Biology subjects

Magnusson, E.

Publications and source records attributed to Magnusson, E..

3 recordsLinked to original sources

bioRxiv2024

Prioritizing Metabolic Gene Regulators through Multi-Omic Network Integration in Maize

Gene regulatory networks (GRNs) link transcription factors (TFs) to the biological processes they control. Assembling them remains difficult because the relevant data types (gene expression, protein-DNA interactions, and genetic variation) are large, heterogeneous, and rarely combined. Here, we developed and benchmarked a framework that integrates these data into TF-function predictions in maize. We assembled four complementary TF-target gene network layers, based on expression, protein-DNA interaction, trans-expression quantitative trait loci (eQTL), and cis-eQTL-supported interaction, from 46 Random Forest (RF)-inferred regulatory networks, 283 protein-DNA interaction assays, and eQTLs derived from 16 million SNPs across 304 inbred lines. Together these layers comprised ~4.6 million interactions. We then compared three strategies for integrating them, benchmarking each against published TF knockout data. A network-based approach, which represents every gene as a low-dimensional vector (embedding) learned from the combined network, outperformed the two overlap-based strategies, annotating over eight times more TFs (~3,000), agreeing most closely with gene knockout responses where predictions existed, and remaining robust when individual layers lacked data. The predictions recovered TF functions and predicted new regulators of hormone, developmental, and metabolic processes, which we prioritized per process and mapped to specific conditions. Using similarity on the low-dimensional vector representation (embedding), we further identified candidate functionally redundant or diverged TF paralogs. Because it relies only on data types now common across species, the framework provides a generalizable template for prioritizing regulatory genes in maize and other plants.

plant biology↗

bioRxiv2023

MOXD1 is a gate-keeper of organ homeostasis and functions as a tumor-suppressor in neuroblastoma

Neuroblastoma is a childhood cancer believed to result from dysfunctional development. Its origin during embryogenesis remains poorly understood. The lack of appropriate models has hindered in-depth mapping of tumor-driving events. Here, we identify a novel tumor-suppressor gene that predicts poor survival in high-risk disease, by applying bulk and single cell RNA sequencing data of neuroblastoma and human fetal adrenal glands. Trunk neural crest-specific MOXD1 discriminates cell populations during normal and tumor development, with implications for deciphering neuroblastoma cell origin. We created an embryonic conditional knockout model and show that cell type-specific loss of MOXD1 leads to disrupted organ homeostasis and failed adrenal gland formation, home for neuroblastoma. We show that MOXD1 is a tumor suppressor gene in zebrafish, chick, and mice in vivo models. One-Sentence SummaryNeural crest-specific MOXD1 is a de novo tumor-suppressor gene in childhood cancers arising during embryogenesis.

cancer biology↗

bioRxiv2021

Applying cis-regulatory codes to predict conserved and variable heat and cold stress response in maize

Changes in gene expression are important for response to abiotic stress. Transcriptome profiling performed on maize inbred and hybrid genotypes subjected to heat or cold stress identifies many transcript abundance changes in response to these environmental conditions. Motifs that are enriched near differentially expressed genes were used to develop machine learning models to predict gene expression responses to heat or cold. The best performing models utilize the sequences both upstream and downstream of the transcription start site. Prediction accuracies could be improved using models developed for specific co-expression clusters compared to using all up- or down-regulated genes or by only using motifs within unmethylated regions. Comparisons of expression responses in multiple genotypes were used to identify genes with variable response and to identify cis- or trans-regulatory variation. Models trained on B73 data have lower performance when applied to Mo17 or W22, this could be improved by using models trained on data from all genotypes. However, the models have low accuracy for correctly predicting genes with variable responses to abiotic stress. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and provides a framework for developing models to predict expression response to abiotic stress across multiple genotypes. One sentence summaryTranscriptome profiling of maize inbred and hybrid seedlings subjected to heat or cold stress was used to identify key cis-regulatory elements and develop models to predict gene expression responses.

plant biology↗