Synthesis, Raman, FT-IR, NMR spectroscopic characterization, antimicrobial activity, cytotoxicity and DNA binding of new mixed aza-oxo-thia macrocyclic compounds

Hydrogen-bonded assemblies in the molecular crystals of 2,2'-thiodiacetic acid with ethylenediamine and o-phenylenediamine

Carboxylate molecular crystals have been of interest due to the presence of hydrogen bonding, which plays a significant role in chemical and crystal engineering, as well as in supramolecular chemistry. Acid-base adducts possess hydrogen bonds which increase the thermal and mechanical stability of the crystal. 2,2'-Thiodiacetic acid (Tda) is a versatile ligand that has been widely explored, employing its multidendate and chelating coordination abilities with many metals; however, charge-transfer complexes of thiodiacetic acid have not been reported. Two salts, namely ethylenediaminium 2,2'-thiodiacetate, C₂H₁₀N₂²⁺·C₄H₄O₄S₂^2-, denoted Tdaen, and 2-aminoanilinium 2-(carboxymethylsulfanyl)acetate, C₆H₉N₂⁺·C₄H₅O₄S^-, denoted Tdaophen, were synthesized and characterized by IR, ¹H and ¹³C NMR spectroscopies, and single-crystal X-ray diffraction. In these salts, Tda reacts with the aliphatic (ethylenediamine) and aromatic (o-phenylenediamine) diamines, and deprotonates them to form anions with different valencies and different supramolecular networks. In Tdaen, the divalent Tda^2- anions form one-dimensional linear supramolecular chains and these are extended into a three-dimensional sandwich-type supramolecular network by interaction with the ethylenediaminium cations. However, in Tdaophen, the monovalent Tda^- anions form one-dimensional zigzag supramolecular chains, which are extended into a three-dimensional supramolecular network by interaction with the 2-aminoanilinium cations. Thus, both three-dimensional structures display different ring motifs. The structures of these diamines, which are influenced by hydrogen-bonded assemblies in the molecular crystals, are discussed in detail.

Characterization and prediction of the mechanism of action of antibiotics through NMR metabolomics

Background

The emergence of antibiotic resistant pathogenic bacteria has reduced our ability to combat infectious diseases. At the same time the numbers of new antibiotics reaching the market have decreased. This situation has created an urgent need to discover novel antibiotic scaffolds. Recently, the application of pattern recognition techniques to identify molecular fingerprints in ‘omics’ studies, has emerged as an important tool in biomedical research and laboratory medicine to identify pathogens, to monitor therapeutic treatments or to develop drugs with improved metabolic stability, toxicological profile and efficacy. Here, we hypothesize that a combination of metabolic intracellular fingerprints and extracellular footprints would provide a more comprehensive picture about the mechanism of action of novel antibiotics in drug discovery programs.

Results

In an attempt to integrate the metabolomics approach as a classification tool in the drug discovery processes, we have used quantitative ¹H NMR spectroscopy to study the metabolic response of Escherichia coli cultures to different antibiotics. Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis. The metabolic fingerprints and footprints of bacterial cultures were affected in a distinct manner and provided complementary information regarding intracellular and extracellular targets such as protein synthesis, DNA and cell wall. While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied. In addition, using a training set of E. coli fingerprints extracted after treatment with different antibiotic classes, the mode of action of streptomycin, tetracycline and carbenicillin could be correctly predicted.

Conclusion

The metabolic profiles of E. coli treated with antibiotics with intracellular and extracellular targets could be separated in fingerprint and footprint analysis, respectively and provided complementary information. Based on the specific fingerprints obtained for different classes of antibiotics, the mode of action of several antibiotics could be predicted. The same classification approach should be applicable to studies of other pathogenic bacteria.