Dysbiosis in Maize Leaf Endosphere Microbiome is Associated with Domestication

Dysbiosis in Maize Leaf Endosphere Microbiome is Associated with Domestication | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dysbiosis in Maize Leaf Endosphere Microbiome is Associated with Domestication Ilksen Topcu, Julio S Bernal, Sanjay Antony-Babu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4850295/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2026 Read the published version in Frontiers in Microbiomes → Version 1 posted You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background 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. Materials and methods 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 ]. Results 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. 3 A) and Chao1 (Fig. 3 B) 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. 3 A). 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 1 A). 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 1 B). 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 ). Discussion 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. Conclusion 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. Declarations 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. References P. Vandenkoornhuyse, S. L. Baldauf, C. Leyval, J. Straczek, and J. P. W. 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Zuo, Q. Hu, and X. He, “Plant species shape the bacterial communities on the phyllosphere in a hyper-arid desert,” Microbiological Research , vol. 269, p. 127314, Apr. 2023, doi: 10.1016/j.micres.2023.127314. Table 1 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 PF) 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 R 2 F Pr (>F) Genotype 0.33653 7.81 0.001 Residual 0.66347 Total 1 B P lant group Comparison R 2 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 R 2 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 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4850295","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341150715,"identity":"22793b46-6a3d-4f3c-824a-eee23ade2179","order_by":0,"name":"Ilksen Topcu","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Ilksen","middleName":"","lastName":"Topcu","suffix":""},{"id":341150716,"identity":"1d629efc-7dd5-4bbc-8460-b3ce2f1bda4d","order_by":1,"name":"Julio S Bernal","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Julio","middleName":"S","lastName":"Bernal","suffix":""},{"id":341150717,"identity":"e9731379-c9bb-4172-9d2b-6b85f9bd7647","order_by":2,"name":"Sanjay Antony-Babu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYJACAwYGGyiTjXgtaRKkaQGCwyRokW8/e6DgZ9v5Ov5pZwwYPpQdJsJRZ/ISDHvbbktI3M4xYJxxjhgtDDkGBrxALQxALcy8bURoke9/Y2D4t+2chDxIy19itDDcyDEw5m07IGEA0sJIjBaDG28MjGXOJUtuvJ1WcLDnXDoxDssxM3xTZscvdzt544MfZdZEOAwYFwaM0Og4QJR6IGB+wPCHWLWjYBSMglEwIgEAqPY52XWXn5sAAAAASUVORK5CYII=","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":true,"prefix":"","firstName":"Sanjay","middleName":"","lastName":"Antony-Babu","suffix":""}],"badges":[],"createdAt":"2024-08-02 19:36:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4850295/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4850295/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3389/frmbi.2026.1735358","type":"published","date":"2026-02-22T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64407509,"identity":"9128db52-9dd9-4d8a-ad9e-989e318df948","added_by":"auto","created_at":"2024-09-12 18:08:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":560051,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap illustrating the relative abundance of the top 40 taxa dominant OTUs at genus-level in maize genotypes. The genotypes are ordered to span the evolution of maize: perennial teosinte, Balsas teosinte, Mexican landrace maize, Mexican inbred maize, US landrace maize, and US inbred maize, as shown at the top. The heatmap shows the most dominant taxa are either less abundant or entirely absent in maize landraces and elite inbreds compared to teosintes.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4850295/v1/06d974ead6c10328434c33d7.png"},{"id":64407507,"identity":"a1b12eb1-7c6c-4d90-afb7-ac4ed85942e5","added_by":"auto","created_at":"2024-09-12 18:08:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":320625,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram showing the number of shared and unique OTUs between maize genotypes in the leaf endosphere microbiota. The core microbial community of the genotypes consisted of 36 OTUs. 78 OTUs were shared by all genotypes except the perennial teosinte and Mexican landrace maize. The Balsas teosinte has 18 unique bacterial taxa, US landrace maize 1, and Mexican and US elite inbred groups 9, and 14 unique bacterial taxa, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4850295/v1/b5dade3e6522e584ff104088.png"},{"id":64407887,"identity":"d577caa0-e0d8-470b-ac80-2421df834988","added_by":"auto","created_at":"2024-09-12 18:16:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":563800,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots and histograms showing leaf endosphere alpha diversity indices observed in maize genotypes. In the left plot, Teosinte, Mexican maize, and US maize were compared using Tukey's test. In the right plot, nested groups within Teosinte (perennial and Balsas), Mexican maize (landrace and inbred), and US maize (landrace and inbred) were compared using a-priori contrasts. The Shannon and Chao1 indices exhibit a pattern of decreasing diversity and richness from Teosinte to Mexican maize (landrace and elite inbred) and to US maize (landrace and elite inbred). Teosintes generally showed greater diversity and richness in the leaf endopshere microbiota (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4850295/v1/82ad9f27a1b3e0973cef7b08.png"},{"id":64407513,"identity":"faa71ac0-4409-41fa-9ec3-18d531e90edc","added_by":"auto","created_at":"2024-09-12 18:08:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":226804,"visible":true,"origin":"","legend":"\u003cp\u003eLeaf endosphere beta diversity is represented as a principal coordinates analysis (PCoA) plot based on Bray-Curtis dissimilarity values among maize genotypes. Each point represents a single sample, and the shape and colors indicate individual maize genotype PCoA plot. PCoA plot demonstrate significant differences in leaf endopshere microbiota composition between teosintes and maize plant group along the first axis (PCo 1, 28.1%), while differences along the second axis (PCo2, 11.7 %) were comparatively minor. The distinct pattern observed between teosintes and maize along the fist axis suggested that domestication has influenced beta diveristy of leaf endosphere bacterial communities.