Dysbiosis in Maize Leaf Endosphere Microbiome is Associated with Domestication

doi:10.21203/rs.3.rs-4850295/v1

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Dysbiosis in Maize Leaf Endosphere Microbiome is Associated with Domestication

https://doi.org/10.21203/rs.3.rs-4850295/v1

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Background

The effect of domestication and breeding on maize leaf endosphere microbiota is scarcely understood, a knowledge gap is vital to be filled given their roles in plant health. We examined the leaf endosphere microbial communities associated with three plant-groups; teosinte, landraces and elite inbred maize, with the latter including both Mexican and US lines. Particularly, we used 16S-V4 region amplicon sequencing of the leaf endosphere microbiomes to infer how the microbial community of elite inbred maize may have been shaped by the crop’s evolution, and whether they were affected by: (i) the transition from a perennial life history to an annual life history in the wild; (ii) transformation of annual life into landrace maize via domestication; (iii) the northward spread of landrace maize from Mexico to the US; and (iii) breeding of landrace maizes to produce elite inbreds. Additionally, we investigated biomarker taxa, and likely functional profiles using LEfSe analysis, network analysis, and FAPROTAX.

Results

The leaf endosphere microbial community differed among the plant-groups and genotypes, and was markedly affected by domestication, as indicated by a decline in bacterial diversity and changes in microbial community structure between wild (teosinte) and domesticated (maize) Zea. While the microbial community structure was highly stringent and regulated in the teosintes, post-domestication maize landraces and elite inbreds showed high variability, suggesting microbial dysbiosis in the leaf endosphere associated with domestication, and consistent with predictions of the Anna Karenina principle. As such, this finding marks the first evidence of dysbiosis associated with plant domestication. Co-occurrence network analyses revealed the complexity of the network structure increased with domestication. Furthermore, FAPROTAX predictions suggested that the teosintes possessed higher cellulolytic, chitinolytic, and nitrate respiration functions, while the maize landraces and elite inbreds showed higher fermentation and nitrate reduction functions.

Conclusions

Our results showed the leaf endosphere microbial community structures are consistent with community alterations associated with dysbiosis. Altogether, our findings enhanced our understanding of the effects of anthropogenic processes such as crop domestication, spread, and breeding on the leaf endosphere of elite maize cultivars, and may guide the development of evolutionarily- and ecologically sustainable biofertilizers and biocontrol agents.

Plant domestication

Maize

Teosinte

Zea mays mays

Zea mays parviglumis

Zea diploperennis

microbiome

Leaf endosphere

Co-occurrence networks

The plant microbiome consists of numerous taxa of microorganisms, including bacteria, fungi, archaea, protists, and viruses, which play essential roles in many plant functions, such as growth, nutrient uptake, plant resistance against pathogens and insects, and plant tolerance of abiotic stresses [1], [2], [3], [4]. The microbial component of plant holobiomes coevolved with their host plants and occur in specialized niches, such as the rhizosphere (space nearest plant roots that is inhabited by microorganisms), phyllosphere (surface of aboveground plant tissues that is inhabited by microorganisms), and endosphere (internal above- and belowground plant tissues) [2]. The structure and composition of microbiome communities colonizing plant niches can be influenced by numerous variables (e.g., plant genotype, soil type, biotic and abiotic environmental variables, plant development stage, and geographical location), and plant survival and reproduction are mediated by microbiome communities [5], [6], [7], [8], [9]. Moreover, host plants selectively recruit particular beneficial microorganisms in their niche compartments and, in return, the recruited microbial assemblages enhance the survival and reproduction of their hosts [10], [11]. For example, rhizosphere microbiota are known to play essential roles in soil nutrient acquisition and enhance plant defense against biotic stressors [12], [13], [10]. Similarly, phyllosphere and leaf endosphere microbiota play crucial roles in defense against plant pathogens and other important plant processes [14], [15], [16], [17], [4].

The processes of plant domestication, geographic spread, and improvement resulted in significant reductions in the genetic diversity of crop species and shaped their microbial assemblages [18], [19], [20], [21], [22], [23]. Maize (Zea mays mays) is one of the most widely cultivated cereal crops globally and the Americas is the crop’s top-producing region [24]. Maize is the product of Balsas teosinte (Zea mays parviglumis) domestication, which began ca. 9,200 years ago in the Pacific lowlands of southern Mexico [25]. Several post-domestication processes, including farmer (artificial) and natural selection, geographic spread, and modern breeding led to dramatic morphological- and physiological trait changes in maize [26], [27]. Artificial selection by farmers after domestication produced a wide variety of landrace cultivars [28], [29]. Dispersal to North America and the present-day USA began as early as 2,100 years ago, and was followed by in-situ development of landrace cultivars, followed by synthetic cultivars derived from simple crossed of landraces in the 1800s, and modern elite inbred and hybrid cultivars beginning ca. 100 years ago [30], [31], [32], [33], [34]

