Summary Glycine betaine (GBT) is a compatible solute in high concentrations in marine microorganisms. As a component of labile organic matter, GBT has complex biochemical potential as a substrate for microbial use that is unconstrained in the environment. Here we determine the uptake kinetics and metabolic fate of GBT in two natural microbial communities in the North Pacific characterized by different nitrate concentrations. Dissolved GBT had maximum uptake rates of 0.36 and 0.56 nM h−1 with half‐saturation constants of 79 and 11 nM in the high nitrate and low nitrate stations respectively. During multiday incubations, most GBT taken into cells was retained as a compatible solute. Stable isotopes derived from the added GBT were also observed in other metabolites, including choline, carnitine and sarcosine, suggesting that GBT was used for biosynthesis and for catabolism to pyruvate and ammonium. Where nitrate was scarce, GBT was primarily metabolized via demethylation to glycine. Gene transcript data were consistent with SAR11 using GBT as a source of methyl groups to fuel the methionine cycle. Where nitrate concentrations were higher, more GBT was partitioned for lipid biosynthesis by both bacteria and eukaryotic phytoplankton. Our data highlight unexpected metabolic pathways and potential routes of microbial metabolite exchange.

Microbial eukaryotes, critical links in aquatic food webs, are unicellular, but some, such as choanoflagellates, form multicellular colonies. Are there consequences to predator avoidance of being unicellular vs. forming larger colonies? Choanoflagellates share a common ancestor with animals and are used as model organisms to study the evolution of multicellularity. Escape in size from protozoan predators is suggested as a selective factor favoring evolution of multicellularity. Heterotrophic protozoans are categorized as suspension feeders, motile raptors, or passive predators that eat swimming prey which bump into them. We focused on passive predation and measured the mechanisms responsible for the susceptibility of unicellular vs. multicellular choanoflagellates, Salpingoeca helianthica, to capture by passive heliozoan predators, Actinosphaerium nucleofilum, which trap prey on axopodia radiating from the cell body. Microvideography showed that unicellular and colonial choanoflagellates entered the predator's capture zone at similar frequencies, but a greater proportion of colonies contacted axopodia. However, more colonies than single cells were lost during transport by axopodia to the cell body. Thus, feeding efficiency (proportion of prey entering the capture zone that were engulfed in phagosomes) was the same for unicellular and multicellular prey, suggesting that colony formation is not an effective defense against such passive predators.

Allelopathic macroalgae have been shown to have significant negative effects on corals via the transfer of toxic compounds. The interaction that takes place between allelopathic macroalgae and other algae, however, has not been studied in detail. Here, the effects of the allelopathic Chlorodesmis fastigiata on other macroalgae were analyzed. These effects were first tested on complete coral and macroalgal individuals over several days, then on small samples of the macroalgal species when exposed to isolated toxins. However, neither experiment found significant negative effects on either Sargassum mangarevense or Boodlea kaeneana due to the interaction between these algae and the toxin produced Berkeley, CA 94720 USA 6 7 Abstract. Allelopathic macroalgae have been shown to have significant negative effects on corals 8 via the transfer of toxic compounds. The interaction that takes place between allelopathic 9 macroalgae and other algae, however, has not been studied in detail. Here, the effects of the 10 allelopathic Chlorodesmis fastigiata on other macroalgae were analyzed. These effects were first 11 tested on complete individuals on multi-day time scales, then on small samples of the macroalgal 12 species exposed to isolated toxins over the scale of minutes. However, neither experiment found 13 significant negative effects on either Sargassum mangarevense or Boodlea kaeneana due to the 14 interaction between these algae and the toxin produced by C. fastigiata. Distribution and 15 abundance of C. fastigiata were also assessed around the island of Mo'orea in French Polynesia. 18Introduction 19 Coral reef health and conservation have become topics of much conversation in recent years. For 20 the most part, these discussions center around large-scale environmental changes such as climate 21 change and ocean acidification (Hoegh-Guldberg et al., 2007), increased sedimentation rates 22 (Rogers 1990), nutrient fluxes (Fabricius, 2005), and a rise in fishing pressure (Jackson et al. 23 2001). These negatively affect reef health and create an increased risk of colonization by 24 macroalgae (McCook, 1999). Some algae facilitate this colonization process via the transfer of 25 toxic nonpolar compounds directly onto the coral (Rasher and Hay, 2010;Rasher et al, 2011; 26 Bonaldo and Hay, 2014). Chlorodesmis fastigiata, commonly known as turtleweed, exemplifies 27 this allelopathic interaction and its diterpene toxins causes appreciable bleaching of sensitive 28 corals in merely a few days (Rasher and Hay, 2010;Rasher et al. 2011;Bonaldo and Hay 2014). 29It is not known exactly how the algal toxins function, but they may act by blocking 30 photosynthesis. Previous studies on aquatic and marine algae have shown that toxins produced by 31 algae can inhibit the light-dependent reactions of photosynthesis. (Patterson et al, 1979; Patterson 32 and Harris, 1983) This is especially true if the toxin is nonpolar and has a low molecular weight 33 (Leflaive and Ten-Hage, 2007; Smith and Thanh, 2007) such as is the case for the ...