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4850295/v1/6c840557bf82d0a41eeb0189.png"},{"id":64407511,"identity":"0c55da23-5080-4fe9-b48a-57bf281d933a","added_by":"auto","created_at":"2024-09-12 18:08:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":393065,"visible":true,"origin":"","legend":"\u003cp\u003eLinear discriminant analysis (LDA) effect size (LEfSe) analysis of biomarker taxa at order level among\u003cem\u003e \u003c/em\u003emaize genotypes. Horizontal bars represent enriched taxa and the effect size of each taxon, and the colors indicate the genotype. LDA score cutoff 4 with an alpha value of 0.05 was used to distinguish bacterial taxon. LEfSe analysis indicates that the majority of differentially abundant taxa were more prevalent in the teosinte genotypes, including both perennial and Balsas varieties. US landrace and inbred line genotype groups had one taxa at the order level. There were two orders in Mexican landrace. Balsas teosinte exhibited ten orders, while the perennial teosinte showed six. No differentially abundant taxa were found in Mexican inbred.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4850295/v1/48c0fcb94145b2d155f7c30e.png"},{"id":64407508,"identity":"923980dc-490a-4037-897d-0bce29e58836","added_by":"auto","created_at":"2024-09-12 18:08:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3601695,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence network of the leaf endosphere community for six genotypes (perennial teosinte, Balsas teosinte, Mexican landrace, Mexican inbred, US landrace, and US inbred). Connections are based on Spearman's rank correlation coefficients (optimized coefficient threshold and adjusted p-value \u0026lt; 0.01). Blue and red edges indicate positive and negative interactions between bacterial taxa, respectively. Operational taxonomic units (OTUs) are represented by colored nodes at the phylum level. Node degree is scaled proportionally to the number of connections. Highest number of positive edges were observed in perennial teosinte (710), followed by US landrace (674), Mexican inbred (656), and US inbred (631). Negative edges were only found in perennial teosinte (51) and Mexican landrace (1). The dominant taxonomic composition in the network analysis included more nodes from Proteobacteria, Bacteroidetes, and Actinobacteria.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4850295/v1/fda6242030a926a91659ed77.png"},{"id":64407888,"identity":"05b82cd0-3123-40a4-bc36-f86af0220eb0","added_by":"auto","created_at":"2024-09-12 18:16:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":320626,"visible":true,"origin":"","legend":"\u003cp\u003eTopological characteristics of leaf endosphere networks of maize genotypes. Number of nodes, number of positive edges, average degree, number of negative edges, average path length, average clustering coefficient, modularity and average weighted degree are prepresented as bar graph. Perennial teosinte exhibited the highest number of total nodes (205) and also the highest number of total positive (710) and negative edges (51). In contrast, the Mexican landrace displayed the lowest number of total edges (54), while Balsas teosinte had the lowest number of positive edges (122). Notably, Balsas teosinte, US landrace, Mexican inbred, and US inbred had no negative edges. Perennial teosinte also demonstrated the highest average degree (7.4) and modularity (0.83), whereas Balsas teosinte showed the lowest values for these metrics, with an average degree of 3.6 and modularity of 0.48.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4850295/v1/8649b3375d3041b53b26a816.png"},{"id":64408227,"identity":"ec74dee6-93c6-4644-a1bd-d05410806a3d","added_by":"auto","created_at":"2024-09-12 18:24:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":218397,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional Annotation of Prokaryotic Taxa (FAPROTAX) analysis of leaf endopshere of maize genotypes. FAPROTAX predictions indicated that the teosintes exhibited higher cellulolytic, chitinolytic, nitrogen respiration, and nitrate respiration functions, while maize landraces and elite inbreds displayed higher fermentation and nitrate reduction functions.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4850295/v1/b9e5a6b4439ed90fd7ffe292.png"},{"id":103256081,"identity":"21b56504-5bc4-437a-a031-aa9967da835a","added_by":"auto","created_at":"2026-02-23 16:54:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7497458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4850295/v1/8a730642-ee77-491b-8e6f-46c4fb190f83.pdf"},{"id":64407516,"identity":"dd6e68d7-ac1a-405c-a91e-f8e6397b2cd5","added_by":"auto","created_at":"2024-09-12 18:08:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10094369,"visible":true,"origin":"","legend":"","description":"","filename":"DysbiosisinMaizeLeafEndospheresupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4850295/v1/57f4caf7750fdd978f077885.