Nanoparticle biological activity, biocompatibility and fate can be directly affected by layers of readily adsorbed host proteins in biofluids. Here, we report a study on the interactions between human blood plasma proteins and nanoparticles with a controlled systematic variation of properties using 18O-labeling and LC-MS-based quantitative proteomics. We developed a novel protocol to both simplify isolation of nanoparticle bound proteins and improve reproducibility. LC-MS analysis identified and quantified 88 human plasma proteins associated with polystyrene nanoparticles consisting of three different surface chemistries and two sizes, as well as, for four different exposure times (for a total of 24 different samples). Quantitative comparison of relative protein abundances was achieved by spiking an 18O-labeled “universal” reference into each individually processed unlabeled sample as an internal standard, enabling simultaneous application of both label-free and isotopic labeling quantification across the entire sample set. Clustering analysis of the quantitative proteomics data resulted in distinctive patterns that classified the nanoparticles based on their surface properties and size. In addition, temporal data indicated that the formation of the stable protein corona was at equilibrium within 5 min. The comprehensive quantitative proteomics results obtained in this study provide rich data for computational modeling and have potential implications towards predicting nanoparticle biocompatibility.

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We evaluate statistical models used in two-hypothesis tests for identifying peptides from tandem mass spectrometry data. The null hypothesis H(0), that a peptide matches a spectrum by chance, requires information on the probability of by-chance matches between peptide fragments and peaks in the spectrum. Likewise, the alternate hypothesis H(A), that the spectrum is due to a particular peptide, requires probabilities that the peptide fragments would indeed be observed if it was the causative agent. We compare models for these probabilities by determining the identification rates produced by the models using an independent data set. The initial models use different probabilities depending on fragment ion type, but uniform probabilities for each ion type across all of the labile bonds along the backbone. More sophisticated models for probabilities under both H(A) and H(0) are introduced that do not assume uniform probabilities for each ion type. In addition, the performance of these models using a standard likelihood model is compared to an information theory approach derived from the likelihood model. Also, a simple but effective model for incorporating peak intensities is described. Finally, a support-vector machine is used to discriminate between correct and incorrect identifications based on multiple characteristics of the scoring functions. The results are shown to reduce the misidentification rate significantly when compared to a benchmark cross-correlation based approach.

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The effects of climate change on soil organic matter—its structure, microbial community, carbon storage, and respiration response—remain uncertain and widely debated. In addition, the effects of climate changes on ecosystem structure and function are often modulated or delayed, meaning that short-term experiments are not sufficient to characterize ecosystem responses. This study capitalized on a long-term reciprocal soil transplant experiment to examine the response of dryland soils to climate change. The two transplant sites were separated by 500 m of elevation on the same mountain slope in eastern Washington state, USA, and had similar plant species and soil types. We resampled the original 1994 soil transplants and controls, measuring CO2 production, temperature response, enzyme activity, and bacterial community structure after 17 years. Over a laboratory incubation of 100 days, reciprocally transplanted soils respired roughly equal cumulative amounts of carbon as non-transplanted controls from the same site. Soils transplanted from the hot, dry, lower site to the cooler and wetter (difference of -5°C monthly maximum air temperature, +50 mm yr-1 precipitation) upper site exhibited almost no respiratory response to temperature (Q10 of 1.1), but soils originally from the upper, cooler site had generally higher respiration rates. The bacterial community structure of transplants did not differ significantly from that of untransplanted controls, however. Slight differences in local climate between the upper and lower Rattlesnake locations, simulated with environmental control chambers during the incubation, thus prompted significant differences in microbial activity, with no observed change to bacterial structure. These results support the idea that environmental shifts can influence soil C through metabolic changes, and suggest that microbial populations responsible for soil heterotrophic respiration may be constrained in surprising ways, even as shorter- and longer-term soil microbial dynamics may be significantly different under changing climate.