Abstract
The female spikes (fruits) of Piper longum are widely used in Ayurvedic, Siddha and Unani medicine systems to treat respiratory and digestive disorders. The spikes are rich in piperine, a pharmacologically active amide alkaloid and a potent bioavailability enhancer, which accumulates to the highest level during the dark-green stage of spike development. Plant-associated microbiota influence the plant’s fitness, response, and production of economically important metabolites. Considering the economic importance of piperine and other spike-derived alkaloids, understanding microbial community dynamics during spike development would be key to bioprospecting for economically important metabolites. In the present study, the structural diversity of microbial communities associated with early (SI), mid (SII), and late (SIII) stages of spike development in P. longum has been analysed by Illumina-based amplicon sequencing of 16S rRNA gene and ITS region. Results revealed that spike development significantly drives the diversity and abundance of spike-associated microbiota, especially bacterial communities. Cyanobacteria and Ascomycota constituted the most abundant bacterial and fungal phyla, respectively, across all stages of spike development. Interestingly, Halomonas, Kushneria and Haererehalobacter were found to be exclusively associated with SIII (corresponding to economically important) stage of spike development. Sphingomonas, Mortierella, Cladosporium and Vishniacozyma constituted the core microbiome of the spike. Besides, PICRUSt analysis revealed that amino acid metabolism was the most dominant metabolic function attributed to spike-associated bacterial communities. To the best of our knowledge, this is the first study to investigate the endomicrobiome dynamics during spike development in a medicinal plant species.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12298-023-01352-2.
Keywords: Spike, Plant development, Microbiome, Amplicon sequencing, Medicinal plant, Piper longum
Introduction
Our understanding of the role of the microbiome in plant responses has remarkably increased in the past few decades. The plant endosphere is known to be predominantly inhabited by bacterial and fungal communities, which fine-tune the overall plant health and fitness (Mishra et al. 2021a). Most studies investigating the influence of plant development on microbiome structure have focussed on the rhizosphere microbiome. One such study, assessed the impact of plant development on microbial community structure during the young seedling, adult stage, flowering and senescence in pea, wheat and sugarbeet (Houlden et al. 2008). Using a combination of culture-dependent approach and denaturing gradient gel electrophoresis, the authors concluded that while the rhizospheric bacterial and fungal community structure was more or less stable in pea and wheat, dynamic shifts were observed in sugarbeet rhizosphere during its life cycle. Further, the role of plants in selecting specific microbial communities, especially during the mature plant stage (vs. seedling stage) was also recorded. Chaparro et al. (2014) studied the rhizosphere microbiome at seedling, vegetative, bolting, and flowering stages in Arabidopsis. They reported that plants at particular stages of development selectively recruit a subset of microbiota to assist or suit specific plant functions.
Piper longum is widely used in Ayurvedic, Siddha and Unani medicine systems. It is a dioecious species, with unisexual flowers borne in spikes on separate male and female plants (Babu et al. 2006). The female flowers of P. longum are yellow or creamy white in colour, and are borne in spikes. The mature fruit is a black-coloured drupe, of ~ 2–3 cm in length (Kanimozhi and Sujatha 2015; Gajurel et al. 2021). The female spikes (commonly known as pippali in the trade of commerce) are shorter (~ 2–3 cm long) than the male spikes (6–7 cm long), and are rich in piperine, piperlongumine and other bioactive compounds (Rajopadhye et al. 2012; Kanimozhi and Sujatha 2015). Hence, female spikes are in great demand for use in more than 100 Ayurvedic formulations to treat respiratory and digestive disorders. Further, piperine is considered to be the most potent bioenhancer, with reports showing 30–200% increase in bioavailability of different classes of drugs (Atal and Bedi 2010). Also, female spikes are widely used as a spice for seasoning dishes. According to National Medicinal Plant Board, P. longum is a high-volume trade plant in the Indian market (Ved and Goraya 2008; NMPB).
Our previous studies have reported the endophytic microbial diversity in P. longum using both culture-dependent and culture-independent approaches (Mintoo et al. 2019; Mishra et al. 2021b). Spikes were found to harbour specific microbial communities with the potential to produce bioactive compounds including piperine (Mishra et al. 2021b). Besides, recently the growth promoting effects of P. longum bacterial endophytes on seed germination and early seedling development in wheat and maize have also been reported (Phurailatpam et al. 2022). Therefore, considering the demand of P. longum worldwide, and the prevailing role of the microbiome in affecting plant health and responses, the study aims to assess the temporal dynamics in microbiota abundance and composition with respect to the stage of spike development (Supplementary Fig. S1).
Materials and methods
Sampling
The female spikes of P. longum at three different stages of development (SI, SII, and SIII) were harvested from healthy, female plants of P. longum. Stage SI represents spikes < 1 cm in length, SII represents spikes with a length of 1–2 cm, and SIII represents spikes > 2 cm in length (Supplementary Fig. S1). Three biological replicates for each stage were collected from asymptomatic plants maintained in University of Lucknow, Lucknow (26.8633° N, 80.9360° E).
