Interaction and assembly processes of abundant and rare microbial communities during a diatom bloom process

Diatom blooms can significantly influence the dynamics of microbial communities, yet little is known about the interaction and assembly mechanisms of abundant and rare taxa during bloom process. Here, using 16S rRNA gene amplicon sequencing, we investigated the co-occurrence patterns and assembly processes of abundant and rare microbial communities during an early spring diatom bloom in Xiangshan bay. Our results showed that α-diversity indices in the rare subcommunity (RS) were significantly higher than those in the abundant and common subcommunities. β-Diversity of the RS was the highest among three subcommunities, and the variation of β-diversity in the three subcommunities was mainly induced by species turnover, which was also the highest in the RS. The assembly of microbial communities was mainly driven by the neutral processes, but the roles of neutral processes might differ in each subcommunity. Co-occurrence network analysis revealed that abundant and common operational taxonomic units were more often located in central positions within the network. Most of the modules in the network were specific to a particular bloom stage, owing to the succession of Skeletonema costatum. Overall, these findings expand current understanding of the microbial interaction and assembly mechanisms in marine environment suffering harmful algal bloom disturbance.     

Biogeography and ecological diversity patterns of rare and abundant bacteria in oil‐contaminated soils

Revealing the biogeographies and ecologies of rare and abundant microorganisms is crucial to understand ecosystem diversity and function. In this study, we investigated the biogeographic assemblies and ecological diversity patterns of rare and abundant bacteria in long‐term oil‐contaminated soils at intervals of 46–360 km by performing high‐throughput sequencing of 16S rRNA genes. The results clearly revealed distinct distribution patterns for rare and abundant bacteria in soil samples. Rare taxa were unevenly distributed; however, abundant taxa were ubiquitous across all samples. Both rare and abundant subcommunities showed significant distance–decay relationships, and their assemblies were driven by different factors. The rare subcommunity primarily exhibited a spatially structured distribution (i.e., stochastic processes), while edaphic factors (i.e., deterministic processes) largely contributed to the structure of the abundant subcommunity. A network analysis revealed closer relationships between abundant bacteria and their heightened influence on other co‐occurrences in the community compared with rare species. In conclusion, rare microbial taxa may play potential roles in maintaining ecosystem diversity, although they do not appear to be central to microbial networks. Abundant microbes are vital for microbial co‐occurrences in oil‐contaminated soils, and high relative abundance and ubiquitous distribution suggest potential roles in the degradation of organic pollutants.     

The Latent Dirichlet Allocation model with covariates (LDAcov): A case study on the effect of fire on species composition in Amazonian forests

Understanding and predicting the effect of global change phenomena on biodiversity is challenging given that biodiversity data are highly multivariate, containing information from tens to hundreds of species in any given location and time. The Latent Dirichlet Allocation (LDA) model has been recently proposed to decompose biodiversity data into latent communities. While LDA is a very useful exploratory tool and overcomes several limitations of earlier methods, it has limited inferential and predictive skill given that covariates cannot be included in the model. We introduce a modified LDA model (called LDAcov) which allows the incorporation of covariates, enabling inference on the drivers of change of latent communities, spatial interpolation of results, and prediction based on future environmental change scenarios. We show with simulated data that our approach to fitting LDAcov is able to estimate well the number of groups and all model parameters. We illustrate LDAcov using data from two experimental studies on the long‐term effects of fire on southeastern Amazonian forests in Brazil. Our results reveal that repeated fires can have a strong impact on plant assemblages, particularly if fuel is allowed to build up between consecutive fires. The effect of fire is exacerbated as distance to the edge of the forest decreases, with small‐sized species and species with thin bark being impacted the most. These results highlight the compounding impacts of multiple fire events and fragmentation, a scenario commonly found across the southern edge of Amazon. We believe that LDAcov will be of wide interest to scientists studying the effect of global change phenomena on biodiversity using high‐dimensional datasets. Thus, we developed the R package LDAcov to enable the straightforward use of this model.     

