Druggability Analysis of Protein Targets for Drug Discovery to Combat Listeria monocytogenes.

Extensive research has been conducted to identify key proteins governing stress responses, virulence, and antimicrobial resistance, as well as to elucidate their interactions within Listeria monocytogenes. While these proteins hold promise as potential targets for novel strategies to control L. monocytogenes, given their critical roles in regulating the pathogen's metabolism, additional analysis is needed to further assess their druggability-the chance of being effectively bound by small-molecule inhibitors. In this work, 535 binding pockets of 46 protein targets for known drugs (mainly antimicrobials) were first analyzed to extract 13 structural features (e.g., hydrophobicity) in a ligand-protein docking platform called Molsoft ICM Pro. The extracted features were used as inputs to develop a logistic regression model to assess the druggability of protein binding pockets, with a value of one if ligands can bind to the protein pocket. The developed druggability model was then used to evaluate 23 key proteins from L. monocytogenes that have been identified in the literature. The following proteins are predicted to be high-potential druggable targets: GroEL, FliH/FliI complex, FliG, FlhB, FlgL, FlgK, InlA, MogR, and PrfA. These findings serve as an initial point for future research to identify specific compounds that can inhibit druggable target proteins and to design experimental work to confirm their effectiveness as drug targets.

Investigation of Potential Drug Targets Involved in Inflammation Contributing to Alzheimer's Disease Progression.

Alzheimer's disease has become a major public health issue. While extensive research has been conducted in the last few decades, few drugs have been approved by the FDA to treat Alzheimer's disease. There is still an urgent need for understanding the disease pathogenesis, as well as identifying new drug targets for further drug discovery. Alzheimer's disease is known to arise from a build-up of amyloid beta (Aß) plaques as well as tangles of tau proteins. Along similar lines to Alzheimer's disease, inflammation in the brain is known to stem from the degeneration of tissue and build-up of insoluble materials. A minireview was conducted in this work assessing the genes, proteins, reactions, and pathways that link brain inflammation and Alzheimer's disease. Existing tools in Systems Biology were implemented to build protein interaction networks, mainly for the classical complement pathway and G protein-coupled receptors (GPCRs), to rank the protein targets according to their interactions. The top 10 protein targets were mainly from the classical complement pathway. With the consideration of existing clinical trials and crystal structures, proteins C5AR1 and GARBG1 were identified as the best targets for further drug discovery, through computational approaches like ligand-protein docking techniques.

A Comprehensive Study of Historical Detection Data for Pathogen Isolates from U.S. Cattle.

Foodborne pathogens pose substantial health hazards and result in considerable economic losses in the U.S. Fortunately, the National Center for Biotechnology Information Pathogen Detection Isolates Browser (NPDIB) provides valuable access to antimicrobial resistance (AMR) genes and antimicrobial assay data. This study aimed to conduct the first comprehensive investigation of AMR genes in pathogens isolated from U.S. cattle over the past decade, driven by the urgent need to address the dangers of AMR specifically originating in pathogens isolated from U.S. cattle. In this study, around 28,000 pathogen isolate samples were extracted from the NPDIB and then analyzed using multivariate statistical methods, mainly principal component analysis (PCA) and hierarchical clustering (H-clustering). These approaches were necessary due to the high dimensions of the raw data. Specifically, PCA was utilized to reduce the dimensions of the data, converting it to a two-dimensional space, and H-clustering was used to better identify the differences among data points. The findings from this work highlighted Salmonella enterica and Escherichia coli as the predominant pathogens among the isolates, with E. coli being the more concerning pathogen due to its increasing prevalence in recent years. Moreover, tetracycline was observed as the most commonly resistant antimicrobial, with the resistance genes mdsA, mdsB, mdtM, blaEC, and acrF being the most prevalent in pathogen isolates from U.S. cattle. The occurrence of mdtM, blaEC, acrF, and glpT_E448k showed an increase in pathogens isolated from U.S. cattle in recent years. Furthermore, based on the data collected for the locations of AMR cases, Texas, California, and Nebraska were the major areas carrying major AMR genes or antimicrobials with detected resistance. The results from this study provide potential directions for targeted interventions to mitigate pathogens' antimicrobial resistance in U.S. cattle.