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dysbiosis in Maize Leaf Endosphere Microbiome is Associated with Domestication","fulltext":[{"header":"Background","content":"\u003cp\u003eThe 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 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 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) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For example, rhizosphere microbiota are known to play essential roles in soil nutrient acquisition and enhance plant defense against biotic stressors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, phyllosphere and leaf endosphere microbiota play crucial roles in defense against plant pathogens and other important plant processes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe processes of plant domestication, geographic spread, and improvement resulted in significant reductions in the genetic diversity of crop species and shaped their microbial assemblages [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Maize (\u003cem\u003eZea mays mays\u003c/em\u003e) is one of the most widely cultivated cereal crops globally and the Americas is the crop\u0026rsquo;s top-producing region [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Maize is the product of Balsas teosinte (\u003cem\u003eZea mays parviglumis\u003c/em\u003e) domestication, which began ca. 9,200 years ago in the Pacific lowlands of southern Mexico [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Artificial selection by farmers after domestication produced a wide variety of landrace cultivars [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMicrobial 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 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The Anna Karenina Principle (AKP), viz. \u0026ldquo;all healthy microbiota are alike; each disease-associated microbiota is sick in its own way\u0026rdquo; [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], is used in microbiome studies to describe whether plants host a dysfunctional microbiota [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. While dysbiosis has been demonstrated well in human and animal microbiomes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], little is known in plant-associated microbiomes. Dysbiosis is linked to poor health in hosts [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and the current knowledge of plant microbiome dysbiosis, although limited, comes from the study of plant diseases [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Additionally, a defect in pattern-triggered immunity and the absence of a specific metabolite, previously linked to phyllosphere, are associated with dysbiosis in plants [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Here, we report on the assemblages of endophytic bacterial microbiota of leaves in a suite of teosintes (\u003cem\u003eZea\u003c/em\u003e spp. other than maize) and maize genotypes spanning the evolution of maize: perennial teosinte (\u003cem\u003eZea diploperennis\u003c/em\u003e), 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 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], 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.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and growth\u003c/h2\u003e \u003cp\u003eA 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 \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). 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 Tuxpe\u0026ntilde;o because its distribution overlaps that of Balsas teosinte and with the area where maize was domesticated [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeeds 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 \u0026times; 25 cm, diam \u0026times; 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\u0026deg;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCollection of leaf endosphere samples\u003c/h2\u003e \u003cp\u003eAbove-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 \u0026minus;\u0026thinsp;80\u0026deg;C until further processing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction\u003c/h2\u003e \u003cp\u003eDNA was extracted from the leaf tissues using the ZymoBIOMICS\u0026trade; 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\u0026prime;-AGAGTTTGATCATGGCTCAG-3\u0026prime;) as forward and 1492R (5\u0026prime;-GGTTACCTTGTTACGACTT-3\u0026prime;) as reverse primer [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. 10 \u0026micro;l PCR reaction was containing 5 \u0026micro;l of mix KAPA2G Fast HotStart ReadyMix PCR Kit (KAPA Biosystems, Wodurn, MA), 3 \u0026micro;l Molecular Biology Grade Water, CorningTM, 0.5 \u0026micro;l of each primer, and 1 \u0026micro;l of 10\u0026thinsp;\u0026minus;\u0026thinsp;2 DNA from the leaf sample. PCR reaction mixture was amplified as follows: 95\u0026deg;C for 3 mins, then 95\u0026deg;C for 15 sec, 48\u0026deg;C for 15 sec, and followed by 72\u0026deg;C for 30 sec for 39 cycles. Subsequently, a final extension step was performed at 72\u0026deg;C for 1 min, and the reaction was held at 10\u0026deg;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).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMetabarcoding and Illumina Mi Seq sequencing\u003c/h2\u003e \u003cp\u003eMetabarcoding library preparation and sequencing was carried out by TxGen - Genomics and Bioinformatics Services, Texas A\u0026amp;M University, College Station (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.txgen.tamu.edu\u003c/span\u003e\u003cspan address=\"https://www.txgen.tamu.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Briefly, the V4 region of the 16S rRNA gene was amplified using NEXTflex-16S V4 Amplicon-Seq Library Prep Kit 2.0\u0026ndash;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 [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRaw amplicon data were performed using MOTHUR software (v.1.48.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mothur.org/wiki/miseq_sop/\u003c/span\u003e\u003cspan address=\"https://mothur.org/wiki/miseq_sop/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed online December 2022) following the default settings [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. 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 \u0026ldquo;classify.otu\u0026rdquo; command in mothur with reference data using the SILVA database (release 138.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mothur.org/wiki/silva_reference_files/\u003c/span\u003e\u003cspan address=\"https://mothur.org/wiki/silva_reference_files/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Before conducting statistical analysis, singleton OTUs were removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eOTU data and taxonomic information on OTUs were analyzed for all statistical calculations and data visualization in R [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and JMP Pro 17 statistical software [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. 