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Ietswaart, Robert

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Ietswaart

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Robert

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Ietswaart, Robert

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2017 - 20202

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Author's Publications: search.filters.isAuthorOfPublication.1974adba-8b6b-484a-8504-96d5e5f188b6

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    Cell-Size-Dependent Transcription of FLC and Its Antisense Long Non-Coding RNA COOLAIR Explain Cell-to-Cell Expression Variation
    (Elsevier BV, 2017-06-28) Ietswaart, Robert; Rosa, Stefanie; Wu, Zhe; Dean, Caroline; Howard, Martin
    Single-cell quantification of transcription kinetics and variability promotes a mechanistic understanding of gene regulation. Here, using single-molecule RNA fluorescence in situ hybridization and mathematical modeling, we dissect cellular RNA dynamics for Arabidopsis FLOWERING LOCUS C (FLC). FLC expression quantitatively determines flowering time and is regulated by antisense (COOLAIR) transcription. In cells without observable COOLAIR expression, we quantify FLC transcription initiation, elongation, intron processing, and lariat degradation, as well as mRNA release from the locus and degradation. In these heterogeneously sized cells, FLC mRNA number increases linearly with cell size, resulting in a large cell-to-cell variability in transcript level. This variation is accounted for by cell-size-dependent, Poissonian FLC mRNA production, but not by large transcriptional bursts. In COOLAIR-expressing cells, however, antisense transcription increases with cell size and contributes to FLC transcription decreasing with cell size. Our analysis therefore reveals an unexpected role for antisense transcription in modulating the scaling of transcription with cell size.
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    Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology
    (Elsevier BV, 2020-07) Ietswaart, Robert; Arat, Seda; Chen, Amanda X.; Farahmand, Saman; Kim, Bumjun; DuMouchel, William; Armstrong, Duncan; Fekete, Alexander; Sutherland, Jeffrey J.; Urban, Laszlo
    Background: 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. Methods: Here, we analyse 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. Findings: 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. Amongst 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. Interpretation: These associations provide a comprehensive resource to support drug development and human biology studies. Funding: This study was not supported by any formal funding bodies.