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Article

Comparative Metagenomic Profiling of Seed-Borne Microbiomes in a Landrace and a Hybrid Maize Variety

by
Sarah Henaut-Jacobs
1,
Beatriz Elisa Barcelos Cyríaco
2,
Francisnei Pedrosa-Silva
1,
Fabio Lopes Olivares
2,* and
Thiago Motta Venancio
1,*
1
Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes 28013-602, RJ, Brazil
2
Laboratório de Biologia Celular e Tecidual, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes 28013-602, RJ, Brazil
*
Authors to whom correspondence should be addressed.
Seeds 2024, 3(4), 528-539; https://doi.org/10.3390/seeds3040035
Submission received: 15 August 2024 / Revised: 13 September 2024 / Accepted: 24 September 2024 / Published: 26 September 2024
(This article belongs to the Special Issue Progress in Multi-omics and Bioinformatics Approaches: Seed Microbiome and Metagenome)
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Abstract

:
The plant seed-borne microbiome comprises microorganisms vertically inherited from the mother plant. This microbiome is often linked to early-life protection and seedling growth promotion. Herein, we compare the seed-borne bacteriomes of a commercial hybrid (Santa Helena) and a landrace maize variety (Sol da Manhã). The landrace variety displays a more diverse seed-borne microbiome, featuring a variety of taxa across samples with an average Shannon’s diversity index of 1.12 compared to 0.45 in the hybrid variety. The landrace variety also showed a greater alpha diversity of 165.8, in contrast to 144.1 in the hybrid. Although both microbiomes lack a functional nitrogen fixation apparatus, we found a remarkably distinct presence of genes associated with phytohormone production and phosphate solubilization, particularly in the landrace variety. In addition, we recovered 18 metagenome-assembled genomes (MAGs), including four from potentially novel species. Collectively, our results allow for a better understanding of the contrasting diversity between maize varieties. The higher potential for phytohormone production in landraces, the absence of nif genes in both varieties, and the identification of core microbiome taxa offer valuable insights into how microbial communities impact plant health and development. This knowledge could pave the way for more sustainable and innovative agricultural practices in crop management.

1. Introduction

When examining the intricate world of microscopic ecosystems, plants are dynamic habitats in their own right. Plants rely heavily on the microbial communities within and around them [1], engaging in a myriad of complex interactions that can be beneficial, antagonistic, or neutral [2]. The plant bacteriome comprises bacteria acquired from the surrounding soil and those vertically transmitted during seed development. These vertically inherited bacteria comprise the resident or seed-borne microbiome. The seed-borne microbiome is often associated with seedling growth and protection [3].
Factors such as plant genotype and environmental conditions can influence plant microbiome composition and functions, emphasizing the dynamic nature of these bacterial communities [4]. Different plant genotypes are likely to diverge in their seed-borne microbiomes due to artificial selection and recruitment of other bacteria from the soil [5]. In addition, neighboring plants [6] and long-term processes such as global warming [7] can influence a plant microbiome. Maize is a suitable model for studying seed-borne microbiomes. Besides its great economic relevance as the second most produced agricultural commodity in the world [8], maize has a well documented evolutionary and domestication history, valuable germplasm collections, and other genetic resources [9,10].
Although previous works have shown a difference in microbiome composition between hybrid and landrace maize varieties [11], the roles of seed-borne bacteria and their implications for plant development remain unclear. Some studies suggest that plant domestication may lead to a decrease in the transmission capability of the seed-borne microbiome [12], which would gradually compromise its composition and diversity in crop varieties used in intensive agriculture systems. Such a reduction in seed-borne microbial diversity may be linked to negative outcomes, like increased disease susceptibility [13].
Metagenomic studies have also shown a strong correlation between reduced microbiome diversity and declines in both crop productivity and disease resistance [7,14]. Within a plant core microbiome, several taxa play pivotal roles, such as Pantoea, renowned for their diverse plant-growth-promoting traits [15,16]. Furthermore, specific bacterial genes as observed in Pseudomonads [17], can also enhance the capability of plant–microbe interactions [18]. Notably, the maize microbiome exhibits a prevalence of families like Rhizobiaceae, Burkholderiaceae, and Microbacteriaceae, particularly during early plant development [19]. These families are usually associated with traits such as phosphate solubilization and nitrogen metabolism [20,21,22,23].
With a rising emphasis on sustainable agriculture, bioinoculants have been adopted over the last four decades, a market that grows at an annual rate of 10% worldwide [24]. By integrating environmentally compatible inoculants with insights from large-scale microbiome analyses, we can modify the “stage 0” of the seed microbiome. This approach aims to enhance crop productivity and protection [3] while also compensating for any potential reduction in seed-borne microbial diversity in intensive crop varieties. Currently, the most effective way to increase crop production is through the widespread use of chemical pesticides and fertilizers. However, this demand for chemical inputs increases dependency on imported products that compromise the sovereignty of several countries for food, fiber, and energy production [25,26]. Furthermore, the intensive use of agrichemicals is often associated with groundwater contamination [27], human diseases [28], and the death of pollinating insects [29]. In this scenario, the adoption of sustainable practices based on plant growth-promoting bacteria (PGPB) constitutes an attractive alternative [30]. PGPB typically promote plant growth through diverse mechanisms of action such as biofertilization, bioprotection, or biostimulation [31].
In the present work, we investigate the seed-borne bacteriomes of maize varieties derived from two distinct breeding strategies. We evaluated the microbiome of a commercial hybrid and a landrace maize variety, the latter originating from organic production in an agroforestry ecosystem. We analyzed the structure and diversity of these microbiomes, with particular emphasis on their differences and potential to promote plant growth.

2. Methods

2.1. Maize Varieties

The varieties used were the “SHS 5050” double hybrid commercial variety (SH) and the “Sol da Manhã” homozygous landrace variety (SOL). SH is a hybrid variety from a local Santa Helena retailer with production in northwest Minas Gerais, Brazil. This company works to improve maize yield and uniformity, dealing with a large-scale production area irrigated under a central pivot. The SOL variety was donated by the family of the agribusiness farmer Jamil Bráz Corinto to Prof. Samuel Kamphorst (UNILA, Brazil). This variety originated from agricultural growth corridors in Santo Antônio do Rio Verde, Goiás, Brazil. SOL was initially cultivated in indigenous lands and has been cultivated in agroforestry systems for over 15 years, a period during which it has undergone meticulous participatory mass selection. The cultivation of SOL is a collaborative effort supported by EMBRAPA and in collaboration with the Movimento Camponês Popular (MCP).

