Integration of Phenotypes in Microbiome Networks for Designing Synthetic Communities: a Study of Mycobiomes in the Grafted Tomato System.

Understanding factors influencing microbial interactions, and designing methods to identify key taxa that are candidates for synthetic communities, or SynComs, are complex challenges for achieving microbiome-based agriculture. Here, we study how grafting and the choice of rootstock influences root-associated fungal communities in a grafted tomato system. We studied three tomato rootstocks (BHN589, RST-04-106, and Maxifort) grafted to a BHN589 scion and profiled the fungal communities in the endosphere and rhizosphere by sequencing the internal transcribed spacer (ITS2). The data provided evidence for a rootstock effect (explaining ~2% of the total captured variation, P < 0.01) on the fungal community. Moreover, the most productive rootstock, Maxifort, supported greater fungal species richness than the other rootstocks or controls. We then constructed a phenotype-operational taxonomic unit (OTU) network analysis (PhONA) using an integrated machine learning and network analysis approach based on fungal OTUs and associated tomato yield as the phenotype. PhONA provides a graphical framework to select a testable and manageable number of OTUs to support microbiome-enhanced agriculture. We identified differentially abundant OTUs specific to each rootstock in both endosphere and rhizosphere compartments. Subsequent analyses using PhONA identified OTUs that were directly associated with tomato fruit yield and others that were indirectly linked to yield through their links to these OTUs. Fungal OTUs that are directly or indirectly linked with tomato yield may represent candidates for synthetic communities to be explored in agricultural systems. IMPORTANCE The realized benefits of microbiome analyses for plant health and disease management are often limited by the lack of methods to select manageable and testable synthetic microbiomes. We evaluated the composition and diversity of root-associated fungal communities from grafted tomatoes. We then constructed a phenotype-OTU network analysis (PhONA) using these linear and network models. By incorporating yield data in the network, PhONA identified OTUs that were directly predictive of tomato yield and others that were indirectly linked to yield through their links to these OTUs. Follow-up functional studies of taxa associated with effective rootstocks, identified using approaches such as PhONA, could support the design of synthetic fungal communities for microbiome-based crop production and disease management. The PhONA framework is flexible for incorporation of other phenotypic data, and the underlying models can readily be generalized to accommodate other microbiome or 'omics data.

Inhibitory Bacterial Diversity and Mucosome Function Differentiate Susceptibility of Appalachian Salamanders to Chytrid Fungal Infection.

Mucosal defenses are crucial in animals for protection against pathogens and predators. Host defense peptides (antimicrobial peptides, AMPs) as well as skin-associated microbes are key components of mucosal immunity, particularly in amphibians. We integrate microbiology, molecular biology, network-thinking, and proteomics to understand how host and microbially derived products on amphibian skin (referred to as the mucosome) serve as pathogen defenses. We studied defense mechanisms against chytrid pathogens, Batrachochytrium dendrobatidis (Bd) and B. salamandrivorans (Bsal), in four salamander species with different Batrachochytrium susceptibilities. Bd infection was quantified using qPCR, mucosome function (i.e., ability to kill Bd or Bsal zoospores in vitro), skin bacterial communities using 16S rRNA gene amplicon sequencing, and the role of Bd-inhibitory bacteria in microbial networks across all species. We explored the presence of candidate-AMPs in eastern newts and red-backed salamanders. Eastern newts had the highest Bd prevalence and mucosome function, while red-back salamanders had the lowest Bd prevalence and mucosome function, and two-lined salamanders and seal salamanders were intermediates. Salamanders with highest Bd infection intensity showed greater mucosome function. Bd infection prevalence significantly decreased as putative Bd-inhibitory bacterial richness and relative abundance increased on hosts. In co-occurrence networks, some putative Bd-inhibitory bacteria were found as hub-taxa, with red-backs having the highest proportion of protective hubs and positive associations related to putative Bd-inhibitory hub bacteria. We found more AMP candidates on salamanders with lower Bd susceptibility. These findings suggest that salamanders possess distinct innate mechanisms that affect chytrid fungi. IMPORTANCE How host mucosal defenses interact, and influence disease outcome is critical in understanding host defenses against pathogens. A more detailed understanding is needed of the interactions between the host and the functioning of its mucosal defenses in pathogen defense. This study investigates the variability of chytrid susceptibility in salamanders and the innate defenses each species possesses to mediate pathogens, thus advancing the knowledge toward a deeper understanding of the microbial ecology of skin-associated bacteria and contributing to the development of bioaugmentation strategies to mediate pathogen infection and disease. This study improves the understanding of complex immune defense mechanisms in salamanders and highlights the potential role of the mucosome to reduce the probability of Bd disease development and that putative protective bacteria may reduce likelihood of Bd infecting skin.

Unique microbial diversity, community composition, and networks among Pacific Islander endocervical and vaginal microbiomes with and without Chlamydia trachomatis infection in Fiji.

IMPORTANCE: Chlamydia trachomatis (Ct) is the most common sexually transmitted bacterium globally. Endocervical and vaginal microbiome interactions are rarely examined within the context of Ct or among vulnerable populations. We evaluated 258 vaginal and 92 paired endocervical samples from Fijian women using metagenomic shotgun sequencing. Over 37% of the microbiomes could not be classified into sub-community state types (subCSTs). We, therefore, developed subCSTs IV-D0, IV-D1, IV-D2, and IV-E-dominated primarily by Gardnerella vaginalis-to improve classification. Among paired microbiomes, the endocervix had a significantly higher alpha diversity and, independently, higher diversity for high-risk human papilloma virus (HPV) genotypes compared to low-risk and no HPV. Ct-infected endocervical networks had smaller clusters without interactions with potentially beneficial Lactobacillus spp. Overall, these data suggest that G. vaginalis may generate polymicrobial biofilms that predispose to and/or promote Ct and possibly HPV persistence and pathogenicity. Our findings expand on the existing repertoire of endocervical and vaginal microbiomes and fill in knowledge gaps regarding Pacific Islanders.

Soil Nitrogen Treatment Alters Microbiome Networks Across Farm Niches.

Agriculture is fundamental for food production, and microbiomes support agriculture through multiple essential ecosystem services. Despite the importance of individual (i.e., niche specific) agricultural microbiomes, microbiome interactions across niches are not well-understood. To observe the linkages between nearby agricultural microbiomes, multiple approaches (16S, 18S, and ITS) were used to inspect a broad coverage of niche microbiomes. Here we examined agricultural microbiome responses to 3 different nitrogen treatments (0, 150, and 300 kg/ha/yr) in soil and tracked linked responses in other neighbouring farm niches (rumen, faecal, white clover leaf, white clover root, rye grass leaf, and rye grass root). Nitrogen treatment had little impact on microbiome structure or composition across niches, but drastically reduced the microbiome network connectivity in soil. Networks of 16S microbiomes were the most sensitive to nitrogen treatment across amplicons, where ITS microbiome networks were the least responsive. Nitrogen enrichment in soil altered soil and the neighbouring microbiome networks, supporting our hypotheses that nitrogen treatment in soil altered microbiomes in soil and in nearby niches. This suggested that agricultural microbiomes across farm niches are ecologically interactive. Therefore, knock-on effects on neighbouring niches should be considered when management is applied to a single agricultural niche.

Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities.

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.