Biology subjects

Luu, K.

Publications and source records attributed to Luu, K..

7 recordsLinked to original sources

bioRxiv2024

Harnessing RNA-based DNA repair pathways for targeted gene editing

Recent studies have revealed a role for RNA in the repair of DNA double-strand breaks. Here, we show that the asymmetric DNA overhangs generated by the small TevSaCas9 dual nuclease informs a simple and robust editing strategy in human cells whereby Pol{theta} and Rad52 are recruited to repair the double-strand break. The 2-nt, 3 DNA overhang generated by the I-TevI nuclease domain of TevSaCas9 hybridizes with the 3 end of a co-localized repair template guide RNA to specifically license repair. Substitutions that destabilize the repair duplex reduce editing efficiency. Targeted RNA-templated repair (rep-editing) harnesses cellular RNA-based DNA repair pathways to introduce precise nucleotide edits, deletions and insertions in human cells with high efficiency and fidelity independent of co-delivered repair functions. The small size of TevSaCas9 and RNA repair template offers delivery advantages over size-constrained or multi-component editing systems.

molecular biology↗

bioRxiv2022

Patterns of Structural Variation Define Prostate Cancer Across Disease States

The complex genomic landscape of prostate cancer evolves across disease states under therapeutic pressure directed toward inhibiting androgen receptor (AR) signaling. While significantly altered genes in prostate cancer have been extensively defined, there have been fewer systematic analyses of how structural variation shapes the genomic landscape of this disease across disease states. We uniformly characterized structural alterations across 278 localized and 143 metastatic prostate cancers profiled by whole genome and transcriptome sequencing. We observed distinct significantly recurrent breakpoints in localized and metastatic castration-resistant prostate cancers (mCRPC), with pervasive alterations in noncoding regions flanking the AR, MYC, FOXA1, and LSAMP genes enriched in mCRPC and TMPRSS2-ERG rearrangements enriched in localized prostate cancer. We defined nine subclasses of mCRPC based on signatures of structural variation, each associated with distinct genetic features and clinical outcomes. Our results comprehensively define patterns of structural variation in prostate cancer and identify clinically actionable subgroups based on whole genome profiling.

genomics↗

bioRxiv2021

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal Cis-Regulatory Elements

PurposePrior studies demonstrate the significance of specific cis-regulatory variants in retinal disease, however determining the functional impact of regulatory variants remains a major challenge. In this study, we utilize a machine learning approach, trained on epigenomic data from the adult human retina, to systematically quantify the predicted impact of cis-regulatory variants. MethodsWe used human retinal DNA accessibility data (ATAC-seq) to determine a set of 18.9k high-confidence putative cis-regulatory elements. 80% of these elements were used to train a machine learning model utilizing a gapped k-mer support vector machine-based approach. In silico saturation mutagenesis and variant scoring was applied to predict the functional impact of all potential single nucleotide variants within cis-regulatory elements. Impact scores were tested in a 20% hold-out dataset and compared to allele population frequency, phylogenetic conservation, transcription factor (TF) binding motifs, and existing massively parallel reporter assay (MPRA) data. ResultsWe generated a model that distinguishes between human retinal regulatory elements and negative test sequences with 95% accuracy. Among a hold-out test set of 3.7k human retinal CREs, all possible single nucleotide variants (SNVs) were scored. Variants with negative impact scores correlated with reduced population allele frequency, higher phylogenetic conservation of the reference allele, disruption of predicted TF binding motifs, and massively-parallel reporter expression. ConclusionsWe demonstrated the utility of human retinal epigenomic data to train a machine learning model for the purpose of predicting the impact of non-coding regulatory sequence variants. Our model accurately scored sequences and predicted putative transcription factor binding motifs. This approach has the potential to expedite the characterization of pathogenic non-coding sequence variants in the context of unexplained retinal disease.

genomics↗

bioRxiv2020

STAT3 Serine phosphorylation is required for TLR4 metabolic reprogramming and IL-1β expression

