Comparison of the Responses of Soil Fungal Community to Straw, Inorganic Fertilizer, and Compost in a Farmland in the Loess Plateau

Determining the response of soil fungi in sensitive ecosystems to external environmental disturbances is an important, yet little-known, topic in microbial ecology. In this study, we evaluated the impact of traditional fertilization management practices on the composition, co-occurrence pattern, and functional groups of fungal communities in loessial soil.

Contrasting Community Assembly Mechanisms Underlie Similar Biogeographic Patterns of Surface Microbiota in the Tropical North Pacific Ocean

Bacteria and microeukaryotes are extremely diverse groups in the ocean, where they regulate elemental cycling and energy flow. Studies of marine microbial ecology have benefited greatly from the rapid progress that has been made in genomic sequencing and theoretical microbial ecology.

The microbial ecology of Candida albicans strains CHN1 and SC5314 in mice

Strain SC5314 is the most widely studied strain of Candida albicans. Despite C. albicans being the most commonly isolated yeast from the human gastrointestinal (GI) microbiome, strain SC5314 does not stably colonize the mouse GI tract long term, even after antibiotic disruption. In contrast, strain CHN1 will stably colonize the mouse GI tract long term. Comparative genomic analysis of strain CHN1 indicates that it belongs to a different evolutionary clade of C. albicans than strain SC5314. Previous studies from our laboratory have shown that colonization by strain CHN1 causes a change in the GI bacterial microbiome of mice and predisposes them to more robust Th2 immune responses. Despite this, little is known about the GI microbial ecology of SC5314 vs. CHN1 and subsequent host responses. Using a short-term antibiotic disruption model in C57BL/6 mice, we have been able to observe significantly different colonization kinetics between these two C. albicans strains, with CHN1 establishing stable long-term colonization. In contrast, colonization by SC5314 was lower, highly variable and cage-dependent. C. albicans colonization kinetics impacted the composition of the bacterial microbiome with a marked effect on the levels of Lactobacillus and Enterococcus. qPCR analysis of 46 host immune response genes did not detect significant differences in host gene expression between SC5134 and CHN1 colonized mice, except for chitinase expression. Thus, these studies suggest that yeast-bacteria interactions in the microbiome may be far more important in determining long-term colonization potential of C. albicans and secondary immunomodulatory effects.

From Diversity to Complexity: Microbial Networks in Soils

Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, an definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research.