Metagenomics is a powerful tool for understanding organismal interactions; however, classification, profiling and detection of interactions at the strain level remain challenging. We present an automated pipeline, quantitative metagenomic alignment and taxonomic exact matching (Qmatey), that performs a fast exact matching-based alignment and integration of taxonomic binning and profiling. It interrogates large databases without using metagenome-assembled genomes, curated pan-genes or k-mer spectra that limit resolution. Qmatey minimizes misclassification and maintains strain level resolution by using only diagnostic reads as shown in the analysis of amplicon, quantitative reduced representation and shotgun sequencing datasets. Using Qmatey to analyze shotgun data from a synthetic community with 35% of the 26 strains at low abundance (0.01–0.06%), we revealed a remarkable 85–96% strain recall and 92–100% species recall while maintaining 100% precision. Benchmarking revealed that the highly ranked Kraken2 and KrakenUniq tools identified 2–4 more taxa (92–100% recall) than Qmatey but produced 315–1752 false positive taxa and high penalty on precision (1–8%). The speed, accuracy and precision of the Qmatey pipeline positions it as a valuable tool for broad-spectrum profiling and for uncovering biologically relevant interactions.

Plant–insect interactions are often influenced by host- or insect-associated metagenomic community members. The relative abundance of insects and the microbes that modulate their interactions were obtained from sweetpotato (Ipomoea batatas) leaf-associated metagenomes using quantitative reduced representation sequencing and strain/species-level profiling with the Qmatey software. Positive correlations were found between whitefly (Bemisia tabaci) and its endosymbionts (Candidatus Hamiltonella defensa, Candidatus Portiera aleyrodidarum, and Rickettsia spp.) and negative correlations with nitrogen-fixing bacteria that implicate nitric oxide in sweetpotato–whitefly interaction. Genome-wide associations using 252 975 dosage-based markers, and metagenomes as a covariate to reduce false positive rates, implicated ethylene and cell wall modification in sweetpotato–whitefly interaction. The predictive abilities (PA) for whitefly and Ocypus olens abundance were high in both populations (68%–69% and 33.3%–35.8%, respectively) and 69.9% for Frankliniella occidentalis. The metagBLUP (gBLUP) prediction model, which fits the background metagenome-based Cao dissimilarity matrix instead of the marker-based relationship matrix (G-matrix), revealed moderate PA (35.3%–49.1%) except for O. olens (3%–10.1%). A significant gain in PA after modeling the metagenome as a covariate (gGBLUP, ≤11%) confirms quantification accuracy and that the metagenome modulates phenotypic expression and might account for the missing heritability problem. Significant gains in PA were also revealed after fitting allele dosage (≤17.4%) and dominance effects (≤4.6%). Pseudo-diploidized genotype data underperformed for dominance models. Including segregation-distorted loci (SDL) increased PA by 6%–17.1%, suggesting that traits associated with fitness cost might benefit from the inclusion of SDL. Our findings confirm the holobiont theory of host–metagenome co-evolution and underscore its potential for breeding within the context of G × G × E interactions.

As an alternative to host resistance, transgenic expression of entomocidal and antimicrobial proteins from Bacillus thuringiensis ( Bt) in maize can mitigate Fusarium ear rot (FER). This study evaluated FER in Bt and conventional maize and the role of microbes in the kernel-associated metagenome using quantitative reduced representation sequencing. Our results revealed significant differences in FER severity across environments and varieties and between inoculation treatments. The lower FER scores of conventional maize Spectrum 6416 relative to other varieties highlighted resistant alleles in maize germplasm, whereas strong correlations indicated FER-induced yield loss. Mitigation of FER by transgenes was validated by about threefold enrichment of Fusarium verticillioides ( Fv) postinoculation compared with nonsignificant Fv enrichment in conventional maize. Although the major causal pathogen of FER, Fv, was the most abundant species in the metagenomes ( r = 0.41 to 0.49), a similar degree of correlation was observed between FER and several Fusarium spp. ( r = 0.2 to 0.56). The potential FER-suppressing properties of Talaromyces stipitatus and Ustilago maydis were indicated by negative correlations with FER ( r = –0.22 to –0.42), Fv, and some Fusarium spp. The more FER-resistant varieties consistently enriched for potential FER-suppressing Burkholderia cenocepacia (negatively correlated with Fv). This suggests host genetic background-dependent recruitment of beneficial microbes that suppress pathogens and that microbe–microbe interactions play a role in FER severity. Quantitative and species/strain-level metagenomic profiles hold promise for identifying robust disease-suppressing microbes and microbes that act in synergy with pathogens and for developing holobiont-aware breeding strategies that account for host–microbiome coevolution and host genotype, microbiome/metagenome, and environment interactions. [ ] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Non-genetic variation limits the identification of novel maize germplasm with genetic markers for reduced Aspergillus flavus infection and aflatoxin contamination. Aflatoxin measurements can vary substantially within fields containing the same germplasm following inoculation with A. flavus. While some variation is expected due to microenvironmental differences, components of field screening methodologies may also contribute to variability in collected data. Therefore, the objective of this study is to test the effects of three different shelling methods (whole ear (WE), ear end removal (EER), and inoculation site-surrounding (ISS)) to obtain bulk samples from maize on aflatoxin measurements. Five ears per row of three inbred lines and two hybrids were inoculated with A. flavus, then shelled using the three different methods, and aflatoxin was quantified. Overall, EER and ISS resulted in reduced coefficients of variance (CVs) in comparison to WE for both inbred and hybrid maize lines, with two exceptions. Susceptible B73 showed increased CVs with both EER and ISS compared to WE, and resistant Mp719’s EER CVs marginally increased compared to WE. While WE is the standard practice for most breeding programs due to its technical simplicity, EER and ISS may allow for finely phenotyping parental lines for further breeding applications.

Fusarium head blight (FHB) is caused by a complex of Fusarium species, including F. graminearum and F. poae . The effect of inoculation timing on disease following single or combined inoculations with these species remains unclear. This study evaluated area under the disease progress curve (AUDPC), Fusarium ‐damaged kernels (FDK), thousand‐kernel weight (TKW), and species DNA after inoculation of winter wheat cv. SS8641 with F. poae , F. graminearum alone or combined under controlled conditions. Plants were inoculated 0, +3, +6 or + 9 days relative to complete heading/early anthesis. Treatment and inoculation timing significantly affected AUDPC, FDK and TKW ( p < 0.0001); the treatment × inoculation timing interaction was significant for TKW ( p < 0.0001), but not for AUDPC ( p = 0.73) or FDK ( p = 0.40). At 0 days, AUDPC was 69.2, 889.4 and 639.6 for F. poae alone, F. graminearum alone and combined inoculation, respectively; FDK was 6.6%, 73.1% and 52.9%, respectively. TKW was lowest at 0 days in treatments containing F. graminearum (11.8–15.9 g) compared with F. poae alone (35.2 g) and the mock control (37.0 g). In FDK fractions, F. poae DNA was undetected in co‐inoculated samples, whereas F. graminearum DNA did not differ significantly between single‐species and co‐inoculation treatments ( p = 0.119). These results indicate that disease and kernel‐weight responses were primarily driven by F. graminearum and that co‐inoculation with F. poae did not significantly alter disease outcomes or F. graminearum DNA accumulation relative to F. graminearum alone under the conditions tested.