Surface sterilization and genomic DNA isolation from spikes
The spike samples were surface-sterilized by washing in running tap water, followed by immersing in 70% ethanol and 4% sodium hypochlorite for 5 s and 90 s, respectively, and finally rinsing in sterile distilled water (Suryanarayanan et al. 1998). Next, genomic DNA was isolated from surface-sterilized plant samples using QIAGEN’s DNeasy PowerSoil Pro kits. The samples with 260/280 values of ~ 1.8 to 2 (as checked by small volume spectrophotometer), were processed for PCR amplification.
PCR amplification of 16S rRNA gene and ITS region
Approximately 40 ng of the extracted DNA was used as a template for the amplification of V3-V4 region of 16S rRNA gene using 357F and 806R primers (Klindworth et al. 2013). For amplification of ITS region of fungal rRNA, the ITS1 forward and ITS2 reverse primers were used (White et al. 1990). The PCR conditions used for amplification of both 16S and ITS amplicons were an initial denaturation of 95 °C for 3 min, followed by 25 cycles of denaturation at 95 °C for 15 s, primer annealing at 60 °C for 15 s, and extension at 72 °C for 30 s, and a final extension at 72 °C for 10 min and hold at 4 °C. PCR products were checked on 1.2% agarose gel and a quality check was done with a small-volume spectrophotometer.
The subsequent steps including preparation of 16S rRNA and ITS amplicon libraries, quality check and sequencing of libraries was performed following the protocol of Mishra et al. (2021b). The 16S rRNA gene and ITS region sequencing reads have been submitted to the Sequence Read Archive of the National Centre for Biotechnology Information under the Bioproject number, PRJNA860932 and PRJNA869034, respectively.
Data processing
The 16S rRNA and ITS sequencing data were processed using QIIME 2 microbiome analysis tool (Bolyen et al. 2019). The data were filtered for low-quality sequences by applying the low count (prevalence level 20% for minimum count 4) and low variance (inter-quartile range of 10%) filters. Next, the variability in sampling and sequencing depth was addressed by rarefying the data to the minimum library size and lowest sequencing depth. KRAKEN 2, the taxonomic classification system was used for OTU calling at a cut-off of 0.97 (Wood et al. 2019). A representative sequence from each OTU was selected, followed by its taxonomic assignment using GreenGenes database for 16S rRNA gene sequences and UNITE for ITS sequences. The alpha diversity analysis was performed by Chao1, Shannon, Simpson and Fisher using the ANOVA statistical test. Beta diversity analysis was performed using Bray–Curtis index distance method based on Permutational MANOVA (PERMANOVA) statistical method. The heatmaps were constructed at the genus level using Ward cluster algorithm based on Euclidean distance measure.
Advanced analysis
The correlation network between microbial communities was built using SparCC (Sparse Correlations for Compositional data) algorithm at a p-value of 0.05. The red lines indicate a positive correlation, while the blue indicate a negative correlation between the taxa pairs. Further, Kruskal–Wallis rank sum test was performed to detect features with significant differential abundance with regards to class labels, followed by Linear Discriminant Analysis to evaluate the relevance or effect size of differentially abundant features. The data were analyzed with the p-value cut-off of 0.1 with FRD-adjusted filter and Log LDA score of 2.0.
The potential metabolic functions of bacterial communities associated with spike development were obtained by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis (Langille et al. 2013). The potential trophic modes and functional guilds were predicted for fungal communities using the FUNGuild (https://github.com/UMNFuN/FUNGuild) database (Nguyen et al. 2016). The core microbiota analysis was performed by selecting the bacterial and fungal taxa present at a sample prevalence of 20% and relative abundance of 0.1%.
Results
The medicinal plant P. longum shows a staggered or non-synchronous flowering pattern. The spikes take around two months to mature from the time of emergence (Gajurel et al. 2021; Piper longum—Vikaspedia). Therefore, at any time point during the flowering season, spikes at different stages of spike development could be seen (Supplementary Figure 1). Commercially, the female spikes are picked at blackish green stage (i.e., fully mature but unripe), when they are most pungent due to highest concentration of piperine (Joshi et al. 2013; Khound et al. 2019). This is because the piperine content of fruits increases with maturity, from ~ 0.53% (at 14–16 days) to ~ 0.9% (at 40–45 days), after which the “quality” (in trade terminology) is said to decline (Babu et al. 2006).
