Clinical proteomics最新文献

{"title":"Kinase inhibitor pulldown assay (KiP) for clinical proteomics.","authors":"Alexander B Saltzman, Doug W Chan, Matthew V Holt, Junkai Wang, Eric J Jaehnig, Meenakshi Anurag, Purba Singh, Anna Malovannaya, Beom-Jun Kim, Matthew J Ellis","doi":"10.1186/s12014-023-09448-3","DOIUrl":"10.1186/s12014-023-09448-3","url":null,"abstract":"<p><p>Protein kinases are frequently dysregulated and/or mutated in cancer and represent essential targets for therapy. Accurate quantification is essential. For breast cancer treatment, the identification and quantification of the protein kinase ERBB2 is critical for therapeutic decisions. While immunohistochemistry (IHC) is the current clinical diagnostic approach, it is only semiquantitative. Mass spectrometry-based proteomics offers quantitative assays that, unlike IHC, can be used to accurately evaluate hundreds of kinases simultaneously. The enrichment of less abundant kinase targets for quantification, along with depletion of interfering proteins, improves sensitivity and thus promotes more effective downstream analyses. Multiple kinase inhibitors were therefore deployed as a capture matrix for kinase inhibitor pulldown (KiP) assays designed to profile the human protein kinome as broadly as possible. Optimized assays were initially evaluated in 16 patient derived xenograft models (PDX) where KiP identified multiple differentially expressed and biologically relevant kinases. From these analyses, an optimized single-shot parallel reaction monitoring (PRM) method was developed to improve quantitative fidelity. The PRM KiP approach was then reapplied to low quantities of proteins typical of yields from core needle biopsies of human cancers. The initial prototype targeting 100 kinases recapitulated intrinsic subtyping of PDX models obtained from comprehensive proteomic and transcriptomic profiling. Luminal and HER2 enriched OCT-frozen patient biopsies subsequently analyzed through KiP-PRM also clustered by subtype. Finally, stable isotope labeled peptide standards were developed to define a prototype clinical method. Data are available via ProteomeXchange with identifiers PXD044655 and PXD046169.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"21 1","pages":"3"},"PeriodicalIF":2.8,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10790396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139472174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic biomarker discovery based on proteome landscape of Chinese lung adenocarcinoma","authors":"Yuqi Huang, Sheng Ma, Jun-Yu Xu, Kun Qian, Yaru Wang, Yi Zhang, Minjia Tan, Ting Xiao","doi":"10.1186/s12014-023-09449-2","DOIUrl":"https://doi.org/10.1186/s12014-023-09449-2","url":null,"abstract":"Despite recent innovations in imaging and genomic screening promotes advance in diagnosis and treatment of lung adenocarcinoma (LUAD), there remains high mortality of LUAD and insufficient understanding of LUAD biology. Our previous study performed an integrative multi-omic analysis of LUAD, filling the gap between genomic alterations and their biological proteome effects. However, more detailed molecular characterization and biomarker resources at proteome level still need to be uncovered. In this study, a quantitative proteomic experiment of patient-derived benign lung disease samples was carried out. After that, we integrated the proteomic data with previous dataset of 103 paired LUAD samples. We depicted the proteomic differences between non-cancerous and tumor samples and among diverse pathological subtypes. We also found that up-regulated mitophagy was a significant characteristic of early-stage LUAD. Additionally, our integrative analysis filtered out 75 potential prognostic biomarkers and validated two of them in an independent LUAD serum cohort. This study provided insights for improved understanding proteome abnormalities of LUAD and the novel prognostic biomarker discovery offered an opportunity for LUAD precise management.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"106 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139102530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of putative ovarian cancer serum protein biomarkers using a multiplexed targeted mass spectrometry assay","authors":"Joohyun Ryu, Kristin L. M. Boylan, Carly A. I. Twigg, Richard Evans, Amy P. N. Skubitz, Stefani N. Thomas","doi":"10.1186/s12014-023-09447-4","DOIUrl":"https://doi.org/10.1186/s12014-023-09447-4","url":null,"abstract":"Ovarian cancer is the most lethal gynecologic malignancy in women, and high-grade serous ovarian cancer (HGSOC) is the most common subtype. Currently, no clinical test has been approved by the FDA to screen the general population for ovarian cancer. This underscores the critical need for the development of