Biology subjects

Barger, Z.

Publications and source records attributed to Barger, Z..

3 recordsLinked to original sources

bioRxiv2026

METTL1 gene dosage and catalytic activity define a targetable vulnerability in pediatric cancers

METTL1 is responsible for catalyzing N7-methylguanosine (m7G) methylation of tRNAs. METTL1-dependent oncogenic mechanisms include both its methyltransferase catalytic activity and, in specific instances, non-catalytic interactions that maintain aminoacylated tRNA levels in malignant cells. We show that METTL1 copy number gain and elevated expression are a feature of pediatric tumors that are METTL1 dependent, using Cas13-mediated RNA to reduce the threshold of METTL1 mRNA and protein, which disrupts both catalytic and non-catalytic functions. In cell lines with METTL1 gene amplification, specific degradation of METTL1 mRNA impaired colony formation, triggered cell death, and decreased the translation efficiency of a subset of genes enriched for codons decoded m7G-modified tRNAs. METTL1 inhibition was accompanied by downregulation of c-MYC, n-MYC and MCL-1 protein expression. METTL1 silencing synergizes with drugs targeting anti-apoptotic members of the BCL2 family, notably BCL-xL. Importantly, the catalytic activity of METTL1 was required to rescue cells from the deleterious consequences of METTL1 silencing. We conclude that inhibiting the catalytic activity of METTL1 has potential therapeutic efficacy, particularly in tumors with METTL1 amplification. Synergistic combinations with BH3-mimetic drugs may be a promising therapeutic combination.

cancer biology↗

bioRxiv2024

Engineered paediatric tumours retain patient tumour genotype and phenotype for precision medicine.

Precision medicine for paediatric and adult cancers that includes drug sensitivity profiling, can identify effective therapies for individual patients. However, obtaining adequate biopsy samples for high-throughput (HTP) screening remains challenging, with tumours needing to be expanded in culture or patient-derived xenografts - this is time-consuming and often unsuccessful. Herein, we have developed paediatric patient-derived tumour models using an engineered extracellular matrix (ECM) tissue mimic hydrogel system and HTP 3D bioprinting. Gene expression analysis from neuroblastoma and sarcoma patients identified key components of the ECM in these tumour types. Engineered hydrogels with ECM-mimic peptides were used to create patient-specific tumour organoids, modelling tumour growth conditions. Expanded tumour organoids recapitulated the genetic and phenotypic characteristics of the original tumours and retained tumourgenicity. Screening of these models identified individualised drug sensitivities. Our approach offers a timely and clinically relevant technology platform for precision medicine in paediatric cancers, potentially transforming preclinical testing across cancer types.

cancer biology↗

bioRxiv2019

Robust, automated sleep scoring by a compact neural network with distributional shift correction

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.

neuroscience↗