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Paulsen, A. A.

Publications and source records attributed to Paulsen, A. A..

2 recordsLinked to original sources

Physiological, Behavioral, and Genetic Factors that Shape Interactions in a Plant-Growth-Promoting Maize Rhizosphere Synthetic Community

Profiling microbiomes is an important way to understand the function and composition of communities in the wild, but natural microbiomes are often highly complex and often unamendable to experimentation to reveal cause and effect relationships. By using a small group of cultivable strains to represent those found in the wild, synthetic communities are one solution to this problem. Here we describe the MAize Rhizosphere Synthetic Community (MARSc), a genome-enabled 31-member bacterial community representative of the diversity found on the roots of maize grown in Iowa soils. This community is built around Pseudomonas putida KT2440, a model maize rhizosphere colonist and synthetic biology chassis. We characterized microbe-microbe interactions and biofilm formation of MARSc members in a variety of environmental contexts, finding that both behaviors are broadly controlled by nutrient levels. Genomic analysis and microbiome profiling of these organisms revealed that annotated biofilm genes (such as surface attachment and exopolysaccharide production) correlated to rhizosphere colonization, but neither trait correlated to in vitro biofilm formation. In vitro interactions assay findings were surprisingly consistent with co-correlations of rhizosphere abundance amongst MARSc members on roots. Finally, we found that when applied to the roots, MARSc can increase maize growth under nitrogen-limiting conditions. Altogether, MARSc is a useful tool for identifying some of the factors influencing rhizosphere microbiome assembly and will be a strong foundation for further work in this area. ImportanceThe microbiome surrounding the roots of plants can play an integral role in plant health and growth, and it is composed of thousands of species of microbes that are specific to the plant and environment it is grown in. However, due to this complexity, little is known about the means by which microbiomes form. In this study, we developed the MAize Rhizosphere Synthetic community (MARSc) to gain a better understanding of the formation and function of microbiomes on the roots of plants. This consortium consists of 30 bacterial isolates plus Pseudomonas putida KT2440, a model maize root colonist. In this study, we investigated the interactions between these organisms, their genomes, cultural characteristics, and growth on the roots of maize. We show that MARSc increases maize growth under low nitrogen fertilizer, suggesting it will be a useful tool for identifying the mechanisms behind microbiome formation and plant growth promotion.

microbiology↗

A Bioinformatic Pipeline for Consensus Taxonomic Classification of Long-Read Amplicons

Characterizing community composition is fundamental to understanding microbial community function. Recent advances in Oxford Nanopore Technology (ONT) long-read sequencing now allow community profiling using full-length gene amplicons, affording better taxonomic resolution than standard short-amplicon Illumina sequencing. However, robust ONT-compatible profiling workflows are lacking. To address this, we have created the Amplicon Consensus Taxonomy (ACT) pipeline for classifying long-read amplicons. ACT combines output from three existing pipelines - Emu, Sintax, and LACA - to leverage the strengths of each while offsetting their individual limitations. We also developed the ACT database (ACT-DB), a sequence-similarity-aware reference database that clusters highly similar sequences into multi-taxa groups to reduce overclassification. We benchmarked ACT performance against Emu and Sintax using a defined simple mock community, simulated datasets, and a complex rhizosphere community supplemented with novel species. While ACT exhibited generally comparable or superior performance across datasets, ACT demonstrated a marked advantage over Emu and Sintax in identifying novel and low-abundance taxa in both simple and complex communities, resulting in significantly higher species-richness estimates that better reflected those observed in prior Illumina amplicon studies. Furthermore, by clustering ambiguous reference sequences, ACT-DB allowed ACT to resolve reads to meaningful multi-species groups, improving resolution without coercing artificial precision. Together, ACT and ACT-DB form a robust long-read amplicon profiling workflow that confidently identifies known species while reducing overclassification and preserving low-abundance and unknown taxa. IMPORTANCEMicrobial communities are frequently characterized by amplicon sequencing of marker genes, such as the bacterial 16S rRNA gene and fungal ITS region. Historically, the standard profiling method has been Illumina sequencing of 200-300 bp amplicons, but improved accuracy of ONT long-read sequencing means it is now possible to sequence amplicons spanning full genes of any size, prompting the need for tools optimized for long amplicons. Here, we describe the ACT bioinformatic pipeline for assigning taxonomy to amplicons of any length. We evaluated ACT performance using full-length 16S amplicon data relative to that of two commonly used pipelines. Additionally, we developed a sequence ambiguity-aware ACT database (ACT-DB) of 16S rRNA sequences to further improve classification accuracy and resolution.

microbiology↗