Investigation of Potential Drug Targets for Cholesterol Regulation to Treat Alzheimer's Disease.

Despite extensive research and seven approved drugs, the complex interplay of genes, proteins, and pathways in Alzheimer's disease remains a challenge. This implies the intricacies of the mechanism for Alzheimer's disease, which involves the interaction of hundreds of genes, proteins, and pathways. While the major hallmarks of Alzheimer's disease are the accumulation of amyloid plaques and tau protein tangles, excessive accumulation of cholesterol is reportedly correlated with Alzheimer's disease patients. In this work, protein-protein interaction analysis was conducted based upon the genes from a clinical database to identify the top protein targets with most data-indicated involvement in Alzheimer's disease, which include ABCA1, CYP46A1, BACE1, TREM2, GSK3B, and SREBP2. The reactions and pathways associated with these genes were thoroughly studied for their roles in regulating brain cholesterol biosynthesis, amyloid beta accumulation, and tau protein tangle formation. Existing clinical trials for each protein target were also investigated. The research indicated that the inhibition of SREBP2, BACE1, or GSK3B is beneficial to reduce cholesterol and amyloid beta accumulation, while the activation of ABCA1, CYP46A1, or TREM2 has similar effects. In this study, Sterol Regulatory Element-Binding Protein 2 (SREBP2) emerged as the primary protein target. SREBP2 serves a pivotal role in maintaining cholesterol balance, acting as a transcription factor that controls the expression of several enzymes pivotal for cholesterol biosynthesis. Novel studies suggest that SREBP2 performs a multifaceted role in Alzheimer's disease. The hyperactivity of SREBP2 may lead to heightened cholesterol biosynthesis, which suggested association with the pathogenesis of Alzheimer's disease. Lowering SREBP2 levels in an Alzheimer's disease mouse model results in reduced production of amyloid-beta, a major contributor to Alzheimer's disease progression. Moreover, its thoroughly analyzed crystal structure allows for computer-aided screening of potential inhibitors; SREBP2 is thus selected as a prospective drug target. While more protein targets can be added onto the list in the future, this work provides an overview of key proteins involved in the regulation of brain cholesterol biosynthesis that may be further investigated for Alzheimer's disease intervention.

Protein Interaction Network Analysis to Investigate Stress Response, Virulence, and Antibiotic Resistance Mechanisms in Listeria monocytogenes.

Listeria monocytogenes is a deadly and costly foodborne pathogen that has a high fatality rate in the elderly, pregnant women, and people with weakened immunity. It can survive under various stress conditions and is a significant concern for the food industry. In this work, a data analysis approach was developed with existing tools and databases and used to create individual and combined protein interaction networks to study stress response, virulence, and antimicrobial resistance and their interaction with L. monocytogenes. The networks were analyzed, and 28 key proteins were identified that may serve as potential targets for new strategies to combat L. monocytogenes. Five of the twenty-eight proteins (i.e., sigB, flaA, cheA, cheY, and lmo0693) represent the most promising targets because they are highly interconnected within the combined network. The results of this study provide a new set of targets for future work to identify new strategies to improve food preservation methods and treatments for L. monocytogenes.

An In-Vitro Study of the Expansion and Transcriptomics of CD4+ and CD8+ Naïve and Memory T Cells Stimulated by IL-2, IL-7 and IL-15.

The growth of T cells ex vivo for the purpose of T cell therapies is a rate-limiting step in the overall process for cancer patients to achieve remission. Growing T cells is a fiscally-, time-, and resource-intensive process. Cytokines have been shown to accelerate the growth of T cells, specifically IL-2, IL-7, and IL-15. Here a design of experiments was conducted to optimize the growth rate of different naïve and memory T cell subsets using combinations of cytokines. Mathematical models were developed to study the impact of IL-2, IL-7, and IL-15 on the growth of T cells. The results show that CD4+ and CD8+ naïve T cells grew effectively using moderate IL-2 and IL-7 in combination, and IL-7, respectively. CD4+ and CD8+ memory cells favored moderate IL-2 and IL-15 in combination and moderate IL-7 and IL-15 in combination, respectively. A statistically significant interaction was observed between IL-2 and IL-7 in the growth data of CD4+ naïve T cells, while the interaction between IL-7 and IL-15 was found for CD8+ naïve T cells. The important genes and related signaling pathways and metabolic reactions were identified from the RNA sequencing data for each of the four subsets stimulated by each of the three cytokines. This systematic investigation lays the groundwork for studying other T cell subsets.

