Biology subjects

Lloyd, A.

Publications and source records attributed to Lloyd, A..

5 recordsLinked to original sources

bioRxiv2018

Assessing the response of small RNA populations to allopolyploidy using resynthesized Brassica napus allotetraploids

Allopolyploidy, combining interspecific hybridization with whole genome duplication, has had significant impact on plant evolution. Its evolutionary success is related to the rapid and profound genome reorganizations that allow neo-allopolyploids to form and adapt. Nevertheless, how neo-allopolyploid genomes adapt to regulate their expression remains poorly understood. The hypothesis of a major role for small non-coding RNAs (sRNAs) in mediating the transcriptional response of neo-allopolyploid genomes has progressively emerged. Generally, 21-nt sRNAs mediate post-transcriptional gene silencing (PTGS) by mRNA cleavage whereas 24-nt sRNAs repress transcription (transcriptional gene silencing, TGS) through epigenetic modifications. Here, we characterize the global response of sRNAs to allopolyploidy in Brassica, using three independently resynthesized B. napus allotetraploids surveyed at two different generations in comparison with their diploid progenitors. Our results suggest an immediate but transient response of specific sRNA populations to allopolyploidy. These sRNA populations mainly target non-coding components of the genome but also target the transcriptional regulation of genes involved in response to stresses and in metabolism; this suggests a broad role in adapting to allopolyploidy. We finally identify the early accumulation of both 21- and 24-nt sRNAs involved in regulating the same targets, supporting a PTGS-to-TGS shift at the first stages of the neo-allopolyploid formation. We propose that reorganization of sRNA production is an early response to allopolyploidy in order to control the transcriptional reactivation of various non-coding elements and stress-related genes, thus ensuring genome stability during the first steps of neo-allopolyploid formation.

evolutionary biology↗

bioRxiv2018

Modelling sex-specific crossover patterning in Arabidopsis

Interference is a major force governing the patterning of meiotic crossovers. A leading model describing how interference influences crossover-patterning is the beam film model, a mechanical model based on the accumulation and redistribution of crossover-promoting stress along the chromosome axis. We use the beam-film model in conjunction with a large Arabidopsis reciprocal back-cross data set to gain mechanistic insights into the differences between male and female meiosis and crossover patterning. Beam-film modelling suggests that the underlying mechanics of crossover patterning and interference are identical in the two sexes, with the large difference in recombination rates and distributions able to be entirely explained by the shorter chromosome axes in females. The modelling supports previous indications that fewer crossovers occur via the class II pathway in female meiosis and that this could be explained by reduced DNA double strand breaks in female meiosis, paralleling the observed reduction in synaptonemal complex length between the two sexes. We also demonstrate that changes in the strength of suppression of neighboring class I crossovers can have opposite effects on effective interference depending on the distance between two genetic intervals.

genetics↗

bioRxiv2017

The Basic Reproductive Number for Disease Systems with Multiple Coupled Heterogeneities

In mathematical epidemiology, a well-known formula describes the impact of heterogeneity on the basic reproductive number, R0, for situations in which transmission is separable and for which there is one source of variation in susceptibility and one source of variation in infectiousness. This formula is written in terms of the magnitudes of the heterogeneities, as quantified by their coefficients of variation, and the correlation between them. A natural question to ask is whether analogous results apply when there are multiple sources of variation in susceptibility and/or infectiousness. In this paper we demonstrate that with three or more coupled heterogeneities, R0 under separable transmission depends on details of the distribution of the heterogeneities in a way that is not seen in the well-known simpler situation. We provide explicit formulae for the cases of multivariate normal and multivariate log-normal distributions, showing that R0 can again be expressed in terms of the magnitudes of the heterogeneities and the pairwise correlations between them. The formulae, however, differ between the two multivariate distributions, demonstrating that no formula of this type applies generally when there are three or more coupled heterogeneities. We see that the results of the formulae are approximately equal when heterogeneities are relatively small and show that an earlier result in the literature (Koella, 1991) should be viewed in this light. We provide numerical illustrations of our results and discuss a setting in which coupled heterogeneities are likely to have a major impact on the value of R0. We also describe a rather surprising result: in a system with three heterogeneities, R0 can exhibit non-monotonic behavior with increasing levels of heterogeneity, in marked contrast to the familiar two heterogeneity setting in which R0 either increases or decreases with increasing heterogeneity.

epidemiology↗

bioRxiv2017

Evaluating Strategies For Reversing CRISPR-Cas9 Gene Drives

A gene drive biases inheritance of a gene so that it increases in frequency within a population even when the gene confers no fitness benefit. There has been renewed interest in environmental releases of engineered gene drives due to recent proof of principle experiments with the CRISPR-Cas9 system as a drive mechanism. Release of modified organisms, however, is controversial, especially when the drive mechanism could theoretically alter all individuals of a species. Thus, it is desirable to have countermeasures to reverse a drive if a problem arises. Several genetic mechanisms for limiting or eliminating gene drives have been proposed and/or developed, including synthetic resistance, reversal drives, and immunizing reversal drives. While predictions about efficacy of these mechanisms have been optimistic, we lack detailed analyses of their expected dynamics. We develop a discrete time model for population genetics of a drive and proposed genetic countermeasures. Efficacy of drive reversal varies between countermeasures. For some parameter values, the model predicts unexpected behavior including polymorphic equilibria and oscillatory dynamics. The timing and number of released individuals containing a genetic countermeasure can substantially impact outcomes. The choice among countermeasures by researchers and regulators will depend on specific goals and population parameters of target populations.

evolutionary biology↗

bioRxiv2017

Impact Of Sequencing Depth And Read Length On Single Cell RNA Sequencing Data: Lessons From T Cells

Single cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCR{beta}), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.\n\nWe studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCR{beta} reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. TCR{beta} were detected in 1,027 cells (79%), with a success rate between 81% and 100% for datasets with at least 250,000 (PE) reads of length >50 bp.\n\nSufficient read length and sequencing depth can control technical noise to enable accurate identification of TCR{beta} and gene expression profiles from scRNA-seq data of T cells.

bioinformatics↗