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Abstract <bold>Background</bold>Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation. <bold>Results</bold>Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability. <bold>Conclusions</bold>Our empirical study shows that the proposed cross-validation method is useful for hyper-parameter selection (training) and comparing the quality of inferred networks between different algorithms (testing). This advancement represents a significant step forward in microbiome network analysis, providing researchers with a reliable tool for understanding complex microbial interactions. The method’s applicability extends beyond microbiome studies to other fields where network inference from high-dimensional compositional data is crucial, such as gene regulatory networks and ecological food webs. Our framework establishes a new standard for validation in network inference, potentially accelerating discoveries in microbial ecology and human health. 

Measuring the growth rate of a microorganism is a simple yet profound way to quantify its effect on the world. The absolute growth rate of a microbial population reflects rates of resource assimilation, biomass production and element transformation—some of the many ways in which organisms affect Earth’s ecosystems and climate. Microbial fitness in the environment depends on the ability to reproduce quickly when conditions are favourable and adopt a survival physiology when conditions worsen, which cells coordinate by adjusting their relative growth rate. At the population level, relative growth rate is a sensitive metric of fitness, linking survival and reproduction to the ecology and evolution of populations. Techniques combining omics and stable isotope probing enable sensitive measurements of the growth rates of microbial assemblages and individual taxa in soil. Microbial ecologists can explore how the growth rates of taxa with known traits and evolutionary histories respond to changes in resource availability, environmental conditions and interactions with other organisms. We anticipate that quantitative and scalable data on the growth rates of soil microorganisms, coupled with measurements of biogeochemical fluxes, will allow scientists to test and refine ecological theory and advance process-based models of carbon flux, nutrient uptake and ecosystem productivity. Measurements of in situ microbial growth rates provide insights into the ecology of populations and can be used to quantitatively link microbial diversity to soil biogeochemistry. 

The growth rate of a microorganism is a simple yet profound way to quantify its impact on the world. Microbial fitness in the environment depends on the ability to reproduce quickly when conditions are favorable and adopt a survival physiology when conditions worsen, which cells coordinate by adjusting their growth rate. At the population level, per capita growth rate is a sensitive metric of fitness, linking survival and reproduction to the ecology and evolution of populations. The absolute growth rate of a microbial population reflects rates of resource assimilation, biomass production, and element transformation, some of the many ways that organisms affect Earth’s ecosystems and climate. For soil microorganisms, most of our understanding of growth is based on observations made in culture. This is a crucial limitation given that many soil microbes are not readily cultured and in vitro conditions are unlikely to reflect conditions in the wild. New approaches in ‘omics and stable isotope probing make it possible to sensitively measure growth rates of microbial assemblages and individual taxa in nature, and to couple these measurements to biogeochemical fluxes. Microbial ecologists can now explore how the growth rates of taxa with known traits and evolutionary histories respond to changes in resource availability, environmental conditions, and interactions with other organisms. We anticipate that quantitative and scalable data on the growth rates of soil microorganisms will allow scientists to test and refine ecological theory and advance processbased models of carbon flux, nutrient uptake, and ecosystem productivity. Measurements of in situ microbial growth rates provide insights into the ecology of populations and can be used to quantitatively link microbial diversity to soil biogeochemistry.  

Soil carbon stocks in the tundra and underlying permafrost have become increasingly vulnerable to microbial decomposition due to climate change. The microbial responses to Arctic warming must be understood in order to predict the effects of future microbial activity on carbon balance in a warming Arctic. 

Abstract Study of life history strategies may help predict the performance of microorganisms in nature by organizing the complexity of microbial communities into groups of organisms with similar strategies. Here, we tested the extent that one common application of life history theory, the copiotroph-oligotroph framework, could predict the relative population growth rate of bacterial taxa in soils from four different ecosystems. We measured the change of in situ relative growth rate to added glucose and ammonium using both 18O–H2O and 13C quantitative stable isotope probing to test whether bacterial taxa sorted into copiotrophic and oligotrophic groups. We saw considerable overlap in nutrient responses across most bacteria regardless of phyla, with many taxa growing slowly and few taxa that grew quickly. To define plausible life history boundaries based on in situ relative growth rates, we applied Gaussian mixture models to organisms’ joint 18O–13C signatures and found that across experimental replicates, few taxa could consistently be assigned as copiotrophs, despite their potential for fast growth. When life history classifications were assigned based on average relative growth rate at varying taxonomic levels, finer resolutions (e.g., genus level) were significantly more effective in capturing changes in nutrient response than broad taxonomic resolution (e.g., phylum level). Our results demonstrate the difficulty in generalizing bacterial life history strategies to broad lineages, and even to single organisms across a range of soils and experimental conditions. We conclude that there is a continued need for the direct measurement of microbial communities in soil to advance ecologically realistic frameworks. 

