Biology subjects

Majewski, I. J.

Publications and source records attributed to Majewski, I. J..

8 recordsLinked to original sources

bioRxiv2024

Mbd4 and MutSα protect cells from spontaneous deamination of 5-methylcytosine.

5-Methylcytosine (5mC) is a common source of somatic mutations. Deamination of 5mC to thymine generates a G/T mismatch, which occurs spontaneously and must be repaired prior to DNA replication to avoid mutation. We generated genetically engineered mice and cell lines to define DNA repair pathways that protect against methylation damage. We observed a low background mutation rate in mouse bone marrow or colon, typically 0.2-0.5 CG>TG mutations/genome/day. This increased 3-7 fold in cells lacking the glycosylase Methyl-binding domain 4 (Mbd4), one of the few glycosylases capable of excising thymine from G/T mismatches. We found no role for Thymine DNA glycosylase (Tdg) in methylation damage repair. Instead, our results support cooperation between Mbd4 and the mismatch repair (MMR) complex MutS (Msh6:Msh2), evident through elevated rates of methylation damage in Msh6-deficient cells; increasing to 2.6-4.8 CG>TG mutations/genome/day in primary cells and up to 13.9 CG>TG mutations/genome/day in cell lines. Our findings support the view that MutS has DNA repair activity outside of replication. While loss of Mbd4 elevates methylation damage selectively, the broader functionality of MutS explains why mutational signatures linked to Msh6-deficiency are variable and reflect the replicative history of the cell.

molecular biology↗

bioRxiv2021

ALLSorts: a RNA-Seq classifier for B-Cell Acute Lymphoblastic Leukemia.

B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. Subtypes within B-ALL are distinguished by characteristic structural variants and mutations, which in some instances strongly correlate with responses to treatment. The World Health Organisation (WHO) recognises seven distinct classifications, or subtypes, as of 2016. However, recent studies have demonstrated that B-ALL can be segmented into 23 subtypes based on a combination of genomic features and gene expression profiles. A method to identify a patients subtype would have clear clinical utility. Despite this, no publically available classification methods using RNA-Seq exist for this purpose. Here we present ALLSorts: a publicly available method that uses RNA-Seq data to classify B-ALL samples to 18 known subtypes and five meta-subtypes. ALLSorts is the result of a hierarchical supervised machine learning algorithm applied to a training set of 1223 B-ALL samples aggregated from multiple cohorts. Validation revealed that ALLSorts can accurately attribute samples to subtypes and can attribute multiple subtypes to a sample. Furthermore, when applied to both paediatric and adult cohorts, ALLSorts was able to classify previously undefined samples into subtypes. ALLSorts is available and documented on GitHub (https://github.com/Oshlack/AllSorts/). Key PointsO_LIALLSorts is a gene expression classifier for B-cell acute lymphoblastic leukemia, which predicts 18 distinct genomic subtypes - including those designated by the World Health Organisation (WHO) and provisional entities. C_LIO_LITrained and validated on over 2300 B-ALL samples, representing each subtype and a variety of clinical features. C_LIO_LICorrectly identified subtypes in 91% of cases in a held-out dataset and between 82-93% across a newly combined cohort of paediatric and adult samples. C_LIO_LIALLSorts assigned subtypes to samples with previously unknown driver events. C_LI ALLsorts is an accurate, comprehensive and freely available classification tool that distinguishes subtypes of B-cell acute lymphoblastic leukemia from RNA-sequencing.

