Chemotaxonomic patterns in intracellular metabolites of marine microbial plankton

Durham, Bryndan P.; Boysen, Angela K.; Carlson, Laura T.; Boccamazzo, Rachel; Deodato, Chloe R.; Qin, Wei; Cattolico, Rose Ann; Armbrust, E. Virginia; Ingalls, A. E.

doi:10.3389/fmars.2022.864796

Cited by 15 publications

(13 citation statements)

References 99 publications

(100 reference statements)

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“…Maximizing the efficiency of resource utilization when resources are available presents a selective advantage to bacteria 51 . Given that different algal hosts synthesize and release a unique composition of metabolites 52,53 , including methylated compounds 54,55 , the differential bacterial response may represent host-specific adaptations. It remains to be determined how closely the spectrum of methylated compounds generated by specific microalgae corresponds to the demethylation abilities of the associated bacteria.…”

Section: Discussionmentioning

confidence: 99%

Reducing the Bacterial Lag Phase Through Methylated Compounds: Insights from Algal-Bacterial Interactions

Sperfeld,

Narváez-Barragán,

Malitsky

et al. 2023

Preprint

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Short lag times are beneficial for heterotrophic bacteria that compete for resources in environments with fluctuating organic carbon levels. We found that the marine model bacterium Phaeobacter inhibens achieves shorter lag times in the presence of nano- to micromolar concentrations of N- or S-methylated compounds, which are abundantly produced by microalgae. To understand the underlying mechanism, we studied algal-bacterial co-cultures and bacterial pure cultures during their lag phase using transcriptomics analyses, 13C-labeled metabolomics, gene knock-out experiments and enzymatic characterizations. Our findings highlight methyl group synthesis as a bottleneck during the bacterial lag phase, which can be overcome by assimilating methyl groups from external sources. Our study reveals a fundamental aspect of the bacterial lag phase, emphasizing the importance of studying bacterial physiology within an ecological framework, particularly in the context of microbial interactions.

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Section: Discussionmentioning

confidence: 99%

Reducing the Bacterial Lag Phase Through Methylated Compounds: Insights from Algal-Bacterial Interactions

Sperfeld,

Narváez-Barragán,

Malitsky

et al. 2023

Preprint

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“…This intrinsic background signal of DA may drift in the environment asynchronously of Pseudo-nitzschia and dab gene expression. Related work has linked microbial taxa to chemotaxonomic “fingerprints” of metabolites [51]. Studies like these have improved our ability to connect taxa to metabolites that they take up, modify or produce.…”

Section: Discussionmentioning

confidence: 99%

High Resolution Biological and Physical Sampling Reveals Expression of Domoic Acid Biosynthetic Genes at Frontal Zones

Thukral,

Aron,

Rabines

et al. 2023

Preprint

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Ocean microbes are the foundation of marine food webs, regulating carbon cycling and ecosystem dynamics. How they proliferate, die, move, and interact is regulated by physical, chemical, and biological factors that are dynamic and challenging to quantify in the natural environment. A significant limitation in many marine field studies is the inability to continuously sample the ever-changing ocean environment over space and time. In this study, we integrated spatiotemporal and multi-omic sample collection in an intensive sampling effort of phytoplankton ecology in Monterey Bay, California during the spring of 2021. Sampling methods coupled: (1) manual shipboard CTD sampling, (2) autonomous sampling using a Long-Range Autonomous Underwater Vehicle (LRAUV) equipped with an Environmental Sampling Processor (ESP), and (3) high-resolution physical measurements by an autonomous vertical profiler (Wirewalker). Sampling occurred as upwelling waned alongside declining domoic acid (DA) and low abundances of toxigenic Pseudo-nitzschia. Conditions needed to spark a widespread and toxic Pseudo-nitzschia bloom were absent, yet low-level DA was driven by similar mechanisms to those causing elevated DA. Three DA biosynthetic intermediate molecules were reported in the environment for the first time. Both shipboard and ESP sampling approaches identified DA biosynthetic gene expression at frontal zones. DA and expression of dabA, the gene encoding the first committed step of DA biosynthesis, were higher in association with recently upwelled water that supplied nutrients for growth and DA biosynthesis. Detection of subtle variations in dab gene expression in response to environmental variation provide a window into the ecological dynamics underpinning major toxic events.

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“…Culture samples used as the validation sets for this paper have been previously described by [ 23 ] and on Metabolomics Workbench (Project ID PR001317).…”

Section: Methodsmentioning

confidence: 99%

Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics

Kumler,

Hazelton,

Ingalls

2023

BMC Bioinformatics

Self Cite

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Background 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. Results 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. Conclusions 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.

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Chemotaxonomic patterns in intracellular metabolites of marine microbial plankton

Cited by 15 publications

References 99 publications

Reducing the Bacterial Lag Phase Through Methylated Compounds: Insights from Algal-Bacterial Interactions

Reducing the Bacterial Lag Phase Through Methylated Compounds: Insights from Algal-Bacterial Interactions

High Resolution Biological and Physical Sampling Reveals Expression of Domoic Acid Biosynthetic Genes at Frontal Zones

Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics

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