Biological ice nucleators (IN) function as catalysts for freezing at relatively warm temperatures (warmer than ؊10°C).We examined the concentration (per volume of liquid) and nature of IN in precipitation collected from Montana and Louisiana, the Alps and Pyrenees (France), Ross Island (Antarctica), and Yukon (Canada). The temperature of detectable ice-nucleating activity for more than half of the samples was > ؊5°C based on immersion freezing testing. Digestion of the samples with lysozyme (i.e., to hydrolyze bacterial cell walls) led to reductions in the frequency of freezing (0 -100%); heat treatment greatly reduced (95% average) or completely eliminated ice nucleation at the measured conditions in every sample. These behaviors were consistent with the activity being bacterial and/or proteinaceous in origin. atmosphere ͉ climate ͉ microbial dissemination ͉ biological ice nuclei A t subzero temperatures warmer than Ϫ40°C, aerosol particles in clouds initiate freezing through the heterogeneous nucleation of ice directly from water vapor or by freezing droplets via several mechanisms: deposition, condensation, contact, and immersion freezing (1). These processes lead to ice formation in clouds that can trigger precipitation. A diverse range of natural and anthropogenic particles, referred to as ice-forming nuclei or ice nucleators (IN), are capable of initiating the ice phase (2). The maximum temperature at which an IN can initiate freezing is specific to that nucleator, but they function similarly by providing templates for the aggregation of individual water molecules in the configuration of an ice embryo, resulting in a subsequent phase change and the cascade of crystal formation (3). Consequently, knowledge of the nature and sources of IN in the atmosphere is important for understanding the meteorological processes responsible for precipitation. The most active naturally occurring IN are biological in origin and have the capacity to catalyze freezing at temperatures near Ϫ2°C (4). The most widespread and well-studied biological aerosols with icenucleating activity are comprised of certain species of plantassociated bacteria (Pseudomonas syringae, Pseudomonas viridiflava, Pseudomonas fluorescens, Pantoea agglomerans, and Xanthomonas campestris), but also fungi (e.g., Fusarium avenaceum), algae such as Chlorella minutissima, and birch pollen (5). P. syringae (6 -8) and F. avenaceum (7) in particular have been detected in atmospheric aerosols and clouds. Icenucleating strains of P. syringae possess a 120-to 180-kDa ice nucleation active protein in their outer membrane comprised of contiguous repeats of a consensus octapeptide; the protein binds water molecules in an ordered arrangement, providing a nucleating template that enhances ice crystal formation (9).Based on reports of ice-nucleating bacteria at altitudes of several kilometers (6, 10) and the warm temperatures at which they function as ice nuclei (Ϫ2°C to Ϫ7°C; ref. Our previous work on snowfall collected from a variety of midand high-latitude locations...

BackgroundThree methods were developed for the application of stoichiometry-based network analysis approaches including elementary mode analysis to the study of mass and energy flows in microbial communities. Each has distinct advantages and disadvantages suitable for analyzing systems with different degrees of complexity and a priori knowledge. These approaches were tested and compared using data from the thermophilic, phototrophic mat communities from Octopus and Mushroom Springs in Yellowstone National Park (USA). The models were based on three distinct microbial guilds: oxygenic phototrophs, filamentous anoxygenic phototrophs, and sulfate-reducing bacteria. Two phases, day and night, were modeled to account for differences in the sources of mass and energy and the routes available for their exchange.ResultsThe in silico models were used to explore fundamental questions in ecology including the prediction of and explanation for measured relative abundances of primary producers in the mat, theoretical tradeoffs between overall productivity and the generation of toxic by-products, and the relative robustness of various guild interactions.ConclusionThe three modeling approaches represent a flexible toolbox for creating cellular metabolic networks to study microbial communities on scales ranging from cells to ecosystems. A comparison of the three methods highlights considerations for selecting the one most appropriate for a given microbial system. For instance, communities represented only by metagenomic data can be modeled using the pooled method which analyzes a community's total metabolic potential without attempting to partition enzymes to different organisms. Systems with extensive a priori information on microbial guilds can be represented using the compartmentalized technique, employing distinct control volumes to separate guild-appropriate enzymes and metabolites. If the complexity of a compartmentalized network creates an unacceptable computational burden, the nested analysis approach permits greater scalability at the cost of more user intervention through multiple rounds of pathway analysis.

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