Biology subjects

Smart, B. C.

Publications and source records attributed to Smart, B. C..

5 recordsLinked to original sources

bioRxiv2024

Understanding genetic architecture overcomes tradeoffs between seed quality and insect resistance

The sunflower (Helianthus annuus) pericarp protects the seed within from both abiotic and biotic stresses. Achenes with stronger pericarps are less susceptible to damage from insect feeding. Complicating the genetic improvement of pericarp strength is the negative correlation between pericarp thickness (a component of strength) and oil content. As breeding efforts have increased oil content, there has been a concomitant decrease in pericarp thickness. A logical sunflower improvement goal is to improve oil content while preserving pericarp strength through genetic mechanisms independent of the tradeoffs with pericarp thickness. To determine the genetic basis of oil content, pericarp strength, and thickness, we identified QTL in two populations; the Sunflower Association Mapping panel (Mandel et al., 2011) and a recombinant inbred line (RIL) population derived from a thin pericarp oilseed inbred (HA 467) crossed to a thick pericarp open pollinated variety from Turkiye (PI 170415). A region on chromosome 15 was associated with neighboring QTL for banded moth resistance, oil content, and pericarp thickness, partially underlying the trade-offs among these traits. Additional QTL on chromosome 5 and 14 for pericarp strength provide fewer trade-offs with oil content. QTL for pericarp strength on chromosome 5 and pericarp thickness on chromosome 16 were associated with large structural variants, with candidate gene presence/absence variation between the haplotypes on chromosome 5. Understanding the origin and nature of phenotypic tradeoffs is beneficial to plant biologists and sunflower breeders as they seek to understand the origin and genetic architecture of adaptive and maladaptive traits.

genetics↗

bioRxiv2024

Variant filters using segregation information improve mapping of nectar-production genes in sunflower (Helianthus annuus L.)

Accurate variant calling is critical for identifying the genetic basis of complex traits, yet filters used in variant detection and validation may inadvertently exclude valuable genetic information. In this study, we compare common sequencing depth filters, used to eliminate error-prone variants associated with repetitive regions and technical issues, with a biologically relevant filtering approach that targets expected population-level Mendelian segregation. The resulting variant sets were evaluated in the context of nectar volume QTL mapping in sunflower (Helianthus annuus L.). Our previous research failed to detect a significant interval containing a strong candidate gene for nectar production (HaCWINV2). We removed certain hard filters and implemented a Chi-square goodness-of-fit test to retain variants that segregate according to expected genetic ratios. We hypothesized that this will enhance mapping resolution and capture key genetic regions previously missed. We demonstrate that biologically relevant filtering retains more significant QTL and candidate genes, including HaCWINV2, while removing variants due to technical errors more effectively, and accounted for 48.55% of phenotypic variation. In finding nine putative homologs of Arabidopsis genes with nectary function within QTL regions, we demonstrate that this filtering strategy, which considers biological contexts, has a higher power of true variant detection than the commonly used variant depth filtering strategy. PLAIN LANGUAGE SUMMARYIn genomic research, identifying genetic markers is key to understanding complex traits, but traditional methods for filtering genetic data can sometimes miss important information. In this study, we explored a new data filtering approach for mapping genes related to nectar production in sunflower. We applied a more flexible filtering method that considers how markers are expected to segregate in breeding populations. Our previous work failed to identify an important gene previously hypothesized to be involved in nectar production, likely due to overly strict filtering. Our improved approach identified nine sunflower genes related to nectar production genes in the model species Arabidopsis thaliana, as compared to zero genes identified from the previous filtering strategy. This study highlights the value of using flexible, biologically relevant filtering methods, which can lead to better results in plant genomic studies. CORE IDEASO_LIDiscovering biologically meaningful variants from sequence data requires a careful and critical view of bioinformatic workflows. C_LIO_LIThe use of arbitrary filters can remove significant genomic variation that contributes to the phenotype of interest. C_LIO_LIArbitrary filters can also fail to remove variant call errors. C_LIO_LIA Chi-square filtering strategy based on segregation ratio retained a larger number of valid variants. C_LIO_LIMore candidate regions with putative nectar-related genes and better statistical support were discovered. C_LI