Investigation of Antimicrobial Resistance Genes in Listeria monocytogenes from 2010 through to 2021

Antimicrobial resistance (AMR) is a serious public health issue. Due to resistance to current antibiotics and a low rate of development of new classes of antimicrobials, AMR is a leading cause of death worldwide. Listeria monocytogenes is a deadly foodborne pathogen that causes listeriosis for the immunocompromised, the elderly, and pregnant women. Unfortunately, antimicrobial resistance has been reported in L. monocytogenes. This study conducted the first comprehensive statistical analysis of L. monocytogenes isolate data from the National Pathogen Detection Isolate Browser (NPDIB) to identify the trends for AMR genes in L. monocytogenes. Principal component analysis was firstly used to project the multi-dimensional data into two dimensions. Hierarchical clustering was then used to identify the significant AMR genes found in L. monocytogenes samples and to assess changes during the period from 2010 through to 2021. Statistical analysis of the data identified fosX, lin, abc-f, and tet(M) as the four most common AMR genes found in L. monocytogenes. It was determined that there was no increase in AMR genes during the studied time period. It was also observed that the number of isolates decreased from 2016 to 2020. This study establishes a baseline for the ongoing monitoring of L. monocytogenes for AMR genes.

Discovery of MurA Inhibitors as Novel Antimicrobials through an Integrated Computational and Experimental Approach.

The bacterial cell wall is essential for protecting bacteria from the surrounding environment and maintaining the integrity of bacteria cells. The MurA enzyme, which is an essential enzyme involved in bacterial cell wall synthesis, could be a good drug target for antibiotics. Although fosfomycin is used clinically as a MurA inhibitor, resistance to this antibiotic is a concern. Here we used molecular docking-based virtual screening approaches to identify potential MurA inhibitors from 1.412 million compounds from three databases. Thirty-three top compounds from virtual screening were experimentally tested in Listeria innocua (Gram-positive bacterium) and Escherichia coli (Gram-negative bacterium). Compound 2-Amino-5-bromobenzimidazole (S17) showed growth inhibition effect in both L. innocua and E. coli, with the same Minimum Inhibitory Concentration (MIC) value of 0.5 mg/mL. Compound 2-[4-(dimethylamino)benzylidene]-n-nitrohydrazinecarboximidamide (C1) had growth inhibition effect only in L. innocua, with a MIC value of 0.5 mg/mL. Two FDA-approved drugs, albendazole (S4) and diflunisal (S8), had a growth inhibition effect only in E. coli, with a MIC value of 0.0625 mg/mL. The identified MurA inhibitors could be potential novel antibiotics. Furthermore, they could be potential fosfomycin substitutes for the fosfomycin-resistant strains.

A Continuous Markov-Chain Model for the Simulation of COVID-19 Epidemic Dynamics.

To address the urgent need to accurately predict the spreading trend of the COVID-19 epidemic, a continuous Markov-chain model was, for the first time, developed in this work to predict the spread of COVID-19 infection. A probability matrix of infection was first developed in this model based upon the contact frequency of individuals within the population, the individual's characteristics, and other factors that can effectively reflect the epidemic's temporal and spatial variation characteristics. The Markov-chain model was then extended to incorporate both the mutation effect of COVID-19 and the decaying effect of antibodies. The developed comprehensive Markov-chain model that integrates the aforementioned factors was finally tested by real data to predict the trend of the COVID-19 epidemic. The result shows that our model can effectively avoid the prediction dilemma that may exist with traditional ordinary differential equations model, such as the susceptible-infectious-recovered (SIR) model. Meanwhile, it can forecast the epidemic distribution and predict the epidemic hotspots geographically at different times. It is also demonstrated in our result that the influence of the population's spatial and geographic distribution in a herd infection event is needed in the model for a better prediction of the epidemic trend. At the same time, our result indicates that no simple derivative relationship exists between the threshold of herd immunity and the virus basic reproduction number R0. The threshold of herd immunity achieved through natural immunity is significantly higher than 1 - 1/R0. These not only explain the theoretical misconceptions of herd immunity thresholds in herd immunity theory but also provide a guidance for predicting the optimal vaccination coverage. In addition, our model can predict the temporal and spatial distribution of infections in different epidemic waves. It is implied from our model that it is challenging to eradicate COVID-19 in the short term for a large population size and a wide spatial distribution. It is predicted that COVID-19 is likely to coexist with humans for a long time and that it will exhibit multipoint epidemic effects at a later stage. The statistical evidence is consistent with our prediction and strongly supports our modeling results.

