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Rhizosphere microbiome exerts a significant role in plant health, influencing nutrient availability, disease resistance, and overall plant growth. Establishing a robust and efficient nodulation process is essential for optimal nitrogen fixation in legumes like soybeans. Different soybean genotypes exhibit variations in their rhizosphere microbiome, potentially impacting nitrogen fixation through nodulation. However, a detailed understanding of how specific soybean genotypes influence rhizosphere microbial communities and nodulation patterns remains limited. Our study aims to investigate the relationship between rhizosphere microbial abundance and plant growth in four soybean genotypes. We evaluated plant growth parameters, including biomass, leaf area, and stomatal conductance, and identified significant genotypic differences in nodulation. Specifically, genotypes PI 458505 and PI 603490 exhibited high levels of nodulation, while PI 605839A and PI 548400 displayed low nodulation. 16S rRNA gene amplicon sequencing revealed diverse bacterial communities in the rhizosphere, with Proteobacteria as the dominant phylum. High-nodulation genotypes harbored more diverse microbial communities enriched with Actinobacteria and Acidobacteriota, while low-nodulation genotypes showed higher abundances of Firmicutes and Planctomycetota. Alpha and beta diversity analyses confirmed distinct microbial community structures between high- and low-nodulation groups. Our findings suggest that the rhizosphere microbiome significantly influences soybean growth and nodulation, highlighting the potential for genotype-driven strategies to enhance plant-microbe interactions and improve soybean productivity.
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This content will become publicly available on September 1, 2026
Harnessing machine learning and synthetic microbial communities to control harmful algal blooms
Freshwater with high quality is crucial for both public health and aquatic biodiversity. However, freshwater resources face numerous challenges, including the proliferation of harmful algal blooms (HABs) caused by various cyanobacterial species that are generally triggered by human activities like agricultural runoff and wastewater. Native algicidal microbiomes may offer potential solutions, although challenges remain in utilizing microbial resources to mitigate HABs in freshwater environments. The combination of synthetic microbial community and probiotic development approaches with robust machine learning tools could allow us to harness native microbiomes to address water quality issues caused by HABs in large water bodies. A meta-analysis of around 100 research studies regarding algicidal bacteria-algae interactions was conducted to quantitatively assess the potential of taxonomically diverse microbial species in controlling HABs in freshwater ecosystems. Meta-analysis findings revealed that diverse species from common freshwater bacterial phyla such as Actinobacteria, Bacteroidota, Firmicutes, and Proteobacteria exhibited 50100 % algicidal activity against different algal species depending on interacting species and environmental conditions. Algicidal taxa (mainly against Microcystis aeruginosa) from both Actinobacteria and Firmicutes primarily included Actinomycetes and Bacillus species. However, Bacteroidota and alpha/beta Proteobacteria exhibited algicidal activity against a broader range of algal species, thus highlighting their potential for controlling multi-species HABs in freshwater environments. Based on this quantitative analysis, the current review puts forward synthetic microbial communities and machine-learning based frameworks to develop microbial solutions for protecting freshwater resources from HABs invasions.
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- Award ID(s):
- 2100777
- PAR ID:
- 10626798
- Publisher / Repository:
- Total Environment Engineering
- Date Published:
- Journal Name:
- Total Environment Engineering
- Volume:
- 4
- Issue:
- C
- ISSN:
- 2950-631X
- Page Range / eLocation ID:
- 100030
- Subject(s) / Keyword(s):
- FreshwaterHarmful algal bloomAlgicidal taxaMachine learningCyanobacteriaBioremediation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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