Examining the Role of Ballast Water in the Global Translocation of Microorganisms
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Ballast water is a known vector for the global translocation of microorganisms. Research into the ballast microbiome recently accelerated following a ballast-associated outbreak of Vibrio cholerae in Peru during the 1990s that killed over 10,000 people. Over the last two decades there has been increasing regulation surrounding ballast water treatment with the aim of protecting human and environmental health, recently culminating in the approval of the International Maritime Organization Ballast Water Management Convention in September 2017. The Convention requires shipowners to install and use of ballast water treatment systems within an established timeline. However, many basic questions remain surrounding the composition of the ballast water microbiome. This dissertation strives to address several of these questions, which will allow shipowners, regulators, and enforcement agencies to make more informed decisions in an uncertain space.
The first aim of this dissertation is to characterize the bacterial microbiome of ballast water aboard vessels arriving at several ports, and identify characteristics to explain observed variations. Published research that utilizes high throughput sequencing (HTS) technology to explore microbial community dynamics is relatively rare. In this study, 16S rRNA gene sequencing and metabarcoding were used to perform the most comprehensive microbiological survey of ballast water arriving to hub ports to date. In total, 41 ballast, 20 harbor, and 6 open ocean water samples were characterized from four world ports (Shanghai, China; Singapore; Durban, South Africa; Los Angeles, California). In addition, total coliforms, Enterococcus, and E. coli were cultured to evaluate adherence to International Maritime Organization standards for ballast discharge. Five of the 41 vessels – all of which were loaded in China – did not comply with standards for at least one indicator organism. Dominant bacterial taxa of ballast water at the class level were Alphaproteobacteria, Gammaproteobacteria, and Bacteroidia. Ballast water samples were composed of significantly lower proportions of Oxyphotobacteria than either ocean or harbor samples. Linear discriminant analysis (LDA) effect size (LEfSe) and machine learning were used to identify and test potential biomarkers for classifying sample types (ocean, harbor, ballast). Eight candidate biomarkers were used to achieve 81% (k nearest neighbors) to 88% (random forest) classification accuracy. Further research of these biomarkers could aid the development of techniques to rapidly assess ballast water origin.
The first portion of the second aim of this dissertation evaluates the prevalence of indicator organisms and antibiotic resistance genes (ARGs) in ballast water compared to harbor and ocean water. The Ballast Water Management Convention, which sets forth guidelines regarding indicator organisms in ballast water, entered into force in September 2017. Notably, antibiotic resistance is absent from the Convention. We collected a total of 74 ballast and harbor samples from Singapore; Shanghai, China; Durban, South Africa; and Los Angeles, California. Eight ocean samples were collected for comparison. This research examines the concentration of indicator organisms and prevalence of three antibiotic resistance genes (ARGs). The ARGs examined in this study range from ubiquitous (sul1 – sulfonamide) to common (tetM – tetracycline) to rare (vanA – vancomycin). In ballast samples, there were significantly higher concentrations of E. coli in Singapore and China when compared to South Africa (Singapore, p = 0.040) and California (Singapore, p < 0.001; China, p = 0.038). Harbor samples from China had significantly higher concentrations of E. coli than Singapore (p = 0.049) and California (p = 0.001). When compared to ocean samples, there were significantly higher concentrations of normalized tetM in ballast samples from California (p = 0.011) and Singapore (p = 0.019) and in harbor samples from California (p = 0.018), Singapore (p = 0.010), and South Africa (p = 0.008). These findings indicate that there are differential microbial loads in different ports. Furthermore, there appears to be elevated levels of certain ARGs in ballast and harbor water when compared to ocean water, which may indicate that ballast is either translocating higher concentrations of certain ARGs or that conditions in the ballast tanks are placing selective pressure in favor of some ARGs.
The second portion of the second aim of this dissertation evaluates the prevalence of fungal pathogens in ballast water compared to harbor and ocean water. Several recent studies have explored the ballast water microbiome, but few have examined the fungal mycobiome and, to our knowledge, no studies have examined fungal pathogens in ballast. The fungal mycobiome was characterized by collecting a total of 65 ballast, harbor, and ocean samples from four major ports and sequencing the fungal internal transcribed spacer (ITS) region. A literature review of the resulting taxa was performed to identify well-studied fungal pathogens. Hosts of the identified pathogens included corals, humans, animals, plants, and crops. Of ballast samples, 21.4% had at least one fungal taxon pathogenic to corals and 81.0% had at least one fungal taxon pathogenic to humans. The majority of the fungal community in 19% of ballast samples were pathogenic taxa. A significantly higher proportion of the fungal community was composed of pathogens in Shanghai compared to all other sample sites (p = 0.025). The identification of fungal pathogens in ballast, especially those affecting corals and humans, highlights the need to further research the ballast microbiome to protect human and environmental health from the threat of fungal pathogen introductions via ballast.
The first portion of the third aim of this dissertation examines correlations between the bacterial and fungal microbiome. This chapter utilizes high throughput sequencing (HTS) and machine learning to examine and integrate the 16S and 18S rRNA genes and fungal ITS region. These sequencing regions were examined using the SILVA v132 and UNITE reference databases. The highest correlation was found between the communities in Silva_16S and UNITE_ITS (0.74). There was a higher proportion of positive inter-kingdom correlations than positive intra-kingdom interactions (p = 0.032). Understanding the reasons for this difference will require additional research under more controlled conditions. Finally, a machine learning model was used to examine the accuracy of assignment when using each sequencing region and reference database. There was significantly higher accuracy when using SILVA v132 (0.814) when compared to UNITE (0.664) (p < 0.001). In the short term, future research with the goal of classifying ballast water samples based on location or ballast water residence time should be performed using the 16S rRNA gene and SILVA v132 reference database. Future research to curate other sequencing regions or the UNITE reference database in the aquatic ecosystem may improve the utility of these tools when attempting to classify ballast water.
The second portion of the third aim of this dissertation examines correlations between the bacterial microbiome and non-target chemical analysis. To our knowledge, no literature is available that examines the interaction between microbes and chemicals in ballast water. This study addresses this gap in the literature by examining correlations between bacterial taxa and non-target chemical compounds. All strong interdomain and intradomain Pearson correlations (i.e. |r| > 0.7) were positive (54 interactions); however, the majority of Pearson correlations at all levels were negative (25,497 of 33,920; 75.2%). The reasons for this pattern are unclear and further research to isolate specific bacterial taxa and non-target chemicals may provide useful insight. In addition, machine learning was performed using bacterial, chemical, and bacterial and chemical markers. The bacterial markers appeared to perform well at differentiating California, China, and South Africa; however, accuracy was poor when classifying Singapore samples. Chemical markers appeared to supplement this deficiency, and the lowest out-of-bag error was achieved using a combined bacterial-chemical marker set with 6 features (6.67%). Further research with a larger sample size is necessary to appropriately test the markers identified in this work; however, this research serves as a proof-of-concept for a combined bacterial-chemical machine learning classification approach to ballast and harbor water samples.
High throughput sequencing
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