Investigation of the Genes Involved in the Outbreaks of Escherichia coli and Salmonella spp. in the United States.

Salmonella spp. and Escherichiacoli (E. coli) are two of the deadliest foodborne pathogens in the US. Genes involved in antimicrobial resistance, virulence, and stress response, enable these pathogens to increase their pathogenicity. This study aims to examine the genes detected in both outbreak and non-outbreak Salmonella spp. and E. coli by analyzing the data from the National Centre for Biotechnology Information (NCBI) Pathogen Detection Isolates Browser database. A multivariate statistical analysis was conducted on the genes detected in isolates of outbreak Salmonella spp., non-outbreak Salmonella spp., outbreak E. coli, and non-outbreak E. coli. The genes from the data were projected onto a two-dimensional space through principal component analysis. Hierarchical clustering was then used to quantify the relationship between the genes in the dataset. Most of the outlier genes identified in E. coli isolates are virulence genes, while outlier genes identified in Salmonella spp. are mainly involved in stress response. Gene epeA, which encodes a high-molecular-weight serine protease autotransporter of Enterobacteriaceae (SPATE) protein, along with subA and subB that encode cytotoxic activity, may contribute to the pathogenesis of outbreak E. coli. The iro operon and ars operon may play a role in the ecological success of the epidemic clones of Salmonella spp. Concurrent relationships between esp and ter operons in E. coli and pco and sil operons in Salmonella spp. are found. Stress-response genes (asr, golT, golS), virulence gene (sinH), and antimicrobial resistance genes (mdsA and mdsB) in Salmonella spp. also show a concurrent relationship. All these findings provide helpful information for experiment design to combat outbreaks of E. coli and Salmonella spp.

An Integrated Computational and Experimental Approach to Identifying Inhibitors for SARS-CoV-2 3CL Protease.

The newly evolved SARS-CoV-2 has caused the COVID-19 pandemic, and the SARS-CoV-2 main protease 3CLpro is essential for the rapid replication of the virus. Inhibiting this protease may open an alternative avenue toward therapeutic intervention. In this work, a computational docking approach was developed to identify potential small-molecule inhibitors for SARS-CoV-2 3CLpro. Totally 288 potential hits were identified from a half-million bioactive chemicals via a protein-ligand docking protocol. To further evaluate the docking results, a quantitative structure activity relationship (QSAR) model of 3CLpro inhibitors was developed based on existing small molecule inhibitors of the 3CLproSARS- CoV- 1 and their corresponding IC50 data. The QSAR model assesses the physicochemical properties of identified compounds and estimates their inhibitory effects on 3CLproSARS- CoV- 2. Seventy-one potential inhibitors of 3CLpro were selected through these computational approaches and further evaluated via an enzyme activity assay. The results show that two chemicals, i.e., 5-((1-([1,1'-biphenyl]-4-yl)-2,5-dimethyl-1H-pyrrol-3-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione and N-(4-((3-(4-chlorophenylsulfonamido)quinoxalin-2-yl)amino)phenyl)acetamide, effectively inhibited 3CLpro SARS-CoV-2 with IC50's of 19 ± 3 µM and 38 ± 3 µM, respectively. The compounds contain two basic structures, pyrimidinetrione and quinoxaline, which were newly found in 3CLpro inhibitor structures and are of high interest for lead optimization. The findings from this work, such as 3CLpro inhibitor candidates and the QSAR model, will be helpful to accelerate the discovery of inhibitors for related coronaviruses that may carry proteases with similar structures to SARS-CoV-2 3CLpro.

Investigation of the Impact from IL-2, IL-7, and IL-15 on the Growth and Signaling of Activated CD4+ T Cells.

