Biology subjects

Gaynor, C.

Publications and source records attributed to Gaynor, C..

3 recordsLinked to original sources

bioRxiv2025

Purkinje cell collaterals preferentially target a subtype of molecular layer interneuron

In addition to providing outputs from the cerebellar cortex, Purkinje cell (PC) axon collaterals target other PCs, molecular layer interneurons (MLIs), and Purkinje layer interneurons (PLIs). It was recently shown that MLIs consist of two subtypes, but the properties of PC synapses onto these subtypes was not known and it was assumed that all PC collateral to MLI synapses would provide positive feedback to PCs. Clarifying the PC connectivity onto MLI subtypes is vital to understating the influence of feedback from PC collaterals because MLI1s primarily inhibit PCs whereas MLI2s mainly inhibit MLI1s and disinhibit PCs. Here we use a combination of serial EM and optogenetic studies to characterize PC synapses onto MLI subtypes in mice. EM reconstructions show that PCs make 53% of their synapses onto other PCs, 32% onto PLIs, 6% onto MLI1s and 7% onto MLI2s. Since there are far more MLI1s than MLI2s, each MLI2 is expected to receive many more synapses than each MLI1. In slice experiments, optogenetic activation of PCs evokes inhibitory currents in most MLI2s, but primarily disinhibits MLI1s. We also find that candelabrum cells, a type of PLI, form many more synapses onto MLI1s than MLI2s. It is therefore expected that both PC-MLI2-MLI1-PC and PC-PLI-MLI1-PC pathways allow increased PC firing to disinhibit MLI1s, which are known to reduce dendritic PC calcium signals and suppress plasticity at granule cell to PC synapses. These pathways provide negative feedback that act in concert with PC-PC synapses to counter elevations in PC firing. Significance StatementPurkinje cells (PCs) influence processing by inhibiting neurons in the cerebellar cortex, including other PCs, molecular layer interneurons (MLIs) and Purkinje layer interneurons (PLIs). The influence of PC-MLI synapses is not known because there are recently identified MLI subtypes with opposing effects: MLI1s inhibit PCs whereas MLI2s inhibit MLI1s and disinhibit PCs. We used serial EM and optogenetic studies to characterize PC synapses onto MLI subtypes and found that PCs preferentially inhibit MLI2s and disinhibit MLI1s. We also found that candelabrum cells (a type of PLI) preferentially inhibit MLI1s. These findings establish that PC-PC synapses, the PC-MLI2-MLI1-PC pathway and the PC-candelabrum cell-MLI1-PC pathway act together to allow alterations in PC firing to provide negative feedback to other PCs.

neuroscience↗

bioRxiv2020

Using high-throughput phenotypes to enable genomic selection by inferring genotypes

In this paper we develop and test a method which uses high-throughput phenotypes to infer the genotypes of an individual. The inferred genotypes can then be used to perform genomic selection. Previous methods which used high-throughput phenotype data to increase the accuracy of selection assumed that the high-throughput phenotypes correlate with selection targets. When this is not the case, we show that the high-throughput phenotypes can be used to determine which haplotypes an individual inherited from their parents, and thereby infer the individuals genotypes. We tested this method in two simulations. In the first simulation, we explored, how the accuracy of the inferred genotypes depended on the high-throughput phenotypes used and the genome of the species analysed. In the second simulation we explored whether using this method could increase genetic gain a plant breeding program by enabling genomic selection on non-genotyped individuals. In the first simulation, we found that genotype accuracy was higher if more high-throughput phenotypes were used and if those phenotypes had higher heritability. We also found that genotype accuracy decreased with an increasing size of the species genome. In the second simulation, we found that the inferred genotypes could be used to enable genomic selection on non-genotyped individuals and increase genetic gain compared to random selection, or in some scenarios phenotypic selection. This method presents a novel way for using high-throughput phenotype data in breeding programs. As the quality of high-throughput phenotypes increases and the cost decreases, this method may enable the use of genomic selection on large numbers of non-genotyped individuals.

genetics↗

bioRxiv2020

Phasing and imputation of single nucleotide polymorphism data of missing parents of bi-parental plant populations

This paper presents an extension to a heuristic method for phasing and imputation of genotypes of descendants in bi-parental populations so that it can phase and impute genotypes of parents of bi-parental populations that are fully ungenotyped or partially genotyped. The imputed genotypes of the parent are then used to impute low-density genotyped descendants of the bi-parental population to high-density. The extension works in three steps. First, it identifies whether a parent has no or low-density genotypes available and it identifies all of its relatives that have high-density genotypes. Second, using the high-density information of relatives, it determines whether the parent is homozygous or heterozygous for a given locus. Third, it phases heterozygous positions of the parent by matching haplotypes to its relatives. We implemented the new algorithm in an extension of the AlphaPlantImptue software and tested its accuracy of imputing missing parent genotypes in simulated bi-parental populations from different scenarios. We also tested the accuracy of imputation of the missing parents descendants using the true genotype of the parent and compared this to using the imputed genotypes of the parent. Our results show that across all scenarios, the accuracy of imputation of a parent, measured as the correlation between true and imputed genotypes, was > 0.98 and did not drop below [~] 0.96. The imputation accuracy of a parent was always higher when it was inbred than when it was outbred and when it had low-density genotypes. Including ancestors of the parent at HD, increasing the number of crosses and the number of high-density descendants all increased the accuracy of imputation. The high imputation accuracy achieved for the parent across all scenarios translated to little or no impact on the accuracy of imputation of its descendants at low-density. Key MessageNew fast and accurate method for phasing and imputation of SNP chip genotypes within diploid bi-parental plant populations.

genetics↗