Project description:Where phages are used to treat bacterial contaminations and infections, multiple phages are typically applied at once as a cocktail. When two or more phages in the cocktail attack the same bacterium, the combination may produce better killing than any single phage (synergy) or the combination may be worse than the best single phage (interference). Synergy is of obvious utility, especially if it can be predicted a priori, but it remains poorly documented with few examples known. This study addresses synergy in which one phage improves adsorption by a second phage. It first presents evidence of synergy from an experimental system of two phages and a mucoid E. coli host. The synergy likely stems from a tailspike enzyme produced by one of the phages. We then offer mathematical models and simulations to understand the dynamics of synergy and the enhanced magnitude of bacterial control possible. The models and observations complement each other and suggest that synergy may be of widespread utility and may be predictable from easily observed phenotypes.
Project description:BackgroundBreast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge.ResultsThis study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects.ConclusionsThe framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment.
Project description:The global rise of antibiotic resistance has renewed interest in phage therapy, as an alternative to antibiotics to eliminate multidrug-resistant (MDR) bacterial pathogens. However, optimizing the broad-spectrum efficacy of phage therapy remains a challenge. In this study, we addressed this issue by employing strategies to improve antimicrobial efficacy of phage therapy against MDR Klebsiella pneumoniae strains, which are notorious for their resistance to conventional antibiotics. This includes the selection of broad host range phages, optimization of phage formulation, and combinations with last-resort antibiotics. Our findings unveil that having a broad host range was a dominant trait of isolated phages, and increasing phage numbers in combination with antibiotics significantly enhanced the suppression of bacterial growth. The decreased incidence of bacterial infection was explained by a reduction in pathogen density and emergence of bacterial resistance. Furthermore, phage-antibiotic synergy (PAS) demonstrated considerable broad-spectrum antibacterial potential against different clades of clinical MDR K. pneumoniae pathogens. The improved treatment outcomes of optimized PAS were also evident in a murine model, where mice receiving optimized PAS therapy demonstrated a reduced bacterial burden in mouse tissues. Taken together, these findings offer an important development in optimizing PAS therapy and its efficacy in the elimination of MDR K. pneumoniae pathogens.ImportanceThe worldwide spread of antimicrobial resistance (AMR) has posed a great challenge to global public health. Phage therapy has become a promising alternative against difficult-to-treat pathogens. One important goal of this study was to optimize the therapeutic efficiency of phage-antibiotic combinations, known as phage-antibiotic synergy (PAS). Through comprehensive analysis of the phenotypic and genotypic characteristics of a large number of CRKp-specific phages, we developed a systematic model for phage cocktail combinations. Crucially, our finding demonstrated that PAS treatments not only enhance the bactericidal effects of colistin and tigecycline against multidrug-resistant (MDR) K. pneumoniae strains in in vitro and in vivo context but also provide a robust response when antibiotics fail. Overall, the optimized PAS therapy demonstrates considerable potential in combating diverse K. pneumoniae pathogens, highlighting its relevance as a strategy to mitigate antibiotic resistance threats effectively.
Project description:Bacteriophage (phage) therapy is being explored as a possible response to the antimicrobial resistance public health emergency. Administering a mixture of different phage types as a cocktail is one proposed strategy for therapeutic applications, but the optimal method for formulating phage cocktails remains a major challenge. Each phage strain has complex pharmacokinetic/pharmacodynamic (PK/PD) properties which depend on the nano-scale size, target-mediated, self-dosing nature of each phage strain, and rapid selection of resistant subpopulations. The objective of this study was to explore the pharmacodynamics (PD) of three unique and clinically relevant anti-Pseudomonas phages after simulation of dynamic dosing strategies. The Hollow Fiber Infection Model (HFIM) is an in vitro system that mimics in vivo pharmacokinetics (PK) with high fidelity, providing an opportunity to quantify phage and bacteria concentration profiles over clinical time scales with rich sampling. Exogenous monotherapy-bolus (producing max concentrations of Cmax = 7 log10 PFU/mL) regimens of phages LUZ19, PYO2, and E215 produced Pseudomonas aeruginosa nadirs of 0, 2.14, or 2.99 log10 CFU/mL after 6 h of treatment, respectively. Exogenous combination therapy bolus regimens (LUZ19 + PYO2 or LUZ19 + E215) resulted in bacterial reduction to <2 log10 CFU/mL. In contrast, monotherapy as a continuous infusion (producing a steady-state concentration of Css,avg = 2 log10PFU/mL) was less effective at reducing bacterial densities. Specifically, PYO2 failed to reduce bacterial density. Next, a mechanism-based mathematical model was developed to describe phage pharmacodynamics, phage-phage competition, and phage-dependent adaptive phage resistance. Monte Carlo simulations supported bolus dose regimens, predicting lower bacterial counts with bolus dosing as compared to prolonged phage infusions. Together, in vitro and in silico evaluation of the time course of phage pharmacodynamics will better guide optimal patterns of administration of individual phages as a cocktail.
