Following are the research areas that we are working on:
- Computational approaches to identify inhibitors for drug targets that are responsible for human diseases (e.g., cancers, viruses, and infections)
- Systems biology approaches to study metabolisms of foodborne pathogens
- Development of mathematical models for optimizing T cell growth and for reducing side effects of immunotherapy
- Big data analysis of pathogens, viruses, and diseases
- Design of sediment microbial fuel cells (for STEM OutReach)
Diseases are generally caused by malfunctioned biochemical reactions. Inhibiting the proteins involved those reactions is one way for combating diseases. Those proteins are called drug targets for inhibition. Automated molecular docking is the most commonly used computational approach that evaluates the binding of small-molecule ligands like compounds to a target protein with a known crystal 3D structure. Molecular docking provides an avenue for a high-throughput virtual screening of ligands, and it has been widely implemented in drug discovery research for hit identification. We are developing docking-based computational approaches for identifying small molecule inhibitors to combat antimicrobial resistant pathogens, COVID-19 virus and cancers.
One onging project is identifying natural compounds to inhibit foodborne pathogen Listeria monocytogenes, a human foodborne pathogen that causes listeriosis with high-rate mortality. Listeria monocytogenes has been reported to be resistant to commonly used antibiotics. For example, the FosX protein enables Listeria monocytogenes survive from the treatment of Fosfomycin. We have developed a computational platform to identify natural compounds for inhibiting the FosX protein so that Fosfomycin is able to eliminate Listeria monocytogenes. We demonstrated that two phenolic acids, i.e., caffeic acid and chlorogenic acid, were predicted as high-affinity FosX inhibitors from the ligand-docking platform. Experiments with these compounds indicated that: the cocktail of either caffeic acid (1.5mg/mL) or chlorogenic acid (3mg/mL) with Fosfomycin (50mg/L) was able to significantly inhibit the growth of the pathogen. Our work in this project was published in ACS Omega (Zhang et al., 2020), and our result was selected as the journal cover image (Zhang et al., 2020).
Pathogens induce serious infections and cause life-threatening diseases such as pneumonia. While antimicrobials are generally effective in treating pathogens, pathogens adapt their metabolisms under the stress conditions and become resistant to antimicrobials. Biofilm formation is one of the most important strategies used by pathogens in the stress response. Specifically, it has been reported that 10 - 1000 times enhanced resistance to antimicrobials is acquired once bacteria form biofilms. While the following paragraph summarizes our work on studying the biofilm formation of clinical pathogens, we now extend our work to foodborne pathogens under other stress conditions like low temperature and low pH values.
We are developing systems biology approaches to metabolism of pathogens in biofilms, and identify therapeutic targets against biofilm-associated bacterial infections. Since Pseudomonas aeruginosa is one of the leading causes of nosocomial infections in hospitalized patients and display resistance to a wide array of antibiotics, it is chosen as the example microorganism to be studied in this project. We have studied the biofilm formation of single gene mutants of Pseudomonas aeruginosa and identified the gene targets that can be used to eliminate Pseudomonas aeruginosa before it forms a biofilm (Xu et al., 2013). The biofilm formation of Pseudomonas aeruginosa under vaious nutrient conditions (Xu et al., 2015) or treated by antibiotics (Xu et al., 2016) has been studied. In addition to Pseudomonas aeruginosa, we have worked with USDA ARS on studying the stress response of foodborne pathogens Salmonella Typhimurium (Ribaudo et al., 2017) and Staphylococcus aureus (Li et al., 2016) treated by antimicrobial plant extracts. We now focus on identifying effective compounds to treat antimicroibal-resistant pathogens, especially those persister cells in biofilms.
