After a large-scale radiological accident, early-response biomarkers to assess radiation exposure over a broad dose range are not only the basis of rapid radiation triage, but are also the key to the rational use of limited medical resources and to the improvement of treatment efficiency. Because of its high throughput, rapid assays and minimally invasive sample collection, metabolomics has been applied to research into radiation exposure biomarkers in recent years. Due to the complexity of radiobiological effects, most of the potential biomarkers are both dose-dependent and time-dependent. In reality, it is very difficult to find a single biomarker that is both sensitive and specific in a given radiation exposure scenario. Therefore, a multi-parameters approach for radiation exposure assessment is more realistic in real nuclear accidents. In this study, untargeted metabolomic profiling based on gas chromatography-mass spectrometry (GC-MS) and targeted amino acid profiling based on LC-MS/MS were combined to investigate early urinary metabolite responses within 48 h post-exposure in a rat model. A few of the key early-response metabolites for radiation exposure were identified, which revealed the most relevant metabolic pathways. Furthermore, a panel of potential urinary biomarkers was selected through a multi-criteria approach and applied to early triage following irradiation. Our study suggests that it is feasible to use a multi-parameters approach to triage radiation damage, and the urinary excretion levels of the relevant metabolites provide insights into radiation damage and repair.

Protein phosphorylation, one of the main protein post-translational modifications, is required for regulating various life activities. Kinases and phosphatases that regulate protein phosphorylation in humans have been targeted to treat various diseases, particularly cancer. High-throughput experimental methods to discover protein phosphosites are laborious and time-consuming. The burgeoning databases and predictors provide essential infrastructure to the research community. To date, >60 publicly available phosphorylation databases and predictors each have been developed. In this review, we have comprehensively summarized the status and applicability of major online phosphorylation databases and predictors, thereby helping researchers rapidly select tools that are most suitable for their projects. Moreover, the organizational strategies and limitations of these databases and predictors have been highlighted, which may facilitate the development of better protein phosphorylation predictors in silico.

The mechanisms shaping the amino acids recruitment pattern into the proteins in the early life history presently remains a huge mystery. In this study, we conducted genome-wide analyses of amino acids usage and genetic codons structure in 7270 species across three domains of life. The carried-out analyses evidenced ubiquitous usage bias of amino acids that were likely independent from codon usage bias. Taking advantage of codon usage bias, we performed pseudotime analysis to re-determine the chronological order of the species emergence, which inspired a new species relationship by tracing the imprint of codon usage evolution. Furthermore, the multidimensional data integration showed that the amino acids A, D, E, G, L, P, R, S, T and V might be the first recruited into the last universal common ancestry (LUCA) proteins. The data analysis also indicated that the remaining amino acids most probably were gradually incorporated into proteogenesis process in the course of two long-timescale parallel evolutionary routes: I→F→Y→C→M→W and K→N→Q→H. This study provides new insight into the origin of life, particularly in terms of the basic protein composition of early life. Our work provides crucial information that will help in a further understanding of protein structure and function in relation to their evolutionary history.