a robust methodology combined with novel technology to detect diagnostic biomarkers for HGSOC in the sera of women. Targeted mass spectrometry (MS) can be used to identify and quantify specific peptides/proteins in complex biological samples with high accuracy, sensitivity, and reproducibility. In this study, we sought to develop and conduct analytical validation of a multiplexed Tier 2 targeted MS parallel reaction monitoring (PRM) assay for the relative quantification of 23 putative ovarian cancer protein biomarkers in sera. To develop a PRM method for our target peptides in sera, we followed nationally recognized consensus guidelines for validating fit-for-purpose Tier 2 targeted MS assays. The endogenous target peptide concentrations were calculated using the calibration curves in serum for each target peptide. Receiver operating characteristic (ROC) curves were analyzed to evaluate the diagnostic performance of the biomarker candidates. We describe an effort to develop and analytically validate a multiplexed Tier 2 targeted PRM MS assay to quantify candidate ovarian cancer protein biomarkers in sera. Among the 64 peptides corresponding to 23 proteins in our PRM assay, 24 peptides corresponding to 16 proteins passed the assay validation acceptability criteria. A total of 6 of these peptides from insulin-like growth factor-binding protein 2 (IBP2), sex hormone-binding globulin (SHBG), and TIMP metalloproteinase inhibitor 1 (TIMP1) were quantified in sera from a cohort of 69 patients with early-stage HGSOC, late-stage HGSOC, benign ovarian conditions, and healthy (non-cancer) controls. Confirming the results from previously published studies using orthogonal analytical approaches, IBP2 was identified as a diagnostic biomarker candidate based on its significantly increased abundance in the late-stage HGSOC patient sera compared to the healthy controls and patients with benign ovarian conditions. A multiplexed targeted PRM MS assay was applied to detect candidate diagnostic biomarkers in HGSOC sera. To evaluate the clinical utility of the IBP2 PRM assay for HGSOC detection, further studies need to be performed using a larger patient cohort.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"40 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139084539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"sBioSITe enables sensitive identification of the cell surface proteome through direct enrichment of biotinylated peptides.","authors":"Kishore Garapati, Husheng Ding, M Cristine Charlesworth, Yohan Kim, Roman Zenka, Mayank Saraswat, Dong-Gi Mun, Sandip Chavan, Ashish Shingade, Fabrice Lucien, Jun Zhong, Richard K Kandasamy, Akhilesh Pandey","doi":"10.1186/s12014-023-09445-6","DOIUrl":"10.1186/s12014-023-09445-6","url":null,"abstract":"<p><strong>Background: </strong>Cell surface proteins perform critical functions related to immune response, signal transduction, cell-cell interactions, and cell migration. Expression of specific cell surface proteins can determine cell-type identity, and can be altered in diseases including infections, cancer and genetic disorders. Identification of the cell surface proteome remains a challenge despite several enrichment methods exploiting their biochemical and biophysical properties.</p><p><strong>Methods: </strong>Here, we report a novel method for enrichment of proteins localized to cell surface. We developed this new approach designated surface Biotinylation Site Identification Technology (sBioSITe) by adapting our previously published method for direct identification of biotinylated peptides. In this strategy, the primary amine groups of lysines on proteins on the surface of live cells are first labeled with biotin, and subsequently, biotinylated peptides are enriched by anti-biotin antibodies and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS).</p><p><strong>Results: </strong>By direct detection of biotinylated lysines from PC-3, a prostate cancer cell line, using sBioSITe, we identified 5851 peptides biotinylated on the cell surface that were derived from 1409 proteins. Of these proteins, 533 were previously shown or predicted to be localized to the cell surface or secreted extracellularly. Several of the identified cell surface markers have known associations with prostate cancer and metastasis including CD59, 4F2 cell-surface antigen heavy chain (SLC3A2) and adhesion G protein-coupled receptor E5 (CD97). Importantly, we identified several biotinylated peptides derived from plectin and nucleolin, both of which are not annotated in surface proteome databases but have been shown to have aberrant surface localization in certain cancers highlighting the utility of this method.