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\u0026rsquo;s tests; genotype means were compared using five \u003cem\u003ea priori\u003c/em\u003e 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 \u003cem\u003eP\u003c/em\u003e value for each \u003cem\u003ea priori\u003c/em\u003e contrast was set to 0.010 to maintain the experiment-wise error rate at 0.05 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. To measure bacterial beta diversity, we used Principal Coordinate Analysis (PCoA) based on Bray\u0026ndash;Curtis and weighted UniFrac metrics [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. PCoA and NMDS analyses were performed using \"phyloseq\", \"microbial \", \u0026ldquo;microeco\u0026rdquo;, \u0026ldquo;file2meco\u0026rdquo;, and \u0026ldquo;magrittr\u0026rdquo; R packages [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. NMDS plots were visualized in R, while the PCoA plot was visualized in JMP Pro 17 statistical software [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] using output file form microeco package. Weighted UniFrac metric was calculated in Mothur using \u0026ldquo;unifrac.weighted\u0026rdquo; and visualized as PCoA plot in R. The output files from \u0026ldquo;pcoa\u0026rdquo; command line in Mothur were utilized in R generating the plot. R packages including \u0026ldquo;rgl\u0026rdquo;, \u0026ldquo;vegan\u0026rdquo;,\u0026rdquo;tidyverse\u0026rdquo;, \u0026ldquo;ggtext\u0026rdquo;, \u0026ldquo;ggplot2\u0026rdquo;, and \u0026ldquo;stats\u0026rdquo; were employed for visualization of Weighted UniFrac plot [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Cluster dendrogram based on Bray\u0026ndash; Curtis similarity was calculated from OTUs with the \"vegdist\" function in R [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLinear discriminant analysis effect sizes (LEfSe) were used to determine differentially abundant features between plant types [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. LefSe was calculated by setting a \u003cem\u003ep-value\u003c/em\u003e 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\", \u0026ldquo;microeco\u0026rdquo;, \u0026ldquo;file2meco\u0026rdquo;, and \u0026ldquo;ggplot2\u0026rdquo; R packages [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. 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\u0026ldquo;ggVennDiagram\u0026rdquo; package and visualized using the ggVennDiagram Shiny app (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bio-spring.shinyapps.io/ggVennDiagram\u003c/span\u003e\u003cspan address=\"https://bio-spring.shinyapps.io/ggVennDiagram\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Shared OTUs were defined as core microbiome [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNetwork 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\u0026thinsp;\u0026gt;\u0026thinsp;0.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and visualized in GEPHI (0.10.1) [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. The network complexity (edge [or linkage] density among taxa) of leaf endopshere networks was defined according to previous studies [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. 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) [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe 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 \u0026ldquo;screen.seqs\u0026rdquo;, \u0026ldquo;unique.seqs\u0026rdquo; and \u0026ldquo;align.seqs\u0026rdquo; command lines. We reran the \u0026quot;screen.seqs\u0026quot; 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 \u0026quot;unique.seqs\u0026quot; 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 \u0026ldquo;remove.lineage\u0026rdquo; command and obtained 99,623 total and 24,666 unique bacterial sequences, and after removing singleton sequences obtained 430 bacterial OTUs for further analysis.\u003c/p\u003e\n\u003cp\u003eThe 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. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Marked decreases in the relative abundances of the genera \u003cem\u003eDevosia, Caulobacter, Chitinophaga\u003c/em\u003e, and \u003cem\u003eDyadobacter\u003c/em\u003e, among others, are evident from the teosintes to the maizes. A few genera seemed more abundant in maize compared to teosinte, such as \u003cem\u003ePanteo, Staphylococcus, Acinetobacter, Corynebacterium\u003c/em\u003e, and \u003cem\u003eRalstonia\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). 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. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe shared and unique OTUs out of the 430 OTUs for each of the genotypes were assessed and visualized as a Venn diagram (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). There were 36 OTUs shared by all plant genotypes, making them the central core microbial community shared by all the \u003cem\u003eZea\u003c/em\u003e 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.\u003c/p\u003e\n\u003cp\u003eThe loss of diversity associated with domestication was particularly evident in the diversity indices associated with the plantgroups and genotypes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Comparisons among Shannon (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA) and Chao1 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB) 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\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). 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\u0026thinsp;=\u0026thinsp;0.005, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eWe 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. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e); 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\u0026thinsp;=\u0026thinsp;0.33653, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), plant group (R2\u0026thinsp;=\u0026thinsp;0.28636, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and nested within plant group (R2\u0026thinsp;=\u0026thinsp;0.1.3734, P\u0026thinsp;\u0026lt;\u0026thinsp;0.005) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Comparisons among plant groups of leaf endopshere microbiota indicated significant effects of effect of domestication in beta diversity (Teosinte vs Mexican maize, P\u0026thinsp;=\u0026thinsp;0.001) while comparisons among genotypes revealed significant effects of domestication (Balsas teosinte vs. Mexican landrace, P\u0026thinsp;=\u0026thinsp;0.005) and breeding in Mexico (Mexican landrace vs Mexican inbred, P\u0026thinsp;=\u0026thinsp;0.027) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). 