In the present study, for targeted amplicon sequencing of spike-associated microbial communities, bacterial 16S rRNA gene and fungal ITS region were amplified from nine spike samples representing three replicates of each of the three stages (SI, SII, SIII) of spike development in P. longum. A total of 22,55,270 and 14,47,434 high-quality reads were generated in 16S rRNA gene and ITS amplicon libraries, respectively. Thereafter, 16S rRNA sequencing reads were clustered into an average of 124,163.125 OTUs, and ITS sequencing reads were clustered into an average of 30,459.1 OTUs at 97% sequence similarity (Supplementary Tables 1, 2). The alpha diversity analyses for 16S rRNA gene and ITS sequences have been represented in Supplementary Figure 2 and 3, respectively. The alpha diversity analysis reveals the number of taxa or relative abundance of taxa within the sample. The alpha diversity analyses revealed that SIII stage distinctly harbors the most abundant and diverse bacterial communities, followed by SII, and least at the SI stage of spike development (Supplementary Figure 2). Similarly, the diversity of fungal communities was highest at SIII stage, while the SI and SII stages showed equivalent values (Supplementary Figure 3). Next, beta diversity analysis was performed to determine how similar or dissimilar the microbial communities are between different samples (here, different stages of spike development). The results of beta diversity analysis showed that the bacterial communities of all the replicates of the SI stage form one group, distinct and away from the replicates of SII and SIII stages (Supplementary Figure 4A). There was much more variation in fungal communities among the replicates across the three stages of spike development (Supplementary Figure 4B).
The rarefaction curves representing species richness for a given sample size for 16S rRNA gene and ITS segment amplicon sequencing have been represented in Supplementary Figure 5. A fair representation of microbial communities (i.e., both abundant and rare species) in the samples could be inferred from the plateau-like phase seen in the rarefaction curves for both 16S rRNA gene and ITS amplicon sequencing (Supplementary Figure 5A and B).
Taxonomy plot analysis of bacterial and fungal communities across spike development stages
To study the dynamics of plant-associated microbial communities during spike development, the average relative abundance of bacterial and fungal communities across different stages of development (SI, SII and SIII stages) was studied. The 16S rRNA amplicon analysis revealed that Cyanobacteria were the most dominant (constituting > 80%) phylum across all the stages of spike development, followed by Proteobacteria. The abundance of Bacteroidetes and Firmicutes phyla increased during mid-to-later stages of spike development. Interestingly, the abundance of several phyla, such as Planctomyces, Actinobacteria, Acidobacteria and Chloroflexi, was highest during the SIII (late) stage of spike development (Fig. 1A). Further, a similar trend was observed at the class level, where Clostridia, Anaerolineae, Actinobacteria, Bacilli and Acidobacteria-6 were the most abundant during the SIII stage of spike development (Fig. 1B). At the genera level, the most abundant taxa during early stage (SI) of spike development included Sphingomonas, Herbaspirillum, Sphingobacterium and Corynebacterium (Fig. 2A). The mid stage (SII) of spike development was marked by an increased abundance of Bifidobacterium, T78, Pseudomonas, Prevotella, Bacteroides, Faecalibacterium, Ruminococcus, Methanosaeta, Acinetobacter, Anaerolinea, Fusobacterium, Collinsella, Clostridium, Streptococcus, Catenibacterium, Erwinia, Succinivibrio, Salmonella, Balneimonas, Klebsiella, Dialister, SHD-231, Serratia and Truepera. The bacterial genera abundant during the late stage (SIII) were Halomonas, Kushneria and Haererehalobacter; these were almost absent from the previous stages of spike development. The other genera that were the most abundant during SIII stage included Pseudonocardia, Stenotrophomonas, Candidatus Solibacter, Rhodococcus and Planctomyces (Fig. 2A).
Fig. 1.
Relative abundance (in percentage) of microbial communities during spike development: Bacterial taxa at phylum (A) and class (B) levels; fungal taxa at phylum (C) and class (D) levels. The number of OTUs on Y-axis represents the average of three replicates for each of the three stages: early (SI), mid (SII), and late (SIII) stages of spike development in P. longum
Fig. 2.
Relative abundance of bacterial (A) and fungal (B) genera during early (SI), mid (SII), and late (SIII) stages of spike development. The inset figures show the taxa with abundance higher than the main graph. Bars represent the mean and error bars show standard error (n = 3). Statistical significance was calculated by Kruskal Waalis test with Benjamini–Hochberg correction at significance level p ≤ 0.05 (denoted by *)
The fungal microbiome associated with different stages of spike development was studied by ITS amplicon sequencing, which revealed that Ascomycota was the most abundant phylum, followed by Mucoromycota and Basidiomycota (Fig. 1C). The phylum Zoopagomycota was abundant during early stage (SI) but declined during mid and late stages of spike development. Further, the fungal communities belonging to class Basidiobolomycetes, Tremellomycetes, Saccharomycetes, Cystobasidiomycetes and Wallemiomycetes were the most abundant during the early stage of spike development; the relative abundance of these taxa was found to decline during later stages of spike development (Fig. 1D). On the other hand, the classes Sordariomycetes and Ustilaginomycetes drastically increased in abundance in SIII stage of development. In the mid-stage of spike development, Dothideomycetes constituted the most abundant (> 60%) class. The most abundant genera during early spike development included Mortierella, Wallemia, Fusarium and Phoma. Some fungal genera such as Trichothecium, Coniothyrium, Botryosporium, Candida, Saccharomycopsis, Lambertella, Malassezia, Ochroconis and Strelitziana were specifically abundant during the mid-stage of spike development. Interestingly, the fungal genera Colletotrichum, Pyrenochaeta, Verticillium, Pseudozyma, Gibellulopsis, Cryptococcus, Acremonium, Microascus, Acrocalymma, Sarocladium, Torula, Chaetomium and Sterigmatomyces increased in abundance from SI to SIII stages (Fig. 2B).
