Brendan J. M. Bohannan

Soil microbes regulate whether carbon is retained in soils or returned to the atmosphere through respiration, but the extent to which microbial community characteristics improve the prediction of heterotrophic soil CO ₂ production beyond predictions by environmental controls remains unclear. We tested this across a topographically structured forest-to-marsh gradient in coastal Oregon by measuring heterotrophic soil respiration, soil physicochemical properties, PLFA-based microbial biomass, metagenomic taxonomic composition, and functional gene-based trait indicators. Across the gradient, soil moisture increased from forest to marsh, while mineral soil and organic matter C:N decreased. These environmental shifts were accompanied by strong but uneven microbial responses: total microbial, fungal, and bacterial biomass declined from forest to marsh, taxonomic composition showed the strongest structuring by environmental conditions, and functional gene-based indicators showed mixed relationships with the gradient. Environmental model comparisons identified soil moisture and organic layer C as the strongest baseline predictors of respiration. Among microbial descriptors, only a small subset improved respiration prediction beyond this environmental baseline. The fungal:bacterial (F:B) biomass ratio produced the largest increase in model fit and the greatest reduction in AICc, whereas the best taxonomic and functional-gene predictors yielded more minor gains. Our results showed that the microbial descriptors most responsive to environmental gradients were not the ones most useful for predicting soil respiration; instead, a relatively simple biomass-partitioning metric captured respiration-relevant microbial variation more effectively than finer taxonomic and genomic descriptors. This suggests that F:B ratio may be especially useful for representing respiration-relevant microbial variation in local landscape-scale studies and for future carbon cycle models applied across heterogeneous ecosystem transition zones.

Predatory bacteria such as Bdellovibrio are emerging as ecological modulators in microbial communities by restructuring community composition, yet their roles in host-associated microbiomes remain poorly understood. Using Caenorhabditis elegans as model host and its defined microbiota, we investigated how two Bdellovibrio strains with distinct prey ranges ( B. tiberii MYbb2 and B. krueschi MYbb4) affect microbial community composition and host life-history traits. Both strains consistently altered microbiome composition, with MYbb4 causing more pronounced alpha-diversity shifts and MYbb2 selectively enriching strains of the genus Ochrobactrum which coincided with higher host median lifespan. Genome-based predictions indicate that de novo vitamin B12 synthesis by Ochrobactrum underlies the observed host phenotype, which was confirmed through quantitative measurements of the vitamin in mono-cell cultures. Employing the acdh-1 p::GFP transcriptional reporter strain, we confirmed that a diet of B12-producing bacteria suppresses the B12-independent propionate detoxification pathway in the host, demonstrating that bacterially produced B12 is bioavailable to C. elegans . Exogenous B12 supplementation assays further confirmed the lifespan-extending effect. Together, these results suggest that predation-driven enrichment of B12-producing bacteria maintains B12 levels sufficient to detoxify propionyl-CoA via the B12-dependent pathway, preventing the accumulation of toxic metabolic byproducts that would otherwise arise under B12-limiting conditions and reduce host lifespan. Our findings demonstrate that predatory bacteria are important drivers of microbiome structure with direct consequences for host physiology, representing an underappreciated ecological mechanism for microbiome modulation.

Endosomes are organelles that sort and traffic materials to different cellular compartments to enable performance of other organelles. One group of organelles supported by endosomes are lysosome related organelles (LROs) – which perform specialized functions using endocytosed or recycled materials from endosomes. LROs include melanosomes, lytic granules, platelet dense granules, lamellar bodies, and more. Individuals with endosome trafficking disorders commonly present with oculocutaneous albinism due to insufficient trafficking to – and maturation of – melanosomes. While the loss of pigment is an easily identifiable phenotype, mutations in endosome trafficking can also cause multi-organ system symptoms due to more broad disruption of LROs. Different endosome trafficking mutations impact LRO maturation at different degrees of severity and can impact a subset or all LROs leading to a wide range of symptomologies. Endosome trafficking disorders can present with oculocutaneous albinism (caused by mutated melanosomes), blood diathesis (caused by mutated platelet dense granules), pulmonary fibrosis (caused by mutated lamellar bodies), or chronic inflammation and inflammatory bowel disease (IBD) (caused by mutated lytic granules), as well as other symptoms. The shared inflammatory patterns of Hermansky-Pudlak Syndrome (HPS) and Inflammatory Bowel Disease (IBD) are well studied. However, impacts of this shared gastrointestinal inflammation on the gut microbiome remain unexplored. Using a spontaneously identified mutant family of threespine stickleback fish, we characterize changes in LROs and symptoms of oculocutaneous albinism. We further demonstrate that the shared gastrointestinal phenotypes of HPS and IBD extend to the gut microbiome showing similar shifts in microbial communities consistent with IBD. We argue endosome trafficking models should be explored as models to study the gut microbiome in addition to inflammatory phenotypes in IBD, and importantly, be used to test microbial/dietary interventions for both IBD and endosome trafficking disorders.

