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Open Access Publications from the University of California

Scholarly Works (6 results)

  • Thesis
  • Peer Reviewed
UC Davis Electronic Theses and Dissertations (2022)

Foodborne outbreaks have been continuously reported during the past decade in the United States. Overall, 129 outbreaks of pathogenic Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica infections linked to fresh produce occurred from 2010 to 2021 in the U.S., causing a total of 8,889 illnesses with 2,412 hospitalizations and 90 deaths. Among the different kinds of fresh produce, Romaine lettuce (RL) is one of the most frequently reported variety of fresh produce associated with the outbreaks. Pathogen contamination can occur at any point during the preharvest, e.g., soil, irrigation water, and insect; and during the postharvest, e.g., produce processing, distribution, and preparation. Intensive studies have shown that foodborne pathogens can persist in RL for a period of time. Unfortunately, current washing strategies cannot eliminate the potential pathogens. The fact is that foodborne pathogens present in fresh produce are typically at low levels, making the detection of pathogens even more challenging. The development of next-generation sequencing techniques provides us with a better understanding of the native microbiota present in RL. The RL microbiota data allow us to not only understand the connection between native microbiota and pathogen contamination but also potentially can be used as a tool to predict produce safety and shelf-life. In this dissertation, I first investigated the impact of storage time, season, and brands on bacterial populations in commercially washed, chopped, and bagged RL by using culture-dependent and culture-independent methods. The results showed that RL had almost no differences in aerobic plate count (APC) and anaerobic plate count (AnPC) between “Use By” date (UBD) and 5 days after the UBD (UBD5) at 4 °C, but differences were seen between RL from two seasons as well as among three brands of RL. At the same time, the results based on 16S rRNA gene amplicon DNA sequencing showed no difference in bacterial diversity and composition between UBD and UBD5 at 4 oC, while the results showed that the season when the RL was harvested generated a significant impact on the diversity and composition of bacterial communities. The brand is the factor that significantly shaped the population as well, indicating that harvest locations, processing strategies, and distribution conditions may impact bacterial populations (Chapter 2). Through my artificial inoculation study (Chapter 3), I found the native microbiota in RL impacted the behaviors of Listeria monocytogenes (LM) at 4 °C. LM decreased in abundance from UBD to UBD5 in the late-season RL but remained at the same levels in the early-season RL. Results of 16S rRNA gene amplicon DNA sequencing with differential abundance analysis showed that the identified Leuconostoc, Lactococcus, and Erwinia in late-season RL may be the potential reason for the LM decrease. In addition, Pseudomonas, Chryseobacterium, Duganella, and Listeria were identified as indicators for RL inoculated with a high level of LM, and Serratia, Pedobacter, Janthinobacterium, Flavobacterium, and Weissella were identified as indicators for RL inoculated with a low level of LM. Through culture-based methods, 32 antagonistic bacteria were isolated and confirmed by using the double-layer and spot-on-lawn methods. Three species were identified by using Sanger sequencing and whole genome sequencing, with these species being Carnobacterium maltaromaticum, Lactococcus lactis, and Bacillus filamentous. Quantitative PCR was used to further confirm and quantify the three antagonistic species in RL. The results showed no difference in abundances of C. maltaromaticum and Lc. lactis between RL from two seasons, while B. filamentosus presented higher abundances in late-season RL than in early-season RL. Additional research is needed to better confirm the antagonist function of B. filamentosus by using the co-culturing method and better identify the anti-LM mechanism of B. filamentosus. During sequencing data processing, up to more than 50% of sequence loss occurred during alignment and denoising, which may cause important bacteria species information to be missing or lost. To test the hypothesis, I explored an alignment-free strategy, k-mer hashes, by establishing classifiers to predict fresh produce contamination and quality reduction. The k-mer hash approach was compared against the amplicon sequence variant (ASV) approach with a typical denoising step. The results showed that random forests (RF)-based classifiers for fresh produce safety and quality that were trained on 7-mer hash-preprocessed publicly available datasets had significantly higher classification accuracy than those using the ASV datasets, supporting our hypothesis that data preprocessing strategies without sequence loss (k-mer hash) retain more important information about bacteria species for produce safety and quality classification than the approach with sequence loss (ASV). In addition, the integration of multiple produce microbiota datasets led RF-based classifiers to have a higher classification accuracy than the classifiers trained with individual datasets. The RF-based classifiers based on integrated datasets identified more consistent and generalizable indicators associated with fresh produce safety and quality (Chapter 4). In summary, this dissertation systematically profiled the structures of bacterial populations in RL and investigated how to better utilize culture-dependent methods, culture-independent methods, and publicly available datasets to identify bacteria associated with produce safety and quality and to isolate competitive exclusion bacteria that can potentially be used as biocontrol agents to reduce foodborne pathogen contamination of produce.

