Biology subjects

Thompson, J. F.

Publications and source records attributed to Thompson, J. F..

7 recordsLinked to original sources

bioRxiv2026

CRISPR/Cas9 Optimization Using Electronic Genome Mapping: Potential Role for Studying Human Genetic Disease

CRISPR Cas9's ability to bind targeted and non-targeted sites and its functional interactions with other proteins and domains have been studied using many technologies. These studies are often limited by using oligos as substrates or by a reliance on predicted binding sites. We show how electronic genome mapping (EGM) with its ability to detect single-molecule DNA nicks can determine how gRNA mismatches impact targeted versus off-target binding across the whole human genome in vitro. Cas9 was found to be capable of binding a single site adjacent to the FXN repeat expansion responsible for Friedreich Ataxia using a gRNA specific for that sequence while gRNAs matching a repetitive sequence were able to bind hundreds of sites. Binding to perfectly matched gRNA sites was compared to sites with mismatches at each gRNA position. The PAM site and nearby seed region bases were confirmed as critical for binding. In contrast, single mismatches toward the gRNA 5' end allowed off-target binding. We also used EGM to determine how physically close the bound dCas9/gRNA complex can be relative to nearby DNA modification sites and still allow activity. Bound CRISPR/dCas9 inhibits nickases with recognition sites 4-7 bp away from the gRNA/PAM. Because EGM can simultaneously evaluate many mismatched off-target sites, these insights may be extended to other CRISPR systems and enable optimization of Cas proteins and gRNAs. This is the first example of EGM being used to characterize proteins and RNAs. This is also the first study demonstrating the combined application of CRISPR and EGM methodology and its potential role in the detection of specific repeat regions in the human genome that are associated with human repeat expansion disorders.

genomics↗

bioRxiv2023

Genome-wide assays to characterize rAAV integration into human genomic DNA in vivo

Adeno-associated viral (AAV) vectors are used to treat genetic diseases, expressing therapeutic genes from both extrachromosomal episomes and payloads that integrate into the host genome. Assays were developed to evaluate HR-mediated on-target integration and the potential occurrence of off-target integration. While many studies have addressed elements of these processes, proper characterization requires long-read sequencing to ensure that integrated viral DNA is examined and not the more prevalent episomes. We used Oxford Nanopore to characterize integrated DNA and scan the whole genome for off-target integrations. These assays were applied to cell-based and in vivo models to study vectors that correct phenylketonuria (PKU), caused by loss of phenylalanine hydroxylase (PAH). Administration of the human-specific vector in a humanized-liver mouse xenograft model resulted in stable, nuclease-free integration into PAH. Because detection of rare integration events in a much larger pool of episomal DNA is subject to artifacts, careful assay validation was required. A long-read, genome-wide assay capable of detecting on- and off-target vector integrations showed no evidence of off-target integration. Artifactual false positive events were below the limit of blank. These data support rAAV as an investigational therapeutic for genetic diseases and reinforce the need for characterization of integration assays to avoid artifacts.

genomics↗

bioRxiv2020

Sensitive detection of DNA contamination in tumor samples via microhaplotypes

Low levels of sample contamination with other human DNAs can have disastrous effects on the accurate identification of somatic variation in tumor samples. Detection of sample contamination in DNA is often based on low frequency variants that indicate if more than a single source of DNA is present. This strategy works with standard DNA samples but can be problematic in solid tumor FFPE samples because there are often huge variations in allele frequency (AF) due to copy number changes arising from gains and losses across the genome. The variable AFs make detection of contamination challenging. To avoid this, we counted microhaplotypes to assess sample contamination. Microhaplotypes are sets of variants on the same sequencing read that can be unambiguously phased. Instead of measuring AF, the number of microhaplotypes is determined. Contamination detection becomes based on fundamental genomic properties, linkage disequilibrium (LD) and the diploid nature of human DNA, rather than variant frequencies. We optimized microhaplotype panel content and selected 164 SNV sets located in regions already being sequenced within a cancer panel. Thus, contamination detection uses existing sequence data. LD data from the 1000 Genomes Project is used to make the panel ancestry agnostic, providing the same sensitivity for contamination detection with samples from individuals of African, East Asian, and European ancestry. Detection of 1% contamination with no matching normal sample is possible. The methods described here can also be extended to other DNA mixtures such as forensic and non-invasive prenatal testing samples where DNA mixes can be similarly detected. The microhaplotype method allows sensitive detection of DNA contamination in FFPE tumor and other samples when deep coverage with Illumina or other high accuracy NGS is used.

