Corey, S. | Search bioRxiv

bioRxiv2026

Hog1/p38 and ZAKα drive Shwachman-Diamond syndrome and provide targets to improve cell growth

Shwachman-Diamond syndrome (SDS) is a ribosomopathy characterized by neutropenia, pancreatic insufficiency, skeletal defects, and predisposition to leukemia. Most cases result from biallelic SBDS mutations that impairing 80S ribosome and polysome assembly. In yeast lacking SDO1 (the SBDS ortholog), growth slows dramatically and the p38 ortholog Hog1 signaling is elevated by multiple types of stress. SBDS-deficient HeLa cells exhibited reduced proliferation and slowed cell cycling. The p38 kinase was constitutively activated in SBDS mutants and SDS patient-derived blood cells. Because ZAK detects ribosome dysfunction, its activation links ribosomal defects to stress kinase pathways in SDS. Suppressing p38 or its upstream activator ZAK restored cell growth and reduced stress signaling. These findings reveal an evolutionarily conserved-independent mechanism via p38 drives SDS pathophysiology and identifies stress kinases as potential therapeutic targets for ribosomal dysfunction.

cell biology↗

bioRxiv2019

Predicting Minimal Residual Disease in Acute Myeloid Leukemia through Stochastic Modeling of Clonality

Event-free and overall survival remains poor for acute myeloid leukemia (AML). Chemo-resistant clones contributing to relapse of the disease arise from minimal residual disease (MRD) rather than resulting from newly acquired mutations during or after chemotherapy. MRD is the presence of measurable leukemic cells using non-morphologic assays. It is considered a strong predictor of relapse. The dynamics of clones comprising MRD is poorly understood and is considered influenced by a form of Darwinian selection. We propose a stochastic model based on a multitype (multi-clone) age-dependent Markov branching process to study how random events in MRD contribute to the heterogeneity in response to treatment in a cohort of six patients from The Cancer Genome Atlas database with whole genome sequencing data at two time points. Our model offers a more accurate understanding of how relapse arises and which properties allow a leukemic clone to thrive in the Darwinian competition among leukemic and normal hematopoietic clones. The model suggests a quantitative relationship between MRD and time to relapse and therefore may aid clinicians in determining when and how to implement treatment changes to postpone or prevent the time to relapse.\n\nAuthor summaryRelapse affects about 50% of AML patients who achieved remission after treatment, and the prognosis of relapsed AML is poor. Current evidence has shown that in many patients, mutations giving rise to relapse are already present at diagnosis and remain in small numbers in remission, defined as the minimal residual disease (MRD). We propose a mathematical model to analyze how MRD develops into relapse, and how random events in MRD may affect the patients fate. This work may aid clinicians in predicting the range of outcomes of chemotherapy, given mutational data at diagnosis. This can help in choosing treatment strategies that reduce the risk of relapse.

cancer biology↗