Microbial taxa relevant to plant survival and reproduction may be absent or underrepresented in the microbial assemblages of modern crop cultivars, plausibly because such cultivars have been bred to thrive under conditions in which crop nutrition and defense against insects and pathogens depend on use of synthetic fertilizers and pesticides [19], [35], [36]. Hence it is important to evaluate whether and how the microbial community structure in modern crops differs from the structures in their domesticated and wild ancestors, which typically thrive in the absence of synthetic fertilizers and pesticides. Particularly, it is relevant to evaluate whether crop microbial community structure was affected by domestication and post-domestication processes, e.g., farmer selection, geographic spread, and modern breeding, or whether signs of diminished microbial recruitment are evident. Additionally, it is important to evaluate whether microbial dysfunction, i.e., dysbiosis, is associated with domestication and post-domestication processes. Subjected to abiotic or biotic stresses, plant (and animal) microbiomes may fall under dysbiosis, which is evident as a loss of biodiversity and imbalances in their microbial communities [37], [38], [39]. The Anna Karenina Principle (AKP), viz. “all healthy microbiota are alike; each disease-associated microbiota is sick in its own way” [40], is used in microbiome studies to describe whether plants host a dysfunctional microbiota [37], [41]. While dysbiosis has been demonstrated well in human and animal microbiomes [37], [41], little is known in plant-associated microbiomes. Dysbiosis is linked to poor health in hosts [42] and the current knowledge of plant microbiome dysbiosis, although limited, comes from the study of plant diseases [43], [44], [45], [39]. For instance, a study conducted on the rhizosphere of dead, but standing, Korean fir trees revealed a state of dysbiosis characterized by a noticeable decrease in the richness and diversity of the microbiota within the rhizosphere when compared to the microbiota of healthy trees [39]. Additionally, a defect in pattern-triggered immunity and the absence of a specific metabolite, previously linked to phyllosphere, are associated with dysbiosis in plants [46], [47]. Understanding the underlying conditions that lead to microbial dysfunction, and fitness costs in plant hosts, is important because it may inform the study of beneficial microbiomes and the development of microbial inoculants for sustainable crop production.

The student presented here is the first, to the best of our knowledge, to assess whether and how domestication, geographic spread, and modern breeding affected the bacterial community of the maize leaf endosphere. Some effects of domestication and breeding on the microbial assembly of the maize rhizosphere were reported in prior studies [48], [22]. Here, we report on the assemblages of endophytic bacterial microbiota of leaves in a suite of teosintes (Zea spp. other than maize) and maize genotypes spanning the evolution of maize: perennial teosinte (Zea diploperennis), Balsas teosinte, landrace maize cultivars, and elite inbred maize cultivars. Using this suite we inferred on effects of the perennial to annual life history transition in teosintes (perennial vs Balsas teosinte), domestication (Balsas teosinte vs. Mexican landrace maize), geographic spread (Mexican vs USA landrace maize), and modern breeding (Mexican and US landrace vs elite inbred maize). We addressed whether the structural and compositional aspects of the leaf endophytic bacterial community were mediated by life history evolution, domestication, geographic spread, and modern breeding, and whether dysbiosis is associated with domestication or other processes. Numerous studies have documented marked losses of genetic and trait diversity associated with maize domestication [49], [50], [33], [51], [52], [53], and it is plausible that such losses parallel changes in the leaf endophytic bacterial community. We hypothesized that transition from perennial to annual life history in teosintes, and maize domestication, geographic spread, and breeding would each be associated with: i) reduction in the diversity and richness of the microbial community recruited within the leaf endosphere; and ii) reductions in stringency and increases in variability in processes mediating recruitment. We also investigated whether these changes were associated with signatures of dysbiosis in terms of alpha- and beta- diversities.

Plant materials and growth

A suite of 17 teosinte and maize accessions corresponding to six genotypes contained within three plant groups were selected to represent the evolution of maize from perennial teosinte to elite inbred maize. Specifically, this suite included the following three plant groups, each with two genotypes: (i) teosinte, including perennial teosinte (1 accession) and Balsas teosinte (6 accessions); (ii) Mexican maize, including Mexican landrace maize (1 accession) and Mexican elite inbred maize (2 accessions); and, (iii) US maize, including US landrace maize (3 accessions) and US elite inbred maize (4 accessions) (Table S1). Using these plantgroups and genotypes we addressed whether the leaf endosphere bacterial community was affected by: (i) the transition from a perennial to an annual life history (comparison: perennial vs Balsas teosinte); (ii) domestication (Balsas teosinte vs Mexican landrace); (iii) northward spread (Mexican landrace vs US landrace); (iv) breeding in Mexico (Mexican landrace vs Mexican elite inbred); and (v) breeding in USA (US landrace vs US elite inbred). Domestication effects were inferred using the maize landrace Tuxpeño because its distribution overlaps that of Balsas teosinte and with the area where maize was domesticated [25], [54].

Seeds were surface sterilized by soaking in 1% Tween20 followed by 5% bleach solution, both steps for one minute each. This was followed by alcohol wash with 70% ethanol twice in less than one minute and a final rinse with sterile reverse osmosis water (RO water) for one minute. The surface-sterilized seeds were transferred to sterile paper prewet with water in sterile Petri dishes for three days in a dark space for pre-germination. One germinated seed was transplanted per cone-tainer pot (4 × 25 cm, diam × length) that contained equal mixture (v/v) of play sand (Quikrete, Atlanta, Georgia, USA) and SunGro Sunshine LC1 soil mix (SunGro, Agawam, Massachusetts, USA). Before use, the soil-sand mixture was autoclaved thrice at 121°C for 1 hour at 24hrs intervals to reduce the initial microbial load. Five replicates per accession (except CML277 with three replicates because of poor germination) were grown for 4 weeks in a growth room under artificial LED lights and 1-week greenhouse condition with natural light.

Collection of leaf endosphere samples

Above-ground biomass was harvested from the 5-week-old seedlings. The entire above-ground mass was mostly made of leaf matter or curled up leaf with little or no real stem. The leaf samples were surface sterilized by soaking in a 5% bleach solution for one minute, a single rinse of 70% ethanol, and rinsed with sterile RO water. Sterilized leaf samples were dabbed on sterile paper to remove excess moisture and sliced into small pieces. The sample tissues were flash-frozen and stored at − 80°C until further processing.

