Metabolic analysis of the response of Pseudomonas putida DOT-T1E strains to toluene using Fourier transform infrared spectroscopy and gas chromatography mass spectrometry

Optimization of metabolomic data processing using NOREVA

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.

Comparison of liver and plasma metabolic profiles in piglets of different ages as animal models for paediatric population

Liver plays an important role in drug metabolism, so studying the grade of maturation of this organ would help to develop more appropriate dosing regimens for paediatric populations. Nevertheless, considering the invasive nature of liver analyses there are obvious ethical boundaries, particularly in babies and children. In this work, we investigated the suitability of blood plasma as an alternative matrix to evaluate the biological age of liver. With this aim, we studied the correlation of plasma and liver metabolomic profiles obtained by HPLC-TOF-MS for piglets of different ages (newborns, neonates and infants). By means of Pearson correlation analysis we observed that 360 and 1784 pairs of metabolite features were significantly correlated in positive and negative ionization mode, respectively. Procrustes analysis was applied in order to assess the similarity of the clustering resulting from the data obtained from the two matrices and the two ionisation modes. The Procrustes distances were low for both ESI+ (0.3753) and ESI- (0.3673) and, hence, liver and plasma are expected to provide similar discriminatory information. Furthermore, we found that Multiblock Principal Component Analysis (MB-PCA) readily allowed us to combine the data obtained from both matrices and to better understand the clustering according to the three study groups. Considering all these results, we suggest that plasma can provide valuable insight into the maturation grade of liver in order to provide accurate dosing in paediatric population.

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data

Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.

Toluene degradation via a unique metabolic route in indigenous bacterial species

Tanneries are the primary source of toluene pollution in the environment and toluene due to its hazardous effects has been categorized as persistent organic pollutant. Present study was initiated to trace out metabolic fingerprints of three toluene-degrading bacteria isolated from tannery effluents of Southern Punjab. Using selective enrichment and serial dilution methods followed by biochemical, molecular and antibiotic resistance analysis, isolated bacteria were subjected to metabolomics analysis. GC-MS/LC-MS analysis of bacterial metabolites helped to identify toluene transformation products and underlying pathways. Three toluene-metabolizing bacteria identified as Bacillus paralicheniformis strain KJ-16 (IUBT4 and IUBT24) and Brevibacillus agri strain NBRC 15538 (IUBT19) were found tolerant to toluene and capable of degrading toluene. Toluene-degrading potential of these isolates was detected to be IUBT4 (10.35 ± 0.084 mg/h), IUBT19 (14.07 ± 3.14 mg/h) and IUBT24 (11.1 ± 0.282 mg/h). Results of GC-MS analysis revealed that biotransformation of toluene is accomplished not only through known metabolic routes such as toluene 3-monooxygenase (T3MO), toluene 2-monooxygenase (T2MO), toluene 4-monooxygenase (T4MO), toluene methyl monooxygenase (TOL), toluene dioxygenase (Tod), meta- and ortho-ring fission pathways. But additionally, confirmed existence of a unique metabolic pathway that involved conversion of toluene into intermediates such as cyclohexene, cyclohexane, cyclohexanone and cyclohexanol. LC-MS analysis indicated the presence of fatty acid amides, stigmine, emmotin A and 2, 2-dinitropropanol in supernatants of bacterial cultures. As the isolated bacteria transformed toluene into relatively less toxic molecules and thus can be preferably exploited for the eco-friendly remediation of toluene.

Commentary on "Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks"

This is an invited review/commentary by the first and last authors of a paper that was the most cited in FEMS Microbiology Letters for 1996, presently showing in excess of 150 citations at Web of Science, and over 200 at Google Scholar. It was the first paper in which diffuse reflectance absorbance FT-IR spectroscopy was used with a supervised learning method in the form of artificial neural networks, and showed that this combination could succeed in discriminating a series of closely related, clinically relevant, Gram-positive bacterial strains.