Comparative abundance of bacterial and fungal genera during spike development
In the heat maps (Fig. 3A and B), each row represents a bacterial/fungal community, and the columns represent average (from three replicates of each stage) OTU abundance during each spike development stage. The high abundance of genera has been indicated by red colour, while low abundance is represented by green colour.
Fig. 3.
Heatmaps showing average relative abundance of fungal (A) and bacterial (B) genera during early (SI), mid (SII), and late (SIII) stages of spike development. Red and green colours represent increase and decrease, respectively, in the OTU abundance during each stage. The heatmaps were constructed at the genus level using Ward cluster algorithm based on Euclidean distance measurements
As evident from the heat map shown in Fig. 3A, there was a remarkable shift in the abundance of fungal taxa during spike development. The fungal genera showing the highest abundance at the SI stage included Cystobasidium, Phaeosphaeria, Starmerella, Calonectria, Microdochium, Geotrichum, Trichoderma, Eutypella, Wallemia, Vishniacozyma, Lambertella, Neophysalospora, Ophiocordyceps, Pichia, Taphrina, Madurella, Plectosphaera, Naganishia, Basidiobolus, Hannaella, Phoma, Epicoccum, Pseudocercospora, Lepidosphaeria, Lophiostoma, Coniella, Latorua, Podospora and Rhodotorula. Likewise, the SII stage was marked by predominant genera such as Toxicocladosporium, Bullera, Nigrospora, Candida, Melanodothis, Aureobasidium, Medicopsis, Stagonospora, Pyrenochaeta, Strelitziana, Acrocalymma, Bartalinia, Saccharomycopsis, Cladosporium, Coniothyrium, Neopyrenochaeta, Ochroconis, Botryosporium, Pseudocoleophoma and Trichothecium. During SIII stage, the economically important stage of spike development, the most abundant fungal members included Acremonium, Gibellulopsis, Torula, Chaetomella, Monocillium, Sarocladium, Curvularia, Eupenidiella, Chaetomium, Fusarium, Pseudozyma, Microascus, Sterigmatomyces, Aspergillus, Verticillium, Sarcinomyces, Gliomastix, Rhexothecium, Metarhizium, Podosphaera, Malassezia, Mortierella, Gliocladiopsis, Penicillium, Cercospora, Dendryphiella and Volutella. Interestingly, the abundance level of some fungal genera such as Alternaria, Symmetrospora, Colletotrichum and Cryptococcus, increased with spike maturity (Fig. 3A). The early (SI) stage of spike development was marked by an increased abundance of bacterial members such as Methylobacterium, Corynebacterium, Sphingomonas, Herbaspirillum, Kineococccus, Agrobacterium, Sediminibacterium, Deinococcus, Acinetobacter and Catenibacterium (Fig. 3B); their levels declined during SII and SIII stages. On the other hand, a higher number of bacterial taxa were found to increase in abundance during the later stages (SII and SIII) of spike development. These include Faecalibacterium, Ruminococcus, Devosia, Virgisporangium, Gluconacetobacter, Prevotella_1, Succinovibrio, Candidatus_Solibacter, Gemmata, Methanosaeta, Mycobacterium, Erwinia, SHD_231, Dialister, T78, Steroidobacter, Klebsiella and Methanobrevibacter. The bacterial genera that showed the highest abundance during the SIII stage included Azospirillum, Rhodococcus, Pseudomonas, Stenotrophomonas, Pseudonocardia, Fusobacterium, Kaistobacter, Chloronema, Phascolarctobacterium, Eubacterium, vadinCA02, Planctomyces, Bacteroides, Lactobacillus and Symbiobacterium. Only five bacterial genera, namely Streptomyces, Clostridium, Prevotella, Serratia and Trabulsiella, showed the highest abundance during the SII stage of spike development (Fig. 3B).
Most abundant genera during each stage of spike development
The ten most abundant bacterial genera during early spike (SI) development were Sphingomonas, Herbaspirillum, Lactobacillus, Streptomyces, Pseudomonas, Bifidobacterium, Acinetobacter, Methylobacterium, Bacteroides and T78 (Fig. 4A-i). Similarly, the ten most abundant bacterial genera during mid-spike (SII) development were Methylobacterium, Bifidobacterium, Mycobacterium, Pseudomonas, Sediminibacterium, Acinetobacter, Bacterioides, Enhydrobacter, Corynebacterium and Dechloromonas (Fig. 4A-ii). The late spike (SIII) development stage showed high abundance of Herbaspirillum, Lactobacillus, Streptomyces, Pseudomonas, Bifidobacterium, Acinetobacter, Methylobacterium, Bacteroides, T78 and Mycobacterium (Fig. 4A-iii).
