Abstract

A new methodology is proposed for the non-target protein analysis of samples from wastewater treatment plants (WWTP). Proteins captured using polymeric (polycaprolactonediol) probes placed along different sites of a WWTP were extracted, digested and analyzed by LC-MS/MS. Selection of the Regions of Interest (ROI) from the raw MS data allowed their storage with the most relevant information present in the analyzed wastewater samples. Application of the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) chemometrics method to the ROI MS data resolved the elution profiles and spectral fingerprints of the components present in the analyzed samples. Partial Least Squares-Discriminant Analysis (PLS-DA) of the peak heights of the elution profiles of these components could distinguish which of them changed more significantly along the different steps of the WWTP. Principal Component Analysis (PCA) of ROI data, and of ROIMCR and PLS-DA results allowed the clustering of the samples according to the different WWTP sites. A clear improvement in their discrimination was observed when PCA was applied to the results of ROIMCR and PLS-DA, compared with the results obtained considering only the unprocessed ROI data. Finally, when ROIMCR and PLS-DA results were analyzed using Proteome Discoverer, 281, 314, and 58 peptides were respectively identified at the three sampling sites in the case of ROIMCR, and 137, 123, and 31 peptides in the case of PLS-DA. From these peptides, a total number of 38 proteins are postulated to be present in the WWTP analyzed samples.

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