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1. INTRODUCTION

Plants have co-existed with various beneficial microbes that interact in different regions, i.e., rhizospheric, epiphytic, and endophytic regions. All types of microbes interacting in the different regions show different proximity, and they tend to benefit from plant growth and development [1,2]. Among the three regions of interaction, endophytic microbial interaction has the closest proximity to plants. The endophytic microbial community resides inside the plant’s healthy tissues and establishes itself at particular sites, including leaf, seed, roots, and stem, without causing any damage to the host plant. Endophytic microbes could be transmitted to the plants either vertically or horizontally, and different groups of microbes could be inhibited [3,4]. The endophytic microbials intrude the plant structure through root hair development, root injuries, and tissue damage, and different microbes, including bacteria, fungi, and archaea, enter the plants [5].

Endophytic fungi play a very important role in plant growth promotion, as they help in the protection and growth of plants. The fungal endophytes protect the plants from both biotic and abiotic stresses from which a huge amount of damage has been reported [6,7]. Plant protection from biotic factors such as microbial pathogens and pests is achieved by preventing entry. If pathogens enter the plant structure, endophytic fungi prevent further establishment of pathogens by limiting the available resources such as nutrients or by producing antibiotics and hydrogen cyanide, which inhibit the growth of the pathogens [8,9]. Under abiotic stress such as drought, salinity, extreme temperature, heavy metals, nutrient-depleted environment, and extreme pH, plants’ chlorophyll pigmentation, carbon oxide, soil fertility, and photosynthetic rate decrease, and metal accumulation and reactive oxygen species increase, which help the endophytic fungi in alleviating all such problems. Endophytic fungi alleviate abiotic stress by activating the plant stress response system and producing various anti-stress agents, which mitigate the plant stress effects [10].

Apart from mitigating abiotic and biotic stress, endophytic fungi supply the soluble form of nutrients and several phytohormones through various mechanisms such as solubilization of potassium and phosphorus, and production of phytohormones such as auxin, which promote plant production and growth [11]. Endophytic fungi are also a source of various secondary metabolites, which also enhance plant growth [12,13]. All these mechanisms that protect and promote plants have been exhibited by the diversity of fungal endophytes but despite being diverse many species are unknown. The biodiversity study of fungal endophytes is very important for further exploration as they hold a plethora of biotechnological applications in the field of agriculture, environment, and industry, and it has attracted the attention of many ecologists, chemists, and taxonomists [14-16].

To have in-depth knowledge of all the benefits of the fungal endophytes, the very first step is culturing, and a huge number of the endophytic fungi are unexplored as they cannot be cultured on the plate. The isolation or culturing of microbes is very important for the study and exploration applications and biodiversity [17]. The omics tool is a promising approach with which the fungal diversity and interaction of plants and fungi could be explored, and these applications could be studied well for future applications and ease. Different omics approaches such as genomics, transcriptomics, metagenomics, metatranscriptomics, proteomics, metaproteomics, and metabolomics [Figure 1] have been known [17,18]. In the present review, the need and omics approaches for understanding the plant–fungal endophyte interaction have been discussed in detail.

Figure 1: Different omics tools for better understanding of plant–fungal interactions. Adapted from Kumar et al. [120]. [Click here to view]

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2. NEED FOR OMICS TOOLS

Access to large-scale omics datasets has revolutionized the field of biology and led to the emergence of systematic approaches to advance the understanding of biological processes [19]. In recent years, omics approaches have walked into people’s vision. The technology with higher sensitivity and an extensive range of applications is thus favored by the scientific community [20]. The need for omics approaches has been realized in a range of research areas such as system microbiology, microbiome analysis, genotype–phenotype interactions, food and nutrition, disease biology, and natural product discovery [21-25]. The omics technology emerged from integrative analysis, ergo, related to the ability to identify a large number of biomolecules and the ability to resolve the dynamics of the ecosystem in establishing their interactions. The advent of this highly uniform technology to understand the complexity of interactions between plants and microbial communities at the molecular level, coupled with the rapid development of the computational tools that are needed to sort and analyze such data, has revolutionized the science of plant–microbial interactions. The efficient functioning of a biological system needs a synergistic interaction between different components, and the integration of omics datasets with rigorous statistical analysis provides information about the complete system [18,26].

The omics techniques are of major importance in unraveling the complexity of the interactions between endophytic fungal communities and their host plants, thereby providing valuable insights into the mechanisms at the molecular level and promising applications of these relationships. The majority of the discoveries in the area of mutualistic relations between fungal endophytes and plants have been completely based on traditional scientific approaches, but the integration of omics is recent to this field [17]. Multi-omics approaches could be applied to study plant–endophyte interactions. Biochemical, physiological, and molecular investigations have well-evidenced the benefits imparted to the host plants by the associated endophytes, especially in terms of growth promotion of their host, enhancing stress resistance and metabolic capabilities; and further knowledge of the complexity of these mechanisms could be gained by adopting a multi-omics approach.

Genomics provides a view of the entire genomic-level information of endophytic fungal communities, helping researchers to find novel genes coding for potent metabolic compounds. Moreover, it also provides insights into the existing biodiversity of endophytes, phylogenetic lineage, evolution, and eco-physiological information [27]. Metagenomics is a significant technique that allows the direct analysis of the entire genome within environmental samples. Transcriptomics and proteomics impart knowledge about the profiling of gene expression and proteomes assembled by endophytic fungal communities, respectively. Furthermore, multi-omics technologies, when coupled with the metabolomics study, can progressively resolve aspects of the relationships between endophyte infection, accumulation of the metabolites, and stress alleviation in host plants [28]. Thus, none of the omics approaches is complete.

The integration of the data generated from metagenomics with metatranscriptomics and metaproteomics can help in detailing the intricacies involved in the establishment of endophytism. Similarly, data interpretation from transcriptomics or proteomics is incomplete in the absence of information from genomics. The use of combinatorial omics tools can help in resolving the enigma existing in the endophyte–host relationship [29]. Implementing multiple omics is thus a novel approach to understanding the multiple functions of endophytic fungi and how they interact with their host plants, and it can further encourage researchers to explore potential strains of fungal endophytes and their bioactive compounds [8].

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3. OMICS APPROACH FOR UNDERSTANDING THE PLANT–FUNGAL ENDOPHYTE INTERACTION

3.1. Genomics

There is a lack of knowledge regarding the fundamental physiological features of the fungal endophyte and host interaction; thus, it’s very important to dig deeper for knowledge [30]. Genome analysis has been a new tool to steadily look into endophytic fungi and plant interactions and to reveal essential attributes such as mineral acquisition, nitrogen fixation, and phytohormone production [31,32]. A genetic characteristic that affects colonizing favoritism and many other bioactivities, both directly and indirectly, has been identified through whole-genome research of endophytic microbiomes. Genomics helps identify desired genes that are involved in the production of antibiotics, the endophytic secretary system, insertion elements, resistance to antibiotics, surface attachment, transport systems, and other metabolic pathways that help promote plant development. These studies have advanced our understanding of endophyte ecology and evolution. Gene acyl homoserine lactone synthases, hyperadherence factors, hydrolases, and fusaric acid resistance proteins demonstrate the endophytic bacteria’s (Pantoea ananatis) biotechnological potentials [33]. Endophytic members of the fungal order are of great interest since they have the ability to promote plant growth and resist stress. It has been suggested that the genome sequence of Piriformospora indica could belong to a plant probiotic [34].

Since the development of high-throughput genome sequencing technology, there has been a noticeable increase in the number of whole-genome sequencing research [35]. The genetic landscape of endophytic fungi has been clarified through the use of high-throughput sequencing technologies such as next-generation sequencing (NGS) systems from Illumina, PacBio, and Oxford Nanopore [36]. Fungal endophytes can produce secondary metabolites, and their genetic makeup and host–environment adaptations can all be understood through whole-genome sequencing [37]. It is possible to find conserved genes, distinctive traits, putative virulence factors, and symbiotic genes by comparing the genomes of various endophytic fungi. The genome of Alternaria sp. is 34.70 Mb in size [38]. Apart from the phytopathogenic Alternaria species, endophytic species are known to produce numerous secondary metabolites as well. These metabolites have unexpected benefits as medicines due to their anti-inflammatory, anti-microbial, antiviral, and anti-carcinogenic mechanisms of action. Furthermore, the genomes of Hypoxylon pulicicidum (41.44 Mb) and Hypoxylon sp. (45.30 Mb) have been sequenced [39,40]. The endophytic fungi namely, Pestalotiopsis fici has been reported to secrete various secondary metabolites such as chloropestolides, chloropupukeananin, chloropupukeanolides, chloropupukeanone, pestalodiols, and pestalofones. These secondary metabolites exhibit various biological activities such as inhibition of HIV-1 replication, prevention of tumor cytotoxicity, and acting as antifungal agents. The complete genome of P. fici reveals a high concentration of enzymes that are active on carbohydrates, especially pectinase, and a notable number of genes that are involved in the synthesis of secondary metabolites. Its capacity to generate naturally occurring compounds with a wide range of biological functions is indicated by the presence of genes involved in the production of secondary metabolites [41]. Based on antiSMASH 4.0, a study identified 65 gene clusters in the endophytic fungal strain Calcarisporium arbuscula NRRL 3705 that code to produce secondary metabolites and also identified the gene cluster responsible for aurovertin production. Furthermore, the researchers hypothesized several gene clusters related to the synthesis of mycotoxins, including alternariol, citrinin, aflatoxin, isoflavipucine, and destruxin. Additionally, it was found that twenty-three of the sixty-five gene clusters contained genes that encoded for the synthesis of polyketide synthases (PKS), whereas the other twelve gene clusters contained genes encoding for the synthesis of non-ribosomal peptide synthases (NRPS). Moreover, fragments per kilobase of exon per million mapped fragments (FPKM)-based RNA sequencing was used to evaluate gene expression using reference genes such as gpdA, tubC, and actA as housekeeping genes [42]. The various roles that fungal endophytes play in plant ecosystems and agriculture are becoming better understood through genetic research. They emphasize how these fungi could be used in a variety of industries, including agriculture and medicine.

3.2. Metagenomics

One of the fundamental components of genomic research that reveals the genome, or DNA identity and integrity, of endophytic microorganisms from plant samples is metagenomics [43]. Metagenomic techniques have high importance for the study of fungal endophytes due to the fact that metagenomics provides a reliable tool for investigation into the diversity, potential development, and ecological functions of these fungi in host plants and habitats to which they are related [44]. Metagenomics makes the study of fungal diversity in plant tissues and other environmental materials feasible. It gives the count and type of fungal species, giving an insight into the composition of fungal endophyte communities. Technologies based on a high-throughput metagenomic approach provide detailed insights about the morphology, physiology, and dynamics of the microbiome. Fungal endophytic strains have been screened using these methods. It has given scientists a variety of tools for the rapid and economic analysis of DNA sequences from environmental samples [30].

By employing the mNGS technique, the complicated properties of bacteria in plants can be studied without the necessity of culture. In addition, a number of endophytic bacteria and fungi from sorghum and oak plants have been examined using Illumina and Roche’s technique of 454 pyrosequencing [45,46]. Shotgun metagenome sequencing analysis is performed on various bioinformatics platforms, MG-RAST and PiCRUST, along with accompanying functional pipelines that help show both the taxonomy and potential functional genes implicated in plant growth promotion [47,48].

