Comparison of Three Quantitative Phosphoproteomic Strategies to Study Receptor Tyrosine Kinase Signaling

There are three quantitative phosphoproteomic strategies most commonly used to study receptor tyrosine kinase (RTK) signaling. These strategies quantify changes in: (1) all three forms of phosphosites (phosphoserine, phosphothreonine and phosphotyrosine) following enrichment of phosphopeptides by titanium dioxide or immobilized metal affinity chromatography; (2) phosphotyrosine sites following anti- phosphotyrosine antibody enrichment of phosphotyrosine peptides; or (3) phosphotyrosine proteins and their binding partners following anti-phosphotyrosine protein immunoprecipitation. However, it is not clear from literature which strategy is more effective. In this study, we assessed the utility of these three phosphoproteomic strategies in RTK signaling studies by using EphB receptor signaling as an example. We used all three strategies with stable isotope labeling with amino acids in cell culture (SILAC) to compare changes in phosphoproteomes upon EphB receptor activation. We used bioinformatic analysis to compare results from the three analyses. Our results show that the three strategies provide complementary information about RTK pathways

Transgenic <i>Bacillus thuringiensis</i> (Bt) Rice Is Safer to Aquatic Ecosystems than Its Non-Transgenic Counterpart

<div><p>Rice lines genetically modified with the crystal toxin genes from <i>Bacillus thuringiensis</i> (Bt) have experienced rapid development, with biosafety certificates for two Bt rice lines issued in 2009. There has still been no commercial release of these lines yet due to public concerns about human health and environmental risks. Some studies confirmed that Bt rice was as safe as conventional rice to non-target organisms when pesticides were not applied, however, pesticides are still required in Bt rice to control non-lepidopteran pests. In this study, we assessed the environmental effects of two Bt rice lines expressing either the <i>cry1Ab/1Ac</i> or <i>cry2A</i> genes, respectively, by using zooplanktons as indicator species under normal field management practices using pesticides when required. In the whole rice growing season, non-Bt rice was sprayed 5 times while Bt rice was sprayed 2 times, which ensured both rice achieved a normal yield. Field investigations showed that rice type (Bt and non-Bt) significantly influenced zooplankton abundance and diversity, which were up to 95% and 80% lower in non-Bt rice fields than Bt rice fields. Laboratory rearing showed that water from non-Bt rice fields was significantly less suitable for the survival and reproduction of <i>Daphnia magna</i> and <i>Paramecium caudatum</i> in comparison with water from Bt rice fields. Higher pesticide residues were detected in the water from non-Bt than Bt rice fields, accounting for the bad performance of zooplankton in non-Bt field water. Our results demonstrate that Bt rice is safer to aquatic ecosystems than non-Bt rice, and its commercialization will be beneficial for biodiversity restoration in rice-based ecosystems.</p></div

Survival (A) and reproductive rate (B) of <i>D. magna</i> reared in water collected from non-Bt rice and Bt rice plots.

<p>Water was collected 6 days after the final pesticide sprays and the culture medium was renewed every 3 days with field water collected on that very day. Non-Bt and Bt rice plots were sprayed with pesticides when rice pests exceeded the action threshold (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104270#pone.0104270.s001" target="_blank">Table S1</a>). Error bars indicated standard error. Different letters capped on the bars indicate significant difference at <i>p</i> = 0.05 (LSD post hoc test after ANOVA).</p

Yield of Bt and non-Bt rice. Non-Bt rice = Minghui 63, Bt rice 1 = Minghui 63 <i>cry1Ab/1Ac</i>, Bt rice 2 = Minghui 63 <i>cry2A</i>.

<p>Non-Bt rice as well as Bt rice were sprayed with pesticides when insect or fungal pests reached the action threshold. Error bars indicate the standard error. Planned comparison of means showed that the yield of non-Bt rice and Bt rice did not differ significantly (d.f. = 6; <i>t</i> = 1.303; <i>p</i> = 0.240).</p

Abundance (individuals L⁻¹) and diversity (number of species) of Rotifera, Cladocera and Copepoda in Bt and non-Bt rice plots.

<p>Non-Bt and Bt rice plots were sprayed with pesticides when rice pests exceeded the action threshold (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104270#pone.0104270.s001" target="_blank">Table S1</a>). The first and the second investigation were conducted 5 days and 12 days after the final spray, respectively. The values presented in the table are untransformed means ± SE of each treatment. Means followed by the same letter within the same column in each investigation do not differ significantly at <i>p</i> = 0.05 (LSD post hoc test after ANOVA, n = 3).</p

Pesticide residue ± SE (µg L⁻¹) in water collected from non-Bt and Bt rice plots.

