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Data associated with "Comparative analysis of core microbiome assignments: implications for ecological synthesis". Paper was published in mSystems, 2023. The concept of a core microbiome has been broadly used to refer to the consistent presence of a set of taxa across multiple samples within a given habitat. The assignment of taxa to core microbiomes can be performed by several methods based on the abundance and occupancy (i.e., detection across samples) of individual taxa. These approaches have led to methodological inconsistencies, with direct implications for ecological interpretation. Here, we reviewed a set of methods most commonly used to infer core microbiomes in divergent systems. We applied these methods using large datasets and analyzed simulations to determine their accuracy in core microbiome assignments. Our results show that core taxa assignments vary significantly across methods and dataset types, with occupancy-based methods most accurately defining true core membership. We also found the ability of these methods to accurately capture core assignments to be contingent upon the distribution of taxon abundance and occupancy in the dataset. Finally, we provide specific recommendations for further studies using core taxa assignments and discuss the need for unifying methodical approaches toward data processing to advance ecological synthesis.

Harnessing disease-suppressive microbiomes constitutes a promising strategy for optimizing plant growth. However, relatively little information is available about the relationship between bulk and rhizosphere soil microbiomes. Here, the assembly of banana bulk soil and rhizosphere microbiomes was investigated in a monoculture system consisting of bio-organic (BIO) and organic management practices. Applying BIO practice in newly reclaimed fields resulted in a high-efficiency biocontrol rate, thus providing a promising strategy for pre-control of Fusarium wilt disease. The soil microbiota was further characterized by MiSeq sequencing and quantitative PCR. The results indicate that disease suppression was mediated by the structure of a suppressive rhizosphere microbiome with respect to distinct community composition, diversity and abundance. Overall microbiome suppressiveness was primarily related to a particular set of enriched bacterial taxa affiliated with Pseudomonas, Terrimonas, Cupriavidus, Gp6, Ohtaekwangia and Duganella. Finally, structural equation modeling was used to show that the changes in bulk soil bacterial community determined its induced rhizosphere bacterial community, which serves as an important and direct factor in restraining the pathogen. Collectively, this study provides an integrative approach to disentangle the biological basis of disease-suppressive microbiomes in the context of agricultural practice and soil management.

Additional file 1: Table S1. Nutrient content (g/kg) present in the pineapple and banana residues. Table S2. Nutrient content corrections (g/pot) carried out across treatments. Table S3. Primers and probe sequences used in this study. Table S4. Proportion of culturable Fusarium spp. obtained in each treatment. Table S5. Fusarium wilt disease severity and symptoms across treatments. Table S6. Taxonomic identification of fungal OTUs. Table S7. Fusarium spp. isolation and strain level identification. Fig. S1 Correlation analysis between FocTR4 target gene copies and the Fusarium wilt disease incidence in two independent experiments (indicated as first and second seasons. Correlation analyses were based on Spearman and the P-values were corrected for False Discovery Rate (FDR). Fig. S2 a Principal coordinate analysis (PCoA) of soil bacterial communities based on the Bray-Curtis distances. BS: above-ground banana residue, BR: below-ground banana residue, PS: above-ground pineapple residue, PR: below-ground pineapple residue, and CK: control treatment without residue. ANOSIM: analysis of similarities. b Structural equation model (SEM) linking the microbial (bacterial and fungal) community compositions (based on PCoA), the pathogen FocTR4 density (copy numbers, qPCR), and their respective relationships with the disease incidence. R2, χ2, and P-values denote the fit of the model. c Redundancy analysis (RDA). “***” represents the P < 0.001. AP: rapid available phosphorus, AK: soil available kalium, NO3-: nitrate nitrogen, NH4+: ammonium nitrogen, pH: pH value, EC: electrical conductivity, TOC: total