As an initial step, ITS region sequencing was used for analyzing the fungal communities of samples, but it was restricted to a few samples. These were usually culture-driven analyses. Furthermore, the identity of a fungal endophyte isolated from the medicinal tree, namely Aquilaria malaccensis, was determined using internal transcribed spacer region sequencing, and a detailed analysis of it was presented [49]. The new procedure for the ARISA technique has been introduced to identify the diversity of fungal species present in the environment. This specifically looked into the lengths of ITS1 rDNA for the various fungal species [50]. The first publication using NGS to analyze the ITS region of a residential endophytic species in a South African host plant was published [51]. Illumina fungal sequencing was used in another study to enable the creation of the network, providing the occurrence and co-occurrence of symbiosis [52]. Endophytic fungus populations in the leaves of Pleioblastus amarus, Bambusa rigida, and Phyllostachys edulis were surveyed using robust ITS sequences from the Illumina MiSeqTM high-throughput. The most prominent enriched endophytic fungus species were Cladosporium, Trichomerium, unclassified_p_Ascomycota, Sporobolomyces, and Camptophora [53].

A metagenomics method allows for opening up the hidden potential of uncultured microbial communities, or endophytes, beyond the information that is obtainable from the genomes of particular taxa [54]. The method entails sampling every individual’s DNA and determining the genetic content. Based on the metagenomic procedure, Langa-lomba et al. provided information on the fungal microbiota of two Somontano Vineyards under the PDO, which are located in Huesca, Spain. Its findings showed a unique mycobiota of the inner wood (and, to a lesser extent, of other organs of plants) composed of microorganisms that have been frequently mentioned in related previous research whose priming effect the grape plant promotes, which is variable depending on the genotype under consideration, the management strategy used, or the crop age [55]. A study by Pais et al. [56] reveals that metagenomic analysis has led to the discovery of hidden relationships between fungal density, plant disease variability, and genetic distance in Cornus florida (Cornaceae), which implies that there is an association between the phylogenetic patterns among the fungi involved in the plant disease.

Both metagenomics and computational methods have now become important molecular approaches for determining the functional genes of fungal endophytes from their host plants. One of the chosen model plants used to study the relationship of fungal endophytes with different host plant tissues with diverse levels of biological strength is Ephedra sinica [8]. Considering the multitude of endophytic members isolated from the genera Talaromyces, Aporospora, and Aspergillus all from the same plant root, a particular and prolific prevalence of endophytic fungal strains of the genus Phyllosticta has been reported from the roots and stem of Ephedra sinica [57].

3.3. Transcriptomics

The field of transcriptomics is one of the subfields of molecular biology that focuses on the transcriptome of an organism—all RNA molecules produced by the genome at a time [58]. Transcriptomics focuses on the quantification of gene expression at the RNA level, including the production of all types of RNA molecules, including messenger RNA (mRNA), short noncoding RNA (ncRNA), and small RNA. A common technique in transcriptomics is RNA-Seq [58]. The entire fungal endophyte transcriptome is scrawled using RNA sequencing (RNA-Seq). It provides researchers with data on unique transcripts, alternative splicing, and gene expression levels in different conditions to help better interpret the dynamic changes in gene expression during the interaction with the host. The other approach for transcriptome analysis is microarray technology [59]. Microarrays allow researchers to simultaneously gauge the expression levels of hundreds of genes. Through transcritomics, genes that are activated or deactivated in response to external stimuli or specific situations can be traced. The interpretation of gene expression pattern changes within different biological conditions that heavily rely on differential gene expression analysis [59].

It has been found that transcriptomics is an applicable approach for studying microbial communities associated with various plants [60,61]. Transcriptomics helps in understanding the mechanisms of adaptation of microbial communities in environments that are changing by comparing the transcriptomes of interacting species groups. While most of the research based on the genome and metagenome of endophytes lists the presence or absence of some specific genes, understanding endophytic phenomena requires an understanding of the expression of certain genes in different microenvironments. A thorough analysis of the symbioticity of bacterial and host plant genes, which are differentially expressed genes (DEGs), would shed light on the basic aspects and mechanisms of their mutualistic functions.

By comparing the transcriptomes of endophyte-free and endophyte-infected plants, the identification of the underlying mechanisms of endophyte-mediated disease resistance and plant growth promotion abilities can be simplified. Comparative studies in contrast to differentially expressed patterns of endophytes in the host plant and outside can be of use to find the aspects of interaction involved in maintaining linkage. Moreover, research may likewise focus on the differential expression of numerous host plant genes under different conditions, whether or not endophytes are present. Suppression subtractive hybridization (SSH), microarray analysis, and SOLiD-SAGE-like approaches are effectively incorporated into differential expression analysis [30,62]. Transcriptomics that rely on genomes offers critical support for successful genome use. The elucidation of the endophytic lifestyle of symbionts is thus enabled by the integrated approach of genome and transcriptome analysis. One of the early transcriptome-based studies focused on the genes associated with the pyrimidine metabolism of Epulorhiza sp., an endophyte isolated from the roots of Anoectochilus roxburghii [17]. It has been observed that the grass Festuca rubra that has the endophytic fungus Epichloë festucae integrated with it demonstrates very different antifungal gene expression patterns [62]. This highlights the fact that endophyte–grass symbiosis is marked by molecular interactions and alterations in gene expression [Table 1].

Table 1: Transcriptomics studies unveiled the benefits of endophytic fungi to their host plants.

S. No.	Endophyte	Host Plant	Benefits	References
1.	Piriformospora indica	Hordeum vulgare	Induction of systemic disease resistance	Molitor et al. [63]
2.	Fusarium verticillioides	Zea mays	Decrease in the negative effects of phytopathogens	Jonkers et al. [64]
3.	Colletotrichum tropicale	Theobroma cacao	Alterations in the physiology, metabolism, and morphology of the host; resistance to infections and herbivores	Mejía et al. [65]
4.	Epichloë festucae	Lolium perenne L. cv Samson	Modifications to the host’s development, specifically in the areas of trichome production and cell wall biogenesis; resistance to fungal infections and dehydration	Dupont et al. [66]
5.	Piriformospora indica	Hordeum vulgare	Salt stress tolerance	Ghaffari et al. [67]
6.	Epichloë coenophiala	Lolium arundinaceum	Resistance to disease and responses to abiotic stress	Dinkins et al. [68]
7.	Chaetomium cupreum	Eucalyptus globulus	Tolerance to heavy metals; intricate control of auxin metabolism and biosynthesis to promote plant growth	Ortiz et al. [69]
8.	Exophiala pisciphila	Zea mays	Tolerance to heavy metals by the remodeled host cell walls	Shen et al. [70]
9.	Pestalotiopsis sp. strain 9143	Platycladus orientalis	Expose growth regulation and sulfur exchange in the initial stages of symbiotic interaction	Shaffer et al. [71]
10.	Trichoderma virens	Zea mays	Development as a biocontrol agent	Malinich et al. [72]
11.	Alternaria burnsii NCIM 1409	Nothapodytes nimmoniana	Insights into the camptothecin biosynthesis ability	Natarajan et al. [73]
12.	Aspergillus oryzae YRA3	Atractylis carduus	A probable biological agent to control Rhizoctonia root rot of sorghum	Rashad et al. [74]

3.4. Metatranscriptomics

Metatranscriptomics is an emerging field that pertains to the pattern characterization of gene expression by sequencing the expressed gene in the microbial community and has led to the discovery of many unknown plant–pathogen interactions [75]. In metatranscriptome due to the dominance of rRNA a strong community profile is generated for organisms belonging to different domains of life. This technique has been applied to study diversified niches such as oceans, soil, and rhizosphere of plants [76-79]. The simpler definition for metatranscriptome could be the total group of RNA content or that gives an overall picture of the gene expression of a microbial community. The primary aim of metatranscriptomics is to go in-depth detailing of the active metabolic pathways performed in the environment. The transcriptomes of an individual whether in the community or pure culture are dominated by the ribosomal RNA and this dominance in 16S and 23S subunits of prokaryotes can be visualized using the agarose gels.

This advanced RNA sequencing technique employed on the collected samples from numerous ecosystems helps in the study of microbial diversity, plant–microbe interactions, isolation of plant growth-promoting microbes, and also identification of potential biological control agents [80]. The neighboring plant species share their mycorrhizal symbionts despite being associated with different mycorrhizal fungi such as ericoid, ectomycorrhizal, and arbuscular mycorrhizal fungi. It is the composition of the plant community that helps in forming the silhouettes of fungal community. Also these fungal communities undergo seasonal changes that may differ between consecutive years, and therefore making the process of their development acyclic. This gives an understanding that the different taxa, their existing correlation, and their interaction with the associated plant community cannot be explained with the assessment of parameters within a set period of time, and here the use of metatranscirptomes and RNA-based methods could aid better in the analysis of their responses better [81].

A study was conducted in Argentina for the characterization of four different fungi involved in the grapevine trunk diseases locally known as “Hoja de malvón” affecting grapes. Here a metatranscriptomic approach is used for the different aspects of the process of characterization from the construction of molecular marker to kmer count evaluation. As a result, a number of microorganisms with a negative association with the disease pathogen were identified as potential biological control agents. In another microbiome study conducted on mummified peach fruits in Korea, an amalgamation of metagenomics and metatranscriptomics was used. The results showed the co-inhabitance of fungi and bacteria, and by combining the DNA shotgun and RNA sequencing the diversity of microbial community was increased [82]. A metatranscriptomic comparison of interactions between endophytic and pathogenic Fusarium with the Arabidopsis plant revealed plant transcriptional plasticity at the early level of infection and therefore giving an understanding of gene regulation to different responses generated by fungal endophytes and pathogen complexes [83]. The metatranscriptomic analyses of grapes revealed the complex interaction dynamics of its microbiome and also highlighted the differences in expressed functional genes of filamentous and yeast fungi during noble rot and gray rot [84].

3.5. Proteomics

The real workhorses living in the cell and reacting to the surrounding environment with a denotation of the active state of the cell are proteins. A large number of research efforts have been invested in the understanding of plant–microbe interaction. Proteomics is used to get an insight into the plant defense mechanisms performed during conditions of biotic stress [85]. Proteomics is defined as the study of proteins extensively describing their abundance, modification, and binding nature with associated networks. In simpler terms, it is the study of different proteins that are expressed by an organism [86]. Proteomics is compatible with working alongside other functional genomics such as transcriptomics and metabolomics. The use of mass spectrometry in recent years has become dominant in the field of proteomic evaluations [30]. It has brought a certain kind of revolution to the fields of research, agriculture, and clinical trials. The different kinds of techniques that are being used in proteomics are gel based (fluorescent two-dimensional difference gel electrophoresis, two-dimensional gel electrophoresis) and gel free (multidimensional protein identification technology, isotope-coded affinity tags, isobaric tagged for relative and absolute quantitation, mass spectrophotometry, and MALDI-TOF) [75].

The advancement in bioinformatics tools and techniques has made functional identification studies in proteomics more accessible and easier. There are several database and search algorithms such as SEQUEST, Mascot, PeptideProphet, ProteinProphet, and DBParser that aid in proteomic analysis [87-90]. A larger percentage of crop losses every year are caused by fungi though the bacteria result in most number of plant diseases. Proteomics have been used to describe the plant–fungi interactions related to biotrophic and necrotrophic fungi groups. It could provide a better understanding of their relationship at both quantitative and qualitative levels and enhance knowledge of management strategies. Though a lot of consideration is given to plant and fungi associations with the help of proteomics, there is still a need to devolve more into solving the constitutional queries. The mutually beneficial relationship shared among the plants and fungi helps in the in-depth adjustment of plant metabolism and the regulation of different molecular mechanisms. Also, many of these mechanisms are not able to be characterized in a proper manner [91].

