Fluxer: a web application to compute, analyze and visualize genome-scale metabolic flux networks.

Next-generation sequencing has paved the way for the reconstruction of genome-scale metabolic networks as a powerful tool for understanding metabolic circuits in any organism. However, the visualization and extraction of knowledge from these large networks comprising thousands of reactions and metabolites is a current challenge in need of user-friendly tools. Here we present Fluxer (https://fluxer.umbc.edu), a free and open-access novel web application for the computation and visualization of genome-scale metabolic flux networks. Any genome-scale model based on the Systems Biology Markup Language can be uploaded to the tool, which automatically performs Flux Balance Analysis and computes different flux graphs for visualization and analysis. The major metabolic pathways for biomass growth or for biosynthesis of any metabolite can be interactively knocked-out, analyzed and visualized as a spanning tree, dendrogram or complete graph using different layouts. In addition, Fluxer can compute and visualize the k-shortest metabolic paths between any two metabolites or reactions to identify the main metabolic routes between two compounds of interest. The web application includes >80 whole-genome metabolic reconstructions of diverse organisms from bacteria to human, readily available for exploration. Fluxer enables the efficient analysis and visualization of genome-scale metabolic models toward the discovery of key metabolic pathways.

Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of ß-glucosidase function in Cellvibrio japonicus.

Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in ß-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially-useful strains. Importantly, the model is able to explain the role of the different ß-glucosidases and their behavior under genetic perturbations. This integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.

mergem: merging, comparing, and translating genome-scale metabolic models using universal identifiers.

Numerous methods exist to produce and refine genome-scale metabolic models. However, due to the use of incompatible identifier systems for metabolites and reactions, computing and visualizing the metabolic differences and similarities of such models is a current challenge. Furthermore, there is a lack of automated tools that can combine the strengths of multiple reconstruction pipelines into a curated single comprehensive model by merging different drafts, which possibly use incompatible namespaces. Here we present mergem, a novel method to compare, merge, and translate two or more metabolic models. Using a universal metabolic identifier mapping system constructed from multiple metabolic databases, mergem robustly can compare models from different pipelines, merge their common elements, and translate their identifiers to other database systems. mergem is implemented as a command line tool, a Python package, and on the web-application Fluxer, which allows simulating and visually comparing multiple models with different interactive flux graphs. The ability to merge, compare, and translate diverse genome scale metabolic models can facilitate the curation of comprehensive reconstructions and the discovery of unique and common metabolic features among different organisms.

Exposure to PFAS chemicals induces sex-dependent alterations in key rate-limiting steps of lipid metabolism in liver steatosis.

Toxicants with the potential to bioaccumulate in humans and animals have long been a cause for concern, particularly due to their association with multiple diseases and organ injuries. Per- and polyfluoro alkyl substances (PFAS) and polycyclic aromatic hydrocarbons (PAH) are two such classes of chemicals that bioaccumulate and have been associated with steatosis in the liver. Although PFAS and PAH are classified as chemicals of concern, their molecular mechanisms of toxicity remain to be explored in detail. In this study, we aimed to identify potential mechanisms by which an acute exposure to PFAS and PAH chemicals can induce lipid accumulation and whether the responses depend on chemical class, dose, and sex. To this end, we analyzed mechanisms beginning with the binding of the chemical to a molecular initiating event (MIE) and the consequent transcriptomic alterations. We collated potential MIEs using predictions from our previously developed ToxProfiler tool and from published steatosis adverse outcome pathways. Most of the MIEs are transcription factors, and we collected their target genes by mining the TRRUST database. To analyze the effects of PFAS and PAH on the steatosis mechanisms, we performed a computational MIE-target gene analysis on high-throughput transcriptomic measurements of liver tissue from male and female rats exposed to either a PFAS or PAH. The results showed peroxisome proliferator-activated receptor (PPAR)-α targets to be the most dysregulated, with most of the genes being upregulated. Furthermore, PFAS exposure disrupted several lipid metabolism genes, including upregulation of fatty acid oxidation genes (Acadm, Acox1, Cpt2, Cyp4a1-3) and downregulation of lipid transport genes (Apoa1, Apoa5, Pltp). We also identified multiple genes with sex-specific behavior. Notably, the rate-limiting genes of gluconeogenesis (Pck1) and bile acid synthesis (Cyp7a1) were specifically downregulated in male rats compared to female rats, while the rate-limiting gene of lipid synthesis (Scd) showed a PFAS-specific upregulation. The results suggest that the PPAR signaling pathway plays a major role in PFAS-induced lipid accumulation in rats. Together, these results show that PFAS exposure induces a sex-specific multi-factorial mechanism involving rate-limiting genes of gluconeogenesis and bile acid synthesis that could lead to activation of an adverse outcome pathway for steatosis.