organic carbon, DI: disease incidence, FocRT4: Fusarium oxysporum f. sp. cubense tropical race 4. Fig. S3 Linear discriminant analysis (LDA). a Analysis including below-ground crop residues and the control treatment, and b analysis including above-ground crop residues and the control treatment. BS: above-ground banana residue, BR: below-ground banana residue, PS: above-ground pineapple residue, PR: below-ground pineapple residue, and CK: control treatment without residue. Fig. S4 Phylogenetic reconstructions of a F. solani isolates (FS11, FS17, and FS18) and OTU3, b A. fumigatus isolates (AS-7, AS-12, and AS-15) and OTU15, and c OTU11. Each independent phylogenetic reconstruction included best match sequences obtained from the NCBI database for taxonomical inferences. Fig. S5 a Correlation plots displaying the relationships between the target gene copy numbers (qPCR) of specific fungal taxa (A. fumigatus and F. solani) in soil and their respective relative abundances in soil obtained by Illumina Miseq sequencing. b Correlation plots displaying the relationships between the target gene copy numbers (qPCR) of specific fungal taxa in crop residues (A. fumigatus and F. solani) and the target gene copy numbers (qPCR) of specific fungal taxa in soil. Correlation analyses were based on Spearman and the P-values were corrected for False Discovery Rate (FDR). Fig. S6 a Results from the multiplex-PCR system based on the targets TEF-1α (650bp, positive control) and FocTR4 (463bp). b Results from the multiplex-PCR system based on the targets TEF-1α (650bp, positive control) and FocTR4 (VCG 01213/16) (455bp). F.s: F. solani (negative control). Fig. S7 Results obtained from the co-culture experiments to test the potential of F. solani and A. fumigatus isolates in inhibiting the colony growth of the pathogen FocTR4. a, b Display the potential effects on FocTR4 colony growth mediated by volatile compounds and secreted substances produced by F. solani. c, d Display the potential inhibiting effects on FocTR4 colony growth mediated by volatile compounds and secreted substances produced by A. fumigatus. e Antagonistic effects of sterile fermentation fluid from A. fumigatus on FocRT4, CK: added water, W: added fluid medium, A.F (7, 12,15): added sterile fermentation fluid from A. fumigatus. Fig. S8 a Number of carbon source utilization by each individual fungal taxa thought time. b Carbon source metabolic rate of each fungal taxa indicated by the average well color development (AWCD). c Average well color (AWC) values of common carbon source utilization between FocTR4 and each individual fungal taxa tested at the 96 hours time point. d Percentage of common carbon source utilization between FocTR4 and each individual fungal taxa tested at the 96 hours time point. Different lowercase letters indicate statistically significant differences (P < 0.05) according to Tukey's HSD test. Fig. S9 Correlation analyses between the colony growth (in diameter) of each specific fungal taxa and the nutrient contents (mg/kg) in the residue extracts. a Pathogen FocTR4, b A. fumigatus, and c F. solani. TN: total nitrogen, TC: total carbon, TP: total phosphorus, TK: total kalium. Correlation analyses were based on Spearman and the P-values were corrected for False Discovery Rate (FDR). Fig. S10 Visualization of the colony growth of the pathogen FocTR4 on media supplemented with 2% (a) and 2%, 5% and 10% (b) of plant reside extracts (BSE, BRE, PSE, PRE). c Nutrient contents (mg/kg) in each crop reside extract. BSE: above-ground banana residue extract, BRE: below-ground banana residue extract, PSE: above-ground pineapple residue extract, PRE: below-ground pineapple residue extract, CKE: control using sterile deionized water. TN: total nitrogen, TC: total carbon, TP: total phosphorus, TK: total kalium. Different lowercase letters indicate statistically significant differences (P < 0.05) according to Tukey's HSD test. Fig. S11 Absolute abundance of FocTR4 target gene copies in soil across different inoculation treatments. a first season and b second season (two independent experiments). CKW-F: control inoculated with sterile water, EC-F: control inoculated with Escherichia coli, A.F-F: inoculation with a combination of three A. fumigatus isolates (A.F-7, A.F-12, and A.F-15), F.S-F: inoculation with a combination of three F. solani isolates (F.S-11, F.S-17, and F.S-18). Different lowercase letters indicate statistically significant differences (P < 0.05) according to Tukey's HSD test.