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 (=\u0026thinsp;average distance to centroid) was significantly greater in maize, both Mexican and US, compared to teosinte (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig. S5A). Interestingly, within-cluster spread increased significantly with northward spread of maize (Mexican vs US landrace, P\u0026thinsp;=\u0026thinsp;0.001) and breeding in USA (US landrace vs US inbred, P\u0026thinsp;=\u0026thinsp;0.003), but was unaffected by the perennial to annual life history transition in the teosintes (P\u0026thinsp;=\u0026thinsp;0.597), domestication (P\u0026thinsp;=\u0026thinsp;0.292), and breeding in Mexico (P\u0026thinsp;=\u0026thinsp;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.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003eThe 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. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). 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\u0026thinsp;=\u0026thinsp;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).\u003c/div\u003e\n\u003cp\u003eA co-occurrence network analysis was performed to examine the relationship of phylum abundances in the leaf endosphere microbiota (Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). 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. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). 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. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eLastly, FAPROTAX functional prediction analysis showed that the perennial and Balsas teosinte genotypes had higher nitrate and nitrogen respiration interactions than the maize genotypes (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). 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. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we examined the leaf endosphere microbiota of a suite of six teosinte and maize (\u003cem\u003eZea\u003c/em\u003e 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 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] we expected that teosinte\u0026rsquo;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.\u003c/p\u003e\n\u003ch3\u003eMaize domestication significantly affected α-diversity and β-diversityof leaf endosphere microbial community\u003c/h3\u003e\n\u003cp\u003eWe 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\u0026rsquo;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 [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eOur 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 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. This is consistent with studies showing that host plants play important roles in shaping their endophytic bacteria communities [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e], [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Any underlying mechanisms and consequences of diminished selection effect remain to be explored.\u003c/p\u003e \u003cp\u003eCollectively, 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 [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e], [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e], [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. For instance, mutations of immunity-related genes in \u003cem\u003eArabidopsis\u003c/em\u003e, and disease in Korean fir and chili pepper were associated with reductions in the diversity of their microbial communities [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. 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) [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. AKP suggests a rise of stochastic over deterministic processes mediating microbial community composition within the holobiont [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e], [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. 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\u0026rsquo;s ability to cope with biotic and abiotic stresses [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], particularly as environments change rapidly under climate change. Teosintes defend against herbivorous insects and pathogens by a variety of means [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e], [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e], [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e], [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e], [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e], 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 [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDifferences in the structure and assemblage of leaf endosphere microbial community associated with maize domestication, northward spread, and breeding\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe 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 [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e], leaf endosphere of prairie plants [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e], and leaf microbiota of \u003cem\u003eArabidopsis\u003c/em\u003e [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. Furthermore, we found several taxa were depleted from the teosinte group, including perennial and Balsas teosinte, to the maize inbred lines, including \u003cem\u003eDevosia\u003c/em\u003e and \u003cem\u003eCaulobacter\u003c/em\u003e (Proteobacteria), and \u003cem\u003eChitinophaga\u003c/em\u003e and \u003cem\u003eDyadobacter\u003c/em\u003e (Bacteroidetes). Moreover, five genera showed higher abundance in maize plant group, \u003cem\u003ePantoea, Staphylococcus, Acinetobacter, Corynebacterium\u003c/em\u003e, Ralstonia. These results are consistent with those of previous research [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e] which identified \u003cem\u003ePantoea\u003c/em\u003e spp. as the dominant taxa in hybrid maize cultivars at an early growth stage. Similarly, our study highlighted \u003cem\u003ePantoea and Ralstonia\u003c/em\u003e as dominant taxa in leaf samples from elite inbred lines, including lines from Mexico and US. Additionally, \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eCorynebacterium\u003c/em\u003e were also among the top 20 genera in the relative abundance analysis of that research [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. Interestingly, the depleted genera observed in elite inbred maize in our study were also not detected in the previous research.