During the last 20 years the rate at which new antimicrobial agents are produced has decreased dramatically, with concomitant increase in the number of pathogens that are becoming multidrug resistant. Together these have created a patient healthcare risk and this is of great concern. A crucial aspect for the discovery of new antibiotics is the development of new techniques that allow rapid and accurate characterization of the mode of action of the pharmacophore. In this work UV resonance Raman (UVRR) spectroscopy has been developed to monitor the concentration effect of antibiotics on bacterial cells. UVRR was conducted at 244 nm and spectra were collected in typically 60 s. Supervised multivariate analysis and 2D correlation spectroscopy were used to evaluate whether the UVRR spectra contained valuable information that could be used to study the mode of action of antibiotics. The clustering pattern in the discriminant factors space correlated directly to the concentration of amikacin, and partial least squares (PLS) regression analysis of the UVRR spectra was able to predict the concentration of amikacin to which bacterial cells had been exposed. 2D correlation spectroscopy contour maps indicated that spectral changes due to the presence of amikacin in the growth media occur according to the known mode of action of the studied antibiotic. Therefore, we conclude that UVRR spectroscopy, when coupled with chemometrics and 2D correlation spectroscopy, constitutes a powerful approach for the development and screening of new antibiotics.

Metabolomic technologies are increasingly being applied to study biological questions in a range of different settings from clinical through to environmental. As with other high-throughput technologies, such as those used in transcriptomics and proteomics, metabolomics continues to generate large volumes of complex data that necessitates computational management. Making sense of this wealth of information also requires access to sufficiently detailed and well annotated meta-data. Here we provide standard reporting requirements for describing biological samples, taken from an environmental context and involved in metabolomic experiments. It is our intention that these reporting requirements should guide and support the standardised annotation, dissemination

The release of active pharmaceutical ingredients (APIs) into the environment is an ecologically important topic for study because, whilst APIs have been designed to have a wide range of biological properties for the target of interest (usually in man), little information on potential ecological risks is currently available regarding their effects on the organisms that inhabit the environment. In this study, the algae Micrasterias hardyi was exposed to propranolol, metoprolol (beta-adrenergic receptor agonist drugs) and mefenamic acid (a non steroidal anti-inflammatory drug), at concentrations ranging between 0.002-0.2 mM. Initial studies showed that Fourier transform infrared (FT-IR) spectroscopy on algal homogenates illustrated that all three APIs had a quantitative effect on the metabolism of the organisms and it was possible to estimate the level of API exposure from the FT-IR metabolic fingerprints using partial least squares (PLS) regression. From the inspection of the PLS loadings matrices it was possible to elucidate that all drugs caused effects on protein and lipid levels. Most strikingly propranolol had significant effects on the lipid components of the cell. These were dramatically reduced possibly as a consequence of loss of membrane integrity. In order to investigate this further, FT-IR microspectroscopy was used to generate detailed metabolic fingerprinting maps. These chemical maps revealed that all the drugs had a dramatic effect on the distribution of various chemical species throughout the algae, and that all drugs had an affect on protein and lipid levels. In particular, as noted in the PLS analyses for propranolol treated cells, the lipid complement found in the lipid storage areas in the processes of M. hardyi was greatly reduced. This illustrates the power of spatial metabolic fingerprinting for investigating abiotic stresses on complex biological species.

No abstract

No abstract