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

Particulate matter (PM) is a ubiquitous component of air pollution that is epidemiologically linked to human pulmonary diseases. PM chemical composition varies widely, and the development of high-throughput experimental techniques enables direct profiling of cellular effects using compositionally unique PM mixtures. Here, we show that in a human bronchial epithelial cell model, exposure to three chemically distinct PM mixtures drive unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability, DNA damage responses, and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Finally, we observed that PM mixtures with higher cadmium content induced increased DNA damage and drove redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of individual cellular morphologies provides a robust, high-throughput approach to gauge the effects of environmental stressors on biological systems and score cellular susceptibilities to pollution.

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.

A strategy to quantify myofibroblast activation on a continuous spectrum

Myofibroblasts are a highly secretory and contractile cell phenotype that are predominant in wound healing and fibrotic disease. Traditionally, myofibroblasts are identified by the de novo expression and assembly of alpha-smooth muscle actin stress fibers, leading to a binary classification: "activated" or "quiescent (non-activated)". More recently, however, myofibroblast activation has been considered on a continuous spectrum, but there is no established method to quantify the position of a cell on this spectrum. To this end, we developed a strategy based on microscopy imaging and machine learning methods to quantify myofibroblast activation in vitro on a continuous scale. We first measured morphological features of over 1000 individual cardiac fibroblasts and found that these features provide sufficient information to predict activation state. We next used dimensionality reduction techniques and self-supervised machine learning to create a continuous scale of activation based on features extracted from microscopy images. 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 myofibroblast activation and provide an imaging-based strategy to quantify the position of a cell on that spectrum.

Computational evolution of an RNA-binding protein towards enhanced oxidized-RNA binding

The oxidation of RNA has been implicated in the development of many diseases. Among the four ribonucleotides, guanosine is the most susceptible to oxidation, resulting in the formation of 8-oxo-7,8-dihydroguanosine (8-oxoG). Despite the limited knowledge about how cells regulate the detrimental effects of oxidized RNA, cellular factors involved in its regulation have begun to be identified. One of these factors is polynucleotide phosphorylase (PNPase), a multifunctional enzyme implicated in RNA turnover. In the present study, we have examined the interaction of PNPase with 8-oxoG in atomic detail to provide insights into the mechanism of 8-oxoG discrimination. We hypothesized that PNPase subunits cooperate to form a binding site using the dynamic SFF loop within the central channel of the PNPase homotrimer. We evolved this site using a novel approach that initially screened mutants from a library of beneficial mutations and assessed their interactions using multi-nanosecond Molecular Dynamics simulations. We found that evolving this single site resulted in a fold change increase in 8-oxoG affinity between 1.2 and 1.5 and/or selectivity between 1.5 and 1.9. In addition to the improvement in 8-oxoG binding, complementation of K12 Δpnp with plasmids expressing mutant PNPases caused increased cell tolerance to H₂O₂. This observation provides a clear link between molecular discrimination of RNA oxidation and cell survival. Moreover, this study provides a framework for the manipulation of modified-RNA protein readers, which has potential application in synthetic biology and epitranscriptomics.

Applying a New REFINE Approach in Zymomonas mobilis Identifies Novel sRNAs That Confer Improved Stress Tolerance Phenotypes

As global controllers of gene expression, small RNAs represent powerful tools for engineering complex phenotypes. However, a general challenge prevents the more widespread use of sRNA engineering strategies: mechanistic analysis of these regulators in bacteria lags far behind their high-throughput search and discovery. This makes it difficult to understand how to efficiently identify useful sRNAs to engineer a phenotype of interest. To help address this, we developed a forward systems approach to identify naturally occurring sRNAs relevant to a desired phenotype: RNA-seq Examiner for Phenotype-Informed Network Engineering (REFINE). This pipeline uses existing RNA-seq datasets under different growth conditions. It filters the total transcriptome to locate and rank regulatory-RNA-containing regions that can influence a metabolic phenotype of interest, without the need for previous mechanistic characterization. Application of this approach led to the uncovering of six novel sRNAs related to ethanol tolerance in non-model ethanol-producing bacterium Zymomonas mobilis. Furthermore, upon overexpressing multiple sRNA candidates predicted by REFINE, we demonstrate improved ethanol tolerance reflected by up to an approximately twofold increase in relative growth rate compared to controls not expressing these sRNAs in 7% ethanol (v/v) RMG-supplemented media. In this way, the REFINE approach informs strain-engineering strategies that we expect are applicable for general strain engineering.