Fig. 4.
Ten most abundant bacterial (A) and fungal (B) genera during early (SI), mid (SII), and late (SIII) stages of spike development. The unclassified/unknown reads were not considered. Bars represent the mean and error bars show standard error (n = 3)
Strikingly, there was conservation in the most abundant fungal genera during spike development. For example, Mortierella, Cladosporium, Basidiobolus, Vishniacozyma, Pichia, Penicillium, Wallemia, Coniothyrium, Lambertella and Colletotrichum constituted the top ten abundant fungal genera during early spike (SI) development (Fig. 4B-i). Similarly, the ten most abundant fungal genera during mid-spike (SII) development were Mortierella, Cladosporium, Basidiobolus, Penicillium, Pichia, Coniella, Phoma, Vishniacozyma, Hannaella and Podosphaera (Fig. 4B-ii). The late spike (SIII) development stage showed a high abundance of Mortierella, Cladosporium, Vishniacozyma, Pichia, Fusarium, Basidiobolus, Coniothyrium, Aspergillus, Phoma and Microascus (Fig. 4C-iii).
Microbial communities common and unique to each spike development stage
The Venn diagrams show that 20, 61, and 23 bacterial genera (Supplementary Figure 6A) and 18, 10, and 26 fungal genera (Supplementary Figure 6B) were unique to SI, SII, and SIII stages, respectively. The microbial communities unique to each stage, and those common to all the stages, have been presented in Tables 1 and 2.
Table 1.
Microbial genera unique to each stage of spike development
| Early (SI) stage | Mid (SII) stage | Late (SIII) stage | |||
|---|---|---|---|---|---|
| Bacterial | Fungal | Bacterial | Fungal | Bacterial | Fungal |
|
Actinopolymorpha Asteroleplasma Asticcacaulis BF311 Bosea Candidatus Cardinium Cellvibrio Dokdonella Gracilibacter Gramella Haliscomenobacter Methylibium Microbacterium Propionibacterium SC103 Shimazuella Sphingobacterium Sulfuricurvum Ureibacillus YRC22 |
Acanthotrema Allomyces Apiotrichum Ascobolus Clonostachys Cordana Fusoidiella Humicola Lasiodiplodia Phialophora Preussia Pyricularia Rhodosporidiobolus Sonoraphlyctis Westerdykella Xeromyces Zopfiella Zymoseptoria |
Acholeplasma Acidaminococcus Actinobacillus Actinocorallia Actinomyces Aeromicrobium Alloiococcus Anabaena Aquicella Arsenicicoccus Arthronema BD2-6 Blvii28 Brevundimonas Campylobacter Candidatus Entotheonella Candidatus Xiphinematobacter Crocinitomix Cupriavidus Dechloromonas Desulfobacca Desulfococcus Desulforhopalus Desulfosarcina Fusibacter HTCC2207 Hyphomicrobium Kosmotoga Kribbella Lachnospira Lautropia LCP-26 Leuconostoc Microcoleus Neisseria Oribacterium Ornithobacterium Oxalobacter Peptoniphilus Pimelobacter Planktothrix Porphyromonas Prosthecobacter Proteus Pseudidiomarina Pseudoramibacter_ Eubacterium Ramlibacter Reinekea Robiginitalea Roseburia Roseomonas Saccharothrix Salinicoccus Shewanella Shinella Sporosarcina Sutterella Syntrophomonas Thalassospira WAL_1855D WCHB1-05 |
Blastobotrys Conioscypha Cutaneotrichosporon Dicyma Meira Neophaeosphaeria Rachicladosporium Saitozyma Ustilago Wojnowicia |
4–1929 Afifella Alkaliphilus Arthrobacter Azohydromonas Brevibacterium C1_B004 CF231 Isosphaera Methanobacterium Nonomuraea Paracoccus PD-UASB-13 Perlucidibaca Pilimelia Planktothricoides Pseudochrobactrum Psychrobacter RFN20 Slackia Sporomusa Sulfurospirillum Syntrophobacter |
Anthracocystis Ceratobasidium Cercosporella Coriolopsis Ctenomyces Exserohilum Heterochaete Hyalotiella Hypoxylon Italiomyces Kernia Lectera Leptoxyphium Malaysiasca Musicillium Mycothermus Papiliotrema Parengyodontium Pseudoacrodictys Rhinocladiella Sporobolomyces Thyridariella Ustilaginoidea Verruconis Zeloasperisporium Zygosporium |
Table 2.
