High-throughput peptide quantification using mTRAQ reagent triplex

<p>Abstract</p> <p>Background</p> <p>Protein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents.</p> <p>Methods and results</p> <p>Here we propose a novel data analysis algorithm for peptide quantification in triplex mTRAQ experiments. It improved the accuracy of quantification in two features. First, it identified and separated triplex isotopic clusters of a peptide in each full MS scan. We designed a schematic model of triplex overlapping isotopic clusters, and separated triplex isotopic clusters by solving cubic equations, which are deduced from the schematic model. Second, it automatically determined the elution areas of peptides. Some peptides have similar atomic masses and elution times, so their elution areas can have overlaps. Our algorithm successfully identified the overlaps and found accurate elution areas. We validated our algorithm using standard protein mixture experiments.</p> <p>Conclusions</p> <p>We showed that our algorithm was able to accurately quantify peptides in triplex mTRAQ experiments. Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.</p

An Exploratory Pilot Study with Plasma Protein Signatures Associated with Response of Patients with Depression to Antidepressant Treatment for 10 Weeks.

Major depressive disorder (MDD) is a leading cause of global disability with a chronic and recurrent course. Recognition of biological markers that could predict and monitor response to drug treatment could personalize clinical decision-making, minimize unnecessary drug exposure, and achieve better outcomes. Four longitudinal plasma samples were collected from each of ten patients with MDD treated with antidepressants for 10 weeks. Plasma proteins were analyzed qualitatively and quantitatively with a nanoflow LC-MS/MS technique. Of 1153 proteins identified in the 40 longitudinal plasma samples, 37 proteins were significantly associated with response/time and clustered into six according to time and response by the linear mixed model. Among them, three early-drug response markers (PHOX2B, SH3BGRL3, and YWHAE) detectable within one week were verified by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) in the well-controlled 24 patients. In addition, 11 proteins correlated significantly with two or more psychiatric measurement indices. This pilot study might be useful in finding protein marker candidates that can monitor response to antidepressant treatment during follow-up visits within 10 weeks after the baseline visit

Formyl-methionine as an N-degron of a eukaryotic N-end rule pathway

In bacteria, nascent proteins bear the pretranslationally generated N-terminal (Nt) formyl-methionine (fMet) residue. Nt-fMet of bacterial proteins is a degradation signal, termed fMet/N-degron. In contrast, proteins synthesized by cytosolic ribosomes of eukaryotes were presumed to bear unformylated Nt-Met. Here we found that the yeast formyltransferase Fmt1, although imported into mitochondria, could also produce Nt-formylated proteins in the cytosol. Nt-formylated proteins were strongly up-regulated in stationary phase or upon starvation for specific amino acids. This up-regulation strictly required the Gcn2 kinase, which phosphorylates Fmt1 and mediates its retention in the cytosol. We also found that the Nt-fMet residues of Nt-formylated proteins act as fMet/N-degrons, and identified the Psh1 ubiquitin ligase as the recognition component of this eukaryotic fMet/N-end rule pathway, which destroys Nt-formylated proteins

Formyl-methionine as an N-degron of a eukaryotic N-end rule pathway

In bacteria, nascent proteins bear the pretranslationally generated N-terminal (Nt) formyl-methionine (fMet) residue. Nt-fMet of bacterial proteins is a degradation signal, termed fMet/N-degron. In contrast, proteins synthesized by cytosolic ribosomes of eukaryotes were presumed to bear unformylated Nt-Met. Here we found that the yeast formyltransferase Fmt1, although imported into mitochondria, could also produce Nt-formylated proteins in the cytosol. Nt-formylated proteins were strongly up-regulated in stationary phase or upon starvation for specific amino acids. This up-regulation strictly required the Gcn2 kinase, which phosphorylates Fmt1 and mediates its retention in the cytosol. We also found that the Nt-fMet residues of Nt-formylated proteins act as fMet/N-degrons, and identified the Psh1 ubiquitin ligase as the recognition component of this eukaryotic fMet/N-end rule pathway, which destroys Nt-formylated proteins

Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer

The 5-year survival rate in the early and late stages of ovarian cancer differs by 63%. In addition, a liquid biopsy is necessary because there are no symptoms in the early stage and tissue collection is difficult without using invasive methods. Therefore, there is a need for biomarkers to achieve this goal. In this study, we found blood-based metabolite or protein biomarker candidates for the diagnosis of ovarian cancer in the 20 clinical samples (10 ovarian cancer patients and 10 healthy control subjects). Plasma metabolites and proteins were measured and quantified using mass spectrometry in ovarian cancer patients and control groups. We identified the differential abundant biomolecules (34 metabolites and 197 proteins) and statistically integrated molecules of different dimensions to better understand ovarian cancer signal transduction and to identify novel biological mechanisms. In addition, the biomarker reliability was verified through comparison with existing research results. Integrated analysis of metabolome and proteome identified emerging properties difficult to grasp with the single omics approach, more reliably interpreted the cancer signaling pathway, and explored new drug targets. Especially, through this analysis, proteins (PPCS, PMP2, and TUBB) and metabolites (L-carnitine and PC-O (30:0)) related to the carnitine system involved in cancer plasticity were identified