Adoptive immunotherapies like CAR-T-cells, aim to provide the patient specific treatment. On the other hand, CAR-T cell therapy costs as much as $375,000 for a one-time treatment. The high cost of CAR-T cell therapy is due the slow growth of T cells. In addition, CAR-T cell therapy can cause deadly side effects like cytokine release syndromes in which concentrations of certain cytokines surge sharply in the first 24 hours. We address these issues by: 1) optimizing the cytokine concentrations in the T cell growth medium to accelerate the T cell growth; 2) developing models to quantify the concentrations of cytokines released during T cell therapy. While we are still working on the project in which we accelerate the growth of CD4+/CD8+ naïve and effectors T cells, we have developed a mathematical model to quantify the severity of cytokine release syndrome. On the basis of our model, we effectively demonstrated a selective approach to reduce the severity of CRS with sequential cytokine inhibition targets. We achieved the reduction of grades by applying the insight from the sensitivity analysis, beginning with the most sensitive targets. Cytokines IL-1, IL-8, TNF-α, INF-γ, IL-6, IL-2, IL-4, IL-10, and IL-12 were in turn the best targets for inhibition to alleviate CRS. Our work has been published in Hopkins, et al., 2019. In addition, we have developed another model in which the dynamics of acute phase proteins haptoglobin, fibrinogen, and albumin can be quantified over time during the acute phase response that may happen during T cell therapy (Xu et al., 2015).
Investigation of data from existing databases on pathogens, viruses and diseases can provide meaningful information on how to eliminate pathogens and viruses and how to prevent diseases. We implement big data analysis techniques to extract useful information from those disease databases. One ongoing project deals with analyzing antimicrobial resistance gene data from the NCBI Pathogen Detection Isolates Browser (NPDIB). In particular, we studied the antimicrobial resistance gene data obtained from environmental samples (Yang et al, 2020), clinical samples (Li et al., 2019),and compared gene patterns between clinical and environmental samples (Hua et al., 2020).
As for environmental samples (Yang et al, 2020), we performed a multivariate statistical analysis of antimicrobial resistance gene data from eight different countries : the US, the UK, China, Brazil, Mexico, Canada, Australia, and South Africa. Our study indicates that: 1) Salmonella enterica and E. coli and Shigella as the most common AMR foodborne pathogens; 2) chicken as the most prevalent meat carrier of antimicrobial resistance; and 3) South Africa had the most statistically unique resistance pattern.
As for clinical samples (Li et al., 2019), we found that aph(3”)-Ib, aph(6)-Id, blaTEM-1, and qacEdelta1 were shared among all six countries (i.e., Australia, Brazil, China, South Africa, the UK, and the US). The most shared pathogens responsible for carrying the most important genes in the six countries in the clinical setting were Acinetobacter baumannii, E. coli and Shigella, Klebsiella pneumoniae and Salmonella enterica. South Africa carried the least similar antimicrobial genes to the other countries in clinical isolates.
As for comparing environmental and clinical sample (Hua et al., 2020), our statistical analysis of the data indicates that the genes fosA, oqxB, ble, floR, fosA7, mcr-9.1, aadA1, aadA2, ant(2’’)-Ia, aph(3’’)-Ib, aph(3’)-Ia, aph(6)-Id, blaTEM-1, qacEdelta1, sul1, sul2, tet(A), and tet(B) were mostly detected for both clinical and environmental settings. Ampicillin, ceftriaxone, gentamicin, tetracycline, and cefoxitin were the antimicrobials which got the most resistance in both settings. The historical profiles of these genes, pathogens and antimicrobials indicated that higher occurrence frequencies generally took place earlier in the environmental setting than the clinical setting.
Microbial fuel cells (MFCs) can take advantage of microbial interaction with an electrode and produce electric energy directly from organic compounds in waste water or sediment. It may provide a sustainable way to treat water treatment or provide power in remote regions. The performance of MFCs depends on the optimization of design parameters such as the metabolism of microorganisms used to form biofilms on the anode and produce electricity, the substrate loading patterns, the external electrical resistance, and the fuel cell configuration. We are developing comprehensive kinetic models to investigate the influence from the design and operation parameters on the microbial activity and the power production of MFCs. We have developed a mathematical model to quantify the dynamic behavior of microbial desalination cells (Ping et al., 2014). We now design an experimental platform for high-school or undergraduate students to test different configurations of sedimental microbial fuel cells. This is one of the STEM Outreach activities in Dr. Huang's lab.