Microbial genera common to all the stages of spike development
| Bacterial members | Fungal members |
|---|---|
|
Achromobacter Acinetobacter Actinoplanes Adhaeribacter Agrobacterium Agromyces Alicyclobacillus Ammoniphilus Anaerolinea Anaeromyxobacter Ardenscatena Bacillus Bacteroides Balneimonas Bdellovibrio Bifidobacterium Blautia Bradyrhizobium Brevibacillus Bulleidia Caldilinea Caloramator Candidatus Solibacter Catenibacterium Chloronema Chryseobacterium Clostridium Collinsella Comamonas Coprococcus Corynebacterium Dactylosporangium Desulfovibrio Devosia Dialister Dysgonomonas Enterococcus Erwinia Euzebya Faecalibacterium Fimbriimonas Flavisolibacter Flavobacterium Fusobacterium Gemmata Gluconacetobacter Haemophilus Herbaspirillum Hymenobacter Janthinobacterium Kaistobacter Kineococcus Klebsiella Lactobacillus Leptolyngbya Luteolibacter Lysobacter Marinobacter Megasphaera Methanosaeta Methylobacterium Methylophaga |
Acremonium Acrocalymma Alternaria Ascochyta Aspergillus Aureobasidium Bartalinia Basidiobolus Bipolaris Bullera Candida Cercospora Chaetomella Chaetomium Cladosporium Colletotrichum Coniella Coniothyrium Coprinellus Cryptococcus Curvularia Cystobasidium Debaryomyces Epicoccum Eutypella Fusarium Gibellulopsis Gliocladiopsis Gliomastix Hannaella Lambertella Lophiostoma Madurella Malassezia Medicopsis Melanodothis Microascus Microdochium Monocillium Mortierella Mycosphaerella Myrothecium Neopyrenochaeta Nigrospora Ophiocordyceps Penicillium Peniophora Phaeosphaeria Phoma Plectosphaera Podosphaera Polypaecilum Pseudozyma Pyrenochaeta Queiroziella Rhodotorula Saccharomycopsis Sarocladium Sterigmatomyces Strelitziana Symmetrospora Taphrina Torula |
| Bacterial members | Fungal members |
|---|---|
|
Mycobacterium Nitrospira Nocardia Nocardioides Opitutus Oscillochloris Oscillospira Paenibacillus Parabacteroides Phascolarctobacterium Pirellula Planctomyces Prevotella Proteiniclasticum Pseudoalteromonas Pseudomonas Pseudonocardia Rhodococcus Rhodoplanes Rothia Rubrobacter Ruminococcus Salmonella Sedimentibacter Sediminibacterium Serratia Sphingomonas Staphylococcus Stenotrophomonas Steroidobacter Streptococcus Streptomyces Symbiobacterium T78 Trabulsiella Treponema vadinCA02 Virgisporangium |
Toxicocladosporium Trichoderma Trichothecium Verticillium Vishniacozyma Volutella Wallemia |
Further, some of these core bacterial and fungal communities underwent remarkable changes in their abundance during different stages, as evident from the heat maps (Fig. 3). The results of core microbiome analysis carried out at a sample prevalence of 20% and relative abundance of 0.1% have been shown in Fig. 5. The core microbial communities of the spike at a prevalence value of 1 included Sphingomonas, Mortierella, Cladosporium and Vishniacozyma (Fig. 5A and B).
Fig. 5.
Core microbiome of spike-associated bacterial (A) and fungal (B) communities. The data was visualized with the sample prevalence of 20% and relative abundance of 0.1%
Discussion
Recent studies on plant microbiomes have highlighted the previously neglected role of endophytic microbiota on plant health and productivity (Singh et al. 2022). It has been proposed that microbes, with their short life span, are more potent than plants in adapting to environmental factors or a particular niche. In addition, endophytes have been reported to produce a range of bioactive compounds, including plant metabolites such as taxol, camptothecin, hypericin, etc. (reviewed by Mishra et al. 2021c, 2022). Therefore, exploring the plant-associated microbiome for solving issues ranging from agricultural sustainability to bioprospecting is pertinent.
Despite the advancements in our understanding of the plant–microbe association, the effect of the developmental stage on the endophytic microbiome is poorly understood. Here, we have investigated the diversity and abundance of bacterial and fungal communities during different stages of spike development in P. longum. The female spikes of P. longum are rich in pharmacologically important bioactive compounds, especially piperine and piperlongumine (Rajopadhye et al. 2012; Basak and Mohapatra 2015). In a previous study, we reported the endophytic bacterial and fungal communities associated with leaf and spike tissues of P. longum (Mishra et al. 2021b). The spikes were preferentially associated with microbial communities that have been reported to produce economically important secondary metabolites, including piperine. Therefore, we were interested in studying the microbiome associated with spikes, from the young to the mature phase. Spike samples representing three stages (early, mid, and late) of spike development were investigated by targeted amplicon sequencing of 16S rRNA gene and ITS region to determine changes, if any, in the abundance and composition of endophytic microbial communities during development. To rule out the interference of environmental and edaphic factors on microbial structure and composition, spikes borne on P. longum plants growing in the same geographical location, under the same environmental conditions were collected on the same day.
