Biology subjects

Lea, R. A.

Publications and source records attributed to Lea, R. A..

5 recordsLinked to original sources

bioRxiv2020

KLF17 promotes human naïve pluripotency but is not required for its establishment

Current knowledge of the transcriptional regulation of human pluripotency is incomplete, with lack of inter-species conservation observed. Single-cell transcriptomics of human embryos previously enabled us to identify transcription factors, including the zinc-finger protein KLF17, that are enriched in the human epiblast and naive hESCs. Here we show that KLF17 is expressed coincident with the known pluripotency factors NANOG and SOX2 across human blastocyst development. We investigate the function of KLF17 in pluripotency using primed and naive hESCs for gain- and loss-of-function analyses. We find that ectopic expression of KLF17 in primed hESCs is sufficient to induce a naive-like transcriptome and that KLF17 can drive transgene-mediated resetting to naive pluripotency. This implies a role for KLF17 in establishing naive pluripotency. However, CRISPR-Cas9-mediated knockout studies reveal that KLF17 is not required for naive pluripotency acquisition in vitro. Transcriptome analysis of naive hESCs identifies subtle effects on metabolism and signalling following KLF17 loss of function, and possible redundancy with the related factor, KLF5. Overall, we show that KLF17 is sufficient, but not necessary, for naive pluripotency under the given in vitro conditions. Summary statementInvestigating KLF17 in human pluripotency reveals that it is sufficient, but not necessary, to establish naive hESCs. We posit that KLF17 is a peripheral regulator, like KLF2 in the mouse.

developmental biology↗

bioRxiv2020

Identification of novel mitochondrial and mitochondrial related genetic loci associated with exercise response in the Gene SMART study

Mitochondria supply intracellular energy requirements during exercise. Specific mitochondrial haplogroups and mitochondrial genetic variants have been associated with athletic performance, and exercise responses. However, these associations were discovered using underpowered, candidate gene approaches, and consequently have not been replicated. Here, we used whole-mitochondrial genome sequencing, in conjunction with high-throughput genotyping arrays, to discover novel genetic variants associated with exercise responses in the Gene SMART (Skeletal Muscle Adaptive Response to Training) cohort (n=62 completed). We performed a Principal Component Analysis of cohort aerobic fitness measures to build composite traits and test for variants associated with exercise outcomes. None of the mitochondrial genetic variants but nine nuclear encoded variants in eight separate genes were found to be associated with exercise responses (FDR<0.05) (rs11061368: DIABLO, rs113400963: FAM185A, rs6062129 and rs6121949: MTG2, rs7231304: AFG3L2, rs2041840: NDUFAF7, rs7085433: TIMM23, rs1063271: SPTLC2, rs2275273: ALDH18A1). Additionally, we outline potential mechanisms by which these variants may be contributing to exercise phenotypes. Our data suggest novel nuclear-encoded SNPs and mitochondrial pathways associated with exercise response phenotypes. Future studies should focus on validating these variants across different cohorts and ethnicities. AUTHOR SUMMARYPrevious exercise genetic studies contain many flaws that impede the growth in knowledge surrounding change in exercise outcomes. In particular, exercise studies looking at mtDNA variants have looked at very small portions of the mitochondrial genome. Mitochondria are the power house of the cell and therefore understanding the mitochondrial genetics behind adaptations to training can help us fill knowledge gaps in current research. Here, we utilised a new mitochondrial genetic sequencing technique to examine all mitochondrial and mitochondrial related genetic variations. We have shown that there were no mitochondrial specific variants that influenced exercise training however there were 9 related variants that were significantly associated with exercise phenotypes. Additionally, we have shown that building composite traits increased the significance of our association testing and lead to novel findings. We will be able to understand why response to training is so varied and increase the effectiveness of exercise training on a host of metabolic disorders.

genetics↗

bioRxiv2019

Cell-type specific analysis of heterogeneous methylation signal using a Bayesian model-based approach

MotivationEpigenome-wide studies are often performed using heterogeneous methylation samples, especially when there is no prior information as to which cell-types are disease associated. While much work has been done in ascertaining cell-type fractions and removing cell-type heterogeneity variation, relatively little work has been done in identifying cell-type specific variation in heterogeneous samples.\n\nResultsIn this paper, we present a Bayesian model-based approach for making cell-type specific inferences in heterogeneous settings, by using a logit-Normal sampling distribution and incorporating a priori knowledge of cell-type lineage. The method is applied to the detection of cell-type specific sex effects in methylation, where cell-type information is present as an independent verification of the results. Panels derived from this method contained more loci where CD8+T, CD19+B and Natural Killer cell-types were differentially methylated. The analysis suggests that an ensemble approach with this method included could be used for discovering cell-type specific methylation changes.\n\nAvailabilityhttps://github.com/danwkenn/Bayes_CDM

bioinformatics↗

bioRxiv2017

Accounting For Cell Lineage And Sex Effects In The Identification Of Cell-Specific DNA Methylation Using A Bayesian Model Selection Algorithm

Cell- and sex-specific differences in DNA methylation are major sources of epigenetic variation in whole blood. Failure to account for these confounders may lead to substantial bias in the identification of differentially methylated CpGs and predicted levels of differential methylation. Previous studies have provided evidence of cell-specific methylation, but all of these have been restricted to the detection of differential methylation in a single cell type. We developed a Bayesian model selection algorithm for the identification of cell-specific methylation profiles that incorporates knowledge of shared cell lineage, to accommodate differential methylation in one or more cell types. Under the proposed methodology, sex-specific differences in methylation by cell type are also assessed. Using publicly available cell-sorted methylation data, we show that 51.3% of female CpG markers and 61.4% of male CpG markers identified were associated with differential methylation in more than one cell type. The impact of cell lineage on differential methylation was also highlighted. An evaluation of sex-specific differences revealed marked differences in CD56+NK methylation, within both single and multi-cell dependent methylation patterns. Our findings demonstrate the need to account for cell lineage in studies of differential methylation and associated sex effects.

genetics↗

bioRxiv2017

Bootstrap Distillation: Non-parametric Internal Validation of GWAS Results by Subgroup Resampling

Genome-wide Association Studies are carried out on a large number of genetic variants in a large number of people, allowing the detection of small genetic effects that are associated with a trait. Natural variation of genotypes within populations means that any particular sample from the population may not represent the true genotype frequencies within that population. This may lead to the observation of marker-disease associations when no such association exists.\n\nA bootstrap population sub-sampling technique can reduce the influence of allele frequency variation in producing false-positive results for particular samplings of the population. In order to utilise bioinformatics in the service of a serious disease, this sub-sampling method has been applied to the Type 1 Diabetes dataset from the Wellcome Trust Case Control Consortium in order to evaluate its effectiveness.\n\nWhile previous literature on Type 1 Diabetes has identified some DNA variants that are associated with the disease, these variants are not informative for distinguishing between disease cases and controls using genetic information alone (AUC=0.7284). Population sub-sampling filtered out noise from genome-wide association data, and increased the chance of finding useful associative signals. Subsequent filtering based on marker linkage and testing of marker sets of different sizes produced a 5-SNP signature set of markers for Type 1 Diabetes. The group-specific markers used in this set, primarily from the HLA region on chromosome 6, are considerably more informative than previously known associated variants for predicting T1D phenotype from genetic data (AUC=0.8395). Given this predictive quality, the signature set may be useful alone as a screening test, and would be particularly useful in combination with other clinical cofactors for Type 1 Diabetes risk.

bioinformatics↗