<p>Field water was collected 10 days after the final spray (see table S1 for the spraying date). Pesticide residues were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS).</p

Population growth of <i>P. caudatum</i> in culture medium made of field water.

<p>Field water was collected from non-Bt rice and Bt rice plots 11 days after the final pesticide spray (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104270#pone.0104270.s001" target="_blank">Table S1</a>). Error bars indicate the standard error.</p

In-Depth Quantitative Proteomic Analysis of de Novo Protein Synthesis Induced by Brain-Derived Neurotrophic Factor

Measuring the synthesis of new proteins in the context of a much greater number of pre-existing proteins can be difficult. To overcome this obstacle, bioorthogonal noncanonical amino acid tagging (BONCAT) can be combined with stable isotope labeling by amino acid in cell culture (SILAC) for comparative proteomic analysis of de novo protein synthesis (BONLAC). In the present study, we show that alkyne resin-based isolation of l-azidohomoalanine (AHA)-labeled proteins using azide/alkyne cycloaddition minimizes contamination from pre-existing proteins. Using this approach, we isolated and identified 7414 BONCAT-labeled proteins. The nascent proteome isolated by BONCAT was very similar to the steady-state proteome, although transcription factors were highly enriched by BONCAT. About 30% of the methionine residues were replaced by AHA in our BONCAT samples, which allowed for identification of methionine-containing peptides. There was no bias against low-methionine proteins by BONCAT at the proteome level. When we applied the BONLAC approach to screen for brain-derived neurotrophic factor (BDNF)-induced protein synthesis, 53 proteins were found to be significantly changed 2 h after BDNF stimulation. Our study demonstrated that the newly synthesized proteome, even after a short period of stimulation, can be efficiently isolated by BONCAT and analyzed to a depth that is similar to that of the steady-state proteome

In-Depth Quantitative Proteomic Analysis of de Novo Protein Synthesis Induced by Brain-Derived Neurotrophic Factor

Measuring the synthesis of new proteins in the context of a much greater number of pre-existing proteins can be difficult. To overcome this obstacle, bioorthogonal noncanonical amino acid tagging (BONCAT) can be combined with stable isotope labeling by amino acid in cell culture (SILAC) for comparative proteomic analysis of de novo protein synthesis (BONLAC). In the present study, we show that alkyne resin-based isolation of l-azidohomoalanine (AHA)-labeled proteins using azide/alkyne cycloaddition minimizes contamination from pre-existing proteins. Using this approach, we isolated and identified 7414 BONCAT-labeled proteins. The nascent proteome isolated by BONCAT was very similar to the steady-state proteome, although transcription factors were highly enriched by BONCAT. About 30% of the methionine residues were replaced by AHA in our BONCAT samples, which allowed for identification of methionine-containing peptides. There was no bias against low-methionine proteins by BONCAT at the proteome level. When we applied the BONLAC approach to screen for brain-derived neurotrophic factor (BDNF)-induced protein synthesis, 53 proteins were found to be significantly changed 2 h after BDNF stimulation. Our study demonstrated that the newly synthesized proteome, even after a short period of stimulation, can be efficiently isolated by BONCAT and analyzed to a depth that is similar to that of the steady-state proteome

Deep Coverage of Global Protein Expression and Phosphorylation in Breast Tumor Cell Lines Using TMT 10-plex Isobaric Labeling

Labeling peptides with isobaric tags is a popular strategy in quantitative bottom-up proteomics. In this study, we labeled six breast tumor cell lysates (1.34 mg proteins per channel) using 10-plex tandem mass tag reagents and analyzed the samples on a Q Exactive HF Quadrupole-Orbitrap mass spectrometer. We identified a total of 8,706 proteins and 28,186 phosphopeptides, including 7,394 proteins and 23,739 phosphosites common to all channels. The majority of technical replicates correlated with a <i>R</i>² ≥ 0.98, indicating minimum variability was introduced after labeling. Unsupervised hierarchical clustering of phosphopeptide data sets successfully classified the breast tumor samples into Her2 (epidermal growth factor receptor 2) positive and Her2 negative groups, whereas mRNA abundance did not. The tyrosine phosphorylation levels of receptor tyrosine kinases, phosphoinositide-3-kinase, protein kinase C delta, and Src homology 2, among others, were significantly higher in the Her2 positive than the Her2 negative group. Despite ratio compression in MS2-based experiments, we demonstrated the ratios calculated using an MS2 method are highly correlated (<i>R</i>² > 0.65) with ratios obtained using MS3-based quantitation (using a Thermo Orbitrap Fusion mass spectrometer) with reduced ratio suppression. Given the deep coverage of global and phosphoproteomes, our data show that MS2-based quantitation using TMT can be successfully used for large-scale multiplexed quantitative proteomics