Introduction

Microorganisms are the most abundant and diverse forms of life, estimated to constitute up to 60% of the Earth’s biomass (Singh et al. 2009). Widely distributed in nature, they can withstand extreme environmental conditions, presenting mechanisms that allow their adaptation to the most diverse niches (Jayadev and Navami 2014). Microbial interactions play a fundamental role in balancing various natural processes, including those established between plants and microorganisms. Due to the ability to inhabit multiple environments with high microbial richness, plants interact with microorganisms essential for ecosystem balance (Kumar et al. 2016).

Such “plant-microorganism” interactions can occur in different ways and levels. Almost all the plant organs interact with microbes at some stage of their development, from germination to senescence. In general, in addition to plants providing a protected habitat for microorganisms, several organic and inorganic compounds they produce represent a rich source of nutrients, thus providing an environment favorable to the colonization of microbial communities. In contrast, microbial communities also can interfere (directly or indirectly) in plant physiology through pathogenic, commensal, mutualistic, or amensalistic interactions (Kumar et al. 2016; Schirawski and Perlin 2018).

Plant colonization by various microorganisms can influence plant health (beneficial, neutral, or harmful) and development (Müller et al. 2016). The processes of colonization and establishment of beneficial or pathogenic interactions between plant-microorganisms require the modulation of the plant immune system by microorganisms, thus leading to a complex and intricate relationship (Chadha 2019). The ability of plants to simultaneously benefit from the microbiota’s favorable roles and tolerate or resist pathogenic activities performed by different microorganisms has aroused several researchers’ attention over the years.

The microbiome—a set of microorganisms associated with a host plant species is composed of bacterial communities and other organisms, including viruses, fungi, archaea, and nematodes, which contribute to relevant functions in the biology of the host (Orozco-Mosqueda et al. 2018). It plays an essential role in plant physiology through involvement in biotic and abiotic factors (Kaul et al. 2017). Besides, it exerts a positive influence on plant development by contributing microorganisms associated with the plant, including nutrition, protection against pathogens, and stress tolerance (Mitter et al. 2019). Consequently, elucidating and understanding aspects of the plant microbiome is essential for identifying microorganisms that can be better explored to promote plants’ growth and health under different conditions.

In turn, endophytes regard plant endosymbiotic microorganisms—often bacteria or fungi. They colonize plants’ inter- and/or intracellular locations during all or part of the plant’s life cycle, with no apparent disease symptoms. Endophytes are known to enhance plant growth and nutrient gain, sometimes improving the plant’s ability to tolerate abiotic and biotic stresses. Some endophytes produce phytohormones and other bioactive compounds of biotechnological interest, including pharmaceutical drugs (Gouda et al. 2016).

To distinguish the whole endophyte community (here called “endophytome”) from other microorganisms that inhabit plants (including pathogenic ones) is challenging. In this context, studies on plant microbiome have opened new possibilities to infer who are “the good and the bad guys” or even who is there without significant consequences. Thus, there are still fundamental questions that need to be clarified, including the interaction (at the molecular level) between host-microorganisms and the potential of applying this knowledge in various areas of expertise (van der Heijden and Schlaeppi 2015). Although different microorganisms colonize plants, most microbiome research studies are related to isolated taxonomic groups (mainly prokaryotes). Thus, the joint analysis of other microbial groups and their interactions within the microbiota and with the plant host is a concept that is on the rise and can contribute to the elucidation of the critical microbiome’s functional characteristics (Berg 2015).

Given the significance of the microbiome for host plants, further investigations are required to fill the gaps concerning the complex communication between microorganisms and plants and exploit these resources to promote plant species’ tolerance to adverse conditions. Therefore, new studies integrating data from interdisciplinary areas have been carried out, ranging from microbiology and molecular biology to agronomy. In this context, the present review presents an overview of the plant microbiome, the plant niches where microbial communities’ interactions are established, and the main tools (including bioinformatics) that help study those communities at the omics level. Emerging studies on non-model plant microbiomes are addressed and, finally, the prospects for its application are discussed, aiming to promote more sustainable agricultural production.

Characterization of the microbiome and its communities in plant tissues

Microorganisms associated with plants can inhabit from the host’s surface (epiphytes) to the internal tissues (endophytes) (Kumar et al. 2017b), whereas endophytic seem to have a most important functional role. In plants, interactions with the microbiota can occur in three distinct compartments: (i) the rhizosphere (in the soil, under the influence of the roots) (Fig. 1a); (ii) the endosphere (in the internal tissues) (Fig. 1b), and (iii) the phyllosphere (in the aerial part) (Fig. 1c) (Sekar and Kandavel 2010). These microenvironments provide biotic and abiotic conditions necessary to maintain plant-associated microbial life.

Fig. 1
figure 1

Schematic representation of a plant in its environment, indicating its main compartments where microbial communities can inhabit: phyllosphere (a); endosphere (b); rhizosphere (c); and those present in the soil (d). Microorganisms horizontal transmission can be established with the host plant from the air or soil microbiome. In the latter case, root exudates (white circles) play an important role in recruiting microorganisms from the soil to the rhizosphere. In contrast, vertical transmission can occur through advancing colonization in different plant tissues by microorganisms or between generations of plants through seeds (e)

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The plant microbiome’s structuring comprises a dynamic process reflecting changes in the microbial community, given the environmental conditions and plant developmental stage (Müller et al. 2016). The complex interaction between the processes of selection, dispersion, drift, and speciation influences a given community’s structuring and dynamics (Vellend 2016). The balance between these processes in the plant-microbiome relationship depends on the plant’s genotype, soil composition, stress type (biotic/abiotic), and agricultural practices.

It has been proposed that the host species strongly influence the composition of the plant microbiome (Thijs et al. 2016). A comparative study was carried out between the rhizosphere’s microbial communities and the roots endosphere of Setaria italic Roem. & Schult. (Poaceae) and its wild relative S. viridis (L.) Thell. Such studies reveal distinct communities between both species, especially regarding Betaproteobacteria and Firmicutes and, among archaea, the Methanobacteria and Methanomicrobia. Such differences reflect the critical role of the genotype in selecting members of the prokaryotic community involved in the plant-soil interaction (Chaluvadi and Bennetzen 2018).

In contrast, the composition of the root bacterial community of selected lycopods, ferns, angiosperms, and gymnosperms indicated the soil as the primary determining factor. However, a significant correlation with the phylogeny of host plants was identified (Yeoh et al. 2017). Similar results were found in studying the rhizosphere and endosphere of different rice cultivars (Oryza sativa L., Poaceae), where the soil played a more substantial role than the host genotype in the formation of the microbial community (Xu et al. 2020). Considering the phyllosphere, the host species also assumes an essential role in the composition of bacterial communities, as observed in seven different tree species in Brazil’s Atlantic forest (Lambais et al. 2014).