农田狼蛛亚群落结构的研究

本文对南宁市郊农田九种生境狼蛛亚群落结构进行了讨论。群落有7属37种狼蛛。文中揭示了农田各生境区间狼蛛亚群落的主要成分及其主要属性差异的原因。以狼蛛类群为指示动物,用群落相似性比例和聚类分析法,能较好地区分幼豹蛛(Pardosa pusiola)与奇异獾蛛(Trochosa ruricola)和拟环纹豹蛛(Pardosa pseudoannulata)与拟水狼蛛(Pirata subpiraticus)是构成农田区旱地和水田生境型具有代表性的两个狼蛛亚群落主要成分。     

Mechanisms governing avian phylosymbiosis: Genetic dissimilarity based on neutral and MHC regions exhibits little relationship with gut microbiome distributions of Galápagos mockingbirds

The gut microbiome of animals, which serves important functions but can also contain potential pathogens, is to varying degrees under host genetic control. This can generate signals of phylosymbiosis, whereby gut microbiome composition matches host phylogenetic structure. However, the genetic mechanisms that generate phylosymbiosis and the scale at which they act remain unclear. Two non‐mutually exclusive hypotheses are that phylosymbiosis is driven by immunogenetic regions such as the major histocompatibility complex (MHC) controlling microbial composition, or by spatial structuring of neutral host genetic diversity via founder effects, genetic drift, or isolation by distance. Alternatively, associations between microbes and host phylogeny may be generated by their spatial autocorrelation across landscapes, rather than the direct effects of host genetics. In this study, we collected MHC, microsatellite, and gut microbiome data from separate individuals belonging to the Galápagos mockingbird species complex, which consists of four allopatrically distributed species. We applied multiple regression with distance matrices and Bayesian inference to test for correlations between average genetic and microbiome similarity across nine islands for which all three levels of data were available. Clustering of individuals by species was strongest when measured with microsatellite markers and weakest for gut microbiome distributions, with intermediate clustering of MHC allele frequencies. We found that while correlations between island‐averaged gut microbiome composition and both microsatellite and MHC dissimilarity existed across species, these relationships were greatly weakened when accounting for geographic distance. Overall, our study finds little support for large‐scale control of gut microbiome composition by neutral or adaptive genetic regions across closely related bird phylogenies, although this does not preclude the possibility that host genetics shapes gut microbiome at the individual level.     

Point-estimating observer models for latent cause detection

The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2^N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several “point-estimating” observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by “committing” to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data.     

Abundant fungi adapt to broader environmental gradients than rare fungi in agricultural fields

Soil communities are intricately linked to ecosystem functioning, and a predictive understanding of how communities assemble in response to environmental change is of great ecological importance. Little is known about the assembly processes governing abundant and rare fungal communities across agro‐ecosystems, particularly with regard to their environmental adaptation. By considering abundant and rare taxa, we tested the environmental thresholds and phylogenetic signals for ecological preferences of fungal communities across complex environmental gradients to reflect their environmental adaptation, and explored the factors influencing their assembly based on the large‐scale soil survey in agricultural fields across eastern China. We found that the abundant taxa exhibited remarkably broader response thresholds and stronger phylogenetic signals for the ecological preferences across environmental gradients compared to the rare taxa. Neutral processes played a key role in shaping the abundant subcommunity compared to the rare subcommunity. Null model analysis revealed that the abundant subcommunity was less clustered phylogenetically and governed primarily by dispersal limitation, while homogeneous selection was the major assembly process in the rare subcommunity. Soil available sulfur was the major factor mediating the balance between stochastic and deterministic processes of both the abundant and rare subcommunities, as indicated by an increase in stochasticity with higher available sulfur concentration. Based on macroecological spatial scale datasets, our study revealed the potential broader environmental adaptation of abundant fungal taxa compared to rare fungal taxa, and identified the factors mediating their distinct community assembly processes in agricultural fields. These results contribute to our understanding of the mechanisms underlying the generation and maintenance of fungal diversity in response to global environmental change.     