Project description:Steel slag has been proven to be an effective environment remediation media for acid neutralization, and a potential aid to mitigate acid mine drainage (AMD). Yet its acid neutralization capacity (ANC) is frequently inhibited by precipitate after a period of time, while the characteristics of the precipitate formation process are unclear yet. In this study, ANC for basic oxygen steel slag was conducted by neutralization experiments with dilute sulfuric acid (0.1 M) and real AMD. Some partially neutralized steel slag samples were determined by X-ray diffraction (XRD), scanning electron microscopy combined with an energy dispersive spectrometer (SEM-EDS), and N2 adsorption tests to investigate the potential formation process of the precipitate. The results indicated that Ca-bearing constitutes leaching and sulfate formation were two main reactions throughout the neutralization process. A prominent transition turning point from leaching to precipitate was at about 40% of the neutralization process. Tricalcium silicate (Ca3SiO5) played a dominant role in the alkalinity-releasing stage among Ca-bearing components, while the new-formed well crystalline CaSO4 changed the microstructure of steel slag and further hindered alkaline components releasing. For steel slag of 200 mesh size, the ANC value for the steel slag sample was 8.23 mmol H+/g when dilute sulfate acid was used. Neutralization experiments conducted by real AMD confirmed that the steel slag ANC was also influenced by the high contaminants, such as Fe2+, due to the hydroxides precipitate reactions except for sulfate formation reactions.
Project description:We evaluated the sensitivity and time resolution of a technique for photochemical reaction monitoring based on the interferometric detection of the deformation of liquid films. The reaction products change the local surface tension and induce Marangoni flow in the liquid film. As a model system, we consider the irradiation of the aliphatic hydrocarbon squalane with broadband deep-UV light. We developed a numerical model that quantitatively reproduces the flow patterns observed in the experiments. Moreover, we present self-similarity solutions that elucidate the mechanisms governing different stages of the dynamics and their parametric dependence. Surface tension changes as small as Δγ = 10-6 N/m can be detected, and time resolutions of <1 s can be achieved.
Project description:Online social media platforms are opening up new opportunities to analyse human behaviour on an unprecedented scale. In some cases, the fast, cheap measurements of human behaviour gained from these platforms may offer an alternative to gathering such measurements using traditional, time consuming and expensive surveys. Here, we use geotagged photographs uploaded to the photo-sharing website Flickr to quantify international travel flows, by extracting the location of users and inferring trajectories to track their movement across time. We find that Flickr based estimates of the number of visitors to the United Kingdom significantly correlate with the official estimates released by the UK Office for National Statistics, for 28 countries for which official estimates are calculated. Our findings underline the potential for indicators of key aspects of human behaviour, such as mobility, to be generated from data attached to the vast volumes of photographs posted online.
Project description:Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for "active modules"--regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.
Project description:Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i.e., focus on separating normal data from the rest of the space. Such methods are based on the assumption of separability of normal and anomalous classes, and subsequently do not take into account any available samples of anomalies. Nonetheless, in practical settings, some anomalous samples are often available; however, usually in amounts far lower than required for a balanced classification task, and the separability assumption might not always hold. This leads to an important task-incorporating known anomalous samples into training procedures of anomaly detection models. In this work, we propose a novel model-agnostic training procedure to address this task. We reformulate one-class classification as a binary classification problem with normal data being distinguished from pseudo-anomalous samples. The pseudo-anomalous samples are drawn from low-density regions of a normalizing flow model by feeding tails of the latent distribution into the model. Such an approach allows to easily include known anomalies into the training process of an arbitrary classifier. We demonstrate that our approach shows comparable performance on one-class problems, and, most importantly, achieves comparable or superior results on tasks with variable amounts of known anomalies.
Project description:Despite thousands of reported studies unveiling gene-level signatures for complex diseases, few of these techniques work at the single-sample level with explicit underpinning of biological mechanisms. This presents both a critical dilemma in the field of personalized medicine as well as a plethora of opportunities for analysis of RNA-seq data. In this study, we hypothesize that the "Functional Analysis of Individual Microarray Expression" (FAIME) method we developed could be smoothly extended to RNA-seq data and unveil intrinsic underlying mechanism signatures across different scales of biological data for the same complex disease. Using publicly available RNA-seq data for gastric cancer, we confirmed the effectiveness of this method (i) to translate each sample transcriptome to pathway-scale scores, (ii) to predict deregulated pathways in gastric cancer against gold standards (FDR<5%, Precision=75%, Recall =92%), and (iii) to predict phenotypes in an independent dataset and expression platform (RNA-seq vs microarrays, Fisher Exact Test p<10(-6)). Measuring at a single-sample level, FAIME could differentiate cancer samples from normal ones; furthermore, it achieved comparative performance in identifying differentially expressed pathways as compared to state-of-the-art cross-sample methods. These results motivate future work on mechanism-level biomarker discovery predictive of diagnoses, treatment, and therapy.