bioinformatics↗

bioRxiv2021

Life without mismatch repair

Mismatch repair (MMR) is a critical defence against mutation, but we lack quantification of its activity on different DNA lesions during human life. We performed whole-genome sequencing of normal and neoplastic tissues from individuals with constitutional MMR deficiency to establish the roles of MMR components, tissue type and disease state in somatic mutation rates. Mutational signatures varied extensively across genotypes, some coupled to leading-strand replication, some to lagging-strand replication and some independent of replication, implying that the various MMR components engage different forms of DNA damage. Loss of MSH2 or MSH6 (MutS), but not MLH1 or PMS2 (MutL), caused 5-methylcytosine-dependent hypermutation, indicating that MutS is the pivotal complex for repairing spontaneous deamination of methylated cytosines in humans. Neoplastic change altered the distribution of mutational signatures, particularly accelerating replication-coupled indel signatures. Each component of MMR repairs 1-10 lesions/day per normal human cell, and many thousands of additional events during neoplastic transformation. HighlightsO_LIMMR repairs 1-10 lesions/day in every normal cell and thousands more in tumor cells C_LIO_LIMMR patterns and rates are shaped by genotype, tissue type and malignant transformation C_LIO_LIMSH2 and MSH6 are pivotal for repairing spontaneous deamination of methylated cytosine C_LIO_LIReplication indels and substitutions vary by leading versus lagging strand and genotype C_LI

genomics↗

bioRxiv2020

CHK2 inhibition provides a strategy to suppress hematological toxicity from PARP inhibitors.

Cancer patients treated with poly (ADP-ribose) polymerase inhibitors (PARPi) experience various side effects, with hematological toxicity being most common. Short term treatment of mice with olaparib resulted in depletion of reticulocytes, B cell progenitors and immature thymocytes, whereas longer treatment induced broader myelosuppression. We performed a CRISPR/Cas9 screen targeting DNA repair genes to identify strategies to suppress hematological toxicity. The screen revealed that sgRNAs targeting the serine/threonine kinase CHK2 were enriched following olaparib treatment. Genetic or pharmacological inhibition of CHK2 blunted PARPi response in lymphoid and myeloid cell lines, and in primary pre-B/pro-B cells. Using a Cas9 base editor, we found that blocking CHK2-mediated phosphorylation of p53 also impaired olaparib response. Our results identify the p53 pathway as a major determinant of the acute response to PARPi in normal blood cells and demonstrate that targeting CHK2 can short-circuit this response. Cotreatment with a CHK2 inhibitor did not antagonise olaparib response in ovarian cancer cells. Selective inhibition of CHK2 may spare blood cells from the toxic influence of PARPi and broaden the utility of these drugs.

cancer biology↗

bioRxiv2020

MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data

Genomic rearrangements can modify gene function by altering transcript sequences, and have been shown to be drivers in both cancer and rare diseases. Although there are now many methods to detect structural variants from Whole Genome Sequencing (WGS), RNA sequencing (RNA-seq) remains under-utilised as a technology for the detection of gene altering structural variants. Calling fusion genes from RNA-seq data is well established, but other transcriptional variants such as fusions with novel sequence, tandem duplications, large insertions and deletions, and novel splicing are difficult to detect using existing approaches. To identify all types of variants in transcriptomes, we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference free approach, which combines de novo assembly of transcripts with differential expression analysis, to identify up-regulated novel variants in a case sample. We validated MINTIE on simulated and real data sets and compared it with eight other approaches for finding novel transcriptional variants. We found MINTIE was able to detect >85% of variants while no other method was able to achieve this. We applied MINTIE to RNA-seq data from a cohort of acute lymphoblastic leukemia (ALL) patient samples and identified several clinically relevant variants, including a recurrent unpartnered fusion involving the tumour suppressor gene RB1, and variants in ALL-associated genes: tandem duplications in IKZF1 and PAX5, and novel splicing in ETV6. We further demonstrate the utility of MINTIE to identify rare disease variants using RNA-seq, including the discovery of an inter-chromosomal translocation in the DMD gene in a patient with muscular dystrophy. We posit that MINTIE will be able to identify new disease variants across a range of cancers and other disease types.

