Biology subjects

Blombery, P.

Publications and source records attributed to Blombery, P..

3 recordsLinked to original sources

bioRxiv2025

Shared and distinct sequence-function signatures define different modes of human TpoR activation

The human thrombopoietin receptor (hTpoR) exists primarily as JAK2-associated monomers that become activated when converted to dimeric forms that support JAK trans-phosphorylation. This can be achieved by several different modes of stimuli, including the natural ligand Tpo, biologic agonists that bind the same site as Tpo, small-molecule drugs that bind the transmembrane (TM) domain, oncogenic mutations in and near the TM domain, and by association with constitutively active JAK V617F or a mutant form of the chaperone protein calreticulin. It is unclear how the dimeric structures induced by synthetic agonists and mutations relate to one another, and whether any of these induce the same active structure as the native ligand Tpo, yet this has important implications both for fundamental cytokine receptor biology and for development of targeted interventions for hTpoR-driven myeloproliferative diseases. Here we used deep mutational scanning (DMS) across the TM and juxtamembrane (JM) regions of hTpoR to extract feature-rich sequence-function signatures across a variety of different activating contexts. While each displayed some unique features, synthetic agonists and activating mutations all exhibited strong dependence on a common TM interface that is consistent with previous models of a left-handed, near-parallel helix dimer with H499 facing lipid. In contrast, Tpo-mediated activation was broadly insensitive to TM-JM substitutions, indicating that it does not rely on the same interface. Modeling with AlphaFold 3 (AF3) consistently yielded a right-handed, "splayed" helix dimer that is close at the extracellular face, contains H499 in the interface and diverges toward the cytosolic face, resting on an intracellular amphipathic JM helix that lies parallel to the membrane, which is also observed in a DMS/AF3 analysis of human erythropoietin receptor. This splayed Tpo-bound dimer could be stably inserted into a lipid bilayer with associated JAK2 using molecular dynamics and is supported by experiments showing that most or all of the TM domain can be replaced by poly-valine, with little effect on Tpo-driven activation but catastrophic effects on responses to synthetic ligands. Our data support at least two different structural modes of hTpoR activation that reconcile prior biochemical models, rationalize patient variants, and inform mechanism-based agonist and antagonist design.

biochemistry↗

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

JAFFAL: Detecting fusion genes with long read transcriptome sequencing

Massively parallel short read transcriptome sequencing has greatly expanded our knowledge of fusion genes which are drivers of tumor initiation and progression. In cancer, many fusions are also important diagnostic markers and targets for therapy. Long read transcriptome sequencing allows the full length of fusion transcripts to be discovered, however, this data has a high rate of errors and fusion finding algorithms designed for short reads do not work. While numerous fusion finding algorithms now exist for short read RNA sequencing data, there are few methods to detect fusions using third generation or long read sequencing data. Fusion finding in long read sequencing will allow the discovery of the full isoform structure of fusion genes. Here we present JAFFAL, a method to identify fusions from long-read transcriptome sequencing. We validated JAFFAL using simulation, cell line and patient data from Nanopore and PacBio. We show that fusions can be accurately detected in long read data with JAFFAL, providing better accuracy than other long read fusion finders and with similar performance as state-of-the-art methods applied to short read data. By comparing Nanopore transcriptome sequencing protocols we find that numerous chimeric molecules are generated during cDNA library preparation that are absent when RNA is sequenced directly. We demonstrate that JAFFAL enables fusions to be detected at the level of individual cells, when applied to long read single cell sequencing. Moreover, we demonstrate JAFFAL can identify fusions spanning three genes, highlighting the utility of long reads to characterise the transcriptional products of complex structural rearrangements with unprecedented resolution. JAFFAL is open source and available as part of the JAFFA package at https://github.com/Oshlack/JAFFA/wiki.

bioinformatics↗