Biology subjects

Bonnette, J. E.

Publications and source records attributed to Bonnette, J. E..

5 recordsLinked to original sources

bioRxiv2026

Chromosome-level Atlantic switchgrass assemblies reveal a supernumerary B-Chromosome with transcriptional and evolutionary impacts

Supernumerary B-Chromosomes are extra genetic elements that frequently deviate from Mendelian inheritance to ensure their own survival. Their evolutionary origins and biological impacts within natural plant populations remain largely unexplored. Switchgrass (Panicum virgatum) is a model for studies of local adaptation, but reliance on a single reference genome has limited the detection of lineage-specific genomic content. We generated chromosome-level genome assemblies from the diverged Atlantic switchgrass subpopulation and discovered a previously undetected genomic element: a ~34 Mb supernumerary B-Chromosome, validated by Hi-C contact mapping and long-read depth, which indicates two to four B copies per nucleus. The B-Chromosome has a bipartite architecture including a gene-rich, single-copy proximal region (~8 Mb) and a repeat-rich, gene-poor distal region (~27 Mb) dominated by LTR/Ty3 (Gypsy) elements and punctuated by Mutator TIR peaks. High-resolution mapping reveals a highly rearranged chimera of fragments from multiple autosomes, biased toward the N-subgenome. Intact LTR retrotransposons on the B-Chromosome are younger than those on the A-Chromosomes, especially in its distal region, consistent with a B-Chromosome that arose and expanded within the Atlantic lineage. Population screening and phylogenetic analysis indicate that the B-Chromosome is largely restricted to the Atlantic subpopulation and likely has a single origin. In a typical family, 70% of open-pollinated progeny of B-positive maternal plants were B-positive, with substantial variation among families. Finally, B-Chromosome presence is associated with expression differences in A-Chromosome genes that differ between genetic backgrounds. Together, our results show how accessory chromosomes contribute to the evolutionary and genomic diversity of natural plant populations.

genomics↗

bioRxiv2026

Uncovering genetic mechanisms underlying trait variation in switchgrass using explainable artificial intelligence

Uncovering the genetic architecture of quantitative traits is challenging because polygenic control yields small individual gene effects and because gene-gene and genotype-by-environment interactions add further complexity. To understand the genetic basis of polygenic traits and their plasticity across environments, we integrated genome-wide SNPs and RNA-seq transcript data with interpretable statistical and machine learning models in a switchgrass (Panicum virgatum) diversity panel grown at contrasting field sites in Michigan and Texas. Notably, in addition to single environments, our trait prediction models were able to predict phenotypic differences, across environments i.e., plasticity. By interpreting trait prediction models with explainable machine learning methods, we identified genes that are the most predictive of flowering time and annual biomass production across environments, based on their associated gene expression levels and nearby SNPs. This approach recovered canonical flowering regulators and revealed novel, environment-specific candidate flowering genes. Further, transcriptome models consistently recovered more switchgrass genes homologous to experimentally validated genes in Arabidopsis and rice than SNP-based models. Feature interaction scores from the models also allow the identification of trait and environment-dependent gene-gene interactions, where flowering time showed stronger and more abundant interactions than biomass. While some of the interactions identified are consistent with the link between flowering time and yield, most are novel predictors that need to be further evaluated. These findings provide insight into the genetic basis of trait variation and plasticity across environments and help prioritize candidate genes for functional validation and cultivar improvement.

genetics↗

bioRxiv2022

Natural variation identifies new effectors of water use efficiency in Arabidopsis.

Water use efficiency (WUE) is the ratio of biomass gained per unit of water consumed; thus, it can be altered by genetic factors that affect either side of the ratio. In the present study, we exploited natural variation for WUE as an unbiased approach to discover loci affecting either biomass accumulation or water use as factors affecting WUE. Genome-wide association (GWAS) analysis using integrated WUE measured through carbon isotope discrimination ({delta}13C) of Arabidopsis thaliana accessions identified genomic regions associated with WUE. Reverse genetic analysis of 70 candidate genes selected based on the GWAS results and transcriptome data identified 25 genes affecting WUE as measured by gravimetric and {delta}13C analyses. Mutants of four genes had higher WUE than wild type, while mutants of the other 21 genes had lower WUE. The differences in WUE were caused by either altered biomass or water consumption (or both). Stomatal density was not a primary cause of altered WUE in these mutants. Leaf surface temperatures indicated that transpiration differed for mutants of 16 genes, but generally biomass accumulation had greater effect on WUE. The genes we identified are involved in diverse cellular processes including hormone and calcium signaling, meristematic activity, photosynthesis, flowering time, leaf/vasculature development, and cell wall composition; however, none of them had been previously linked to WUE or traits related to plant water relations. Thus, our study successfully identified new effectors of WUE that can be used to understand the genetic basis of WUE and improve crop productivity.

plant biology↗

bioRxiv2021

Correlational selection and genetic architecture promote the leaf economics spectrum in a perennial grass

The leaf economics spectrum (LES) is hypothesized to result from a trade-off between resource acquisition and conservation. Yet few studies have examined the evolutionary mechanisms behind the LES, perhaps because most species exhibit relatively specialized leaf economics strategies. In a genetic mapping population of the phenotypically diverse grass Panicum virgatum, we evaluate two interacting mechanisms that may drive LES evolution: 1) genetic architecture, where multiple traits are coded by the same gene (pleiotropy) or by genes in close physical proximity (linkage), and 2) correlational selection, where selection acts non-additively on combinations of multiple traits. We found evidence suggesting that shared genetic architecture (pleiotropy) controls covariation between two pairs of leaf economics traits. Additionally, at five common gardens spanning 17 degrees of latitude, correlational selection favored particular combinations of leaf economics traits. Together, these results demonstrate how the LES can evolve within species.

ecology↗

bioRxiv2018

The genetic architecture of shoot and root trait divergence between upland and lowland ecotypes of a perennial grass.

Introduction Introduction Materials and Methods Results Discussion Conclusion Author Contributions: References Adaptation to abiotic stress is an important driver of contemporary evolution in plant populations. Abiotic stressors have been implicated as driving factors in ecological speciation (Stebbins, 1952;Lexer & Fay, 2005), where populations have diverged across a number of traits, exhibit different niche characteristics, and eventually become reproductively isolated (Clausen, 1951; Lowry, 2012; Yardeni et al., 2016). Local adaptation to soil water availability is an especially important driver of plant evolution (Stebbins, 1952; Rajakaruna, 2004; ...

plant biology↗