The human microbiome in evolution

The trillions of microbes living in the gut—the gut microbiota—play an important role in human biology and disease. While much has been done to explore its diversity, a full understanding of our microbiomes demands an evolutionary perspective. In this review, we compare microbiomes from human populations, placing them in the context of microbes from humanity’s near and distant animal relatives. We discuss potential mechanisms to generate host-specific microbiome configurations and the consequences of disrupting those configurations. Finally, we propose that this broader phylogenetic perspective is useful for understanding the mechanisms underlying human–microbiome interactions.     

Bacterial rarity in a subarctic stream network: Biodiversity patterns,assembly mechanisms and types of rarity

Dendritic stream networks are an intriguing subject for exploring the spatial and temporal variability of the rare and common bacterial biosphere, yet very few such studies have been conducted. We sampled riverine bacterioplankton at 13 sites in a subarctic riverine network across 3 years, with five sampling times each year. Ordinations showed a consistent pattern of downstream shift for both rare and abundant subcommunities. We also detected a temporal signal, with seasonal community shifts reflecting changes in water temperature and groundwater contribution, and an inter-annual pattern where the year 2018 differed from other years. Phylogenetic turnover of the rare subcommunity indicated homogeneous selection, whereas the abundant subcommunity was mainly stochastically structured. Transiently rare taxa were the dominant type of rarity with the highest proportion at the headwater regions. The bacterioplankton community was characterized by a small group of core taxa that occurred at most sites with little temporal variation, a very large number of permanently or transiently rare taxa, and taxa shifting through time between the rare and abundant biosphere. While this basic structure could have been detected with less extensive temporal replication, a comprehensive understanding of the rare biosphere in riverine bacterioplankton can only be achieved via inter-annual, spatially replicated sampling that covers the whole stream network.     

Modeling disease incidence data with spatial and spatio temporal dirichlet process mixtures

Disease incidence or mortality data are typically available as rates or counts for specified regions, collected over time. We propose Bayesian nonparametric spatial modeling approaches to analyze such data. We develop a hierarchical specification using spatial random effects modeled with a Dirichlet process prior. The Dirichlet process is centered around a multivariate normal distribution. This latter distribution arises from a log-Gaussian process model that provides a latent incidence rate surface, followed by block averaging to the areal units determined by the regions in the study. With regard to the resulting posterior predictive inference, the modeling approach is shown to be equivalent to an approach based on block averaging of a spatial Dirichlet process to obtain a prior probability model for the finite dimensional distribution of the spatial random effects. We introduce a dynamic formulation for the spatial random effects to extend the model to spatio-temporal settings. Posterior inference is implemented through Gibbs sampling. We illustrate the methodology with simulated data as well as with a data set on lung cancer incidences for all 88 counties in the state of Ohio over an observation period of 21 years.     

The effect of rhizosphere microbes outweighs host plant genetics in reducing insect herbivory

Rhizosphere microbes affect plant performance, including plant resistance against insect herbivores; yet, a direct comparison of the relative influence of rhizosphere microbes versus plant genetics on herbivory levels and on metabolites related to defence is lacking. In the crucifer Boechera stricta, we tested the effects of rhizosphere microbes and plant population on herbivore resistance, the primary metabolome, and select secondary metabolites. Plant populations differed significantly in the concentrations of six glucosinolates (GLS), secondary metabolites known to provide herbivore resistance in the Brassicaceae. The population with lower GLS levels experienced ~60% higher levels of aphid (Myzus persicae) attack; no association was observed between GLS and damage by a second herbivore, flea beetles (Phyllotreta cruciferae). Rhizosphere microbiome (disrupted vs. intact native microbiome) had no effect on plant GLS concentrations. However, aphid number and flea beetle damage were respectively about three‐ and seven‐fold higher among plants grown in the disrupted versus intact native microbiome treatment. These differences may be attributable to shifts in primary metabolic pathways previously implicated in host defence against herbivores, including increases in pentose and glucoronate interconversion among plants grown with an intact microbiome. Furthermore, native microbiomes with distinct community composition (as estimated from 16s rRNA amplicon sequencing) differed two‐fold in their effect on host plant susceptibility to aphids. The findings suggest that rhizosphere microbes, including distinct native microbiomes, can play a greater role than population in defence against insect herbivores, and act through metabolic mechanisms independent of population.     