All plants and animals are host to a community of microorganisms, their microbiotas, which have crucial influences on the life history and performance of their hosts. Despite the importance of such host–microbiota relationships, relatively little is known about the role microbiotas have in mediating evolution of the host and entire host–microbe assemblages. This knowledge gap is partly due to the lack of theoretical frameworks that generate testable predictions on the evolutionary dynamics of host–microbiota systems. In this Perspective, we argue that the foundation for such frameworks exists in evolutionary theory. We highlight four examples of theoretical models—niche construction, indirect genetic effects, maternal effects and multilevel selection—that capture important aspects of host–microbiome evolution. We outline how each of these frameworks can provide key insights into the evolution of host–microbiota systems while also suggesting expansions of current theory to incorporate processes unique to host–microbe assemblages; for instance, focusing on nuances in microbiota transmission and ecological microbial community dynamics. Expanding evolutionary theory to accommodate host–microbiota associations is key for a more integrative understanding of evolution, and the approach outlined here can guide future empirical research on the function and evolution of these ubiquitous interactions.

Background: Environmental microbiomes, such as those in recirculating aquaculture systems (RAS), can play a key role in shaping host-associated microbial communities. In zebrafish ( Danio rerio ) research, these interactions can introduce uncontrolled sources of variation, potentially confounding experimental outcomes across multiple facilities. Despite widespread zebrafish use in microbiome studies, few have characterized the microbial composition of both tank water and fish across multiple independent facilities to evaluate the consequences of environmental microbiome variation on the host microbiome. Results: We compared water and zebrafish gut microbiomes across five aquaculture facilities—two in the United States and three in Norway—using a nested sampling design and 16S rRNA gene sequencing. Alpha diversity was consistently higher in tank water than in fish guts, and beta diversity metrics revealed distinct clustering by sample type, facility, and location. Differences in microbial community composition were significant across facilities, with both water and fish samples exhibiting facility-specific profiles. Similarity Percentage analysis identified taxonomic groups driving these differences, while Fast Expectation-Maximization for Microbial Source Tracking detected measurable contributions of tank water microbiota to zebrafish gut communities. Bray-Curtis dissimilarity values were lowest between fish and water from the same tank and increased with geographic and facility distance, indicating local microbial overlap. Relative abundance patterns and ordination plots further supported distinct and structured microbial assemblages across systems. Conclusions: This study demonstrates that zebrafish aquaculture systems harbor unique microbial communities shaped by both environmental and geographic factors, with tank water acting as a potential source of gut-associated microbes. These findings underscore the importance of incorporating environmental microbiome assessments into zebrafish experimental design, particularly for studies focused on host-microbe interactions. Without such consideration, unaccounted variation in environmental microbiota may affect microbiome composition and reduce cross-study reproducibility. Moving forward, standardized reporting of environmental conditions and microbial composition across facilities will be critical for strengthening reproducibility and interpretation in zebrafish microbiome research.

The gut and external tissues of most animals are colonized by communities of microorganisms that can influence the health, development, and fitness of the host. The composition of these communities can vary greatly between individuals within a host species, and both selective factors (e.g. host immune response) and neutral processes (e.g. random loss of microbial cells) have been shown to contribute to this variation. While it is known that microbiome composition differs between tissues within an individual host, less is known about the ecological processes that underline these differences. To address this, we investigated whether the contribution of neutral ecological processes to microbiome assembly differs between external (skin and scale) and internal (gut) host tissues for a diverse panel of animal hosts. To do this, we fit a neutral ecological model to microbial communities from external and internal tissues across a variety of animal hosts. Strikingly, we discovered that the neutral model was equally or better fit to skin or scale microbial communities across all hosts, suggesting that neutral processes play a larger role in the assembly of skin or scale microbiomes compared to gut microbiomes. Furthermore, we observed that this trend is robust to different definitions of the metacommunity (i.e. the microbial taxa available to colonize a host). Finally, we leveraged a simulation framework to compare the model fits of empirical vs simulated microbial communities. We found that neutral model fits to empirical communities can differ from simulated communities, emphasizing the importance of temporal sampling in profiling animal microbiomes.

