A kinetic platform for in silico modeling of the metabolic dynamics in Escherichia coli

Weak links: Advancing target-based drug discovery by identifying the most vulnerable targets

Mycobacterium tuberculosis remains the most common infectious killer worldwide despite decades of antitubercular drug development. Effectively controlling the tuberculosis (TB) pandemic will require innovation in drug discovery. In this review, we provide a brief overview of the two main approaches to discovering new TB drugs-phenotypic screens and target-based drug discovery-and outline some of the limitations of each method. We then explore recent advances in genetic tools that aim to overcome some of these limitations. In particular, we highlight a novel metric to prioritize essential targets, termed vulnerability. Stratifying targets based on their vulnerability presents new opportunities for future target-based drug discovery campaigns.

Validated In Silico Population Model of Escherichia coli

Flux balance analysis (FBA) and ordinary differential equation models have been instrumental in depicting the metabolic functioning of a cell. Nevertheless, they demonstrate a population's average behavior (summation of individuals), thereby portraying homogeneity. However, living organisms such as Escherichia coli contain more biochemical reactions than engaging metabolites, making them an underdetermined and degenerate system. This results in a heterogeneous population with varying metabolic patterns. We have formulated a population systems biology model that predicts this degeneracy by emulating a diverse metabolic makeup with unique biochemical signatures. The model mimics the universally accepted experimental view that a subpopulation of bacteria, even under normal growth conditions, renders a unique biochemical state, leading to the synthesis of metabolites and persister progenitors of antibiotic resistance and biofilms. We validate the platform's predictions by producing commercially important heterologous (isobutanol) and homologous (shikimate) metabolites. The predicted fluxes are tested in vitro resulting in 32- and 42-fold increased product of isobutanol and shikimate, respectively. Moreover, we authenticate the platform by mimicking a bacterial population in the presence of glyphosate, a metabolic pathway inhibitor. Here, we observe a fraction of subsisting persisters despite inhibition, thus affirming the signature of a heterogeneous populace. The platform has multiple uses based on the disposition of the user.

Validated In Silico Model for Biofilm Formation in Escherichia coli

Using Escherichia coli as the representative biofilm former, we report here the development of an in silico model built by simulating events that transform a free-living bacterial entity into self-encased multicellular biofilms. Published literature on ∼300 genes associated with pathways involved in biofilm formation was curated, static maps were created, and suitably interconnected with their respective metabolites using ordinary differential equations. Precise interplay of genetic networks that regulate the transitory switching of bacterial growth pattern in response to environmental changes and the resultant multicomponent synthesis of the extracellular matrix were appropriately represented. Subsequently, the in silico model was analyzed by simulating time-dependent changes in the concentration of components by using the R and python environment. The model was validated by simulating and verifying the impact of key gene knockouts (KOs) and systematic knockdowns on biofilm formation, thus ensuring the outcomes were comparable with the reported literature. Similarly, specific gene KOs in laboratory and pathogenic E. coli were constructed and assessed. MiaA, YdeO, and YgiV were found to be crucial in biofilm development. Furthermore, qRT-PCR confirmed the elevation of expression in biofilm-forming clinical isolates. Findings reported in this study offer opportunities for identifying biofilm inhibitors with applications in multiple industries. The application of this model can be extended to the health care sector specifically to develop novel adjunct therapies that prevent biofilms in medical implants and reduce emergence of biofilm-associated resistant polymicrobial-chronic infections. The in silico framework reported here is open source and accessible for further enhancements.

Validation of CoaBC as a Bactericidal Target in the Coenzyme A Pathway of Mycobacterium tuberculosis

Mycobacterium tuberculosis relies on its own ability to biosynthesize coenzyme A to meet the needs of the myriad enzymatic reactions that depend on this cofactor for activity. As such, the essential pantothenate and coenzyme A biosynthesis pathways have attracted attention as targets for tuberculosis drug development. To identify the optimal step for coenzyme A pathway disruption in M. tuberculosis, we constructed and characterized a panel of conditional knockdown mutants in coenzyme A pathway genes. Here, we report that silencing of coaBC was bactericidal in vitro, whereas silencing of panB, panC, or coaE was bacteriostatic over the same time course. Silencing of coaBC was likewise bactericidal in vivo, whether initiated at infection or during either the acute or chronic stages of infection, confirming that CoaBC is required for M. tuberculosis to grow and persist in mice and arguing against significant CoaBC bypass via transport and assimilation of host-derived pantetheine in this animal model. These results provide convincing genetic validation of CoaBC as a new bactericidal drug target.

