@article{engels_kamat_pafilis_li_agrawal_haller_phillip_contreras_2023, title={Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells}, volume={3}, ISSN={["2752-6542"]}, DOI={10.1093/pnasnexus/pgad415}, abstractNote={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.}, number={1}, journal={PNAS NEXUS}, author={Engels, Sean M. and Kamat, Pratik and Pafilis, G. Stavros and Li, Yukang and Agrawal, Anshika and Haller, Daniel J. and Phillip, Jude M. and Contreras, Lydia M.}, year={2023}, month={Dec} }
 @article{al'abri_li_haller_crook_2022, title={A Novel Method of Inducible Directed Evolution to Evolve Complex Phenotypes}, volume={12}, ISSN={["2331-8325"]}, DOI={10.21769/BioProtoc.4535}, abstractNote={Directed evolution is a powerful technique for identifying beneficial mutations in defined DNA sequences with the goal of improving desired phenotypes. Recent methodological advances have made the evolution of short DNA sequences quick and easy. However, the evolution of DNA sequences >5kb in length, notably gene clusters, is still a challenge for most existing methods. Since many important microbial phenotypes are encoded by multigene pathways, they are usually improved via adaptive laboratory evolution (ALE), which while straightforward to implement can suffer from off-target and hitchhiker mutations that can adversely affect the fitness of the evolved strain. We have therefore developed a new directed evolution method (Inducible Directed Evolution, IDE) that combines the specificity and throughput of recent continuous directed evolution methods with the ease of ALE. Here, we present detailed methods for operating Inducible Directed Evolution (IDE), which enables long (up to 85kb) DNA sequences to be mutated in a high throughput manner via a simple series of incubation steps. In IDE, an intracellular mutagenesis plasmid (MP) tunably mutagenizes the pathway of interest, located on the phagemid (PM). MP contains a mutagenic operon ( danQ926, dam, seqA, emrR, ugi , and cda1 ) that can be expressed via the addition of a chemical inducer. Expression of the mutagenic operon during a cell cycle represses DNA repair mechanisms such as proofreading, translesion synthesis, mismatch repair, and base excision and selection, which leads to a higher mutation rate. Induction of the P1 lytic cycle results in packaging of the mutagenized phagemid, and the pathway-bearing phage particles infect naïve cells, generating a mutant library that can be screened or selected for improved variants. Successive rounds of IDE enable optimization of complex phenotypes encoded by large pathways (as of this writing up to 36 kb), without requiring inefficient transformation steps. Additionally, IDE avoids off-target genomic mutations and enables decoupling of mutagenesis and screening steps, establishing it as a powerful tool for optimizing complex phenotypes in E. coli .}, number={20}, journal={BIO-PROTOCOL}, author={Al'Abri, Ibrahim S. and Li, Zidan and Haller, Daniel J. and Crook, Nathan}, year={2022}, month={Oct} }
 @article{al'abri_haller_li_crook_2022, title={Inducible directed evolution of complex phenotypes in bacteria}, volume={2}, ISSN={["1362-4962"]}, url={https://doi.org/10.1093/nar/gkac094}, DOI={10.1093/nar/gkac094}, abstractNote={Directed evolution is a powerful method for engineering biology in the absence of detailed sequence-function relationships. To enable directed evolution of complex phenotypes encoded by multigene pathways, we require large library sizes for DNA sequences >5-10 kb in length, elimination of genomic hitchhiker mutations, and decoupling of diversification and screening steps. To meet these challenges, we developed Inducible Directed Evolution (IDE), which uses a temperate bacteriophage to package large plasmids and transfer them to naive cells after intracellular mutagenesis. To demonstrate IDE, we evolved a 5-gene pathway from Bacillus licheniformis that accelerates tagatose catabolism in Escherichia coli, resulting in clones with 65% shorter lag times during growth on tagatose after only two rounds of evolution. Next, we evolved a 15.4 kb, 10-gene pathway from Bifidobacterium breve UC2003 that aids E. coli's utilization of melezitose. After three rounds of IDE, we isolated evolved pathways that both reduced lag time by more than 2-fold and enabled 150% higher final optical density. Taken together, this work enhances the capacity and utility of a whole pathway directed evolution approach in E. coli.}, journal={NUCLEIC ACIDS RESEARCH}, publisher={Oxford University Press (OUP)}, author={Al'Abri, Ibrahim S. and Haller, Daniel J. and Li, Zidan and Crook, Nathan}, year={2022}, month={Feb} }