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BRCore provides a unified framework for identification and ecological interpretation of core microbiomes across time and space, enhancing robustness and reproducibility in microbiome data analysis. Is supplement to Software: https://github.com/germs-lab/BRCore/tree/v2.0.7 (URL)
Investigation of microbial gene function is essential to the elucidation of ecological roles and complex genetic interactions that take place in microbial communities. While microbiome studies have increased in prevalence, the lack of viable in situ editing strategies impedes experimental progress, rendering genetic knowledge and manipulation of microbial communities largely inaccessible. Here, we demonstrate the utility of phage-delivered CRISPR-Cas payloads to perform targeted genetic manipulation within a community context, deploying a fabricated ecosystem (EcoFAB) as an analog for the soil microbiome. First, we detail the engineering of two classical phages for community editing using recombination to replace nonessential genes through Cas9-based selection. We show efficient engineering of T7, then demonstrate the expression of antibiotic resistance and fluorescent genes from an engineered λ prophage within an Escherichia coli host. Next, we modify λ to express an APOBEC-1-based cytosine base editor (CBE), which we leverage to perform C-to-T point mutations guided by a modified Cas9 containing only a single active nucleolytic domain (nCas9). We strategically introduce these base substitutions to create premature stop codons in-frame, inactivating both chromosomal ( lacZ ) and plasmid-encoded genes (mCherry and ampicillin resistance) without perturbation of the surrounding genomic regions. Furthermore, using a multigenera synthetic soil community, we employ phage-assisted base editing to induce host-specific phenotypic alterations in a community context both in vitro and within the EcoFAB, observing editing efficiencies from 10 to 28% across the bacterial population. The concurrent use of a synthetic microbial community, soil matrix, and EcoFAB device provides a controlled and reproducible model to more closely approximate in situ editing of the soil microbiome.
Despite the clear ecological significance of the microbiomes inhabiting groundwater and connected ecosystems, our current understanding of their habitats, functionality, and the ecological processes controlling their assembly have been limited. In this study, an efficient pipeline combining geochemistry, high-throughput Fluidigm TM functional gene amplification and sequencing was developed to analyze the suspended and attached microbial communities inhabiting five groundwater monitoring wells in the Illinois Basin, USA. The dominant taxa in the suspended and the attached microbial communities exhibited significantly different spatial and temporal changes in both alpha- and beta-diversity. Further analyses of representative functional genes affiliated with N 2 fixation (nifH), methane oxidation (pmoA), and sulfate reduction (dsrB, and aprA), suggested functional redundancy within the shallow aquifer microbiomes. While more diversified functional gene taxa were observed for the suspended microbial communities than the attached ones except for pmoA, different levels of changes over time and space were observed between these functional genes. Notably, deterministic and stochastic ecological processes shaped the assembly of microbial communities and functional gene reservoirs differently. While homogenous selection was the prevailing process controlling assembly of microbial communities, the neutral processes (e.g., dispersal limitation, drift and others) were more important for the functional genes. The results suggest complex and changing shallow aquifer microbiomes, whose functionality and assembly vary even between the spatially proximate habitats and fractions. As a result, this research underscored the importance to include all the interface components for a more holistic understanding of the biogeochemical processes in aquifer ecosystems, which is also instructive for practical applications.
To overcome CRISPR-Cas defense systems, many phages and mobile genetic elements (MGEs) encode CRISPR-Cas inhibitors called anti-CRISPRs (Acrs). Nearly all characterized Acrs directly bind Cas proteins to inactivate CRISPR immunity. Here, using functional metagenomic selection, we describe AcrIIA22, an unconventional Acr found in hypervariable genomic regions of clostridial bacteria and their prophages from human gut microbiomes. AcrIIA22 does not bind strongly to SpyCas9 but nonetheless potently inhibits its activity against plasmids. To gain insight into its mechanism, we obtained an X-ray crystal structure of AcrIIA22, which revealed homology to PC4-like nucleic acid–binding proteins. Based on mutational analyses and functional assays, we deduced that acrIIA22 encodes a DNA nickase that relieves torsional stress in supercoiled plasmids. This may render them less susceptible to SpyCas9, which uses free energy from negative supercoils to form stable R-loops. Modifying DNA topology may provide an additional route to CRISPR-Cas resistance in phages and MGEs.
