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Abstract Delineation of microbial habitats within the soil matrix and characterization of their environments and metabolic processes are crucial to understand soil functioning, yet their experimental identification remains persistently limited. We combined single- and triple-energy X-ray computed microtomography with pore specific allocation of¹³C labeled glucose and subsequent stable isotope probing to demonstrate how long-term disparities in vegetation history modify spatial distribution patterns of soil pore and particulate organic matter drivers of microbial habitats, and to probe bacterial communities populating such habitats. Here we show striking differences between large (30-150 µm Ø) and small (4-10 µm Ø) soil pores in (i) microbial diversity, composition, and life-strategies, (ii) responses to added substrate, (iii) metabolic pathways, and (iv) the processing and fate of labile C. We propose a microbial habitat classification concept based on biogeochemical mechanisms and localization of soil processes and also suggests interventions to mitigate the environmental consequences of agricultural management. 

Abstract Delineation of microbial habitats within the soil matrix and characterization of their environments are crucial to understand soil functioning and carbon (C) cycling. Yet, experimental identification of microbial communities populating specific micro-habitats and assessments of their biochemical properties have been persistently limited. Here we demonstrate how long-term disparities in vegetation history modify spatial distribution patterns and properties of soil pores and particulate organic matter (POM), and show striking differences in bacterial communities populating pores of contrasting sizes in soils from three vegetation systems on the same soil type: an intensive corn (Zea mays L.) rotation, monoculture switchgrass (Panicum virgatum L.), and restored North American prairie. We combined single- and triple-energy X-ray computed microtomography (µCT) with pore specific allocation of 13 C labeled glucose and subsequent stable isotope probing (¹³C-DNA/RNA-SIP) to show that large (30-150 µm Ø) and small (4-10 µm Ø) soil pores differed in (i) microbial diversity, composition, and life-strategies, (ii) responses to added substrate, (iii) metabolic pathways, and (iv) the processing and fate of labile C. Results demonstrate that soil pores created by different plant communities differ in ways that strongly influence microbial composition and activity, and thus impact ecosystem processes such as decomposition, nitrogen processing, and carbon sequestration. A proposed classification scheme may improve biogeochemical models of soil processes and as well suggest interventions to mitigate the environmental consequences of agricultural management. 

The goals of this study were to predict the genes associated with the biodegradation of organic contaminants and to examine microbial community structure in samples from two contaminated sites. The approach involved a predictive bioinformatics tool (PICRUSt2) targeting genes from twelve KEGG xenobiotic biodegradation pathways (benzoate, chloroalkane and chloroalkene, chlorocyclohexane and chlorobenzene, toluene, xylene, nitrotoluene, ethylbenzene, styrene, dioxin, naphthalene, polycyclic aromatic hydrocarbons, and metabolism of xenobiotics by cytochrome P450). Further, the predicted phylotypes associated with functional genes early in each pathway were determined. Phylogenetic analysis indicated a greater diversity in the sediment compared to the groundwater samples. The most abundant genera for sediments/microcosms included Pseudomonas, Methylotenera, Rhodococcus, Stenotrophomonas, and Brevundimonas, and the most abundant for the groundwater/microcosms included Pseudomonas, Cupriavidus, Azospira, Rhodococcus, and unclassified Burkholderiaceae. Genes from all twelve of the KEGG pathways were predicted to occur. Seven pathways contained less than twenty-five genes. The predicted genes were lowest for xenobiotics metabolism by cytochrome P450 and ethylbenzene biodegradation and highest for benzoate biodegradation. Notable trends include the occurrence of the first genes for trinitrotoluene and 2,4-dinitrotoluene degradation. Also, the complete path from toluene to benzoyl-CoA was predicted. Twenty-two of the dioxin pathway genes were predicted, including genes within the first steps. The following phylotypes were associated with the greatest number of pathways: unclassified Burkholderiaceae, Burkholderia-Caballeronia-Paraburkholderia, Pseudomonas, Rhodococcus, unclassified Betaproteobacteria, and Polaromonas. This work illustrates the value of PICRUSt2 for predicting biodegradation potential and suggests that a subset of phylotypes could be important for the breakdown of organic contaminants or their metabolites.