While CAR-T therapy is a growing and promising area of cancer research, it is limited by high cost and the difficulty of consistently culturing T-cells to therapeutically relevant concentrations ex-vivo. Cytokines IL-2, IL-7 and IL-15 have been found to stimulate the growth of T cells, however, the optimized combination of these three cytokines for T cell proliferation is unknown. In this study, we designed an integrated experimental and modeling approach to optimize cytokine supplementation for rapid expansion in clinical applications. We assessed the growth data for statistical improvements over no cytokine supplementation and used a systems biology approach to identify genes with the highest magnitude of expression change from control at several time points. Further, we developed a predictive mathematical model to project the growth rate for various cytokine combinations, and investigate genes and reactions regulated by cytokines in activated CD4+ T cells. The most favorable conditions from the T cell growth study and from the predictive model align to include the full range of IL-2 and IL-7 studied, and at lower levels of IL-15 (6 ng/mL or 36 ng/mL). The highest growth rates were observed where either IL-2 or IL-7 was at the highest concentration tested (15 ng/mL for IL-2 and 80 ng/mL for IL-7) while the other was at the lowest (1 ng/mL for IL-2 and 6 ng/mL for IL-7), or where both IL-2 and IL-7 concentrations are moderate-corresponding to condition keys 200, 020, and 110 respectively. This suggests a synergistic interaction of IL-2 and IL-7 with regards to promoting optimal proliferation and survival of the activated CD4+ T cells. Transcriptomic data analysis identified the genes and transcriptional regulators up/down-regulated by each of the cytokines IL-2, IL-7, and IL-15. It was found that the genes with persistent expressing changes were associated with major pathways involved in cell growth and proliferation. In addition to influencing T cell metabolism, the three cytokines were found to regulate specific genes involved in TCR, JAK/STAT, MAPK, AKT and PI3K-AKT signaling. The developed Fuzzy model that can predict the growth rate of activated CD4+ T cells for various combinations of cytokines, along with identified optimal cytokine cocktails and important genes found in transcriptomic data, can pave the way for optimizing activated CD4 T cells by regulating cytokines in the clinical setting.

The Inhibitory Effect of Plant Extracts on Growth of the Foodborne Pathogen, Listeria monocytogenes.

Listeria monocytogenes is a foodborne pathogen responsible for about 1600 illnesses each year in the United States (US) and about 2500 confirmed invasive human cases in European Union (EU) countries. Several technologies and antimicrobials are applied to control the presence of L. monocytogenes in food. Among these, the use of natural antimicrobials is preferred by consumers. This is due to their ability to inhibit the growth of foodborne pathogens but not prompt negative safety concerns. Among natural antimicrobials, plant extracts are used to inactivate L. monocytogenes. However, there is a large amount of these types of extracts, and their active compounds remain unexplored. The aim of this study was to evaluate the antibacterial activity against L. monocytogenes of about 800 plant extracts derived from plants native to different countries worldwide. The minimal inhibitory concentrations (MICs) were determined, and scanning electron microscopy (SEM) was used to verify how the plant extracts affected L. monocytogenes at the microscopic level. Results showed that 12 of the plant extracts had inhibitory activity against L. monocytogenes. Future applications of this study could include the use of these plant extracts as new preservatives to reduce the risk of growth of pathogens and contamination in the food industry from L. monocytogenes.

Comparison of Antimicrobial Resistance Detected in Environmental and Clinical Isolates from Historical Data for the US.

Antimicrobial resistance (AMR) has become an urgent public health issue, as pathogens are becoming increasingly resistant to commonly used antimicrobials. While AMR isolate data are available in the NCBI Pathogen Detection Isolates Browser (NPDIB) database, few researches have been performed to compare antimicrobial resistance detected in environmental and clinical isolates. To address this, this work conducted the first multivariate statistical analysis of antimicrobial-resistance pathogens detected in NPDIB clinical and environmental isolates for the US from 2013 to 2018. The highly occurring AMR genes and pathogens were identified for both clinical and environmental settings, and the historical profiles of those genes and pathogens were then compared for the two settings. It was found that Salmonella enterica and E. coli and Shigella were the highly occurring AMR pathogens for both settings. Additionally, 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 in the clinical setting.

Synergistic Effect of Chlorogenic Acid and Caffeic Acid with Fosfomycin on Growth Inhibition of a Resistant Listeria monocytogenes Strain.