genomics↗

bioRxiv2020

Cross-Platform Omics Prediction procedure enables precision medicine in patients with stage-III melanoma

There is no consensus methodology that can account for the variation in omics signatures when they are acquired across different platforms and times. This poses a significant barrier to the implementation of valuable biomarkers into clinical practice. We present a novel procedure (Cross-Platform Omics Prediction) that accounts for these variations and demonstrate its utility in three risk models for different diseases that is suitable for prospective and multi-centre clinical implementation.

bioinformatics↗

bioRxiv2020

Anatomic position determines oncogenic specificity in melanoma

Oncogenic alterations to DNA are not transforming in all cellular contexts1,2. This may be due to pre-existing transcriptional programs in the cell of origin. Here, we define anatomic position as a major determinant of why cells respond to specific oncogenes. Cutaneous melanoma arises throughout the body, whereas the acral subtype arises on the palms of the hands, soles of the feet, or under the nails3. We sequenced the DNA of cutaneous and acral melanomas from a large cohort of human patients and found a specific enrichment for BRAF mutations in cutaneous melanoma but CRKL amplifications in acral melanoma. We modeled these changes in transgenic zebrafish models and found that CRKL-driven tumors predominantly formed in the fins of the fish. The fins are the evolutionary precursors to tetrapod limbs, indicating that melanocytes in these acral locations may be uniquely susceptible to CRKL. RNA profiling of these fin/limb melanocytes, compared to body melanocytes, revealed a positional identity gene program typified by posterior HOX13 genes. This positional gene program synergized with CRKL to drive tumors at acral sites. Abrogation of this CRKL-driven program eliminated the anatomic specificity of acral melanoma. These data suggest that the anatomic position of the cell of origin endows it with a unique transcriptional state that makes it susceptible to only certain oncogenic insults.

cancer biology↗

bioRxiv2018

SNP Selection and Concordance in Consumer Genetics Testing

The use of Direct To Consumer (DTC) genetic testing for predicting health risks and a variety of other phenotypes has been extensively discussed. Additionally, there have been wide ranging discourses on privacy and ethical concerns. Much less attention has been paid to what most people actually use DTC testing for: ancestry determination. Furthermore, comparison of the platforms used by different companies and how they have chosen SNPs to address the questions of health and ancestry have not been broadly reported. When SNPs across three genotyping platforms are compared, only 16-18% of SNPs with reported genotypes are shared across all platforms. Only 110,051 of the more than 600,000 SNPs are called on all three panels examined (Ancestry, 23andMe and MyHeritage). SNPs genotyped on all platforms are highly concordant with only two SNPs having discordant calls. When the SNPs unique to a single panel are examined, it is apparent that each company has its own strategy for choosing SNPs. When each platform is examined, the unique SNPs have different frequencies, ethnic selectivities, and chromosomal locations. Because each company separates the world into different, overlapping geographical regions, it is impossible to do an exact comparison of ancestry results. Factoring in the ways the regions overlap, congruent results are generated for the major contributors to ancestry.

genomics↗

bioRxiv2018

Epigenetic Profiling for the Molecular Classification of Metastatic Brain Tumors

Optimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet these challenges, we generated genome-scale DNA methylomes of the three most frequent types of brain metastases: melanoma, breast, and lung cancers (n=96). Using supervised machine learning and integration of multiple DNA methylomes from normal, primary, and metastatic tumor specimens (n=1,860), we unraveled epigenetic signatures specific to each type of metastatic brain tumor and constructed a three-step DNA methylation-based classifier (BrainMETH) that categorizes brain metastases according to the tissue of origin and therapeutically-relevant subtypes. BrainMETH predictions were supported by routine histopathologic evaluation. We further characterized and validated the most predictive genomic regions in a large cohort of brain tumors (n=165) using quantitative methylation-specific PCR. Our study highlights the importance of brain tumor-defining epigenetic alterations, which can be utilized to further develop DNA methylation profiling as a critical tool in the histomolecular stratification of patients with brain metastases.

cancer biology↗