Overexpression of Bcl-2 induces STAT-3 activation via an increase in mitochondrial superoxide

We recently reported a novel interaction between Bcl-2 and Rac1 and linked that to the ability of Bcl-2 to induce a pro-oxidant state in cancer cells. To gain further insight into the functional relevance of this interaction, we utilized computer simulation based on the protein pathway dynamic network created by Cellworks Group Inc. STAT3 was identified among targets that positively correlated with Rac1 and/or Bcl-2 expression levels. Validating this, the activation level of STAT3, as marked by p-Tyr705, particularly in the mitochondria, was significantly higher in Bcl-2-overexpressing cancer cells. Bcl-2-induced STAT3 activation was a function of GTP-loaded Rac1 and NADPH oxidase (Nox)-dependent increase in intracellular superoxide (O₂^•−). Furthermore, ABT199, a BH-3 specific inhibitor of Bcl-2, as well as silencing of Bcl-2 blocked STAT3 phosphorylation. Interestingly, while inhibiting intracellular O₂^•− blocked STAT3 phosphorylation, transient overexpression of wild type STAT3 resulted in a significant increase in mitochondrial O₂^•− production, which was rescued by the functional mutants of STAT3 (Y705F). Notably, a strong correlation between the expression and/or phosphorylation of STAT3 and Bcl-2 was observed in primary tissues derived from patients with different sub-sets of B cell lymphoma. These data demonstrate the presence of a functional crosstalk between Bcl-2, Rac1 and activated STAT3 in promoting a permissive redox milieu for cell survival. Results also highlight the potential utility of a signature involving Bcl-2 overexpression, Rac1 activation and STAT3 phosphorylation for stratifying clinical lymphomas based on disease severity and chemoresistance.

In silico-based high-throughput screen for discovery of novel combinations for tuberculosis treatment

There are currently 18 drug classes for the treatment of tuberculosis, including those in the development pipeline. An in silico simulation enabled combing the innumerably large search space to derive multidrug combinations. Through the use of ordinary differential equations (ODE), we constructed an in silico kinetic platform in which the major metabolic pathways in Mycobacterium tuberculosis and the mechanisms of the antituberculosis drugs were integrated into a virtual proteome. The optimized model was used to evaluate 816 triplets from the set of 18 drugs. The experimentally derived cumulative fractional inhibitory concentration (∑FIC) value was within twofold of the model prediction. Bacterial enumeration revealed that a significant number of combinations that were synergistic for growth inhibition were also synergistic for bactericidal effect. The in silico-based screen provided new starting points for testing in a mouse model of tuberculosis, in which two novel triplets and five novel quartets were significantly superior to the reference drug triplet of isoniazid, rifampin, and ethambutol (HRE) or the quartet of HRE plus pyrazinamide (HREZ).

In silico modeling predicts drug sensitivity of patient-derived cancer cells

BACKGROUND

Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach.

METHODS

Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents.

RESULTS

Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings.

CONCLUSIONS

These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.

Assessment of Mycobacterium tuberculosis pantothenate kinase vulnerability through target knockdown and mechanistically diverse inhibitors

