Biology subjects

Feltus, F. A.

Publications and source records attributed to Feltus, F. A..

7 recordsLinked to original sources

bioRxiv2025

A haplotype-resolved, chromosome-scale genome assembly for the southern live oak, Quercus virginiana

Hybridization is a major force driving diversification, migration, and adaptation in Quercus species. While population genetics and phylogenetics have traditionally been used for studying these processes, advances in sequencing technology now enable us to incorporate comparative and pan-genomic approaches as well. Here we present a highly contiguous, chromosome-scale and haplotype-resolved genome assembly for the southern live oak, Quercus virginiana, the first reference genome for section Virentes, as part of the American Campus Tree Genomes (ACTG) program. Originating from a clone of Auburn Universitys historic "Toomers Oak," this assembly contributes to the pool of genomic resources for investigating recombination, haplotype variation, and structural genomic changes influencing hybridization potential in this clade and across Quercus. It also provides insights into the architecture of the putative centromeric regions within the genus. Alongside other oak references, the Q. virginiana genome will support research into the evolution and adaptation of the Quercus genus.

genomics↗

bioRxiv2023

The Single-Cell Transcriptome Program of Nodule Development Cellular Lineages in Medicago truncatula

Legumes can establish a symbiotic relationship with nitrogen-fixing rhizobia by developing nodules after root exposure to lipo-chito-oligosaccharides secreted by the bacteria. Nodule development initiates with anticlinal mitotic divisions in the pericycle and endodermal and inner cortical cells, establishing cell lineages that ultimately form each nodule compartment. We characterized these lineages by isolating and sequencing the transcriptome of Medicago truncatula single nuclei derived from uninoculated roots and roots undergoing early nodule development at 24, 48, and 96 hours after inoculation. To enrich samples for cells responding to the rhizobia, we complemented the analysis of the Medicago wild-type genotype A17 with a mutant for the autoregulation of nodulation, sunn-4. Analysis of cell lineage trajectories derived from the cortex indicates that their transcriptome is initially enriched for cytokinin perception and signaling while repressing auxin accumulation. As these cells differentiate to form nodules, expression of genes related to auxin biosynthesis, transport, and signaling was enhanced, while genes involved in cytokinin degradation were activated as lineages bifurcated to form the nodule meristem and infection zones. While the contribution of auxin and cytokinin in nodule development has been recognized, this single-cell resource quantifies the expression of each of their regulators, receptors and targets as cells divide and differentiate to form each nodule compartment.

plant biology↗

bioRxiv2023

A laser capture microdissection transcriptome of M. truncatula roots responding to rhizobia reveals spatiotemporal tissue expression patterns of genes involved in nodule signaling and organogenesis

We report a public resource for examining the spatiotemporal RNA expression of 54,893 M. truncatula genes during the first 72 hours of response to rhizobial inoculation. Using a methodology that allows synchronous inoculation and growth of over 100 plants in a single media container, we harvested the same segment of each root responding to rhizobia in the initial inoculation over a time course, collected individual tissues from these segments with laser capture microdissection, and created and sequenced RNA libraries generated from these tissues. We demonstrate the utility of the resource by examining the expression patterns of a set of genes induced very early in nodule signaling, as well as two gene families (CLE peptides and nodule specific PLAT-domain proteins) and show that despite similar whole root expression patterns, there are tissue differences in expression between the genes. Using a rhizobial response data set generated from transcriptomics on intact root segments, we also examined differential temporal expression patterns and determined that, after nodule tissue, the epidermis and cortical cells contained the most temporally patterned genes. We circumscribed gene lists for each time and tissue examined and developed an expression pattern visualization tool. Finally, we explored transcriptomic differences between the inner cortical cells that become nodules and those that do not, confirming that the expression of ACC synthases distinguishes inner cortical cells that become nodules and provide and describe potential downstream genes involved in early nodule cell division.

plant biology↗

bioRxiv2023

Transcriptome analysis of Medicago truncatula Autoregulation of Nodulation mutants reveals that disruption of the SUNN pathway causes constitutive expression changes in a small group of genes, but the overall response to rhizobia resembles wild type, including induction of TML1 and TML2.