Listeria monocytogenes, a human foodborne pathogen that causes listeriosis with high-rate mortality, has been reported to be resistant to commonly used antibiotics. New antibiotics or cocktails of existing antibiotics with synergistic compounds are in high demand for treating this multi-drug-resistant pathogen. Fosfomycin is one of the novel and promising therapeutic antibiotics for the treatment of listeriosis. However, some L. monocytogenes strains with the FosX gene were recently reported to survive from the fosfomycin treatment. This work aims to identify FosX inhibitors that can revive fosfomycin in treating resistant L. monocytogenes. Since structures and activities of the FosX protein in L. monocytogenes have been well studied, we used an integrated computational and experimental approach to identify FosX inhibitors that show synergistic effect with fosfomycin in treating resistant L. monocytogenes. Specifically, automated ligand docking was implemented to perform virtual screening of the Indofine natural-product database and FDA-approved drugs to identify potential inhibitors. An in vitro bacterial growth inhibition test was then utilized to verify the effectiveness of identified compounds combined with fosfomycin in inhibiting the resistant L. monocytogenes strains. 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 (1.5 mg/mL) or chlorogenic acid (3 mg/mL) with fosfomycin (50 mg/L) was able to significantly inhibit the growth of the pathogen. The finding of this work implies that the combination of fosfomycin with either caffeic acid or chlorogenic acid is of potential to be used in the clinical treatment of Listeria infections.

Investigation of Incidents and Trends of Antimicrobial Resistance in Foodborne Pathogens in Eight Countries from Historical Sample Data.

Antimicrobial resistance (AMR) causes millions of illnesses every year, threatening the success of lifesaving antibiotic therapy and, thus, public health. To examine the rise and spread of antimicrobial resistance around the world, our study performs 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. Multi-dimensional data points were projected onto a two-dimensional plane using principal component analysis and organized into a dendrogram utilizing hierarchical clustering to identify significant AMR genes and pathogens. Outlier genes/pathogens were typically involved in high occurrences of antimicrobial resistance, and they were able to indicate the trend of antimicrobial resistance in the future. Statistical analysis of the data identified: (1) tet(A), aph(3'')-Ib, aph(6)-Id, blaEC, blaTEM-1, qacEdelta1, sul1, sul2, and aadA1 as the nine most common AMR genes among the studied countries; (2) Salmonella enterica and E. coli and Shigella as the most common AMR foodborne pathogens; and (3) chicken as the most prevalent meat carrier of antimicrobial resistance. Our study shows that the overall number of reported antimicrobial resistance cases in foodborne pathogens is generally rising. One potential contributing factor for this is the increasing antimicrobial usage in the growing livestock industry.

Data-Driven Analysis of Antimicrobial Resistance in Foodborne Pathogens from Six States within the US.

Foodborne pathogens cause thousands of illnesses across the US each year. However, these pathogens gain resistance to the antimicrobials that are commonly used to treat them. Typically, antimicrobial resistance is caused by mechanisms encoded by multiple antimicrobial-resistance genes. These are carried through pathogens found in foods such as meats. It is, thus, important to study the genes that are most related to antimicrobial resistance, the pathogens, and the meats carrying antimicrobial-resistance genes. This information can be further used to correlate the antimicrobial-resistance genes found in humans for improving human health. Therefore, we perform the first multivariate statistical analysis of the antimicrobial-resistance gene data provided in the NCBI Pathogen Detection Isolates Browser database, covering six states that are geographically either in close proximity to one another (i.e., Pennsylvania (PA), Maryland (MD), and New York (NY)) or far (i.e., New Mexico (NM), Minnesota (MN), and California (CA)). Hundreds of multidimensional data points were projected onto a two-dimensional space that was specified by the first and second principal components, which were then categorized with a hierarchical clustering approach. It turns out that aadA, aph(3''), aph(3'')-Ib, aph(6)-I, aph(6)-Id, bla, blaCMY, tet, tet(A), and sul2 constructed the assembly of ten genes that were most commonly involved in antimicrobial resistance in these six states. While geographically close states like PA, MD and NY share more similar antimicrobial-resistance genes, geographically far states like NM, MN, and CA also contain most of these common antimicrobial-resistance genes. One potential reason for this spread of antimicrobial-resistance genes beyond the geographic limitation is that animal meats like chicken and turkey act as the carriers for the nationwide spread of these genes.

