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Supplemental sequence processing and R statistical analysis for publication which compares the microbiome of 27 Zea cultivars: 12 Inbred maize genotypes, 9 hybrids, and 6 wild teosinte. The project contains amplicon data for various genes: 16S rRNA, ITS, bacterial amoA, Archeal amoA, nirS, nirK, and nosZ. In addition to functional potential assay data, and N2O flux.
This project is an implementation of the Bayesian Multitask Learning (BayesMTL) from the paper Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for Microbiome Analysis.
Data and analysis to accompany our submitted paper characterizing the LAMPS Miscanthus Microbiome comparing 16S rRNA gene DNA sequencing of Miscanthus in a staggered-start experiment. Authors for this analysis include Fernando Igne Rocha, Lanying Ma, and Adina Howe.
We present Genomeocean, a foundational genome language model that represents the microbial genome sequences from complex environmental samples. By training on a large, diverse metagenomic dataset, Genomeocean learns species-specific sequence composition and can generate long, realistic open reading frames (ORFs). Our model employs a Byte-pair-encoding (BPE) tokenization strategy, allowing it to efficiently process large genomic datasets and generate long sequences up to 50kb. We demonstrate that fine-tuning Genomeocean can generate novel gene clusters encoding biosynthetic pathways, showcasing its ability to model both fundamental and complex biological processes. Our work establishes Genomeocean as a powerful tool for understanding microbial genome biology and paves the way for its application in a range of fields, from synthetic biology to microbiome research.
Background: The first two weeks of post-hatch (PH) growth in broilers (meat-type birds) are critical for gut development and microbiota colonization. In the current broiler production system, chicks may not receive feed and water for 24 to 72 h due to variations in hatching time and hatchery management. Post-hatch feed delay affects body weight, feed efficiency, mortality, and gut development. The goal of this study was to investigate changes in the microbiome in broiler chickens early PH and the effect of delayed access to feed on the microbiota. Results: Chicks either received feed and water immediately after hatch or access to feed was delayed for 48 h to mimic commercial hatchery settings (treatment, TRT). Both groups were sampled (n = 6) at -48, 0, 4 h, and 1 (24 h), 2 (48 h), 3 (72 h), 4 (96 h), 6 (144 h), 8 (192 h), 10 (240 h), 12 (288 h) and 14 (336 h) days PH. Ileal (IL) and cecal (CE) epithelial scrapings (mucosal bacteria, M) and digesta (luminal bacteria, L) were collected for microbiota analysis. Microbiota was determined by sequencing the V3-V4 region of bacterial 16S rRNA and analyzed using QIIME2. The microbiota of early ileal and cecal samples were characterized by high abundance of unclassified bacteria. Among four bacterial populations (IL-L, IL-M, CE-L, CE-M), IL-M was the least affected by delayed access to feed early PH. Both alpha and beta diversities were affected by delayed access to feed PH in IL-L, CE-M and CE-L. However, the development effect was more pronounced. In all four bacterial populations, significant changes due to developmental effect (time relative to hatch) was observed in taxonomic composition, with transient changes of bacterial taxa during the first two weeks PH. Delayed access to feed has limited influence on bacterial composition with only a few genera and species affected in all four bacterial populations. Predicted function based on 16S rRNA was also affected by delayed access to feed PH with most changes in metabolic pathway richness observed in IL-L, CE-L and CE-M. Conclusions: These results show transient changes in chicken microbiota biodiversity during the first two weeks PH and indicate that delayed access to feed affects microbiota development. Proper microbiota development could be an important factor in disease prevention and antibiotic use in broiler chickens. Moreover, significant differences in response to delayed access to feed PH between luminal and mucosal bacterial populations strongly suggests the need for separate analysis of these two populations.
Abstract Bacterial communities are ubiquitous, found in natural ecosystems, such as soil, and within living organisms, like the human microbiome. The dynamics of these communities in diverse environments depend on factors such as spatial features of the microbial niche, biochemical kinetics, and interactions among bacteria. Moreover, in many systems, bacterial communities are influenced by multiple physical mechanisms, such as mass transport and detachment forces. One example is gut mucosal communities, where dense, closely packed communities develop under the concurrent influence of nutrient transport from the lumen and fluid-mediated detachment of bacteria. In this study, we model a mucosal niche through a coupled agent-based and finite-volume modeling approach. This methodology enables us to model bacterial interactions affected by nutrient release from various sources while adjusting individual bacterial kinetics. We explored how the dispersion and abundance of bacteria are influenced by biochemical kinetics in different types of metabolic interactions, with a particular focus on the trade-off between growth rate and yield. Our findings demonstrate that in competitive scenarios, higher growth rates result in a larger share of the niche space. In contrast, growth yield plays a critical role in neutralism, commensalism, and mutualism interactions. When bacteria are introduced sequentially, they cause distinct spatiotemporal effects, such as deeper niche colonization in commensalism and mutualism scenarios driven by species intermixing effects, which are enhanced by high growth yields. Moreover, sub-ecosystem interactions dictate the dynamics of three-species communities, sometimes yielding unexpected outcomes. Competitive, fast-growing bacteria demonstrate robust colonization abilities, yet they face challenges in displacing established mutualistic systems. Bacteria that develop a cooperative relationship with existing species typically obtain niche residence, regardless of their growth rates, although higher growth yields significantly enhance their abundance. Our results underscore the importance of bacterial niche dynamics in shaping community properties and succession, highlighting a new approach to manipulating microbial systems.
