Biology subjects

Ietswaart, R.

Publications and source records attributed to Ietswaart, R..

6 recordsLinked to original sources

bioRxiv2024

Early-stage lung adenocarcinoma is driven by an injury-associated, plastic cell state dependent on a KRAS-ITGA3-SRC axis

Glycine 12 mutations in the GTPase KRAS (KRASG12) are a known initiating event for lung adenocarcinoma (LUAD) with broad clinical relevance. KRASG12 mutations promote cell-intrinsic rewiring of the lung alveolar type II progenitor (AT2) cells, but to what extent such changes interplay with pathways essential for lung homeostasis and cell fate is unclear. We used single-cell RNA-seq (scRNA-seq) from AT2-mesenchyme organoid co-cultures, mouse models, and stage IA LUAD patient samples to identify conserved regulators of AT2 cell transcriptional dynamics and the impact of KRASG12D with temporal resolution. In AT2WT organoids, a transient injury/plasticity state preceded AT2 self-renewal and AT1 differentiation. Early-stage AT2KRAS cells exhibited perturbed gene expression dynamics most noted by retention of the injury/plasticity state. At later time points in tumorigenesis, AT2KRAS cells consisted of heterogeneous populations that could be defined by either the injury state or high expression of an AT2 cell signature. The injury state in AT2KRAS cells of LUAD in patients, mice, and organoids was distinguishable from AT2WT states by altered receptor expression, including co-expression of ITGA3 and SRC. The combination of clinically relevant KRASG12D and SRC inhibitors to target the oncogenic injury cell state impaired AT2KRAS organoid growth. Thus, an injury/plasticity signature characterized as an essential step in lung repair is used during alveolar cell self-renewal and during initiation and progression of LUAD. Early-stage lung cancer may be susceptible to intervention by targeting the oncogenic-specific nature of this cell state.

cancer biology↗

bioRxiv2023

Proximal termination generates a transcriptional state that determines the rate of establishment of Polycomb silencing

Chromatin-mediated transcriptional silencing by Polycomb Repressive Complex 2 (PRC2) is critical for gene regulation in development and environmental responses. However, the mechanism and timescales controlling de novo establishment of PRC2 silencing are unclear. Here, we investigate PRC2 silencing at Arabidopsis FLOWERING LOCUS C (FLC), known to involve co-transcriptional RNA processing, histone demethylation activity, and PRC2 function; but so far not mechanistically connected. We develop and then test a computational model that describes how proximal polyadenylation/termination mediated by the RNA binding protein FCA induces H3K4me1 removal by the histone demethylase FLD. H3K4me1 removal feeds back to reduce RNA Pol II processivity and thus enhance early termination, thereby repressing productive transcription. The model predicts that this transcription-coupled repression controls the level of transcriptional antagonism to Polycomb action, Thus, the effectiveness of this repression dictates the timescale for establishment of Polycomb H3K27me3 silencing. Experimental validation of these model predictions allowed us to mechanistically connect co-transcriptional processing to setting the level of productive transcription at the locus, which then determines the rate of the ON to OFF switch to PRC2 silencing.

molecular biology↗

bioRxiv2023

Regulatory principles of human mitochondrial gene expression revealed by kinetic analysis of the RNA life cycle

Oxidative phosphorylation (OXPHOS) complexes, encoded by both mitochondrial and nuclear DNA, are essential producers of cellular ATP, but how nuclear and mitochondrial gene expression steps are coordinated to achieve balanced OXPHOS biogenesis remains unresolved. Here, we present a parallel quantitative analysis of the human nuclear and mitochondrial messenger RNA (mt-mRNA) life cycles, including transcript production, processing, ribosome association, and degradation. The kinetic rates of nearly every stage of gene expression differed starkly across compartments. Compared to nuclear mRNAs, mt-mRNAs were produced 700-fold higher, degraded 5-fold faster, and accumulated to 170-fold higher levels. Quantitative modeling and depletion of mitochondrial factors, LRPPRC and FASTKD5, identified critical points of mitochondrial regulatory control, revealing that the mitonuclear expression disparities intrinsically arise from the highly polycistronic nature of human mitochondrial pre-mRNA. We propose that resolving these differences requires a100-fold slower mitochondrial translation rate, illuminating the mitoribosome as a nexus of mitonuclear co-regulation.

cell biology↗

bioRxiv2022

Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle

Dissecting the myriad regulatory mechanisms controlling eukaryotic transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered roles for DDX3X and PABPC4 in nuclear export. For hundreds of genes, most transcripts were degraded within the nucleus, while the remaining molecules were exported and persisted with stable lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, a machine learning model identified additional molecular features that underlie the diverse life cycles of mammalian mRNAs.

genomics↗

bioRxiv2019

GeneWalk identifies relevant gene functions for a biological context using network representation learning

The primary bottleneck in high-throughput genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Existing methods such as Gene Ontology (GO) enrichment analysis provide insight at the gene set level. For individual genes, GO annotations are static and biological context can only be added by manual literature searches. Here, we introduce GeneWalk (github.com/churchmanlab/genewalk), a method that identifies individual genes and their relevant functions under a particular experimental condition. After automatic assembly of an experiment-specific gene regulatory network, GeneWalk quantifies the similarity between vector representations of each gene and its GO annotations through representation learning, yielding annotation significance scores that reflect their functional relevance for the experimental context. We demonstrate the use of GeneWalk analysis of RNA-seq and nascent transcriptome (NET-seq) data from human cells and mouse brains, validating the methodology. By performing gene- and condition-specific functional analysis that converts a list of genes into data-driven hypotheses, GeneWalk accelerates the interpretation of high-throughput genetics experiments.

bioinformatics↗

bioRxiv2019

Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology

Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines. Here, we analyze in vitro secondary pharmacology of common (off) targets for 2134 marketed drugs. To associate these drugs with human ADRs, we utilized FDA Adverse Event Reports and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles. By evaluating Gini importance scores of model features, we identify 221 target-ADR associations, which co-occur in PubMed abstracts to a greater extent than expected by chance. Among these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which further validate our machine learning approach. Evidence on bile acid metabolism supports our identification of associations between the Bile Salt Export Pump and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 40 ADRs. These associations provide a comprehensive resource to support drug development and human biology studies.

pharmacology and toxicology↗