A multiview model for relative and absolute microbial abundances

Novel community data in ecology-properties and prospects

New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce 'novel community data' as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning.

From hype to hope: Considerations in conducting robust microbiome science

Microbiome science has been one of the most exciting and rapidly evolving research fields in the past two decades. Breakthroughs in technologies including DNA sequencing have meant that the trillions of microbes (particularly bacteria) inhabiting human biological niches (particularly the gut) can be profiled and analysed in exquisite detail. This microbiome profiling has profound impacts across many fields of research, especially biomedical science, with implications for how we understand and ultimately treat a wide range of human disorders. However, like many great scientific frontiers in human history, the pioneering nature of microbiome research comes with a multitude of challenges and potential pitfalls. These include the reproducibility and robustness of microbiome science, especially in its applications to human health outcomes. In this article, we address the enormous promise of microbiome science and its many challenges, proposing constructive solutions to enhance the reproducibility and robustness of research in this nascent field. The optimisation of microbiome science spans research design, implementation and analysis, and we discuss specific aspects such as the importance of ecological principals and functionality, challenges with microbiome-modulating therapies and the consideration of confounding, alternative options for microbiome sequencing, and the potential of machine learning and computational science to advance the field. The power of microbiome science promises to revolutionise our understanding of many diseases and provide new approaches to prevention, early diagnosis, and treatment.

Dynamic metabolites: A bridge between plants and microbes

Plant metabolites have a great influence on soil microbiomes. Although few studies provided insights into plant-microbe interactions, we still know very little about how plants recruit their microbiome. Here, we discuss the dynamic progress that typical metabolites shape microbes by a variety of factors, such as physiographic factors, cultivar factors, phylogeny factors, and environmental stress. Several kinds of metabolites have been reviewed, including plant primary metabolites (PPMs), phytohormones, and plant secondary metabolites (PSMs). The microbes assembled by plant metabolites in return exert beneficial effects on plants, which have been widely applied in agriculture. What's more, we point out existing problems and future research directions, such as unclear mechanisms, few species, simple parts, and ignorance of absolute abundance. This review may inspire readers to study plant-metabolite-microbe interactions in the future.

Ammonium oxidizing bacterial populations in South African activated sludge wastewater treatment plants

This is the first study that describes ammonium oxidizing bacterial populations and correlations of these populations with a range of criteria in activated sludge wastewater treatment plants in South Africa. In this study, not only the influent but also the activated sludge chemistry was comprehensively characterized. Multivariate statistical analyses were used to determine the relative significances of the geographical location (factor: site), wastewater treatment plant process (factor: configuration), seasonality (factor: season), and environmental parameters on the ammonium oxidizing bacterial genera in six municipal activated sludge wastewater treatments plants from two sites (the cities of Cape Town and Ekurhuleni). The geographical location (site) was significant for selection of the ammonium oxidizing genera (Global ANOSIM R value = 0.538, p = 0.001). It was established that the inter-site differences were not climatic in origin, nor related to the composition of the influent, but were rather driven by the activated sludge chemistry. It was found using BEST analysis that the activated sludge ammonia, activated sludge total phosphate, and activated sludge total chemical oxygen demand were the most significant (p < 0.001) drivers for ammonium oxidizing bacterial selection (ANOSIM Global R value 0.419) and were significantly higher in the activated sludge from the City of Cape Town wastewater treatment plants. Nitrosospira was the most abundant ammonium oxidizing bacterial genus, with notably higher relative and estimated actual abundances in the City of Cape Town wastewater treatment plants than the City of Ekurhuleni wastewater treatment plants. The strong selection of Nitrosospira in the City of Cape Town wastewater treatment plants with higher nutrient concentrations strongly suggests that high concentrations of activated sludge ammonia, activated sludge total phosphate, and activated sludge total chemical oxygen demand are key selective drivers for this genus. PRACTITIONER POINTS: First comprehensive study describing ammonium oxidizing bacterial populations in Southern African domestic activated sludge wastewater treatment plants. The geographical location (site) was significant for selection of different ammonium oxidizing genera (Global ANOSIM R value = 0.538, p = 0.001). Inter-site differences driven by the activated sludge chemistry, not climate or influent wastewater composition. Selection of Nitrosospira driven by high concentrations of activated sludge ammonia, total phosphate and total chemical oxygen demand.

The recovery of the microbial community after plaque removal depends on periodontal health status

Plaque accumulation and microbial community changes are important causes of periodontal disease. Cleaned plaque microorganisms will reattach to form biofilms, but the recovery and outcome of plaque microbial communities in different periodontal health states remain unknown. In this study, we tracked the biofilm remodeling process in 206 dental plaque samples from 40 healthy periodontal, gingivitis and periodontitis volunteers at 6 time points before and after supragingival scaling. We found that microbial communities of different periodontal states changed asynchronously during the process, and the more severe the periodontal disease condition, the more lagged the recovery of plaque microorganisms to their original state after cleaning; this reflected a higher degree of plaque development in periodontitis samples. The plaque index and bleeding index were significantly correlated with plaque recovery, especially the recovery of bacteria such as Abiotrophia and Capnocytophaga. Meanwhile, we found that the microbial community structure of different periodontal health states was most similar at the Day 3 after plaque cleaning, and the communities gradually differentiated and developed in different directions. Abiotrophia and other bacteria might play an important role in determining the development trend of plaque biofilms. The discovery of specific time points and bacteria was of great value in clarifying the pathogenesis of periodontal disease and in seeking targets for prevention and treatment.

BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations

Quantifying the differential abundance (DA) of specific taxa among experimental groups in microbiome studies is challenging due to data characteristics (e.g., compositionality, sparsity) and specific study designs (e.g., repeated measures, meta-analysis, cross-over). Here we present BIRDMAn (Bayesian Inferential Regression for Differential Microbiome Analysis), a flexible DA method that can account for microbiome data characteristics and diverse experimental designs. Simulations show that BIRDMAn models are robust to uneven sequencing depth and provide a >20-fold improvement in statistical power over existing methods. We then use BIRDMAn to identify antibiotic-mediated perturbations undetected by other DA methods due to subject-level heterogeneity. Finally, we demonstrate how BIRDMAn can construct state-of-the-art cancer-type classifiers using The Cancer Genome Atlas (TCGA) dataset, with substantial accuracy improvements over random forests and existing DA tools across multiple sequencing centers. Collectively, BIRDMAn extracts more informative biological signals while accounting for study-specific experimental conditions than existing approaches.

Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data

It is well-known that human gut microbiota form an ecosystem where microbes interact with each other. Due to complex underlying interactions, some microbes may correlate nonlinearly. There are no measures in the microbiome literature we know of that quantify these nonlinear relationships. Here, we develop a methodology called Sparse Estimation of Correlations among Microbiomes (SECOM) for estimating linear and nonlinear relationships among microbes while maintaining the sparsity. SECOM accounts for both sample and taxon-specific biases in its model. Its statistical properties are evaluated analytically and by comprehensive simulation studies. We test SECOM in two real data sets, namely, forehead and palm microbiome data from college-age adults, and Norwegian infant gut microbiome data. Given that forehead and palm are related to skin, as desired, SECOM discovers each genus to be highly correlated between the two sites, but that is not the case with any of the competing methods. It is well-known that infant gut evolves as the child grows. Using SECOM, for the first time in the literature, we characterize temporal changes in correlations among bacterial families during a baby's first year after birth.

Extracting abundance information from DNA ‐based data

The accurate extraction of species-abundance information from DNA-based data (metabarcoding, metagenomics) could contribute usefully to diet analysis and food-web reconstruction, the inference of species interactions, the modelling of population dynamics and species distributions, the biomonitoring of environmental state and change, and the inference of false positives and negatives. However, multiple sources of bias and noise in sampling and processing combine to inject error into DNA-based data sets. To understand how to extract abundance information, it is useful to distinguish two concepts. (i) Within-sample across-species quantification describes relative species abundances in one sample. (ii) Across-sample within-species quantification describes how the abundance of each individual species varies from sample to sample, such as over a time series, an environmental gradient or different experimental treatments. First, we review the literature on methods to recover across-species abundance information (by removing what we call "species pipeline biases") and within-species abundance information (by removing what we call "pipeline noise"). We argue that many ecological questions can be answered with just within-species quantification, and we therefore demonstrate how to use a "DNA spike-in" to correct for pipeline noise and recover within-species abundance information. We also introduce a model-based estimator that can be used on data sets without a physical spike-in to approximate and correct for pipeline noise.

Competition-based screening helps to secure the evolutionary stability of a defensive microbiome

BACKGROUND

The cuticular microbiomes of Acromyrmex leaf-cutting ants pose a conundrum in microbiome biology because they are freely colonisable, and yet the prevalence of the vertically transmitted bacteria Pseudonocardia, which contributes to the control of Escovopsis fungus garden disease, is never compromised by the secondary acquisition of other bacterial strains. Game theory suggests that competition-based screening can allow the selective recruitment of antibiotic-producing bacteria from the environment, by providing abundant resources to foment interference competition between bacterial species and by using Pseudonocardia to bias the outcome of competition in favour of antibiotic producers.

RESULTS

Here, we use RNA-stable isotope probing (RNA-SIP) to confirm that Acromyrmex ants can maintain a range of microbial symbionts on their cuticle by supplying public resources. We then used RNA sequencing, bioassays, and competition experiments to show that vertically transmitted Pseudonocardia strains produce antibacterials that differentially reduce the growth rates of other microbes, ultimately biassing the bacterial competition to allow the selective establishment of secondary antibiotic-producing strains while excluding non-antibiotic-producing strains that would parasitise the symbiosis.

CONCLUSIONS

Our findings are consistent with the hypothesis that competition-based screening is a plausible mechanism for maintaining the integrity of the co-adapted mutualism between the leaf-cutting ant farming symbiosis and its defensive microbiome. Our results have broader implications for explaining the stability of other complex symbioses involving horizontal acquisition.

Assessing the Relationship Between Nitrate-Reducing Capacity of the Oral Microbiome and Systemic Outcomes

The significance of the oral microbiome in the generation of the nitric oxide (NO) via the enterosalivary nitrate-nitrite-nitric oxide pathway is increasingly recognized, directly linking the oral microbiome to cardiometabolic outcomes influenced by NO. The objective of this chapter is to outline a strategy of identifying pathway-specific bacterial taxa or predicted genes of interest from 16S rRNA data, specifically in the enterosalivary pathway of nitrate reduction, and analyzing their relationship with cardiometabolic outcomes using multivariable regression models.