Background: The olive tree is of particular economic interest in the Mediterranean basin. Researchers have conducted several studies on one of the most devastating disorders affecting this tree, the Verticillium wilt, which causes substantial economic losses in numerous areas. We analyzed metatranscriptomic samples taken from a previous study conducted on leaves and roots of Olea europaea that were infected with Verticillium dahliae. In addition, we also analyzed mechanically damaged roots. The aim of our approach is to describe the dynamics of the root microbiome after severe perturbations. Results: Our results not only describe the dynamics of the microbial community associated with the disturbance, but also show the high complexity of these systems and explain how this can lead to a conflicting assignment of the various types of parasitism observed in a specific organism. Conclusions: Our findings indicate that this infection, although led by Verticillium, is driven not by a single species, but by a polymicrobial consortium that also includes natural endophytes of the olive tree. This community contains both biotrophic and necrotrophic organisms that alternate and live together during the infection. In addition, opportunistic organisms appear that take profit not from plant tissues, but from new emerging populations of microorganisms. Therefore, this system can be described as a complex biological system composed of different interacting communities. Notably, our work has important considerations when it comes to classifying the type of parasitism of a given species.
Abstract Anaerobic fungi and methanogenic archaea are two classes of microorganisms found in the rumen microbiome that metabolically interact during lignocellulose breakdown. Here, stable synthetic co-cultures of the anaerobic fungus Caecomyces churrovis and the methanogen Methanobacterium bryantii (not native to the rumen) were formed, demonstrating that microbes from different environments can be paired based on metabolic ties. Transcriptional and metabolic changes induced by methanogen co-culture were evaluated in C. churrovis across a variety of substrates to identify mechanisms that impact biomass breakdown and sugar uptake. A high-quality genome of C. churrovis was obtained and annotated, which is the first sequenced genome of a non-rhizoid-forming anaerobic fungus. C. churrovis possess an abundance of CAZymes and carbohydrate binding modules and, in agreement with previous studies of early-diverging fungal lineages, N6-methyldeoxyadenine (6mA) was associated with transcriptionally active genes. Co-culture with the methanogen increased overall transcription of CAZymes, carbohydrate binding modules, and dockerin domains in co-cultures grown on both lignocellulose and cellulose and caused upregulation of genes coding associated enzymatic machinery including carbohydrate binding modules in family 18 and dockerin domains across multiple growth substrates relative to C. churrovis monoculture. Two other fungal strains grown on a reed canary grass substrate in co-culture with the same methanogen also exhibited high log2-fold change values for upregulation of genes encoding carbohydrate binding modules in families 1 and 18. Transcriptional upregulation indicated that co-culture of the C. churrovis strain with a methanogen may enhance pyruvate formate lyase (PFL) function for growth on xylan and fructose and production of bottleneck enzymes in sugar utilization pathways, further supporting the hypothesis that co-culture with a methanogen may enhance certain fungal metabolic functions. Upregulation of CBM18 may play a role in fungal–methanogen physical associations and fungal cell wall development and remodeling.
Background - Nudibranchs comprise a group of > 6000 marine soft-bodied mollusk species known to use secondary metabolites (natural products) for chemical defense. The full diversity of these metabolites and whether symbiotic microbes are responsible for their synthesis remains unexplored. Another issue in searching for undiscovered natural products is that computational analysis of genomes of uncultured microbes can result in detection of novel biosynthetic gene clusters; however, their in vivo functionality is not guaranteed which limits further exploration of their pharmaceutical or industrial potential. To overcome these challenges, we used a fluorescent pantetheine probe, which produces a fluorescent CoA-analog employed in biosynthesis of secondary metabolites, to label and capture bacterial symbionts actively producing these compounds in the mantle of the nudibranch Doriopsilla fulva. Results - We recovered the genome of Candidatus Doriopsillibacter californiensis from the Ca. Tethybacterales order, an uncultured lineage of sponge symbionts not found in nudibranchs previously. It forms part of the core skin microbiome of D. fulva and is nearly absent in its internal organs. We showed that crude extracts of D. fulva contained secondary metabolites that were consistent with the presence of a beta-lactone encoded in Ca. D. californiensis genome. Beta-lactones represent an underexplored group of secondary metabolites with pharmaceutical potential that have not been reported in nudibranchs previously. Conclusions - Altogether, this study shows how probe-based, targeted sorting approaches can capture bacterial symbionts producing secondary metabolites in vivo.