Optimizing performance of semi-continuous cell culture in an ambr15™ microbioreactor using dynamic flux balance modeling.

The ambr bioreactors are single-use microbioreactors for cell line development and process optimization. With operating conditions for large-scale biopharmaceutical production properly scaled down, microbioreactors such as the ambr15™ can potentially be used to predict the effect of process changes such as modified media or different cell lines. While there have been some recent studies evaluating the ambr15™ technology as a scale-down model for fed-batch operations, little has been reported for semi-continuous or continuous operation. Gassing rates and dilution rates in the ambr15™ were varied in this study to attempt to replicate performance of a perfusion process at the 5 L scale. At both scales, changes to metabolite production and consumption, and cell growth rate and therapeutic protein production were measured. Conditions were identified in the ambr15™ bioreactor that produced metabolic shifts and specific metabolic and protein production rates that are characteristic of the corresponding 5 L perfusion process. A dynamic flux balance (DFB) model was employed to understand and predict the metabolic changes observed. The DFB model predicted trends observed experimentally, including lower specific glucose consumption and a switch from lactate production to consumption when dissolved CO2 was maintained at higher levels in the broth. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:420-431, 2018.

A Genome-Scale Modeling Approach to Quantify Biofilm Component Growth of Salmonella Typhimurium.

Salmonella typhimurium (S. typhimurium) is an extremely dangerous foodborne bacterium that infects both animal and human subjects, causing fatal diseases around the world. Salmonella's robust virulence, antibiotic-resistant nature, and capacity to survive under harsh conditions are largely due to its ability to form resilient biofilms. Multiple genome-scale metabolic models have been developed to study the complex and diverse nature of this organism's metabolism; however, none of these models fully integrated the reactions and mechanisms required to study the influence of biofilm formation. This work developed a systems-level approach to study the adjustment of intracellular metabolism of S. typhimurium during biofilm formation. The most advanced metabolic reconstruction currently available, STM_v1.0, was 1st extended to include the formation of the extracellular biofilm matrix. Flux balance analysis was then employed to study the influence of biofilm formation on cellular growth rate and the production rates of biofilm components. With biofilm formation present, biomass growth was examined under nutrient rich and nutrient deficient conditions, resulting in overall growth rates of 0.8675 and 0.6238 h-1 respectively. Investigation of intracellular flux variation during biofilm formation resulted in the elucidation of 32 crucial reactions, and associated genes, whose fluxes most significantly adapt during the physiological response. Experimental data were found in the literature to validate the importance of these genes for the biofilm formation of S. typhimurium. This preliminary investigation on the adjustment of intracellular metabolism of S. typhimurium during biofilm formation will serve as a platform to generate hypotheses for further experimental study on the biofilm formation of this virulent bacterium.

A systems biology approach to investigate the antimicrobial activity of oleuropein.

Oleuropein and its hydrolysis products are olive phenolic compounds that have antimicrobial effects on a variety of pathogens, with the potential to be utilized in food and pharmaceutical products. While the existing research is mainly focused on individual genes or enzymes that are regulated by oleuropein for antimicrobial activities, little work has been done to integrate intracellular genes, enzymes and metabolic reactions for a systematic investigation of antimicrobial mechanism of oleuropein. In this study, the first genome-scale modeling method was developed to predict the system-level changes of intracellular metabolism triggered by oleuropein in Staphylococcus aureus, a common food-borne pathogen. To simulate the antimicrobial effect, an existing S. aureus genome-scale metabolic model was extended by adding the missing nitric oxide reactions, and exchange rates of potassium, phosphate and glutamate were adjusted in the model as suggested by previous research to mimic the stress imposed by oleuropein on S. aureus. The developed modeling approach was able to match S. aureus growth rates with experimental data for five oleuropein concentrations. The reactions with large flux change were identified and the enzymes of fifteen of these reactions were validated by existing research for their important roles in oleuropein metabolism. When compared with experimental data, the up/down gene regulations of 80% of these enzymes were correctly predicted by our modeling approach. This study indicates that the genome-scale modeling approach provides a promising avenue for revealing the intracellular metabolism of oleuropein antimicrobial properties.