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Morán, Xosé Anxelu G.

Publications and source records attributed to Morán, Xosé Anxelu G..

Water aging and the quality of organic carbon sources drive niche partitioning of the active bathypelagic prokaryotic microbiome

Abstract Due to the scarcity of organic matter (OM) sources in the bathypelagic (1000–4000 m depth), prokaryotic metabolism is believed to be concentrated on particles originating from the surface. However, the structure of active bathypelagic prokaryotic communities and how it changes across environmental gradients remains unexplored. Using a combination of 16S rRNA gene and transcripts sequencing, metagenomics, and substrate uptake potential measurements, here we aimed to explore how water masses aging and the quality of OM influence the structure of the active microbiome, and the potential implications for community function. We found that the relative contribution of taxa with a free‐living lifestyle to the active microbiome increased in older water masses that were enriched in recalcitrant OM, suggesting that these prokaryotes may also play a substantial role in the bathypelagic metabolism of vast areas of the ocean. In comparison to particle‐associated prokaryotes, free‐living prokaryotes exhibited lower potential metabolic rates, and harbored a limited number of two‐component sensory systems, suggesting they have less ability to sense and respond to environmental cues. In contrast, particle‐associated prokaryotes carried genes for particle colonization and carbohydrate utilization that were absent in prokaryotes with a free‐living lifestyle. Consistently, we observed that prokaryotic communities inhabiting older waters displayed reduced abilities to colonize particles, and higher capabilities to use complex carbon sources, compared to communities in waters with a higher proportion of labile OM. Our results provide evidence of regionalization of the bathypelagic active prokaryotic microbiome, unveiling a niche partitioning based on the quality of OM.

Marine & Freshwater Biology↗

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells

Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removal efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t – 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance.

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