DNA extraction

DNA was extracted from the leaf tissues using the ZymoBIOMICS™ DNA Miniprep Kit (Zymo Research, Irvine, CA, USA) and the protocol was modified (See detailed Method S1). DNA concentration was quantified using SpectraMax QuickDrop Micro-Volume Spectrophotometer, and the extracted DNA was verified by 1% agarose gel electrophoresis. According to the results of QuickDrop, the samples, which have less than 0.800 A260/A230 ratios, were tested whether they can be amplified by PCR (and hence can be used to produce metabarcoding libraries) with universal primers 27F (5′-AGAGTTTGATCATGGCTCAG-3′) as forward and 1492R (5′-GGTTACCTTGTTACGACTT-3′) as reverse primer [55]. 10 µl PCR reaction was containing 5 µl of mix KAPA2G Fast HotStart ReadyMix PCR Kit (KAPA Biosystems, Wodurn, MA), 3 µl Molecular Biology Grade Water, CorningTM, 0.5 µl of each primer, and 1 µl of 10 − 2 DNA from the leaf sample. PCR reaction mixture was amplified as follows: 95°C for 3 mins, then 95°C for 15 sec, 48°C for 15 sec, and followed by 72°C for 30 sec for 39 cycles. Subsequently, a final extension step was performed at 72°C for 1 min, and the reaction was held at 10°C continually. PCR products were run on 1% agarose gel and confirmed for the presence of bacterial 16S rRNA genes. DNA concentration was quantified using Qubit dsDNA HS Assay kit by Qubit 2.0 fluorometer (Life Technologies, Carlsbad, USA).

Metabarcoding and Illumina Mi Seq sequencing

Metabarcoding library preparation and sequencing was carried out by TxGen - Genomics and Bioinformatics Services, Texas A&M University, College Station (https://www.txgen.tamu.edu). Briefly, the V4 region of the 16S rRNA gene was amplified using NEXTflex-16S V4 Amplicon-Seq Library Prep Kit 2.0–12 and primers (BIOO Scientific, Austin, TX, USA). The libraries were sequenced using the Illumina MiSeq MCS 2.5.0.5 and RTA 1.18.54 software with default parameter settings. Sequencer.bcl basecall files were formatted into fastq files using bcl2fastq 2.20 script configureBclToFastq.pl. The quality of Illumina Mi Seq sequencing reads was assessed with FastQC [56].

Raw amplicon data were performed using MOTHUR software (v.1.48.0, https://mothur.org/wiki/miseq_sop/, accessed online December 2022) following the default settings [57]. The recommended SOP was followed except for the maximum length adjusted to 320 to accommodate our assembled read lengths. Sequences were clustered into operational taxonomic units (OTUs). The consensus taxonomy of the OTUs was generated using the “classify.otu” command in mothur with reference data using the SILVA database (release 138.1, https://mothur.org/wiki/silva_reference_files/) [58]. Before conducting statistical analysis, singleton OTUs were removed.

Statistical analyses

OTU data and taxonomic information on OTUs were analyzed for all statistical calculations and data visualization in R [59] and JMP Pro 17 statistical software [60]. Bacterial alpha diversity, including the Chao1, Shannon, Simpson, Observed and Fisher indices, was estimated using JMP software. The results of alpha diversity were visualized and statistical analyesis were conducted using JMP software [60]. We performed alpha diversity analysis of bacterial communities in the leaf endosphere of maize using a nested analysis of variance (ANOVA) model that included plant group (teosinte, Mexican maize, US maize) and genotype (perennial teosinte, Balsas teosinte, Mexican landrace, Mexican elite inbred, US landrace, US elite inbred) nested within plant group. As warranted, after ANOVA plant group means were separated using Tukey’s tests; genotype means were compared using five a priori contrasts: perennial vs Balsas teosinte; Balsas teosinte vs Mexican landrace; Mexican landrace vs US landrace; Mexican landrace vs Mexican elite inbred; and, US landrace vs US elite inbred. The critical P value for each a priori contrast was set to 0.010 to maintain the experiment-wise error rate at 0.05 [61]. To measure bacterial beta diversity, we used Principal Coordinate Analysis (PCoA) based on Bray–Curtis and weighted UniFrac metrics [62], [63]. A nested permutational analysis of variance (PERMANOVA) was performed to illustrate the differences in community composition among plant group (teosinte, Mexican maize, US maize) and genotype (perennial teosinte, Balsas teosinte, Mexican landrace, Mexican elite inbred, US landrace, US elite inbred) nested within plant group. Pairwise comparisons of genotype and plant group were calculated using Multivariate Analysis of Variance (MANOVA) based on adjusted false discovery rate (FDR) P-values. Further, Nonmetric multidimensional scaling (NMDS) analysis was conducted to test for differences in beta diversity between plant samples [64]. PCoA and NMDS analyses were performed using "phyloseq", "microbial ", “microeco”, “file2meco”, and “magrittr” R packages [65], [66], [67], [64], [68], [69]. NMDS plots were visualized in R, while the PCoA plot was visualized in JMP Pro 17 statistical software [60] using output file form microeco package. Weighted UniFrac metric was calculated in Mothur using “unifrac.weighted” and visualized as PCoA plot in R. The output files from “pcoa” command line in Mothur were utilized in R generating the plot. R packages including “rgl”, “vegan”,”tidyverse”, “ggtext”, “ggplot2”, and “stats” were employed for visualization of Weighted UniFrac plot [70], [71], [72], [73], [74], [59]. Cluster dendrogram based on Bray– Curtis similarity was calculated from OTUs with the "vegdist" function in R [74].

