Biology subjects

Van der Laan, L.

Publications and source records attributed to Van der Laan, L..

5 recordsLinked to original sources

bioRxiv2025

Heat Stress and Soil Microbial Disturbance Influence Soybean Root Metabolite, Microbiome Profiles, and Nodulation

Heat stress is a major limiting factor for soybean productivity worldwide. Recent studies have highlighted the critical role of the plant microbiome in enhancing plant resilience to heat stress. However, our understanding of the molecular and physiological mechanisms underlying root-microbiome interactions under heat stress remains limited. To elucidate the role of native soil microbes in the heat tolerance of soybean genotypes, we analyzed rhizosphere bacterial and fungal communities via 16S rRNA and ITS sequencing, and characterized root metabolites and anatomical traits in response to microbiome composition and heat stress. Soybean plants were grown under controlled conditions in either natural soil containing native microbiota or in microbiome-disturbed soil (via 3-hour autoclaving), under both optimal and elevated temperature regimes. Alpha and beta diversity analyses revealed significant microbial shifts between treatments. Distinct clustering of bacterial, fungal, and metabolite profiles was observed under high temperature and microbial disturbance. Nodule-forming bacteria such as Rhizobium and Janthinobacterium were markedly suppressed, and belowground traits exhibited sensitivity, with significantly reduced nodule numbers and nodulation efficiency under high temperature and soil microbial perturbation. Non-targeted root metabolomics identified 372 differentially accumulated metabolites. Integrative multi-omics analysis revealed associations between metagenomic profiles, metabolite levels, and nitrogen-fixation traits, implying a coordinated modulation of root physiological processes. These findings contribute to a growing understanding of how heat stress interacts with rhizosphere microbial communities and may support future efforts in breeding climate-resilient soybean cultivars.

plant biology↗

bioRxiv2025

Time Series GWAS for Iron Deficiency Chlorosis Tolerance in Soybean using Aerial Imagery

AbstractThe use of drones has become a commonly used tool by plant scientists to aid in plant phenotyping endeavors. Iron deficiency chlorosis (IDC) is a commonly observed abiotic stress in soybean fields with high soil pH levels. IDC severity is visually classified, and recent work has shown that digital imaging techniques using both ground and UAS-acquired imagery can be utilized for automated severity ratings. In our study, we compared the classification accuracy of two flight altitudes to determine the optimal flight parameters for IDC phenotyping. In addition to this, we investigated the ability to use image-predicted scores for genome wide association study (GWAS), as well as the effect of IDC on traits such as canopy area and canopy growth and development. We also report a tool for semi-automated plot extraction from orthomosaic images that can be easily integrated with UAS. We noted that 43 days after planting was an ideal time for IDC severity ratings as the highest number of significant SNPs were reported at this timepoint.

genetics↗

bioRxiv2024

High Temperature and Microbiome Conditions Affect Gene Expression in Soybean

Heat stress is increasingly a problem in global agriculture production, both in increasing occurrences and extended durations. Understanding the molecular mechanisms of the soybean heat stress response is essential for breeding heat tolerant soybeans. Plant associated microbiomes are known to mitigate adverse effects from abiotic stress. Soybean heat stress studies have primarily focused on response to short periods of stress, and how soybean responds on a transcriptional level to a soil microbiome is poorly understood. We hypothesize a soil microbiome may help soybean survive long-term heat stress exposure. We used RNA-seq to measure the transcriptional responses in four soybean exposed to two temperature regimes and grown in two soil microbiome conditions. We identified unique responses to temperature based on the soil microbiome conditions and to the different genotypes, with fewer changes across genotypes in response to a soil microbiome. Our findings provide insights on the interaction of soil microbiome with heat stress response in soybean and identify gene targets to further study the soybean heat stress tolerance with applications to develop improved varieties.