Our extended genotype—An argument for the study of domesticated microbes

We interpret the domesticated organisms—plants, animals, and the domesticated microbes used for food fermentation—as an extended genotype of humans due to their close relationship with our species. We propose to analyse the role of microbes in traditionally fermented food with the approaches used in the human microbiome project, and we expect to find associations with ethnic groups, explaining part of human (culinary) culture.     

Microbiome-immune interactions in tuberculosis

Tuberculosis (TB) remains an infectious disease of global significance and a leading cause of death in low- and middle-income countries. Significant effort has been directed towards understanding Mycobacterium tuberculosis genomics, virulence, and pathophysiology within the framework of Koch postulates. More recently, the advent of “-omics” approaches has broadened our appreciation of how “commensal” microbes have coevolved with their host and have a central role in shaping health and susceptibility to disease. It is now clear that there is a diverse repertoire of interactions between the microbiota and host immune responses that can either sustain or disrupt homeostasis. In the context of the global efforts to combatting TB, such findings and knowledge have raised important questions: Does microbiome composition indicate or determine susceptibility or resistance to M. tuberculosis infection? Is the development of active disease or latent infection upon M. tuberculosis exposure influenced by the microbiome? Does microbiome composition influence TB therapy outcome and risk of reinfection with M. tuberculosis? Can the microbiome be actively managed to reduce risk of M. tuberculosis infection or recurrence of TB? Here, we explore these questions with a particular focus on microbiome-immune interactions that may affect TB susceptibility, manifestation and progression, the long-term implications of anti-TB therapy, as well as the potential of the host microbiome as target for clinical manipulation.     

中国白酒酒曲微生物组研究进展及趋势

酒曲是中国白酒酿造过程的糖化发酵剂,对酿造酒体的品质与风味有重要影响,其质量与其中的微生物结构和酶系组成有重要关联.近年来基于现代生物学技术对白酒酒曲微生物组的研究表明,该体系具有丰富的科学内涵与广阔的应用价值.酒曲微生物组具有微生物物种丰度高、酶系功能复杂多样等特点,并与制备环境存在显著交互作用.基于酒曲微生物组特点...     

Monotone spline‐based least squares estimation for panel count data with informative observation times

This article discusses the statistical analysis of panel count data when the underlying recurrent event process and observation process may be correlated. For the recurrent event process, we propose a new class of semiparametric mean models that allows for the interaction between the observation history and covariates. For inference on the model parameters, a monotone spline‐based least squares estimation approach is developed, and the resulting estimators are consistent and asymptotically normal. In particular, our new approach does not rely on the model specification of the observation process. The proposed inference procedure performs well through simulation studies, and it is illustrated by the analysis of bladder tumor data.     

Are you what you eat? A highly transient and prey‐influenced gut microbiome in the grey house spider Badumna longinqua

