microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Genomic insights of Fictibacillus terranigra sp. nov., a versatile metabolic bacterium from Amazonian Dark Earths

The Amazon rainforest, a hotspot for biodiversity, is a crucial research area for scientists seeking novel microorganisms with ecological and biotechnological significance. A key region within the Amazon rainforest is the Amazonian Dark Earths (ADE), noted for supporting diverse plant and microbial communities, and its potential as a blueprint for sustainable agriculture. This study delineates the isolation, morphological traits, carbon source utilization, and genomic features of Fictibacillus terranigra CENA-BCM004, a candidate novel species of the Fictibacillus genus isolated from ADE. The genome of Fictibacillus terranigra was sequenced, resulting in 16 assembled contigs, a total length of 4,967,627 bp, and a GC content of 43.65%. Genome annotation uncovered 3315 predicted genes, encompassing a wide range of genes linked to various metabolic pathways. Phylogenetic analysis indicated that CENA-BCM004 is a putative new species, closely affiliated with other unidentified Fictibacillus species and Bacillus sp. WQ 8-8. Moreover, this strain showcased a multifaceted metabolic profile, revealing its potential for diverse biotechnological applications. It exhibited capabilities to antagonize pathogens, metabolize multiple sugars, mineralize organic matter compounds, and solubilize several minerals. These insights substantially augment our comprehension of microbial diversity in ADE and underscore the potential of Fictibacillus terranigra as a precious resource for biotechnological endeavors. The genomic data generated from this study will serve as a foundational resource for subsequent research and exploration of the biotechnological capabilities of this newly identified species.

An overview of Parafrankia (Nod+/Fix+) and Pseudofrankia (Nod+/Fix-) interactions through genome mining and experimental modeling in co-culture and co-inoculation of Elaeagnus angustifolia

In many frankia, the ability to nodulate host plants (Nod+) and fix nitrogen (Fix+) is a common strategy. However, some frankia within the Pseudofrankia genus lack one or two of these traits. This phenomenon has been consistently observed across various actinorhizal nodule isolates, displaying Nod- and/or Fix- phenotypes. Yet, the mechanisms supporting the colonization and persistence of these inefficient frankia within nodules, both with and without symbiotic strains (Nod+/Fix+), remain unclear. It is also uncertain whether these associations burden or benefit host plants. This study delves into the ecological interactions between Parafrankia EUN1f and Pseudofrankia inefficax EuI1c, isolated from Elaeagnus umbellata nodules. EUN1f (Nod+/Fix+) and EuI1c (Nod+/Fix-) display contrasting symbiotic traits. While the prediction suggests a competitive scenario, the absence of direct interaction evidence implies that the competitive advantage of EUN1f and EuI1c is likely contingent on contextual factors such as substrate availability and the specific nature of stressors in their respective habitats. In co-culture, EUN1f outperforms EuI1c, especially under specific conditions, driven by its nitrogenase activity. Iron-depleted conditions favor EUN1f, emphasizing iron's role in microbial competition. Both strains benefit from host root exudates in pure culture, but EUN1f dominates in co-culture, enhancing its competitive traits. Nodulation experiments show that host plant preferences align with inoculum strain abundance under nitrogen-depleted conditions, while consistently favoring EUN1f in nitrogen-supplied media. This study unveils competitive dynamics and niche exclusion between EUN1f and EuI1c, suggesting that host plant may penalize less effective strains and even all strains. These findings highlight the complex interplay between strain competition and host selective pressure, warranting further research into the underlying mechanisms shaping plant-microbe-microbe interactions in diverse ecosystems.

IMPORTANCE

While Pseudofrankia strains typically lack the common traits of ability to nodulate the host plant (Nod-) and/or fix nitrogen (Fix-), they are still recovered from actinorhizal nodules. The enigmatic question of how and why these unconventional strains establish themselves within nodule tissue, thriving either alongside symbiotic strains (Nod+/Fix+) or independently, while considering potential metabolic costs to the host plant, remains a perplexing puzzle. This study endeavors to unravel the competitive dynamics between Pseudofrankia inefficax strain EuI1c (Nod+/Fix-) and Parafrankia strain EU1Nf (Nod+/Fix+) through a comprehensive exploration of genomic data and empirical modeling, conducted both in controlled laboratory settings and within the host plant environment.

