i-RUBY: a novel software for quantitative analysis of highly accurate shotgun-proteomics liquid chromatography/tandem mass spectrometry data obtained without stable-isotope labeling of proteins.

We developed a novel software named i-RUBY (identification-Related qUantification-Based strategY algorithm for liquid chromatography/tandem mass spectrometry (LC/MS/MS) data) that enables us to perform fully automatic ion current-based spectral feature analysis of highly accurate data obtained by LC/MS/MS. At the 1st step, this software utilizes accurate peptide/protein identification information for peak detection and peak matching among measurements. Then, at the 2nd step, it picks yet unidentified peaks and matches them to the peaks identified at the 1st step by a linear interpolation algorithm. The analysis of human plasma externally spiked with a known amount of yeast alcohol dehydrogenase 1 showed a good linear relationship between the amount of protein spiked and the quantitative values obtained by i-RUBY analysis. Experiment using human plasma digests spiked with a mixture of known amounts of synthetic peptides derived from two yeast proteins, alcohol dehydrogenase 1 and glucose-6-phospate isomerase, showed the expansion by the 2nd step of i-RUBY of the lower quantification limits to 1/10 to 1/1000 of those reached only by identified peaks at the 1st step. Good correlations between the i-RUBY results and the amount of proteins were confirmed by the analysis of real samples, i.e., sera of normal subjects and cancer patients, by comparing quantitative values of acute-phase proteins obtained by i-RUBY analysis of LC/MS/MS data with those obtained by an immunological method using Bio-Plex. These results taken together show that i-RUBY is a useful tool for obtaining dependable quantitative information from highly accurate shotgun-proteomics LC/MS/MS data.

Environmentally Driven Migration in a Social Network Game.

Cooperative behaviors are common in humans, and they are the fundamental basis of our society. Theoretical and experimental studies have modeled environments where the behaviors of humans, or agents, have been restricted to analyze their social behavior. However, it is important that such studies can be generalized to a less restrictive environment in order to understand human society. Social network games (SNGs) provide a powerful tool for the quantitative study of human behavior using a less restrictive environment than in previous studies. We focused on multilevel selection, one of the theoretical frameworks used to study the evolution of cooperation. The evolution of cooperation by multilevel selection requires that the continual assortment between cooperators and noncooperators is generated; thus, humans may have acquired mechanisms that ensure assortment (e.g., migration between groups). This study aims to investigate this mechanism in a less restrictive environment. For this purpose, we researched the effect of migration based on data analysis in an SNG where the players could behave more freely than was possible in the environments used in the previous studies. We showed that players maintained assortment between cooperators and defectors in this SNG, where it appears that environmentally driven migration generated the assortment.

Proteomic biomarkers for acute interstitial lung disease in gefitinib-treated Japanese lung cancer patients.

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10(-25)), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.