Linear discriminant analysis effect sizes (LEfSe) were used to determine differentially abundant features between plant types [75]. LefSe was calculated by setting a p-value of less than 0.05 and the logarithmic LDA (linear discriminant analysis) effect size score threshold was set to 4.0. The LEfSe analysis was conducted using "phyloseq", “microeco”, “file2meco”, and “ggplot2” R packages [65], [70], [67], [69]. The relative abundances of leaf endosphere microbiota at the phylum and genus levels were generated in R. A Venn diagram, which shows unique or shared OTUs between genotypes, and were calculated in R using“ggVennDiagram” package and visualized using the ggVennDiagram Shiny app (https://bio-spring.shinyapps.io/ggVennDiagram) [76], [77]. Shared OTUs were defined as core microbiome [78].

Network analysis was performed to define the correlations of leaf microbial community composition among the genotypes. The correlations were constructed based on robust Spearman correlation (r > 0.7, p < 0.01) and visualized in GEPHI (0.10.1) [79]. The network complexity (edge [or linkage] density among taxa) of leaf endopshere networks was defined according to previous studies [80], [81]. In addition, the topology characteristics of network analysis, such as the number of nodes and degree, were calculated. Finally, the bacterial functional profiles of the six genotypes were determined through Functional Annotation of Prokaryotic Taxa (FAPROTAX) [82].

We sequenced the bacterial 16S rRNA V4 region from 83 leaf samples from 17 plant accessions corresponding to six genotypes spanning the evolution of maize from perennial teosinte to elite inbred maize. From these samples we obtained 3,921,857 bacterial sequences, which yielded 2,714,777 total sequences and 189,434 unique sequences after running “screen.seqs”, “unique.seqs” and “align.seqs” command lines. We reran the "screen.seqs" command, per the Mothur pipeline, to ensure that all the sequences aligned with the same region and obtained 2,545,257 total sequences and 156,886 unique sequences. We reran the "unique.seqs" step once more after quality filtering to eliminate duplicates, which yielded the same total number of sequences as the previous step but 151,799 unique sequences. After these steps, we ran chimera clustering and removal steps which yielded 2,490,998 total sequences and 73,348 unique sequences. Finally, we removed non-target taxonomic lineages using the “remove.lineage” command and obtained 99,623 total and 24,666 unique bacterial sequences, and after removing singleton sequences obtained 430 bacterial OTUs for further analysis.

The OTUs fell into a total of 172 genera from 16 phyla, according to taxa similarities with sequences in the SILVA database. The most dominant taxa among the 40 most abundant (per relative abundance) bacterial genera are either lower in abundance or completely missing in the maize landraces and elite inbreds compared to the teosintes (Fig. 1). Marked decreases in the relative abundances of the genera Devosia, Caulobacter, Chitinophaga, and Dyadobacter, among others, are evident from the teosintes to the maizes. A few genera seemed more abundant in maize compared to teosinte, such as Panteo, Staphylococcus, Acinetobacter, Corynebacterium, and Ralstonia (Fig. 1). At the phylum level, Proteobacteria (67.5%), Bacteroidetes (14.2%), Actinobacteria (9.0%), Firmicutes (3.0%) and TM7 (1.9%) were the most dominant phyla in maize leaf endosphere bacterial communities. Notably, the relative abundance of Actinobacteria was consistently high in the teosintes and varied between low and high in maize, suggesting a marked effect of domestication (Fig. S1).

The shared and unique OTUs out of the 430 OTUs for each of the genotypes were assessed and visualized as a Venn diagram (Fig. 2). There were 36 OTUs shared by all plant genotypes, making them the central core microbial community shared by all the Zea genotypes studied here. Overall, the core microbial community of the six genotypes included 36 OTUs in 31 unique genera. We also noted that 78 OTUs were shared by all except the perennial teosinte and Mexican landrace genotypes. The Balsas teosinte, US landrace, and Mexican and US elite inbred genotypes had 18, 1, 9, and 14 unique bacterial taxa, respectively, while the perennial teosinte and Mexican landrace genotypes had no unique bacterial taxa. The teosintes, perennial and Balsas, shared 84 OTUs that were unique to them. The absence of these OTUs in the maize genotypes suggests an effect of maize domestication and signifies major losses of leaf microbiota associated with agriculture.

The loss of diversity associated with domestication was particularly evident in the diversity indices associated with the plantgroups and genotypes (Fig. 3). Comparisons among Shannon (Fig. 3A) and Chao1 (Fig. 3B) indices of leaf endopshere microbiota among the plant groups revealed a trend of declining diversity and richness from teosinte to Mexican maize (landrace and elite inbred) and US maize (landrace and elite inbred), as well as a consistent decline between the Balsas teosinte and Mexican landrace genotypes, indicating an effect of domestication (P < 0.0001, Fig. 3). These findings were further supported by the observed OTUs, and Simpson and Fisher indices (Fig. S2). Breeding (i.e., landrace vs elite inbred maize comparison) did not have a consistent effect on OTU diversity as its effect was significant only per Shannon index and for Mexican maize (p = 0.005, Fig. 3A).