Generating Detailed Spectral Libraries for Canine Proteomes Obtained from Serum and Urine

Abstract Domestic dogs (Canis lupus familiaris) are popular companion animals. Increase in medical expenses associated with them and demand for extending their lifespan in a healthy manner has created the need to develop new diagnostic technology. Companion dogs also serve as important animal models for non-clinical research as they can provide various biological phenotypes. Proteomics have been increasingly used on dogs and humans to identify novel biomarkers of various diseases. Despite the growing applications of proteomics in liquid biopsy in veterinary medicine, no publicly available spectral assay libraries have been created for the proteome of canine serum and urine. In this study, we generated spectral assay libraries for the two-representative liquid-biopsy samples using mid-pH fractionation that allows in-depth understanding of proteome coverage. The resultant canine serum and urine spectral assay libraries include 1,132 and 4,749 protein groups and 5,483 and 25,228 peptides, respectively. We built these complimentary accessible resources for proteomic biomarker discovery studies through ProteomeXchange with the identifier PXD034770

Temperature-Dependent Fracture Resistance of Silicon Nanopillars during Electrochemical Lithiation

During the lithation of silicon anodes, the solid-state diffusion of lithium into LixSi follows the Arrhenius law, the resulting morphology and fracture behavior are determined by the silicon anode operation temperature. Here, we reveal the temperature dependence of the lithiation mechanics of crystalline silicon nanopillars (SiNPs) via microscopic observations of the anisotropic growth and fracture behavior. We fabricated 1D SiNP structures with various orientations (&lt; 100 &gt;, &lt; 110 &gt;, and &lt; 111 &gt;) as working electrodes and operated them at temperatures ranging from -20 to 40 degrees C. The lithiation of crystalline silicon at low temperatures exhibited preferential volume expansion along &lt; 110 &gt; and decreased fracture resistance. Furthermore, low temperatures caused the catastrophic fracture of amorphous silicon after the second lithiation. Our findings demonstrate the importance of silicon anode temperature control to prevent mechanical fracture during the cycle of lithium-ion batteries in harsh environments (e.g., electric vehicles in winter)

Page curves for tripartite systems

We investigate information flow and Page curves for tripartite systems. We prepare a tripartite system (say, A, B, and C) of a given number of states and calculate information and entropy contents by assuming random states. Initially, every particle was in A (this means a black hole), and as time goes on, particles move to either B (this means Hawking radiation) or C (this means a broadly defined remnant, including a non-local transport of information, the last burst, an interior large volume, or a bubble universe, etc). If the final number of states of the remnant is smaller than that of Hawking radiation, then information will be stored by both the radiation and the mutual information between the radiation and the remnant, while the remnant itself does not contain information. On the other hand, if the final number of states of the remnant is greater than that of Hawking radiation, then the radiation contains negligible information, while the remnant and the mutual information between the radiation and the remnant contain information. Unless the number of states of the remnant is large enough compared to the entropy of the black hole, Hawking radiation must contain information; and we meet the menace of black hole complementarity again. Therefore, this contrasts the tension between various assumptions and candidates of the resolution of the information loss problem. © 2017 IOP Publishing Ltd.FALS

Plasma Protein Biomarkers Associated with Higher Ovarian Cancer Risk in BRCA1/2 Carriers

Ovarian cancer (OC) is the most lethal gynecologic malignancy and in-time diagnosis is limited because of the absence of effective biomarkers. Germline BRCA1/2 genetic alterations are risk factors for hereditary OC; risk-reducing salpingo-oophorectomy (RRSO) is pursued for disease prevention. However, not all healthy carriers develop the disease. Therefore, identifying predictive markers in the BRCA1/2 carrier population could help improve the identification of candidates for preventive RRSO. In this study, plasma samples from 20 OC patients (10 patients with BRCA1/2 wild type (wt) and 10 with the BRCA1/2 variant (var)) and 20 normal subjects (10 subjects with BRCA1/2wt and 10 with BRCA1/2var) were analyzed for potential biomarkers of hereditary OC. We applied a bottom-up proteomics approach, using nano-flow LC-MS to analyze depleted plasma proteome quantitatively, and potential plasma protein markers specific to the BRCA1/2 variant were identified from a comparative statistical analysis of the four groups. We obtained 1505 protein candidates from the 40 subjects, and SPARC and THBS1 were verified by enzyme-linked immunosorbent assay. Plasma SPARC and THBS1 concentrations in healthy BRCA1/2 carriers were found to be lower than in OC patients with BRCA1/2var. If plasma SPARC concentrations increase over 337.35 ng/mL or plasma THBS1 concentrations increase over 65.28 μg/mL in a healthy BRCA1/2 carrier, oophorectomy may be suggested