</p><p><strong>Conclusions: </strong>Detection of biotinylation sites on cell surface proteins using sBioSITe provides a reliable method for identifying cell surface proteins. This strategy complements existing methods for detection of cell surface expressed proteins especially in discovery-based proteomics approaches.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"56"},"PeriodicalIF":3.8,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138486897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative proteomics analysis based on data-independent acquisition reveals the effect of Shenling Baizhu powder (SLP) on protein expression in MAFLD rat liver tissue.","authors":"Sufei Song, Jixian Zheng, Dongmei Zhao, Anni Zheng, Ye Zhu, Qiuling Xu, Tao Liu","doi":"10.1186/s12014-023-09442-9","DOIUrl":"10.1186/s12014-023-09442-9","url":null,"abstract":"<p><strong>Background: </strong>Metabolic associated fatty liver disease (MAFLD) has become the most common chronic liver disease worldwide, and it is also a high-risk factor for the development of other metabolic diseases. Shenling Baizhu powder (SLP) is a traditional Chinese herbal formula with good clinical efficacy against MAFLD. However, its molecular mechanism for the treatment of MAFLD is still not fully understood. This study used quantitative proteomics analysis to reveal the SLP action mechanism in the treatment of MAFLD by discovering the effect of SLP on protein expression in the liver tissue of MAFLD rats.</p><p><strong>Materials and methods: </strong>Q-Orbitrap LC-MS/MS was used to identify the incoming blood compounds of SLP. The 18 SD male rats were randomly divided into 3 groups (n = 6): control group, HFD group and SLP group. The HFD group and SLP group were established as MAFLD rat models by feeding them a high-fat diet for 4 weeks. Afterwards, the SLP group was treated with SLP (10.89 g/kg/d) for 3 weeks. Biochemical parameters and liver pathological status were measured. Rat liver tissue was analyzed using DIA-based quantitative proteomics and the DEPs were validated by western blotting analysis.</p><p><strong>Results: </strong>A total of 18 active compounds of SLP were identified and isolated to enter the bloodstream. Comparison of DEPs between control group vs. HFD group and HFD group vs. SLP group revealed that SLP restored the expression of 113 DEPs. SLP catalyzes oxidoreductase activity and binding activity on mitochondria and endoplasmic reticulum to promote lipid oxidative catabolism, maintain oxoacid metabolic homeostasis in vivo and mitigate oxidative stress-induced hepatocyte injury. And 52 signaling pathways including PPAR signaling, arachidonic acid metabolism and glycine, serine and threonine metabolism were enriched by KEGG. PPI topology analysis showed that Cyp4a2, Agxt2, Fabp1, Pck1, Acsm3, Aldh1a1, Got1 and Hmgcs2 were the core DEPs. The western blotting analysis verified that SLP was able to reverse the increase in Fabp1 and Hmgcs2 and the decrease in Pck1 induced by HFD, and the results were consistent proteomic data.</p><p><strong>Conclusion: </strong>SLP ameliorates hepatic steatosis to exert therapeutic effects on MAFLD by inhibiting the expression of lipid synthesis genes and inhibiting lipid peroxidation in mitochondria. This study provides a new idea and basis for the study of SLP in the treatment of MAFLD and provides an experimental basis for the clinical application of SLP.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"55"},"PeriodicalIF":3.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138458353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomic analysis of chromophobe renal cell carcinoma and benign renal oncocytoma biopsies reveals shared metabolic dysregulation.","authors":"Luis B Carvalho, Susana Jorge, Hugo López-Fernández, Carlos Lodeiro, Rajiv Dhir, Luis Campos Pinheiro, Mariana Medeiros, Hugo M Santos, José L Capelo","doi":"10.1186/s12014-023-09443-8","DOIUrl":"10.1186/s12014-023-09443-8","url":null,"abstract":"<p><strong>Background: </strong>This study investigates the proteomic landscapes of chromophobe renal cell carcinoma (chRCC) and renal oncocytomas (RO), two subtypes of renal cell carcinoma that together account for approximately 10% of all renal tumors. Despite their histological similarities and shared origins, chRCC is a malignant tumor necessitating aggressive intervention, while RO, a benign growth, is often subject to overtreatment due to difficulties in accurate differentiation.</p><p><strong>Methods: </strong>We conducted a label-free quantitative proteomic analysis on solid biopsies of chRCC (n = 5), RO (n = 5), and normal adjacent tissue (NAT, n = 5). The quantitative analysis was carried out by comparing protein abundances between tumor and NAT specimens. Our analysis identified a total of 1610 proteins across all samples, with 1379 (85.7%) of these proteins quantified in at least seven out of ten LC‒MS/MS runs for one renal tissue type (chRCC, RO, or NAT).