Endophytic fungi reside inside the living cells of the host plant. They do not sporulate and are in a mutualistic relationship with the host plant. They are an important form of bioresource. Endophytic fungi have abundant benefits and applications in the fields of agriculture, industry, and medicine. With the help of protein profiling, one gets an overall snapshot of pathogens’ conversion to endophytes. In the last five decades, plant–fungus interaction has been a keen topic of interest for researchers, although the information related to sequences in public databases is limited. This gives an opportunity for proteomics to be associated with the comparative analysis of plants. Also, in modern times, fungal biology is also facing challenges in understanding the function, expression, and regulation of the whole protein content coding by the fungal genes. And this understanding is vital for assessing plant–fungal interactions, fungal colonization, and pathogenicity. The use of proteomics in this regard plays a vital role in mapping the changes in plant–microbe interaction studies [92].

A study was undertaken to conduct a comprehensive proteomics analysis of arbuscular mycorrhizal fungi-modulated proteins both at the root and shoot/leaves levels. The results concluded the presence of several protein transporters helping in mineral uptake, along with the ribosomal translational apparatus playing a big role in the systemic reprogramming of translation [93]. An experiment was conducted using transcriptomics and proteomics techniques on endophyte-inoculated and free plants to evaluate the effect of fungal endophyte Gilmaniella sp. AL12 on Atractylodes lancea plant’s metabolism and other related regulatory processes at both translational and transcriptional levels. It was concluded that the fungal endophyte weakens the immune response of the host plant, their interaction increased the biomass and sesquiterpenoid content, increased photosynthesis, expanded the TCA and glycolysis cycle, and enhanced metabolic flux [94]. Entomopathogenic fungi such as Beauveria bassiana, Lecanicillium cf. psalliotae, and Lecanicillium dimorphum may act as endophytes colonizing the palm tree tissue. The proteomic techniques were used to study the interaction of these fungi with the date palm tree Phoenix dactylifera L. at the molecular level. An endophytic colonization of these fungi modulating plant defense responses, energy metabolism, and possibility of modulating cell division-related proteins in the in vitro palms was suggested [95].

Piriformospora indica is known to promote growth and help in the survival of a diverse number of plants in times of abiotic stress, especially during drought. The barley plant leaves exposed to moisture stress were inoculated with P. indica and its response was characterized using proteomics and metabolomics. The other conclusion drawn from the interaction was that the colonization of roots by the fungal endophyte increased the activity of photosystem and electron transfer chain, and in addition to this promotion it accumulated protective proteins pertaining to functions such as energy modulation, photorespiration, autophagy, primary metabolism, and transporters. In another study related to Piriformospora indica, quantitative proteomic analysis of the fungus and its interaction with the host plant and rhizospheric bacteria was performed for the reflection of associated, hidden proteins and enzymes. Also a protocol was described for better extraction of the cellular proteins from the fungus. The protocol included 2D gel electrophoresis after the interaction with Azotobacter chroococcum in the axenic culture [96]. Serendipita indica-colonized barley plants were analyzed for proteomic analysis, and it was revealed that the fungus improved photosynthesis in barley plant, Hordeum vulgare L. under salt stress conditions and provided the protein profiling of the functional proteins [97].

3.6. Metaproteomics

Metaproteomics revolves around the functional identification of the expressed metagenome functional expression and also includes the interpretation of the associated metabolic functions that occur in a community during the sample collection. It is also referred to as whole-community proteomics. Metaproteomics plays a significant role in the functional identification of new genes expressed in stress conditions and also in the genomic diversity of microbiome complex in a particular environment [98,99]. The extraction of total protein from the microenvironment can be done using either direct or indirect lysis. Through direct lysis the total protein content can be extracted directly from the plant endosphere in a natural or stressed condition. Also, the analysis of protein fingerprints can be done to study the potential of endophytes and the effect of their metabolite production. In the case of indirect lysis, the extraction of total protein content is carried out from already isolated endophytes in a stressed environment. The 2D gel electrophoresis is used for obtaining protein fingerprints that can be further used for the analysis of the role of endophytes in stressful conditions [98,96]. Metaproteomics uses the high-end performance of mass spectrophotometers for the exclusive protein characterization expressed by a microbial community in a given sample [30]. It quantifies the peptides and proteins by spot intensity analysis on the gels and in liquid chromatography by protein tagging. But for measuring the relative abundance signal intensity/spectra counts may be used [100-102]. A study was conducted to evaluate the relationship of symbiotic microbiota with Dermatocarpon miniatum’s entire lichen thallus by using the metaproteomic analysis. The result showcased the identification of 138 proteins after the use of SDS-PAGE, LC/MS analysis, and Mascot search in UniRef100 and Swiss-Prot databases. Both the proteins from lichens and fungi were associated with different microbial communities, and they also gave extended input to the fungal and algal associations [103].

3.7. Metabolomics

In many biological domains, the advent of high-throughput technology known as “omics” has proven to be tremendously advantageous [104]. Metabolomics has surfaced as a potentially valuable technique for phenotypic characterization in dynamic contexts [105]. The goal of a field of study known as “metabolomics” is to identify and quantify each and every metabolite that exists in a given organism [106]. Small molecules (less than 1 kDa) that are either products or intermediates in metabolic processes are commonly referred to as metabolites. The understanding of the interactions between plant microbes has grown significantly in the past few years, but the chemical communication that results in priming is still not widely understood [107]. As a supplement to existing “omics” techniques, metabolomics provides the ability to characterize changes to the metabolomes of interacting species through a variety of sophisticated bioanalytical techniques combined with chemometrics and bioinformatics tools [108]. Gaining more insight into the separate metabolisms of the plant and its endophyte, as well as the metabolic interplay supporting the interactome, can be facilitated by metabolomics [109].

Metabolomic analysis frequently entails a number of processes, including sample preparation, measurement, and data processing [110]. In metabolomics, quenching is a technique used to stop metabolite turnover, especially during sample preparation and collection. This mechanism is quite efficient in processing most key metabolites, such as sugars, amino acids, organic acids, and carbohydrates. Secondary metabolites, which are a group of metabolites derived from three families, including phenolics, alkaloids, terpenes, and steroids, typically have a much slower turnover rate and are more chemically stable than primary metabolites, which must be quenched during sample preparation [111]. These subsequent types of metabolites are sometimes of higher interest to traditional medicine. Samples are extracted after quenching, and this procedure typically uses a range of organic or inorganic solvents, such as methanol, ethanol, ethyl acetate, or hexane, depending on the target metabolites [112]. Identification of metabolites is as important a step in metabolomics as extraction and quantification. There are two different but complementary ways to accomplish this: untargeted and targeted [113].

Metabolites that have been isolated from fungi can be characterized using a variety of methods [114]. Sample preparation, data collection, data mining and analysis, statistical modeling, signature biomarkers, and biochemical interpretation are all included in an adaptable metabolomics flowchart [115]. Various techniques have been employed by scientists for conducting metabolite profiling [116]. Several detection techniques are commonly used to separate and identify compounds, including liquid chromatography–ultraviolet and visible spectrum or diode array detection [LC-UV (DAD)], liquid chromatography–mass spectrometry (LC-MS), LC-MS/MS, various forms of high-performance liquid chromatography (HPLC), gas chromatography–mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) imaging, gas–liquid chromatography, and liquid chromatography/time of flight–mass spectrometry (LC-TOF/MS) [28,117-119]. The spatial metabolome may be utilized based on desorption electrospray ionization-imaging mass spectrometry (DESI-IMS), matrix-assisted laser desorption/ionization-IMS (MALDI-IMS), as well as airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) when traditional MS is unable to reliably detect spatial–temporal occurrences of metabolites [28].

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4. CONCLUSION

Fungi, the very important part of Eukarya domain, have been known since a long time and are found in various niches. This category of organism plays various significant roles in the functioning of various organisms, including plants. Several fungi are associated with the internal tissues of plants, which play a vital role in their survivability. After a long period of research, endophytic fungi are known to have a wide range of applications in various industries, and several species have been recognized as industrially important. However, after a long study, a huge number of fungi are still unknown because they cannot be cultured, so omics tools are a solution with the help of which unknown fungal species could be researched and their interactions could be studied. In future, several omics tools could be used to study the fungal diversity residing inside the plants, and their applications could be explored.

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5. ACKNOWLEDGMENTS

The authors are grateful to the Department of Environment, Science & Technology (DEST), Shimla funded project “Development of Microbial Consortium as Bio-inoculants for Drought and Low Temperature Growing Crops for Organic Farming in Himachal Pradesh” for providing the facilities and financial support.

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6. AUTHOR CONTRIBUTIONS

The manuscript was written by TK, RN, BS,SK, SSK and DK. Manuscript was reviewed and edited by SS, SR, SS, NY, MK, and AKR. Manuscript was edited and revised by AKR, SS and SR. The concept manuscript was given by ANY.

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7. CONFLICT OF INTEREST

The authors report no financial or any other conflicts of interest in this work.

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8. ETHICAL APPROVALS

This study does not involve experiments on animals or human subjects.

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9. DATA AVAILABILITY

All the data is available with the authors and shall be provided upon request.

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10. USE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED TECHNOLOGY

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11. PUBLISHER’S NOTE

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REFERENCES

1.  Verma P, Yadav AN, Kumar V, Singh DP, Saxena AK. Beneficial plant-microbes interactions: biodiversity of microbes from diverse extreme environments and its impact for crop improvement. In: Singh DP, Singh HB, Prabha R, editors. Plant-microbe interactions in agro-ecological perspectives: Volume 2: microbial interactions and agro-ecological impacts. Singapore: Springer Singapore; 2017, p. 543–80.

2.  Yadav AN. Beneficial plant-microbe interactions for agricultural sustainability. J Appl Biol Biotechnol. 2021;9:1–4.

3.  Suman A, Yadav AN, Verma P. Endophytic microbes in crops: diversity and beneficial impact for sustainable agriculture. In: Singh DP, Singh HB, Prabha R, editors. Microbial Inoculants in Sustainable Agricultural Productivity: Vol. 1: Research Perspectives. New Delhi: Springer; 2016, p. 117–43.

4.  Yadav AN. Biodiversity and biotechnological applications of host-specific endophytic fungi for sustainable agriculture and allied sectors. Acta Sci Microbiol. 2018;1:1–5.

5.  Rana KL, Kour D, Kaur T, Devi R, Yadav AN, Yadav N, et al. Endophytic microbes: biodiversity, plant growth-promoting mechanisms and potential applications for agricultural sustainability. Antonie van Leeuwenhoek. 2020;113:1075–7.

6.  Yadav AN. Endophytic fungi for plant growth promotion and adaptation under abiotic stress conditions. Acta Sci Agric. 2019;3:91–3.

7.  Yadav AN, Mishra S, Kour D, Yadav N, Kumar A. Agriculturally Important Fungi for Sustainable Agriculture, Volume 1: Perspective for Diversity and Crop Productivity. Cham: Springer International Publishing; 2020.

8.  Verma V, Srivastava A, Garg SK, Singh VP, Arora PK. Incorporating omics-based tools into endophytic fungal research. Biotechnol Notes. 2024;5:1–7.

9.  Yadav AN, Mishra S, Kour D, Yadav N, Kumar A. Agriculturally important fungi for sustainable agriculture, Volume 2: functional annotation for crop protection. Cham: Springer International Publishing; 2020.

10.  Adeleke BS, Babalola OO. Meta-omics of endophytic microbes in agricultural biotechnology. Biocatal Agric Biotechnol. 2022;42:102332.

11.  Rana KL, Kour D, Sheikh I, Yadav N, Yadav AN, Kumar V, et al. Biodiversity of endophytic fungi from diverse niches and their biotechnological applications. In: Singh BP, editor. Advances in Endophytic Fungal Research: Present Status and Future Challenges. Cham: Springer International Publishing; 2019. p. 105–44.

12.  Yadav AN. Recent trends in mycological research, Volume 1: agricultural and medical perspective. Cham: Springer; 2020.

