Type 2 diabetes results from aberrant regulation of the phosphorylation cascade in beta-cells. Phosphorylation in pancreatic beta-cells has not been examined extensively, except with regard to subcellular phosphoproteomes using mitochondria. Thus, robust, comprehensive analytical strategies are needed to characterize the many phosphorylated proteins that exist, because of their low abundance, the low stoichiometry of phosphorylation, and the dynamic regulation of phosphoproteins. In this study, we attempted to generate data on a large-scale phosphoproteome from the INS-1 rat pancreatic beta-cell line using linear ion trap MS/MS. To profile the phosphoproteome in-depth, we used comprehensive phosphoproteomic strategies, including detergent-based protein extraction (SDS and SDC), differential sample preparation (in-gel, in-solution digestion, and FASP), TiO2 enrichment, and MS replicate analyses (MS2-only and multiple-stage activation). All spectra were processed and validated by stringent multiple filtering using target and decoy databases. We identified 2467 distinct phosphorylation sites on 1419 phosphoproteins using 4 mg of INS-1 cell lysate in 24 LC-MS/MS runs, of which 683 (27.7%) were considered novel phosphorylation sites that have not been characterized in human, mouse, or rat homologues. Our informatics data constitute a rich bioinformatics resource for investigating the function of reversible phosphorylation in pancreatic beta-cells. In particular, novel phosphorylation sites on proteins that mediate the pathology of type 2 diabetes, such as Pdx-1, Nkx.2, and Srebf1, will be valuable targets in ongoing phosphoproteomics studies.

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Advances in targeted medications have improved the survival rate of breast cancer patients with molecular marker-positive tumors. To date, immunohistochemistry (IHC) has remained as the standard method for quantifying the markers including HER2, ER, and PR. Nevertheless, IHC-based grading is subjective, because the results depend on trained individuals’ eye rather than numerical quantities. Thus, alternative methods that can account for quantitative levels of markers are gaining popularity, including targeted proteomics by mass spectrometry (MS). However, technical limitations have impeded the application of MS-based protein quantification to pathological FFPE slides that contain low amounts of cross-linked proteins. To challenge this, we developed a parallel reaction monitoring-mass spectrometry (PRM-MS) method to measure the expression levels of breast cancer markers. After developing the method using cell lines, we performed PRM-MS using 51 individuals’ FFPE samples. As a result, we obtained numerical measures of targets, quantifying 13 peptides of 4 markers in a single analysis per sample. The results correlated well with the IHC readings of experienced pathologists. Moreover, the results distinguished a gray zone in HER2 classification, which IHC alone failed to do. This proof-of-concept study demonstrates the application of targeted proteomics in pathologic slides, further supporting the applicability of MS-based approaches in precision medicine.

This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled (training (n = 100) and validation set (n = 80)). Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified five marker candidate proteins (LRG1, APCS, BCHE, C7, and FCN3). When this five-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Furthermore, the ensemble model was an independent predictor of rapid progression (hazard ratio (HR), 2.889; 95% confidence interval (CI), 1.612-5.178; P value < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024-3.848; P value = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.

Glycoproteins have many important biological functions. In particular, aberrant glycosylation has been observed in various cancers, such as liver cancer. A well-known glycoprotein biomarker is α-fetoprotein (AFP), a surveillance biomarker for hepatocellular carcinoma (HCC) that contains a glycosylation site at asparagine 251. The low diagnostic sensitivity of AFP led researchers to focus on AFP-L3, which has the same sequence as conventional AFP but contains a fucosylated glycan. AFP-L3 has high affinity for Lens culinaris agglutinin (LCA) lectin, prompting many groups to use it for detecting AFP-L3. However, a few studies have identified more effective lectins for fractionating AFP-L3. In this study, we compared the amounts of enriched AFP-L3 with five fucose-specific lectinsLCA, Lotus tetragonolobus lectin (LTL), Ulex europaeus agglutinin I (UEA I), Aleuria aurantia lectin (AAL), and Aspergillus oryzae lectin (AOL)to identify better lectins and improve HCC diagnostic assays using mass spectrometry (MS). Our results indicate that LTL was the most effective lectin for capturing AFP-L3 species, yielding approximately 3-fold more AFP-L3 than LCA from the same pool of HCC serum samples. Thus, we recommend the use of LTL for AFP-L3 assays, given its potential to improve the diagnostic sensitivity in patients having limited results by conventional LCA assay. The MS data have been deposited to the PeptideAtlas (PASS01752).

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

Background Metastasis of breast cancer to distal organs is fatal. However, few studies have identified biomarkers that are associated with distant metastatic breast cancer. Furthermore, the inability of current biomarkers, such as HER2, ER, and PR, to differentiate between distant and nondistant metastatic breast cancers accurately has necessitated the development of novel biomarker candidates. Methods An integrated proteomics approach that combined filter-aided sample preparation, tandem mass tag labeling (TMT), high pH fractionation, and high-resolution MS was applied to acquire in-depth proteomic data from FFPE distant metastatic breast cancer tissues. A bioinformatics analysis was performed with regard to gene ontology and signaling pathways using differentially expressed proteins (DEPs) to examine the molecular characteristics of distant metastatic breast cancer. In addition, real-time polymerase chain reaction (RT-PCR) and invasion/migration assays were performed to validate the differential regulation and function of our protein targets. Results A total of 9441 and 8746 proteins were identified from the pooled and individual sample sets, respectively. Based on our criteria, TUBB2A was selected as a novel biomarker candidate. The metastatic activities of TUBB2A were subsequently validated. In our bioinformatics analysis using DEPs, we characterized the overall molecular features of distant metastasis and measured differences in the molecular functions of distant metastatic breast cancer between breast cancer subtypes. Conclusions Our report is the first study to examine the distant metastatic breast cancer proteome using FFPE tissues. The depth of our dataset allowed us to discover a novel biomarker candidate and a proteomic characteristics of distant metastatic breast cancer. Distinct molecular features of various breast cancer subtypes were also established. Our proteomic data constitute a valuable resource for research on distant metastatic breast cancer.

The multiplexed product-ion scan mode using QqQ-MS generates systematically detectable peptide transitions in a single liquid chromatography/MS run, in which we were able to identify SRM peptides that represent known target proteins in complex biological samples. The method presented here is easy to implement and has high-throughput capabilities as a result of the short analysis time. It is therefore well suited for the design of optimal SRM experiments.