Molecular characterization of dissolved organic nitrogen and phosphorus in agricultural runoff and surface waters.

Agricultural runoff is a significant contributor to nitrogen (N) and phosphorus (P) pollution in water bodies. Limited information is available about the molecular characteristics of the dissolved organic N (DON) and P (DOP) species in the agricultural runoff and surface waters. We employed Fourier Transform-Ion Cyclotron Resonance-Mass Spectrometry (FT-ICR-MS) to investigate the changes in the molecular characteristics of DON and DOP at three watershed positions (upstream water, runoff from agricultural fields, and downstream waters). Across three watershed locations, more-bioavailable compounds (such as amino sugars, carbohydrates, lipids, and proteins) accounted for <5% of DON and 4-31% of DOP molecules, whereas less-bioavailable compounds (such as lignin, tannins, condensed hydrocarbons, and unsaturated hydrocarbons) were >95% of DON and 69-96% of DOP. Of the dissolved organic matter, runoff waters from agricultural fields contained the greatest proportion of DON formulas (20-25%) than upstream (18%) and downstream (13-14%) waters, indicating the presence of a greater diversity of DON species in the runoff. Various nutrient sources present in agricultural fields such as crop residues, soil organic matter, and transformed fertilizers likely contributed to the diverse composition of DON and DOP in the runoff, which were likely altered as the surface water traversed along the flow pathways in the watershed. The presence of more-bioavailable molecules detected in upstream compared to agricultural runoff and downstream waters suggests that photochemical and/or microbial processes likely altered the characteristics of DON and DOP compounds. The findings of this study increase our understanding of DON and DOP compounds lability and transformations in runoff and surface waters , which may be useful in quantifying the contribution of organic N and P sources to water quality impairment in aquatic ecosystems.

Video frame prediction of microbial growth with a recurrent neural network.

The recent explosion of interest and advances in machine learning technologies has opened the door to new analytical capabilities in microbiology. Using experimental data such as images or videos, machine learning, in particular deep learning with neural networks, can be harnessed to provide insights and predictions for microbial populations. This paper presents such an application in which a Recurrent Neural Network (RNN) was used to perform prediction of microbial growth for a population of two Pseudomonas aeruginosa mutants. The RNN was trained on videos that were acquired previously using fluorescence microscopy and microfluidics. Of the 20 frames that make up each video, 10 were used as inputs to the network which outputs a prediction for the next 10 frames of the video. The accuracy of the network was evaluated by comparing the predicted frames to the original frames, as well as population curves and the number and size of individual colonies extracted from these frames. Overall, the growth predictions are found to be accurate in metrics such as image comparison, colony size, and total population. Yet, limitations exist due to the scarcity of available and comparable data in the literature, indicating a need for more studies. Both the successes and challenges of our approach are discussed.

The role of oxygen in stimulating methane production in wetlands.

Methane (CH4 ), a potent greenhouse gas, is the second most important greenhouse gas contributor to climate change after carbon dioxide (CO2 ). The biological emissions of CH4 from wetlands are a major uncertainty in CH4 budgets. Microbial methanogenesis by Archaea is an anaerobic process accounting for most biological CH4 production in nature, yet recent observations indicate that large emissions can originate from oxygenated or frequently oxygenated wetland soil layers. To determine how oxygen (O2 ) can stimulate CH4 emissions, we used incubations of Sphagnum peat to demonstrate that the temporary exposure of peat to O2 can increase CH4 yields up to 2000-fold during subsequent anoxic conditions relative to peat without O2 exposure. Geochemical (including ion cyclotron resonance mass spectrometry, X-ray absorbance spectroscopy) and microbiome (16S rDNA amplicons, metagenomics) analyses of peat showed that higher CH4 yields of redox-oscillated peat were due to functional shifts in the peat microbiome arising during redox oscillation that enhanced peat carbon (C) degradation. Novosphingobium species with O2 -dependent aromatic oxygenase genes increased greatly in relative abundance during the oxygenation period in redox-oscillated peat compared to anoxic controls. Acidobacteria species were particularly important for anaerobic processing of peat C, including in the production of methanogenic substrates H2 and CO2 . Higher CO2 production during the anoxic phase of redox-oscillated peat stimulated hydrogenotrophic CH4 production by Methanobacterium species. The persistence of reduced iron (Fe(II)) during prolonged oxygenation in redox-oscillated peat may further enhance C degradation through abiotic mechanisms (e.g., Fenton reactions). The results indicate that specific functional shifts in the peat microbiome underlie O2 enhancement of CH4 production in acidic, Sphagnum-rich wetland soils. They also imply that understanding microbial dynamics spanning temporal and spatial redox transitions in peatlands is critical for constraining CH4 budgets; predicting feedbacks between climate change, hydrologic variability, and wetland CH4 emissions; and guiding wetland C management strategies.

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions.

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P. aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density and local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models.

Assembly and Tracking of Microbial Community Development within a Microwell Array Platform.

The development of microbial communities depends on a combination of complex deterministic and stochastic factors that can dramatically alter the spatial distribution and activities of community members. We have developed a microwell array platform that can be used to rapidly assemble and track thousands of bacterial communities in parallel. This protocol highlights the utility of the platform and describes its use for optically monitoring the development of simple, two-member communities within an ensemble of arrays within the platform. This demonstration uses two mutants of Pseudomonas aeruginosa, part of a series of mutants developed to study Type VI secretion pathogenicity. Chromosomal inserts of either mCherry or GFP genes facilitate the constitutive expression of fluorescent proteins with distinct emission wavelengths that can be used to monitor community member abundance and location within each microwell. This protocol describes a detailed method for assembling mixtures of bacteria into the wells of the array and using time-lapse fluorescence imaging and quantitative image analysis to measure the relative growth of each member population over time. The seeding and assembly of the microwell platform, the imaging procedures necessary for the quantitative analysis of microbial communities within the array, and the methods that can be used to reveal interactions between microbial species area all discussed.