Paul Christensen | Search PubMed

PubMed2026

Endothelial PERK restricts lymphoid regeneration by reducing DLL4-NOTCH3 signaling at the Pre-B niche.

Delayed immune recovery after hematopoietic stem cell (HSC) transplantation is associated with a poor clinical outcome. We study the role of unfolded protein response (ER stress) in hematopoietic regeneration within the bone marrow (BM) microenvironment. We reveal that BM endothelium PERK activation is a prominent feature of patients with leukemia and is a hallmark response in mice following ionizing irradiation. Ablating endothelial Perk boosts NOTCH ligand DLL4 expression and promotes DLL4-dependent early HSC and B progenitor regeneration. Single-cell analysis reveals that endothelial DLL4 activates NOTCH3 expressed by mesenchymal stroma cells, and that the PERK-DLL4 axis coordinates the regulation of lymphoid commitment. NOTCH3 is critical for the upregulation of IL7 following irradiation and the expansion of lymphoid progenitors. These findings not only unveil an ER stress-controlled vascular-stroma signaling mechanism in regenerative hematopoiesis but also highlight PERK blockade as a promising strategy to improve immune recovery after myeloablative transplantation.

CP: cell biology↗

PubMed2024

Using intrahost single nucleotide variant data to predict SARS-CoV-2 detection cycle threshold values.

Over the last four years, each successive wave of the COVID-19 pandemic has been caused by variants with mutations that improve the transmissibility of the virus. Despite this, we still lack tools for predicting clinically important features of the virus. In this study, we show that it is possible to predict the PCR cycle threshold (Ct) values from clinical detection assays using sequence data. Ct values often correspond with patient viral load and the epidemiological trajectory of the pandemic. Using a collection of 36,335 high quality genomes, we built models from SARS-CoV-2 intrahost single nucleotide variant (iSNV) data, computing XGBoost models from the frequencies of A, T, G, C, insertions, and deletions at each position relative to the Wuhan-Hu-1 reference genome. Our best model had an R2 of 0.604 [0.593-0.616, 95% confidence interval] and a Root Mean Square Error (RMSE) of 5.247 [5.156-5.337], demonstrating modest predictive power. Overall, we show that the results are stable relative to an external holdout set of genomes selected from SRA and are robust to patient status and the detection instruments that were used. This study highlights the importance of developing modeling strategies that can be applied to publicly available genome sequence data for use in disease prevention and control.

SARS-CoV-2↗