bioinformatics↗

bioRxiv2020

Detecting copy number alterations in RNA-Seq using SuperFreq

Calling copy number alterations (CNAs) from RNA-Seq is challenging, because differences in gene expression mean that read depth across genes varies by several orders of magnitude and there is a paucity of informative single nucleotide polymorphisms (SNPs). We previously developed SuperFreq to analyse exome data of tumours by combining variant calling and copy number estimation in an integrated pipeline. Here we have used the SuperFreq framework for the analysis of RNA sequencing (RNA-Seq) data, which allows for the detection of absolute and allele sensitive CNAs. SuperFreq uses an error-propagation framework to combine and maximise the information available in the read depth and B-allele frequencies of SNPs (BAFs) to make CNA calls on RNA-seq data. We used data from The Cancer Genome Atlas (TCGA) to evaluate the CNA called from RNA-Seq with those generated from SNP-arrays. When ploidy estimates were consistent, we found excellent agreement with CNAs called from DNA of over 98% of the genome for acute myeloid leukaemia (TCGA-AML, n=116) and 87% for colorectal cancer (TCGA-CRC, n=377), which has a much higher CNA burden. As expected, the sensitivity of CNA calling from RNA-Seq was dependent on gene density. Nonetheless, using RNA-Seq SuperFreq detected 78% of CNA calls covering 100 or more genes with a precision of 94%. Recall dropped markedly for focal events, but this also depended on the signal intensity. For example, in the CRC cohort SuperFreq identified 100% (7/7) of cases with high-level amplification of ERBB2, where the copy number was typically >20, but identified only 6% (1/17) of cases with moderate amplification of IGF2, typically 4 or 5 copies over a smaller region (median 5 flanking genes for IGF2, compared to 20 for ERBB2). We were able to reproduce the relationship between mutational load and CNA profile in CRC using RNA-Seq alone. SuperFreq offers an integrated platform for identification of CNAs and point mutations from RNA-seq in cancer transcriptomes. The software is implemented in R and is available through GitHub: https://github.com/ChristofferFlensburg/SuperFreq.

bioinformatics↗

bioRxiv2019

Finding a suitable library size to call variants in RNA-seq

BackgroundRNA-Seq allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the overall cost and the quality of the results. While several studies analyse the effect that library size has on differential expression analyses, sensitivity analysis for variant detection has received far less attention. ResultsWe simulated shallower sequencing depths by downsampling 45 AML samples that are part of the Leucegene project, which were originally sequenced at high depth. We compared the sensitivity of six methods of recovering validated mutations on the same samples. The methods compared are a combination of three popular callers (MuTect, VarScan, and VarDict) and two filtering strategies. We observed an incremental loss in sensitivity when simulating libraries of 80M, 50M, 40M, 30M and 20M fragments, with the largest loss detected with less than 30M fragments (below 90%). The sensitivity in recovering indels varied markedly between callers, with VarDict showing the highest sensitivity (60%). Single nucleotide variant sensitivity is relatively consistent across methods, apart from MuTect, whose default filters need adjustment when using RNA-Seq. We also analysed 136 RNA-Seq samples from the TCGA-LAML cohort, assessing the change in sensitivity between the initial libraries (average 59M fragments) and after downsampling to 40M fragments. When considering single nucleotide variants in recurrently mutated myeloid genes we found a comparable performance, with a 3% average loss in sensitivity using 40M fragments. ConclusionsBetween 30M and 40M fragments are needed to recover 90%-95% of the initial variants on recurrently mutated myeloid genes. To extend this result to another cancer type, an exploration of the characteristics of its mutations and gene expression patterns is suggested.

bioinformatics↗

bioRxiv2017

Clinker: visualising fusion genes detected in RNA-seq data

Genomic profiling efforts have revealed a rich diversity of oncogenic fusion genes, and many are emerging as important therapeutic targets. While there are many ways to identify fusion genes from RNA-seq data, visualising these transcripts and their supporting reads remains challenging. Clinker is a bioinformatics tool written in Python, R and Bpipe, that leverages the superTranscript method to visualise fusion genes. We demonstrate the use of Clinker to obtain interpretable visualisations of the RNA-seq data that lead to fusion calls. In addition, we use Clinker to explore multiple fusion transcripts with novel breakpoints within the P2RY8-CRLF2 fusion gene in B-cell Acute Lymphoblastic Leukaemia (B-ALL).\n\nAvailability and ImplementationClinker is freely available from Github https://github.com/Oshlack/Clinker under a MIT License.\n\nContactalicia.oshlack@mcri.edu.au

bioinformatics↗