ASU Scholarship Showcase

In vitro models that mimic in vivo host-pathogen interactions are needed to evaluate candidate drugs that inhibit bacterial virulence traits. We established a new approach to study Pseudomonas aeruginosa biofilm susceptibility on biotic surfaces, using a three-dimensional (3-D) lung epithelial cell model. P. aeruginosa formed antibiotic resistant biofilms on 3-D cells without affecting cell viability. The biofilm-inhibitory activity of antibiotics and/or the anti-biofilm peptide DJK-5 were evaluated on 3-D cells compared to a plastic surface, in medium with and without fetal bovine serum (FBS). In both media, aminoglycosides were more efficacious in the 3-D cell model. In serum-free medium, most antibiotics (except polymyxins) showed enhanced efficacy when 3-D cells were present. In medium with FBS, colistin was less efficacious in the 3-D cell model. DJK-5 exerted potent inhibition of P. aeruginosa association with both substrates, only in serum-free medium. DJK-5 showed stronger inhibitory activity against P. aeruginosa associated with plastic compared to 3-D cells. The combined addition of tobramycin and DJK-5 exhibited more potent ability to inhibit P. aeruginosa association with both substrates. In conclusion, lung epithelial cells influence the efficacy of most antimicrobials against P. aeruginosa biofilm formation, which in turn depends on the presence or absence of FBS.

There are many proteomic applications that require large collections of purified protein, but parallel production of large numbers of different proteins remains a very challenging task. To help meet the needs of the scientific community, we have developed a human protein production pipeline. Using high-throughput (HT) methods, we transferred the genes of 31 full-length proteins into three expression vectors, and expressed the collection as N-terminal HaloTag fusion proteins in Escherichia coli and two commercial cell-free (CF) systems, wheat germ extract (WGE) and HeLa cell extract (HCE). Expression was assessed by labeling the fusion proteins specifically and covalently with a fluorescent HaloTag ligand and detecting its fluorescence on a LabChip[superscript ®] GX microfluidic capillary gel electrophoresis instrument. This automated, HT assay provided both qualitative and quantitative assessment of recombinant protein. E. coli was only capable of expressing 20% of the test collection in the supernatant fraction with ≥20 μg yields, whereas CF systems had ≥83% success rates. We purified expressed proteins using an automated HaloTag purification method. We purified 20, 33, and 42% of the test collection from E. coli, WGE, and HCE, respectively, with yields ≥1 μg and ≥90% purity. Based on these observations, we have developed a triage strategy for producing full-length human proteins in these three expression systems.

In eutherians, the placenta acts as a barrier and conduit at the maternal-fetal interface. Syncytiotrophoblasts, the multinucleated cells that cover the placental villous tree surfaces of the human placenta, are directly bathed in maternal blood and are formed by the fusion of progenitor cytotrophoblasts that underlie them. Despite their crucial role in fetal protection, many of the events that govern trophoblast fusion and protection from microbial infection are unknown. We describe a three-dimensional (3D)–based culture model using human JEG-3 trophoblast cells that develop syncytiotrophoblast phenotypes when cocultured with human microvascular endothelial cells. JEG-3 cells cultured in this system exhibit enhanced fusogenic activity and morphological and secretory activities strikingly similar to those of primary human syncytiotrophoblasts. RNASeq analyses extend the observed functional similarities to the transcriptome, where we observed significant overlap between syncytiotrophoblast-specific genes and 3D JEG-3 cultures. Furthermore, JEG-3 cells cultured in 3D are resistant to infection by viruses and Toxoplasma gondii, which mimics the high resistance of syncytiotrophoblasts to microbial infections in vivo. Given that this system is genetically manipulatable, it provides a new platform to dissect the mechanisms involved in syncytiotrophoblast development and microbial resistance.