plant biology↗

bioRxiv2024

Embedded Extremum Seeking: A Bio-synthetic Optimiser

In cybergentics, internal control uses synthetic genetic circuits to bypass the sensing and actuation limits of external control. Currently, embedded controllers are limited to basic controllers, like PID. Here, we introduce the Embedded Extremum Seeking Controller, the first example of a bio-synthetic optimiser. Extremum Seeking combines an integral and oscillator circuit to adjust system parameters and achieve a desired optimal steady state. This system uses an integral circuit and oscillator that already exist in synthetic biology. The Extremum Seeker is demonstrated in silico on a Labour Division system, where the controller tunes a kinematic parameter, optimising the ratio of the culture subpopulations to maximise production. This controller paves the way for embedded controllers that precisely direct cellular behaviour without needing external actuation and sensing.

synthetic biology↗

bioRxiv2024

Kernel Filter-Based Adaptive Controllers For Cybergenetics Applications

Cybergenetics is an advancing field that seeks to implement control theory within biological systems. When applying feedback control for the regulation of gene expression or cell proliferation, model-based control strategies can be applied; in this context, online adaptive mathematical models can be used to keep models in tune with the current behaviour of the biological system. Controllers are often constrained by their sampling rate, which is usually relatively low when using microfluidics/microscopy platforms. Current adaptive filters can lead to an inaccurate predictive model when operating with a low sampling rate, leading to sub-optimal control. Here, we propose a kernel filter that can fit model parameters online to produce a more accurate predictive model that can be included within an adaptive model predictive control scheme. The use of the kernel filter is demonstrated in in silico and in vitro experiments, where we control a synthetic gene oscillator and a P53 oscillator, and observe a synthetic toggle switch. Our results show that the kernel filter outperforms a particle filter when used for parameter estimation in both the predictive model accuracy and when included within an adaptive model-based controller.

systems biology↗

bioRxiv2023

Chromosome-scale Genome Assembly of Lewis Flax (Linum lewisii Pursh.)

The shift from self-incompatibility to self-compatibility is a frequent evolutionary transition in flowering plants with numerous ecological and evolutionary consequences. It is also an advantageous transition for domestication of new crop plants, as self-compatibility makes it considerably easier to drive adaptively important alleles to fixation. In the flax genus, Linum, self-incompatibility is linked to the floral polymorphism known as heterostyly, where plants exhibit distinct floral morphs with different positioning of male and female reproductive organs. Heterostyly has been lost multiple times independently across the flax genus, leading to homostyly and self-compatibility, but the genetic causes of this transition are not fully understood. Here, we present a near telomere-to-telomere genome assembly of "Maple Grove" Lewis flax (Linum lewisii, 2n = 2x = 18), a homostylous wild blue flax species native to North America. By comparison to the genome of close heterostylous relative L. perenne, we found that the coding sequence of heterostyly candidate gene TSS1 is deleted in the Lewis flax genome, which could underlie its transition from heterostyly to homostyly. Analysis of chromosomal synteny between Lewis flax and common flax (L. usitatissimum) further revealed a striking amount of chromosomal rearrangements, which will complicate the use of comparative genomics to accelerate domestication of Lewis flax as a new perennial oilseed crop. The final, primary haplotype was 845 Mb in length and comprised 9 pseudochromosomes and 324 unplaced scaffolds with a contig N50 of 17.9 Mb. Annotation of the assembly revealed 19,593 protein-coding genes. This genomic resource will inform ongoing breeding efforts and will support its use in native ecosystem restoration and in other native plantings across western North America.

genomics↗