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Biology subjects

Baird, D. M.

Publications and source records attributed to Baird, D. M..

2 recordsLinked to original sources

Loss of BCL-3 sensitises colorectal cancer cells to DNA damage, revealing a role for BCL-3 in double strand break repair by homologous recombination

ObjectiveThe proto-oncogene BCL-3 is upregulated in a subset of colorectal cancers (CRC) and increased expression of the gene correlates with poor patient prognosis. The aim is to investigate whether inhibiting BCL-3 can increase the response to DNA damage in CRC. DesignThe function of BCL-3 in DNA damage response was studied in vitro using siRNA and CRISPR-Cas9 genome editing and in vivo using Bcl3-/- mice. DNA damage induced by {gamma}-irradiation and/or cisplatin was quantified using H2AX and RAD51 foci, repair pathways investigated using HR/NHEJ assays and treatment with the PARP inhibitor olaparib. ResultSuppression of BCL-3 increases double strand break number and decreases homologous recombination in CRC cells, supported by reduced RAD51 foci number and increased sensitivity to PARP inhibition. Importantly, a similar phenotype is seen in Bcl3-/- mice, where the intestinal crypts of these mice exhibit sensitivity to DNA damage and a greater number of double strand breaks compared to wild type mice. Furthermore Apc.Krasmutant x Bcl3-/- mice exhibit increased DNA damage and reduced RAD51+ cells compared to their wild type counterparts when treated with cisplatin. ConclusionThis work identifies BCL-3 as a regulator of the cellular response to DNA damage and suggests that elevated BCL-3 expression could increase resistance of tumour cells to DNA damaging agents including radiotherapy. These findings offer a rationale for targeting BCL-3 in CRC as an adjuvant to conventional therapies and suggest that BCL-3 expression in tumours could be a useful biomarker in stratification of rectal cancer patients for neo-adjuvant chemoradiotherapy.

cancer biology↗

Dysgu: efficient structural variant calling using short or long reads

Structural variation (SV) plays a fundamental role in genome evolution and can underlie inherited or acquired diseases such as cancer. Long-read sequencing technologies have led to improvements in the characterization of structural variants (SVs), although paired-end sequencing offers better scalability. Here, we present dysgu, which calls SVs or indels using paired-end or long reads. Dysgu detects signals from alignment gaps, discordant and supplementary mappings, and generates consensus contigs, before classifying events using machine learning. Additional SVs are identified by remapping of anomalous sequences. Dysgu outperforms existing state-of-the-art tools using paired-end or long-reads, offering high sensitivity and precision whilst being among the fastest tools to run. We find that combining low coverage paired-end and long-reads is competitive in terms of performance with long-reads at higher coverage values.

bioinformatics↗