Despite serving as the primary entry portal for coxsackievirus B (CVB), little is known about CVB infection of the intestinal epithelium, owing at least in part to the lack of suitable in vivo models and the inability of cultured cells to recapitulate the complexity and structure associated with the gastrointestinal (GI) tract. Here, we report on the development of a three-dimensional (3-D) organotypic cell culture model of Caco-2 cells to model CVB infection of the gastrointestinal epithelium. We show that Caco-2 cells grown in 3-D using the rotating wall vessel (RWV) bioreactor recapitulate many of the properties of the intestinal epithelium, including the formation of well-developed tight junctions, apical-basolateral polarity, brush borders, and multicellular complexity. In addition, transcriptome analyses using transcriptome sequencing (RNA-Seq) revealed the induction of a number of genes associated with intestinal epithelial differentiation and/or intestinal processes in vivo when Caco-2 cells were cultured in 3-D. Applying this model to CVB infection, we found that although the levels of intracellular virus production were similar in two-dimensional (2-D) and 3-D Caco-2 cell cultures, the release of infectious CVB was enhanced in 3-D cultures at early stages of infection. Unlike CVB, the replication of poliovirus (PV) was significantly reduced in 3-D Caco-2 cell cultures. Collectively, our studies show that Caco-2 cells grown in 3-D using the RWV bioreactor provide a cell culture model that structurally and transcriptionally represents key aspects of cells in the human GI tract and can thus be used to expand our understanding of enterovirus-host interactions in intestinal epithelial cells.

The United State generates the most waste among OECD countries, and there are adverse effects of the waste generation. One of the most serious adverse effects is greenhouse gas, especially CH4, which causes global warming. However, the amount of waste generation is not decreasing, and the United State recycling rate, which could reduce waste generation, is only 26%, which is lower than other OECD countries. Thus, waste generation and greenhouse gas emission should decrease, and in order for that to happen, identifying the causes should be made a priority. The research objective is to verify whether the Environmental Kuznets Curve relationship is supported for waste generation and GDP across the U.S. Moreover, it also confirmed that total waste generation and recycling waste influences carbon dioxide emissions from the waste sector. The annual-based U.S. data from 1990 to 2012 were used. The data were collected from various data sources, and the Granger causality test was applied for identifying the causal relationships. The results showed that there is no causality between GDP and waste generation, but total waste and recycling generation significantly cause positive and negative greenhouse gas emissions from the waste sector, respectively. This implies that the waste generation will not decrease even if GDP increases. And, if waste generation decreases or recycling rate increases, the greenhouse gas emission will decrease. Based on these results, it is expected that the waste generation and carbon dioxide emission from the waste sector can decrease more efficiently.

Construction waste management has become extremely important due to stricter disposal and landfill regulations, and a lesser number of available landfills. There are extensive works done on waste treatment and management of the construction industry. Concepts like deconstruction, recyclability, and Design for Disassembly (DfD) are examples of better construction waste management methods. Although some authors and organizations have published rich guides addressing the DfD's principles, there are only a few buildings already developed in this area. This study aims to find the challenges in the current practice of deconstruction activities and the gaps between its theory and implementation. Furthermore, it aims to provide insights about how DfD can create opportunities to turn these concepts into strategies that can be largely adopted by the construction industry stakeholders in the near future.

Previous studies in building energy assessment clearly state that to meet sustainable energy goals, existing buildings, as well as new buildings, will need to improve their energy efficiency. Thus, meeting energy goals relies on retrofitting existing buildings. Most building energy models are bottom-up engineering models, meaning these models calculate energy demand of individual buildings through their physical properties and energy use for specific end uses (e.g., lighting, appliances, and water heating). Researchers then scale up these model results to represent the building stock of the region studied.

Studies reveal that there is a lack of information about the building stock and associated modeling tools and this lack of knowledge affects the assessment of building energy efficiency strategies. Literature suggests that the level of complexity of energy models needs to be limited. Accuracy of these energy models can be elevated by reducing the input parameters, alleviating the need for users to make many assumptions about building construction and occupancy, among other factors. To mitigate the need for assumptions and the resulting model inaccuracies, the authors argue buildings should be described in a regional stock model with a restricted number of input parameters. One commonly-accepted method of identifying critical input parameters is sensitivity analysis, which requires a large number of runs that are both time consuming and may require high processing capacity.