Histological evaluation of mineral trioxide aggregate and enamel matrix derivative combination in direct pulp capping: An in vivo study.

AIM: The aim of this study is to evaluate the response of human pulp tissue to mineral trioxide aggregate (MTA), Emdogain (EMD), and combination of MTA/EMD. MATERIALS AND METHODS: This study was performed on sixty intact first and second premolars of human maxillary and mandibular teeth. A standard pulpal exposure was done on all the teeth and was divided into three groups of twenty teeth each and was capped with MTA, EMD, and MTA/EMD combination. The final restoration was done with resin-modified glass ionomer cement. The teeth were then extracted on the 15th or 45th day and histological evaluation done. RESULTS: Differences in inflammatory response and thickness of dentin bridge formation of the exposed pulp to the three different groups were statistically evaluated using Chi-square and Mann-Whitney tests and were found to be significant. No significant difference was found between MTA/EMD and MTA in terms of calcified bridge formation and pulp inflammatory response to the capping materials. CONCLUSIONS: MTA and MTA/EMD combination produced a better quality hard tissue response compared with the use of EMD.

Comparative Evaluation of Microleakage Between Nano-Ionomer, Giomer and Resin Modified Glass Ionomer Cement in Class V Cavities- CLSM Study.

INTRODUCTION: Marginal integrity of adhesive restorative materials provides better sealing ability for enamel and dentin and plays an important role in success of restoration in Class V cavities. Restorative material with good marginal adaptation improves the longevity of restorations. AIM: Aim of this study was to evaluate microleakage in Class V cavities which were restored with Resin Modified Glass Ionomer Cement (RMGIC), Giomer and Nano-Ionomer. MATERIALS AND METHODS: This in-vitro study was performed on 60 human maxillary and mandibular premolars which were extracted for orthodontic reasons. A standard wedge shaped defect was prepared on the buccal surfaces of teeth with the gingival margin placed near Cemento Enamel Junction (CEJ). Teeth were divided into three groups of 20 each and restored with RMGIC, Giomer and Nano-Ionomer and were subjected to thermocycling. Teeth were then immersed in 0.5% Rhodamine B dye for 48 hours. They were sectioned longitudinally from the middle of cavity into mesial and distal parts. The sections were observed under Confocal Laser Scanning Microscope (CLSM) to evaluate microleakage. Depth of dye penetration was measured in millimeters. STATISTICAL ANALYSIS: The data was analysed using the Kruskal Wallis test. Pair wise comparison was done with Mann Whitney U Test. A p-value<0.05 is taken as statistically significant. RESULTS: Nano-Ionomer showed less microleakage which was statistically significant when compared to Giomer (p=0.0050). Statistically no significant difference was found between Nano Ionomer and RMGIC (p=0.3550). There was statistically significant difference between RMGIC and Giomer (p=0.0450). CONCLUSION: Nano-Ionomer and RMGIC showed significantly less leakage and better adaptation than Giomer and there was no statistically significant difference between Nano-Ionomer and RMGIC.