Biology subjects

Abdollahzadeh, E.

Publications and source records attributed to Abdollahzadeh, E..

3 recordsLinked to original sources

bioRxiv2025

Genome-wide analysis of FSHD cell lines using Nanopore sequencing reveals allele-specific differences at DUX4 target genes and complex repeats

Facioscapulohumeral muscular dystrophy (FSHD) is linked to a monoallelic contraction of primate-specific 3.3kb D4Z4 macrosatellite repeats on the disease-permissive chromosome 4q (4qA haplotype) with additional mutations of a chromatin regulator SMCHD1 acting as a disease modifier. DNA hypomethylation at the D4Z4 repeat and resulting abnormal derepression of the embryonic transcription factor DUX4 encoded in the D4Z4 repeat are the hallmark of FSHD. In order to investigate the impact of FSHD mutations within as well as outside of the disease loci, we performed Nanopore direct-RNA and genomic sequencing to characterize global and D4Z4-specific changes in isoform expression and DNA methylation using CRISPR-engineered human skeletal myoblast lines carrying FSHD mutations (D4Z4 contraction and SMCHD1 mutation) compared to the isogenic parental healthy control line. Nanopore sequencing allowed us to characterize the entire unedited control and contracted D4Z4 arrays as well as distinguish differential methylation patterns at the disease locus on chromosome 4qA from those at a nearly identical nonpathogenic D4Z4 repeat arrays on chromosome 10 and disease non-permissive 4qB allele. We observe hypomethylation both at the DUX4 locus and globally in FSHD mutant cell lines in myoblasts as well as in myotubes. DUX4 target gene expression is correlated with promoter hypomethylation. De novo haplotype phasing of genomic and RNA reads reveals allele- and isoform-specific expression of DUX4 target genes as well as highly expressed DUX4 target pseudogenes that may contribute to disease pathogenesis. Taken together, our results indicate significant impact of FSHD mutations not only on D4Z4 allele, but also DUX4 targets and repeat regions in the genome, which may be collectively contributing to the FSHD pathogenesis.

genomics↗

bioRxiv2025

Dogme: A nextflow pipeline for reprocessing nanopore RNA and DNA modifications

MotivationThe Oxford Nanopore Technologies (ONT) platform allows for the direct detection of RNA and DNA modifications from unamplified nucleic acids, which is a significant advantage over other platforms. However, the rapid updates to ONT basecalling models and the evolving landscape of computational tools for modification detection bring about challenges for reproducible and standardized analyses. To address these challenges, we developed Dogme, which is a Nextflowbased workflow that automates the processing of ONT data, including basecalling, alignment, modification detection, and transcript quantification. Dogme automates the reprocessing of ONT POD5 files by integrating basecalling using Dorado, read mapping using minimap2 and subsequent analysis steps such as running modkit. The pipeline supports three major types of ONT sequencing data - direct RNA (dRNA), complementary DNA (cDNA), and genomic DNA (gDNA) - enabling comprehensive analyses across different library preparations. Dogme facilitates detection of diverse RNA modifications supported by Dorado such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), inosine, pseudouridine, 2-Omethylation (Nm) and DNA methylation, while concurrently quantifying full-length transcript isoforms LR-Kallisto for transcript quantification for dRNA and cDNA. ResultsWe applied Dogme to three separate mouse C2C12 myoblast replicates using direct RNA sequencing on MinION flow cells. We detected an average of 147,879 m6A, 86,673 m5C, 21,242 inosine, 24,540 pseudouridine, and 83,841 2- O-methylation sites per replicate with 96,581 m6A, 43,446 m5C, 8,825 inosine, 10,048 pseudouridine, and 30,157 2-O- methylation sites detected in all three biological replicates. The pipeline produced reproducible modification profiles and transcript expression levels across replicates, demonstrating its utility for integrative long-read transcriptomic and epigenomic analyses. AvailabilityDogme is implemented in Nextflow and is freely available under the MIT license at https://github.com/mortazavilab/dogme, with documentation provided for installation and usage.

genomics↗

bioRxiv2025

Systematic cell-type resolved transcriptomes of 8 tissues in 8 lab and wild-derived mouse strains captures global and local expression variation

Mapping the impact of genomic variation on gene expression facilitates an understanding of the molecular basis of complex phenotypic traits and disease predisposition. Mouse models provide a controlled and reproducible framework for capturing the breadth of genomic variation observed in different genotypes across a wide variety of tissues. As part of the IGVF consortiums effort to catalog the effects of genetic variation, we uniformly characterized the transcriptomes of eight tissues from each mouse founder strain used to derive the Collaborative Cross strains, comprising five classical laboratory inbred strains and three wild-derived inbred strains. We sequenced samples from four male and four female replicates per tissue using single-nucleus RNA-seq to generate an "8-cube" dataset of 5.2 million nuclei across 106 cell types and cell states. As expected, the overall extent of transcriptome variation correlates positively with genetic divergence across the strains with the greatest differential between PWK/PhJ and CAST/EiJ. At the individual tissue level, heart and brain are relatively more similar across strains compared with gonads, adrenal, skeletal muscle, kidney, and liver. Further analyses revealed substantial strain variation, often concentrated in a few cell types as well as cell-state signatures that especially reflect strain-associated immune and metabolic trait differences. The founder 8-cube dataset provides rich transcriptome variation signatures to help explain strain-specific phenotypic traits and disease states, as illustrated by examples in tissue-resident immune cells, muscle degeneration, kidney sex differences, and the hypothalamicpituitary-adrenal axis. This data further provides a systematic foundation for the analysis of these tissues in the founder strains as well as the Collaborative Cross.

genomics↗