Grazing exclusion and land abandonment are commonly adopted to restore degraded ecosystems in semiarid and arid regions worldwide. However, the temporal variation in the soil- versus root-associated microbiome over plant species turnover during secondary succession has rarely been quantified. Using the chronosequence restored from fenced grassland and abandoned farmlands on the Loess Plateau of China, we characterized the dynamics of the soil- and root-associated microbiome of host plant with different dominance statuses during secondary succession from 0 to 40 years. Our results revealed that the root microhabitat, the host plant and their interactions were the main contributors to the bacterial community shift (R 2 = 15.5%, 8.1%, and 22.3%, respectively), and plant interspecies replacement had a greater effect on the shift in the root-associated microbial community than intraspecies replacement did during succession. The root-associated bacterial community of pioneer plants was particularly responsive to succession, especially the endosphere community. Endosphere microbial diversity was positively correlated with host plant coverage change, and the diversity and abundance of taxon recruitment into the endosphere of pioneer plants from the surrounding environment decreased as succession progressed. The community assembly processes also indicated that the endosphere microbiota are strongly selected in younger host plants, whereas stochastic processes dominate in aged host plants. Our study provides evidence of the unique response of the root-associated microbiome to the replacement of plant species during secondary succession, and the function of endosphere microbes should be considered when studying plant–microbe feedback.
Populus trichocarpa is an ecologically important tree species and economically important biofeedstock. Belowground, P. trichocarpa interacts with diverse microorganisms in the rhizosphere and root endosphere. These plant-microbial interactions can bolster a variety of plant processes, ranging from pathogen suppression to drought tolerance, yet we know little about the impact of chronic drought stress on P. trichocarpa’s belowground microbiomes. To investigate the interactive effect of chronic drought on belowground microbial communities across genetically different P. trichocarpa hosts, we assessed archaeal/bacterial and fungal communities within the root endosphere, rhizosphere, and surrounding bulk soil of selected genotypes in a long-term drought experiment in Boardman, OR, USA. We sequenced the 16S rRNA and ITS2 gene region on samples collected from 16 distinct P. trichocarpa genotypes in plots with full or reduced irrigation. Eight of these genotypes have been previously identified as drought tolerant while the other eight genotypes were drought susceptible. While reduced irrigation influenced the composition of every archaeal/bacterial microbiome compartment, fungal communities were only affected in the rhizosphere and bulk soil compartments. Drought-tolerant bacteria, such as Actinobacteria, were differentially abundant in reduced irrigation across all belowground microbiomes. Host drought-tolerance influenced plant-associated microbiome compartments but had little impact on the bulk soil compartment. Drought-tolerant trees were enriched for potential growth-promoting microorganisms in the root endosphere and rhizosphere, including Sphingomonas bacteria and ectomycorrhizal fungi. Overall, associations of growth-promoting microbes in drought resistant P. trichocarpa genotypes can be leveraged to improve biofeedstock productivity in regions prone to periodic drought.
The operation of modern wastewater treatment plants (WWTPs) is driven by activated sludge microbiota, a complex assemblage of trophically interacting microorganisms. Microbial predation is crucial to fundamental understanding of how biological interactions drive microbiome structuring and functioning of WWTPs. However, predatory bacteria have received little attention regarding their diversity, activity, and ecological function in activated sludge, limiting the exploitation of food web interactions for wastewater microbiome engineering. Here, by using rRNA-stable isotope probing of activated sludge microbiota with 13 C-labeled prey bacteria, we uncovered diverse as-yet-uncultivated putative predatory bacteria that actively incorporated 13 C-biomass. Myxobacteria, especially Haliangium and the mle1-27 clade, were found as the dominant active predators, refreshing conventional views based on a few predatory isolates of Bdellovibrionota from WWTPs. The identified predatory bacteria showed more selective predation on prey compared with the protists dominated by ciliates, providing in situ evidence for inter-domain predation behavior divergence in activated sludge. Putative predatory bacteria were tracked over a two-year microbiome monitoring effort at a local WWTP, revealing the predominance of Myxococcota (6.5 ± 1.3%) over Bdellovibrionota (1.0 ± 0.2%) lineages. Phylogenetic analysis unveiled highly diverse myxobacteria inhabiting activated sludge and suggested a habitat filtering effect in global WWTPs. Further mining of a global activated sludge microbiome dataset revealed the prevalence of Myxococcota (5.4 ± 0.1%) species and potential impacts of myxobacterial predation on process performance. Collectively, our findings provided unique insights into the predating activity, diversity, and prevalence of Myxococcota species in activated sludge, highlighting their links with wastewater treatment processes via trophic regulation of enteric and functional bacteria.
