Plant microbiomes and nutrient metabolism interactions

Introduction

Plant microbiomes and nutrient metabolism interactions are essential for improving nutrient cycling and uptake, which supports sustainable agriculture. In soybeans (Glycine max), symbiotic microbes like Bradyrhizobium japonicum enable nitrogen fixation, reducing the need for synthetic fertilizers (Göttfert and Krishnan, 2001). Additionally, sulfur-transforming microbes play a key role in providing plant-available sulfur, critical for amino acid and coenzyme synthesis.

Advancements in omics technologies, such as metagenomics and transcriptomics, have deepened our understanding of these interactions. Metagenomics reveals microbial community composition and function, while transcriptomics highlights gene expression patterns in microbes and plants under different conditions (Wood et al., 2019). These insights can optimize nutrient use efficiency, reduce fertilizer dependency, and promote sustainable farming practices, aligning with global goals for eco-friendly agriculture (Mendes et al., 2013).

Literature Review and Research Gap

Microbial Contributions to Nutrient Metabolism

Plant-associated microbes play a crucial role in mediating nitrogen (N) fixation and sulfur (S) assimilation through specialized biochemical pathways. Diazotrophic bacteria like Bradyrhizobium japonicum form symbiotic relationships with soybean roots, facilitating nitrogen fixation via nitrogenase enzymes, which convert atmospheric nitrogen into forms usable by plants (Göttfert and Krishnan, 2001).

Similarly, sulfur-metabolizing bacteria, such as Desulfovibrio species, are instrumental in converting inorganic sulfur into plant-accessible forms, supporting sulfur-dependent metabolic processes like protein synthesis and enzyme function (Cypionka, 2000). Recent research highlights the regulatory influence of microbial communities on nitrogen and sulfur cycles, especially under varying environmental conditions such as nutrient availability and soil type (Mendes et al., 2013; Mueller et al., 2000). These findings emphasize the importance of microbial interactions in maintaining nutrient balance and enhancing plant growth.

Omics-Based Approaches

Advanced omics technologies, such as metagenomics and transcriptomics, have transformed the study of microbial communities. Metagenomics unveils the genetic potential of microbial populations by identifying taxonomic and functional profiles, while transcriptomics provides insights into gene expression dynamics, shedding light on active metabolic pathways within microbes and their plant hosts (Wood et al., 2019). Complementary to these approaches, elemental analysis techniques like inductively coupled plasma mass spectrometry (ICP-MS) enable precise quantification of nutrient levels in plant tissues, bridging the gap between microbial activity and plant nutrient uptake (Kanehisa et al., 2019). However, despite their potential, these methods are seldom integrated into a cohesive framework to simultaneously study nitrogen and sulfur metabolism in soybean microbiomes (Love et al., 2014).

Research Gap

Although nitrogen fixation has been extensively studied in soybeans, the interconnected regulation of nitrogen and sulfur metabolic pathways by plant-associated microbes remains poorly understood. There is limited exploration of how microbial activities in both nutrient cycles are influenced by environmental factors such as soil pH, nutrient availability, and plant developmental stages. Furthermore, the integration of metagenomics, transcriptomics, and elemental analysis to establish functional correlations between microbial activity and nutrient metabolism in crop systems is still in its infancy. Addressing these gaps will enhance our understanding of microbial contributions to crop nutrient efficiency and their implications for sustainable agriculture.

Also Read About: Phosphoregulation of Plant Cytoskeleton

Hypothesis

Microbial communities associated with soybeans modulate nitrogen and sulfur metabolic pathways in a coordinated manner, and these interactions can be elucidated by integrating omics techniques with elemental analysis.

Objectives

1. To characterize the microbial community composition associated with soybean roots under varying nutrient conditions using metagenomics.
2. To identify differentially expressed genes involved in nitrogen and sulfur metabolism using transcriptomic analysis.
3. To quantify nitrogen and sulfur uptake in soybeans and correlate these with microbial functional profiles using elemental analysis.
4. To explore the influence of environmental factors (e.g., soil type and nutrient availability) on microbial contributions to sulfur and nitrogen pathways.

Materials and Methods

Experimental Design

The experiment will involve growing soybeans (Glycine max) under controlled greenhouse conditions to evaluate the effects of varying nitrogen (N) and sulfur (S) levels on plant-microbe interactions and nutrient metabolism. Nitrogen and sulfur will be supplied at three levels: deficient, optimal, and excess. To examine environmental variability, three soil types—acidic (pH < 6.0), neutral (pH 6.5–7.5), and alkaline (pH > 8.0)—will be used. The experimental design will be a randomized complete block design (RCBD) with five replicates for each treatment, ensuring statistical robustness. Environmental parameters such as temperature, humidity, and light will be maintained at optimal levels for soybean growth.