Nodule number regulation in legumes is controlled by a feedback loop that integrates nutrient and rhizobia symbiont status signals to regulate nodule development. Signals from the roots are perceived by shoot receptors, including a CLV1-like receptor-like kinase known as SUNN in the annual medic Medicago truncatula. In the absence of functional SUNN, the autoregulation feedback loop is disrupted, resulting in hypernodulation. To elucidate early autoregulation mechanisms disrupted in SUNN mutants, we searched for genes with altered expression in the loss-of-function sunn-4 mutant and included the rdn1-2 autoregulation mutant for comparison. We identified constitutively altered expression of small groups of genes in sunn-4 roots, including higher levels of transcription factor NF-YA2, and in sunn-4 shoots. All genes with verified roles in nodulation that were induced in wild type roots during the establishment of nodules were also induced in sunn-4, including, surprisingly, autoregulation genes TML2 and TML1. Among all genes with a differential response to rhizobia in wild type roots, only an isoflavone-7-O-methyltransferase gene (Medtr7g014510) was found to be unresponsive in sunn-4. In shoot tissues of wild type, eight rhizobia-responsive genes were identified, including a MYB family transcription factor gene (Medtr3111880) which remained at a baseline level in sunn-4; three genes were found to be induced by rhizobia in shoots of sunn-4 but not wild type. We also cataloged the temporal induction profiles of many small secreted peptide (MtSSP) genes in nodulating root tissues, encompassing members of twenty-four peptide families, including the CLE and IRON MAN families. The discovery that expression of TML genes in roots, a key factor in inhibiting nodulation in response to autoregulation signals, is also triggered in sunn-4 in the section of roots analyzed suggests that the mechanism of TML regulation in M. truncatula may be more complex than published models.

plant biology↗

bioRxiv2022

Computational Speed-Up of Large-Scale, Single-Cell Model Simulations Via a Fully-Integrated SBML-Based Format

SummaryLarge-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed up simulation by ~4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Availability and ImplementationSource code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.1.0 implemented in python, and supported on Linux, Windows, and MacOS (via Docker). ContactMarc Birtwistle mbirtwi@clemson.edu Supplementary informationN/A

systems biology↗

bioRxiv2020

A Simple and Efficient Pipeline for Construction, Merging, Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic Models

Mechanistic models of how single cells respond to different perturbagens can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Our lab previously constructed one of the largest mechanistic models for single mammalian cell regulation of proliferation and death (774 species, 141 genes, 8 ligands, 2400 reactions). However, this, as many other large-scale models, was written using licensed software (MATLAB) with intricate programming structure, impeding alteration, expansion, and sharing. Here, we generated a new foundation for this model, which includes a python-based creation and simulation pipeline converting a few structured text files into an SBML-compatible format. This new open-source model (named SPARCED) is high-performance- and cloud-computing compatible and enables the study of virtual cell population responses at the single-cell level. We applied this new model to a subset of the LINCS MCF10A Data Cube, which observed that IFN{gamma} acts as an anti-proliferative factor, but the reasons why were unknown. After expanding the SPARCED model with an IFN{gamma} signaling module (to 950 species, 150 genes, 9 ligands, 2500 reactions), we ran stochastic single-cell simulations for two different putative crosstalk mechanisms and looked at the number of cycling cells in each case. Our model-based analysis suggested, and experiments support that these observations are better explained by IFN{gamma}-induced SOCS1 expression sequestering activated EGF receptors, thereby downregulating AKT activity, as opposed to direct IFN{gamma}-induced upregulation of p21 expression. This work forms a foundation for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically predictive mechanistic models.

systems biology↗

bioRxiv2017

Widespread Genotype-Phenotype Correlations in Intellectual Disability

BackgroundLinking genotype to phenotype is a major aim of genetics research, yet many complex conditions continue to hide their underlying biochemical mechanisms. Recent research provides evidence that relevant gene-phenotype associations are discoverable in the study of intellectual disability (ID). Here we expand on that work, identifying distinctive gene interaction modules with unique enrichment patterns reflective of associated clinical features in ID.\n\nMethodsTwo hundred twelve forms of monogenic ID were curated according to comorbidities with autism and epilepsy. These groups were further subdivided according to secondary clinical symptoms of complex versus simple facial dysmorphia and neurodegenerative-like features due to their clinical prominence, modest symptom overlap, and probable etiological divergence. An aggregate gene interaction ID network for these phenotype subgroups was discovered using via a public database of known gene interactions: protein-protein, genetic, and mRNA coexpression. Additional annotation resources (Gene Ontology, Human Phenotype Ontology, TRANSFAC/JASPAR, and KEGG/WikiPathways) were utilized to assess functional and phenotypic enrichment modules within the full ID network.\n\nResultsPhenotypic analysis revealed high rates of complex facial dysmorphia in ID with comorbid autism. In contrast, neurodegenerative-like features were overrepresented in ID with epilepsy. Network analysis subsequently showed that gene groups divided according to clinical features of interest resulted in distinctive interaction clusters, with unique functional enrichments according to module.\n\nConclusionsThese data suggest that specific comorbid and secondary clinical features in ID are predictive of underlying genotype. In summary, ID form unique clusters, which are comprised of individual conditions with remarkable genotypic and phenotypic overlap.

genetics↗