Spike development is marked by remarkable shifts in the microbiota
The 16S rRNA gene and ITS amplicon sequencing analysis clearly showed that the spike endosphere microbiota was not static; it underwent significant changes both in abundance and diversity at different stages of spike development. Further, while some microbial communities were exclusively or preferentially associated with certain developmental stages, others were present throughout spike development. However, it must be noted that even those present at all the stages or common microbiota underwent a striking change in their abundance level during spike development. Notably, the mid and late stages of spike development were accompanied by a more heterogeneous assemblage of bacterial communities encompassing members from various classes, namely Gamma Proteobacteria, Clostridia, Anaerolineae, Actinobacteria and Bacilli. The fungal communities belonging to Ustilaginomycetes and Dithediomycetes showed an increase in abundance, while those belonging to Tremellomycetes, Saccharomycetes and Wallemiomycetes decreased in abundance as the spike development progressed. These interesting trends in the relative abundance of these microbial taxa could indicate an underlying function; this remains a task for the future.
Interestingly, Mortierella, Cladosporium and Basidiobolus more or less remained the three most abundant fungal genera during spike development. Further, Mortierella and Cladosporium (along with Vishniacozyma and Sphingomonas) were also found to represent the core microbial communities of spikes. In a previous study, M. alpina CS10E4 has been reported to enhance the production of apocarotenoids, namely crocin, picrocrocin and safranal, improve growth parameters, and stress tolerance in Crocus sativus (Wani et al. 2017). Moreover, M. elongata strains isolated from Populus field sites have been found to improve plant biomass in various crop species, including Populus, Citrullus lanatus, Zea mays, Solanum lycopersicum, and Cucurbita (Zhang et al. 2020). Cladosporium is another core endophytic fungus of P. longum spikes. Previous reports have demonstrated the ability of endophytic Cladosporium strains to produce bioactive compounds such as polyketides from a Cladosporium strain isolated from Excoecaria agallocha (Wang et al. 2018), and Huperzine A, which is used in the treatment of Alzheimer’s disease (Zhang et al. 2011). Further, Cladosporium-mediated synthesis of silver nanoparticles has been found to have antioxidant, anti-diabetic and anti-Alzheimer activity (Popli et al. 2018). Endophytic strains of Sphingomonas have been reported to demonstrate plant growth promoting properties, including the production of IAA and gibberellins (Khan et al. 2014) and alleviation of heavy metal stresses (Bilal et al. 2018; Wang et al. 2020). The other abundant genus in spikes, Herbaspirillum, has been reported to occur as diazotrophic bacterial endophyte in various Gramineae members (Olivares et al. 1996; Pedrosa et al. 2011).
Since all the spike samples were collected from the plants (P. longum plants show staggered flowering pattern), growing in the same area and at the same time, the underlying cause for the change in spike-associated microbiota is most likely development-specific. Besides, the stage-specific association or dynamics of spike-associated microbiota could be explained by two possibilities: either the host (plant) genotype actively recruits or increases the titre of some members at a specific stage of spike development, or the microbial communities regulate their number in response to metabolites or factors present at specific stages of development.
Identification of biomarkers, core and stage-specific microbiota
The occurrence of microbial taxa across different samples could be used as a parameter to determine the core microbial communities (Neu et al. 2021). In the present study, Sphingomonas, Mortierella, Cladosporium and Vishniacozyma represented the core microbiota of spikes; these were shared among all the spike development stages at significant cut-off values. It could be inferred that core endophyte communities would have evolved to stably colonize the particular niche (plant endosphere), and have most likely developed an intricate functional or physiological association with the host. The knowledge of core microbiota would enable targeted culturing protocols for isolating and characterizing the core microbial communities. The investigation of the core microbiome would provide crucial insights into the in planta function of these microbial communities. Often considered as obligate partners of the host, core microbiome could also be defined and delineated based on the “ecological core”, “functional core” or “temporal core” (Neu et al. 2021).
Next, three derived analyses, namely Linear Discriminant Analysis (LDA) Effect Size (LEfSe), correlation network analysis, and PICRUSt and functional guild analysis, were performed to gain clues on the functional role(s) of spike-associated microbiome. LEfSe analysis was performed to determine the OTUs/microbial taxa that would explain the differences observed between different stages of spike development. In other words, the bacterial and fungal communities that could be used as biomarkers for particular stages of spike development have been identified based on changes in abundance. Based on the results of LEfSe analysis, it could be inferred that Kushneria was highly abundant at the SIII stage, while Methanobrevibacter and Magnetospirillum were at the SII stage (Fig. 6A). Likewise, the corresponding fungal biomarkers for the SIII stage were Pseudozyma, Verticillium, Gibellulopsis, Acremonium, Cryptococcus and Sarocladium (Fig. 6B).
Fig. 6.