Sample Collection

Soybean plants will be sampled at two key growth stages: vegetative (V6) and reproductive (R2). Samples will include roots, rhizosphere soil, and shoots. Rhizosphere soil will be collected by gently shaking off loosely adhered soil and preserving tightly bound soil for microbial analysis. Plant tissues will be harvested, rinsed to remove contaminants, and divided into sub-samples for subsequent analyses. All samples will be immediately frozen in liquid nitrogen to preserve biological integrity and stored at −80°C until further processing.

Metagenomics

To study microbial community composition and functional potential, DNA will be extracted from rhizosphere soil using a commercial kit, such as the DNeasy PowerSoil Pro Kit (Qiagen), optimized for soil microbiome studies. Quality and quantity of DNA will be assessed using spectrophotometry and gel electrophoresis. Sequencing libraries will be prepared using Illumina protocols and sequenced on a NovaSeq platform to generate high-throughput metagenomic data. Bioinformatic analysis will include taxonomic classification using Kraken2 (Wood et al., 2019) and functional annotation of genes with the KEGG database (Kanehisa et al., 2019). This approach will provide insights into microbial taxa and their roles in nitrogen and sulfur metabolism.

Transcriptomics

Root tissues will be used to analyze gene expression patterns associated with plant-microbe interactions. RNA will be extracted using TRIzol reagent (Invitrogen), followed by purification to remove contaminants. RNA quality will be confirmed using an Agilent Bioanalyzer. RNA sequencing libraries will be prepared and sequenced using Illumina platforms. Differential gene expression analysis will be performed with DESeq2 (Love et al., 2014) to identify key genes involved in sulfur and nitrogen metabolic pathways. Functional enrichment analysis will be conducted to link differentially expressed genes to metabolic pathways of interest.

Elemental Analysis

To quantify nitrogen and sulfur concentrations, plant tissues (roots, shoots, and leaves) will be dried, ground into a fine powder, and digested in nitric acid using a microwave digestion system. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) will be employed to measure elemental concentrations with high precision. The results will be integrated with metagenomic and transcriptomic data to infer functional correlations between microbial activity, nutrient uptake, and plant performance.

Statistical Analysis

All data will be subjected to statistical analyses using R and Python software. Multivariate statistical techniques, such as principal component analysis (PCA) and redundancy analysis (RDA), will be used to identify patterns and correlations between treatments, microbial communities, and nutrient levels. Network analysis will be conducted using Cytoscape to visualize interactions between microbial taxa, genes, and metabolic pathways. Significance of treatment effects will be tested using analysis of variance (ANOVA) and appropriate post-hoc tests, ensuring rigorous evaluation of results.

By combining experimental, analytical, and computational methods, this study aims to provide comprehensive insights into the microbial contributions to sulfur and nitrogen metabolism in soybean systems.

Expected Outcomes

1. Identification of key microbial taxa and functional genes associated with sulfur and nitrogen pathways.
2. Insights into the co-regulation of nutrient pathways mediated by the microbiome under varying environmental conditions.
3. A framework for integrating omics data to study plant-microbe interactions at the systems level.

Timeline

The project is expected to be completed within three years, with specific milestones for each phase:

• Year 1: Experimental setup, soil and plant sample collection, metagenomic sequencing.
• Year 2: Transcriptomic analysis, elemental analysis.
• Year 3: Data integration, statistical analysis, manuscript preparation.

References

Göttfert, M., and Krishnan, H. (2001). Nodulation Genes and Type III Secretion Systems in Rhizobia. American Society of Agronomy.

Cypionka, H. (2000). Oxygen Respiration by Desulfovibrio Species. Annual Review of Microbiology, 54, 827-848.

Mendes, R., et al. (2013). The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiology Reviews,37(50, 634–663.

Mueller, R., et al. (2000). Nutrient enrichment increases size of Zostera marina shoots and enriches for sulfur and nitrogen cycling bacteria in root-associated microbiomes. FEMS Microbiology Ecology, 96(8).

Wood, D. E., et al. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology, 20, 257.

Kanehisa, M., et al. (2019). KEGG as a reference resource for gene and protein annotation. Nucleic Acids Research, 44 (D1), D457–D462.

Love, M. I., et al. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.

Pozo, M.J., et al. (2004) Jasmonates – Signals in Plant-Microbe Interactions. J Plant Growth Regul 23, 211–222.