13.  Abdel-Azeem AM, Yadav AN, Yadav N, Usmani Z. Industrially important fungi for sustainable development, Vol-1: biodiversity and ecological perspective. Cham: Springer; 2021.

14.  Rana KL, Kour D, Kaur T, Devi R, Negi C, Yadav AN, et al. Endophytic fungi from medicinal plants: biodiversity and biotechnological applications. In: Kumar A, Radhakrishnan EK, editors. Microbial Endophytes, Woodhead Publishing; 2020. p. 273–305.

15.  Zheng YK, Qiao XG, Miao CP, Liu K, Chen YW, Xu LH, et al. Diversity, distribution and biotechnological potential of endophytic fungi. Ann Microbiol. 2016;66:529–42.

16.  Kour D, Rana KL, Yadav N, Yadav AN, Singh J, Rastegari AA, et al. Agriculturally and industrially important fungi: current developments and potential biotechnological applications. In: Yadav AN, Singh S, Mishra S, Gupta A, editors. Recent Advancement in White Biotechnology through Fungi, Volume 2: Perspective for Value-Added Products and Environments. Cham: Springer International Publishing; 2019. p. 1–64.

17.  Chetia H, Kabiraj D, Bharali B, Ojha S, Barkataki MP, Saikia D, et al. Exploring the benefits of endophytic fungi via omics. In: Singh BP, editor. Advances in Endophytic Fungal Research: Present Status and Future Challenges. Cham: Springer International Publishing; 2019. p. 51–81.

18.  Diwan D, Rashid MM, Vaishnav A. Current understanding of plant-microbe interaction through the lenses of multi-omics approaches and their benefits in sustainable agriculture. Microbiol Res. 2022;265:127180.

19.  Misra BB, Langefeld C, Olivier M, Cox LA. Integrated omics: tools, advances and future approaches. J Mol Endocrinol. 2019;62:R21–45.

20.  Cao L, Zhang Q, Miao R, Lin J, Feng R, Ni Y, et al. Application of omics technology in the research on edible fungi. Curr Res Food Sci. 2023;6:100430.

21.  Kato H, Takahashi S, Saito K. Omics and integrated omics for the promotion of food and nutrition science. J Tradit Complement Med. 2011;1:25–30.

22.  Fondi M, Liò P. Multi-omics and metabolic modelling pipelines: challenges and tools for systems microbiology. Microbiol Res. 2015;171:52–64.

23.  Muller EE, Pinel N, Laczny CC, Hoopmann MR, Narayanasamy S, Lebrun LA, et al. Community-integrated omics links dominance of a microbial generalist to fine-tuned resource usage. Nat Commun. 2014;5:5603.

24.  Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype–phenotype interactions. Nat Rev Genet. 2015;16:85–97.

25.  Yang JY, Karr JR, Watrous JD, Dorrestein PC. Integrating ‘-omics’ and natural product discovery platforms to investigate metabolic exchange in microbiomes. Curr Opin Cheml Biol. 2011;15:79–87.

26.  Graw S, Chappell K, Washam CL, Gies A, Bird J, Robeson MS, et al. Multi-omics data integration considerations and study design for biological systems and disease. Mol Omics. 2021;17:170–85.

27.  Suryanarayanan TS. Endophyte research: going beyond isolation and metabolite documentation. Fungal Ecol. 2013;6:561–8.

28.  Chen XL, Sun MC, Chong SL, Si JP, Wu LS. Transcriptomic and metabolomic approaches deepen our knowledge of plant–endophyte interactions. Front Plant Sci. 2022;12:700200.

29.  Subudhi E, Sahoo RK, Dey S, Das A, Sahoo K. Unraveling plant-endophyte interactions: an omics insight. In: Jha S, editor. Endophytes and Secondary Metabolites. Cham: Springer; 2018. p. 1–19.

30.  Kaul S, Sharma T, Dhar MK. “Omics” tools for better understanding the plant–endophyte interactions. Front Plant Sci. 2016;7:955.

31.  Sagita R, Quax WJ, Haslinger K. Current state and future directions of genetics and genomics of endophytic fungi for bioprospecting efforts. Front Bioeng Biotechnol. 2021;9:649906.

32.  Dudeja SS, Suneja-Madan P, Paul M, Maheswari R, Kothe E. Bacterial endophytes: molecular interactions with their hosts. J Basic Microbiol. 2021;61:475–505.

33.  Dubey A, Malla MA, Kumar A, Dayanandan S, Khan ML. Plants endophytes: unveiling hidden agenda for bioprospecting toward sustainable agriculture. Crit Rev Biotechnol. 2020;40:1210–31.

34.  Kumar M, Sharma R, Dua M, Tuteja N, Johri AK. “Electrotransformation” transformation system for root endophytic fungus Piriformospora indica. In: Varma A, Kost G, Oelmüller R, editors. Piriformospora indica: Sebacinales and Their Biotechnological Applications. Berlin: Springer Berlin Heidelberg; 2013. p. 309–21.

35.  Qiang-long Z, Shi L, Peng G, Fei-shi L. High-throughput sequencing technology and its application. J Northeast Agric Univ. 2014;21:84–96.

36.  Aragona M, Haegi A, Valente MT, Riccioni L, Orzali L, Vitale S, et al. New-generation sequencing technology in diagnosis of fungal plant pathogens: a dream comes true? J Fungi. 2022;8:737.

37.  van der Does HC, Rep M. Adaptation to the host environment by plant-pathogenic fungi. Ann Rev Phytopathol. 2017;55:427–50.

38.  Lu Y, Ye C, Che J, Xu X, Shao D, Jiang C, et al. Genomic sequencing, genome-scale metabolic network reconstruction, and in silico flux analysis of the grape endophytic fungus Alternaria sp. MG1. Microb Cell Fact. 2019;18:1–16.

39.  Nicholson MJ, Van de Bittner KC, Ram A, Bustamante LY, Scott B, Parker EJ. Draft genome sequence of the filamentous fungus Hypoxylon pulicicidum ATCC 74245. Genome Announc. 2018;6:e01380–17.

40.  Shaw JJ, Berbasova T, Sasaki T, Jefferson-George K, Spakowicz DJ, Dunican BF, et al. Identification of a fungal 1, 8-cineole synthase from Hypoxylon sp. with specificity determinants in common with the plant synthases. J Biol Chem. 2015;290:8511–26.

41.  Silva DPD, Cardoso MS, Macedo AJ. Endophytic fungi as a source of antibacterial compounds—a focus on gram-negative bacteria. Antibiotics. 2022;11:1509.

42.  Herndon N, Shelton J, Gerischer L, Ioannidis P, Ninova M, Dönitz J, et al. Enhanced genome assembly and a new official gene set for Tribolium castaneum. BMC Genom. 2020;21:1–13.

43.  Handelsman J. Metagenomics: application of genomics to uncultured microorganisms. Microbiol Mol Biol Rev. 2004;68:669–85.

44.  Sahoo RK, Gaur M, Subudhi E. Chapter 4 - function profiling of microbial community. In: Singh HB, Gupta VK, Jogaiah S, editors. New and Future Developments in Microbial Biotechnology and Bioengineering. Amsterdam: Elsevier; 2019. p. 77–85.

45.  Maropola MKA, Ramond JB, Trindade M. Impact of metagenomic DNA extraction procedures on the identifiable endophytic bacterial diversity in Sorghum bicolor (L. Moench). J Microbiol Methods. 2015;112:104–17.

46.  Nguyen MH, Shin KC, Lee JK. Fungal community analyses of endophytic fungi from two oak species, Quercus mongolica and Quercus serrata, in Korea. Mycobiology. 2021;49:385–95.

47.  Hong CE, Kim JU, Lee JW, Bang KH, Jo IH. Metagenomic analysis of bacterial endophyte community structure and functions in Panax ginseng at different ages. 3 Biotech. 2019;9:1–8.

48.  Toole DR, Zhao J, Martens-Habbena W, Strauss SL. Bacterial functional prediction tools detect but underestimate metabolic diversity compared to shotgun metagenomics in southwest Florida soils. Appl Soil Ecol. 2021;168:104129.

49.  Premalatha K, Kalra A. Molecular phylogenetic identification of endophytic fungi isolated from resinous and healthy wood of Aquilaria malaccensis, a red listed and highly exploited medicinal tree. Fungal Ecol. 2013;6:205–11.

50.  Weig AR, Peršoh D, Werner S, Betzlbacher A, Rambold G. Diagnostic assessment of mycodiversity in environmental samples by fungal ITS1 rDNA length polymorphism. Mycol Prog. 2013;12:719–25.

51.  Kemler M, Garnas J, Wingfield MJ, Gryzenhout M, Pillay KA, Slippers B. Ion Torrent PGM as tool for fungal community analysis: a case study of endophytes in Eucalyptus grandis reveals high taxonomic diversity. PLoS One. 2013;8:e81718.

52.  Toju H, Tanabe AS, Ishii H. Ericaceous plant–fungus network in a harsh alpine–subalpine environment. Mol Ecol. 2016;25:3242–57.

53.  Yan K, Zhang J, Cai Y, Cao G, Meng L, Soaud SA, et al. Comparative analysis of endophytic fungal communities in bamboo species Phyllostachys edulis, Bambusa rigida, and Pleioblastus amarus. Sci Rep. 2023;13:20910.

54.  Dinsdale EA, Edwards RA, Hall D, Angly F, Breitbart M, Brulc JM, et al. Functional metagenomic profiling of nine biomes. Nature. 2008;452:629–32.

55.  Langa-Lomba N, Grimplet J, Sánchez-Hernández E, Martín-Ramos P, Casanova-Gascón J, Julián-Lagunas C, et al. Metagenomic study of fungal microbial communities in two PDO Somontano Vineyards (Huesca, Spain): effects of age, plant genotype, and initial phytosanitary status on the priming and selection of their associated microorganisms. Plants. 2023;12:2251.

56.  Pais A, Ristaino J, Whetten R, Xiang QYJ. Metagenomic study reveals hidden relationships among fungal diversity, variation of plant disease, and genetic distance in Cornus florida (Cornaceae). Front Plant Sci. 2023;14:1282188.

57.  Zhang Q, Xue XZ, Miao SM, Cui JL, Qin XM. Differential relationship of fungal endophytic communities and metabolic profiling in the stems and roots of Ephedra sinica based on metagenomics and metabolomics. Symbiosis. 2020;81:115–25.

58.  Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T. Transcriptomics technologies. PLoS Comput Biol. 2017;13:e1005457.

59.  Govindarajan R, Duraiyan J, Kaliyappan K, Palanisamy M. Microarray and its applications. J Pharm Bioallied Sci. 2012;4:S310–2.

60.  Molina LG, Cordenonsi da Fonseca G, Morais GLd, de Oliveira LFV, Carvalho JBd, Kulcheski FR, et al. Metatranscriptomic analysis of small RNAs present in soybean deep sequencing libraries. Genet Mol Biol. 2012;35:292–303.

61.  Sheibani-Tezerji R, Rattei T, Sessitsch A, Trognitz F, Mitter B. Transcriptome profiling of the endophyte Burkholderia phytofirmans PsJN indicates sensing of the plant environment and drought stress. mBio. 2015;6:e00621–15.

62.  Ambrose KV, Belanger FC. SOLiD-SAGE of endophyte-infected red fescue reveals numerous effects on host transcriptome and an abundance of highly expressed fungal secreted proteins. PLoS One. 2012;7:e53214.

63.  Molitor A, Zajic D, Voll LM, Pons-Kühnemann J, Samans B, Kogel KH, et al. Barley leaf transcriptome and metabolite analysis reveals new aspects of compatibility and Piriformospora indica–mediated systemic induced resistance to powdery mildew. Mol Plant Microb Interact. 2011;24:1427–39.