The multivariate regression interpretation of the Gaussian chain graph model simultaneously parametrizes (i) the direct effects of p predictors on q outcomes and (ii) the residual partial covariances between pairs of outcomes. We introduce a new method for fitting sparse versions of these models with spike-and-slab LASSO (SSL) priors. We develop an Expectation Conditional Maximization algorithm to obtain sparse estimates of the p × q matrix of direct effects and the q × q residual precision matrix. Our algorithm iteratively solves a sequence of penalized maximum likelihood problems with self-adaptive penalties that gradually filter out negligible regression coefficients and partial covariances. Because it adaptively penalizes individual model parameters, our method is seen to outperform fixed-penalty competitors on simulated data. We establish the posterior contraction rate for our model, buttressing our method’s excellent empirical performance with strong theoretical guarantees. Using our method, we estimated the direct effects of diet and residence type on the composition of the gut microbiome of elderly adults.
Abstract The gut microbiome plays a major role in human health; however, little is known about the structural arrangement of microbes and factors governing their distribution. In this work, we present an in silico agent-based model (ABM) to conceptually simulate the dynamics of gut mucosal bacterial communities. We explored how various types of metabolic interactions, including competition, neutralism, commensalism, and mutualism, affect community structure, through nutrient consumption and metabolite exchange. Results showed that, across scenarios with different initial species abundances, cross-feeding promotes species coexistence. Morphologically, competition and neutralism resulted in segregation, while mutualism and commensalism fostered high intermixing. In addition, cooperative relations resulted in community properties with little sensitivity to the selective uptake of metabolites produced by the host. Moreover, metabolic interactions strongly influenced colonization success following the invasion of newcomer species. These results provide important insights into the utility of ABM in deciphering complex microbiome patterns.
The Birch effect, a pulse of CO2 release that occurs when dry soil is rewet, is commonly observed, yet the underlying biogeochemistry remains elusive. Using multi-omics data, real-time mass spectrometry and modeling approaches, we investigated the molecular response to rewetting of a soil microbiome exposed to drought for one and two weeks. The microbiome response was evaluated through analysis of transcript, protein, metabolite, and respiration profiles and metabolic modeling using an enhanced version of the Metabolite Expression Metabolic Network Integration for Pathway Identification and Selection (MEMPIS) algorithm (Roy Chowdhury et al, mSystems, 2019).
Abstract Background Microbe-microbe interactions between members of the plant rhizosphere are important but remain poorly understood. A more comprehensive understanding of the molecular mechanisms used by microbes to cooperate, compete, and persist has been challenging because of the complexity of natural ecosystems and the limited control over environmental factors. One strategy to address this challenge relies on studying complexity in a progressive manner, by first building a detailed understanding of relatively simple subsets of the community and then achieving high predictive power through combining different building blocks (e.g., hosts, community members) for different environments. Herein, we coupled this reductionist approach with high-resolution mass spectrometry-based metaproteomics to study molecular mechanisms driving community assembly, adaptation, and functionality for a defined community of ten taxonomically diverse bacterial members of Populus deltoides rhizosphere co-cultured either in a complex or defined medium. Results Metaproteomics showed this defined community assembled into distinct microbiomes based on growth media that eventually exhibit composition and functional stability over time. The community grown in two different media showed variation in composition, yet both were dominated by only a few microbial strains. Proteome-wide interrogation provided detailed insights into the functional behavior of each dominant member as they adjust to changing community compositions and environments. The emergence and persistence of select microbes in these communities were driven by specialization in strategies including motility, antibiotic production, altered metabolism, and dormancy. Protein-level interrogation identified post-translational modifications that provided additional insights into regulatory mechanisms influencing microbial adaptation in the changing environments. Conclusions This study provides high-resolution proteome-level insights into our understanding of microbe-microbe interactions and highlights specialized biological processes carried out by specific members of assembled microbiomes to compete and persist in changing environmental conditions. Emergent properties observed in these lower complexity communities can then be re-evaluated as more complex systems are studied and, when a particular property becomes less relevant, higher-order interactions can be identified.
Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. Here, in this study, we introduce a new MDS-based ordination method, “F-informed MDS,” which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community’s response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.
The rhizosphere is a small region surrounding plant roots that is enriched in biochemicals from root exudates and populated with fungi, nematode, and bacteria. Interaction of rhizosphere organisms with plants is mainly promoted by exudates from the roots. Root exudates contain biochemicals that come from primary and secondary metabolisms of plants. These biochemicals attract microbes, which influence plant nutrition. The rhizosphere bacteria (microbiome) are vital to plant nutrient uptake and influence biotic and abiotic stress and pathogenesis. Pseudomonas is a genus of gammaproteobacteria known for its ubiquitous presence in natural habitats and its striking ecological, metabolic, and biochemical diversity. Within the genus, members of the Pseudomonas fluorescens group are common inhabitants of soil and plant surfaces, and certain strains function in the biological control of plant disease, protecting plants from infection by soilborne and aerial plant pathogens. The soil bacterium Pseudomonas protegens Pf-5 (also known as Pseudomonas fluorescensPf-5) is a well-characterized biological strain, which is distinguished by its prolific production of the secondary metabolite, pyoverdine. Knowledge of the distribution of P. fluorescens secretory activity around plant roots is very important for understanding the interaction between P. fluorescens and plants and can be achieved by real time tracking of pyoverdine. To achieve the capability of real-time tracking in soil, we have used a structure-switching SELEX strategy to select high affinity ssDNA and 2’ F-Y-RNA aptamers with specificity for pyoverdine over other siderophores. Pyoverdines from various strains possess a conserved chromophore along with strain-specific peptide chain. Some of the isolated aptamers bind to chromophore of pyoverdine. These will identify pyoverdines from a variety of pseudomonads. Identification of aptamers that selectively bind the peptide portion of pyoverdine Pf-5 is underway. The identified aptamers that are broadly and narrowly specific for pyoverdines or Pf-5 will be optimized for integration into an electrochemical biosensor to track, in real time, P fluorescens activity and other pseudomonads in and around the rhizosphere.
Knowledge of microbial gene functions comes from manipulating the DNA of individual strains in isolation from their natural communities. While this approach to microbial genetics has been foundational, its requirement for culturable microorganisms has left the majority of microbes and their interactions genetically unexplored. Here, we describe a generalizable strategy for editing the genomes of specific organisms within microbial communities. We identified genetically tractable bacteria within a community using Environmental Transformation Sequencing (ET-Seq), an approach in which non-targeted transposon integrations are mapped and quantified following community delivery. We next developed and used DNA-editing All-in-one RNA-guided CRISPR-Cas Transposase (DART) systems for targeted DNA insertion into organisms identified as tractable by ET-Seq, enabling organism- and locus-specific genetic manipulation within the community context. To illustrate the utility of our approach, we selectively edited closely related strains, measured gene fitness, and enriched targeted members within soil and infant gut microbiota. These results establish a new paradigm for targeted community editing relevant to research and applications on medical, agricultural, and industrial microbiomes.
High-dimensional mixed-effects models are an increasingly important form of regression in which the number of covariates rivals or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate descent algorithm that lacks guarantees of convergence to a global optimum. Here, we empirically study the behavior of this algorithm on simulated and real examples of three types of data that are common in modern biology: transcriptome, genome-wide association, and microbiome data. Our simulations provide new insights into the algorithm’s behavior in these settings, and, comparing the performance of two popular penalties, we demonstrate that the smoothly clipped absolute deviation (SCAD) penalty consistently outperforms the least absolute shrinkage and selection operator (LASSO) penalty in terms of both variable selection and estimation accuracy across omics data. To empower researchers in biology and other fields to fit models with the SCAD penalty, we implement the algorithm in a Julia package, HighDimMixedModels.jl .
Warming alters soil microbial traits through ecological and evolutionary processes, directly influencing the decomposition of organic matter, which significantly affects global soil carbon emissions. Yet, soil carbon models largely ignore these processes and their implications for global responses to warming. Here, we incorporate eco-evolutionary theory into a mechanistic model describing microbial soil carbon decomposition to address the question of whether such processes could have consequential effects on climate carbon feedbacks globally. We assume that a key trait of microbes, their resource allocation to production of exoenzymes (which facilitate decomposition of organic matter)—is optimized to environmental temperatures by natural selection. We find that eco-evolutionary optimization results in microbes allocating more resources to enzyme production under warming. When applied at the global scale, eco-evolutionary optimization enhances the biological realism of soil carbon models and significantly amplifies global soil carbon loss by 2100. Our results highlight the significant potential of microbial eco-evolutionary responses to influence carbon cycle feedbacks to climate change, and motivate an urgent need for more comprehensive data to accurately quantify the adaptive potential of microbiomes in response to climate change.
The advance of high-throughput molecular biology tools allows in-depth profiling of microbial communities in soils, which possess a high diversity of prokaryotic microorganisms. Amplicon-based sequencing of 16S rRNA genes is the most common approach to studying the richness and composition of soil prokaryotes. To reliably detect different taxonomic lineages of microorganisms in a single soil sample, an adequate pipeline including DNA isolation, primer selection, PCR amplification, library preparation, DNA sequencing, and bioinformatic post-processing is required. Besides DNA sequencing quality and depth, the selection of PCR primers and PCR amplification reactions arguably have the largest influence on the results. This study tested the performance and potential bias of two primer pairs, i.e., 515F (Parada)-806R (Apprill) and 515F (Parada)-926R (Quince) in the standard pipelines of 16S rRNA gene Illumina amplicon sequencing protocol developed by the Earth Microbiome Project (EMP), against shotgun metagenome-based 16S rRNA gene reads. The evaluation was conducted using five differently managed soils. We observed a higher richness of soil total prokaryotes by using reverse primer 806R compared to 926R, contradicting to in silico evaluation results. Both primer pairs revealed various degrees of taxon-specific bias compared to metagenome-derived 16S rRNA gene reads. Nonetheless, we found consistent patterns of microbial community variation associated with different land uses, irrespective of primers used. Total microbial communities, as well as ammonia oxidizing archaea (AOA), the predominant ammonia oxidizers in these soils, shifted along with increased soil pH due to agricultural management. In the unmanaged low pH plot abundance of AOA was dominated by the acid-tolerant NS-Gamma clade, whereas limed agricultural plots were dominated by neutral-alkaliphilic NS-Delta/NS-Alpha clades. This study stresses how primer selection influences community composition and highlights the importance of primer selection for comparative and integrative studies, and that conclusions must be drawn with caution if data from different sequencing pipelines are to be compared.
Periodontitis is a chronic inflammatory disease associated with persistent oral microbial dysbiosis. The human β-glucuronidase (GUS) degrades constituents of the periodontium and is used as a biomarker for periodontitis severity. However, the human microbiome also encodes GUS enzymes, and the role of these factors in periodontal disease is poorly understood. Here, we define the 53 unique GUSs in the human oral microbiome and examine diverse GUS orthologs from periodontitis-associated pathogens. Oral bacterial GUS enzymes are more efficient polysaccharide degraders and processers of biomarker substrates than the human enzyme, particularly at pHs associated with disease progression. Using a microbial GUS-selective inhibitor, we show that GUS activity is reduced in clinical samples obtained from individuals with untreated periodontitis and that the degree of inhibition correlates with disease severity. Together, these results establish oral GUS activity as a biomarker that captures both host and microbial contributions to periodontitis, facilitating more efficient clinical monitoring and treatment paradigms for this common inflammatory disease.