2.2. Planting

Maize seeds from both varieties were rinsed with sterile distilled water five times and soaked for five hours to break dormancy and standardize germination. The seeds were carefully distributed into sterile 2 L glass jars filled with 700 g of Basaplant substrate sieved through a 2 mm mesh previously autoclaved for four one-hour cycles for complete sterilization. We irrigated the substrate with 250 mL of autoclaved distilled water and divided it into three distinct sectors, each containing 10 seeds. We sealed the glass jars with cotton to allow gas exchange while maintaining the seedlings under axenic conditions throughout the incubation period. We conducted the experiment in a Biological Oxygen Demand (BOD) incubator (100 µmol/m2/s), maintaining the temperature at 28 °C “day” and 25 °C “night”, following a 12 h light/12 h dark cycle for 7 days. The choice to work with germinated seeds was so that the microbiome would mimic the real scenario of plant development in the substrate instead of studying only the quiescent seed microbiome [15,32].

2.3. 16S and Metagenomic Sequencing

The rhizospheric substrate was collected by brushing it from the seedlings and DNA extraction conducted using the DNeasy PowerSoil Pro Kit (Qiagen—Hilden, Germany) following the manufacturer’s instructions. DNA was sequenced using an Illumina NextSeq instrument at NGS (Piracicaba, Brazil). We also sequenced a DNA sample from the autoclaved substrate to serve as a negative control (“C1”) for contamination in shotgun sequencing. The raw sequencing reads were submitted to NCBI SRA under BioProject PRJNA1069023. For 16S rRNA sequencing, we used the Wizard Genomic DNA Purification Kit (Promega – Madison, Wisconsin, United States) followed by sequencing using the Sanger method using the 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-TACGGYTACCTTGTTACGACTT-3′) universal primers [33].
QIIME2 v. 2023.5.1 [34] was used to process 16S metataxonomic data and to remove reads from mitochondrial and chloroplast 16S rRNA. The SILVA database Release 138 [35] was used to infer taxonomic classification.
For metagenome analysis, we used Bowtie2 v. 2.3.4.3 [36] and Samtools v. 1.9 [37] to remove fragments of maize DNA. Sequencing quality was evaluated with FastQC v. 0.12.1 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/, accessed on 7 August 2023) and low-quality fragments trimmed using Trimmomatic v. 0.39 [38]. The taxonomic distribution of the reads was conducted with Kaiju v. 1.6.2 [39]. Microbiome diversity analyses were conducted with VEGAN [40].
For optimizing the screening of plant growth promotion capacity in the metagenomes, the microbiome biotechnological potential was inferred using an in-house database of genes associated with different plant growth promotion traits (i.e., nitrogen fixation, phosphate solubilization, phytases, ACC deaminase, and auxin production) (Pedrosa–Silva, Henaut–Jacobs, Venancio, in preparation). This database was constructed by selecting genes widely known for their plant growth promotion capabilities. The database also contains copies of each gene from every different genera that are available in UniProt. These genes were compared with the metagenomes using Usearch [41] with a 60% identity threshold.

2.4. Metagenome-Assembled Genomes (MAGs)

Metagenome assembly was conducted using SPAdes v. 3.15.5 [42] with the “--meta” parameter. Assembly quality was inferred using QUAST v. 5.0.2 [43] to help choose the best assembly parameters. Only contigs longer than 1750 base pairs were retained. MAGs were binned with MetaBAT2 v. 2.2.15 [44] (Kang et al., 2019). The GTDB toolkit Release 08-RS214 [45] was used to infer the taxonomy and BUSCO v. 5.4.2 [46] to assess the completeness and duplication levels of the MAGs. The redundancy between the MAGs was estimated with pyani v. 0.2.12 and their genes were predicted with Prokka v. 1.13 [47]. To categorize the MAGs, the MIMAG methodology was used [48], and we retained only non-redundant MAGs that were classified as High-quality or Finished. These were then submitted to GenBank for further analysis.

3. Results and Discussion

3.1. Metagenomics Sequencing Depth Satisfactorily Captured Sample Diversity

We started the analysis with the C1 control sample, which had 1881 operational taxonomic units (OTUs) (Tables S1 and S2). Most of these OTUs are exclusive to C1, suggesting that they result from residual DNA from bacteria killed during sterilization. Conversely, the OTUs from SH and SOL are remarkably more abundant than those of C1, most likely because of living bacteria in these samples. These results support the observation that the DNA reads sequenced from the SH and SOL samples were indeed from the seed-borne microbiome, with virtually no substrate DNA contamination.
To assess the coverage of our data, we estimated the taxa per sample (Figure 1) and computed the Good’s coverage index for each sample (Table 1). These analyses demonstrated that our sequencing data achieved robust taxonomic coverage, capturing 80% of the microbial diversity at 10 million reads in both varieties. Notably, the Good’s index is in line with the rarefaction curves, with only a slight variation where SH exhibits marginally lower Good’s indexes in comparison to SOL. SH samples also exhibited a more delayed plateau phase (Figure 1), which is consistent with their greater number of singletons (Table 1) that probably originated from low-abundance bacteria that are unlikely to significantly contribute to the community structure. Nevertheless, we kept these singletons for downstream analysis because of the overall low bacterial DNA amounts in our samples.

3.2. The Hybrid Maize Genotype Harbors a Homogeneous Seed-Borne Bacteriome

We sequenced 124,744 and 120,881 16S rRNA partial gene sequences in the SH and SOL samples, respectively (Table 1). In our 16S metataxonomic data, we observed a substantial abundance of the Burkholderiaceae family in SH and SOL, mainly from the Burkholderia and Paraburkholderia genera (Figure 2B). Previous studies reported the prevalence of the Burkholderiaceae family in maize rhizosphere microbiomes [11], which could suggest a higher likelihood of these bacteria being inherited across plant generations. Importantly, Burkholderia species have been associated with nitrogen fixation and stress resistance in maize [49,50]. Another possible explanation for this persistence is the biological profile of the Burkholderiaceae family. Known for their endophytic lifestyle, they possess a highly developed motility apparatus, which could enhance their ability to colonize seeds [51]. One of the most frequent species in both maize varieties was Burkholderia cenocepacia (Table S1), which is part of the Burkholderia cepacia complex that has been widely reported as associated with plant growth promotion [52]. The inoculation of B. cenocepacia improved maize production (cob length, number of grains per cob, grain weight, and 100-grain weight) in the field, with better results when combined with Alcaligenes aquatilis [53].
It is important to acknowledge that some B. cenocepacia strains are pathogenic to humans, particularly to immunocompromised individuals [54]. Conversely, other B. cenocepacia strains are non-pathogenic and deemed safe [55,56]. Hence, it is feasible to strategically incorporate non-pathogenic strains in inoculants or synthetic microbiomes to harness the beneficial features of B. cenocepacia as a PGPB while mitigating potential risks to human health.
Burkholderiaceae accounted for over 90% of the SH microbiome. In addition, all classified reads from the SH 16S metataxonomic data were assigned to the Burkholderia–Caballeronia–Paraburkholderia genera (Figure 2B). In contrast, the SOL samples displayed a substantial presence of various taxa and variable proportions of Burkholderiaceae in their composition (Table S1). Principal Component Analysis (PCA) further highlighted this distinction, with the SH samples almost completely overlapping, while the SOL samples were more dispersed (Figure 3). We can also see a greater separation of SOL1 from the other samples on the X-axis, which might reflect the lower prevalence of Burkholderia in this specific sample based on the contribution of this taxa to the PCA.
The homogeneity found in the SH samples mirrors the inherent genetic uniformity of the hybrid variety. It is well established that the plant genotype profoundly influences the microbiome composition and that intensive agriculture can reduce microbiome diversity [57]. Previous research has shown that inbred maize varieties exhibit changes in microbiome recruitment over time, with more recently developed germplasms recruiting fewer microbial taxa capable of nitrogen fixation and favoring larger populations of microorganisms that contribute to nitrogen loss [58]. It is reasonable to assume that SH experienced a loss of microbiome diversity due to its long-term use in intensive agriculture when compared to SOL, which is also reflected in the high occurrence of singletons in those samples. Another critical aspect to consider is that the intensive use of chemical fertilizers and pesticides reduces the selective pressure on recruiting a supportive microbiome by plants [59], which might also be the case in commercial varieties like SH.

3.3. The Seed-Borne Microbiome Harbors Multiple Phosphate Solubilization Genes but Is Likely Unable to Fix Nitrogen

Bacteria can promote plant growth through various mechanisms, either directly or indirectly. Here we used a list of manually curated genes to investigate the presence of direct plant-growth promotion mechanisms, including nitrogen fixation, phosphate solubilization, ACC deaminase, and auxin production.
We expected a greater abundance of nitrogen fixation genes in SOL because of the gene loss driven by extensive breeding and chemical nitrogen fertilization in varieties like SH [58,60]. Contrary to our hypothesis, the nifHDK core nitrogenase genes were absent in both varieties. Nevertheless, we found nifB and nifUS, which are involved in the biosynthesis of the FeMo nitrogenase cofactor [61,62] and in providing the essential Fe-S clusters for the biosynthesis of FeMo-Co [63], respectively. In the SH samples, we found nifQ, responsible for molybdenum incorporation, which acts in conjunction with other genes (nifB, nifNV, and nifE) in the biosynthesis of the nitrogenase iron-molybdenum cofactor [64,65] (Figure 4). The relevance of these nif genes in the absence of the core nitrogenase subunits remains unclear. However, it is important to emphasize that even non-intensive agricultural management involves nitrogen fertilization, which could reduce the selective pressure for retaining diazotrophic microorganisms in the seed-borne microbiome.
We found all the pqq genes for inorganic phosphate solubilization in all samples, except for the pqqA gene (not necessary for PQQ cofactor synthesis [66]). Further, phytase genes are absent in metagenomes, supporting the dominance of inorganic phosphate solubilization in the seed-borne microbiome. Seeds are usually rich in stored organic compounds such as phytates, which plants and their microbiomes can access during germination. Furthermore, inorganic phosphate availability in the soil can often be a limiting factor for plant growth [67]. Hence, the evolution of the seed-borne microbiome to prioritize the solubilization of inorganic over organic phosphate is compatible with the general availability of phosphorus in soils used in agriculture [68]. In this scenario, these microbial communities play a crucial role in supporting early seedling growth by making inorganic phosphate available, directly contributing to the seedling’s immediate phosphorus demands.

3.4. The Seed-Borne Microbiome May Affect Phytohormone Production

We found distinctive patterns of auxin biosynthesis genes, with SH harboring the nthB gene, while SOL showed a dominance of ipdC and iaaH. These results show much greater auxin biosynthesis potential in the SOL seed-borne microbiome. In this context, we can hypothesize that increased utilization of nitrogen fertilizers could compensate for any deficiency in microbial-produced auxin, and intensive breeding might render auxin-producing microorganisms dispensable, with plant varieties that are more efficient in nitrogen utilization and auxin biosynthesis being selected [69,70].
We also found the ACC deaminase gene acdS in all samples. ACC deaminases convert ACC in α-ketobutyrate and ammonia [71,72], lowering ethylene levels in the plant [31]. Reduced ethylene levels can delay senescence [73] and increase plant resistance to stressful environments [74] and pathogen attacks [75]. Hence, the prevalence of acdS could be linked to the needs of crop plants under stressful conditions, leading to better crop performance and growth [76].

3.5. Metagenome-Assembled Genomes (MAGs) in Seed-Borne Microbiome

A total of 18 valid bacterial MAGs were recovered from the seed-borne metagenome (Table 2). We identified ten high-quality MAGs that met the stringent criteria of at least 90% genome completeness and up to 5% contamination. There were five medium-quality draft MAGs, with 50 to 90% completeness and less than 10% contamination, and one low-quality draft MAG, with less than 50% genome completeness and more than 10% contamination. Two MAGs exhibited duplication levels exceeding 10% and were likely contaminated.
Remarkably, we discovered four MAGs belonging to novel species, including two from the Curtobacterium, one from Burkholderia, and one from Terriglobus. These four novel MAGs were found in the SOL samples, with SOL1.2 being classified as high quality according to the MIMAG methodology [48]. Furthermore, the two unknown Curtobacterium MAGs were 99% identical, supporting their affiliation with the same novel species.
It is reasonable to assume that identifying a high-quality MAG is more common for abundant strains. Thus, the independent recovery of the same MAG in different samples strongly supports its robust occurrence in the seed-borne microbiome. Species such as Burkholderia gladioli, found in all six samples, might have an essential role in the seed-borne microbiome. Importantly, given the ANI values greater than 99%, we probably found the same B. gladioli strain in all six samples. B. gladioli has been previously reported as a beneficial seed-borne bacteria, showing a good repertoire of phosphatases and no nitrogen fixation genes [77], which is consistent with the results presented herein. B. gladioli has also been shown to enhance plant resistance to Fusarium oxysporum by inducing fungal cell death [78]. We hypothesize that the presence of B. gladioli in all samples could represent a seed-borne biocontrol mechanism to counter phytopathogenic fungi during germination and early post-germination development.
Another interesting MAG is that of a B. cenocepacia strain found in all SH samples. B. cenocepacia is also a known biocontrol agent [79] generally associated with Fusarium wilt protection. Furthermore, B. cenocepacia has been reported to protect against Fusarium root rot by controlling other Fusarium species that may interact with F. oxysporum [80]. The co-occurrence of B. cenocepacia with B. gladioli in the SH seed-borne microbiome may indicate an evolving system in which the maize plant recruits biocontrol agents. Our findings also suggest that these two strains are compatible and could work together, either additively or synergistically. With further validation, these strains might be utilized in a novel biocontrol strategy.

4. Concluding Remarks

In this study, we demonstrated that a landrace maize variety has a more diverse and less uniform (between samples) seed-borne microbiome than a hybrid commercial variety. We identified a higher potential for phytohormone production in the landrace microbiome. Our exploration of MAGs revealed that certain taxa are consistently present across different varieties, supporting the existence of a core seed-borne microbiome. Additionally, we observed an unexpected absence of nif genes in both varieties, which may be linked to the widespread use of nitrogen fertilization, even in non-extensive agricultural environments. Understanding the dynamics of seed-borne microbiome evolution could guide the development of novel precision agriculture applications.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/seeds3040035/s1, Table S1: Metagenomic classification of reads to their higher classification rank, Table S2: Metataxonomy classification based on 16S rRNA.

Author Contributions

Conceptualization: S.H.-J., F.L.O. and T.M.V.; formal analysis: S.H.-J.; computational methodology: S.H.-J. and F.P.-S.; wet lab methodology; B.E.B.C.; writing—original draft: S.H.-J. and T.M.V.; writing—review and editing: S.H.-J., B.E.B.C., F.L.O. and T.M.V.; funding acquisition: F.L.O. and T.M.V.; project administration: F.L.O. and T.M.V. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ; grants E-26/203.014/2018, E-26/201.142/2021, and E-26/201.117/2022), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES; Finance Code 001), and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). The funding agencies had no role in the design of the study, the collection, analysis, and interpretation of the data, or in the writing of the paper.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All of the genomic sequences in this paper were submitted under BioProject PRJNA1069023 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1069023, accessed on 23 September 2024).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Vandenkoornhuyse, P.; Quaiser, A.; Duhamel, M.; Le Van, A.; Dufresne, A. The importance of the microbiome of the plant holobiont. New Phytol. 2015, 206, 1196–1206. [Google Scholar] [CrossRef] [PubMed]
  2. Lyu, D.; Zajonc, J.; Pagé, A.; Tanney, C.A.S.; Shah, A.; Monjezi, N.; Msimbira, L.A.; Antar, M.; Nazari, M.; Backer, R.; et al. Plant Holobiont Theory: The Phytomicrobiome Plays a Central Role in Evolution and Success. Microorganisms 2021, 9, 675. [Google Scholar] [CrossRef]
  3. Jiang, M.; Delgado-Baquerizo, M.; Yuan, M.M.; Ding, J.; Yergeau, E.; Zhou, J.; Crowther, T.W.; Liang, Y. Home-based microbial solution to boost crop growth in low-fertility soil. New Phytol. 2023, 239, 752–765. [Google Scholar] [CrossRef]
  4. de Faria, M.R.; Costa, L.S.A.S.; Chiaramonte, J.B.; Bettiol, W.; Mendes, R. The rhizosphere microbiome: Functions, dynamics, and role in plant protection. Trop. Plant Pathol. 2021, 46, 13–25. [Google Scholar] [CrossRef]
  5. Zhang, J.; Liu, W.; Bu, J.; Lin, Y.; Bai, Y. Host genetics regulate the plant microbiome. Curr. Opin. Microbiol. 2023, 72, 102268. [Google Scholar] [CrossRef] [PubMed]
  6. Dastogeer, K.M.G.; Tumpa, F.H.; Sultana, A.; Akter, M.A.; Chakraborty, A. Plant microbiome–an account of the factors that shape community composition and diversity. Curr. Plant Biol. 2020, 23, 100161. [Google Scholar] [CrossRef]
  7. Trivedi, P.; Batista, B.D.; Bazany, K.E.; Singh, B.K. Plant-microbiome interactions under a changing world: Responses, consequences and perspectives. New Phytol. 2022, 234, 1951–1959. [Google Scholar] [CrossRef]
  8. Erenstein, O.; Jaleta, M.; Sonder, K.; Mottaleb, K.; Prasanna, B.M. Global maize production, consumption and trade: Trends and R&D implications. Food Secur. 2022, 14, 1295–1319. [Google Scholar] [CrossRef]
  9. Eyre-Walker, A.; Gaut, R.L.; Hilton, H.; Feldman, D.L.; Gaut, B.S. Investigation of the bottleneck leading to the domestication of maize. Proc. Natl. Acad. Sci. USA 1998, 95, 4441–4446. [Google Scholar] [CrossRef]
  10. Hake, S.; Ross-Ibarra, J. Genetic, evolutionary and plant breeding insights from the domestication of maize. eLife 2015, 4, e05861. [Google Scholar] [CrossRef]
  11. Wagner, M.R.; Roberts, J.H.; Balint-Kurti, P.; Holland, J.B. Heterosis of leaf and rhizosphere microbiomes in field-grown maize. New Phytol. 2020, 228, 1055–1069. [Google Scholar] [CrossRef] [PubMed]
  12. Berg, G.; Raaijmakers, J.M. Saving seed microbiomes. ISME J. 2018, 12, 1167–1170. [Google Scholar] [CrossRef] [PubMed]
  13. Luo, D.-L.; Huang, S.-Y.; Ma, C.-Y.; Zhang, X.-Y.; Sun, K.; Zhang, W.; Dai, C.-C. Seed-borne bacterial synthetic community resists seed pathogenic fungi and promotes plant growth. J. Appl. Microbiol. 2024, 135, lxae073. [Google Scholar] [CrossRef] [PubMed]
  14. Berendsen, R.L.; Pieterse, C.M.J.; Bakker, P.A.H.M. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012, 17, 478–486. [Google Scholar] [CrossRef]
  15. Johnston-Monje, D.; Gutiérrez, J.P.; Lopez-Lavalle, L.A.B. Seed-Transmitted Bacteria and Fungi Dominate Juvenile Plant Microbiomes. Front. Microbiol. 2021, 12, 737616. [Google Scholar] [CrossRef]
  16. Zhang, X.; Ma, Y.-N.; Wang, X.; Liao, K.; He, S.; Zhao, X.; Guo, H.; Zhao, D.; Wei, H.-L. Dynamics of rice microbiomes reveal core vertically transmitted seed endophytes. Microbiome 2022, 10, 216. [Google Scholar] [CrossRef] [PubMed]
  17. Liu, Z.; Beskrovnaya, P.; Melnyk, R.A.; Hossain, S.S.; Khorasani, S.; O’Sullivan, L.R.; Wiesmann, C.L.; Bush, J.; Richard, J.D.; Haney, C.H. A Genome-Wide Screen Identifies Genes in Rhizosphere-Associated Pseudomonas Required to Evade Plant Defenses. mBio 2018, 9, e00433-18. [Google Scholar] [CrossRef]
  18. Levy, A.; Salas Gonzalez, I.; Mittelviefhaus, M.; Clingenpeel, S.; Herrera Paredes, S.; Miao, J.; Wang, K.; Devescovi, G.; Stillman, K.; Monteiro, F.; et al. Genomic features of bacterial adaptation to plants. Nat. Genet. 2017, 50, 138–150. [Google Scholar] [CrossRef]
  19. Xiong, C.; Singh, B.K.; He, J.-Z.; Han, Y.-L.; Li, P.-P.; Wan, L.-H.; Meng, G.-Z.; Liu, S.-Y.; Wang, J.-T.; Wu, C.-F.; et al. Plant developmental stage drives the differentiation in ecological role of the maize microbiome. Microbiome 2021, 9, 171. [Google Scholar] [CrossRef]
  20. Cordovez, V.; Schop, S.; Hordijk, K.; Dupré de Boulois, H.; Coppens, F.; Hanssen, I.; Raaijmakers, J.M.; Carrión, V.J. Priming of Plant Growth Promotion by Volatiles of Root-Associated Microbacterium spp. Appl. Environ. Microbiol. 2018, 84, e01865-18. [Google Scholar] [CrossRef]
  21. Goldstein, A.H. Recent progress in understanding the molecular genetics and biochemistry of calcium phosphate solubilization by gram negative bacteria. Biol. Agric. Hortic. 1995, 12, 185–193. [Google Scholar] [CrossRef]