Mass spectrometry (MS) is a powerful tool for measuring biomolecules, but the data produced is often difficult to handle computationally because it is stored as a ragged array. In R, this format is typically encoded in complex S4 objects built around environments, requiring an extensive background in R to perform even simple tasks. However, the adoption of tidy data (Wickham, 2014) provides an alternate data structure that is highly intuitive and works neatly with base R functions and common packages, as well as other programming languages. Here, we discuss the current state of R-based MS data processing, the convenience and challenges of integrating tidy data techniques into MS data processing, and present RaMS, a package that produces tidy representations of MS data.

Allelopathic macroalgae have been shown to have significant negative effects on corals via the transfer of toxic compounds. The interaction that takes place between allelopathic macroalgae and other algae, however, has not been studied in detail. Here, the effects of the allelopathic Chlorodesmis fastigiata on other macroalgae were analyzed. These effects were first tested on complete coral and macroalgal individuals over several days, then on small samples of the macroalgal species when exposed to isolated toxins. However, neither experiment found significant negative effects on either Sargassum mangarevense or Boodlea kaeneana due to the interaction between these algae and the toxin produced Berkeley, CA 94720 USA 6 7 Abstract. Allelopathic macroalgae have been shown to have significant negative effects on corals 8 via the transfer of toxic compounds. The interaction that takes place between allelopathic 9 macroalgae and other algae, however, has not been studied in detail. Here, the effects of the 10 allelopathic Chlorodesmis fastigiata on other macroalgae were analyzed. These effects were first 11 tested on complete individuals on multi-day time scales, then on small samples of the macroalgal 12 species exposed to isolated toxins over the scale of minutes. However, neither experiment found 13 significant negative effects on either Sargassum mangarevense or Boodlea kaeneana due to the 14 interaction between these algae and the toxin produced by C. fastigiata. Distribution and 15 abundance of C. fastigiata were also assessed around the island of Mo'orea in French Polynesia. 18Introduction 19 Coral reef health and conservation have become topics of much conversation in recent years. For 20 the most part, these discussions center around large-scale environmental changes such as climate 21 change and ocean acidification (Hoegh-Guldberg et al., 2007), increased sedimentation rates 22 (Rogers 1990), nutrient fluxes (Fabricius, 2005), and a rise in fishing pressure (Jackson et al. 23 2001). These negatively affect reef health and create an increased risk of colonization by 24 macroalgae (McCook, 1999). Some algae facilitate this colonization process via the transfer of 25 toxic nonpolar compounds directly onto the coral (Rasher and Hay, 2010;Rasher et al, 2011; 26 Bonaldo and Hay, 2014). Chlorodesmis fastigiata, commonly known as turtleweed, exemplifies 27 this allelopathic interaction and its diterpene toxins causes appreciable bleaching of sensitive 28 corals in merely a few days (Rasher and Hay, 2010;Rasher et al. 2011;Bonaldo and Hay 2014). 29It is not known exactly how the algal toxins function, but they may act by blocking 30 photosynthesis. Previous studies on aquatic and marine algae have shown that toxins produced by 31 algae can inhibit the light-dependent reactions of photosynthesis. (Patterson et al, 1979; Patterson 32 and Harris, 1983) This is especially true if the toxin is nonpolar and has a low molecular weight 33 (Leflaive and Ten-Hage, 2007; Smith and Thanh, 2007) such as is the case for the ...

Chromatographic peakpicking continues to represent a significant bottleneck in automated LC-MS workflows. Uncontrolled false discovery rates and the lack of manually-calibrated quality metrics require researchers to visually evaluate individual peaks, requiring large amounts of time and breaking replicability. This problem is exacerbated in noisy environmental datasets and for novel separation methods such as hydrophilic interaction columns in metabolomics, creating a demand for a simple, intuitive, and robust metric of peak quality. Here, we manually labeled four HILIC oceanographic particulate metabolite datasets to assess the performance of individual peak quality metrics. We used these datasets to construct a predictive model calibrated to the likelihood that visual inspection by an MS expert would include a given mass feature in the downstream analysis. We implemented two novel peak quality metrics, a custom signal-to-noise metric and a test of similarity to a bell curve, both calculated from the raw data in the extracted ion chromatogram and found that these outperformed existing measurements of peak quality. A simple logistic regression model built on two metrics reduced the fraction of false positives in the analysis from 70-80% down to 1-5% and showed minimal overfitting when applied to novel datasets. We then explored the implications of this quality thresholding on the conclusions obtained by the downstream analysis and found that while only 10% of the variance in the dataset could be explained by depth in the default output from the peakpicker, approximately 40% of the variance was explained when restricted to high-quality peaks alone. We conclude that the poor performance of peakpicking algorithms significantly reduces the power of both univariate and multivariate statistical analyses to detect environmental differences. We demonstrate that simple models built on intuitive metrics and derived from the raw data are more robust and can outperform more complex models when applied to new data. Finally, we show that in properly curated datasets, depth is a major driver of variability in the marine microbial metabolome and identify several interesting metabolite trends for future investigation.