64.  Jonkers W, Rodriguez Estrada AE, Lee K, Breakspear A, May G, Kistler HC. Metabolome and transcriptome of the interaction between Ustilago maydis and Fusarium verticillioides in vitro. Appl Environ Microbiol. 2012;78:3656–67.

65.  Mejía LC, Herre EA, Sparks JP, Winter K, García MN, Van Bael SA, et al. Pervasive effects of a dominant foliar endophytic fungus on host genetic and phenotypic expression in a tropical tree. Front Microbiol. 2014;5:479.

66.  Dupont PY, Eaton CJ, Wargent JJ, Fechtner S, Solomon P, Schmid J, et al. Fungal endophyte infection of ryegrass reprograms host metabolism and alters development. New Phytol. 2015;208:1227–40.

67.  Ghaffari MR, Ghabooli M, Khatabi B, Hajirezaei MR, Schweizer P, Salekdeh GH. Metabolic and transcriptional response of central metabolism affected by root endophytic fungus Piriformospora indica under salinity in barley. Plant Mol Biol. 2016;90:699–717.

68.  Dinkins RD, Nagabhyru P, Graham MA, Boykin D, Schardl CL. Transcriptome response of Lolium arundinaceum to its fungal endophyte Epichloë coenophiala. New Phytol. 2017;213:324–37.

69.  Ortiz J, Soto J, Fuentes A, Herrera H, Meneses C, Arriagada C. The endophytic fungus Chaetomium cupreum regulates expression of genes involved in the tolerance to metals and plant growth promotion in Eucalyptus globulus roots. Microorganisms. 2019;7:490.

70.  Shen M, Schneider H, Xu R, Cao G, Zhang H, Li T, et al. Dark septate endophyte enhances maize cadmium (Cd) tolerance by the remodeled host cell walls and the altered Cd subcellular distribution. Environ Exp Bot. 2020;172:104000.

71.  Shaffer JP, Carter ME, Spraker JE, Clark M, Smith BA, Hockett KL, Baltrus DA, Arnold AE, et al. Transcriptional profiles of a foliar fungal endophyte (Pestalotiopsis, Ascomycota) and its bacterial symbiont (Luteibacter, Gammaproteobacteria) reveal sulfur exchange and growth regulation during early phases of symbiotic interaction. Msystems. 2022;7:e00091–22.

72.  Malinich EA, Wang K, Mukherjee PK, Kolomiets M, Kenerley CM. Differential expression analysis of Trichoderma virens RNA reveals a dynamic transcriptome during colonization of Zea mays roots. BMC Genom. 2019;20:1–19.

73.  Natarajan S, Pucker B, Srivastava S. Genomic and transcriptomic analysis of camptothecin producing novel fungal endophyte-Alternaria burnsii NCIM 1409. Sci Rep. 2023;13:14614.

74.  Rashad YM, Al Tami MS, Abdalla SA. Eliciting transcriptomic and antioxidant defensive responses against Rhizoctonia root rot of sorghum using the endophyte Aspergillus oryzae YRA3. Sci Rep. 2023;13:19823.

75.  Swarupa V, Pavitra K, Shivashankara K, Ravishankar K. Omics-driven approaches in plant–microbe interaction. In: Singh D, Singh H, Prabha R, editors. Microbial Inoculants in Sustainable Agricultural Productivity: Vol. 1: Research Perspectives. New Delhi: Springer; 2016. p. 61–84.

76.  Ottesen EA, Marin R, Preston CM, Young CR, Ryan JP, Scholin CA, et al. Metatranscriptomic analysis of autonomously collected and preserved marine bacterioplankton. ISME J. 2011;5:1881–95.

77.  Shi Y, Tyson GW, Eppley JM, DeLong EF. Integrated metatranscriptomic and metagenomic analyses of stratified microbial assemblages in the open ocean. ISME J. 2011;5:999–1013.

78.  Urich T, Lanzén A, Qi J, Huson DH, Schleper C, Schuster SC. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS One. 2008;3:e2527.

79.  Turner TR, Ramakrishnan K, Walshaw J, Heavens D, Alston M, Swarbreck D, et al. Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. ISME J. 2013;7:2248–58.

80.  Sharaff MM, Subrahmanyam G, Kumar A, Yadav AN. Mechanistic understanding of the root microbiome interaction for sustainable agriculture in polluted soils. In: Rastegari AA, Yadav AN, Yadav N (eds) New and Future Developments in Microbial Biotechnology and Bioengineering. Elsevier, 2020. pp 61-84.

81.  Peršoh D. Plant-associated fungal communities in the light of meta’omics. Fungal Divers. 2015;75:1–25.

82.  Jo Y, Back C-G, Choi H, Cho WK. Comparative microbiome study of mummified peach fruits by metagenomics and metatranscriptomics. Plants. 2020;9:1052.

83.  Guo L, Yu H, Wang B, Vescio K, DeIulio GA, Yang H, et al. Metatranscriptomic comparison of endophytic and pathogenic Fusarium–Arabidopsis interactions reveals plant transcriptional plasticity. Mol Plant Microb Interact. 2021;34:1071–83.

84.  Otto M, Geml J, Hegyi ÁI, Hegyi-Kaló J, Kun J, Gyenesei A, et al. Metatranscriptomic analyses of grapes reveal differences in expressed functional genes of filamentous and yeast fungi during noble rot and grey rot. Fungal Ecol. 2023;65:101277.

85.  Chatterjee M, Gupta S, Bhar A, Chakraborti D, Basu D, Das S. Analysis of root proteome unravels differential molecular responses during compatible and incompatible interaction between chickpea (Cicer arietinum L.) and Fusarium oxysporum f. sp. ciceri Race1 (Foc1). BMC Genom. 2014;15:1–19.

86.  Wilkins MR, Sanchez JC, Gooley AA, Appel RD, Humphery-Smith I, Hochstrasser DF, et al. Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol Genet Eng Rev. 1996;13:19–50.

87.  Kav NN, Srivastava S, Yajima W, Sharma N. Application of proteomics to investigate plant-microbe interactions. Curr Proteom. 2007;4:28–43.

88.  Jayaraman D, Forshey KL, Grimsrud PA, Ané J-M. Leveraging proteomics to understand plant–microbe interactions. Front Plant Sci. 2012;3:44.

89.  Keller M, Hettich R. Environmental proteomics: a paradigm shift in characterizing microbial activities at the molecular level. Microbiol Mol Biol Rev. 2009;73:62–70.

90.  Calderón-González KG, Hernández-Monge J, Herrera-Aguirre ME, Luna-Arias JP. Bioinformatics tools for proteomics data interpretation. In: Mirzaei H, Carrasco M, editors. Modern Proteomics – Sample Preparation, Analysis and Practical Applications. Cham: Springer International Publishing; 2016. p. 281–341.

91.  Jain A, Singh HB, Das S. Deciphering plant-microbe crosstalk through proteomics studies. Microbiol Res. 2021;242:126590.

92.  Pillai TG. Pathogen to endophytic transmission in fungi-a proteomics approach. SOJ Microbiol Infect Dis. 2017;5:1–5.

93.  Domingo G, Vannini C, Bracale M, Bonfante P. Proteomics as a tool to decipher plant responses in arbuscular mycorrhizal interactions: a meta-analysis. Proteomics. 2023;23:2200108.

94.  Yuan J, Zhang W, Sun K, Tang MJ, Chen PX, Li X, et al. Comparative transcriptomics and proteomics of Atractylodes lancea in response to endophytic fungus Gilmaniella sp. AL12 reveals regulation in plant metabolism. Front Microbiol. 2019;10:1208.

95.  Gómez-Vidal S, Salinas J, Tena M, Lopez-Llorca LV. Proteomic analysis of date palm (Phoenix dactylifera L.) responses to endophytic colonization by entomopathogenic fungi. Electrophoresis. 2009;30:2996–3005.

96.  Ghaffari MR, Mirzaei M, Ghabooli M, Khatabi B, Wu Y, Zabet-Moghaddam M, et al. Root endophytic fungus Piriformospora indica improves drought stress adaptation in barley by metabolic and proteomic reprogramming. Environ Exp Bot. 2019;157:197–210.

97.  Sepehri M, Ghaffari M, Khayam Nekoui M, Sarhadi E, Moghadam A, Khatabi B, et al. Root endophytic fungus Serendipita indica modulates barley leaf blade proteome by increasing the abundance of photosynthetic proteins in response to salinity. J Appl Microbiol. 2021;131:1870–89.

98.  Maron PA, Ranjard L, Mougel C, Lemanceau P. Metaproteomics: a new approach for studying functional microbial ecology. Microbial Ecol. 2007;53:486–93.

99.  Dubey RK, Tripathi V, Prabha R, Chaurasia R, Singh DP, Rao CS, et al. Metatranscriptomics and metaproteomics for microbial communities profiling. In: Dubey RK, Tripathi V, Prabha R, et al. editors. Unravelling the Soil Microbiome: Perspectives for Environmental Sustainability. Cham: Springer International Publishing; 2020. p. 51–60.

100.  Seifert J, Herbst FA, Halkjær Nielsen P, Planes FJ, Jehmlich N, Ferrer M, et al. Bioinformatic progress and applications in metaproteogenomics for bridging the gap between genomic sequences and metabolic functions in microbial communities. Proteomics. 2013;13:2786–804.

101.  Mueller RS, Pan C. Sample handling and mass spectrometry for microbial metaproteomic analyses. In: DeLong EF, editors. Methods in Enzymology. Academic Press; 2013. p. 289–303.

102.  Pan C, Banfield JF. Quantitative metaproteomics: functional insights into microbial communities. In: Paulsen IT, Holmes AJ, editors. Environmental Microbiology: Methods and Protocols. Totowa (NJ): Humana Press; 2014. p. 231–40.

103.  Ullah J, Khanum Z, Khan IA, Khalid AN, Musharraf SG, Ali A. Metaproteomics reveals the structural and functional diversity of Dermatocarpon miniatum (L.) W. Mann. Microbiota. Fungal Biol. 2021;125:32–8.

104.  Zhang HW, Lv C, Zhang LJ, Guo X, Shen YW, Nagle DG, et al. Application of omics-and multi-omics-based techniques for natural product target discovery. Biomed Pharmacother. 2021;141:111833.

105.  Monteiro M, Carvalho M, Bastos M, Guedes de Pinho P. Metabolomics analysis for biomarker discovery: advances and challenges. Curr Med Chem. 2013;20:257–71.

106.  Klassen A, Faccio AT, Canuto GAB, da Cruz PLR, Ribeiro HC, Tavares MFM, et al. Metabolomics: definitions and significance in systems biology. In: Sussulini A, editor. Metabolomics: From Fundamentals to Clinical Applications. Cham: Springer International Publishing; 2017. p. 3–17.

107.  Tugizimana F, Mhlongo MI, Piater LA, Dubery IA. Metabolomics in plant priming research: the way forward? Int J Mol Sci. 2018;19:1759.

108.  Jendoubi T. Approaches to integrating metabolomics and multi-omics data: a primer. Metabolites. 2021;11:184.

109.  Castro-Moretti FR, Gentzel IN, Mackey D, Alonso AP. Metabolomics as an emerging tool for the study of plant–pathogen interactions. Metabolites. 2020;10:52.

110.  Salem MA, Perez de Souza L, Serag A, Fernie AR, Farag MA, Ezzat SM, et al. Metabolomics in the context of plant natural products research: from sample preparation to metabolite analysis. Metabolites. 2020;10:37.

111.  Patil AS. Plant secondary metabolites: isolation, characterization & biological properties. Delhi: Studera Press; 2020.

