Biology subjects

Engels, S. M.

Publications and source records attributed to Engels, S. M..

4 recordsLinked to original sources

bioRxiv2025

Cytoplasmic localization of PUS7 facilitates a pseudouridine-dependent enhancement of cellular stress tolerance

Pseudouridine ({Psi}) is an abundant post-transcriptional modification found across all classes of RNA. It has been widely speculated that {Psi} inclusion in mRNAs might provide an avenue for cells to control gene expression post-transcriptionally. Here we demonstrate that one of the principal mRNA pseudouridylating enzymes, pseudouridine synthase 7 (PUS7), exhibits a stress-induced accumulation in the cytoplasm of yeast and human epithelial lung cells. Stress-induced and cytoplasmic localization of PUS7 promote {Psi}-incorporation into hundreds of mRNA targets. Furthermore, engineered PUS7 cytoplasmic localization increases cellular fitness under ROS and divalent metal ion stress. Consistent with this, transcripts modified upon PUS7 cytoplasmic localization are enriched within mRNAs encoding proteins involved in divalent metal metabolism and ROS stress pathways. In contrast, tRNA sites modified by PUS7 ({Psi}13 and {Psi}35 are unperturbed). Quantitative proteomics reveal a reshaping of the proteome upon PUS7 relocalization under stress, with proteins involved in metal and ROS homeostasis being particularly sensitive to PUS7 localization. Collectively, our data demonstrate that PUS7 localization alters mRNA pseudouridylation patterns to modulate protein production and enhance cellular fitness.

biochemistry↗

bioRxiv2025

RNA Binding Proteins KhpA and KhpB Interact with Small Regulatory RNAs and Affect Global Gene Expression in Deinococcus radiodurans

Small regulatory RNAs (sRNAs) in bacteria often associate with RNA-binding proteins to gain intracellular stability and/or to enable regulatory efficiency. While much of the current knowledge about those sRNA binding proteins is derived from studies in Gram-negative organisms, the characterization of such proteins in Gram-positive species is still lagging behind. Here, we identified and characterized two sRNA binding proteins (KhpA and KhpB) in Deinococcus radiodurans, a Gram-positive bacterium that exhibits extreme resistance to radiation and other oxidative stressors. We demonstrate that KhpA and KhpB interact with key sRNAs in D. radiodurans and influence their stabilities. Although KhpA and KhpB interact with each other, they do not bind the sRNAs as a complex. Insightfully, KhpA and KhpB facilitate the interactions of the representative sRNAs PprS and Dsr9 in D. radiodurans with their respective mRNA targets, pprM and DR_1968. Through RNA-seq analysis, we further revealed that KhpA and KhpB have both overlapping and specific roles in a global gene regulation in D. radiodurans. Overall, this study expands our knowledge of posttranscriptional regulation in D. radiodurans and supports the growing consensus that KhpA and KhpB homologs constitute a new family of sRNA binding proteins in Gram-positive bacteria. IMPORTANCEThe bacterium Deinococcus radiodurans is the most radiation-resistant organism identified to date. Understanding the mechanisms of resistance of D. radiodurans is essential for leveraging this bacterium in biomedical and biomanufacturing applications. It was previously revealed that small regulatory RNAs (sRNAs) play crucial roles in the gene regulation of D. radiodurans. However, how these sRNAs are influenced by RNA binding proteins is poorly understood. Here we identified two conserved RNA binding proteins, KhpA and KhpB, as sRNA binding partners in D. radiodurans. These proteins affect the sRNA stability, sRNA-target interaction, and global gene regulation. Characterization of KhpA and KhpB will help us advance the understanding of how post-transcriptional network regulates the physiology and radioresistance of D. radiodurans.

molecular biology↗

bioRxiv2023

Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells

Particulate matter (PM) is a ubiquitous component of indoor and outdoor air pollution that is epidemiologically linked to many human pulmonary diseases. PM has many emission sources, making it challenging to understand the biological effects of exposure due to the high variance in chemical composition. However, the effects of compositionally unique particulate matter mixtures on cells have not been analyzed using both biophysical and biomolecular approaches. Here, we show that in a human bronchial epithelial cell model (BEAS-2B), exposure to three chemically distinct PM mixtures drives unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability and DNA damage responses and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization and structure, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Lastly, we observed that particulate matter mixtures with high contents of heavy metals, such as cadmium and lead, induced larger drops in viability, increased DNA damage, and drove a redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of cellular morphology provides a robust approach to gauge the effects of environmental stressors on biological systems and determine cellular susceptibilities to pollution.

systems biology↗

bioRxiv2022

A Strategy to Quantify Myofibroblast Activation on a Continuous Spectrum

Myofibroblasts are a highly secretory and contractile phenotype most commonly identified by the de novo expression and assembly of alpha-smooth muscle actin stress fibers. Traditionally, this activation process has been thought of as a binary process, with cells being labeled as "activated" or "quiescent (non-activated)". More recently, this view has been expanded to consider activation on a continuous spectrum. However, there is no established method to quantify a cells position on this spectrum, and as a result, the binary labeling system is still widely used. While transcriptomic analyses provide a continuous measure of myofibroblast markers, a faster and more facile screening method is needed. To this end, we utilized optical microscopy and machine learning methods to quantify myofibroblast activation on a spectrum. We first measured size and shape features of over 1,000 individual cardiac fibroblasts and found that these features provide enough information to predict activation state, on the binary scale, with 94% accuracy as compared to manual classification. We next performed dimensionality reduction techniques on these features to create a continuous scale of activation. Importantly, this new classification system captures a range of fibroblast activation states, but still possesses inherent bias due to choice of morphological features. Thus, we next used self-supervised machine learning to create a second continuous labeling system free from biases associated with the manually measured features. Lastly, we compared our findings for mechanically activated cardiac fibroblasts to a distribution of cell phenotypes generated from transcriptomic data using single-cell RNA sequencing. Altogether, these results demonstrate a continuous spectrum of activation from fibroblast to myofibroblast and provide a strategy to quantify a cells position on that spectrum.

bioengineering↗