Detection of microbial components such as lipopolysaccharide (LPS) by Toll-like receptor (TLR)-4 expressed on macrophages induces a robust pro-inflammatory response which has recently been shown to be dependent on metabolic reprogramming 1, 2, 3, 4. These innate metabolic changes have been compared to the Warburg effect (also known as aerobic glycolysis) described in tumour cells 5, 6. However, the mechanisms by which TLR4 activation leads to mitochondrial and glycolytic reprogramming remain unknown. Here we show that TLR4 activation induces a signalling cascade recruiting TRAF6 and TBK-1, while TBK-1 phosphorylates STAT3 on S727. Using a genetically engineered mouse model incapable of undergoing STAT3 Ser727 phosphorylation, we show both ex vivo and in vivo that STAT3 Ser727 phosphorylation is critical for LPS-induced glycolytic reprogramming, the production of the central immune-metabolite succinate and inflammatory cytokine production in a model of LPS-induced inflammation. Our study identifies non-canonical STAT3 activation as the crucial signalling intermediary for TLR4-induced glycolysis, macrophage metabolic reprogramming and inflammation.

immunology↗

bioRxiv2019

Efficient toolkit implementing best practices for principal component analysis of population genetic data

Principal Component Analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (1) capturing Linkage Disequilibrium (LD) structure instead of population structure, (2) projected PCs that suffer from shrinkage bias, (3) detecting sample outliers, and (4) uneven population sizes. In this work, we explore these potential issues when using PCA, and present efficient solutions to these. Following applications to the UK Biobank and the 1000 Genomes project datasets, we make recommendations for best practices and provide efficient and user-friendly implementations of the proposed solutions in R packages bigsnpr and bigutilsr. For example, we find that PC19 to PC40 in the UK Biobank capture complex LD structure rather than population structure. Using our automatic algorithm for removing long-range LD regions, we recover 16 PCs that capture population structure only. Therefore, we recommend using only 16-18 PCs from the UK Biobank to account for population structure confounding. We also show how to use PCA to restrict analyses to individuals of homogeneous ancestry. Finally, when projecting individual genotypes onto the PCA computed from the 1000 Genomes project data, we find a shrinkage bias that becomes large for PC5 and beyond. We then demonstrate how to obtain unbiased projections efficiently using bigsnpr. Overall, we believe this work would be of interest for anyone using PCA in their analyses of genetic data, as well as for other omics data.

genetics↗

bioRxiv2019

CiiiDER: a new tool for predicting and analysing transcription factor binding sites

The availability of large amounts of high-throughput genomic, transcriptomic and epigenomic data has provided opportunity to understand regulation of the cellular transcriptome with an unprecedented level of detail. As a result, research has advanced from identifying gene expression patterns associated with particular conditions to elucidating signalling pathways that regulate expression. There are over 1,000 transcription factors (TFs) in vertebrates that play a role in this regulation. Determining which of these are likely to be controlling a set of genes can be assisted by computational prediction, utilising experimentally verified binding site motifs.\n\nHere we present CiiiDER, an integrated computational toolkit for transcription factor binding analysis, written in the Java programming language, to make it independent of computer operating system. It is operated through an intuitive graphical user interface with interactive, high-quality visual outputs, making it accessible to all researchers. CiiiDER predicts transcription factor binding sites (TFBSs) across regulatory regions of interest, such as promoters and enhancers derived from any species. It can perform an enrichment analysis to identify TFs that are significantly over- or under-represented in comparison to a bespoke background set and thereby elucidate pathways regulating sets of genes of pathophysiological importance.\n\nCiiiDER is available from www.ciiider.org.

bioinformatics↗

bioRxiv2018

How to make use of ordination methods to identify local adaptation: a comparison of genome scans based on PCA and RDA

Ordination is a common tool in ecology that aims at representing complex biological information in a reduced space. In landscape genetics, ordination methods such as principal component analysis (PCA) have been used to detect adaptive variation based on genomic data. Taking advantage of environmental data in addition to genotype data, redundancy analysis (RDA) is another ordination approach that is useful to detect adaptive variation. This paper aims at proposing a test statistic based on RDA to search for loci under selection. We compare redundancy analysis to pcadapt, which is a nonconstrained ordination method, and to a latent factor mixed model (LFMM), which is a univariate genotype-environment association method. Individual-based simulations identify evolutionary scenarios where RDA genome scans have a greater statistical power than genome scans based on PCA. By constraining the analysis with environmental variables, RDA performs better than PCA in identifying adaptive variation when selection gradients are weakly correlated with population structure. Additionally, we show that if RDA and LFMM have a similar power to identify genetic markers associated with environmental variables, the RDA-based procedure has the advantage to identify the main selective gradients as a combination of environmental variables. To give a concrete illustration of RDA in population genomics, we apply this method to the detection of outliers and selective gradients on an SNP data set of Populus trichocarpa (Geraldes et al., 2013). The RDA-based approach identifies the main selective gradient contrasting southern and coastal populations to northern and continental populations in the northwestern American coast.

genomics↗