We used Principal Coordinates Analysis (PCoA) based on Bray-Curtis dissimilarity matrices of bacterial communities to analyze and visualize beta diversity dissimilarities among the six genotypes (Fig. 4); corresponding weighted Unifrac metric and NMDS plots are shown in Fig. S3. PCoA revealed marked divergence in the composition of leaf endosphere microbiota between the teosinte and maize genotypes along the first axis (PCo 1, 28%), while little to no divergence was evident among genotypes along the second axis (PCo 2, 12%). The divergent pattern separating teosinte and maize along the first axis suggested an effect of domestication on beta diversity of leaf endopshere bacterial communities. Nested permutational analysis of variance (PERMANOVA) revealed significant effects of genotype (R2 = 0.33653, P < 0.001), plant group (R2 = 0.28636, P < 0.001) and nested within plant group (R2 = 0.1.3734, P < 0.005) (Table 1A). Comparisons among plant groups of leaf endopshere microbiota indicated significant effects of effect of domestication in beta diversity (Teosinte vs Mexican maize, P = 0.001) while comparisons among genotypes revealed significant effects of domestication (Balsas teosinte vs. Mexican landrace, P = 0.005) and breeding in Mexico (Mexican landrace vs Mexican inbred, P = 0.027) (Table 1B). In addition, hierarchical clustering analysis based on the Bray-Curtis distance of bacterial OTUs from leaf samples showed clear separation between teosinte and maize samples (Fig. S4). Importantly, the within-cluster spread (= average distance to centroid) was significantly greater in maize, both Mexican and US, compared to teosinte (P < 0.0001) (Fig. S5A). Interestingly, within-cluster spread increased significantly with northward spread of maize (Mexican vs US landrace, P = 0.001) and breeding in USA (US landrace vs US inbred, P = 0.003), but was unaffected by the perennial to annual life history transition in the teosintes (P = 0.597), domestication (P = 0.292), and breeding in Mexico (P = 0.027) (Fig. S5B). The decreases in OTU diversity and parallel increases in variation (within-cluster spread) with domestication are consistent with predictions of the Anna Karenina Principle, which predicts that under stress, microbial communities are increasingly shaped by stochastic processes and suffer overall decreases in diversity and increases in the differences between communities.

The LDA effect sizes (LEfSe) analysis, with LDA score cutoff at 4 and alpha value of 0.05, was conducted to identify biomarker taxa at the order level. Single orders exceeded the cutoff in the US landrace and inbred line genotypes, two orders in the Mexican landrace genotype, 10 in the Balsas teosinte genotype, six in the perennial teosinte genotype, and none in the Mexican inbred genotype (Fig. 5). Comparisons of each genotype as a paired group were further analyzed to identify significantly enriched taxa based on the Kruskal-Wallis rank-sum test (with critical P-value P = 0.05). In these comparisons, the top 20 biomarker taxa with the highest LDA scores are shown in Fig. S6. LEfSe analysis showed most of the differentially abundant taxa were enriched in the teosinte genotypes, both perennial and Balsas. Perennial teosinte exhibited only enriched taxa compared to US inbred lines. Mexican inbred lines in comparison to US inbred lines also solely displayed enriched taxa. Similarly, when compared to US landrace lines, Mexican inbred lines showed differential abundance in two taxa. No differentially abundant taxa were found in the analyses comparing perennial teosinte with Balsas teosinte, and Mexican landrace with Mexican inbred maize (Fig. S6).

A co-occurrence network analysis was performed to examine the relationship of phylum abundances in the leaf endosphere microbiota (Figs. 6 and 7). Exclusive of negative edges and average path length, our results revealed a pattern in which network metrics decreased between perennial and Balsas teosinte, and increased between Balsas teosinte and maize. The highest number of total edges, 761, was found in perennial teosinte, while the lowest was observed in Balsas teosinte, with 122. The perennial teosinte showed the highest network complexity (i.e., edge (or linkage) density among taxa) with the number of total edges, and was followed by US landrace (674), Mexican inbred (656), US inbred (631), Mexican landrace (265), and Balsas teosinte (122) (Fig. 7). Furthermore, the Balsas teosinte and all maize genotypes showed a single or no negative edges, and perennial teosinte 51 negative edges. The dominant taxonomic composition of the network analysis was similar across plant genotypes, with more nodes belonging to Proteobacteria, Bacteroidetes, and Actinobacteria (Fig. 6).

Lastly, FAPROTAX functional prediction analysis showed that the perennial and Balsas teosinte genotypes had higher nitrate and nitrogen respiration interactions than the maize genotypes (Fig. 8). In contrast, the Mexican inbred- and US landrace maize genotypes, followed by Mexican landrace and US inbred genotypes had higher fermentation and nitrate reduction interactions compared to the teosinte genotypes (Fig. 8).

In this study, we examined the leaf endosphere microbiota of a suite of six teosinte and maize (Zea spp.) genotypes spanning the evolution of maize from teosintes (perennial and Balsas teosinte) to maize landraces (Fr. Mexico and United States) and maize elite inbreds (Fr. Mexico and United States). Through comparisons among those six genotypes we inferred on effects of transitioning from perennial to annual life history in the teosintes (perennial vs Balsas teosinte), domestication (Balsas teosinte vs Mexican landrace maize), northward spread (Mexican vs US landrace maize), and breeding (Mexican and US landrace vs Mexican and US inbred maize). In line with Anna Karenina principle predictions [37], [46], [40] we expected that teosinte’s transition from perennial to annual life history, and maize domestication, northward spread, and breeding would be associated with decreasing leaf endosphere bacterial community diversity and richness on one hand, and increasing variability in bacterial communities on the other hand. Importantly, we found that domestication in particular, evident both in comparisons between the Balsas teosinte and Mexican landrace genotypes, as well as between teosintes and maizes broadly, was associated with decreasing leaf endosphere bacterial diversity and increasing variability in bacterial communities. This suggested that dysbiosis is associated with maize domestication. Below, we discuss our results showing that maize domestication significantly affected α-diversity and β-diversity of the leaf endosphere microbial community, and other differences in the structure and assemblage of the leaf endosphere microbial community associated with maize domestication, northward spread, and breeding.