\u003c/p\u003e \u003cp\u003eWe identified a few OTUs as known beneficial bacteria, though we did not test their functional properties. For example, we identified \u003cem\u003eMethylobacterium\u003c/em\u003e spp., which are well known phyllosphere colonizers with documented beneficial effects, e.g., production of phytohormones, and enhancement of seed germination and plant growth [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e], [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e], [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e], [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. Previous studies consistently reported Methylobacteriaceae as the most abundant or as a biomarker taxon for leaf microbiota studies in maize [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e], [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. 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. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] 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 [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e], [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e], [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e], [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e], [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eWe 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 [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], grasses [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e], and \u003cem\u003eArabidopsis thaliana\u003c/em\u003e [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. Moreover, Johnston-Monje and Raizada [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] detected a core microbiota of endophytes that remained conserved in \u003cem\u003eZea\u003c/em\u003e seeds. Core microbial communities establish enduring relationships with plant hosts and crucially influence biological processes of their host plants [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e], [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e], [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eWe 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 [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e], [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e], [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. 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 \u0026gt;\u0026thinsp;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 \u0026gt;\u0026thinsp;3-fold higher in maize genotypes (average\u0026thinsp;=\u0026thinsp;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 [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e], [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e], [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]. 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. [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], and at the seedling stage of modern maize cultivars in a study by Xiong et al [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eWe 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 [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e], [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e]. Regulation of functional groups and shifts in functional groups indicate that plant-recruited microbes reflect the current needs of the host plant [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e], [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. 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.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe 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.\u003c/p\u003e \u003cp\u003eAlso, 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\u0026rsquo;s northward spread and breeding. Taken together, our results suggested that the leaf endophytic bacterial assembly in maize was markedly influenced by its domestication.\u003c/p\u003e \u003cp\u003eAltogether, 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI.T. carried out experiments and bioinformatical analyses of microbiota communities,\u0026nbsp;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 \u0026amp; editing. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially funded through USDA-Research, Education, and Economics Information System \u0026ldquo;Deciphering Microbial Functions to Improve Plant Productivity and Sustainable Agriculture\u0026rdquo;. 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 T\u0026uuml;rkiye.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eP. Vandenkoornhuyse, S. L. Baldauf, C. Leyval, J. Straczek, and J. P. W. Young, \u0026ldquo;Extensive Fungal Diversity in Plant Roots,\u0026rdquo; \u003cem\u003eScience\u003c/em\u003e, vol. 295, no. 5562, pp. 2051\u0026ndash;2051, Mar. 2002, doi: 10.1126/science.295.5562.2051.\u003c/li\u003e\n\u003cli\u003eM. A. Hassani, P. Dur\u0026aacute;n, and S. Hacquard, \u0026ldquo;Microbial interactions within the plant holobiont,\u0026rdquo; \u003cem\u003eMicrobiome\u003c/em\u003e, vol. 6, no. 1, p. 58, Dec. 2018, doi: 10.1186/s40168-018-0445-0.\u003c/li\u003e\n\u003cli\u003eP. Trivedi, J. E. Leach, S. G. Tringe, T. Sa, and B. K. Singh, \u0026ldquo;Plant\u0026ndash;microbiome interactions: from community assembly to plant health,\u0026rdquo; \u003cem\u003eNat Rev Microbiol\u003c/em\u003e, vol. 18, no. 11, pp. 607\u0026ndash;621, Nov. 2020, doi: 10.1038/s41579-020-0412-1.\u003c/li\u003e\n\u003cli\u003eK. P. 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Ibny, and F. D. Dakora, \u0026ldquo;Rhizobia as a Source of Plant Growth-Promoting Molecules: Potential Applications and Possible Operational Mechanisms,\u0026rdquo; \u003cem\u003eFront. Sustain. Food Syst.