The Amazon rainforest has been subjected to high rates of deforestation, mostly for pasturelands, over the last few decades. This change in plant cover is known to alter the soil microbiome and the functions it mediates, but the genomic changes underlying this response are still unresolved. In this study, we used a combination of deep shotgun metagenomics complemented by a supervised machine learning approach to compare the metabolic strategies of tropical soil microbial communities in pristine forests and long‐term established pastures in the Amazon. Machine learning‐derived metagenome analysis indicated that microbial community structures (bacteria, archaea and viruses) and the composition of protein‐coding genes were distinct in each plant cover type environment. Forest and pasture soils had different genomic diversities for the above three taxonomic groups, characterised by their protein‐coding genes. These differences in metagenome profiles in soils under forests and pastures suggest that metabolic strategies related to carbohydrate and energy metabolisms were altered at community level. Changes were also consistent with known modifications to the C and N cycles caused by long‐term shifts in aboveground vegetation and were also associated with several soil physicochemical properties known to change with land use, such as the C/N ratio, soil temperature and exchangeable acidity. In addition, our analysis reveals that these alterations in land use can also result in changes to the composition and diversity of the soil DNA virome. Collectively, our study indicates that soil microbial communities shift their overall metabolic strategies, driven by genomic alterations observed in pristine forests and long‐term established pastures with implications for the C and N cycles.

Marine sediment contains some of the most abundant and diverse microbial communities; however, the ecological processes shaping the benthic microbial communities at the regional scale remains poorly understood. Using a high-coverage sampling strategy, 16S rRNA gene sequencing, and ecological null models, we explored variation in the ecological processes governing benthic microbial community assembly in surface sediments across an extensive Southwest Atlantic basin. The relative importance of ecological processes varied between provinces, with drift, dispersal limitation, and homogeneous selection being the three main processes that shaped the communities. Phylogenetic bin-based analysis revealed a complex balance of assembly mechanisms, with drift dominating the majority of the bin assembly of the dominant groups such as Candidatus Nitrosopumilus, Pirellula-like planctomycetes, and Woeseia. The environmental factors driving this processes were associated with sediment characteristics and organic matter quality, although they differed among provinces. Drift emerged as the dominant process, influenced by sediment grain size and depth in deeper regions and organic matter properties on the continental shelf. Dispersal limitation was linked to sediment and bottom water properties, while homogeneous selection was associated with sediment aluminum and hydrocarbon content. These findings highlight the role of spatial variation and environmental factors in benthic microbial community assembly at a regional scale, providing a framework for understanding microbial community assembly in oceanic basins, and emphasizing the need for province-specific management strategies.

Multicellular organisms host a rich assemblage of associated microorganisms, collectively known as their “microbiomes”. Microbiomes have the capacity to influence their hosts’ fitnesses, but the conditions under which such influences contribute to evolution are not clear. This is due in part to a lack of a comprehensive theoretical framework for describing the combined effects of host and associated microbes on phenotypic variation. Here we address this gap by extending the foundations of quantitative genetic theory to include host-associated microbes, as well as alleles of hosts, as factors that explain quantitative host trait variation. We introduce a way to partition host-associated microbiomes into components relevant for predicting a microbiome-mediated response to selection. We then apply our general framework to a simulation model of microbiome inheritance to illustrate principles for predicting host trait dynamics, and to generalize classical narrow and broad sense heritabilities to account for microbial effects. We demonstrate that microbiome-mediated responses to host-level selection can arise from various transmission modes, not solely vertical, but that the contribution of non-vertical modes can depend strongly on host life history. Our work lays a foundation for integrating microbiome-mediated host variation and adaptation into our understanding of natural variation.