Prokaryotic taxa play keystone roles in the soil microbiome associated with woody perennial plants in the genus Buxus

Organic farming promotes the abundance of fungi keystone taxa in bacteria-fungi interkingdom networks

Soil bacteria-fungi interactions are essential in the biogeochemical cycles of several nutrients, making these microbes major players in agroecosystems. While the impact of the farming system on microbial community composition has been extensively reported in the literature, whether sustainable farming approaches can promote associations between bacteria and fungi is still unclear. To study this, we employed 16S, ITS, and 18S DNA sequencing to uncover how microbial interactions were affected by conventional and organic farming systems on maize crops. The Bray-Curtis index revealed that bacterial, fungal, and arbuscular mycorrhizal fungi communities were significantly different between the two farming systems. Several taxa known to thrive in healthy soils, such as Nitrosophaerales, Orbiliales, and Glomus were more abundant in the organic farming system. Constrained ordination revealed that the organic farming system microbial community was significantly correlated with the β-glucosidase activity, whereas the conventional farming system microbial community significantly correlated with soil pH. Both conventional and organic co-occurrence interkingdom networks exhibited a parallel node count, however, the former had a higher number of edges, thus being denser than the latter. Despite the similar amount of fungal nodes in the co-occurrence networks, the organic farming system co-occurrence network exhibited more than 3-fold the proportion of fungal taxa as keystone nodes than the conventional co-occurrence network. The genera Bionectria, Cercophora, Geastrum, Penicillium, Preussia, Metarhizium, Myceliophthora, and Rhizophlyctis were among the fungal keystone nodes of the organic farming system network. Altogether, our results uncover that beyond differences in microbial community composition between the two farming systems, fungal keystone nodes are far more relevant in the organic farming system, thus suggesting that bacteria-fungi interactions are more frequent in organic farming systems, promoting a more functional microbial community.

Identifying keystone species in microbial communities using deep learning

Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep-learning model using microbiome samples collected from this habitat. The well-trained deep-learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data and applied DKI to analyse real data. We found that those taxa with high median keystoneness across different communities display strong community specificity. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

Identifying keystone species in microbial communities using deep learning

Previous studies suggested that microbial communities harbor keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. This is mainly due to our limited knowledge of microbial dynamics and the experimental and ethical difficulties of manipulating microbial communities. Here, we propose a Data-driven Keystone species Identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep learning model using microbiome samples collected from this habitat. The well-trained deep learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data generated from a classical population dynamics model in community ecology. We then applied DKI to analyze human gut, oral microbiome, soil, and coral microbiome data. We found that those taxa with high median keystoneness across different communities display strong community specificity, and many of them have been reported as keystone taxa in literature. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

Archaea: An under-estimated kingdom in livestock animals

Archaea are considered an essential group of gut microorganisms in both humans and animals. However, they have been neglected in previous studies, especially those involving non-ruminants. In this study, we re-analyzed published metagenomic and metatranscriptomic data sequenced from matched samples to explore the composition and the expression activity of gut archaea in ruminants (cattle and sheep) and monogastric animals (pig and chicken). Our results showed that the alpha and beta diversity of each host species, especially cattle and chickens, calculated from metagenomic and metatranscriptomic data were significantly different, suggesting that metatranscriptomic data better represent the functional status of archaea. We detected that the relative abundance of 17 (cattle), 7 (sheep), 20 (pig), and 2 (chicken) archaeal species were identified in the top 100 archaeal taxa when analyzing the metagenomic datasets, and these species were classified as the “active archaeal species” for each host species by comparison with corresponding metatranscriptomic data. For example, The expressive abundance in metatranscriptomic dataset of Methanosphaera cuniculi and Methanosphaera stadtmanae were 30- and 27-fold higher than that in metagenomic abundance, indicating their potentially important function in the pig gut. Here we aim to show the potential importance of archaea in the livestock digestive tract and encourage future research in this area, especially on the gut archaea of monogastric animals.

Characterization of the Soil Bacterial Community from Selected Boxwood Gardens across the United States

In a recent study, we observed a rapid decline of the boxwood blight pathogen Calonectria pseudonaviculata (Cps) soil population in all surveyed gardens across the United States, and we speculated that these garden soils might be suppressive to Cps. This study aimed to characterize the soil bacterial community in these boxwood gardens. Soil samples were taken from one garden in California, Illinois, South Carolina, and Virginia and two in New York in early summer and late fall of 2017 and 2018. Soil DNA was extracted and its 16S rRNA amplicons were sequenced using the Nanopore MinION® platform. These garden soils were consistently dominated by Rhizobiales and Burkholderiales, regardless of garden location and sampling time. These two orders contain many species or strains capable of pathogen suppression and plant fitness improvement. Overall, 66 bacterial taxa were identified in this study that are known to have strains with biological control activity (BCA) against plant pathogens. Among the most abundant were Pseudomonas spp. and Bacillus spp., which may have contributed to the Cps decline in these garden soils. This study highlights the importance of soil microorganisms in plant health and provides a new perspective on garden disease management using the soil microbiome.

One Clonal Lineage of Calonectria pseudonaviculata Is Primarily Responsible for the Boxwood Blight Epidemic in the United States

Boxwood blight caused by Calonectria pseudonaviculata and C. henricotiae is destroying cultivated and native boxwood worldwide, with profound negative economic impacts on the horticulture industry. First documented in the United States in 2011, the disease has now occurred in 30 states. Previous research showed that global C. pseudonaviculata populations prior to 2014 had a clonal structure, and only the MAT1-2 idiomorph was observed. In this study, we examined C. pseudonaviculata genetic diversity and population structure in the United States after 2014, following the expansion of the disease across the country over the past 5 years. Two hundred eighteen isolates from 21 states were genotyped by sequencing 11 simple sequence repeat (SSR) loci and by MAT1 idiomorph typing. All isolates presented C. pseudonaviculata-specific alleles, indicating that C. henricotiae is still absent in the U.S. states sampled. The presence of only the MAT1-2 idiomorph and gametic linkage disequilibrium suggests the prevalence of asexual reproduction. The contemporary C. pseudonaviculata population is characterized by a clonal structure and composed of 13 multilocus genotypes (SSR-MLGs) unevenly distributed across the United States. These SSR-MLGs grouped into two clonal lineages (CLs). The predominant lineage CL2 (93% of isolates) is the primary contributor to U.S. disease expansion. The contemporary U.S. C. pseudonaviculata population is not geographically subdivided and not genetically differentiated from the U.S. population prior to 2014, but is significantly differentiated from the main European population, which is largely composed of CL1. Our findings provide insights into the boxwood blight epidemic that are critical for disease management and breeding of resistant boxwood cultivars.