\u003c/em\u003e, vol. 4, p. 619676, Jan. 2021, doi: 10.3389/fsufs.2020.619676.\u003c/li\u003e\n\u003cli\u003eK. L. Grady, J. W. Sorensen, N. Stopnisek, J. Guittar, and A. Shade, \u0026ldquo;Assembly and seasonality of core phyllosphere microbiota on perennial biofuel crops,\u0026rdquo; \u003cem\u003eNat Commun\u003c/em\u003e, vol. 10, no. 1, p. 4135, Sep. 2019, doi: 10.1038/s41467-019-11974-4.\u003c/li\u003e\n\u003cli\u003eB. Tian \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Beneficial traits of bacterial endophytes belonging to the core communities of the tomato root microbiome,\u0026rdquo; \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, vol. 247, pp. 149\u0026ndash;156, Sep. 2017, doi: 10.1016/j.agee.2017.06.041.\u003c/li\u003e\n\u003cli\u003eN. Stopnisek and A. Shade, \u0026ldquo;Persistent microbiome members in the common bean rhizosphere: an integrated analysis of space, time, and plant genotype,\u0026rdquo; \u003cem\u003eThe ISME Journal\u003c/em\u003e, vol. 15, no. 9, pp. 2708\u0026ndash;2722, Sep. 2021, doi: 10.1038/s41396-021-00955-5.\u003c/li\u003e\n\u003cli\u003eL. Zhang \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;A highly conserved core bacterial microbiota with nitrogen-fixation capacity inhabits the xylem sap in maize plants,\u0026rdquo; \u003cem\u003eNat Commun\u003c/em\u003e, vol. 13, no. 1, p. 3361, Jun. 2022, doi: 10.1038/s41467-022-31113-w.\u003c/li\u003e\n\u003cli\u003eK. Faust and J. Raes, \u0026ldquo;Microbial interactions: from networks to models,\u0026rdquo; \u003cem\u003eNat Rev Microbiol\u003c/em\u003e, vol. 10, no. 8, pp. 538\u0026ndash;550, Aug. 2012, doi: 10.1038/nrmicro2832.\u003c/li\u003e\n\u003cli\u003eY. Deng \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Network succession reveals the importance of competition in response to emulsified vegetable oil amendment for uranium bioremediation,\u0026rdquo; \u003cem\u003eEnvironmental Microbiology\u003c/em\u003e, vol. 18, no. 1, pp. 205\u0026ndash;218, Jan. 2016, doi: 10.1111/1462-2920.12981.\u003c/li\u003e\n\u003cli\u003eK. Feng \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Biodiversity and species competition regulate the resilience of microbial biofilm community,\u0026rdquo; \u003cem\u003eMolecular Ecology\u003c/em\u003e, vol. 26, no. 21, pp. 6170\u0026ndash;6182, Nov. 2017, doi: 10.1111/mec.14356.\u003c/li\u003e\n\u003cli\u003eK. Faust and J. Raes, \u0026ldquo;Microbial interactions: from networks to models,\u0026rdquo; \u003cem\u003eNat Rev Microbiol\u003c/em\u003e, vol. 10, no. 8, pp. 538\u0026ndash;550, Aug. 2012, doi: 10.1038/nrmicro2832.\u003c/li\u003e\n\u003cli\u003eL. Luo \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Variations in phyllosphere microbial community along with the development of angular leaf-spot of cucumber,\u0026rdquo; \u003cem\u003eAMB Expr\u003c/em\u003e, vol. 9, no. 1, p. 76, Dec. 2019, doi: 10.1186/s13568-019-0800-y.\u003c/li\u003e\n\u003cli\u003eJ. Zhou \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Characterization of the core microbiome in tobacco leaves during aging,\u0026rdquo; \u003cem\u003eMicrobiologyOpen\u003c/em\u003e, vol. 9, no. 3, p. e984, Mar. 2020, doi: 10.1002/mbo3.984.\u003c/li\u003e\n\u003cli\u003eJ.-E. Cheng \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Metagenomic analysis of the dynamical conversion of photosynthetic bacterial communities in different crop fields over different growth periods,\u0026rdquo; \u003cem\u003ePLoS ONE\u003c/em\u003e, vol. 17, no. 7, p. e0262517, Jul. 2022, doi: 10.1371/journal.pone.0262517.\u003c/li\u003e\n\u003cli\u003eJ. Liu, X. Sun, Y. Zuo, Q. Hu, and X. He, \u0026ldquo;Plant species shape the bacterial communities on the phyllosphere in a hyper-arid desert,\u0026rdquo; \u003cem\u003eMicrobiological Research\u003c/em\u003e, vol. 269, p. 127314, Apr. 2023, doi: 10.1016/j.micres.2023.127314.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 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\u0026nbsp;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\u0026lt;0.05) (B). Significant values are indicated in bold.\u003c/p\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.30831973898858%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eperMANOVA\u0026nbsp;results for plant group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597063621533444%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.192495921696574%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.902120717781404%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr (\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.30831973898858%\" valign=\"top\"\u003e\n \u003cp\u003ePlant group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597063621533444%\" valign=\"top\"\u003e\n \u003cp\u003e0.28636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.192495921696574%\" valign=\"top\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.902120717781404%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.30831973898858%\" valign=\"top\"\u003e\n \u003cp\u003eGenotype (Plant group)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597063621533444%\" valign=\"top\"\u003e\n \u003cp\u003e1.3734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.192495921696574%\" valign=\"top\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.902120717781404%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.30831973898858%\" valign=\"top\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597063621533444%\" valign=\"top\"\u003e\n \u003cp\u003e0.66347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.192495921696574%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.902120717781404%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.30831973898858%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597063621533444%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.192495921696574%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.902120717781404%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.30831973898858%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eperMANOVA\u0026nbsp;results for genotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597063621533444%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.192495921696574%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.902120717781404%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr (\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.30831973898858%\" valign=\"top\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597063621533444%\" valign=\"top\"\u003e\n \u003cp\u003e0.33653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.192495921696574%\" valign=\"top\"\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.902120717781404%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.30831973898858%\" valign=\"top\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597063621533444%\" valign=\"top\"\u003e\n \u003cp\u003e0.66347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.192495921696574%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.902120717781404%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.30831973898858%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.597063621533444%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.192495921696574%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.902120717781404%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;B\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.81906300484653%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003elant group Comparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.416801292407108%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.054927302100161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.709208400646204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.81906300484653%\" valign=\"top\"\u003e\n \u003cp\u003eTeosinte vs. Mexican maize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.416801292407108%\" valign=\"top\"\u003e\n \u003cp\u003e0.33342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.054927302100161%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.709208400646204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.81906300484653%\" valign=\"top\"\u003e\n \u003cp\u003eMexican maize vs. US maize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.416801292407108%\" valign=\"top\"\u003e\n \u003cp\u003e0.03146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.054927302100161%\" valign=\"top\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.709208400646204%\" valign=\"top\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.81906300484653%\" valign=\"top\"\u003e\n \u003cp\u003eTeosinte vs. US maize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.416801292407108%\" valign=\"top\"\u003e\n \u003cp\u003e0.30054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.054927302100161%\" valign=\"top\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.709208400646204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.48796147672552%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype Comparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.582664526484752%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.48796147672552%\" valign=\"top\"\u003e\n \u003cp\u003ePerennial teosinte vs. Balsas teosinte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e0.03090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.582664526484752%\" valign=\"top\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.48796147672552%\" valign=\"top\"\u003e\n \u003cp\u003eBalsas teosinte vs. Mexican landrace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e0.36439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e18.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.582664526484752%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.48796147672552%\" valign=\"top\"\u003e\n \u003cp\u003eMexican landrace vs. Mexican inbred\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e0.14350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.582664526484752%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.48796147672552%\" valign=\"top\"\u003e\n \u003cp\u003eMexican landrace vs. US landrace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e0.07119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.582664526484752%\" valign=\"top\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.48796147672552%\" valign=\"top\"\u003e\n \u003cp\u003eUs landrace vs. US inbred\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e0.05244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.964686998394864%\" valign=\"top\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.582664526484752%\" valign=\"top\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Plant domestication, Maize, Teosinte, Zea mays mays, Zea mays parviglumis, Zea diploperennis, microbiome, Leaf endosphere, Co-occurrence networks","lastPublishedDoi":"10.21203/rs.3.rs-4850295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4850295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe 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\u0026rsquo;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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe 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) \u003cem\u003eZea\u003c/em\u003e. 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.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur 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.\u003c/p\u003e","manuscriptTitle":"Dysbiosis in Maize Leaf Endosphere Microbiome is Associated with Domestication","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-12 18:07:55","doi":"10.21203/rs.3.rs-4850295/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f7887b94-d048-4ab4-bf1e-2b813eac8a3f","owner":[],"postedDate":"September 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:53:37+00:00","versionOfRecord":{"articleIdentity":"rs-4850295","link":"https://doi.org/10.3389/frmbi.2026.1735358","journal":{"identity":"frontiers-in-microbiomes","isVorOnly":true,"title":"Frontiers in Microbiomes"},"publishedOn":"2026-02-22 00:00:00","publishedOnDateReadable":"February 22nd, 2026"},"versionCreatedAt":"2024-09-12 18:07:55","video":"","vorDoi":"10.3389/frmbi.2026.1735358","vorDoiUrl":"https://doi.org/10.3389/frmbi.2026.1735358","workflowStages":[]},"version":"v1","identity":"rs-4850295","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4850295","identity":"rs-4850295","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}