112.  Pinu FR, Villas-Boas SG, Aggio R. Analysis of intracellular metabolites from microorganisms: quenching and extraction protocols. Metabolites. 2017;7:53.

113.  Cajka T, Fiehn O. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal Chem. 2016;88:524–45.

114.  Frisvad JC, Andersen B, Thrane U. The use of secondary metabolite profiling in chemotaxonomy of filamentous fungi. Mycol Res. 2008;112:231–40.

115.  Li C, Gao Z, Su B, Xu G, Lin X. Data analysis methods for defining biomarkers from omics data. Anal Bioanall Chem. 2022;414:235–50.

116.  Wolfender J-L, Marti G, Thomas A, Bertrand S. Current approaches and challenges for the metabolite profiling of complex natural extracts. J Chromatog A. 2015;1382:136–64.

117.  Khoomrung S, Wanichthanarak K, Nookaew I, Thamsermsang O, Seubnooch P, Laohapand T, et al. Metabolomics and integrative omics for the development of Thai traditional medicine. Front Pharmacol. 2017;8:474.

118.  Xie J, Zhang A, Wang X. Metabolomic applications in hepatocellular carcinoma: toward the exploration of therapeutics and diagnosis through small molecules. RSC Adv. 2017;7:17217–226.

119.  Maheshwari DK. Endophytes: biology and biotechnology. Cham: Springer; 2017.

120.  Kumar R, Bohra A, Pandey AK, Pandey MK, Kumar A. Metabolomics for plant improvement: status and prospects. Front Plant Sci. 2017;8:271676.