Pantothenate kinase (PanK) catalyzes the phosphorylation of pantothenate, the first committed and rate-limiting step toward coenzyme A (CoA) biosynthesis. In our earlier reports, we had established that the type I isoform encoded by the coaA gene is an essential pantothenate kinase in Mycobacterium tuberculosis, and this vital information was then exploited to screen large libraries for identification of mechanistically different classes of PanK inhibitors. The present report summarizes the synthesis and expansion efforts to understand the structure-activity relationships leading to the optimization of enzyme inhibition along with antimycobacterial activity. Additionally, we report the progression of two distinct classes of inhibitors, the triazoles, which are ATP competitors, and the biaryl acetic acids, with a mixed mode of inhibition. Cocrystallization studies provided evidence of these inhibitors binding to the enzyme. This was further substantiated with the biaryl acids having MIC against the wild-type M. tuberculosis strain and the subsequent establishment of a target link with an upshift in MIC in a strain overexpressing PanK. On the other hand, the ATP competitors had cellular activity only in a M. tuberculosis knockdown strain with reduced PanK expression levels. Additionally, in vitro and in vivo survival kinetic studies performed with a M. tuberculosis PanK (MtPanK) knockdown strain indicated that the target levels have to be significantly reduced to bring in growth inhibition. The dual approaches employed here thus established the poor vulnerability of PanK in M. tuberculosis.

Current understanding of skin biology pertinent to skin penetration: skin biochemistry

The purpose of this review is to summarize some of the biochemical or chemical findings that have contributed most significantly to our current understanding of the permeability barrier of the skin. This literature survey covers the period from the 1970s up to the present. This seems appropriate since earlier progress was comprehensively covered in a 1978 review by Bob Scheuplein entitled 'Permeability of the skin: a review of major concepts' and in the earlier review by Scheuplein and Blank entitled 'Permeability of the skin'. Both of these review articles are still being cited, and the earlier one has been cited more than 800 times. Overlap with material covered in these earlier publications will be minimized. The overall significance of findings from some of the most recent years may not yet be determined. The emphasis will be placed on the determination of the composition and structures of the epidermal lipids, especially those of the stratum corneum, key enzymes in the biosynthesis of these lipids and some of the physical chemical properties of these lipids as revealed by X-ray diffraction, infrared spectroscopy and other physical methods.

Genetic and chemical knockdown: a complementary strategy for evaluating an anti-infective target

The equity of a drug target is principally evaluated by its genetic vulnerability with tools ranging from antisense- and microRNA-driven knockdowns to induced expression of the target protein. In order to upgrade the process of antibacterial target identification and discern its most effective type of inhibition, an in silico toolbox that evaluates its genetic and chemical vulnerability leading either to stasis or cidal outcome was constructed and validated. By precise simulation and careful experimentation using enolpyruvyl shikimate-3-phosphate synthase and its specific inhibitor glyphosate, it was shown that genetic knockdown is distinct from chemical knockdown. It was also observed that depending on the particular mechanism of inhibition, viz competitive, uncompetitive, and noncompetitive, the antimicrobial potency of an inhibitor could be orders of magnitude different. Susceptibility of Escherichia coli to glyphosate and the lack of it in Mycobacterium tuberculosis could be predicted by the in silico platform. Finally, as predicted and simulated in the in silico platform, the translation of growth inhibition to a cidal effect was able to be demonstrated experimentally by altering the carbon source from sorbitol to glucose.

Defensin DEFB103 bidirectionally regulates chemokine and cytokine responses to a pro-inflammatory stimulus

Human β defensin DEFB103 acts as both a stimulant and an attenuator of chemokine and cytokine responses: a dichotomy that is not entirely understood. Our predicted results using an in silico simulation model of dendritic cells and our observed results in human myeloid dendritic cells, show that DEFB103 significantly (p < 0.05) enhanced 6 responses, attenuated 7 responses, and both enhanced/attenuated the CXCL1 and TNF responses to Porphyromonas gingivalis hemagglutinin B (HagB). In murine JAWSII dendritic cells, DEFB103 significantly attenuated, yet rarely enhanced, the Cxcl2, Il6, and Csf3 responses to HagB; and in C57/BL6 mice, DEFB103 significantly enhanced, yet rarely attenuated, the Cxcl1, Csf1, and Csf3 responses. Thus, DEFB103 influences pro-inflammatory activities with the concentration of DEFB103 and order of timing of DEFB103 exposure to dendritic cells, with respect to microbial antigen exposure to cells, being paramount in orchestrating the onset, magnitude, and composition of the chemokine and cytokine response.