plant biology↗

bioRxiv2024

Genomic and Phenomic Prediction for Soybean Seed Yield, Protein, and Oil

Developments in genomics and phenomics have provided valuable tools for use in cultivar development. Genomic prediction (GP) has been used in commercial soybean [Glycine max L. (Merr.)] breeding programs to predict grain yield and seed composition traits. Phenomic prediction (PP) is a rapidly developing field that holds the potential to be used for the selection of genotypes early in the growing season. The objectives of this study were to compare the use and performance of GP and PP for predicting soybean seed yield, protein content, and oil content. We additionally conducted Genome Wide Association Studies (GWAS) to identify significant SNPs associated with the traits of interest. These SNPs were also used to train the GP models. The GWAS panel of 292 diverse accessions was grown in six environments in replicated trials. Spectral data were collected at three timepoints during the growing season. A GBLUP model was trained on 268 accessions, while three separate machine learning (ML) models were trained on vegetation indices (VIs) and canopy traits. We observed that for PP, Random Forest (RF) algorithm had the highest rank correlation between the predicted and the actual phenotype rank. PP had a higher correlation coefficient than GP for seed yield, while GP had higher correlation coefficients for seed protein and oil contents. VIs with high feature importance were used as covariates in a new GBLUP model, and a new RF model was trained with the inclusion of selected SNPs from the GWAS results. These models did not outperform the original GP and PP models. These results show the capability of using ML for in-season predictions for specific traits in soybean breeding and provide insights on PP and GP inclusions in breeding programs. PLAIN LANGUAGE SUMMARYObtaining DNA information can often be costly for a public breeding program to obtain, which is a barrier of entry for using genomic prediction in these programs to make selections. Phenomic prediction provides an alternative opportunity that does not require the recurring costs of DNA sequencing. This research aimed to compare genomic and phenomic prediction in a diverse panel of soybeans. We found that phenomic prediction had the highest accuracy for seed yield, while genomic prediction was the best model for seed protein and oil. Combining genomic and phenomic data did not improve the predictive ability of the models. Core ideas O_LIIn-season phenomic prediction is able to outperform genomic prediction for seed yield. C_LIO_LIGenomic prediction outperformed in-season phenomic prediction models for seed protein and oil. C_LIO_LIOverlapping spectral indices were identified as the most predictive for seed yield, protein, and oil. C_LIO_LIFusion of genomic and phenomic prediction did not increase the predictive ability of the combined models. C_LI

plant biology↗

bioRxiv2024

Genetic Dissection of Heat Stress Tolerance in Soybean through Genome-Wide Association Studies and the Use of Genomic Prediction to Enhance Breeding Applications

Rising temperatures and associated heat stress pose an increasing threat to soybean [Glycine max L. (Merr.)] productivity. Due to a limited choice of mitigation strategies, the primary arsenal in crop protection comes from improved genetic stress tolerance. Despite this current and looming threat to soybean production, limited studies have examined the genetics of heat stress tolerance. There is a need to conduct large-scale germplasm screening and genetic studies, including genome-wide association mapping and genomic prediction, to identify genomic regions and useful markers associated with heat tolerance traits that can be utilized in soybean breeding programs. We screened a diverse panel of 450 soybean accessions from MG 0-IV to dissect the genetic architecture of physiological and growth-related traits under optimal and heat stress temperatures and study trait relationships and predictive ability. The genetic architecture information of the response to heat revealed in this study provides insights into the genetics of heat stress tolerance. Thirty-seven significant SNPs were detected, with 20 unique SNPs detected in optimal, 16 detected in heat stress, and a single SNP detected for a heat tolerance index. Only one significant SNP was identified across temperature treatments indicating a genetic divergence in soybean responses to temperature. The genomic prediction worked well for biomass traits, but physiological traits associated with heat stress had poor model accuracy. Through our phenotyping efforts, we identified heat tolerant soybean accessions. The identification of heat tolerant accessions and significant SNPs are useful in heat tolerant variety development through marker-assisted and genomic selection. Core ideasO_LISoybean exhibit phenotypic diversity in response to heat stress. C_LIO_LILarge scale phenotypic screening identified heat tolerant accessions. C_LIO_LIPreviously unreported QTL and SNP associated with biomass and physiological parameters under heat stress are reported. C_LIO_LIGenomic prediction shows promise in abiotic stress breeding applications. C_LI

genomics↗