    • Article
    • Peer Reviewed
    UC Davis Previously Published Works (2022)
    In the United States, due to the limited information about the microbial quality and safety of fresh produce after the labeled open dates, unnecessary discarding of fresh produce in good conditions and food loss have been caused. The aim of this study was to address this knowledge gap and evaluate the microbial quality of commercial chopped Romaine lettuce (RL) on the Use By dates (UBD) and 5 days after the Use By dates (UBD5). The microbial quality was evaluated using culture-dependent and culture-independent methods. Three brands of RL samples, from early and late harvest seasons, were purchased from local grocery stores and stored at 4°C until 5 days after their UBD. On the UBD and UBD5, bagged lettuce was opened, homogenized, diluted, and plated onto plate count agar and anaerobic agar to obtain total aerobic plate counts (APC) and total anaerobic plate counts (AnPC). For the culture-independent method, DNA was extracted from each sample homogenate and used for 16S rRNA gene sequencing. The culture-dependent results showed that there was no significant change in APC or AnPC between UBD and UBD5 samples. The APC and AnPC ranged from 5.71 ± 0.74 to 7.89 ± 0.10 Log CFU/g and 1.75 ± 0.08 to 7.32 ± 0.61 Log CFU/g, respectively. No significant difference in alpha diversity, based on observed features and Shannon index values, was detected between UBD and UBD5 samples using 16S rRNA sequencing. Similarly, no difference was observed in beta diversity based on the Jaccard distance matrixes and the weighted Unifrac distance matrixes. Taxonomic analysis revealed 128 genera in all RL samples. The top five genera were Pseudomonas (with relative abundance ranging from 16.47 to 92.72%), Serratia (0-52.35%), Weissella (0-42.42%), Pantoea (0.17-21.33%), and Lactococcus (0-24.30%). The differential abundance analysis based on the ANCOM test showed that no bacteria were detected to have significantly differential abundance in RL between UBD and UBD5. In summary, both the culture-dependent and culture-independent results showed that there was no significant difference in the microbial quality of RL before and shortly after the UBD.
      Cover page: The Microbial Quality of Commercial Chopped Romaine Lettuce Before and After the Use By Date.
      • Article
      • Peer Reviewed
      UC Davis Previously Published Works (2024)
      UNLABELLED: Small sample sizes and loss of sequencing reads during the microbiome data preprocessing can limit the statistical power of differentiating fresh produce phenotypes and prevent the detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored a machine learning-based k-mer hash analysis strategy to identify DNA signatures predictive of produce safety (PS) and produce quality (PQ) and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step and ASV-based taxonomy strategy. Random forest-based classifiers for PS and PQ using 7-mer hash data sets had significantly higher classification accuracy than those using the ASV data sets. We also demonstrated that the proposed combination of integrating multiple data sets and leveraging a 7-mer hash strategy leads to better classification performance for PS and PQ compared to the ASV method but presents lower PS classification accuracy compared to the feature-selected ASV-based taxonomy strategy. Due to the current limitation of generating taxonomy using the 7-mer hash strategy, the ASV-based taxonomy strategy with remarkably less computing time and memory usage is more efficient for PS and PQ classification and applicable for important taxa identification. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing data sets for the application of machine learning in the area of microbial safety and quality of food. IMPORTANCE: Identification of generalizable indicators for produce safety (PS) and produce quality (PQ) improves the detection of produce contamination and quality decline. However, effective sequencing read loss during microbiome data preprocessing and the limited sample size of individual studies restrain statistical power to identify important features contributing to differentiating PS and PQ phenotypes. We applied machine learning-based models using individual and integrated k-mer hash and amplicon sequence variant (ASV) data sets for PS and PQ classification and evaluated their classification performance and found that random forest (RF)-based models using integrated 7-mer hash data sets achieved significantly higher PS and PQ classification accuracy. Due to the limitation of taxonomic analysis for the 7-mer hash, we also developed RF-based models using feature-selected ASV-based taxonomic data sets, which performed better PS classification than those using the integrated 7-mer hash data set. The RF feature selection method identified 480 PS indicators and 263 PQ indicators with a positive contribution to the PS and PQ classification.
        Cover page: Microbiome-based classification models for fresh produce safety and quality evaluation.Creative Commons 'BY' version 4.0 license
        • Article
        • Peer Reviewed
        UC Davis Previously Published Works (2023)
        Given the involvement of Vibrio parahaemolyticus (Vp) in a wide range of seafood outbreaks, a systematical characterization of Vp fitness and transcriptomic changes at temperatures of critical importance for seafood production and storage is needed. In this study, one of each virulent Vp strain (tdh+ and trh+) was tested. While no difference in survival behavior of the two virulent strains was observed at 10°C, the tdh+ strain had a faster growth rate than the trh+ strain at 30°C. Transcriptomic analysis showed that a significantly higher number of genes were upregulated at 30°C than at 10°C. The majority of differentially expressed genes of Vp at 30°C were annotated to functional categories supporting cellular growth. At 10°C, the downregulation of the biofilm formation and histidine metabolism indicates that the current practice of storing seafood at low temperatures not only protects seafood quality but also ensures seafood safety.
          Cover page: Fitness and transcriptomic analysis of pathogenic Vibrio parahaemolyticus in seawater at different shellfish harvesting temperatures.