Reference

1. Verma P, Yadav AN, Kumar V, Singh DP, Saxena AK. Beneficial plant-microbes interactions: biodiversity of microbes from diverse extreme environments and its impact for crop improvement. In: Singh DP, Singh HB, Prabha R, editors. Plant-microbe interactions in agro-ecological perspectives: Volume 2: microbial interactions and agro-ecological impacts. Singapore: Springer Singapore; 2017, p. 543-80. https://doi.org/10.1007/978-981-10-6593-4_22
2. Yadav AN. Beneficial plant-microbe interactions for agricultural sustainability. J Appl Biol Biotechnol. 2021;9:1-4. https://doi.org/10.7324/JABB.2021.91ed
3. Suman A, Yadav AN, Verma P. Endophytic microbes in crops: diversity and beneficial impact for sustainable agriculture. In: Singh DP, Singh HB, Prabha R, editors. Microbial Inoculants in Sustainable Agricultural Productivity: Vol. 1: Research Perspectives. New Delhi: Springer; 2016, p. 117-43. https://doi.org/10.1007/978-81-322-2647-5_7
4. Yadav AN. Biodiversity and biotechnological applications of host-specific endophytic fungi for sustainable agriculture and allied sectors. Acta Sci Microbiol. 2018;1:1-5.
5. Rana KL, Kour D, Kaur T, Devi R, Yadav AN, Yadav N, et al. Endophytic microbes: biodiversity, plant growth-promoting mechanisms and potential applications for agricultural sustainability. Antonie van Leeuwenhoek. 2020;113:1075-7. https://doi.org/10.1007/s10482-020-01429-y
6. Yadav AN. Endophytic fungi for plant growth promotion and adaptation under abiotic stress conditions. Acta Sci Agric. 2019;3:91-3.
7. Yadav AN, Mishra S, Kour D, Yadav N, Kumar A. Agriculturally Important Fungi for Sustainable Agriculture, Volume 1: Perspective for Diversity and Crop Productivity. Cham: Springer International Publishing; 2020. https://doi.org/10.1007/978-3-030-45971-0
8. Verma V, Srivastava A, Garg SK, Singh VP, Arora PK. Incorporating omics-based tools into endophytic fungal research. Biotechnol Notes. 2024;5:1-7. https://doi.org/10.1016/j.biotno.2023.12.006
9. Yadav AN, Mishra S, Kour D, Yadav N, Kumar A. Agriculturally important fungi for sustainable agriculture, Volume 2: functional annotation for crop protection. Cham: Springer International Publishing; 2020. https://doi.org/10.1007/978-3-030-48474-3
10. Adeleke BS, Babalola OO. Meta-omics of endophytic microbes in agricultural biotechnology. Biocatal Agric Biotechnol. 2022;42:102332. https://doi.org/10.1016/j.bcab.2022.102332
11. Rana KL, Kour D, Sheikh I, Yadav N, Yadav AN, Kumar V, et al. Biodiversity of endophytic fungi from diverse niches and their biotechnological applications. In: Singh BP, editor. Advances in Endophytic Fungal Research: Present Status and Future Challenges. Cham: Springer International Publishing; 2019. p. 105-44. https://doi.org/10.1007/978-3-030-03589-1_6
12. Yadav AN. Recent trends in mycological research, Volume 1: agricultural and medical perspective. Cham: Springer; 2020. https://doi.org/10.1007/978-3-030-60659-6
13. Abdel-Azeem AM, Yadav AN, Yadav N, Usmani Z. Industrially important fungi for sustainable development, Vol-1: biodiversity and ecological perspective. Cham: Springer; 2021. https://doi.org/10.1007/978-3-030-67561-5_1
14. Rana KL, Kour D, Kaur T, Devi R, Negi C, Yadav AN, et al. Endophytic fungi from medicinal plants: biodiversity and biotechnological applications. In: Kumar A, Radhakrishnan EK, editors. Microbial Endophytes, Woodhead Publishing; 2020. p. 273-305. https://doi.org/10.1016/B978-0-12-819654-0.00011-9
15. Zheng YK, Qiao XG, Miao CP, Liu K, Chen YW, Xu LH, et al. Diversity, distribution and biotechnological potential of endophytic fungi. Ann Microbiol. 2016;66:529-42. https://doi.org/10.1007/s13213-015-1153-7
16. Kour D, Rana KL, Yadav N, Yadav AN, Singh J, Rastegari AA, et al. Agriculturally and industrially important fungi: current developments and potential biotechnological applications. In: Yadav AN, Singh S, Mishra S, Gupta A, editors. Recent Advancement in White Biotechnology through Fungi, Volume 2: Perspective for Value- Added Products and Environments. Cham: Springer International Publishing; 2019. p. 1-64. https://doi.org/10.1007/978-3-030-14846-1_1
17. Chetia H, Kabiraj D, Bharali B, Ojha S, Barkataki MP, Saikia D, et al. Exploring the benefits of endophytic fungi via omics. In: Singh BP, editor. Advances in Endophytic Fungal Research: Present Status and Future Challenges. Cham: Springer International Publishing; 2019. p. 51-81. https://doi.org/10.1007/978-3-030-03589-1_4
18. Diwan D, Rashid MM, Vaishnav A. Current understanding of plant-microbe interaction through the lenses of multi-omics approaches and their benefits in sustainable agriculture. Microbiol Res. 2022;265:127180. https://doi.org/10.1016/j.micres.2022.127180
19. Misra BB, Langefeld C, Olivier M, Cox LA. Integrated omics: tools, advances and future approaches. J Mol Endocrinol. 2019;62:R21-45. https://doi.org/10.1530/JME-18-0055
20. Cao L, Zhang Q, Miao R, Lin J, Feng R, Ni Y, et al. Application of omics technology in the research on edible fungi. Curr Res Food Sci. 2023;6:100430. https://doi.org/10.1016/j.crfs.2022.100430
21. Kato H, Takahashi S, Saito K. Omics and integrated omics for the promotion of food and nutrition science. J Tradit Complement Med. 2011;1:25-30. https://doi.org/10.1016/S2225-4110(16)30053-0
22. Fondi M, Liò P. Multi-omics and metabolic modelling pipelines: challenges and tools for systems microbiology. Microbiol Res. 2015;171:52-64. https://doi.org/10.1016/j.micres.2015.01.003
23. Muller EE, Pinel N, Laczny CC, Hoopmann MR, Narayanasamy S, Lebrun LA, et al. Community-integrated omics links dominance of a microbial generalist to fine-tuned resource usage. Nat Commun. 2014;5:5603. https://doi.org/10.1038/ncomms6603
24. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet. 2015;16:85-97. https://doi.org/10.1038/nrg3868
25. Yang JY, Karr JR, Watrous JD, Dorrestein PC. Integrating '-omics' and natural product discovery platforms to investigate metabolic exchange in microbiomes. Curr Opin Cheml Biol. 2011;15:79-87. https://doi.org/10.1016/j.cbpa.2010.10.025
26. Graw S, Chappell K, Washam CL, Gies A, Bird J, Robeson MS, et al. Multi-omics data integration considerations and study design for biological systems and disease. Mol Omics. 2021;17:170-85. https://doi.org/10.1039/D0MO00041H
27. Suryanarayanan TS. Endophyte research: going beyond isolation and metabolite documentation. Fungal Ecol. 2013;6:561-8. https://doi.org/10.1016/j.funeco.2013.09.007
28. Chen XL, Sun MC, Chong SL, Si JP, Wu LS. Transcriptomic and metabolomic approaches deepen our knowledge of plant-endophyte interactions. Front Plant Sci. 2022;12:700200. https://doi.org/10.3389/fpls.2021.700200
29. Subudhi E, Sahoo RK, Dey S, Das A, Sahoo K. Unraveling plant-endophyte interactions: an omics insight. In: Jha S, editor. Endophytes and Secondary Metabolites. Cham: Springer; 2018. p. 1-19. https://doi.org/10.1007/978-3-319-76900-4_2-1
30. Kaul S, Sharma T, Dhar MK. "Omics" tools for better understanding the plant-endophyte interactions. Front Plant Sci. 2016;7:955. https://doi.org/10.3389/fpls.2016.00955
31. Sagita R, Quax WJ, Haslinger K. Current state and future directions of genetics and genomics of endophytic fungi for bioprospecting efforts. Front Bioeng Biotechnol. 2021;9:649906. https://doi.org/10.3389/fbioe.2021.649906
32. Dudeja SS, Suneja-Madan P, Paul M, Maheswari R, Kothe E. Bacterial endophytes: molecular interactions with their hosts. J Basic Microbiol. 2021;61:475-505. https://doi.org/10.1002/jobm.202000657
33. Dubey A, Malla MA, Kumar A, Dayanandan S, Khan ML. Plants endophytes: unveiling hidden agenda for bioprospecting toward sustainable agriculture. Crit Rev Biotechnol. 2020;40:1210-31. https://doi.org/10.1080/07388551.2020.1808584
34. Kumar M, Sharma R, Dua M, Tuteja N, Johri AK. "Electrotransformation" transformation system for root endophytic fungus Piriformospora indica. In: Varma A, Kost G, Oelmüller R, editors. Piriformospora indica: Sebacinales and Their Biotechnological Applications. Berlin: Springer Berlin Heidelberg; 2013. p. 309-21. https://doi.org/10.1007/978-3-642-33802-1_19
35. Qiang-long Z, Shi L, Peng G, Fei-shi L. High-throughput sequencing technology and its application. J Northeast Agric Univ. 2014;21:84-96. https://doi.org/10.1016/S1006-8104(14)60073-8
36. Aragona M, Haegi A, Valente MT, Riccioni L, Orzali L, Vitale S, et al. New-generation sequencing technology in diagnosis of fungal plant pathogens: a dream comes true? J Fungi. 2022;8:737. https://doi.org/10.3390/jof8070737
37. van der Does HC, Rep M. Adaptation to the host environment by plant-pathogenic fungi. Ann Rev Phytopathol. 2017;55:427-50. https://doi.org/10.1146/annurev-phyto-080516-035551
38. Lu Y, Ye C, Che J, Xu X, Shao D, Jiang C, et al. Genomic sequencing, genome-scale metabolic network reconstruction, and in silico flux analysis of the grape endophytic fungus Alternaria sp. MG1. Microb Cell Fact. 2019;18:1-16. https://doi.org/10.1186/s12934-019-1063-7
39. Nicholson MJ, Van de Bittner KC, Ram A, Bustamante LY, Scott B, Parker EJ. Draft genome sequence of the filamentous fungus Hypoxylon pulicicidum ATCC 74245. Genome Announc. 2018;6:e01380-17. https://doi.org/10.1128/genomeA.01380-17
40. Shaw JJ, Berbasova T, Sasaki T, Jefferson-George K, Spakowicz DJ, Dunican BF, et al. Identification of a fungal 1, 8-cineole synthase from Hypoxylon sp. with specificity determinants in common with the plant synthases. J Biol Chem. 2015;290:8511-26. https://doi.org/10.1074/jbc.M114.636159
41. Silva DPD, Cardoso MS, Macedo AJ. Endophytic fungi as a source of antibacterial compounds-a focus on gram-negative bacteria. Antibiotics. 2022;11:1509. https://doi.org/10.3390/antibiotics11111509
42. Herndon N, Shelton J, Gerischer L, Ioannidis P, Ninova M, Dönitz J, et al. Enhanced genome assembly and a new official gene set for Tribolium castaneum. BMC Genom. 2020;21:1-13. https://doi.org/10.1186/s12864-019-6394-6
43. Handelsman J. Metagenomics: application of genomics to uncultured microorganisms. Microbiol Mol Biol Rev. 2004;68:669-85. https://doi.org/10.1128/MMBR.68.4.669-685.2004
44. Sahoo RK, Gaur M, Subudhi E. Chapter 4 - function profiling of microbial community. In: Singh HB, Gupta VK, Jogaiah S, editors. New and Future Developments in Microbial Biotechnology and Bioengineering. Amsterdam: Elsevier; 2019. p. 77-85. https://doi.org/10.1016/B978-0-444-63503-7.00004-8
45. Maropola MKA, Ramond JB, Trindade M. Impact of metagenomic DNA extraction procedures on the identifiable endophytic bacterial diversity in Sorghum bicolor (L. Moench). J Microbiol Methods. 2015;112:104-17. https://doi.org/10.1016/j.mimet.2015.03.012
46. Nguyen MH, Shin KC, Lee JK. Fungal community analyses of endophytic fungi from two oak species, Quercus mongolica and Quercus serrata, in Korea. Mycobiology. 2021;49:385-95. https://doi.org/10.1080/12298093.2021.1948175
47. Hong CE, Kim JU, Lee JW, Bang KH, Jo IH. Metagenomic analysis of bacterial endophyte community structure and functions in Panax ginseng at different ages. 3 Biotech. 2019;9:1-8. https://doi.org/10.1007/s13205-019-1838-x
48. Toole DR, Zhao J, Martens-Habbena W, Strauss SL. Bacterial functional prediction tools detect but underestimate metabolic diversity compared to shotgun metagenomics in southwest Florida soils. Appl Soil Ecol. 2021;168:104129. https://doi.org/10.1016/j.apsoil.2021.104129
49. Premalatha K, Kalra A. Molecular phylogenetic identification of endophytic fungi isolated from resinous and healthy wood of Aquilaria malaccensis, a red listed and highly exploited medicinal tree. Fungal Ecol. 2013;6:205-11. https://doi.org/10.1016/j.funeco.2013.01.005
50. Weig AR, Peršoh D, Werner S, Betzlbacher A, Rambold G. Diagnostic assessment of mycodiversity in environmental samples by fungal ITS1 rDNA length polymorphism. Mycol Prog. 2013;12:719-25. https://doi.org/10.1007/s11557-012-0883-1
51. Kemler M, Garnas J, Wingfield MJ, Gryzenhout M, Pillay KA, Slippers B. Ion Torrent PGM as tool for fungal community analysis: a case study of endophytes in Eucalyptus grandis reveals high taxonomic diversity. PLoS One. 2013;8:e81718. https://doi.org/10.1371/journal.pone.0081718
52. Toju H, Tanabe AS, Ishii H. Ericaceous plant-fungus network in a harsh alpine-subalpine environment. Mol Ecol. 2016;25:3242-57. https://doi.org/10.1111/mec.13680
53. Yan K, Zhang J, Cai Y, Cao G, Meng L, Soaud SA, et al. Comparative analysis of endophytic fungal communities in bamboo species Phyllostachys edulis, Bambusa rigida, and Pleioblastus amarus. Sci Rep. 2023;13:20910. https://doi.org/10.1038/s41598-023-48187-1
54. Dinsdale EA, Edwards RA, Hall D, Angly F, Breitbart M, Brulc JM, et al. Functional metagenomic profiling of nine biomes. Nature. 2008;452:629-32. https://doi.org/10.1038/nature06810
55. Langa-Lomba N, Grimplet J, Sánchez-Hernández E, Martín-Ramos P, Casanova-Gascón J, Julián-Lagunas C, et al. Metagenomic study of fungal microbial communities in two PDO Somontano Vineyards (Huesca, Spain): effects of age, plant genotype, and initial phytosanitary status on the priming and selection of their associated microorganisms. Plants. 2023;12:2251. https://doi.org/10.3390/plants12122251
56. Pais A, Ristaino J, Whetten R, Xiang QYJ. Metagenomic study reveals hidden relationships among fungal diversity, variation of plant disease, and genetic distance in Cornus florida (Cornaceae). Front Plant Sci. 2023;14:1282188. https://doi.org/10.3389/fpls.2023.1282188
57. Zhang Q, Xue XZ, Miao SM, Cui JL, Qin XM. Differential relationship of fungal endophytic communities and metabolic profiling in the stems and roots of Ephedra sinica based on metagenomics and metabolomics. Symbiosis. 2020;81:115-25. https://doi.org/10.1007/s13199-020-00685-w
58. Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T. Transcriptomics technologies. PLoS Comput Biol. 2017;13:e1005457. https://doi.org/10.1371/journal.pcbi.1005457
59. Govindarajan R, Duraiyan J, Kaliyappan K, Palanisamy M. Microarray and its applications. J Pharm Bioallied Sci. 2012;4:S310-2. https://doi.org/10.4103/0975-7406.100283
60. Molina LG, Cordenonsi da Fonseca G, Morais GLd, de Oliveira LFV, Carvalho JBd, Kulcheski FR, et al. Metatranscriptomic analysis of small RNAs present in soybean deep sequencing libraries. Genet Mol Biol. 2012;35:292-303. https://doi.org/10.1590/S1415-47572012000200010