Maize domestication significantly affected α-diversity and β-diversityof leaf endosphere microbial community

We found that the Shannon and Chao1 diversity values in the Balsas teosinte genotype were higher than in the Mexican landrace genotype, and that generally they were higher in the teosintes compared to the maizes. We propose that the reductions are due to an increasing dominance of stochastic over deterministic processes mediating the leaf microbiome’s assemblage. The diversity decreases both in Mexican landrace maize and maizes combined relative to their predecessors indicate that the decreases are associated with domestication, and in parallel with a transition from plant survival and reproduction in a highly variable natural environment to a typically richer and more predictable agricultural environment [83], [84]. In contrast, there were no signficant changes in diversity associated with the transition from perennial to annual life history, northward maize spread, and breeding. To our knowledge, ours is the first study reporting a significant decay in leaf endosphere bacterial diversity and richness associated with maize domestication. Importantly, our study reveals significantly higher bacterial diversity and richness in the leaf endosphere of Balsas teosintes compared to the maize inbreds that serve as parents of commercial hybrid varieties, while highlighting a decline in bacterial diversity with likely implications for maize productivity in environments under stress from climate change.

Our PCoA analysis revealed a clear divergence between teosinte and maize in variation, in addition to diversity, associated with their leaf bacterial communities. In addition, our study highlighted distinct patterns in the clustering of teosintes, including perennial and Balsas teosinte, compared to the more variable distributions observed in maize landraces and elite inbreds. Notably, the beta diversity in leaf bacterial communities exhibited variations among plant groups, with teosinte displaying higher diversity in the leaf endosphere than the maize groups. Consistently, a previous study on the microbiome of wild and domesticated wheat species showed that the bacterial communities in the leaves of wild wheat species were more phylogenetically clustered compared to the bacterial communities in domesticated wheat [20]. In terms of the maize leaf endosphere microbiota study, previous studies have demonstrated no significant differences in the beta diversity of leaf-associated bacterial assemblages among modern maize cultivars [85], [86]. This is consistent with studies showing that host plants play important roles in shaping their endophytic bacteria communities [87], [86], [11], [88]. Thus, variation in the diversity between teosinte and maize plant groups in this study can be attributed to the selectiveness of the host plant. In addition, such selection may be correlated with host plant functional traits and ecological strategies [89], [90]. Any underlying mechanisms and consequences of diminished selection effect remain to be explored.

Collectively, we observed increased beta-diversity and decreased alpha-diversity in leaf-associated bacterial communities in the maize genotypes. This is potentially due to decreased selection and increased relevance of stochastic processes. Patterns similar to these are also noted in association with stress in plants and other hosts, including humans and animals [91], [92], [37], [93], [38]. For instance, mutations of immunity-related genes in Arabidopsis, and disease in Korean fir and chili pepper were associated with reductions in the diversity of their microbial communities [39], [46], [94]. Seemingly, hosts lose beneficial microbiota due to stochastic processes associated with stress. Additionally, the negative effects of stresses are compounded by dysbiosis, following Anna Karenina Principle predictions (AKP) [95], [96], [97]. AKP suggests a rise of stochastic over deterministic processes mediating microbial community composition within the holobiont [37], [98], [40]. The decline in diversity and increase in variability in the bacterial community of the maize leaf endosphere observed in this study are consistent with AKP and we suggest that the maize leaf endosphere is a dysbiotic microbiome. The decline in the diversity of microbial species in leaf endosphere of the maizes compared with the teosintes may impact the crop’s ability to cope with biotic and abiotic stresses [23], [99], particularly as environments change rapidly under climate change. Teosintes defend against herbivorous insects and pathogens by a variety of means [100], [101], [102], [103], [104], [105], [106], [107], and differences in defense strengths and strategies between teosinte and maize seem to be associated with their divergent environments, i.e. typically poorer, wild environments for the former and richer, agricultural environments for the latter [84], [51], [53]. Examining the diversity of leaf endosphere microbiota in wild crop relatives may provide insights to how traits that allow plants to survive in the wild can, alongside other enhancements, be utilized to improve the breeding process.

Differences in the structure and assemblage of leaf endosphere microbial community associated with maize domestication, northward spread, and breeding

We found that Bacteroidetes and Actinobacteria (both Proteobacteria) were the most dominant taxa in the leaf endosphere bacterial communities. Similar compositions have been found in studies of different plant varieties such as the phyllosphere microbiome of sorghum [108], leaf endosphere of prairie plants [109], and leaf microbiota of Arabidopsis [110]. Furthermore, we found several taxa were depleted from the teosinte group, including perennial and Balsas teosinte, to the maize inbred lines, including Devosia and Caulobacter (Proteobacteria), and Chitinophaga and Dyadobacter (Bacteroidetes). Moreover, five genera showed higher abundance in maize plant group, Pantoea, Staphylococcus, Acinetobacter, Corynebacterium, Ralstonia. These results are consistent with those of previous research [111] which identified Pantoea spp. as the dominant taxa in hybrid maize cultivars at an early growth stage. Similarly, our study highlighted Pantoea and Ralstonia as dominant taxa in leaf samples from elite inbred lines, including lines from Mexico and US. Additionally, Staphylococcus and Corynebacterium were also among the top 20 genera in the relative abundance analysis of that research [111]. Interestingly, the depleted genera observed in elite inbred maize in our study were also not detected in the previous research.

