Rapid species and strain differentiation of non-tubercoulous mycobacteria by Fourier-Transform Infrared microspectroscopy.

Rapid identification of non-tuberculous mycobacteria (NTM) species is important in clinical laboratories to stipulate the appropriate therapy and to offer a comprehensive infection control. We applied Fourier-Transform Infrared microspectroscopy to evaluate, whether the most frequent species of NTM can be rapidly and uniformly identified by this method using microcolonies of NTM growing on solid nutrient agar plates. To establish a standardized protocol, the heterogeneity of cell growth within the microcolonies and the reproducibility of measuring the IR spectra from whole mycobacterial microcolonies were first studied. Hierarchical cluster analysis applied to spectra obtained by linear mapping across microcolony imprints from fast- and slow-growing NTM revealed only little spectral variance between the various microcolony zones. In parallel, when repetitive measurements were performed on independently grown whole single microcolonies with diameters of 80 and 140 mum, excellent reproducibility could be achieved, verifying that mycobacterial microcolonies are well suited for FT-IR-based identification. Twenty-eight different and well-defined strains, comprising the most frequent species of NTM isolated in clinical laboratories, were used to create a classification system based on FT-IR spectra from single microcolonies. Hierarchical cluster analysis allowed the assignment of all isolates measured in replicates to their correct species-specific clusters. Additionally, a clear separation of all strains into strain-specific sub-clusters was observed. These results demonstrate the potential of FT-IR microspectroscopy to rapidly differentiate NTM at the species and strain level. The data so far obtained suggest that an extended spectral database, containing more NTM strains and covering a broader biological variance, may provide a practical solution to rapidly identify unknown NTM isolates in routine clinical-microbiological laboratories with the additional possibility to type these microorganisms at the sub-species level.

Analysis of covariance patterns in gene expression data and FT-IR spectra.

The aim of this study was to detect and interpret correlation patterns in several large data matrices from the same biological system using Partial Least Squares Regression (PLSR) in order to get information on the system under investigation. To do this, DNA microarray data and Fourier Transform Infrared (FT-IR) spectra from a designed study where Campylobacter jejuni was exposed to environmental stress conditions, were used. The experimental design included variation in atmospheric conditions, temperature and time. PLSR was first used to analyse each of the two data types separately in order to explore the effect of the experimental parameters on the data. The results showed that both the gene expression and FT-IR spectra were affected by the variations in atmosphere, temperature and time, but that the effect was different for the two types of data. When the DNA microarray data and FT-IR spectra were linked together by PLSR, covariation due to temperature was seen. Both specific genes and ranges in the FT-IR spectra that were connected to the variation in temperature were detected. Some of these are possibly connected to properties of the cell wall of the bacteria. The results in this study show the potential of PLSR for investigation of covariance structures in biological data. By doing this, valuable information about the biological system can be detected and interpreted. It was also shown that the use of FT-IR spectroscopy provided important information about the stress responses in the bacteria, information that was not detected from the DNA microarray data.

FT-IR spectroscopy for identification of closely related lactobacilli.

Fourier Transform Infrared (FT-IR) spectroscopy was used to analyse 56 strains from four closely related species of Lactobacillus, L. sakei, L. plantarum, L. curvatus and L. paracasei. Hierarchical Cluster Analysis (HCA) was used to study the clusters in the data, but in the dendrogram, the spectra were not differentiated into four separate clusters corresponding to species. When the data were analysed with Partial Least Squares Regression (PLSR), the strains were differentiated into four clusters according to species. It was also possible to recognise strains that were incorrectly identified by conventional methods prior to the FT-IR analysis. PLSR was used to identify strains from three of the species, and the results were compared to two other multivariate methods, Soft Independent Modelling of Class Analogy (SIMCA) and K-Nearest Neighbour (KNN). The three methods gave equally good identification results. The results show that FT-IR spectroscopy in combination with PLSR, or other multivariate methods, is well suited for identification of Lactobacillus at the species level, even in quite large data sets.

Evaluation of the robustness of FT-IR spectra of lactobacilli towards changes in the bacterial growth conditions.

In order to use Fourier Transform Infrared (FT-IR) spectroscopy for identification of microorganisms on a routine basis, it is important that the spectra are robust against small, uncontrollable variations in the bacterial growth conditions. In this study, the effect of small variations in growth temperature, growth time, growth medium and atmospheric conditions on the separation of Lactobacillus based on their FT-IR spectra was investigated. The resulting spectra were shown to be robust against the variations in the cultivation conditions, and the separation of both strains and species was unaffected. Larger variations in the growth medium influenced only the separation of strains. FT-IR spectroscopy for identification of lactobacilli therefore seems to be robust against small variations in the cultivation conditions.