One of the primary determining factors for the acquisition and establishment of the microbiome in different compartments of the host plant is the “transmission pattern” (Simon et al. 2019). The so-called “horizontal transmission” can occur through the air-plant (phyllosphere) interface or through the soil, which acts as an essential source of microbial diversity associated with the host plant. In this case, soil microorganisms are attracted to the rhizosphere through root exudates (represented by white circles in Fig. 1). Part of the microorganisms penetrate the plant’s roots and colonize the internal tissues. In turn, “vertical transmission” assumes its role when colonization proceeds to the host plant (seeds) above the soil surface (Zheng and Gong 2019). Wind, water and animals can be involved in the vertical transmission of endophytic microorganisms, but this may occur between generations of plants through seeds. Such endophytes are transmitted from generation to generation since seed-borne transmission assures their presence in seedlings of the next generation (Fig. 1e) (Shade et al. 2017). An interplay between both horizontal and vertical transmission is assumed. In this sense, plant-soil biotic interactions lead to the gradual colonization of a subgroup of microorganisms in the established continuum of plant compartments from the soil to the host tissues (Vandenkoornhuyse et al. 2015).

Rhizosphere—microbial diversity recruited from the soil

The rhizosphere (Fig. 1c) comprises the soil’s narrow region under the influence of the plant’s roots, characterized by a richness in microorganisms, considered one of the most dynamic environments on the planet (Philippot et al. 2013). Several microorganisms inhabit this environment, including bacteria, fungi, protozoa, and multicellular organisms, such as nematodes and some invertebrates. The processes of root exudation, in the form of primary and secondary metabolites, besides signaling in the rhizosphere, influence the selection and colonization of microbial communities in this region (Venturi and Keel 2016).

The rhizosphere’s recruitment of microorganisms is expected, mainly due to the exudation of root compounds (white circles, Fig. 1), which mediate the interaction between plant and soil microorganisms (Fig. 1d). Consequently, plants can alter their exudates’ composition, thus modifying the rhizospheric microbiome, attracting beneficial microorganisms (such as mycorrhizal fungi and growth-promoting rhizobacteria) from the soil, or inhibiting the spread of pathogenic microorganisms (Vives-Peris et al. 2020).

Together with its physical–chemical properties and biogeographic processes, the soil influences the rhizospheric microbiome formation, given its role in defining the root exudation pattern (Philippot et al. 2013). According to Turner et al. (2013b) the rhizospheric microbiome varies between plant species, considering that different hosts can control, harbor, and select various prokaryotic and eukaryotic microorganisms from the soil. For example, in a study on the rhizospheric plant community in temperate forests, based on 16S rRNA (ribosomal RNA) meta-barcode, significant differences were identified between the OTUs (Operational Taxonomic Units) of Milium effusum Lour (Poaceae) and Stachys sylvatica Torr (Lamiaceae), demonstrating the influence of the plant species in determining the composition of the bacterial community of the rhizospheric soil (Ma et al. 2019). Using the same approach (16S rRNA) to analyze the rhizosphere of 19 herbaceous plant species, Dawson et al. (2017) identified OTUs that responded to specific plant taxa. Some plant taxa were related to critical microbial members that influenced plant growth during beneficial interactions, despite representing a small number.

Independent cultivation approaches reveal that the microbial diversity of the rhizosphere is underestimated. Among the various microorganisms characterized as beneficial for plant development and health, nitrogen-fixing bacteria, PGPR (Plant Growth Promoting Rhizobacteria), and mycorrhizal fungi have drawn particular attention. PGPR as an alternative to replacing agrochemicals is a promising approach to developing more sustainable agriculture (Shailendra Singh 2015). PGPR promotes plant growth directly through the production of plant hormones or the availability of nutrients, or indirectly, through antimicrobial metabolites for biocontrol (Arora et al. 2013). As an example of the benefits, Kumari et al. (2018) demonstrated that bacteria isolated from the rhizosphere of mung beans (Vigna radiata R. Wilczek, Leguminosae) had significant PGPR attributes, such as phosphate solubilization and phytohormone production, in addition to the ability to inhibit (in vitro) the growth of the phytopathogen Rhizoctonia solani, the causative agent of root rot in this species. In a metagenomic study of Paspalum scrobiculatum L. (Poaceae), Prabha et al. (2019) identified high taxonomic diversity in the rhizosphere associated with the metabolic capacities of microbial communities to promote plant growth and development, including carbon fixation, nitrogen, phosphorus, response to stress, and phytohormone synthesis. Thus, different microbial members of the rhizosphere may be potential candidates for improving plant development and health during plant–soil interactions.

Endosphere—a selective niche

For a long time, microorganisms inside the plants were erroneously associated with pathogens, which promoted damage in the development and loss of crop yield (Andreote et al. 2014). It has been considered that microorganisms that occupy the endosphere (or internal tissues such as root, stem, or leaves), called endophytes (Turner et al. 2013a) without causing deleterious symptoms, were non-pathogenic. Still, depending on the environmental conditions or host defense response, some can be considered latent pathogens (Schulz and Boyle 2005).

According to their lifestyle, endophytic microorganisms can be classified into two categories: (i) mandatory endophytes, which require the metabolism of the host plant for its growth and survival, whose transmission occurs vertically or through vectors; and (ii) facultative endophytes, which may or may not live within the host plant (and in other habitats) at some stage in their life cycle (Hardoim et al. 2008). Thus, endophytic microorganisms can gain access and colonize the internal tissues of the plant by horizontal (soil) and/or vertical (seed) transmission (Omomowo and Babalola 2019).

Seeds are essential vehicles in establishing the endophytome between successive plant generations through vertical transmission, acting as an initial inoculum in the formation of the microbiome of descendant plants (Shade et al. 2017). Microorganisms that inhabit the seed embryo or endosperm are more likely to be transferred vertically than those present in the outer seed coating (Barret et al. 2016). In addition, the seed endophytome also represents a reservoir of microorganisms able to colonize plant tissues, playing a crucial role in later stages of development, promoting nutrient absorption and resistance to pathogens (Rybakova et al. 2017; Bergna et al. 2018), among other features.

As demonstrated by Rodríguez et al. (2020), the association of healthy seeds with environmental sources of microorganisms ensures the establishment and vigor in the initial stages of seedling development during the assembly of the endophytome. Endophytes from external sources (soil, rhizosphere, phyllosphere, and environment) are also important for assembling the seed microbiome, favoring early plant establishment and vigor (Rodríguez et al. 2020).

The structure of the root endophytic community is guided by three main factors: (i) the soil, including geographical, geological, and edaphoclimatic aspects, which influence the microorganisms that can occupy the soil, affecting the community of endophytes; (ii) the host plant, whose species, stage of development and health affects microbial colonization; (iii) the endophytic microorganism, its ability to penetrate and to colonize the root tissue (Gaiero et al. 2013); and an additional aspect (iv) should be considered regarding the genomic and epigenomic features of each individual of a given species.

Among the factors that influence the bacterial spectrum before endophytic root colonization, the roots' architecture and the composition of the root exudates deserve mention, besides mycorrhization or existing wounds in the penetration region. Once colonization has occurred, other factors are responsible for selecting the bacterial spectrum within the root, such as the dimension of the intercellular space, the disposition of nutrients in apoplastic fluids, besides plant response to endophytic colonization (Hallmann and Berg 2006).

Campisano et al. (2017) proposed that seasonal changes throughout the year and temperature affect the succession of endophytic communities in the stem of grape plants (Vitis vinifera L., Vitaceae) more intensely than in the roots. This study comprised a controlled experiment with different air temperatures (15, 25, or 35 °C), inducing a significant increase in the microbial diversity of stems, which was reversed along the evaluated time. Furthermore, evaluation under field conditions during different seasons (every three months) affected the stem microbial composition more than the root microbiome. The results suggested that grapevine roots offer a more stable compartment for the endophytome, being less susceptible to environmental disturbances.

In general, the microbial diversity of the endosphere tends to be lower than that of the rhizosphere, considering that only a subset of the microorganisms colonizes the rhizoplane. An even smaller fraction is released and proliferates in the endosphere (Edwards et al. 2015). The selectivity of the endosphere is also a determinant of lower microbial diversity. For example, Lundberg et al. (2012) demonstrated that under controlled conditions, the microbiome of Arabidopsis thaliana L. (Brassicaceae) suffers a drastic loss in diversity from the rhizosphere compartment in the root endosphere. In this research, several bacterial taxa typically present in the soil and rhizosphere were absent in the endosphere. Such an absence was due to three possible factors: (i) the activity of the host’s immune system; (ii) to the overcoming by successful colonizers of the endosphere; or (iii) to the metabolic inability to colonize this niche.

Some bacterial and fungal endophytes have demonstrated essential roles in plant growth and development of economically relevant crops when subjected to normal environmental conditions (Díaz-González et al. 2020) or even when exposed to salt stress (Khan et al. 2020). Endophytes can promote the development of plants under different conditions of environmental stresses, such as drought and nitrogen deficiency (Rho et al. 2018). Some endophytic microorganisms can also induce phytohormones’ expression, essential for plant growth, thus essential in arid environmental conditions, besides acting in biological control of phytopathogens (Asaf et al. 2017; Liotti et al. 2018).

Phyllosphere—dynamics and microbial colonization on plant surfaces

The phyllosphere comprises the entire surface of the plant above the ground, predominantly represented by the leaves (Morris and Kinkel 2002). The phyllosphere microbiome comprises different genera of bacteria, filamentous fungi, yeasts, algae, and, occasionally, protozoa and nematodes, with bacteria representing the most abundant group in the leaves (Lindow and Brandl 2003).

The microbial community must undergo constant adaptations since the phyllosphere is directly influenced by abiotic factors, as environmental conditions, climate, ultraviolet radiation, water, and nutrient availability, besides biotic factors as host antimicrobial compounds. Consequently, microbial proliferation tends to be uneven in composition and distribution along the leaf surface (Vorholt 2012; Copeland et al. 2015).

At the species or cultivar level, some studies have shown that plant genotype plays a relevant role in establishing bacterial and fungal communities in the phyllosphere, besides geographic location or season of the year. This was the case for cereal crops (Sapkota et al. 2015) and arboreal species in a forest environment (Laforest-Lapointe et al. 2016). On the other hand, geographic location was identified as a dominant factor in the phyllosphere microbiome of three Tamarix (Tamaricaceae) species in different Israel and the USA regions. In these plants, bacterial and fungal communities of the phyllosphere growing under different climatic conditions were distinct (Finkel et al. 2011). Another study highlighted that (i) seasonal changes, (ii) genotypic factors, and (iii) geographic area (respectively, in this order of relevance) were determinants of the phyllosphere microbiome composition and diversity of arboreal gymnosperms in China (Bao et al. 2020).

The most critical transport sources for forming microbial communities in the phyllosphere include air, water, and soil (Bulgarelli et al. 2013). However, insects, seeds, tree shoots, sedimentation, rain splashes, harvesting, and cultivation practices have also been fundamental (Kumar et al. 2017b). Once in the phyllosphere (by any of these sources), microbial colonization occurs; it tends to be restricted to specific locations, such as stomata, at the base of trichomes, and under the cuticle. Also, most are found in aggregates or microbial biofilms (Leveau and Lindow 2001; Whipps et al. 2008). Microbial communities in the phyllosphere are often associated with plant development, including their participation in the nitrogen fixation process (Abadi et al. 2020), in promoting growth (Mehanathan et al. 2016), and protection against phytopathogens (Wiraswati et al. 2019). Thus, the interactions between the phyllosphere microbiome, the plant, and the multiple environmental factors influence this microbial community’s modulation and favor heterogeneity in species abundance.

Modern molecular technologies have elucidated the importance of the phyllosphere microbiome, offering new opportunities to study microbial communities in structural, functional, and ecological terms. In this sense, recent advances in high-performance omics approaches have the potential to deepen the understanding of microbial communities in the phyllosphere and explore their impact on the ecosystem (Bringel and Couée 2015; Rossmann et al. 2017). In the case of humans, apparently, the irregular distribution of the microbiota along the skin's surface is associated with moisture and the availability of nutrients. They are also critical components in the protection against undesirable pathogens (Byrd et al. 2018). In the case of plants, can it be said that this distribution is analogous?

Metagenomics as a tool for microbiome analysis

The cultivation and classification of new microorganisms figure among the main objectives of microbiology (Handelsman 2004). For years, members of the plant microbiome have been characterized by traditional techniques involving isolation and cultivation (Yoo et al. 2017). However, it is estimated that only a small portion (about 1:1000) of microorganisms from environmental samples can be cultivated by conventional methods (Pace 2018). From this perception, there is an emerging demand to develop new approaches beyond cultivation to access the diversity and ecological functionality of the “microbial world” (Riesenfeld et al. 2004).

The independent culture methods mainly analyze nucleic acids extracted directly from the target sample, enabling access to the microbial community (Correia et al. 2012). Among the currently available approaches for studying the microbiome (Fig. 2), metagenomics stands out (Zeyaullah et al. 2009). In metagenomics, the genome evaluated is a microbial community in a given environment, not the genome of a single microorganism, as assumed in conventional genomics (Guazzaroni et al. 2009).

Fig. 2
figure 2

Different sequencing and bioinformatics strategies based on cultivation‐independent methods to study the plant microbiome. a The amplicon-based approach. Includes the amplification and sequencing of variable regions within rRNA genes (16S, 18S, and ITS). The filtered sequences are grouped into OTUs, which are taxonomically classified based on sequence homology. b Shotgun approaches include metagenomics and metatranscriptomics. Metagenomics comprises the whole metagenome sequencing (WMS), followed by mapping against reference genomes/genes or de novo assembly. Subsequently, the abundance of genes, genomes, and the functional potential of the retrieved sequences can be estimated. In contrast, metatranscriptomics encompasses the sequencing of the complete metatranscriptome, followed by mapping RNA sequences and reference genes to identify active pathways, genes, and microorganisms. The combined application of different methods contributes to a more holistic understanding of the composition and functional characteristics of the plant microbiome

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Metagenomics studies are based on the combination of genomics, bioinformatics, and systems biology for the joint investigation of microbial genomes, collected directly from an environmental sample. Advances, including lower cost of next-generation sequencing (NGS) technologies, new bioinformatics tools, in addition to high-throughput screening methods (HTS), have promoted substantial progress in metagenomic studies (Kumar et al. 2015). In the last decade, different plant compartments, including the rhizosphere (Goss-Souza et al. 2019), phyllosphere (Khoiri et al. 2021), and endosphere (Zhang et al. 2020a), have been the subject to such studies.

A metagenomic analysis begins with collecting a specific environmental sample containing a microbial community of interest, DNA extraction, followed by the sequencing and bioinformatic analysis steps, which involve steps from quality control to comparative analysis (Fig. 3) (Stanchev et al. 2016). Sampling is a primordial and essential step in metagenomic projects, as it is decisive in the quality of the data and, consequently, for the interpretation of the results obtained (Thomas et al. 2012). The main objectives in sampling are to ensure a sufficient amount of microbial biomass for sequencing purposes and minimize contamination of environmental samples. Therefore, care is required in optimizing the collection and storage methods according to the sample of interest (Quince et al. 2017).

Fig. 3
figure 3

Flowchart of the main approaches used in metagenomic projects and studies. DNA from metagenomic samples can be used for analyzes based on amplification and sequencing of marker genes (a–c) or in analyzes based on total sequencing (d–f)

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The efficient extraction of plant DNA is necessary to guarantee that the microbial community associated with each analyzed plant tissue is properly accessed (Fadiji and Babalola 2020). The isolated DNA must represent the totality of microbial cells present in the sample, have a sufficient amount and high quality to ensure the success of the subsequent stages (Thomas et al. 2012). However, the extraction and purification of high-quality DNA, associated with the lack of a standard extraction method for all environmental samples, constitute some of the main obstacles faced by metagenomic studies (Kunin et al. 2008). Several commercial kits were developed for DNA extraction from different types of samples. However, a careful literature review and validation of the protocol or kit of choice is highly recommended to select the most appropriate extraction method (Izard 2015).

Metagenomic analyzes can be divided into two distinct approaches: amplicon and shotgun metagenomics (Gilbert and Dupont 2011), the first also called meta-barcode. Both can be applied to metagenomic projects in an isolated or joint way to promote a better understanding of the structural diversity and/or functional potential of microbial communities, as well as their changes in response to external factors (National Research Council 2007). Further details are presented in the following sections.

Amplicon metagenomics

The study of microbial diversity by amplicon metagenomics is based on analyzing universal and conserved target genes, such as rRNA genes (Li et al. 2014). The 18S rRNA gene is the most commonly used for studying the phylogenetic and taxonomic diversity of eukaryotes (Tanabe et al. 2016) since it has highly conserved regions. However, it also anchors eight hypervariable areas that enable identification at the genus and species level, using sequence data available in public databases (Ki 2012). Likewise, the 16S rRNA gene sequence is used to identify prokaryotes (bacteria and archaea) (Petrosino et al. 2009). In studies of the diversity of fungi in a microbial community, the most frequently used gene is the ITS (Internal Transcribed Spacers). In most cases, it contains a sufficient degree of variation to allow differentiation at the species level (Seifert 2009).

The amplicon metagenomics approach for the identification of microbial diversity involves obtaining DNA directly from an environmental sample (e.g., rhizosphere, phyllosphere, or endosphere), the PCR amplification of 16S, 18S, and/or ITS genes, sequencing the amplicons and analyzing amplicon similarity against databases (ribosomal genes or ITSs) available (Fig. 3) (Silva et al. 2015). The necessary steps for the execution and data processing involve (i) removal of “noise” sequences (denoising); (ii) removal of chimeras; (iii) clustering of OTUs; and (iv) taxonomic classification. The denoising stage is essential because it filters “noise” sequences generated from PCR or sequencing (Kim et al. 2013). Among the tools used for this purpose are Denoiser (Reeder and Knight 2010) and DADA2 (Rosen et al. 2012). Then the removal of chimeric sequences must be performed, as they make it difficult to identify original sequences during the analysis (Edgar et al. 2011). UCHIME (Edgar et al. 2011) is an accurate tool for this type of analysis.

The OTU clustering step consists of aligning and grouping the reads based on their similarity. OTUs are determined from the sequence grouping, using a defined percentage of the difference between them, with a similarity limit of 97%, 95%, and 80% for species, genus, and phylum, respectively (Schloss and Handelsman 2005). Initially, reads are pre-grouped (reducing computational time), and OTUs are selected from 2 approaches: (i) de novo, whose sequences are compared to each other and grouped by similarity, without the use of a reference (Di Bella et al. 2013), through tools such as Cd-hit (Li and Godzik 2006), and UCLUST (Edgar 2010); and (ii) taxonomy-based, where sequences are compared and grouped into OTUs based on their similarity with sequences deposited in databases (Di Bella et al. 2013), using the BLAST tool (Altschul et al. 1997), for example. The taxonomic classification stage allows inferring a taxonomic lineage to an OTU by searching specifically marker genes in reference databases. Some of the databases frequently applied in this type of analysis include Greengenes (DeSantis et al. 2006) and Ribosomal Database Project (RDP) (Cole et al. 2009) for analysis of 16S metagenomic data; Silva (Pruesse et al. 2007), for 16S and 18S; and Unite (Kõljalg et al. 2013) for ITS (Oulas et al. 2015). Thus, analyses based on marker genes are favored because databases cover genes from millions of species and accessions (Breitwieser et al. 2017). A collection of software used for amplicon-based taxonomic annotation of metagenomics data sets is presented in Table 1.

Table 1 Examples of software used for functional and taxonomic annotation of metagenomic data
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Despite its importance as a successful method for characterizing microbial communities, the amplicon metagenomic approach has some major limitations for more comprehensive studies. Since it is based on PCR amplification, there may be an underestimation of species since not all rRNA genes amplify equally (Aguilar and Grasso 2010).

Due to the sequence homology between the bacterial 16S rRNA gene and the organellar DNA (mitochondria and chloroplast) of the host, the application of universal prokaryotic PCR primers results in the co-amplification of the plant host's DNA and consequent substantial presence of plant sequences in the generated dataset (Lefèvre et al. 2020). This co-amplification is generally more problematic for studying endophytic communities (de Souza et al. 2016). Although their removal in silico is feasible, such an abundance impacts coverage of actual microbial diversity (Lefèvre et al. 2020). To overcome this limitation, a promising approach is the application of a Peptide Nucleic Acid (PNA) PCR clamp, which selectively binds to a target region of the plant genome and inhibits its amplification during PCR (Kawasaki and Ryan 2021). This technique has been efficiently applied and significantly reduces host DNA co-amplification during library preparation in different plant species (de Souza et al. 2016; Fitzpatrick et al. 2018; Lefèvre et al. 2020).

Furthermore, although the amplicon metagenomics approach allows obtaining data about the taxonomic composition of a microbial community, information about functional aspects remains unavailable (Aguilar and Grasso 2010). Therefore, the amplicon metagenomic strategy can answer the question: “who is there?” by accessing the taxonomic diversity of the microbial diversity. On the other hand, information on the functional diversity of the studied microbiome, which response to the question: “what are they doing?” can be obtained only through the shotgun metagenomic approach (DeCastro et al. 2016).

Shotgun metagenomics

Shotgun metagenomics is based on sequencing the total DNA of a sample. Considering the advances in the NGS technologies and the development of increasingly efficient bioinformatics tools, shotgun metagenomic sequencing has been used to characterize microbial communities in terms of structure, function, and diversity in different organisms and environments (Abraham et al. 2020; Hagagy et al. 2021; Fadiji et al. 2021).

In this methodology, the DNA of the microbiome is isolated, fragmented, and sequenced (Mande et al. 2012). The result of sequencing is the generation of millions of reads, which undergo a series of bioinformatics analyzes, including quality control (QC), assembly, gene prediction, gene annotation, taxonomic, and comparative analysis (Fig. 3) (Ladoukakis et al. 2014).

In the QC step, low-quality reads and contaminants are filtered (Zhou et al. 2014) using tools such as FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and PRINSEQ5 (Schmieder and Edwards 2011). The assembly maps the sequence data to reconstruct the target, grouping the reads into contigs and contigs into scaffolds (Miller et al. 2010). Two strategies used for assembly are ‘reference’ and/or ‘de novo’, the latter using the OLC approach (Overlap-Layout-Consensus) or an algorithm based on Bruijn’s graph (DBG) (Ji et al. 2011). Specifically, because it involves several genomes from a sample, metagenomic assembly requires algorithms to separate data from different species (Di Bella et al. 2013). Among the assemblers developed for metagenomic data, we can mention MetaSPAdes (Nurk et al. 2017) and MetaVelvet (Namiki et al. 2012).

The mapping of genes within a genome is based on the detection of ORFs (Open Read Frames) followed by determining their viability for translation into functional proteins, thus aiming to determine candidate nucleotide sequences for coding genes (Ladoukakis et al. 2014). Some tools were designed for the detection of genes from the properties of metagenomic data (Di Bella et al. 2013) as FragGeneScan (Kim et al. 2015) and Metagenomics Gene Caller (MGC) (El Allali and Rose 2013), for instance.

Once the coding gene sequences are determined, it is necessary to predict their functions through functional annotation. The annotation is performed using the non-redundant (nr) database of sequences with known functions (Thomas et al. 2012). Examples of such databanks are UniProt (UniProt Consortium 2012), KEGG (Kanehisa et al. 2004), and PFAM (Bateman et al. 2004). Also, large-scale databases such as MG-RAST (Glass et al. 2010) aggregated information from several reference databases, gathered in a single structure, optimizing the automation of processing and functional annotation (Thomas et al. 2012). In the analysis, the taxonomic classification of the various microbial groups present in the sample is considered (Mande et al. 2012). Contigs or reads are compared to available sequences in databanks, assigning them the classification in species or genera. Tools for this purpose include PhyloPythiaS+ (Gregor et al. 2016) and TACOA (Diaz et al. 2009); or even considering the sequence similarity analysis tools available in databases (Oulas et al. 2015), such as MG-RAST v.4 (Meyer et al. 2019) and MEGAN6 (Huson et al. 2016). Additional examples of software for taxonomic and functional annotation of metagenomic data are compiled in Table 1.

In the last step, metagenomic data sets can be compared with MEGAN (Huson and Weber 2013), IMG/M v.5.0 (Chen et al. 2019), and parallel-meta (Su et al. 2014). These programs are based on algorithms that make it possible to compare both functional and taxonomic content with statistical support (Ladoukakis et al. 2014).

The main advantages of the shotgun metagenomic approach comprise the possibility of investigating the total diversity of organisms in a community, as this is not limited by the use of primer pairs in the amplification of target genes (Petrosino et al. 2009), allowing to compare, concomitantly, the relative abundance of all the different organisms present in a given sample. This approach also makes it possible to obtain the entire repertoire of microbial genes that encode a series of essential metabolic functions (Fadiji and Babalola 2020). Among the challenges, the difficulty in computerizing complex metagenomic data, the predominance of the host’s DNA, and the higher cost of sequencing the complete metagenome. However, advances in this research area have led to the circumvention of these obstacles, making it feasible to execute this strategy in a large number of projects and laboratories (Sharpton 2014).

Metagenomics and non-model plant microbiomes

NGS technologies and bioinformatics have become more efficient and affordable, allowing a broader characterization of plant microbiomes. This benefited especially non-model plants, whose cultivation (of the plant itself) is sometimes difficult, and even more, the cultivation and identification of its microbiome with traditional methods. Thus, metagenomic approaches have been successfully applied to evaluate microbiome diversity and functional characteristics of the model and non-model plants (Simon et al. 2019) and compare them to each other.

Since the description of the microbiome associated with the root of healthy A. thaliana plants through metagenomics (Lundberg et al. 2012), several other non-model plants (including crops) had their microbiomes characterized. Investigations of plant microbiomes aim to promote the descriptive or structural analysis of microbial communities associated with plants under different conditions (Sánchez-Cañizares et al. 2017), as illustrated in Table 2, including host plant species (Marasco et al. 2018), developmental stage (Xiong et al. 2021) and temporal differences (Ou et al. 2019).

Table 2 Synthetic description of exemplary studies on different aspects of non-model plant microbiomes reported in the literature, using metagenomics approaches (SA: sequencing of amplicon; SS: DNA shotgun sequencing)
Full size table

Studies have explored the structure and function of the microbiome of non-model species and cultivation under both natural and controlled conditions (Table 2) (Busby et al. 2017; Nadarajah 2019). Some of them investigated the microbiome of invasive plants under both conditions, using metagenomic approach 16S rRNA, providing a description of microbial communities' structural diversity in separate compartments. Studies in this context have identified bacterial members associated with Plant Growth Promoters (PGP), indicating that the microbiome is potentially involved in the colonization capacity of invasive plants in new environments (Cheng et al. 2019; Zhang et al. 2020c), often with challenging environmental conditions (Table 2).

Research with emphasis on the microbiome exploitation of important cultivated species includes, for example, crops such as corn (Zea mays L., Poaceae), soybean (Glycine max (L.) Merr), and rice (O. sativa) (Table 2). Using metagenomic shotgun associated with metabarcoding (16S rRNA and ITS), Xiong et al. (2021) proposed that bacterial communities in leaves and roots of maize played a critical role in early developmental stages, while fungal communities showed a similar pattern in the late developmental stages. This result indicates that the developmental stage of maize strongly influences microbiome assembly, with bacterial and fungal communities possibly having distinct and beneficial ecological roles on the microbiome and host performance throughout plant growth. Using 18S rDNA sequencing, Andreo-Jimenez et al. (2019) analyzed the endophytic fungal diversity of rice. They identified that the richness of fungal OTUs in the root endosphere increased when the plants were subjected to water deficit. The authors proposed that the recruitment of fungi by the roots aimed to mitigate the effects of drought on the host plant.

The use of metagenomics in non-model plants has enabled the investigation of the effects of abiotic stresses in structuring the microbiome of plant species capable of inhabiting places with challenging environmental conditions. This approach is promising since the microbiome may also benefit the plant’s survival or adaptation to environmental conditions (Ahlawat et al. 2018). In this context, studies involving the composition and influence of the plant microbiome on adaptation or tolerance to different environmental stresses, such as drought and salinity, have received particular attention (Table 2). Analyzing the microbiome of Alhagi sparsifolia Shap. (a Leguminosae of desert environments) using metagenomic approaches, Zhang et al. (2020b) found that different plant compartments (rhizosphere and root endosphere) presented other microbial taxa and variable abundances, proposing that the endosphere houses endophytic taxa that contribute to plant drought resistance. Using a similar approach, Zuo et al. (2021) demonstrated that the fungal microbiome of five xerophytic shrubs inhabiting an extremely acidic ecosystem varied among different host plant species, indicating that even under identical environmental conditions, host identity strongly influences the structuring and selection of the fungal community in the inner tissues of leaves, stems, and roots. Furthermore, the dominance of specific fungal members in the endosphere characterized with beneficial effects may be related to the adaptive and ecological success of the studied species in arid environments.

Metatranscriptomics as a tool for studying the microbiome

Metagenomics comprises an initial stage for studies in the microbiome, providing data on the taxonomic profile and whole metagenome sequencing (WMS), allowing potential functional profiling of a microbial community (Aguiar-Pulido et al. 2016). On the other hand, metatranscriptomics covers the sequencing of an environmental sample’s transcriptome, making it possible to analyze the set of expressed RNAs of the associated microbiota (Marchesi and Ravel 2015). In this way, metatranscriptomics complements metagenomic data, allowing the accurate obtaining of the transcriptional profile of the genomes that make up a microbial community submitted to a particular environmental condition (Chandra et al. 2014).

In general, studies with metatranscriptomics involve the following steps: (i) collecting the samples; (ii) isolation of total RNA (for each sample); (iii) removal of rRNA, aiming at enriching the protein-coding mRNA (when the target of the study is gene expression); (iv) cDNA synthesis; (v) sequencing, using NGS technologies; and (vi) data processing and analysis (bioinformatics), including QC, assembly, and differential gene expression analysis (Pérez-Pantoja and Tamames 2015). Metatranscriptomics analyses provide a better understanding of the microbiome functions and enable the discovery of new genes for biotechnological applications (Mukherjee and Reddy 2020).

Since its introduction in the early 2000s, the number of studies based on metatranscriptomics has increased significantly, with investigations on the most varied ecosystems. It has not been different considering plant microbiomes since metatranscriptomics allows us to identify specific functions of microbial members and transcripts involved in the microbiome-plant interaction (Shakya et al. 2019).

Metatranscriptomic analysis of leaves of Colobanthus quitensis (Kunth) Bartl. (Caryophyllaceae) revealed genes differentially expressed in fungal communities (endophytic and epiphytic) in plants subjected to simulated global warming conditions (compared to natural conditions in maritime Antarctica), demonstrating the influence of climatic factors on the modulation of the microbial metatranscriptome. Genes induced in the simulated condition and related to the metabolic and molecular pathways of the involved microorganisms may assist in the adaptation of the species to adverse environmental conditions (Ballesteros et al. 2020). Through independent analysis of the rRNA and mRNA data sets, it was demonstrated that the combined condition of prolonged heat waves and elevated atmospheric CO2 (eCO2) has significant effects on the composition and activity of the rhizosphere microbiome in European pastures (Bei et al. 2019). A considerable decline in fungal abundance was observed in the rhizosphere, with reduced nitrogen metabolism. It increased the production of secondary metabolites in the rhizosphere and roots as a strategy to defend the plant against the extreme heat conditions associated with eCO2 (Bei et al. 2019).

Metatranscriptomics approach allowed the identification of active microbial communities and the expression of a gene associated with the microbiome in the plant carbohydrate metabolism during the fruit ripening process in different watermelon (Citrullus lanatus (Thunb.) Mansf., Cucurbitaceae) cultivars (Saminathan et al. 2018). A similar approach was used to study the xylem sap of vines during the bleeding period to identify their microbiome composition and metabolic activity. Proteobacteria, Basidiomycota, and Ascomycota were the most abundant phyla. The functional analysis pointed out the microbiota activity associated with the growth of microorganisms and resistance to diseases in the xylem sap during the bleeding period (Zheng et al. 2019).

Like any other approach, metatranscriptomics also has limitations. For example, in some cases, RNA extraction (from complex samples) is not a very easy procedure (Bashiardes et al. 2016). Also, the lack of high-quality reference genomes for assigning transcripts to specific microorganisms is a limiting factor (Levy et al. 2018). Despite these difficulties, new tools have been continuously developed in this area and help study active members, gene expression, and pathways involved in microbiomes (Shakya et al. 2019).

Microbiome–plant interactions as an alternative for sustainable agricultural production

Given the world population's constant growth, the great demand for food production makes intensive agricultural output necessary. However, the application of this practice can trigger a series of environmental imbalances, including drought, soils that are poorer in nutrients and more saline, contaminated, increased incidence of pests, pollution of rivers, among others (Browne et al. 2013). In this scenario, plant and soil microbiome exploration as a promising approach to agricultural sustainability has been increasingly relevant (Barea 2015), especially the here called “endophytome”.

The identification of endophytic members of the microbiome with beneficial activities emerges as a powerful strategy to be considered in promoting more sustainable agriculture, reducing the application of fertilizers, and combating phytopathogens to boost better crop yields. Therefore, a greater understanding of the aspects involved in plant-endophytome interaction and the influence of this set of microbes on better plant productivity is necessary. Singh et al. (2020) consider that only then can new methods be explored in the manipulation of the microbiome for the development of sustainable agriculture.

There are different strategies to promote the engineering of the plant microbiome. Among them is the possibility of selection mediated by hosts. Another alternative is to inoculate strains directly into the soil or plant tissues, including rhizosphere, seeds, and seedlings. There is also tissue atomization or direct injection of strains into tissues or wounds of the plant (Orozco-Mosqueda et al. 2018).

The host-mediated microbiome selection approach comprises a technique that applies artificial selection to improve host performance. In this case, microbial communities are selected indirectly through host fitness characteristics that have evolved between generations due to the influence of the microbiome (Mueller and Sachs 2015).

The best strategy for applying endophytic microorganisms in agricultural systems is still not well established, although the most used method consists of direct inoculation in soil or seeds (Cocq et al. 2017). This option was efficiently applied in sugarcane (Saccharum officinarum L., Poaceae) research (da Silva et al. 2012), for example. The spraying of flowers with an inoculum of the endophytic microorganism of interest has also been suggested, aiming at vertical transmission (via seeds) to plants’ next generation. Following this strategy, Mitter et al. (2017) successfully managed to introduce Paraburkholderia phytofirmans PsJN (a powerful grown-promoting bacterium) in the flowers of various crops of agronomic interest, with consequent inclusion in the microbiome on the seeds of the progenies. Even tested in the field, this strategy comprises a new tool to supply beneficial bacteria to plants, helping their development and agricultural production (Mitter et al. 2017).

The effects of inoculants on the native plant-associated (indigenous) microbiome was recently reviewed by Berg et al. (2021). According to the authors, the modulation of the indigenous microbiome is influenced by the action of inoculants, depending on the sampling time (after application), resulting in six types of modulation: (i) transient changes; (ii) stabilization or increase in microbial diversity; (iii) stabilization or enhancement of microbiome uniformity; (iv) restoration of dysbiosis/compensation of a phytopathogen-induced change; (v) targeted changes to members of the plant's beneficial native microbiome; and (vi) phytopathogen suppression. Considering microbial composition, the effects of microbial inoculants lead to changes that simultaneously enrich potentially beneficial members of the indigenous microbiome while suppressing potential pathogens (Berg et al. 2021).

Although different microbiome approaches to improving agricultural productivity have proliferated, there are still some practical challenges to the effectiveness of these strategies in the field. The successful introduction of microbial inoculants depends on their ability to compete with the native plant-associated microbiome (Batista and Singh 2021). To overcome this limitation, selecting indigenous plant microorganisms as inoculants emerges as a promising procedure to explore their potential in promoting plant growth and agricultural productivity (Kouadio et al. 2017; May et al. 2021). In this sense, the selection of endophyte strains adapted to the plant can improve the survival chances and inoculum performance in promoting beneficial effects on plant development (Qiu et al. 2019). Nevertheless, further studies are needed in order to broaden the understanding of how the indigenous microbiome interacts with microbial inoculants to improve microbiome management methods (French et al. 2021).

Another constraint is because a large part of environmental microorganisms cannot be cultivated. Also, the activity of several microbes may be influenced by specific biotic and abiotic factors. Therefore, the integration of emerging approaches in agriculture, synthetic biology, big data, and metagenomics is mandatory to provide a factual basis for exploring the maximum potential of biotechnological applications of the plant microbiome in the development of agricultural and environmental sustainability (Singh et al. 2018).

Among the main objectives of agricultural biotechnology is developing microbial inoculants capable of increasing plant growth and repressing plant diseases, aiming to reduce the dependence on chemical fertilizers and pesticides (Adesemoye et al. 2009). For the development of these inoculants, however, interdisciplinary research studies are necessary, capable of optimizing parameters including, selection of beneficial microorganisms, analysis and selection of those with positive effects on the plant, as well as detecting and evaluating the performance of most promising inoculants in the soil (Pereg and McMillan 2015).

Microbial biotechnology plays an essential role in promoting sustainable agriculture in different ways, including managing biotic and abiotic stresses, increasing productivity, acquiring nutrients, and promoting bioremediation (Singh 2019). Some relevant studies involving potential applications of plant microbes to increase agricultural production sustainably will be reviewed below.

Management of biotic and abiotic stresses

Given the current climate change scenario, the development of crops tolerant to various environmental stresses is essential. Although the genome of the plant itself can confer this type of tolerance, the innumerable interactions with microbial communities can assist in the greater efficiency of this process (Farrar et al. 2014). Rhizobacterial species (Bacillus spp. and Pantoea spp.) isolated from cactus species in a semiarid environment were inoculated in corn seedlings (Z. mays) and conferred a capacity to promote plant growth under water stress conditions (Kavamura et al. 2013). Likewise, rhizobacteria isolated from S. italic have been identified as potential bioinoculants to assist agricultural production in arid regions due to their proven stimulation activity in the germination and growth of seedlings of this species under water deficit (Niu et al. 2018).

Soil salinity comprises another abiotic stress that negatively impacts agricultural production, sometimes leading to soil infertility. Microbiomes isolated from cultures adapted to saline ecosystems can promote plant growth (Yadav and Saxena 2018). For instance, arbuscular mycorrhizal fungi (AMF) isolated from areas and plants affected by salinity conferred tolerance to salt stress in inoculated corn plants (Estrada et al. 2013). A consortium of three strains isolated from PGPR tolerant to salinity from wheat plants increased the growth and yield of this culture under experimental and field saline conditions through PGPR biofertilization (inoculation) technology. Such a strategy has emerged as an alternative to wheat biofertilizer in saline environments, reducing chemical fertilizers (Rajput et al. 2018).

Several phytopathogens lead to a series of diseases responsible for significant losses in crop and harvest productivity worldwide. In this context, using beneficial microorganisms as a control alternative to limit chemical pesticides has encouraged studies over the past decades (Syed Ab Rahman et al. 2018).

Microbial plant communities as a source of biocontrol agents (BCAs) have generated great enthusiasm as a method of sustainable control of plant pathogens, deserving more comprehensive research, especially regarding practical applications in cultivated plants (Massart et al. 2015). For instance, fungi isolated from the endosphere of strawberry leaves (Fragaria vesca L., Rosaceae), especially Paecilomyces, induced a high mortality rate of the pathogen Duponchelia fovealis, emerging as a promising alternative for biocontrol (Amatuzzi et al. 2018).

Biofertilizers for increased productivity

A biofertilizer comprises a substance containing potentially beneficial living microorganisms that colonize their interior or rhizosphere when applied to plant seeds or added to the soil, increasing crop productivity and nutrients supply to the host plant. Microbial biofertilizers represent one of the main alternatives for sustainable agricultural production, minimizing the need to use chemical fertilizers (Reddy et al. 2020; Kour et al. 2020). They improve soil fertility by fixing atmospheric nitrogen, solubilizing insoluble phosphates, and producing plant growth substances in the soil (Mazid and Khan 2015). Biofertilizers include different genera and species of microorganisms. They are categorized into four groups according to their beneficial mechanisms of action: (i) nitrogen fixation (e.g., Azotobacter, Rhizobium, Azospirillum, and Cyanobacteria); (ii) phosphate solubilization (e.g., Pseudomonas striata, Bacillus polymyxa, Penicillium, and Aspergillus); (iii) composting microorganisms (e.g., Bacillus spp.); and (iv) PGPRs (e.g., Pseudomonas and Bacillus species) (Pathak and Kumar 2016; Khan et al. 2018).

Studies in plant microbiomes revealed unexplored microbial consortia that can be used as effective biofertilizers (Meena and Busi 2019). In this perspective, under field conditions, Khaitov et al. (2020) analyzed the effect of the inoculation of rhizobia strains (Rhizobium phaseoli—R9 and Mesorhizobium cicero—R6) isolated from the rhizospheres of 2 legumes—soybean, G. max. and chickpea (Cicer arietinum L.)—in common bean seeds (Phaseolus vulgaris L.). Co-inoculation increased the growth, root nodulation, and yield of P. vulgaris, probably due to higher nitrogen absorption. This indicates that this alternative is better than the inoculation of a single rhizobium strain for biofertilization, improving productivity under salinity. Another example regards endophytic fungi isolated from rice and inoculated as consortia in cucumber seeds (Cucumis sativus L., Cucurbitaceae) were able to efficiently increase plant growth, highlighting the potential use of endophytic fungal isolates as biofertilizers (Syamsia et al. 2021).

Inoculants and their influence on the acquisition of nutrients

Plant growth and productivity require the presence of nutrients in the soil–root interaction zone. The availability of nutrients at this interface is influenced by many factors, including the activity of roots and microorganisms in the rhizosphere. The use of microbial inoculants as an alternative to the application of chemical fertilizers for crop nutrition comprises a promising approach to improve plant development based on more sustainable practices. Evidence indicates that the inoculation of plants with PGPR increases soil nutrients’ bioavailability for plants (Pii et al. 2015). In this context, 4 PGPR (Pseudomonas sp., Pseudomonas sp., Bacillus pumilus, and Paenibacillus polymyxa) were analyzed to investigate their effects on growth, nutrient uptake, and photosynthetic activity in Habanero pepper (Capsicum chinense Jacq., Solanaceae). As a result of the inoculation during sowing, there was a significant increase in growth parameters, photosynthetic activity, and the absorption of nutrients. Seedlings inoculated with P. polymyxa showed higher acquisition of P and K compared to controls. Later, inoculation with this rhizobacterium was pointed out as an alternative for reducing fertilizers in agricultural production (Castillo-Aguilar et al. 2017). Using similar approaches, Kumar et al. (2017a) analyzed the effect of indigenous PGPR isolated from different crops on growth and obtaining nutrients in wheat cultivation. The inoculation of a consortium of 3 PGPR, besides having positive effects, helped in plants growth and yield, also leading to a significant increase in the levels of macro and micronutrients (N, P, Cu, Zn, Mn, and Fe) in wheat grains, also in-field conditions.

Arbuscular mycorrhiza (AM) is the most frequent type of mycorrhizal association. They play a vital role in acquiring macro- and micronutrients for the host plant (Hodge and Storer 2015). These fungi promote an increase in the acquisition of nitrogen, phosphorus, and other soil nutrients by exchanging photosynthetically fixed carbon by the host plant (Kaul et al. 2017). Ortas et al. (2019) observed under field conditions that in different horticultural plants, the effects of inoculation of mycorrhizal fungi on plant yield were higher without phosphorus addition, which led to better plant growth and nutrient absorption (P and Zn). These results showed that mycorrhizal inoculants could reduce the dependence on phosphorus fertilization crop plants.

Microorganisms in the promotion of bioremediation

Bioremediation involves the use of living organisms, plants, and/or microorganisms to mitigate or eliminate environmental pollutants (Chibuike and Obiora 2014). As forms of bioremediation, phytoremediation in plant tissues and rhizoremediation in the rhizosphere are likely to happen naturally in plants. Such a process can be improved by the association with microorganisms capable of helping plants survive in contaminated environments or even in the degradation of contaminants (Eevers et al. 2017). So, phytoremediation is a sustainable, cheaper, and ecological alternative to conventional physicochemical pollutant remediation methods, also applicable in economically emerging countries (Shmaefsky 2020).

Iram et al. (2019) evaluated the effects of inoculation of fungi of the Trichocomaceae family on corn and sunflower (Helianthus annuus L., Asteraceae) for the uptake of heavy metals in contaminated soils of agricultural importance. The study demonstrated that corn and sunflower plants, inoculated with fungi using three different methods, significantly increased the phytoremediation potential to absorb heavy metals in contaminated soils in the Gujranwala region (Pakistan). In another study, Liu et al. (2021) analyzed the effect of using endophytic bacteria-isolated from Phytolacca acinosa Roxb. (Phytolaccaceae) growing in cadmium (Cd) contaminated soil—as inoculants. As a result, inoculated plants of P. acinosa presented increased growth and capacity of accumulating Cd, demonstrating the potential use of endophytes in soil remediation, including those contaminated by heavy metals.

PGPR inoculation may also assist the phytoremediation process; as observed by Khan et al. (2017) in a greenhouse experiment, the rhizobacteria Bacillus subtilis 189 and Pseudomonas fluorescens RB4 were inoculated into seedlings of Catharanthus roseus (L.) G. Don (Apocynaceae) to identify their effects on phytoremediation in soils contaminated with copper (Cu) and lead (Pb) in Pakistan. As a result, the co-inoculation of the two rhizobacteria significantly increased the growth of shoots in contaminated soils, plant biomass and resulted in a more significant accumulation of heavy metals (Cu and Pb) in shoots of C. roseus grown in the contaminated soils as compared with non-inoculated control plants (Khan et al. 2017).

Conclusions and perspectives

The plant microbiome interacts with both the internal and the superficial tissues of the host plant. Its structure and performance are influenced by a series of intrinsic (genome, plant immune system, developmental stage, etc.) and extrinsic factors (such as soil properties and environmental factors). Consequently, there are noticeable differences in the microbiome’s composition between internal and superficial niches on the same plant and between different plant individuals or taxa.

The affordable cost of sequencing and new bioinformatics tools contribute to a more comprehensive description of the structural and microbial metabolic composition of the model and non-model plants. The development of metagenomic and metatranscriptomic approaches allows trespassing limitations of culture-dependent microbiological techniques to study and understand microbiomes. Despite that, the isolation and lab cultivation of specific components of the plant microbiome remains challenging.

Also, sequences from microbiotic origin have been detected in sequencing plant genomes and transcriptomes, mainly being ignored or reported as ‘no hits”. Revisiting raw data of such projects should bring many interesting evidences concerning plant associated-microorganisms.

There is no doubt that essential roles are provided through microbiome-plant interactions in promoting plant growth and defense against biotic and abiotic stresses. Indeed, recent advances in understanding plant microbial communities through metagenomics demonstrate the enormous untapped potential of the plant microbiome. Evidence points out that the inoculation of microorganisms isolated from specific plant microbial communities increases plant growth, yield, and productivity, through different processes.

However, there is a long way to go. Detailed information about the plant-microbiome-environment interaction is available to a limited number of plant species, mainly cultivated, herbaceous, and temperate. Information on tropical plants, specially adapted to challenging environments (so-called extremophiles), is necessary and should shed new light on the complexity of survival in such environments. In this context, the microbiome of pioneer plants, capable of establishing in degraded environments or soils contaminated by heavy metals, should receive investments and special attention.

There are no reports of adverse effects related to the application of endophytes in improving crop production. Therefore, the implementation of these practices in a sustainable way worldwide is promising and can revolutionize agriculture. However, it demands standardized processes that depend on interdisciplinary efforts, especially regarding developing marketed products and appropriate strategies for manipulating plant microbiomes associated with a given culture and environment.

The main question remains: Is the plant microbiome defined by the soil it inhabits, or is the soil microbiome defined/influenced by the plants (and organisms) that colonize a given environment? Examples of both situations can be observed.

Also, few studies discuss the balance between endophytes and known pathogenic microorganisms within the same microbiome. The general idea is that latent pathogenic species may be harmful to the plant under a sudden stressful condition. On the other hand, such “unwanted hosts” may be responsible for a state of alertness, important for constitutive plant defense.

Research efforts on wild plants in their natural environment (also relatives of crop species) may be more elucidative since many factors can affect studies under greenhouse conditions or experimental fields.

The future application of systems biology approaches is mandatory to bring an integrated view of both plant and microbiome molecular processes in a holistic approach.