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Environmentally relevant metagenomes and BONCAT-FACS derived translationally active metagenomes from Powder River Basin coal seams were investigated to elucidate potential genes and functional groups involved in hydrocarbon degradation to methane in coal seams with high- and low-sulfate levels. An advanced subsurface environmental sampler allowed the establishment of coal-associated microbial communities under in situ conditions for metagenomic analyses from environmental and translationally active populations. Metagenomic sequencing demonstrated that biosurfactants, aerobic dioxygenases, and anaerobic phenol degradation pathways were present in active populations across the sampled coal seams. In particular, results suggested the importance of anaerobic degradation pathways under high-sulfate conditions with an emphasis on fumarate addition. Under low-sulfate conditions, a mixture of both aerobic and anaerobic pathways was observed but with a predominance of aerobic dioxygenases. The putative low-molecular-weight biosurfactant, lichysein, appeared to play a more important role compared to rhamnolipids. The methods used in this study-subsurface environmental samplers in combination with metagenomic sequencing of both total and translationally active metagenomes-offer a deeper and environmentally relevant perspective on community genetic potential from coal seams poised at different redox conditions broadening the understanding of degradation strategies for subsurface carbon.

Fungi and bacteria coexist in a wide variety of natural and artificial environments which can lead to their association and interaction – ranging from antagonism to cooperation – that can affect the survival, colonization, spatial distribution and stress resistance of the interacting partners. The use of polymicrobial cultivation approaches has facilitated a more thorough understanding of microbial dynamics in mixed microbial communities, such as those composed of fungi and bacteria, and their influence on ecosystem functions. Mixed (multi-domain) microbial communities exhibit unique associations and interactions that could result in more efficient systems for the degradation and removal of organic pollutants. Several previous studies have reported enhanced biodegradation of certain pollutants when using combined fungal-bacterial treatments compared to pure cultures or communities of either fungi or bacteria (single domain systems). This article reviews: (i) the mechanisms of pollutant degradation that can occur in fungal-bacterial systems (e.g.: co-degradation, production of secondary metabolites, enhancement of degradative enzyme production, and transport of bacteria by fungal mycelia); (ii) case studies using fungal-bacterial co-cultures for the removal of various organic pollutants (synthetic dyes, polycyclic aromatic hydrocarbons, pesticides, and other trace or volatile organic compounds) in different environmental matrices (e.g. water, gas/vapors, soil); (iii) the key aspects of engineering artificial fungal-bacterial co-cultures, and (iv) the current challenges and future perspectives of using fungal-bacterial co-cultures for environmental remediation. [ABSTRACT FROM AUTHOR]

Urea-hydrolysing biofilms are crucial to applications in medicine, engineering, and science. Quantitative information about ureolysis rates in biofilms is required to model these applications. We formulate a novel model of urea consumption in a biofilm that allows different kinetics, for example either first order or Michaelis–Menten. The model is fit to synthetic data to validate and compare two approaches, Bayesian and nonlinear least squares (NLS), commonly used by biofilm practitioners. The shortcomings of NLS motivate the Bayesian approach where a simple Markov Chain Monte Carlo (MCMC) sampler is applied. The model is then fit to real data of influent and effluent urea concentrations from experiments with biofilms of Escherichia coli. Results from synthetic data aid in interpreting results from real data, where first-order and Michaelis–Menten kinetic models are compared. The method shows potential for general applications requiring biofilm kinetic information. [ABSTRACT FROM AUTHOR]