Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome.

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have reanalyzed publicly available mass spectrometry proteomics data sets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,565 phosphosites on serine, threonine, and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety and clustered the data to identify groups of sites with similar patterns across rice family groups. The data has been loaded into UniProt Knowledge-Base─enabling researchers to visualize sites alongside other data on rice proteins, e.g., structural models from AlphaFold2, PeptideAtlas, and the PRIDE database─enabling visualization of source evidence, including scores and supporting mass spectra.

A meta-analysis of rice phosphoproteomics data to understand variation in cell signalling across the rice pan-genome.

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have re-analysed publicly available mass spectrometry proteomics datasets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,522 phosphosites on serine, threonine and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety. The data was clustered to identify groups of sites with similar patterns across rice family groups, for example those highly conserved in Japonica, but mostly absent in Aus type rice varieties - known to have different responses to drought. These resources can assist rice researchers to discover alleles with significantly different functional effects across rice varieties. The data has been loaded into UniProt Knowledge-Base - enabling researchers to visualise sites alongside other data on rice proteins e.g. structural models from AlphaFold2, PeptideAtlas and the PRIDE database - enabling visualisation of source evidence, including scores and supporting mass spectra.

Method for Independent Estimation of the False Localization Rate for Phosphoproteomics.

Phosphoproteomic methods are commonly employed to identify and quantify phosphorylation sites on proteins. In recent years, various tools have been developed, incorporating scores or statistics related to whether a given phosphosite has been correctly identified or to estimate the global false localization rate (FLR) within a given data set for all sites reported. These scores have generally been calibrated using synthetic datasets, and their statistical reliability on real datasets is largely unknown, potentially leading to studies reporting incorrectly localized phosphosites, due to inadequate statistical control. In this work, we develop the concept of scoring modifications on a decoy amino acid, that is, one that cannot be modified, to allow for independent estimation of global FLR. We test a variety of amino acids, on both synthetic and real data sets, demonstrating that the selection can make a substantial difference to the estimated global FLR. We conclude that while several different amino acids might be appropriate, the most reliable FLR results were achieved using alanine and leucine as decoys. We propose the use of a decoy amino acid to control false reporting in the literature and in public databases that re-distribute the data. Data are available via ProteomeXchange with identifier PXD028840.

Proteome Bioinformatics Methods for Studying Histidine Phosphorylation.

In this chapter, we introduce the bioinformatics methods associated with studying histidine phosphorylation (pHis) by LC-MS/MS. We describe methods for converting and preprocessing raw data from MS instruments, the method of searching MS data against a sequence database and scoring the confidence associated with localizing the modification site on the peptide sequence. We also describe methods for performing pathway enrichment once a set of pHis-containing proteins have been identified to understand the putative functions of modified proteins. Several of the methods are relatively straightforward to run but require some theoretical knowledge to optimize parameters and correctly interpret outputs. We also describe some of the theory underpinning statistical considerations, to assist correct usage and interpretation of these bioinformatics methods.

Evaluating the effects of switching from cigarette smoking to using a heated tobacco product on health effect indicators in healthy subjects: study protocol for a randomized controlled trial.

Tobacco heating products (THPs) are a potentially safer alternative to combustible cigarette smoking. Through continued use, THPs may reduce smoking-related disease risk, whilst maintaining the sensorial experience and nicotine delivery sought by smokers. While literature evidence of the biological effects of THP aerosol exposure is increasing, there remains a knowledge gap with respect to substantiation of THP reduced risk potential in longer term real-life use. This randomized, multi-centre, controlled clinical study will test the hypotheses that following a switch from combustible cigarettes to a THP for 1 year, participants will experience a sustained reduction in exposure to tobacco-related toxicants that will lead to favourable changes in health effect indicators associated with smoking-related disease development. Changes in such indicators will be contextualized against smoking cessation and never-smoker cohorts. Up to 280 participants who do not intend to quit smoking will be randomized to continued combustible smoking (arm A, up to n = 80) or a commercially available THP (arm B n = 200). Furthermore, up to 190 participants with a high intent to quit smoking will undergo smoking cessation (arm D), and 40 never smokers will serve as a control group (arm E). Recruitment numbers were determined to be sufficient to achieve n = 50 in arms A, B and D, at study end. Enrolment started in March 2018 and the trial is scheduled to be completed in March 2020. Data from this study will be a valuable addition to the growing body of evidence in the field of understanding the individual and public health impact of THPs.Clinical Trial Registration: https://www.isrctn.com/ISRCTN81075760.