were lower than in other European countries. AMR analysis reveals increase of streptomycin and tetracycline resistant broiler origin C. jejuni strains. WGS demonstrates a high compliance between resistance phenotype and genotype for quinolones and tetracyclines.

Effects of acetate on Kluyveromyces marxianus DSM 5422 growth and metabolism.

Metabolically active cells produce a wide array of metabolites that can inhibit their growth. Acetate is a widely known preservative, and it is also produced by yeast cells during their growth. Kluyveromyces marxianus DSM 5422 is a promising yeast strain that could be employed in biotechnological processes, but the knowledge of its stress physiology is scarce. Here, we investigate the effects of acetate on growth and changes in cell population structure during adaptation to elevated concentrations of acetate in K. marxianus DSM 5422. Our results indicate that acetate inhibits growth in a pH-dependent manner and has pronounced effects if yeast is grown on lactose or galactose. When challenged with acetate, culture extends lag phase, during which cells adapt to elevated acetate concentrations, and growth reoccurs, albeit at a slower rate, when majority of the population is acetate resistant. Acetate resistance is maintained only if acetate is present in the media or if the culture has reached end of active growth phase. This study shows possible caveats in lactose fermentation with K. marxianus and gives a further perspective in non-conventional yeast applications in biotechnology.

Adenine auxotrophy--be aware: some effects of adenine auxotrophy in Saccharomyces cerevisiae strain W303-1A.

Adenine auxotrophy is a commonly used genetic marker in haploid yeast strains. Strain W303-1A, which carries the ade2-1 mutation, is widely used in physiological and genetic research. Yeast extract-based rich medium contains a low level of adenine, so that adenine is often depleted before glucose. This could affect the cell physiology of adenine auxotrophs grown in rich medium. The aim of our study was to assess the effects of adenine auxotrophy on cell morphology and stress physiology. Our results show that adenine depletion halts cell division, but that culture optical density continues to increase due to cell swelling. Accumulation of trehalose and a coincident 10-fold increase in desiccation stress tolerance is observed in adenine auxotrophs after adenine depletion, when compared to prototrophs. Under adenine starvation, long-term survival of W303-1A is lower than during carbon starvation, but higher than during leucine starvation. We observed drastic adenine-dependent changes in cell stress physiology, suggesting that results may be biased when adenine auxotrophs are grown in rich media without adenine supplementation.