  22. Kuang, W.; Chen, W.; Lei, C.; Dai, Y.; Tian, X.; Tang, S.; Qian, Q.; Zhang, C.; Fu, L.; Zhou, G.; et al. Diversity of endophytic bacterial community in rice roots and their roles in phosphate solubilization and plant growth. Rhizosphere 2024, 30, 100877. [Google Scholar] [CrossRef]
  23. Zhang, L.; Zhang, M.; Huang, S.; Li, L.; Gao, Q.; Wang, Y.; Zhang, S.; Huang, S.; Yuan, L.; Wen, Y.; et al. A highly conserved core bacterial microbiota with nitrogen-fixation capacity inhabits the xylem sap in maize plants. Nat. Commun. 2022, 13, 3361. [Google Scholar] [CrossRef] [PubMed]
  24. Elnahal, A.S.M.; El-Saadony, M.T.; Saad, A.M.; Desoky, E.-S.M.; El-Tahan, A.M.; Rady, M.M.; AbuQamar, S.F.; El-Tarabily, K.A. The use of microbial inoculants for biological control, plant growth promotion, and sustainable agriculture: A review. Eur. J. Plant Pathol./Eur. Found. Plant Pathol. 2022, 162, 759–792. [Google Scholar] [CrossRef]
  25. Chojnacka, K.; Moustakas, K.; Witek-Krowiak, A. Bio-based fertilizers: A practical approach towards circular economy. Bioresour. Technol. 2020, 295, 122223. [Google Scholar] [CrossRef]
  26. Grames, J.; Zoboli, O.; Laner, D.; Rechberger, H.; Zessner, M.; Sánchez-Romero, M.; Prskawetz, A. Understanding feedbacks between economic decisions and the phosphorus resource cycle: A general equilibrium model including material flows. Resour. Policy 2019, 61, 311–347. [Google Scholar] [CrossRef]
  27. Dugan, S.T.; Muhammetoglu, A.; Uslu, A. A combined approach for the estimation of groundwater leaching potential and environmental impacts of pesticides for agricultural lands. Sci. Total Environ. 2023, 901, 165892. [Google Scholar] [CrossRef]
  28. Kim, K.-H.; Kabir, E.; Jahan, S.A. Exposure to pesticides and the associated human health effects. Sci. Total Environ. 2017, 575, 525–535. [Google Scholar] [CrossRef]
  29. Ndakidemi, B.; Mtei, K.; Ndakidemi, P.A. Impacts of synthetic and botanical pesticides on beneficial insects. Agric. Sci. China 2016, 7, 364–372. [Google Scholar] [CrossRef]
  30. Fraser, E.; Legwegoh, A.; Kc, K.; CoDyre, M.; Dias, G.; Hazen, S.; Johnson, R.; Martin, R.; Ohberg, L.; Sethuratnam, S.; et al. Biotechnology or organic? Extensive or intensive? Global or local? A critical review of potential pathways to resolve the global food crisis. Trends Food Sci. Technol. 2016, 48, 78–87. [Google Scholar] [CrossRef]
  31. Olanrewaju, O.S.; Glick, B.R.; Babalola, O.O. Mechanisms of action of plant growth promoting bacteria. World J. Microbiol. Biotechnol. 2017, 33, 197. [Google Scholar] [CrossRef] [PubMed]
  32. Shade, A.; Jones, S.E.; Caporaso, J.G.; Handelsman, J.; Knight, R.; Fierer, N.; Gilbert, J.A. Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. mBio 2014, 5, e01371-14. [Google Scholar] [CrossRef]
  33. Heuer, H.; Krsek, M.; Baker, P.; Smalla, K.; Wellington, E.M. Analysis of actinomycete communities by specific amplification of genes encoding 16S rRNA and gel-electrophoretic separation in denaturing gradients. Appl. Environ. Microbiol. 1997, 63, 3233–3241. [Google Scholar] [CrossRef] [PubMed]
  34. Hall, M.; Beiko, R.G. 16S rRNA Gene Analysis with QIIME2. Methods Mol. Biol. 2018, 1849, 113–129. [Google Scholar]
  35. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013, 41, D590–D596. [Google Scholar] [CrossRef]
  36. Langdon, W.B. Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks. BioData Min. 2015, 8, 1. [Google Scholar] [CrossRef]
  37. Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R.; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009, 25, 2078–2079. [Google Scholar] [CrossRef] [PubMed]
  38. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
  39. Menzel, P.; Ng, K.L.; Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 2016, 7, 11257. [Google Scholar] [CrossRef]
  40. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. Off. Organ Int. Assoc. Veg. Sci. 2003, 14, 927–930. [Google Scholar] [CrossRef]
  41. Edgar, R.C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010, 26, 2460–2461. [Google Scholar] [CrossRef] [PubMed]
  42. Bankevich, A.; Nurk, S.; Antipov, D.; Gurevich, A.A.; Dvorkin, M.; Kulikov, A.S.; Lesin, V.M.; Nikolenko, S.I.; Pham, S.; Prjibelski, A.D.; et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 2012, 19, 455–477. [Google Scholar] [CrossRef] [PubMed]
  43. Gurevich, A.; Saveliev, V.; Vyahhi, N.; Tesler, G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 2013, 29, 1072–1075. [Google Scholar] [CrossRef] [PubMed]
  44. Kang, D.D.; Li, F.; Kirton, E.; Thomas, A.; Egan, R.; An, H.; Wang, Z. MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019, 7, e7359. [Google Scholar] [CrossRef]
  45. Chaumeil, P.-A.; Mussig, A.J.; Hugenholtz, P.; Parks, D.H. GTDB-Tk: A toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019, 36, 1925–1927. [Google Scholar] [CrossRef]
  46. Simão, F.A.; Waterhouse, R.M.; Ioannidis, P.; Kriventseva, E.V.; Zdobnov, E.M. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 2015, 31, 3210–3212. [Google Scholar] [CrossRef]
  47. Seemann, T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 2014, 30, 2068–2069. [Google Scholar] [CrossRef]
  48. Bowers, R.M.; Kyrpides, N.C.; Stepanauskas, R.; Harmon-Smith, M.; Doud, D.; Reddy, T.B.K.; Schulz, F.; Jarett, J.; Rivers, A.R.; Eloe-Fadrosh, E.A.; et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 2017, 35, 725–731. [Google Scholar] [CrossRef]
  49. Bevivino, A.; Dalmastri, C.; Tabacchioni, S.; Chiarini, L. Efficacy of Burkholderia cepacia MCI 7 in disease suppression and growth promotion of maize. Biol. Fertil. Soils 2000, 31, 225–231. [Google Scholar] [CrossRef]
  50. Mitter, B.; Petric, A.; Chain, P.S.; Trognitz, F.; Nowak, J.; Compant, S.; Sessitsch, A. Genome analysis, ecology, and plant growth promotion of the endophyte Burkholderia phytofirmans strain PsJN. In Molecular Microbial Ecology of the Rhizosphere; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2013; pp. 865–874. [Google Scholar]