This paper utilizes the Energy, Carbon and Cost Assessment for Buildings Stocks (ECCABS) model, which calculates the net energy demand of buildings and presents aggregated and individual- building-level, demand for specific end uses, e.g., heating, cooling, lighting, hot water and appliances. The model has already been validated using the Swedish, Spanish, and UK building stock data. This paper discusses potential improvements to this model by assessing the feasibility of using stepwise regression to identify the most important input parameters using the data from UK residential sector. The paper presents results of stepwise regression and compares these to sensitivity analysis; finally, the paper documents the advantages and challenges associated with each method.

Low fluid shear force, including that encountered in microgravity models, induces bacterial responses, but the range of bacteria capable of responding to this signal remains poorly characterized. We systematically analyzed a range of Gram negative Enterobacteriaceae for conservation of the low-shear modeled microgravity (LSMMG) response using phenotypic assays, qPCR, and targeted mutations. Our results indicate LSMMG response conservation across Enterobacteriacae with potential variance in up- or down-regulation of a given response depending on genus. Based on the data, we analyzed the role of the trp operon genes and the TrpR regulator in the LSMMG response using targeted mutations in these genes in S. Typhimurium and E. coli. We found no alteration of the LSMMG response compared to WT in these mutant strains under the conditions tested here. To our knowledge, this study is first-of-kind for Citrobacter, Enterobacter, and Serratia, presents novel data for Escherichia, and provides the first analysis of trp genes in LSMMG responses. This impacts our understanding of how LSMMG affects bacteria and our ability to modify bacteria with this condition in the future.

The mission of the DNASU Plasmid Repository is to accelerate research by providing high-quality, annotated plasmid samples and online plasmid resources to the research community through the curated DNASU database, website and repository (http://dnasu.asu.edu or http://dnasu.org). The collection includes plasmids from grant-funded, high-throughput cloning projects performed in our laboratory, plasmids from external researchers, and large collections from consortia such as the ORFeome Collaboration and the NIGMS-funded Protein Structure Initiative: Biology (PSI:Biology). Through DNASU, researchers can search for and access detailed information about each plasmid such as the full length gene insert sequence, vector information, associated publications, and links to external resources that provide additional protein annotations and experimental protocols. Plasmids can be requested directly through the DNASU website. DNASU and the PSI:Biology-Materials Repositories were previously described in the 2010 NAR Database Issue (Cormier, C.Y., Mohr, S.E., Zuo, D., Hu, Y., Rolfs, A., Kramer, J., Taycher, E., Kelley, F., Fiacco, M., Turnbull, G. et al. (2010) Protein Structure Initiative Material Repository: an open shared public resource of structural genomics plasmids for the biological community. Nucleic Acids Res., 38, D743–D749.). In this update we will describe the plasmid collection and highlight the new features in the website redesign, including new browse/search options, plasmid annotations and a dynamic vector mapping feature that was developed in collaboration with LabGenius. Overall, these plasmid resources continue to enable research with the goal of elucidating the role of proteins in both normal biological processes and disease.

As the construction continue to be a leading industry in the number of injuries and fatalities annually, several organizations and agencies are working avidly to ensure the number of injuries and fatalities is minimized. The Occupational Safety and Health Administration (OSHA) is one such effort to assure safe and healthful working conditions for working men and women by setting and enforcing standards and by providing training, outreach, education and assistance. Given the large databases of OSHA historical events and reports, a manual analysis of the fatality and catastrophe investigations content is a time consuming and expensive process. This paper aims to evaluate the strength of unsupervised machine learning and Natural Language Processing (NLP) in supporting safety inspections and reorganizing accidents database on a state level. After collecting construction accident reports from the OSHA Arizona office, the methodology consists of preprocessing the accident reports and weighting terms in order to apply a data-driven unsupervised K-Means-based clustering approach. The proposed method classifies the collected reports in four clusters, each reporting a type of accident. The results show the construction accidents in the state of Arizona to be caused by falls (42.9%), struck by objects (34.3%), electrocutions (12.5%), and trenches collapse (10.3%). The findings of this research empower state and local agencies with a customized presentation of the accidents fitting their regulations and weather conditions. What is applicable to one climate might not be suitable for another; therefore, such rearrangement of the accidents database on a state based level is a necessary prerequisite to enhance the local safety applications and standards.