Stable core microbial communities have been described in numerous animal species and are commonly associated with fitness benefits for their hosts. Recent research, however, highlights examples of species whose microbiota are transient and environmentally derived. Here, we test the effect of diet on gut microbial community assembly in the spider Badumna longinqua. Using 16S rRNA gene amplicon sequencing combined with quantitative PCR, we analyzed diversity and abundance of the spider's gut microbes, and simultaneously characterized its prey communities using nuclear rRNA markers. We found a clear correlation between community similarity of the spider's insect prey and gut microbial DNA, suggesting that microbiome assembly is primarily diet‐driven. This assumption is supported by a feeding experiment, in which two types of prey—crickets and fruit flies—both substantially altered microbial diversity and community similarity between spiders, but did so in different ways. After cricket consumption, numerous cricket‐derived microbes appeared in the spider's gut, resulting in a rapid homogenization of microbial communities among spiders. In contrast, few prey‐associated bacteria were detected after consumption of fruit flies; instead, the microbial community was remodelled by environmentally sourced microbes, or abundance shifts of rare taxa in the spider's gut. The reshaping of the microbiota by both prey taxa mimicked a stable core microbiome in the spiders for several weeks post feeding. Our results suggest that the spider's gut microbiome undergoes pronounced temporal fluctuations, that its assembly is dictated by the consumed prey, and that different prey taxa may remodel the microbiota in drastically different ways.     

Immune and genetic gardening of the intestinal microbiome

The mucosal immune system – consisting of adaptive and innate immune cells as well as the epithelium – is profoundly influenced by its microbial environment. There is now growing evidence that the converse is also true, that the immune system shapes the composition of the intestinal microbiome. During conditions of health, this bidirectional interaction achieves a homeostasis in which inappropriate immune responses to non-pathogenic microbes are averted and immune activity suppresses blooms of potentially pathogenic microbes (pathobionts). Genetic alteration in immune/epithelial function can affect host gardening of the intestinal microbiome, contributing to the diversity of intestinal microbiota within a population and in some cases allowing for unfavorable microbial ecologies (dysbiosis) that confer disease susceptibility.     

Dual inoculation of dark septate endophytes and Trichoderma viride drives plant performance and rhizosphere microbiome adaptations of Astragalus mongholicus to drought

Rhizosphere microbiome adapts their structural compositions to water scarcity and have the potential to mitigate drought stress of plants. To unlock this potential, it is crucial to understand community responses to drought in the interplay between soil properties, water management and exogenous microbes interference. Inoculation with dark septate endophytes (DSE) (Acrocalymma vagum, Paraboeremia putaminum) and Trichoderma viride on Astragalus mongholicus grown in the non-sterile soil was exposed to drought. Rhizosphere microbiome were assessed by Illumina MiSeq sequencing of the 16S and ITS2 rRNA genes. Inoculation positively affected plant growth depending on DSE species and water regime. Ascomycota, Proteobacteria, Actinobacteria, Chloroflexi and Firmicutes were the dominant phyla. The effects of dual inoculation on bacterial community were greater than those on fungal community, and combination of P. putaminum and T. viride exerted a stronger impact on the microbiome under drought stress. The observed changes in soil factors caused by inoculation could be explained by the variations in microbiome composition. Rhizosphere microbiome mediated by inoculation exhibited distinct preferences for various growth parameters. These findings suggest that dual inoculation of DSE and T. viride enriched beneficial microbiota, altered soil nutrient status and might contribute to enhance the cultivation of medicinal plants in dryland agriculture.     

Marginalized models for moderate to long series of longitudinal binary response data

Marginalized models (Heagerty, 1999, Biometrics 55, 688-698) permit likelihood-based inference when interest lies in marginal regression models for longitudinal binary response data. Two such models are the marginalized transition and marginalized latent variable models. The former captures within-subject serial dependence among repeated measurements with transition model terms while the latter assumes exchangeable or nondiminishing response dependence using random intercepts. In this article, we extend the class of marginalized models by proposing a single unifying model that describes both serial and long-range dependence. This model will be particularly useful in longitudinal analyses with a moderate to large number of repeated measurements per subject, where both serial and exchangeable forms of response correlation can be identified. We describe maximum likelihood and Bayesian approaches toward parameter estimation and inference, and we study the large sample operating characteristics under two types of dependence model misspecification. Data from the Madras Longitudinal Schizophrenia Study (Thara et al., 1994, Acta Psychiatrica Scandinavica 90, 329-336) are analyzed.