Biology subjects

Perrin, D.

Publications and source records attributed to Perrin, D..

12 recordsLinked to original sources

bioRxiv2025

CRISPR-DA: Adapting CRISPR gRNA design for detection assays

CRISPR-Cas systems offer a viable alternative to traditional detection and diagnosis methods. However, their effectiveness relies heavily on the selection of appropriate guide RNA sequences. Existing gRNA design tools were primarily developed for gene editing and are not always directly applicable to CRISPR-based detection assays. In particular, alignmentbased methods are still used to estimate gRNA specificity, even though they can miss a substantial portion of off-target sites. In this work, we introduce CRISPR-DA, a CRISPR gRNA design tool for detection assays. We show that it provides a better assessment of gRNA specificity than BLAST, which detected only 33.27 % and 0.43 % of cross-species off-targets in two datasets. Additionally, CRISPR-DA ran two and six times faster than BLAST on these datasets, respectively. Our method incorporates advances from gene-editing guide RNA design tools, including uncertainty-informed guide RNA design, to improve the selection of guides with high on-target activity. CRISPR-DA is available at https://github.com/bmds-lab/CRISPR-DA.

bioinformatics↗

bioRxiv2024

Crackling Cloud: an event-driven, cloud-based CRISPR-Cas9 guide RNA design tool

Gene editing has been revolutionised by the CRISPR-Cas9 technology. The versatility and ease-of-use of the technology far exceeds its predecessors, however, the selection of a high-quality guide RNA (gRNA) is critical to directing it to a target site. Selecting gRNA calls upon high-performance algorithms that evaluate nuclease activity at on-target and off-target sites. While there are a suite of programs available, many struggle to analyse the largest genomes, or their predictive accuracy is low. We have previously published a program, named Crackling that is amongst the fastest and most accurate tools available, however, it requires an end-user to have access to a traditional high-performance computing environment. Here, we present an adaptation of Crackling, named Crackling Cloud, that takes advantage of modern serverless cloud technologies that are widely available to anyone, and do not consume resources and incur costs when sitting idle, but can scale to use large volumes of resources when analyses require that. Crackling Cloud is provided as a templated solution using technologies of Amazon Web Services, and is available for free on GitHub under the terms of the BSD 3-clause licence: https://github.com/bmds-lab/Crackling-AWS

bioinformatics↗

bioRxiv2024

Pyruvate dehydrogenase kinase 1 controls triacylglycerol hydrolysis in cardiomyocytes

Pyruvate dehydrogenase kinase (PDK) 1 is one of four isozymes that inhibit the oxidative decarboxylation of pyruvate to acetyl-CoA via pyruvate dehydrogenase. PDK activity is elevated in fasting or starvation conditions to conserve carbohydrate reserves. PDK has also been shown to increase mitochondrial fatty acid utilization. In cardiomyocytes, metabolic flexibility is crucial for the fulfillment of high energy requirements. The PDK1 isoform is abundant in cardiomyocytes, but its specific contribution to cardiomyocyte metabolism is unclear. Here we show that PDK1 regulates cardiomyocyte fuel preference by mediating triacylglycerol turnover in differentiated H9c2 myoblasts using lentiviral shRNA to knockdown Pdk1. Somewhat surprisingly, PDK1 loss did not affect overall PDH activity, basal glycolysis, or glucose oxidation revealed by oxygen consumption rate experiments and 13C6 glucose labelling. On the other hand, we observed decreased triacylglycerol turnover in H9c2 cells with PDK1 knockdown, which was accompanied by decreased mitochondrial fatty acid utilization following nutrient deprivation. 13C16 palmitate tracing of uniformly labelled acyl chains revealed minimal acyl chain shuffling within triacylglycerol, indicating that the triacylglycerol hydrolysis, and not re-esterification, was dysfunctional in PDK1 suppressed cells. Importantly, PDK1 loss did not significantly impact the cellular lipidome or triacylglycerol accumulation following palmitic acid treatment, suggesting that effects of PDK1 on lipid metabolism were specific to the nutrient-deprived state. We validated that PDK1 loss decreased triacylglycerol turnover in Pdk1 knockout mice. Together, these findings implicate a novel role for PDK1 in lipid metabolism in cardiomyocytes, independent of its canonical roles in glucose metabolism.

biochemistry↗

bioRxiv2024

Leveraging uncertainty quantification to optimise CRISPR guide RNA selection

CRISPR-based genome editing relies on guide RNA sequences to target specific regions of interest. A large number of methods have been developed to predict how efficient different guides are at inducing indels. As more experimental data becomes available, methods based on machine learning have become more prominent. Here, we explore whether quantifying the uncertainty around these predictions can be used to design better guide selection strategies. We demonstrate how using a deep ensemble approach achieves better performance than utilising a single model. This approach can also provide uncertainty quantification. This allows to design, for the first time, strategies that consider uncertainty in guide RNA selection. These strategies achieve precision over 91% and can identify suitable guides for more than 93% of genes in the mouse genome. Our deep ensemble model is available at https://github.com/bmdslab/CRISPR_DeepEnsemble.