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

Microbial evolution-An under-appreciated driver of soil carbon cycling

Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

Metagenomics-Based Microbial Ecological Community Threshold and Indicators of Anthropogenic Disturbances in Estuarine Sediments

Assessing the impacts of cumulative anthropogenic disturbances on estuarine ecosystem health is challenging. Using spatially distributed sediments from the Pearl River Estuary (PRE) in southern China, which are significantly influenced by anthropogenic activities, we demonstrated that metagenomics-based surveillance of benthic microbial communities is a robust approach to assess anthropogenic impacts on estuarine benthic ecosystems. Correlational and threshold analyses between microbial compositions and environmental conditions indicated that anthropogenic disturbances in the PRE sediments drove the taxonomic and functional variations in the benthic microbial communities. An ecological community threshold of anthropogenic disturbances was identified, which delineated the PRE sediments into two groups (H and L) with distinct taxa and functional traits. Group H, located nearshore and subjected to a higher level of anthropogenic disturbances, was enriched with pollutant degraders, putative human pathogens, fecal pollution indicators, and functional traits related to stress tolerance. In contrast, Group L, located offshore and subjected to a lower level of anthropogenic disturbances, was enriched with halotolerant and oligotrophic taxa and functional traits related to growth and resource acquisition. The machine learning random forest model identified a number of taxonomic and functional indicators that could differentiate PRE sediments between Groups H and L. The identified ecological community threshold and microbial indicators highlight the utility of metagenomics-based microbial surveillance in assessing the adverse impacts of anthropogenic disturbances in estuarine sediments, which can assist environmental management to better protect ecosystem health.

Comparative genomics of a vertically transmitted thiotrophic bacterial ectosymbiont and its close free-living relative

Thiotrophic symbioses between sulphur-oxidizing bacteria and various unicellular and metazoan eukaryotes are widespread in reducing marine environments. The giant colonial ciliate Zoothamnium niveum, however, is the only host of thioautotrophic symbionts that has been cultivated along with its symbiont, the vertically transmitted ectosymbiont Candidatus Thiobius zoothamnicola (short Thiobius). Because theoretical predictions posit a smaller genome in vertically transmitted endosymbionts compared to free-living relatives, we investigated whether this is true also for an ectosymbiont. We used metagenomics to recover the high-quality draft genome of this bacterial symbiont. For comparison we have also sequenced a closely related free-living cultured but not formally described strain Milos ODIII6 (short ODIII6). We then performed comparative genomics to assess the functional capabilities at gene, metabolic pathway and trait level. 16S rRNA gene trees and average amino acid identity confirmed the close phylogenetic relationship of both bacteria. Indeed, Thiobius has about a third smaller genome than its free-living relative ODIII6, with reduced metabolic capabilities and fewer functional traits. The functional capabilities of Thiobius were a subset of those of the more versatile ODIII6, which possessed additional genes for oxygen, sulphur and hydrogen utilization and for the acquisition of phosphorus illustrating features that may be adaptive for the unstable environmental conditions at hydrothermal vents. In contrast, Thiobius possesses genes potentially enabling it to utilize lactate and acetate heterotrophically, compounds that may be provided as byproducts by the host. The present study illustrates the effect of strict host-dependence of a bacterial ectosymbiont on genome evolution and host adaptation.

Implications of Soil Microbial Community Assembly for Ecosystem Restoration: Patterns, Process, and Potential

While it is now widely accepted that microorganisms provide essential functions in restoration ecology, the nature of relationships between microbial community assembly and ecosystem recovery remains unclear. There has been a longstanding challenge to decipher whether microorganisms facilitate or simply follow ecosystem recovery, and evidence for each is mixed at best. We propose that understanding microbial community assembly processes is critical to understanding the role of microorganisms during ecosystem restoration and thus optimizing management strategies. We examine how the connection between environment, community structure, and function is fundamentally underpinned by the processes governing community assembly of these microbial communities. We review important factors to consider in evaluating microbial community structure in the context of ecosystem recovery as revealed in studies of microbial succession: (1) variation in community assembly processes, (2) linkages to ecosystem function, and (3) measurable microbial community attributes. We seek to empower restoration ecology with microbial assembly and successional understandings that can generate actionable insights and vital contexts for ecosystem restoration efforts.

Metagenome-assembled genome extraction and analysis from microbiomes using KBase

Uncultivated Bacteria and Archaea account for the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only become possible recently. To realize the hope of capturing Earth's microbial genetic complement and to facilitate the investigation of the functional roles of specific lineages in a given ecosystem, technologies that accelerate the recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high-quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform ( http://www.kbase.us/ ). Overall, these steps take about a day to obtain extracted genomes when starting from smaller environmental shotgun read libraries, or up to about a week from larger libraries. In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAGs, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq and others. While portions of such capabilities are available individually from other resources, the combination of the intuitive usability, data interoperability and integration of tools in a freely available computational resource makes KBase a powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended with additional MAG recovery and analysis tools, via the KBase software development kit (SDK).