ABSTRACT Predation structures food webs, influences energy flow, and alters rates and pathways of nutrient cycling through ecosystems, effects that are well documented for macroscopic predators. In the microbial world, predatory bacteria are common, yet little is known about their rates of growth and roles in energy flows through microbial food webs, in part because these are difficult to quantify. Here, we show that growth and carbon uptake were higher in predatory bacteria compared to nonpredatory bacteria, a finding across 15 sites, synthesizing 82 experiments and over 100,000 taxon-specific measurements of element flow into newly synthesized bacterial DNA. Obligate predatory bacteria grew 36% faster and assimilated carbon at rates 211% higher than nonpredatory bacteria. These differences were less pronounced for facultative predators (6% higher growth rates, 17% higher carbon assimilation rates), though high growth and carbon assimilation rates were observed for some facultative predators, such as members of the genera Lysobacter and Cytophaga , both capable of gliding motility and wolf-pack hunting behavior. Added carbon substrates disproportionately stimulated growth of obligate predators, with responses 63% higher than those of nonpredators for the Bdellovibrionales and 81% higher for the Vampirovibrionales , whereas responses of facultative predators to substrate addition were no different from those of nonpredators. This finding supports the ecological theory that higher productivity increases predator control of lower trophic levels. These findings also indicate that the functional significance of bacterial predators increases with energy flow and that predatory bacteria influence element flow through microbial food webs. IMPORTANCE The word “predator” may conjure images of leopards killing and eating impala on the African savannah or of great white sharks attacking elephant seals off the coast of California. But microorganisms are also predators, including bacteria that kill and eat other bacteria. While predatory bacteria have been found in many environments, it has been challenging to document their importance in nature. This study quantified the growth of predatory and nonpredatory bacteria in soils (and one stream) by tracking isotopically labeled substrates into newly synthesized DNA. Predatory bacteria were more active than nonpredators, and obligate predators, such as Bdellovibrionales and Vampirovibrionales , increased in growth rate in response to added substrates at the base of the food chain, strong evidence of trophic control. This work provides quantitative measures of predator activity and suggests that predatory bacteria—along with protists, nematodes, and phages—are active and important in microbial food webs. 

Mycorrhizal restoration benefits are widely acknowledged, yet factors underpinning this success remain unclear. To illuminate when natural regeneration might be sufficient, we investigated the degree mycorrhizal fungi would colonizePopulus fremontii(Fremont cottonwood) 2 years after the restoration of a riparian corridor, in the presence of an adjacent source. We compared colonization levels across plant populations and ecotypes, and from trees in the planted area to those in natural source populations. Four findings contribute to the theory and application of host–symbiont interactions. (1) Median ectomycorrhizal colonization of trees in the planted area was less than one‐tenth of that within natural source populations (p < 0.05), suggesting that even with adjacent intact habitat, sluggish regeneration would make proactive mycorrhizal restoration beneficial. (2) Within the planted area, median ectomycorrhizal and arbuscule colonization of trees sourced from greater distances were less than one‐third of that for trees sourced locally (p < 0.05), suggesting translocation poses barriers to symbioses. (3) Changes in colonization did not align with plant ecotypes, suggesting that geographic scales of selection for plants and fungi differ. (4) Slight increases in median mycorrhizal colonization (from 0% to 5%) were strongly correlated with increased survival for the plant provenance with lowest survival (r² = 46% andr_s = 48%,p < 0.05), suggesting mycorrhizae are particularly beneficial when plants are under stress (including translocation‐induced stress). This study is novel in demonstrating that mycorrhizal regeneration is slow even in the presence of adjacent intact habitat, and that when colonization could seem negligible, it may still have biological significance.