bioinformatics↗

bioRxiv2024

Fast and scalable off-target assessment for CRISPR guide RNAs using partial matches

The design of CRISPR-Cas9 guide RNAs is not trivial. In particular, it is crucial to evaluate the risk of unintended, off-target modifications, but this is computationally expensive. To avoid a brute-force approach where each guide RNA is compared against every possible CRISPR target site in the genome, we previously introduced Crackling, a guide RNA design tool that relies on exact matches over 4bp subsequences to approximate a neighbourhood and accelerate off-target scoring by greatly reducing the search space. While this was faster than other existing tools, it still generates large neighbourhoods. Here, we aim to further reduce the search space by requiring more, now non-contiguous, exact matches. The new implementation, called Crackling++, is benchmarked against our initial approach and other off-target evaluation tools. We show that it provides the fastest way to assess candidate guide RNAs. By using memorymapped files, it also scales to the largest genomes. Crackling++ is available at https://github.com/bmds-lab/CracklingPlusPlus under the Berkeley Software Distribution (BSD) 3-Clause license.

bioinformatics↗

bioRxiv2022

Circadian ribosome profiling reveals a role for the Period2 upstream opening reading frame in sleep

Many mammalian proteins have circadian cycles of production and degradation, and many of these rhythms are altered post-transcriptionally. We used ribosome profiling to examine post-transcriptional control of circadian rhythms by quantifying RNA translation in the liver over a 24-h period from circadian-entrained mice transferred to constant darkness conditions and by comparing ribosome binding levels to protein levels for 16 circadian proteins. We observed large differences in ribosome binding levels compared to protein levels, and we observed delays between peak ribosome binding and peak protein abundance. We found extensive binding of ribosomes to upstream open reading frames (uORFs) in circadian mRNAs, including the core clock gene Period2 (Per2). An increase in the number of uORFs in the 5UTR was associated with a decrease in ribosome binding in the main coding sequence and a reduction in expression of synthetic reporter constructs. Mutation of the Per2 uORF increased luciferase and fluorescence reporter expression in 3T3 cells and increased luciferase expression in PER2:LUC MEF cells. Mutation of the Per2 uORF in mice increased Per2 mRNA expression, enhanced ribosome binding on Per2, and reduced total sleep time compared to that in wild-type mice. These results suggest that uORFs affect mRNA post-transcriptionally, which can impact physiological rhythms and sleep. Significance StatementPeriod (Per) is an iconic gene in the field of circadian rhythms since its discovery in 1971 by Seymour Benzer and Ronald Konopka in fruit flies. The inhibitory feedback loop of PER protein drives circadian rhythms. We show that Per2 is regulated by an upstream open reading frame (uORF) in the 5 untranslated region of Period2 mRNA. Mutation of the Per2 uORF altered the amplitude of luciferase reporter expression in well-characterized cell culture models. Per2 uORF mutant mice had significantly elevated Per2 mRNA levels and exhibited sleep loss, particularly during light-to-dark and dark-to-light transitions, which suggests a role for uORFs in modulating molecular and physiological circadian rhythms.

cell biology↗

bioRxiv2020

Faster and better CRISPR guide RNA design with the Crackling method

The design of CRISPR-Cas9 guide RNAs is not trivial, and is a computationally demanding task. Design tools need to identify target sequences that will maximise the likelihood of obtaining the desired cut, whilst minimising off-target risk. There is a need for a tool that can meet both objectives while remaining practical to use on large genomes. Here, we present Crackling, a new method that is more suitable for meeting these objectives. We test its performance on 12 genomes and on data from validation studies. Crackling maximises guide efficiency by combining multiple scoring approaches. On experimental data, the guides it selects are better than those selected by others. It also incorporates Inverted Signature Slice Lists (ISSL) for faster off-target scoring. ISSL provides a gain of an order of magnitude in speed, while preserving the same level of accuracy. Overall, this makes Crackling a faster and better method to design guide RNAs at scale. Crackling is available at https://github.com/bmds-lab/Crackling under the Berkeley Software Distribution (BSD) 3-Clause license.

bioinformatics↗

bioRxiv2020

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Mobile health, the collection of data using wearables and sensors, is a rapidly growing field in health research with many applications. Deriving validated measures of disease and severity that can be used clinically or as outcome measures in clinical trials, referred to as digital biomarkers, has proven difficult. In part due to the complicated analytical approaches necessary to develop these metrics. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of Parkinsons Disease (PD) and severity of three PD symptoms: tremor, dyskinesia and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC=0.87), as well as tremor (best AUPR=0.75), dyskinesia (best AUPR=0.48) and bradykinesia (best AUPR=0.95) severity.

