Pharmacogenomic strategies against resistance development in microbial infections

Host defense peptides as effector molecules of the innate immune response: a sledgehammer for drug resistance?

Host defense peptides can modulate the innate immune response and boost infection-resolving immunity, while dampening potentially harmful pro-inflammatory (septic) responses. Both antimicrobial and/or immunomodulatory activities are an integral part of the process of innate immunity, which itself has many of the hallmarks of successful anti-infective therapies, namely rapid action and broad-spectrum antimicrobial activities. This gives these peptides the potential to become an entirely new therapeutic approach against bacterial infections. This review details the role and activities of these peptides, and examines their applicability as development candidates for use against bacterial infections.

Computational multiscale modeling in protein--ligand docking

In biological systems, the binding of small molecule ligands to proteins is a crucial process for almost every aspect of biochemistry and molecular biology. Enzymes are proteins that function by catalyzing specific biochemical reactions that convert reactants into products. Complex organisms are typically composed of cells in which thousands of enzymes participate in complex and interconnected biochemical pathways. Some enzymes serve as sequential steps in specific pathways (such as energy metabolism), while others function to regulate entire pathways and cellular functions [1]. Small molecule ligands can be designed to bind to a specific enzyme and inhibit the biochemical reaction. Inhibiting the activity of key enzymes may result in the entire biochemical pathways being turned on or off [2], [3]. Many small molecule drugs marketed today function in this generic way as enzyme inhibitors. If research identifies a specific enzyme as being crucial to the progress of disease, then this enzyme may be targeted with an inhibitor, which may slow down or reverse the progress of disease. In this way, enzymes are targeted from specific pathogens (e.g., virus, bacteria, fungi) for infectious diseases [4], [5], and human enzymes are targeted for noninfectious diseases such as cardiovascular disease, cancer, diabetes, and neurodegenerative diseases [6].

New trends in pharmacogenomic strategies against resistance development in microbial infections

This review summarizes some of the new trends in the fight against drug resistant bacteria. We review Gram-positive (e.g., S.aureus) and Gram-negative (e.g., Pseudomonas aeruginosa, Helicobacter pylori) bacteria, the current antibiotic resistance situation, as well as resistance spread and some recently discovered resistance mechanisms, such as those based on integrons and complex transposons. We then summarize several current routes to identify new drugs such as cationic antimicrobial peptides, novel acyldepsipeptides, RNA aptamers and lipopeptides. New drug strategies to treat resistant pathogens include eliciting growth in dormant bacteria, or a new way to attack efflux systems. Typical approaches from pharmacogenomics combined with systems biology and bioinformatics support these routes (simulations, metagenomics and metabolic network modeling), as well as the patient treatment (e.g., haplotyping and immune response).

When will the genomics investment pay off for antibacterial discovery?

Effective solutions to antibacterial resistance are among the key unmet medical needs driving the antibacterial industry. A major thrust in a number of companies is the development of agents with new modes of action in order to bypass the increasing emergence of antibacterial resistance. However, few antibacterials marketed in the last 30 years have novel modes of action. Most recently, genomics and target-based screening technologies have been emphasized as a means to facilitate this and expedite the antibacterial discovery process. And although no new antibacterials have yet been marketed as result of these technologies, genomics has delivered well-validated novel bacterial targets as well as a host of genetic approaches to support the antibacterial discovery process. Likewise, high throughput screening technologies have delivered the capacity to perform robust screenings of large compound collections to identify target inhibitors for lead generation. One of the principal challenges still facing antibacterial discovery is to become proficient at optimizing target inhibitors into broad-spectrum antibacterials with appropriate in vivo properties. Genomics-based technologies clearly have the potential for additional application throughout the discovery process especially in the areas of structural biology and safety assessment.

YANA - a software tool for analyzing flux modes, gene-expression and enzyme activities

Background

A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest.

Results

YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at .

Conclusion

A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts.