Multi-omics profiling reveals comprehensive microbe-plant-metabolite regulation patterns for medicinal plant Glycyrrhiza uralensis Fisch.

Glycyrrhiza uralensis Fisch is a medicinal plant widely used to treat multiple diseases in Europe and Asia, and its efficacy largely depends on liquiritin and glycyrrhizic acid. The regulatory pattern responsible for the difference in efficacy between wild and cultivated G. uralensis remains largely undetermined. Here, we collected roots and rhizosphere soils from wild (WT) G. uralensis as well as those farmed for 1 year (C1) and 3 years (C3), generated metabolite and transcript data for roots, microbiota data for rhizospheres and conducted comprehensive multi-omics analyses. We updated gene structures for all 40 091 genes in G. uralensis, and based on 52 differentially expressed genes, we charted the route-map of both liquiritin and glycyrrhizic acid biosynthesis, with genes BAS, CYP72A154 and CYP88D6 critical for glycyrrhizic acid biosynthesis being significantly expressed higher in wild G. uralensis than in cultivated G. uralensis. Additionally, multi-omics network analysis identified that Lysobacter was strongly associated with CYP72A154, which was required for glycyrrhizic acid biosynthesis. Finally, we developed a holistic multi-omics regulation model that confirmed the importance of rhizosphere microbial community structure in liquiritin accumulation. This study thoroughly decoded the key regulatory mechanisms of liquiritin and glycyrrhizic acid, and provided new insights into the interactions of the plant's key metabolites with its transcriptome, rhizosphere microbes and environment, which would guide future cultivation of G. uralensis.

Glycyrrhiza uralensis Fisch. Root-associated microbiota: the multifaceted hubs associated with environmental factors, growth status and accumulation of secondary metabolites.

Glycyrrhiza uralensis Fisch. is an important, perennial medicinal plant whose root microbiome is considered to play an important role in promoting accumulation of effective medicinal ingredients (liquiritin and glycrrhizic acid). Here, we report a comprehensive analysis of the microbial community structural composition and metabolite-plant-microbes association of G. uralensis Fisch. We collected both soil and rhizosphere samples of G. uralensis from different environmental conditions (cultivated and wild) and growth years (grown for one year and three years). Our data revealed higher species diversity in the wild group than in the cultivated group. The core rhizosphere microbiome of G. uralensis comprised 78 genera, including Bacillus, Pseudomonas, Rhizobium, some of which were potential plant beneficial microbes. Our results suggest that the growth of G. uralensis has a correlation with the root-associated microbiota assemblage. Integrated analysis among rhizosphere microbial taxa, plant gene expressions, and liquiritin and glycrrhizic acid accumulation showed that the liquiritin and glycrrhizic acid accumulation exhibited associations with the rhizosphere microbial composition at the genus level. The results provide valuable information to guide cultivation of G. uralensis, and potentially to harness the power of the root-associated microbiota to improve medicinal plant production.

The seasonal changes of the gut microbiome of the population living in traditional lifestyles are represented by characteristic species-level and functional-level SNP enrichment patterns.

BACKGROUND: Most studies investigating human gut microbiome dynamics are conducted on humans living in an urban setting. However, few studies have researched the gut microbiome of the populations living traditional lifestyles. These understudied populations are arguably better subjects in answering human-gut microbiome evolution because of their lower exposure to antibiotics and higher dependence on natural resources. Hadza hunter-gatherers in Tanzania have exhibited high biodiversity and seasonal patterns in their gut microbiome composition at the family level, where some taxa disappear in one season and reappear later. Such seasonal changes have been profiled, but the nucleotide changes remain unexplored at the genome level. Thus, it is still elusive how microbial communities change with seasonal changes at the genome level. RESULTS: In this study, we performed a strain-level single nucleotide polymorphism (SNP) analysis on 40 Hadza fecal metagenome samples spanning three seasons. With more SNP presented in the wet season, eight prevalent species have significant SNP enrichment with the increasing number of SNP calling by VarScan2, among which only three species have relatively high abundances. Eighty-three genes have the most SNP distributions between the wet season and dry season. Many of these genes are derived from Ruminococcus obeum, and mainly participated in metabolic pathways including carbon metabolism, pyruvate metabolism, and glycolysis. CONCLUSIONS: Eight prevalent species have significant SNP enrichments with the increasing number of SNP, among which only Eubacterium biforme, Eubacterium hallii and Ruminococcus obeum have relatively high species abundances. Many genes in the microbiomes also presented characteristic SNP distributions between the wet season and the dry season. This implies that the seasonal changes might indirectly impact the mutation patterns for specific species and functions for the gut microbiome of the population that lives in traditional lifestyles through changing the diet in wet and dry seasons, indicating the role of these variants in these species' adaptation to the changing environment and diets.

Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology.

Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.e. species) of the Species-Centric Networks (SCNs). However, little was understood about the change of correlation between network members (i.e. edges of the SCNs) when the network was disturbed. Here, we introduced a Correlation-Centric Network (CCN) to the microbial research based on the concept of edge networks. In CCN, each node represented a species-species correlation, and edge represented the species shared by two correlations. In this research, we investigated the CCNs and their corresponding SCNs on two large cohorts of microbiome. The results showed that CCNs not only retained the characteristics of SCNs, but also contained information that cannot be detected by SCNs. In addition, when the members of microbial communities were decreased (i.e. environmental disturbance), the CCNs fluctuated within a small range in terms of network connectivity. Therefore, by highlighting the important species correlations, CCNs could unveil new insights when studying not only the functions of target species, but also the stabilities of their residing microbial communities.

Strain-GeMS: optimized subspecies identification from microbiome data based on accurate variant modeling.

MOTIVATION: Subspecies identification is one of the most critical issues in microbiome studies, as it is directly related to their functions in response to the environmental stress and their feedbacks. However, identification of subspecies remains a challenge largely due to the small variation between different strains within the same species. Accurate identification of subspecies primarily relies on variant identification and categorization through microbiome data. However, current SNP calling and subspecies identification for microbiome data remain underdeveloped. RESULTS: In this work, we have proposed Strain-GeMS for subspecies identification from microbiome data, based on solid statistical model for SNP calling, as well as optimized procedure for subspecies identification. Results on simulated, ab initio and in vivo datasets have shown that Strain-GeMS could always generate more accurate results compared with other subspecies identification methods. AVAILABILITY AND IMPLEMENTATION: Strain-GeMS is available at: https://github.com/HUST-NingKang-Lab/straingems. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.