LEfSe analysis of bacterial (A) and fungal (B) OTUs at genus level for biomarker discovery. Group I represents early stage (SI); Group II indicates mid stage (SII) and Group III indicates late stage (SIII) of spike development
The results of the co-relation analysis revealed co-occurrence and interdependence of microbial communities at particular stages. Besides, correlation analysis may indicate ecological interactions between microbial communities. Our results indicated that the abundance of Sphingomonas during spike development was negatively co-related with that of SHD_231 (Fig. 7A and 3B). Interestingly, the correlation analysis performed for fungal communities revealed a highly intricate association among the fungal taxa than the bacterial ones. The significant co-occurrence of microbial communities at a particular stage of spike development indicates a similar niche, and could imply interdependence, teamwork or division of labor for a particular plant response or simply similar physiological requirements of the microbiota.
Fig. 7.
Correlation network for bacterial (A) and fungal (B) genera during early (SI), mid (SII) and late (SIII) stages of spike development. Links between nodes are based on correlations and represent potential interaction(s) among taxa
Metabolic potential and trophic mode analysis of spike-associated microbiota during spike development
The potential metabolic functions of bacterial communities during spike development were predicted by PICRUSt analysis. The results indicated that amino acid biosynthesis and metabolism was the predominant function performed by spike-associated bacteriome (Fig. 8). The other major functions attributed to spike-inhabiting microbial communities included metabolism of nitrogen, nucleic acids and carbohydrates. Further, there was some representation of microbiome-specific carbon fixation in the spike-associated bacterial communities. Overall most biological functions were slightly over-represented during the SIII spike development stage. The category of “amino sugar and nucleotide sugar metabolism” was exclusively represented in the bacterial communities present during the late spike development stage. Likewise, “nicotinate and nicotinamide metabolism” was represented in spike-associated bacteria during the mid- and late-spike development stages (Fig. 8).
Fig. 8.
Prediction of metabolic functions of bacterial communities by PICRUSt analysis. Bar plots display the mean proportion for each of the 15 KEGG pathways that were found to be different in spike-associated bacterial communities during spike development. SI, SII, and SIII represent early, mid, and late stages of development, respectively
Next, the FUNGuild analysis was performed to predict the tropic modes (Fig. 9A) and functional guilds (Fig. 9B) of the spike-associated fungal communities during spike development (Nguyen et al. 2016). The results suggested that the proportion of fungal communities with “pathotroph” and “pathotroph-symbiotroph” mode increased during spike development. Further, the “endophyte-plant pathogen” guild was over-represented in the fungal taxa associated with the later stages of spike development.
Fig. 9.
Stacked bar plots representing the trophic modes (A), and top ten guilds (B) of fungal communities associated with spikes during early (SI), mid (SII) and late (SIII) stages of spike development. The trophic modes and guilds were predicted by FUNGuild analysis
Conclusions
The microbiome constitutes a significant part of the holobiont, and is often considered the “second genome” of an organism. Although the role of plant microbiome in plant health and stress responses is beginning to get attention, its role or abundance vis-a-vis plant development is largely unexplored. This is the first report to investigate the temporal association of endophyte communities in response to spike (fruit) development. Our results indicate that the spike development in P. longum is accompanied by a remarkable shift in microbiota. This transition is especially prominent during the later stages of spike development, which overlaps with the mature, dark-green stage or the “pippali” of commerce. Considering the economic importance of piperine and other spike-derived alkaloids, understanding the composition and abundance of microbial communities in spikes and their dynamics as a function of development, is the key to bioprospecting for economically important metabolites. Based on the results of targeted metagenomic analysis, concerted efforts should be made to isolate and culture promising strains in laboratory. This, followed by designing a synthetic microbial community of selected bacterial and fungal isolates, could act as an in vitro model system wherein factors such as temperature, pH, culture media etc. could be altered, and inducers/inhibitors could be added to investigate their effect on the production of important metabolites. Apart from bioprospecting for economically significant plant metabolites, the findings of the present study could be pursued for their potential applications in biotechnology, agriculture, medicine and environment.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
SM and SS are grateful to the Science and Engineering Research Board-Teachers Associateship for Research Excellence (SERB-TARE) for research grant TAR/2021/000309.
Author contributions
SM and SS conceptualized the research design and procured funding; SM performed the experiments and data analysis; SM wrote the manuscript; SS provided critical inputs and reviewed the manuscript. Both authors have read and approved the submitted manuscript.
Availability of data and material
The 16S rRNA gene and ITS sequencing reads have been deposited in the Sequence Read Archive of the National Centre for Biotechnology Information under the Bioproject number, PRJNA860932 and PRJNA869034, respectively.
Declarations
Conflict of interest
The authors declare no conflict of interest.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Sushma Mishra, Email: mishra_sushma@lkouniv.ac.in.
Shilpi Sharma, Email: shilpi@dbeb.iitd.ac.in.
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Associated Data
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Supplementary Materials
Data Availability Statement
The 16S rRNA gene and ITS sequencing reads have been deposited in the Sequence Read Archive of the National Centre for Biotechnology Information under the Bioproject number, PRJNA860932 and PRJNA869034, respectively.