Biology subjects

Bonito, G. M.

Publications and source records attributed to Bonito, G. M..

4 recordsLinked to original sources

bioRxiv2023

Identification of anti-fungal bioactive terpenoids from the bioenergy crop switchgrass (Panicum virgatum)

Plant derived bioactive small molecules have attracted attention of scientists across fundamental and applied scientific disciplines. We seek to understand the influence of these phytochemicals on functional phytobiomes. Increased knowledge of specialized metabolite bioactivities could inform strategies for sustainable crop production. Our recent investigations of metabolomes of the upland and lowland ecotypes of the bioenergy crop switchgrass (Panicum virgatum) revealed large differences in types and abundances of terpenoid specialized metabolites. We hypothesized that - consistent with accumulating evidence that switchgrass genotype impacts microbiome assembly - differential terpenoid accumulation contributes to switchgrass ecotype-specific microbiome composition. An initial in vitro plate-based disc diffusion screen of 18 switchgrass root derived fungal isolates revealed differential responses to upland- and lowland-isolated metabolites. To identify specific fungal growth-modulating metabolites, we tested fractions from root extracts on three ecologically important fungal isolates - Linnemania elongata, Trichoderma sp. and Fusarium sp. Saponins and diterpenoids were identified as the most prominent antifungal metabolites. Finally, analysis of liquid chromatography-purified terpenoids revealed fungal inhibition structure - activity relationships (SAR). Saponin antifungal activity was primarily determined by the number of sugar moieties - saponins glycosylated at a single core position were inhibitory whereas saponins glycosylated at two core positions were inactive. Saponin core hydroxylation and acetylation were also associated with reduced activity. Diterpenoid activity required the presence of an intact furan ring for strong fungal growth inhibition. This study demonstrates that switchgrass genotype-specific metabolites differentially inhibit fungal isolates from the root and rhizosphere, supporting the hypothesis that these small molecules contribute to microbiome assembly and function.

plant biology↗

bioRxiv2023

The Impact of Species Tree Estimation Error on Cophylogenetic Reconstruction

AO_SCPLOWBSTRACTC_SCPLOWJust as a phylogeny encodes the evolutionary relationships among a group of organisms, a cophylogeny represents the coevolutionary relationships among symbiotic partners. Both are widely used to investigate a range of topics in evolutionary biology and beyond. Both are also primarily reconstructed using computational analysis of biomolecular sequence data as well as other biological character data. The most widely used cophylogenetic reconstruction methods utilize an important simplifying assumption: species phylogenies for each set of coevolved taxa are required as input and assumed to be correct. Many theoretical and experimental studies have shown that this assumption is rarely - if ever - satisfied, and the consequences for cophylogenetic studies are poorly understood. To address this gap, we conduct a comprehensive performance study that quantifies the relationship between species tree estimation error and downstream cophylogenetic estimation accuracy. The study includes performance benchmarking using in silico model-based simulations. Our investigation also includes assessments of cophylogenetic reproducibility using genomic sequence datasets sampled from two important models of symbiosis: soil-associated fungi and their endosymbiotic bacteria, and bobtail squid and their bioluminescent bacterial symbionts. Our findings conclusively demonstrate the major impact that upstream phylogenetic estimation error has on downstream cophylogenetic reconstruction quality.

bioinformatics↗

bioRxiv2021

Non-target impacts of fungicide disturbance on phyllosphere yeasts in different crop species and management systems

O_LIFungicides reduce fungal pathogen populations and are essential to food security. Fungicide disturbance of plant microbiomes has received limited attention. Understanding the impacts of fungicides on crop microbiomes in different cropping systems is vital to minimizing unintended consequences while maintaining their use for plant protection. C_LIO_LIWe used amplicon sequencing of fungi and prokaryotes in maize and soybean microbiomes before and after foliar fungicide application in leaves and roots from plots under long-term no-till and conventional tillage managements. We examine fungicide disturbance and microbiome resilience across these treatments. C_LIO_LIFoliar fungicides directly affected phyllosphere fungal communities, but not root fungal communities or prokaryote communities. Impacts on fungal phyllosphere composition and resiliency were management-dependent and lasted more than thirty days. Fungicides lowered pathogen abundance in maize and soybean and decreased the abundance of Tremellomycetes yeasts, especially the Bulleribacidiaceae, including core microbiome members. C_LIO_LIFungicide application reduced network complexity in the soybean phyllosphere. Bulleribacidiaceae often co-occurred with Sphingomonas and Hymenobacter in control plots, but co-occurrences were altered in fungicide plots. Results indicate that foliar fungicides lower pathogen and non-target fungal abundance and may impact prokaryotes indirectly. No-till management was more resilient following fungicide disturbance and recovery. C_LI

microbiology↗

bioRxiv2017

Coal-Miner: A Coalescent-Based Method For GWA Studies Of Quantitative Traits With Complex Evolutionary Origins

Association mapping (AM) methods are used in genome-wide association (GWA) studies to test for statistically significant associations between genotypic and phenotypic data. The genotypic and phenotypic data share common evolutionary origins - namely, the evolutionary history of sampled organisms - introducing covariance which must be distinguished from the covariance due to biological function that is of primary interest in GWA studies. A variety of methods have been introduced to perform AM while accounting for sample relatedness. However, the state of the art predominantly utilizes the simplifying assumption that sample relatedness is effectively fixed across the genome. In contrast, population genetic theory and empirical studies have shown that sample relatedness can vary greatly across different loci within a genome; this phenomena - referred to as local genealogical variation - is commonly encountered in many genomic datasets. New AM methods are needed to better account for local variation in sample relatedness within genomes.\n\nWe address this gap by introducing Coal-Miner, a new statistical AM method. The Coal-Miner algorithm takes the form of a methodological pipeline. The initial stages of Coal-Miner seek to detect candidate loci, or loci which contain putatively causal markers. Subsequent stages of Coal-Miner perform test for association using a linear mixed model with multiple effects which account for sample relatedness locally within candidate loci and globally across the entire genome.\n\nUsing synthetic and empirical datasets, we compare the statistical power and type I error control of Coal-Miner against state-of-theart AM methods. The simulation conditions reflect a variety of genomic architectures for complex traits and incorporate a range of evolutionary scenarios, each with different evolutionary processes that can generate local genealogical variation. The empirical benchmarks include a large-scale dataset that appeared in a recent high-profile publication. Across the datasets in our study, we find that Coal-Miner consistently offers comparable or typically better statistical power and type I error control compared to the state-of-art methods.\n\nCCS CONCEPTSApplied computing [->] Computational genomics; Computational biology; Molecular sequence analysis; Molecular evolution; Computational genomics; Systems biology; Bioinformatics; Population genetics;\n\nACM Reference formatHussein A. Hejase, Natalie Vande Pol, Gregory M. Bonito, Patrick P. Edger, and Kevin J. Liu. 2017. Coal-Miner: a coalescent-based method for GWA studies of quantitative traits with complex evolutionary origins. In Proceedings of ACM BCB, Boston, MA, 2017 (BCB), 10 pages. DOI: 10.475/123 4

bioinformatics↗