Biology subjects

Kilburn, D.

Publications and source records attributed to Kilburn, D..

3 recordsLinked to original sources

bioRxiv2024

CCG interruptions are unstable and hypermethylated in DM1 patients

Myotonic dystrophy type 1 (DM1) exhibits highly heterogeneous clinical manifestations caused by an unstable CTG repeat expansion reaching up to 4,000 CTG. The dynamics of CTG repeats and clinical variability depends on the CTG repeat number, CNG repeat interruptions, DNA methylation, somatic mosaicism, but also gene modifiers. Around 10% of the DM1 population carries triplet repeat interruptions (CCG, CGG, CTC, CAG), which differ in number and nature between DM1 families. CCG interruptions have been associated with stabilization of the CTG repeat expansions and a milder phenotype in the DM1 population. However, the dynamics and precise role of interruptions in CTG repeat instability remain relatively underexplored due to the complexities of analyzing them. In this study, we showed that the number of CCG interruptions within the expansion varies within tissues and between generations using single-molecule real-time long-read sequencing. Although the interrupted expanded alleles showed global stabilization, the CCG interruptions themselves displayed variability across generations and within somatic tissues. Importantly, our findings reveal, for the first time, CCG hypermethylation within the CTG expansion, which is linked to downstream hypermethylation of the repeat. These results support the hypothesis that methylation of CCG interruptions within the expanded DMPK alleles may contribute to the stabilization of trinucleotide repeats.

molecular biology↗

bioRxiv2023

Explainable Machine Learning Reveals the Role of the Breast Tumor Microenvironment in Neoadjuvant Chemotherapy Outcome

Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled the identification of phenotypic diversity within breast tumor tissues. However, the contribution of these cell phenotypes to tumor biology and treatment response has remained less understood. This is primarily due to the limited number of available samples and the inherent heterogeneity of breast tumors. To address this limitation, we leverage a state-of-the-art scRNA-seq atlas and employ CIBER-SORTx to estimate cell phenotype fractions by de-convolving bulk expression profiles in more than 2000 samples from patients who have undergone Neoad-juvant Chemotherapy (NAC). We introduce a pipeline based on explainable Machine Learning (XML) to robustly explore the associations between different cell phenotype fractions and the response to NAC in the general population as well as different subtypes of breast tumors. By comparing tumor subtypes, we observe that multiple cell types exhibit a distinct association with pCR within each subtype. Specifically, Dendritic cells (DCs) exhibit a negative association with pathological Complete Response (pCR) in Estrogen Receptor positive, ER+, (Luminal A/B) tumors, while showing a positive association with pCR in ER-(Basal-like/HER2-enriched) tumors. Analysis of new spatial cyclic immunoflu-orescence data and publicly available imaging mass cytometry data showed significant differences in the spatial distribution of DCs between ER subtypes. These variations underscore disparities in the engagement of DCs within the tumor microenvironment (TME), potentially driving their divergent associations with pCR across tumor subtypes. Overall, our findings on 28 different cell types provide a comprehensive understanding of the role played by cellular compo-nents of the TME in NAC outcomes. They also highlight directions for further experimental investigations at a mechanistic level.

bioinformatics↗

bioRxiv2019

Efficient de novo assembly of eleven human genomes using PromethION sequencing and a novel nanopore toolkit

Present workflows for producing human genome assemblies from long-read technologies have cost and production time bottlenecks that prohibit efficient scaling to large cohorts. We demonstrate an optimized PromethION nanopore sequencing method for eleven human genomes. The sequencing, performed on one machine in nine days, achieved an average 63x coverage, 42 Kb read N50, 90% median read identity and 6.5x coverage in 100 Kb+ reads using just three flow cells per sample. To assemble these data we introduce new computational tools: Shasta - a de novo long read assembler, and MarginPolish & HELEN - a suite of nanopore assembly polishing algorithms. On a single commercial compute node Shasta can produce a complete human genome assembly in under six hours, and MarginPolish & HELEN can polish the result in just over a day, achieving 99.9% identity (QV30) for haploid samples from nanopore reads alone. We evaluate assembly performance for diploid, haploid and trio-binned human samples in terms of accuracy, cost, and time and demonstrate improvements relative to current state-of-the-art methods in all areas. We further show that addition of proximity ligation (Hi-C) sequencing yields near chromosome-level scaffolds for all eleven genomes.

bioinformatics↗