Effect of pH on the denitrification proteome of the soil bacterium Paracoccus denitrificans PD1222

Denitrification is a respiratory process by which nitrate is reduced to dinitrogen. Incomplete denitrification results in the emission of the greenhouse gas nitrous oxide and this is potentiated in acidic soils, which display reduced denitrification rates and high N2O/N2 ratios compared to alkaline soils. In this work, impact of pH on the proteome of the soil denitrifying bacterium Paracoccus denitrificans PD1222 was analysed with nitrate as sole energy and nitrogen source under anaerobic conditions at pH ranging from 6.5 to 7.5. Quantitative proteomic analysis revealed that the highest difference in protein representation was observed when the proteome at pH 6.5 was compared to the reference proteome at pH 7.2. However, this difference in the extracellular pH was not enough to produce modification of intracellular pH, which was maintained at 6.5 ± 0.1. The biosynthetic pathways of several cofactors relevant for denitrification and nitrogen assimilation like cobalamin, riboflavin, molybdopterin and nicotinamide were negatively affected at pH 6.5. In addition, peptide representation of reductases involved in nitrate assimilation and denitrification were reduced at pH 6.5. Data highlight the strong negative impact of pH on NosZ synthesis and intracellular copper content, thus impairing active NosZ assembly and, in turn, leading to elevated nitrous oxide emissions.

Prediction of Genomewide Conserved Epitope Profiles of HIV-1: Classifier Choice and Peptide Representation

Identification of peptides binding to Major Histocompatibility Complex (MHC) molecules is important for accelerating vaccine development and improving immunotherapy. Accordingly, a wide variety of prediction methods have been applied in this context. In this paper, we introduce (tree-based) ensemble classifiers for such problems and contrast their predictive performance with forefront existing methods for both MHC class I and class II molecules. In addition, we investigate the impact of differing peptide representation schemes on performance. Finally, classifier predictions are used to conduct genomewide scans of a diverse collection of HIV-1 strains, enabling assessment of epitope conservation. We investigated all combinations of six classification methods (classification trees, artificial neural networks, support vector machines, as well as the more recently devised ensemble methods (bagging, random forests, boosting) with four peptide representation schemes (amino acid sequence, select biophysical properties, select quantitative structure-activity relationship (QSAR) descriptors, and the combination of the latter two) in predicting peptide binding to an MHC class I molecule (HLA-A2) and MHC class II molecule (HLA-DR4). Our results show that the ensemble methods are consistently more accurate than the other three alternatives. Furthermore, they are robust with respect to parameter tuning. Among the four representation schemes, the amino acid sequence representation gave consistently (across classifiers) best results. This finding obviates the need for feature selection strategies incurred by use of biophysical and/or QSAR properties. We obtained, and aligned, a diverse set of 32 HIV-1 genomes and pursued genomewide HLA-DR4 epitope profiling by querying with respect to classifier predictions, as obtained under each of the four peptide representation schemes. We validated those epitopes conserved across strains against known T-cell epitopes. Once again, amino acid sequence representation was at least as effective as using properties. Assessment of novel epitope predictions awaits experimental verification.