Background: Viruses impact nearly all organisms on Earth, including microbial communities and their associated biogeochemical processes. In soils, highly diverse viral communities have been identified, with a global distribution seemingly driven by multiple biotic and abiotic factors, especially soil temperature and moisture. However, our current understanding of the stability of soil viral communities across time and their response to strong seasonal changes in environmental parameters remains limited. Here, we investigated the diversity and activity of environmental soil DNA and RNA viruses, focusing especially on bacteriophages, across dynamics’ seasonal changes in a snow-dominated mountainous watershed by examining paired metagenomes and metatranscriptomes. Results: We identified a large number of DNA and RNA viruses taxonomically divergent from existing environmental viruses, including a significant proportion of fungal RNA viruses, and a large and unsuspected diversity of positive single-stranded RNA phages ( Leviviricetes ), highlighting the under-characterization of the global soil virosphere. Among these, we were able to distinguish subsets of active DNA and RNA phages that changed across seasons, consistent with a “seed-bank” viral community structure in which new phage activity, for example, replication and host lysis, is sequentially triggered by changes in environmental conditions. At the population level, we further identified virus-host dynamics matching two existing ecological models: “Kill-The-Winner” which proposes that lytic phages are actively infecting abundant bacteria, and “Piggyback-The-Persistent” which argues that when the host is growing slowly, it is more beneficial to remain in a dormant state. The former was associated with summer months of high and rapid microbial activity, and the latter with winter months of limited and slow host growth. Conclusion: Taken together, these results suggest that the high diversity of viruses in soils is likely associated with a broad range of host interaction types each adapted to specific host ecological strategies and environmental conditions. As our understanding of how environmental and host factors drive viral activity in soil ecosystems progresses, integrating these viral impacts in complex natural microbiome models will be key to accurately predict ecosystem biogeochemistry.
Abstract Background Sulfoquinovose (SQ) is a green-diet-derived sulfonated glucose and a selective substrate for a limited number of human gut bacteria. Complete anaerobic SQ degradation via interspecies metabolite transfer to sulfonate-respiring bacteria produces hydrogen sulfide, which has dose- and context-dependent health effects. Here, we studied potential SQ degradation by the mammalian host and the impact of SQ supplementation on human and murine gut microbiota diversity and metabolism. Results 13 CO 2 breath tests with germ-free C57BL/6 mice gavaged with 13 C-SQ were negative. Also, SQ was not degraded by human intestinal cells in vitro, indicating that SQ is not directly metabolized by mice and humans. Addition of increasing SQ concentrations to human fecal microcosms revealed dose-dependent responses of the microbiota and corroborated the relevance ofAgathobacter rectalisandBilophila wadsworthiain cooperative degradation of SQ to hydrogen sulfide via interspecies transfer of 2,3-dihydroxy-1-propanesulfonate (DHPS). Similar to the human gut microbiome, the genetic capacity for SQ or DHPS degradation is sparsely distributed among bacterial species in the gut of conventional laboratory mice.Escherichia coliandEnterocloster clostridioformiswere identified as primary SQ degraders in the mouse gut. SQ and DHPS supplementation experiments with conventional laboratory mice and their intestinal contents showed that SQ was incompletely catabolized to DHPS. Although someE. clostridioformisgenomes encode an extended sulfoglycolytic pathway for both SQ and DHPS fermentation, SQ was only degraded to DHPS by a mouse-derivedE. clostridioformisstrain. Conclusions Our findings suggest that SQ is solely a nutrient for the gut microbiota and not for mice and humans, emphasizing its potential as a prebiotic. SQ degradation by the microbiota of conventional laboratory mice differs from the human gut microbiota by absence of DHPS degradation activity. Hence, the microbiota of conventional laboratory mice does not fully represent the SQ metabolism in humans, indicating the need for alternative model systems to assess the impact of SQ on human health. This study advances our understanding of how individual dietary compounds shape the microbial community structure and metabolism in the gut and thereby potentially influence host health.
Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices.
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.
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.
Sorghum ( Sorghum bicolor ) is an important food, feed, and fodder crop worldwide and is gaining popularity as an energy crop due to its high potential for biomass production. Some sorghum accessions develop many aerial roots and produce an abundant carbohydrate-rich mucilage after rain. This aerial root mucilage is similar to that observed in landraces of maize ( Zea mays ) from southern Mexico, which have been previously shown to host diazotrophs. In this study, we characterized the aerial root development of several sorghum accessions and the impact of humidity on this trait. We conducted a microbiome study of the aerial root mucilage of maize and sorghum and isolated numerous diazotrophs from field sorghum mucilage. We observed that the prevailing phyla in the mucilage were Pseudomonadota, Bacteroidota, and Bacillota. However, bacterial abundances varied based on the genotype and the location. Using acetylene reduction, 15 N 2 gas feeding, and 15 N isotope dilution assays, we confirmed that these sorghum accessions can acquire about 40% of their nitrogen from the atmosphere through these associations on aerial roots. Nitrogen fixation in sorghum aerial root mucilage offers a promising avenue to reduce reliance on synthetic fertilizers and promote sustainable agricultural practices for food, feed, fodder, and bioenergy production.
Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology ( e . g ., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.
Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized , and so name our tool MetConSIN: Met abolically Con textualized S pecies I nteraction N etworks.
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 .