          • Article
          • Peer Reviewed
          UC Davis Previously Published Works (2019)
          BACKGROUND:Drug-resistant fish pathogens can cause significant economic loss to fish farmers. Since 2012, florfenicol has become an approved drug for treating both septicemia and columnaris diseases in freshwater fish. Due to the limited drug options available for aquaculture, the impact of the therapeutical florfenicol treatment on the microbiota landscape as well as the resistome present in the aquaculture farm environment needs to be evaluated. RESULTS:Time-series metagenomic analyses were conducted to the aquatic microbiota present in the tank-based catfish production systems, in which catfish received standard therapeutic 10-day florfenicol treatment following the federal veterinary regulations. Results showed that the florfenicol treatment shifted the structure of the microbiota and reduced the biodiversity of it by acting as a strong stressor. Planctomycetes, Chloroflexi, and 13 other phyla were susceptible to the florfenicol treatment and their abundance was inhibited by the treatment. In contrast, the abundance of several bacteria belonging to the Proteobacteria, Bacteroidetes, Actinobacteria, and Verrucomicrobia phyla increased. These bacteria with increased abundance either harbor florfenicol-resistant genes (FRGs) or had beneficial mutations. The florfenicol treatment promoted the proliferation of florfenicol-resistant genes. The copy number of phenicol-specific resistance genes as well as multiple classes of antibiotic-resistant genes (ARGs) exhibited strong correlations across different genetic exchange communities (p < 0.05), indicating the horizontal transfer of florfenicol-resistant genes among these bacterial species or genera. Florfenicol treatment also induced mutation-driven resistance. Significant changes in single-nucleotide polymorphism (SNP) allele frequencies were observed in membrane transporters, genes involved in recombination, and in genes with primary functions of a resistance phenotype. CONCLUSIONS:The therapeutical level of florfenicol treatment significantly altered the microbiome and resistome present in catfish tanks. Both intra-population and inter-population horizontal ARG transfer was observed, with the intra-population transfer being more common. The oxazolidinone/phenicol-resistant gene optrA was the most prevalent transferred ARG. In addition to horizontal gene transfer, bacteria could also acquire florfenicol resistance by regulating the innate efflux systems via mutations. The observations made by this study are of great importance for guiding the strategic use of florfenicol, thus preventing the formation, persistence, and spreading of florfenicol-resistant bacteria and resistance genes in aquaculture.
            Cover page: Impacts of florfenicol on the microbiota landscape and resistome as revealed by metagenomic analysis
            • Article
            • Peer Reviewed
            UC Davis Previously Published Works (2017)
            This study developed RNA-based predictive models describing the survival of Vibrio parahaemolyticus in Eastern oysters (Crassostrea virginica) during storage at 0, 4, and 10°C. Postharvested oysters were inoculated with a cocktail of five V. parahaemolyticus strains and were then stored at 0, 4, and 10°C for 21 or 11 days. A real-time reverse transcription-PCR (RT-PCR) assay targeting expression of the tlh gene was used to evaluate the number of surviving V. parahaemolyticus cells, which was then used to establish primary molecular models (MMs). Before construction of the MMs, consistent expression levels of the tlh gene at 0, 4, and 10°C were confirmed, and this gene was used to monitor the survival of the total V. parahaemolyticus cells. In addition, the tdh and trh genes were used for monitoring the survival of virulent V. parahaemolyticus Traditional models (TMs) were built based on data collected using a plate counting method. From the MMs, V. parahaemolyticus populations had decreased 0.493, 0.362, and 0.238 log10 CFU/g by the end of storage at 0, 4, and 10°C, respectively. Rates of reduction of V. parahaemolyticus shown in the TMs were 2.109, 1.579, and 0.894 log10 CFU/g for storage at 0, 4, and 10°C, respectively. Bacterial inactivation rates (IRs) estimated with the TMs (-0.245, -0.152, and -0.121 log10 CFU/day, respectively) were higher than those estimated with the MMs (-0.134, -0.0887, and -0.0732 log10 CFU/day, respectively) for storage at 0, 4, and 10°C. Higher viable V. parahaemolyticus numbers were predicted using the MMs than using the TMs. On the basis of this study, RNA-based predictive MMs are the more accurate and reliable models and can prevent false-negative results compared to TMs.IMPORTANCE One important method for validating postharvest techniques and for monitoring the behavior of V. parahaemolyticus is to establish predictive models. Unfortunately, previous predictive models established based on plate counting methods or on DNA-based PCR can underestimate or overestimate the number of surviving cells. This study developed and validated RNA-based molecular predictive models to describe the survival of V. parahaemolyticus in oysters during low-temperature storage (0, 4, and 10°C). The RNA-based predictive models show the advantage of being able to count all of the culturable, nonculturable, and stressed cells. By using primers targeting the tlh gene and pathogenesis-associated genes (tdh and trh), real-time RT-PCR can evaluate the total surviving V. parahaemolyticus population as well as differentiate the pathogenic ones from the total population. Reliable and accurate predictive models are very important for conducting risk assessment and management of pathogens in food.
              Cover page: Establishment and Validation of RNA-Based Predictive Models for Understanding Survival of Vibrio parahaemolyticus in Oysters Stored at Low Temperatures
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