61. Sheibani-Tezerji R, Rattei T, Sessitsch A, Trognitz F, Mitter B. Transcriptome profiling of the endophyte Burkholderia phytofirmans PsJN indicates sensing of the plant environment and drought stress. mBio. 2015;6:e00621-15. https://doi.org/10.1128/mBio.00621-15
62. Ambrose KV, Belanger FC. SOLiD-SAGE of endophyte-infected red fescue reveals numerous effects on host transcriptome and an abundance of highly expressed fungal secreted proteins. PLoS One. 2012;7:e53214. https://doi.org/10.1371/journal.pone.0053214
63. Molitor A, Zajic D, Voll LM, Pons-Kühnemann J, Samans B, Kogel KH, et al. Barley leaf transcriptome and metabolite analysis reveals new aspects of compatibility and Piriformospora indica-mediated systemic induced resistance to powdery mildew. Mol Plant Microb Interact. 2011;24:1427-39. https://doi.org/10.1094/MPMI-06-11-0177
64. Jonkers W, Rodriguez Estrada AE, Lee K, Breakspear A, May G, Kistler HC. Metabolome and transcriptome of the interaction between Ustilago maydis and Fusarium verticillioides in vitro. Appl Environ Microbiol. 2012;78:3656-67. https://doi.org/10.1128/AEM.07841-11
65. Mejía LC, Herre EA, Sparks JP, Winter K, García MN, Van Bael SA, et al. Pervasive effects of a dominant foliar endophytic fungus on host genetic and phenotypic expression in a tropical tree. Front Microbiol. 2014;5:479. https://doi.org/10.3389/fmicb.2014.00479
66. Dupont PY, Eaton CJ, Wargent JJ, Fechtner S, Solomon P, Schmid J, et al. Fungal endophyte infection of ryegrass reprograms host metabolism and alters development. New Phytol. 2015;208:1227-40. https://doi.org/10.1111/nph.13614
67. Ghaffari MR, Ghabooli M, Khatabi B, Hajirezaei MR, Schweizer P, Salekdeh GH. Metabolic and transcriptional response of central metabolism affected by root endophytic fungus Piriformospora indica under salinity in barley. Plant Mol Biol. 2016;90:699-717. https://doi.org/10.1007/s11103-016-0461-z
68. Dinkins RD, Nagabhyru P, Graham MA, Boykin D, Schardl CL. Transcriptome response of Lolium arundinaceum to its fungal endophyte Epichloë coenophiala. New Phytol. 2017;213:324-37. https://doi.org/10.1111/nph.14103
69. Ortiz J, Soto J, Fuentes A, Herrera H, Meneses C, Arriagada C. The endophytic fungus Chaetomium cupreum regulates expression of genes involved in the tolerance to metals and plant growth promotion in Eucalyptus globulus roots. Microorganisms. 2019;7:490. https://doi.org/10.3390/microorganisms7110490
70. Shen M, Schneider H, Xu R, Cao G, Zhang H, Li T, et al. Dark septate endophyte enhances maize cadmium (Cd) tolerance by the remodeled host cell walls and the altered Cd subcellular distribution. Environ Exp Bot. 2020;172:104000. https://doi.org/10.1016/j.envexpbot.2020.104000
71. Shaffer JP, Carter ME, Spraker JE, Clark M, Smith BA, Hockett KL, Baltrus DA, Arnold AE, et al. Transcriptional profiles of a foliar fungal endophyte (Pestalotiopsis, Ascomycota) and its bacterial symbiont (Luteibacter, Gammaproteobacteria) reveal sulfur exchange and growth regulation during early phases of symbiotic interaction. Msystems. 2022;7:e00091-22. https://doi.org/10.1128/msystems.00091-22
72. Malinich EA, Wang K, Mukherjee PK, Kolomiets M, Kenerley CM. Differential expression analysis of Trichoderma virens RNA reveals a dynamic transcriptome during colonization of Zea mays roots. BMC Genom. 2019;20:1-19. https://doi.org/10.1186/s12864-019-5651-z
73. Natarajan S, Pucker B, Srivastava S. Genomic and transcriptomic analysis of camptothecin producing novel fungal endophyte- Alternaria burnsii NCIM 1409. Sci Rep. 2023;13:14614. https://doi.org/10.1038/s41598-023-41738-6
74. Rashad YM, Al Tami MS, Abdalla SA. Eliciting transcriptomic and antioxidant defensive responses against Rhizoctonia root rot of sorghum using the endophyte Aspergillus oryzae YRA3. Sci Rep. 2023;13:19823. https://doi.org/10.1038/s41598-023-46696-7
75. Swarupa V, Pavitra K, Shivashankara K, Ravishankar K. Omics-driven approaches in plant-microbe interaction. In: Singh D, Singh H, Prabha R, editors. Microbial Inoculants in Sustainable Agricultural Productivity: Vol. 1: Research Perspectives. New Delhi: Springer; 2016. p. 61-84. https://doi.org/10.1007/978-81-322-2647-5_4
76. Ottesen EA, Marin R, Preston CM, Young CR, Ryan JP, Scholin CA, et al. Metatranscriptomic analysis of autonomously collected and preserved marine bacterioplankton. ISME J. 2011;5:1881-95. https://doi.org/10.1038/ismej.2011.70
77. Shi Y, Tyson GW, Eppley JM, DeLong EF. Integrated metatranscriptomic and metagenomic analyses of stratified microbial assemblages in the open ocean. ISME J. 2011;5:999-1013. https://doi.org/10.1038/ismej.2010.189
78. Urich T, Lanzén A, Qi J, Huson DH, Schleper C, Schuster SC. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS One. 2008;3:e2527. https://doi.org/10.1371/journal.pone.0002527
79. Turner TR, Ramakrishnan K, Walshaw J, Heavens D, Alston M, Swarbreck D, et al. Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. ISME J. 2013;7:2248-58. https://doi.org/10.1038/ismej.2013.119
80. Sharaff MM, Subrahmanyam G, Kumar A, Yadav AN. Mechanistic understanding of the root microbiome interaction for sustainable agriculture in polluted soils. In: Rastegari AA, Yadav AN, Yadav N (eds) New and Future Developments in Microbial Biotechnology and Bioengineering. Elsevier, 2020. pp 61-84. https://doi.org/10.1016/B978-0-12-820526-6.00005-1
81. Peršoh D. Plant-associated fungal communities in the light of meta'omics. Fungal Divers. 2015;75:1-25. https://doi.org/10.1007/s13225-015-0334-9
82. Jo Y, Back C-G, Choi H, Cho WK. Comparative microbiome study of mummified peach fruits by metagenomics and metatranscriptomics. Plants. 2020;9:1052. https://doi.org/10.3390/plants9081052
83. Guo L, Yu H, Wang B, Vescio K, DeIulio GA, Yang H, et al. Metatranscriptomic comparison of endophytic and pathogenic Fusarium-Arabidopsis interactions reveals plant transcriptional plasticity. Mol Plant Microb Interact. 2021;34:1071-83. https://doi.org/10.1094/MPMI-03-21-0063-R
84. Otto M, Geml J, Hegyi ÁI, Hegyi-Kaló J, Kun J, Gyenesei A, et al. Metatranscriptomic analyses of grapes reveal differences in expressed functional genes of filamentous and yeast fungi during noble rot and grey rot. Fungal Ecol. 2023;65:101277. https://doi.org/10.1016/j.funeco.2023.101277
85. Chatterjee M, Gupta S, Bhar A, Chakraborti D, Basu D, Das S. Analysis of root proteome unravels differential molecular responses during compatible and incompatible interaction between chickpea (Cicer arietinum L.) and Fusarium oxysporum f. sp. ciceri Race1 (Foc1). BMC Genom. 2014;15:1-19. https://doi.org/10.1186/1471-2164-15-949
86. Wilkins MR, Sanchez JC, Gooley AA, Appel RD, Humphery-Smith I, Hochstrasser DF, et al. Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol Genet Eng Rev. 1996;13:19-50. https://doi.org/10.1080/02648725.1996.10647923
87. Kav NN, Srivastava S, Yajima W, Sharma N. Application of proteomics to investigate plant-microbe interactions. Curr Proteom. 2007;4:28-43. https://doi.org/10.2174/157016407781387357
88. Jayaraman D, Forshey KL, Grimsrud PA, Ané J-M. Leveraging proteomics to understand plant-microbe interactions. Front Plant Sci. 2012;3:44. https://doi.org/10.3389/fpls.2012.00044
89. Keller M, Hettich R. Environmental proteomics: a paradigm shift in characterizing microbial activities at the molecular level. Microbiol Mol Biol Rev. 2009;73:62-70. https://doi.org/10.1128/MMBR.00028-08
90. Calderón-González KG, Hernández-Monge J, Herrera-Aguirre ME, Luna-Arias JP. Bioinformatics tools for proteomics data interpretation. In: Mirzaei H, Carrasco M, editors. Modern Proteomics - Sample Preparation, Analysis and Practical Applications. Cham: Springer International Publishing; 2016. p. 281-341. https://doi.org/10.1007/978-3-319-41448-5_16
91. Jain A, Singh HB, Das S. Deciphering plant-microbe crosstalk through proteomics studies. Microbiol Res. 2021;242:126590. https://doi.org/10.1016/j.micres.2020.126590
92. Pillai TG. Pathogen to endophytic transmission in fungi-a proteomics approach. SOJ Microbiol Infect Dis. 2017;5:1-5. https://doi.org/10.15226/sojmid/5/3/00173
93. Domingo G, Vannini C, Bracale M, Bonfante P. Proteomics as a tool to decipher plant responses in arbuscular mycorrhizal interactions: a meta-analysis. Proteomics. 2023;23:2200108. https://doi.org/10.1002/pmic.202200108
94. Yuan J, Zhang W, Sun K, Tang MJ, Chen PX, Li X, et al. Comparative transcriptomics and proteomics of Atractylodes lancea in response to endophytic fungus Gilmaniella sp. AL12 reveals regulation in plant metabolism. Front Microbiol. 2019;10:1208. https://doi.org/10.3389/fmicb.2019.01208
95. Gómez-Vidal S, Salinas J, Tena M, Lopez-Llorca LV. Proteomic analysis of date palm (Phoenix dactylifera L.) responses to endophytic colonization by entomopathogenic fungi. Electrophoresis. 2009;30:2996-3005. https://doi.org/10.1002/elps.200900192
96. Ghaffari MR, Mirzaei M, Ghabooli M, Khatabi B, Wu Y, Zabet- Moghaddam M, et al. Root endophytic fungus Piriformospora indica improves drought stress adaptation in barley by metabolic and proteomic reprogramming. Environ Exp Bot. 2019;157:197-210. https://doi.org/10.1016/j.envexpbot.2018.10.002
97. Sepehri M, Ghaffari M, Khayam Nekoui M, Sarhadi E, Moghadam A, Khatabi B, et al. Root endophytic fungus Serendipita indica modulates barley leaf blade proteome by increasing the abundance of photosynthetic proteins in response to salinity. J Appl Microbiol. 2021;131:1870-89. https://doi.org/10.1111/jam.15063
98. Maron PA, Ranjard L, Mougel C, Lemanceau P. Metaproteomics: a new approach for studying functional microbial ecology. Microbial Ecol. 2007;53:486-93. https://doi.org/10.1007/s00248-006-9196-8
99. Dubey RK, Tripathi V, Prabha R, Chaurasia R, Singh DP, Rao CS, et al. Metatranscriptomics and metaproteomics for microbial communities profiling. In: Dubey RK, Tripathi V, Prabha R, et al. editors. Unravelling the Soil Microbiome: Perspectives for Environmental Sustainability. Cham: Springer International Publishing; 2020. p. 51-60. https://doi.org/10.1007/978-3-030-15516-2_5
100. Seifert J, Herbst FA, Halkjær Nielsen P, Planes FJ, Jehmlich N, Ferrer M, et al. Bioinformatic progress and applications in metaproteogenomics for bridging the gap between genomic sequences and metabolic functions in microbial communities. Proteomics. 2013;13:2786-804. https://doi.org/10.1002/pmic.201200566
101. Mueller RS, Pan C. Sample handling and mass spectrometry for microbial metaproteomic analyses. In: DeLong EF, editors. Methods in Enzymology. Academic Press; 2013. p. 289-303. https://doi.org/10.1016/B978-0-12-407863-5.00015-0
102. Pan C, Banfield JF. Quantitative metaproteomics: functional insights into microbial communities. In: Paulsen IT, Holmes AJ, editors. Environmental Microbiology: Methods and Protocols. Totowa (NJ): Humana Press; 2014. p. 231-40. https://doi.org/10.1007/978-1-62703-712-9_18
103. Ullah J, Khanum Z, Khan IA, Khalid AN, Musharraf SG, Ali A. Metaproteomics reveals the structural and functional diversity of Dermatocarpon miniatum (L.) W. Mann. Microbiota. Fungal Biol. 2021;125:32-8. https://doi.org/10.1016/j.funbio.2020.10.001
104. Zhang HW, Lv C, Zhang LJ, Guo X, Shen YW, Nagle DG, et al. Application of omics-and multi-omics-based techniques for natural product target discovery. Biomed Pharmacother. 2021;141:111833. https://doi.org/10.1016/j.biopha.2021.111833
105. Monteiro M, Carvalho M, Bastos M, Guedes de Pinho P. Metabolomics analysis for biomarker discovery: advances and challenges. Curr Med Chem. 2013;20:257-71. https://doi.org/10.2174/092986713804806621
106. Klassen A, Faccio AT, Canuto GAB, da Cruz PLR, Ribeiro HC, Tavares MFM, et al. Metabolomics: definitions and significance in systems biology. In: Sussulini A, editor. Metabolomics: From Fundamentals to Clinical Applications. Cham: Springer International Publishing; 2017. p. 3-17. https://doi.org/10.1007/978-3-319-47656-8_1
107. Tugizimana F, Mhlongo MI, Piater LA, Dubery IA. Metabolomics in plant priming research: the way forward? Int J Mol Sci. 2018;19:1759. https://doi.org/10.3390/ijms19061759
108. Jendoubi T. Approaches to integrating metabolomics and multi-omics data: a primer. Metabolites. 2021;11:184. https://doi.org/10.3390/metabo11030184
109. Castro-Moretti FR, Gentzel IN, Mackey D, Alonso AP. Metabolomics as an emerging tool for the study of plant-pathogen interactions. Metabolites. 2020;10:52. https://doi.org/10.3390/metabo10020052
110. Salem MA, Perez de Souza L, Serag A, Fernie AR, Farag MA, Ezzat SM, et al. Metabolomics in the context of plant natural products research: from sample preparation to metabolite analysis. Metabolites. 2020;10:37. https://doi.org/10.3390/metabo10010037
111. Patil AS. Plant secondary metabolites: isolation, characterization & biological properties. Delhi: Studera Press; 2020.
112. Pinu FR, Villas-Boas SG, Aggio R. Analysis of intracellular metabolites from microorganisms: quenching and extraction protocols. Metabolites. 2017;7:53. https://doi.org/10.3390/metabo7040053
113. Cajka T, Fiehn O. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal Chem. 2016;88:524-45. https://doi.org/10.1021/acs.analchem.5b04491
114. Frisvad JC, Andersen B, Thrane U. The use of secondary metabolite profiling in chemotaxonomy of filamentous fungi. Mycol Res. 2008;112:231-40. https://doi.org/10.1016/j.mycres.2007.08.018
115. Li C, Gao Z, Su B, Xu G, Lin X. Data analysis methods for defining biomarkers from omics data. Anal Bioanall Chem. 2022;414:235-50. https://doi.org/10.1007/s00216-021-03813-7
116. Wolfender J-L, Marti G, Thomas A, Bertrand S. Current approaches and challenges for the metabolite profiling of complex natural extracts. J Chromatog A. 2015;1382:136-64. https://doi.org/10.1016/j.chroma.2014.10.091
117. Khoomrung S, Wanichthanarak K, Nookaew I, Thamsermsang O, Seubnooch P, Laohapand T, et al. Metabolomics and integrative omics for the development of Thai traditional medicine. Front Pharmacol. 2017;8:474. https://doi.org/10.3389/fphar.2017.00474
118. Xie J, Zhang A, Wang X. Metabolomic applications in hepatocellular carcinoma: toward the exploration of therapeutics and diagnosis through small molecules. RSC Adv. 2017;7:17217-226. https://doi.org/10.1039/C7RA00698E
119. Maheshwari DK. Endophytes: biology and biotechnology. Cham: Springer; 2017. https://doi.org/10.1007/978-3-319-66541-2
120. Kumar R, Bohra A, Pandey AK, Pandey MK, Kumar A. Metabolomics for plant improvement: status and prospects. Front Plant Sci. 2017;8:271676. https://doi.org/10.3389/fpls.2017.01302

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