bioengineering↗

bioRxiv2018

A benchmark of computational CRISPR-Cas9 guide design methods

The popularity of CRISPR-based gene editing has resulted in an abundance of tools to design CRISPR-Cas9 guides. This is also driven by the fact that designing highly specific and efficient guides is a crucial, but not trivial, task in using CRISPR for gene editing. Here, we thoroughly analyse the performance of 17 design tools. They are evaluated based on runtime performance, compute requirements, and guides generated. To achieve this, we implemented a method for auditing system resources while a given tool executes, and tested each tool on datasets of increasing size, derived from the mouse genome. We found that only five tools had a computational performance that would allow them to analyse an entire genome in a reasonable time, and without exhausting computing resources. There was wide variation in the guides identified, with some tools reporting every possible guide while others filtered for predicted efficiency. Some tools also failed to exclude guides that would target multiple positions in the genome. We also considered a collection of over a thousand guides for which experimental data is available. For the tools that attempt to filter based on efficiency, 65% to 85% of the guides they reported were experimentally found to be efficient, but with limited overlap in the sets produced by different tools. Our results show that CRISPR-Cas9 guide design tools need further work in order to achieve rapid whole-genome analysis and that improvements in guide design will likely require combining multiple approaches.

bioinformatics↗

bioRxiv2018

Haplotype-phased Callithrix jacchus embryonic stem cell line for genome editing using CRISPR/Cas9

Due to anatomical and physiological similarities to humans, the common marmoset (Callithrix jacchus) is an ideal organism for the study human diseases. Researchers are currently leveraging genome-editing technologies such as CRISPR/Cas9 to genetically engineer marmosets for the in vivo biomedical modeling of human neuropsychiatric and neurodegenerative diseases. The genome characterization of these cell lines greatly reinforces these transgenic efforts. It also provides the genomic contexts required for the accurate interpretation of functional genomics data. We performed haplotype-resolved whole-genome characterization for marmoset ESC line cj367 from the Wisconsin National Primate Research Center. This is the first haplotype-resolved analysis of a marmoset genome and the first whole-genome characterization of any marmoset ESC line. We identified and phased single-nucleotide variants (SNVs) and Indels across the genome. By leveraging this haplotype information, we then compiled a list of cj367 ESC allele-specific CRISPR targeting sites. Furthermore, we demonstrated successful Cas9 Endonuclease Dead (dCas9) expression and targeted localization in cj367 as well as sustained pluripotency after dCas9 transfection by teratoma assay. Lastly, we show that these ESCs can be directly induced into functional neurons in a rapid, single-step process. Our study provides a valuable set of genomic resources for primate transgenics in this post-genome era.

genetics↗

bioRxiv2018

Haplotype-resolved and integrated genome analysis of ENCODE cell line HepG2

The HepG2 cancer cell line is one of the most widely-used biomedical research and one of the main cell lines of ENCODE. Vast numbers of functional genomics and epigenomics datasets have been produced to characterize its biology. However, the correct interpretation such data requires an understanding of the cell lines genome sequence and genome structure. Using a variety of sequencing and analysis methods, we identified a wide spectrum of HepG2 genome characteristics: copy numbers of chromosomal segments, SNVs and Indels (corrected for aneuploidy), phased haplotypes extending to entire chromosome arms, loss of heterozygosity, retrotransposon insertions, structural variants (SVs) including complex and somatic genomic rearrangements. We also identified allele-specific expression and DNA methylation genome-wide and assembled an allele-specific CRISPR/Cas9 targeting map.\n\nSIGNIFICANCEHaplotype-resolved and comprehensive whole-genome analysis of a widely-used cell line for cancer research and ENCODE, HepG2, serves as an essential resource for unlocking complex cancer gene regulation using a genome-integrated framework and also provides genomic context for the analysis of ~1,000 functional datasets to date on ENCODE for biological discovery. We also demonstrate how deeper insights into genomic regulatory complexity are gained by adopting a genome-integrated framework.

genomics↗

bioRxiv2017

Comprehensive, Integrated, and Phased Whole-Genome Analysis of the Primary ENCODE Cell Line K562

K562 is widely used in biomedical research. It is one of three tier-one cell lines of ENCODE and also most commonly used for large-scale CRISPR/Cas9 screens. Although its functional genomic and epigenomic characteristics have been extensively studied, its genome sequence and genomic structural features have never been comprehensively analyzed. Such information is essential for the correct interpretation and understanding of the vast troves of existing functional genomics and epigenomics data for K562. We performed and integrated deep-coverage whole-genome (short-insert), mate-pair, and linked-read sequencing as well as karyotyping and array CGH analysis to identify a wide spectrum of genome characteristics in K562: copy numbers (CN) of aneuploid chromosome segments at high-resolution, SNVs and Indels (both corrected for CN in aneuploid regions), loss of heterozygosity, mega-base-scale phased haplotypes often spanning entire chromosome arms, structural variants (SVs) including small and large-scale complex SVs and non-reference retrotransposon insertions. Many SVs were phased, assembled, and experimentally validated. We identified multiple allele-specific deletions and duplications within the tumor suppressor gene FHIT. Taking aneuploidy into account, we re-analyzed K562 RNA-seq and whole-genome bisulfite sequencing data for allele-specific expression and allele-specific DNA methylation. We also show examples of how deeper insights into regulatory complexity are gained by integrating genomic variant information and structural context with functional genomics and epigenomics data. Furthermore, using K562 haplotype information, we produced an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize K562 as well as a framework for the analysis of other cancer genomes.

genomics↗