  51. Pal, G.; Saxena, S.; Kumar, K.; Verma, A.; Sahu, P.K.; Pandey, A.; White, J.F.; Verma, S.K. Endophytic Burkholderia: Multifunctional roles in plant growth promotion and stress tolerance. Microbiol. Res. 2022, 265, 127201. [Google Scholar] [CrossRef]
  52. Rojas-Rojas, F.U.; López-Sánchez, D.; Meza-Radilla, G.; Méndez-Canarios, A.; Ibarra, J.A.; Estrada-de Los Santos, P. The controversial Burkholderia cepacia complex, a group of plant growth promoting species and plant, animals and human pathogens. Rev. Argent. Microbiol. 2019, 51, 84–92. [Google Scholar]
  53. Pande, A.; Pandey, P.; Kaushik, S. Co-inoculation of Burkholderia cepacia and Alcaligenes aquatilis enhances plant growth of maize (Zea mays) under green house and field condition. Korean J. Agric. Sci. 2017, 44, 196–210. [Google Scholar]
  54. Govan, J.R.; Hughes, J.E.; Vandamme, P. Burkholderia cepacia: Medical, taxonomic and ecological issues. J. Med. Microbiol. 1996, 45, 395–407. [Google Scholar] [CrossRef]
  55. Ji, X.; Lu, G.; Gai, Y.; Gao, H.; Lu, B.; Kong, L.; Mu, Z. Colonization of Morus alba L. by the plant-growth-promoting and antagonistic bacterium Burkholderia cepacia strain Lu10-1. BMC Microbiol. 2010, 10, 243. [Google Scholar] [CrossRef] [PubMed]
  56. Li, G.-X.; Wu, X.-Q.; Ye, J.-R. Biosafety and colonization of Burkholderia multivorans WS-FJ9 and its growth-promoting effects on poplars. Appl. Microbiol. Biotechnol. 2013, 97, 10489–10498. [Google Scholar] [CrossRef]
  57. Peixoto, R.S.; Voolstra, C.R.; Sweet, M.; Duarte, C.M.; Carvalho, S.; Villela, H.; Lunshof, J.E.; Gram, L.; Woodhams, D.C.; Walter, J.; et al. Harnessing the microbiome to prevent global biodiversity loss. Nat. Microbiol. 2022, 7, 1726–1735. [Google Scholar] [CrossRef]
  58. Favela, A.; Bohn, M.O.; Kent, A.D. Maize germplasm chronosequence shows crop breeding history impacts recruitment of the rhizosphere microbiome. ISME J. 2021, 15, 2454–2464. [Google Scholar] [CrossRef]
  59. Barros-Rodríguez, A.; Rangseekaew, P.; Lasudee, K.; Pathom-Aree, W.; Manzanera, M. Impacts of Agriculture on the Environment and Soil Microbial Biodiversity. Plants 2021, 10, 2325. [Google Scholar] [CrossRef]
  60. Vicente, E.J.; Dean, D.R. Review of Keeping the nitrogen-fixation dream alive. Proc. Natl. Acad. Sci. USA 2017, 114, 3009–3011. [Google Scholar] [CrossRef]
  61. Arragain, S.; Jiménez-Vicente, E.; Scandurra, A.A.; Burén, S.; Rubio, L.M.; Echavarri-Erasun, C. Diversity and Functional Analysis of the FeMo-Cofactor Maturase NifB. Front. Plant Sci. 2017, 8, 1947. [Google Scholar] [CrossRef]
  62. Rettberg, L.A.; Wilcoxen, J.; Jasniewski, A.J.; Lee, C.C.; Tanifuji, K.; Hu, Y.; Britt, R.D.; Ribbe, M.W. Identity and function of an essential nitrogen ligand of the nitrogenase cofactor biosynthesis protein NifB. Nat. Commun. 2020, 11, 1757. [Google Scholar] [CrossRef] [PubMed]
  63. Zhao, D.; Curatti, L.; Rubio, L.M. Evidence for nifU and nifS participation in the biosynthesis of the iron-molybdenum cofactor of nitrogenase. J. Biol. Chem. 2007, 282, 37016–37025. [Google Scholar] [CrossRef] [PubMed]
  64. Hernandez, J.A.; Curatti, L.; Aznar, C.P.; Perova, Z.; Britt, R.D.; Rubio, L.M. Metal trafficking for nitrogen fixation: NifQ donates molybdenum to NifEN/NifH for the biosynthesis of the nitrogenase FeMo-cofactor. Proc. Natl. Acad. Sci. USA 2008, 105, 11679–11684. [Google Scholar] [CrossRef] [PubMed]
  65. Imperial, J.; Ugalde, R.A.; Shah, V.K.; Brill, W.J. Role of the nifQ gene product in the incorporation of molybdenum into nitrogenase in Klebsiella pneumoniae. J. Bacteriol. 1984, 158, 187–194. [Google Scholar] [CrossRef] [PubMed]
  66. Duine, J.A. Quinoproteins: Enzymes containing the quinonoid cofactor pyrroloquinoline quinone, topaquinone or tryptophan-tryptophan quinone. Eur. J. Biochem./FEBS 1991, 200, 271–284. [Google Scholar] [CrossRef] [PubMed]
  67. Li, L.; Pan, S.; Melzer, R.; Fricke, W. Apoplastic barriers, aquaporin gene expression and root and cell hydraulic conductivity in phosphate-limited sheepgrass plants. Physiol. Plant. 2020, 168, 118–132. [Google Scholar] [CrossRef] [PubMed]
  68. Bhattacharya, A. Changing environmental condition and phosphorus-use efficiency in plants. In Changing Climate and Resource Use Efficiency in Plants; Elsevier: Amsterdam, The Netherlands, 2019; pp. 241–305. [Google Scholar]
  69. Ahmad, R.; Khalid, A.; Arshad, M.; Zahir, Z.A.; Mahmood, T. Effect of compost enriched with N and L-tryptophan on soil and maize. Agron. Sustain. Dev. 2008, 28, 299–305. [Google Scholar] [CrossRef]
  70. Gondek, K.; Mierzwa-Hersztek, M. Effect of soil-applied L-tryptophan on the amount of biomass and nitrogen and sulfur utilization by maize. Agronomy 2021, 11, 2582. [Google Scholar] [CrossRef]
  71. Blaha, D.; Prigent-Combaret, C.; Mirza, M.S.; Moënne-Loccoz, Y. Phylogeny of the 1-aminocyclopropane-1-carboxylic acid deaminase-encoding gene acdS in phytobeneficial and pathogenic Proteobacteria and relation with strain biogeography. FEMS Microbiol. Ecol. 2006, 56, 455–470. [Google Scholar] [CrossRef]
  72. Sapre, S.; Gontia-Mishra, I.; Tiwari, S. ACC deaminase-producing bacteria: A key player in alleviating abiotic stresses in plants. In Plant Growth Promoting Rhizobacteria for Agricultural Sustainability; Springer: Singapore, 2019; pp. 267–291. [Google Scholar]
  73. Kader, A.A. Ethylene-induced senescence and physiological disorders in harvested horticultural crops. HortScience 1985, 20, 54–57. [Google Scholar] [CrossRef]
  74. Morgan, P.W.; Drew, M.C. Ethylene and plant responses to stress. Physiol. Plant. 1997, 100, 620–630. [Google Scholar] [CrossRef]
  75. Lund, S.T.; Stall, R.E.; Klee, H.J. Ethylene regulates the susceptible response to pathogen infection in tomato. Plant Cell 1998, 10, 371–382. [Google Scholar] [CrossRef] [PubMed]
  76. Moon, Y.-S.; Ali, S. A fruitful decade of bacterial ACC deaminase biotechnology: A pragmatic approach towards abiotic stress relief in plants. Theor. Exp. Plant Physiol. 2022, 34, 109–129. [Google Scholar] [CrossRef]
  77. Shao, J.; Miao, Y.; Liu, K.; Ren, Y.; Xu, Z.; Zhang, N.; Feng, H.; Shen, Q.; Zhang, R.; Xun, W. Rhizosphere microbiome assembly involves seed-borne bacteria in compensatory phosphate solubilization. Soil Biol. Biochem. 2021, 159, 108273. [Google Scholar] [CrossRef]
  78. Ahmad, T.; Bashir, A.; Farooq, S.; Riyaz-Ul-Hassan, S. Burkholderia gladioli E39CS3, an endophyte of Crocus sativus Linn.; induces host resistance against corm-rot caused by Fusarium oxysporum. J. Appl. Microbiol. 2022, 132, 495–508. [Google Scholar] [CrossRef]
  79. Ho, Y.-N.; Chiang, H.-M.; Chao, C.-P.; Su, C.-C.; Hsu, H.-F.; Guo, C.-T.; Hsieh, J.-L.; Huang, C.-C. In planta biocontrol of soilborne Fusarium wilt of banana through a plant endophytic bacterium, Burkholderia cenocepacia 869T2. Plant Soil 2015, 387, 295–306. [Google Scholar] [CrossRef]
  80. Tagele, S.B.; Kim, S.W.; Lee, H.G.; Lee, Y.S. Potential of Novel Sequence Type of Burkholderia cenocepacia for Biological Control of Root Rot of Maize (Zea mays L.) Caused by Fusarium temperatum. Int. J. Mol. Sci. 2019, 20, 1005. [Google Scholar] [CrossRef]
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Figure 1. Saturation curve of OTUs found in each sample. Horizontal lines indicate the final number of species from each sample.
Figure 1. Saturation curve of OTUs found in each sample. Horizontal lines indicate the final number of species from each sample.
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Figure 2. Relative abundance of OTUs. (A) Metagenome shotgun sequencing data (higher taxonomic levels are presented between parentheses). OTUs with less than 1% relative abundance across all samples and reads without taxonomic assignment were collectively classified as “Other”; (B) 16S rRNA data.
Figure 2. Relative abundance of OTUs. (A) Metagenome shotgun sequencing data (higher taxonomic levels are presented between parentheses). OTUs with less than 1% relative abundance across all samples and reads without taxonomic assignment were collectively classified as “Other”; (B) 16S rRNA data.
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Figure 3. Principal Component Analysis performed with the OTU diversity and abundance across samples.
Figure 3. Principal Component Analysis performed with the OTU diversity and abundance across samples.
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Figure 4. Functional heatmap of plant-growth promotion genes in each sample.
Figure 4. Functional heatmap of plant-growth promotion genes in each sample.
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Table 1. Complete sample information with sample identification based on the description in the Methods section (Santa Helena variety = SH; Sol da Manhã variety = SOL).
Table 1. Complete sample information with sample identification based on the description in the Methods section (Santa Helena variety = SH; Sol da Manhã variety = SOL).
SampleVarietyNumber of OTUs in Metagenomic DataTotal Read Count16S Read CountSingletonsGood’s IndexShannon’s Diversity IndexAlpha Diversity
SH1SHS-5050174117,853,45440,04612399.999240.46150.28
SH2SHS-5050163720,265,86943,50722599.998790.44138.62
SH3SHS-5050167017,844,95841,19120999.998720.45143.42
SOL1Sol da Manhã189918,881,65041,5684099.999631.56172.00
SOL2Sol da Manhã182118,250,32841,6316999.999550.82158.52
SOL3Sol da Manhã188820,837,84837,6823899.999720.99166.88
Table 2. MAGs’ features and metadata. MAGs classified only at the genus level are presented between parentheses.
Table 2. MAGs’ features and metadata. MAGs classified only at the genus level are presented between parentheses.
AssemblyContigsTotal Length (bp)GC (%)N50L50ClassificationCompleteness (%)Duplication (%)
SH1.2677,326,04666.84159,76512Burkholderia cenocepacia_B97.80.4
SH1.31077,837,77468.31126,33218Burkholderia gladioli91.90.6
SH2.1717,313,02966.85179,01614Burkholderia cenocepacia_B98.10.4
SH2.2827,745,20568.3150,95916Burkholderia gladioli92.10.4
SH3.1667,322,31366.84188,41414Burkholderia cenocepacia_B97.90.4
SH3.31007,780,05768.26120,20220Burkholderia gladioli93.90.4
SOL1.17842,479,334583304264(Terriglobus)49.20.3
SOL1.24723,628,55471.4212,24696(Curtobacterium)91.30
SOL1.75049,566,51367.4435,14878Burkholderia gladioli75.524
SOL1.9184,819,02957.46374,2135Pantoea dispersa69.20.9
SOL1.10588,220,75965.06258,14910Paraburkholderia tropica95.81.2
SOL1.111825,852,37768.8347,27737(Burkholderia)5115
SOL2.3124,843,39657.44690,4502Pantoea dispersa73.60.9
SOL2.4928,328,54467.93222,00612Burkholderia gladioli99.20.7
SOL3.3204,859,99157.45416,5924Pantoea dispersa74.70.9
SOL3.46613,263,73871.345741171(Curtobacterium)76.10.3
SOL3.614358,789,04164.698009303Paraburkholderia tropica59.23.1
SOL3.82037,160,87468.8262,20341Burkholderia gladioli95.20.4
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Henaut-Jacobs, S.; Cyríaco, B.E.B.; Pedrosa-Silva, F.; Olivares, F.L.; Venancio, T.M. Comparative Metagenomic Profiling of Seed-Borne Microbiomes in a Landrace and a Hybrid Maize Variety. Seeds 2024, 3, 528-539. https://doi.org/10.3390/seeds3040035

AMA Style

Henaut-Jacobs S, Cyríaco BEB, Pedrosa-Silva F, Olivares FL, Venancio TM. Comparative Metagenomic Profiling of Seed-Borne Microbiomes in a Landrace and a Hybrid Maize Variety. Seeds. 2024; 3(4):528-539. https://doi.org/10.3390/seeds3040035

Chicago/Turabian Style

Henaut-Jacobs, Sarah, Beatriz Elisa Barcelos Cyríaco, Francisnei Pedrosa-Silva, Fabio Lopes Olivares, and Thiago Motta Venancio. 2024. "Comparative Metagenomic Profiling of Seed-Borne Microbiomes in a Landrace and a Hybrid Maize Variety" Seeds 3, no. 4: 528-539. https://doi.org/10.3390/seeds3040035

APA Style

Henaut-Jacobs, S., Cyríaco, B. E. B., Pedrosa-Silva, F., Olivares, F. L., & Venancio, T. M. (2024). Comparative Metagenomic Profiling of Seed-Borne Microbiomes in a Landrace and a Hybrid Maize Variety. Seeds, 3(4), 528-539. https://doi.org/10.3390/seeds3040035

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