We identified a few OTUs as known beneficial bacteria, though we did not test their functional properties. For example, we identified Methylobacterium spp., which are well known phyllosphere colonizers with documented beneficial effects, e.g., production of phytohormones, and enhancement of seed germination and plant growth [112], [113], [114], [115]. Previous studies consistently reported Methylobacteriaceae as the most abundant or as a biomarker taxon for leaf microbiota studies in maize [116], [117], [11]. In our study, we did not observe Methylobacteriaceae as an indicator taxon among genotypesin LEfSe analysis, though they were found to be more abundant in the teosinte plant group than in Mexican and US elite inbred genotypes. Moreover, we identified that classes Xanthomonadales, Actinomycetales, Burkholderiales, Rhizobiales were enriched in the teosinte group and Mexican landrace genotype as potential biomarkers with different abundances. This is in line with Xion et al. [9] who found Actinobacteria, Burkholderiaceae, and Rhizobiaceae to be abundant in the phylloplane and rhizosphere of maize during the seedling stage, even if they were not identified as biomarker taxa. Many strains within those taxa establish beneficial partnerships with their host plants, including biological nitrogen fixation, plant growth stimulation, and protection against plant pathogens [118], [119], [120], [121], [122]. Our findings demonstrated that biomarker taxa of teosinte were significantly more enriched in that plant group when compared to elite inbred lines, suggesting that wild ancestors may harbor greater diversity of beneficial taxa than crops. Such enriched bacterial taxa may confer functional advantages to their host plants, including increased tolerance of biotic and abiotic stress and greater adaptability to new environments. Further study is needed to better understand the correlations and functions of these taxa in crop wild ancestors, as well as for harnessing them to improve plant growth and health in crops.

We found that the core microbiome consists of 36 taxa shared across six genotypes. The predominant classes within this central core microbiome were Alphaproteobacteria, Gammaproteobacteria, Actinobacteria, and Betaproteobacteria. The taxonomic affiliations observed in our findings are similar to those in other crop studies on the core microbiome of phyllosphere bacterial communities, e.g., tree leaves [89], grasses [123], and Arabidopsis thaliana [110]. Moreover, Johnston-Monje and Raizada [16] detected a core microbiota of endophytes that remained conserved in Zea seeds. Core microbial communities establish enduring relationships with plant hosts and crucially influence biological processes of their host plants [124], [125], [126]. Our finding provides insight into core bacteriome taxa in the leaf endosphere as shaoed by maize domestication and breeding. We suggest that a core bacterial community potentially coexists in mutual syntrophy, which provided a reproducible and conserved suite of taxa during maize domestication and breeding. Further studies are needed to understand the functions of the core bacterial community in relation to the biological functions of the maize host plant.

We constructed co-occurrence networks to explore the effects of domestication, northward spread, and breeding on microbe-microbe interactions across the different plant groups and genotypes. Network analysis provides metrics that allow an initial exploration of the dynamics of microbial interactions. For example, positive edges indicate connections with beneficial or cooperative interaction between nodes, negative edges indicate connections with antagonistic interactions between nodes, and average weighted degree indicates the typical strength of node connections and are useful for between-network comparisons [127], [128], [129]. Across plant genotypes positive edges were ca. 60-fold more frequent than negative edges; indeed, negative edges were nearly absent, except in perennial teosinte. Interestingly, the average number of positive edges across plant groups and genotypes, including Mexican maize and US maize (556.3) was > 4-fold greater than in Balsas teosinte (122). Average weighted degree was highest in the maize genotypes and lowest in Balsas teosinte; notably, it was > 3-fold higher in maize genotypes (average = 9.3) compared to Balsas teosinte (2.8). Finally, the number of nodes was highest in perennial teosinte, lowest in Mexican landrace maize and Balsas teosinte, and intermediate in US landrace, Mexican inbred, and US inbred maize. Perennial and Balsas teosinte exhibited opposite trends from each other in topological characteristics. In addition, Balsas teosinte showed differences in topological characteristics in the leaf endosphere networks compared to the maize plant groups. Negative edges were typically observed outside of the dense part of the network, plausibly indicating competition for resources among different assemblages or the release of antimicrobial substances by certain members through the assemblage interactions [130], [128], [131]. Leaf endosphere network complexity (edge density) among bacterial taxa seemed to decrease with the transition to annual life history in teosinte, whereas it increased with domestication and showed little change with maize's northward spread and breeding. The network complexity within Mexican and US maize inbred genotypes is similar to the pattern observed in one modern cultivar line in a study by Kong et al. [85], and at the seedling stage of modern maize cultivars in a study by Xiong et al [9]. Kong et al. identified differences as well in the network complexity of the phyllosphere bacterial community among modern maize cultivars. Studies comparing the network complexity of leaf endosphere microbial communities in teosintes and maizes are unavailable, so further investigation is needed to understand the differences between Balsas teosinte and maize plant groups in network analysis.

