Comparative study of the effects of biocides and metal oxide nanoparticles on microbial community structure in a stream impacted by hydraulic fracturing.

Our study goal was to investigate the impact of biocides and nanoparticles (NPs) on the microbial diversity in a hydraulic fracturing impacted stream. Biocides and NPs are known for their antimicrobial properties and controlling microbial growth. Previous work has shown that biocides can alter the microbial community composition of stream water and may select for biocide-resistant bacteria. Additional studies have shown that nanoparticles can also alter microbial community composition. However, previous work has often focused on the response to a single compound. Here we provide a more thorough analysis of the microbial community response to three different biocides and three different nanoparticles. A microcosm-based study was undertaken that exposed stream microbial communities to either biocides or NPs. Our results showed a decrease in bacterial abundance with different types of nanoparticles, but an increase in microbial abundance in biocide-amended treatments. The microbial community composition (MCC) was distinct from the controls in all biocide and NP treatments, which resulted in differentially enriched taxa in the treatments compared to the controls. Our results indicate that NPs slightly altered the MCC compared to the biocide-treated microcosms. After 14 days, the MCC in the nanoparticle-treated conditions was similar to the MCC in the control. Conversely, the MCC in the biocide-treated microcosms was distinct from the controls at day 14 and distinct from all conditions at day 0. This finding may point to the use of NPs as an alternative to biocides in some settings.

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring.

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.

Phylogeny and diversity of alkane-degrading enzyme gene variants in the laurentian great lakes and western atlantic.

Many aquatic environments are at risk for oil contamination and alkanes are one of the primary constituents of oil. The alkane hydroxylase (AlkB) is a common enzyme used by microorganisms to initiate the process of alkane-degradation. While many aspects of alkane bioremediation have been studied, the diversity and evolution of genes involved in hydrocarbon degradation from environmental settings is relatively understudied. The majority of work done to-date has focused on the marine environment. Here we sought to better understand the phylogenetic diversity of alkB genes across marine and freshwater settings using culture-independent methods. We hypothesized that there would be distinct phylogenetic diversity of alkB genes in freshwater relative to the marine environment. Our results confirm that alkB has distinct variants based on environment while our diversity analyses demonstrate that freshwater and marine alkB communities have unique responses to oil amendments. Our results also demonstrate that in the marine environment, depth is a key factor impacting diversity of alkB genes.

Biogeographic Patterns in Members of Globally Distributed and Dominant Taxa Found in Port Microbial Communities.

We conducted a global characterization of the microbial communities of shipping ports to serve as a novel system to investigate microbial biogeography. The community structures of port microbes from marine and freshwater habitats house relatively similar phyla, despite spanning large spatial scales. As part of this project, we collected 1,218 surface water samples from 604 locations across eight countries and three continents to catalogue a total of 20 shipping ports distributed across the East and West Coast of the United States, Europe, and Asia to represent the largest study of port-associated microbial communities to date. Here, we demonstrated the utility of machine learning to leverage this robust system to characterize microbial biogeography by identifying trends in biodiversity across broad spatial scales. We found that for geographic locations sharing similar environmental conditions, subpopulations from the dominant phyla of these habitats (Actinobacteria, Bacteroidetes, Cyanobacteria, and Proteobacteria) can be used to differentiate 20 geographic locations distributed globally. These results suggest that despite the overwhelming diversity within microbial communities, members of the most abundant and ubiquitous microbial groups in the system can be used to differentiate a geospatial location across global spatial scales. Our study provides insight into how microbes are dispersed spatially and robust methods whereby we can interrogate microbial biogeography.IMPORTANCE Microbes are ubiquitous throughout the world and are highly diverse. Characterizing the extent of variation in the microbial diversity across large geographic spatial scales is a challenge yet can reveal a lot about what biogeography can tell us about microbial populations and their behavior. Machine learning approaches have been used mostly to examine the human microbiome and, to some extent, microbial communities from the environment. Here, we display how supervised machine learning approaches can be useful to understand microbial biodiversity and biogeography using microbes from globally distributed shipping ports. Our findings indicate that the members of globally dominant phyla are important for differentiating locations, which reduces the reliance on rare taxa to probe geography. Further, this study displays how global biogeographic patterning of aquatic microbial communities (and other systems) can be assessed through populations of the highly abundant and ubiquitous taxa that dominant the system.

Global Comparison of the Bacterial Communities of Bilge Water, Boat Surfaces, and External Port Water.

In the past, ballast water has been a key vector in the ship-mediated dispersal of invasive species. Here, we evaluate the potential for port microorganisms to enter and colonize the hull and bilge water of ships. Due to the small size and ubiquitous nature of bacteria, they also have the potential to be spread through hull fouling and bilge water discharge. The goal of this study was to identify the extent to which the boat microbial community is shaped by the microbial community in the port water where the boat spends most of its time. Here, we compared the microbial communities of the hull and bilge compartments of 20 boats to those of the port water in 20 different ports in five regions around the world. We found that there was a significant difference in microbial diversity between boat and port microbial communities. Despite these differences, we found that Cyanobacteria were present at high abundances in the bilge water of most vessels. Due to the limited light in the bilge, the presence of Cyanobacteria suggests that port microorganisms can enter the bilge. Using source-tracking software, we found that, on average, 40% of the bilge and 52% of the hull microbial communities were derived from water. These findings suggest that the bilge of a vessel contains a diverse microbial community that is influenced by the port microbial community and has the potential to serve as an underappreciated vector for dispersal of life.IMPORTANCE Invasive species have been a worldwide problem for many years. However, the potential for microorganisms to become invasive is relatively underexplored. As the tools to study bacterial communities become more affordable, we are able to perform large-scale studies and examine bacterial communities in higher resolution than was previously practical. This study looked at the potential for bacteria to colonize both boat surfaces and bilge water. We describe the bacterial communities on boats in 20 shipping ports in five regions around the world, describing how these microorganisms were similar to microorganisms found in port water. This suggests that the water influences the bacterial community of a boat and that microorganisms living on a boat could be moved from place to place when the boat travels.