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Most animals and plants have associated microorganisms, collectively referred to as their microbiomes, which can provide essential functions. Given their importance, host-associated microbiomes have the potential to contribute substantially to adaptation of the host-microbiome assemblage (the "metaorganism"). Microbiomes may be especially important for rapid adaptation to novel environments because microbiomes can change more rapidly than host genomes. However, it is not well understood how hosts and microbiomes jointly contribute to metaorganism adaptation. We developed a model system with which to disentangle the contributions of hosts and microbiomes to metaorganism adaptation. We established replicate mesocosms containing the nematode Caenorhabditis elegans co-cultured with microorganisms in a novel complex environment (laboratory compost). After approximately 30 nematode generations (100 days), we harvested worm populations and associated microbiomes, and subjected them to a common garden experiment designed to unravel the impacts of microbiome composition and host genetics on metaorganism adaptation. We observed that adaptation took different trajectories in different mesocosm lines, with some increasing in fitness and others decreasing, and that interactions between host and microbiome played an important role in these contrasting evolutionary paths. We chose two exemplary mesocosms (one with a fitness increase and one with a decrease) for detailed study. For each example, we identified specific changes in both microbiome composition (for both bacteria and fungi) and nematode gene expression associated with each change in fitness. Our study provides experimental evidence that adaptation to a novel environment can be jointly influenced by host and microbiome., (© 2023. The Author(s).)

The Amazon rainforest is a biodiversity hotspot and large terrestrial carbon sink threatened by agricultural conversion. Rainforest-to-pasture conversion stimulates the release of methane, a potent greenhouse gas. The biotic methane cycle is driven by microorganisms; therefore, this study focused on active methane-cycling microorganisms and their functions across land-use types. We collected intact soil cores from three land use types (primary rainforest, pasture, and secondary rainforest) of two geographically distinct areas of the Brazilian Amazon (Santarém, Pará and Ariquemes, Rondônia) and performed DNA stable-isotope probing coupled with metagenomics to identify the active methanotrophs and methanogens. At both locations, we observed a significant change in the composition of the isotope-labeled methane-cycling microbial community across land use types, specifically an increase in the abundance and diversity of active methanogens in pastures. We conclude that a significant increase in the abundance and activity of methanogens in pasture soils could drive increased soil methane emissions. Furthermore, we found that secondary rainforests had decreased methanogenic activity similar to primary rainforests, and thus a potential to recover as methane sinks, making it conceivable for forest restoration to offset greenhouse gas emissions in the tropics. These findings are critical for informing land management practices and global tropical rainforest conservation.

Co-occurrence networks allow for the identification of potential associations among species, which may be important for understanding community assembly and ecosystem functions. We employed this strategy to examine prokaryotic co-occurrence patterns in the Amazon soils and the response of these patterns to land use change to pasture, with the hypothesis that altered microbial composition due to deforestation will mirror the co-occurrence patterns across prokaryotic taxa. In this study, we calculated Spearman correlations between operational taxonomic units (OTUs) as determined by 16S rRNA gene sequencing, and only robust correlations were considered for network construction (-0.80 ≥ P ≥ 0.80, adjusted P < 0.01). The constructed network represents distinct forest and pasture components, with altered compositional and topological features. A comparative analysis between two representative modules of these contrasting ecosystems revealed novel information regarding changes to metabolic pathways related to nitrogen cycling. Our results showed that soil physicochemical properties such as temperature, C/N and H++Al3+ had a significant impact on prokaryotic communities, with alterations to network topologies. Taken together, changes in co-occurrence patterns and physicochemical properties may contribute to ecosystem processes including nitrification and denitrification, two important biogeochemical processes occurring in tropical forest systems.

Analysis of patterns in the distribution of taxa can provide important insights into ecological and evolutionary processes. Microbial biogeographic patterns almost always appear to be weaker than those reported for plant and animal taxa. It is as yet unclear why this is the case. Some argue that microbial diversity scales differently over space because microbial taxa are fundamentally different in their abundance, longevity and dispersal abilities. Others have argued that differences in scaling are an artifact of how we assess microbial biogeography, driven, for example, by differences in taxonomic resolution, spatial scale, sampling effort or community activity/dormancy. We tested these alternative explanations by comparing bacterial biogeographic patterns in soil to those of trees found in a forest in Gabon. Altering taxonomic resolution, excluding inactive individuals, or adjusting for differences in spatial scale were insufficient to change the rate of microbial taxonomic turnover. In contrast, we account for the differences in spatial turnover between these groups by equalizing sampling extent. Our results suggest that spatial scaling differences between microbial and plant diversity are likely not due to fundamental differences in biology, and that sampling extent should be taken into account when comparing the biogeographic patterns of microorganisms and larger organisms.

It is widely assumed that resistance to consumers (e.g., predators or pathogens) comes at a "cost," that is, when the consumer is absent the resistant organisms are less fit than their susceptible counterparts. It is unclear what factors determine this cost. We demonstrate that epistasis between genes that confer resistance to two different consumers can alter the cost of resistance. We used as a model system the bacterium Escherichia coli and two different viruses (bacteriophages), T4 and Λ, that prey upon E. coli. Epistasis tended to reduce the costs of multiple resistance in this system. However, the extent of cost savings and its statistical significance depended on the environment in which fitness was measured, whether the null hypothesis for gene interaction was additive or multiplicative, and subtle differences among mutations that conferred the same resistance phenotype., (© 1999 The Society for the Study of Evolution.)