Biology subjects

Lyon, E.

Publications and source records attributed to Lyon, E..

3 recordsLinked to original sources

bioRxiv2026

Control of grain dimensions via ALLENE OXIDE CYCLASE

Cereal grain is a global food staple. Grain formation starts with fertilization of the embryo sac which generates filial tissues, such as the embryo and endosperm, that grow embedded in the maternal ovule and surrounding ovary wall. Maternal tissues provide nutrition and protection but are progressively reduced and degraded, such that filial tissues dominate the mature grain. While we know some enzymatic players, regulation of this process in temperate cereals like wheat and barley, remains less understood. However, previous work suggested a role for the phytohormone Jasmonate (JA). To learn more, we edited the barley ALLENE OXIDASE CYCLASE (HvAOC) gene encoding a JA biosynthetic enzyme. Defective HvAOC alleles caused enhanced vegetative growth, faster flowering and larger, heavier grain - phenotypes rescued by MeJA treatment. HvAOC control of final grain parameters is parentally-derived, consistent with enriched HvAOC expression in maternal compared to filial tissues. Defective HvAOC function enlarged maternal tissues involved in nutrient transfer, correlating with larger endosperm and higher expression of genes linked to sucrose transport. This work provides the first genetic dissection of JA in barley and highlights a key role for HvAOC in the coordination between growth and death of maternal tissues with grain size and weight.

plant biology↗

bioRxiv2025

Single-cell profiling of human bone marrow reveals multiple myeloma progression is accompanied by an increase in CD56bright bone marrow resident NK cells.

Natural killer (NK) cells play a key role in the innate immune response against tumour progression. While the immune microenvironment in multiple myeloma (MM) becomes increasingly dysfunctional during disease evolution, little is known about changes in the NK cell compartment. Using primary samples from clinical trial patients, we performed detailed phenotypic analyses of bone marrow mononuclear cells from MGUS, SMM, and newly diagnosed MM patients. We found that disease progression is associated with an increase in CD56bright NK cells with a dynamic positive association between this NK cell subset and local tumour burden. We generated a large single-cell RNA sequencing dataset of >100,000 NK cells from healthy donor individuals and plasma cell disorder patients and identify a bone marrow specific CD56bright-like NK cell population (BM-NK) that is enriched in the marrow of MM patients. These findings highlight the evolution of the NK-cell compartment in MM and suggest a role for BM-resident CD56bright NK cells with impaired cytotoxicity in promoting immune evasion.

cancer biology↗

bioRxiv2020

Reducing Sanger Confirmation Testing through False Positive Prediction Algorithms

PurposeClinical genome sequencing (cGS) followed by orthogonal confirmatory testing is standard practice. While orthogonal testing significantly improves specificity it also results in increased turn-around-time and cost of testing. The purpose of this study is to evaluate machine learning models trained to identify false positive variants in cGS data to reduce the need for orthogonal testing. MethodsWe sequenced five reference human genome samples characterized by the Genome in a Bottle Consortium (GIAB) and compared the results to an established set of variants for each genome referred to as a truth-set. We then trained machine learning models to identify variants that were labeled as false positives. ResultsAfter training, the models identified 99.5% of the false positive heterozygous single nucleotide variants (SNVs) and heterozygous insertions/deletions variants (indels) while reducing confirmatory testing of true positive SNVs to 1.67% and indels to 20.29%. Employing the algorithm in clinical practice reduced orthogonal testing using dideoxynucleotide (Sanger) sequencing by 78.22%. ConclusionOur results indicate that a low false positive call rate can be maintained while significantly reducing the need for confirmatory testing. The framework that generated our models and results is publicly available at https://github.com/HudsonAlpha/STEVE.

bioinformatics↗