Browsing by Subject "Desiccation"

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    Salmonella’s Desiccation Survival and Thermal Tolerance: Genetic, Physiological, and Metabolic Factors
    (2017-07) Maserati, Alice
    Salmonella can survive for long periods under extreme desiccation and low water activity conditions (aw < 0.6) while becoming tolerant to heat. This stress tolerance poses a risk for food safety, but relatively little is known about the molecular and cellular processes involved in this adaptation mechanism and its potential for cross-protection. This dissertation consists of three distinct studies focused on elucidating this mechanism. The objective of the first study was to identify the genes involved in Salmonella’s resistance to desiccation. A global transcriptomic analysis comparing S. enterica serovar Typhimurium cells equilibrated to low aw (aw 0.11) and cells equilibrated to high aw (aw 1.0) determined that 719 genes (16% of the total number of genes in the genome) were differentially expressed between the two conditions. The genes that were up-regulated at aw 0.11 (290) were mostly involved in metabolic pathways, DNA replication/repair, regulation of transcription and translation, and virulence. Based on the transcriptomic analysis, we created deletion mutants for two virulence genes, sseD and sopD, and tested their ability to survive desiccation and low aw on glass beads. The two mutants exhibited significant cell viability reductions after desiccation compared to the wild-type and additional decrease after exposure to aw 0.11 for 7 days. Under scanning electron microscopy, the mutants displayed a different cell morphology and extracellular matrix production when compared to the wild-type under the same conditions. The findings of this study suggested that sopD and sseD are required for Salmonella’s survival during desiccation. The objective of the second study was to determine the effect of food and inert matrices, nutrient availability, and growth conditions on desiccation survival and thermal tolerance of S. enterica serovar Typhimurium. Salmonella was grown in LBglc and M9 media, in the presence or absence of EDTA and dipyridyl. Cultures were inoculated on toasted oat cereal (TOC) or glass beads, dried, and equilibrated for a week at aw 0.11 and 1.0, before being thermally treated at 75, 85, 90, and 95oC. For all growth conditions and temperatures tested, cells exposed to aw 0.11 had inactivation rates (δ-values) at least 10-fold longer than cells equilibrated at aw 1.0. Our results showed that growth in the presence of EDTA or Dipyridyl did not have any effect on Salmonella’s thermal tolerance at either aw on TOC. In control conditions, recovery after drying and thermal tolerance was higher on TOC than on glass beads, suggesting that the food matrix was protective for desiccation and thermal treatment. Growth in M9 resulted in lower survival to drying and exposure to low aw on glass beads, compared to LBglc. On the contrary, thermal tolerance increased in cells grown in M9 compared to LBglc at both aw. Cells grown in LBglc and M9 displayed differences in the production of extracellular matrix, in particular during equilibration to aw 0.11 and after thermal treatment at both aw. Additionally, when Salmonella was grown on glass beads in LBglc as biofilm, the thermal tolerance was greater than free cells dried on beads. Our observations suggest that the presence of nutrients during growth and before exposure to desiccation and thermal treatment influenced Salmonella’s ability to survive desiccation and develop thermal tolerance. The objective of the third study was to identify proteins involved in Salmonella’s resistance to desiccation and thermal treatment using iTRAQ. Proteins were extracted from S. enterica servorar Typhimurium cells dried, equilibrated at high aw (1.0) and low aw (0.11), and thermally treated at 75°C. Our analysis determined that 734 proteins were differentially expressed among samples, and of these 175 proteins were the most significant in determining differences in the proteomic profiles among treatments. Based on their proteomic expression profiles, the samples were clustered in two main groups by PCA analysis, “dry” samples and “wet” samples, while we did not observe significant differences between the thermally treated samples and the non-heated samples, at both aw. Protein profiles indicated shifts in cell metabolism in both samples, as well as a strict regulation of DNA repair, replication, transcription, and translation. “Dry” samples had higher levels of 50S and 30S ribosomal proteins, indicating that ribosomal proteins might be important for extra-ribosomal regulation of cellular response even when the synthesis of proteins is slowed down. Stress response proteins were more frequently present in “wet” samples compared to “dry” samples, including SspA, GorA, and Dps, suggesting that “wet” cells were activating stress systems in response to rehydration. In conclusion, our study indicated that pre-adaptation to dry conditions was linked to increased thermal tolerance, while reversion from a dry state into a wet state implied a significant change in protein expression that is linked with reduced thermal tolerance.
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    Use of Machine Learning to Predict the Desiccation Tolerance of Bacteria
    (2021-08) Clipsham, Maia
    For efficient long-term storage and use of bacteria for environmental applications, understanding and identifying desiccation resistance in bacteria is key. In the past, desiccation tolerance was a common way of characterizing bacteria, so there is much data on the desiccation tolerance of a wide range of bacterial species. Since the advent of transcriptomics, multiple papers have been published on the expression level of genes during desiccation stress. Additionally, many reviews have described mechanisms and genes relevant to desiccation tolerance in bacteria, but an overarching framework for the prediction of desiccation survival in bacteria is lacking. Model building based on data collected from the literature has been used to successfully predict aerobic vs anaerobic phenotype, enzyme function and substrate specificity (Robinson et al., 2020; Jabłońska et al, 2019) Building on this wealth of previous research, machine learning was used to create a robust model that predicts desiccation tolerance given bacterial genomes. Validation and accuracy of the machine learning model was tested using a desiccation assay carried out over three months. To build the model, a literature review was conducted to find genes that were upregulated greater than two-fold during desiccation stress in bacteria. From the review, 2609 genes from 11 papers were found and condensed to 1082 non-homologous and non near-zero variance genes. A second literature search was conducted to identify bacterial species with a known desiccation response, either tolerant or sensitive, and a publicly available genome. Thirty-five desiccation tolerant and 33 desiccation sensitive genomes were chosen and then queried for the previously curated desiccation upregulated genes list. Approximately 176,800 genes were analyzed, and genes with non-zero variance were removed. The remaining 75,982 genes are included in the model (Rogozin et al., 2002). A random forest supervised machine learning approach was used to create a preliminary model for desiccation resistance. The genomes were split into 80% training data and 20% test data and the model was run 100 times with different seeds, 10-fold cross validation, and three repeats. The average accuracy for the 100 iterations of the model was 0.898 ± 0.0266, indicating the model could accurately predict the desiccation phenotype of the testing data 89.8% of the time. The experimental validation of the desiccation model looked at the viability of 28 bacteria, seven with documented desiccation phenotypes and 21 bacteria with no known desiccation phenotype. For all organisms tested the model had an accuracy of 0.75 demonstrating good model performance.