We used FAPROTAX analyses to evaluate potential functions of the microbiota of the different plant genotypes. While not conclusive, results from these analyses are useful for indicating future research directions concerning the functional ecology of endophytic bacteria and their host plants. Ecological functions and functional abundance of microbial communities can vary with plant type, plant development, and environment, among other variables [132], [11], [69], [133], [134]. Regulation of functional groups and shifts in functional groups indicate that plant-recruited microbes reflect the current needs of the host plant [132], [134]. The results of FAPROTAX suggested that nitrate reduction, nitrate respiration, fermentation, and cellulolytic activities were most prominent in the two teosinte genotypes, while nitrate reduction and fermentation were prominent among the four maize genotypes. The latter results align with findings from a previous study on maize hybrids [11]. Our findings provide predicted potential functions of the active microbial community in leaf endosphere. However, experimental research is essential to validate and confirm the predicted functional roles of these bacteria in enhancing plant fitness.

The findings presented here suggest that maize domestication played a pivotal role in shaping the assembly of maize leaf endophytes, with the plant genotype being a primary driver of this assembly. This was particularly evident in our comparisons between Balsas teosinte and Mexican landrace maize. Indeed, those comparisons showed significant declines of microbial diversity in the leaf endosphere associated with maize domestication. Strikingly, we found a signature of microbial dysbiosis is also associated with maize domestication. Particularly, a shift in microbial community structure from highly stringent and regulated in Balsas teosinte to loose and unregulated in maize, especially in Mexican landrace maize, the immediate descendent of Balsas teosinte. Taken together, these results are in line with and add support to the Anna Karenina principle in microbial dysbiosis and represent the first evidence of dysbiosis caused by plant domestication.

Also, our study demonstrated that teosintes harbor a greater number of biomarker taxa than maize landraces and elite inbred cultivars. Co-occurrence network analysis demonstrated predominantly positive relationships within each plant group. though we were unable to clearly discern patterns among maize genotypes. Interestingly, the co-occurrence network structure suggested a decrease in complexity with the perennial to annual life history transition in teosinte, an increase with domestication, and little change with maize’s northward spread and breeding. Taken together, our results suggested that the leaf endophytic bacterial assembly in maize was markedly influenced by its domestication.

Altogether, our study contributes to the characterization of the leaf-associated endophytic microbiota composition of maize wild ancestors, landraces, and elite inbred lines. The insights from our research set the stage for advancements in biological control, biofertilizer technologies, and maize breeding strategies, particularly through the examination of microbiomes of wild relative genotypes. Identifying beneficial bacterial microbes and understanding their functions is essential for developing microbial technologies for enhancing the sustainability and resilience of agricultural practices.

Acknowledgements

We would like to thank Prof. Dr. Michael V. Kolomiets for providing Mexican and USA elite inbred line seeds, and Arial Black, Kathy Gonzalez, Elek Nagy, and Sabin Khanal for their generous help in plant harvesting. We thank Vanessa E. Thomas for helping with R codes and Sabin Khanal for the microbiome analyses pipeline.

Author Contributions

I.T. carried out experiments and bioinformatical analyses of microbiota communities, analyzed the data, and wrote the first draft of the manuscript. J.S.B. conceptualized the study, designed the study plant panel, collected teosinte seeds, analyzed the data, and reviewed and edited the manuscript. S.A-B. conceptualized and designed of research funding acquisition, review & editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially funded through USDA-Research, Education, and Economics Information System “Deciphering Microbial Functions to Improve Plant Productivity and Sustainable Agriculture”. The authors would like to thank USDA NIFA award numbers 2021-70006-35321 and 2023-67013-39155, TAMU-CONACyT grant (246391-97140), and USDA HATCH project 1021870 #TEX09714. I.T. was supported in part by the Ministry of National Education of Türkiye.

Data availability

R codes that I used in this study have been deposited in the GitHub (https://github.com/ilksentpc/DysbiosisinMaize). Raw sequence reads can be found on the NCBI Sequence Read Archive under BioProject number PRJNA1137996.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflict of Interest

The authors declare no conflict of interest.

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Table 1. Results of PERMANOVA analysis on leaf endosphere microbiota showed significant effects of genotype, plant group and nested genotype (plant group) (A). Pairwise comparisons based on adjusted false discovery rate (FDR) P-values were calculated using the Benjamini-Hochberg method, resulting from Multivariate Analysis of Variance (MANOVA) among maize genotypes based on Bray-Curtis distance. (FDR adjusted P<0.05) (B). Significant values are indicated in bold.

perMANOVA results for plant group	R²	F	Pr (>F)
Plant group	0.28636	16.6	0.001
Genotype (Plant group)	1.3734	1.94	0.005
Residual	0.66347
Total	1

perMANOVA results for genotype	R²	F	Pr (>F)
Genotype	0.33653	7.81	0.001
Residual	0.66347
Total	1

Plant group Comparison	R²	F	P
Teosinte vs. Mexican maize	0.33342	23	0.001
Mexican maize vs. US maize	0.03146	1.49	0.080
Teosinte vs. US maize	0.30054	29.2	0.001

Genotype Comparison	R²	F	P_adj
Perennial teosinte vs. Balsas teosinte	0.03090	1.05	0.315
Balsas teosinte vs. Mexican landrace	0.36439	18.91	0.005
Mexican landrace vs. Mexican inbred	0.14350	1.84	0.027
Mexican landrace vs. US landrace	0.07119	1.37	0.142
Us landrace vs. US inbred	0.05244	1.82	0.083

No competing interests reported.

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Dysbiosis in Maize Leaf Endosphere Microbiome is Associated with Domestication

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Background

Materials and methods

Plant materials and growth

Collection of leaf endosphere samples

DNA extraction

Metabarcoding and Illumina Mi Seq sequencing

Statistical analyses

Results

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Maize domestication significantly affected α-diversity and β-diversityof leaf endosphere microbial community

Conclusion

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Table 1

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