</p><p><strong>Results: </strong>Our findings revealed significant similarities in the dysregulation of key metabolic pathways, including carbohydrate, lipid, and amino acid metabolism, in both chRCC and RO. Compared to NAT, both chRCC and RO showed a marked downregulation in gluconeogenesis proteins, but a significant upregulation of proteins integral to the citrate cycle. Interestingly, we observed a distinct divergence in the oxidative phosphorylation pathway, with RO showing a significant increase in the number and degree of alterations in proteins, surpassing that observed in chRCC.</p><p><strong>Conclusions: </strong>This study underscores the value of integrating high-resolution mass spectrometry protein quantification to effectively characterize and differentiate the proteomic landscapes of solid tumor biopsies diagnosed as chRCC and RO. The insights gained from this research offer valuable information for enhancing our understanding of these conditions and may aid in the development of improved diagnostic and therapeutic strategies.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"54"},"PeriodicalIF":3.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138451077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of the novel GLAS algorithm as an aid in the detection of liver fibrosis and cirrhosis based on GP73, LG2m, age, and sex.","authors":"Philip M Hemken, Xuzhen Qin, Lori J Sokoll, Laurel Jackson, Fan Feng, Peng Li, Susan H Gawel, Bailin Tu, Zhihong Lin, James Hartnett, David Hawksworth, Bryan C Tieman, Toru Yoshimura, Hideki Kinukawa, Shaohua Ning, Enfu Liu, Fanju Meng, Fei Chen, Juru Miao, Xuan Mi, Xin Tong, Daniel W Chan, Gerard J Davis","doi":"10.1186/s12014-023-09444-7","DOIUrl":"10.1186/s12014-023-09444-7","url":null,"abstract":"<p><strong>Background: </strong>Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. In this pilot study, we tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with liver disease and healthy individuals in two independent cohorts.</p><p><strong>Methods: </strong>To develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model.</p><p><strong>Results: </strong>GP73 and LG2m concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90-0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls.</p><p><strong>Conclusions: </strong>GP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for detection of liver disease in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"53"},"PeriodicalIF":3.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138451078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables.","authors":"Caterina Gabriele, Federica Aracri, Licia Elvira Prestagiacomo, Maria Antonietta Rota, Stefano Alba, Giuseppe Tradigo, Pietro Hiram Guzzi, Giovanni Cuda, Rocco Damiano, Pierangelo Veltri, Marco Gaspari","doi":"10.1186/s12014-023-09439-4","DOIUrl":"10.1186/s12014-023-09439-4","url":null,"abstract":"<p><strong>Background: </strong>Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies.</p><p><strong>Methods: </strong>In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO₂ (titanium dioxide) strategy.</p><p><strong>Results: </strong>Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69).</p><p><strong>Conclusions: </strong>To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"52"},"PeriodicalIF":3.8,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138290539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of in-gel and in-solution proteolysis in the proteome profiling of organ perfusion solutions.","authors":"Corinna M Snashall, Chris W Sutton, Letizia Lo Faro, Carlo Ceresa, Rutger Ploeg, Sadr Ul Shaheed","doi":"10.1186/s12014-023-09440-x","DOIUrl":"10.1186/s12014-023-09440-x","url":null,"abstract":"<p><strong>Purpose: </strong>The organ perfusion solution (perfusate), collected at clinically and temporally significant stages of the organ preservation and transplantation process, provides a valuable insight into the biological status of an organ over time and prior to reperfusion (transplantation) in the recipient. The objective of this study was to assess two bottom-up proteomics workflows for the extraction of tryptic peptides from the perfusate.</p><p><strong>Experimental design: </strong>Two different kinds of perfusate samples from kidney and liver trials were profiled using liquid chromatography-mass spectrometry (LC-MS/MS). The preparation of clean peptide mixtures for downstream analysis was performed considering different aspects of sample preparation; protein estimation, enrichment, in-gel and urea-based in-solution digestion.</p><p><strong>Results: </strong>In-solution digestion of perfusate allowed identification of the highest number of peptides and proteins with greater sequence coverage and higher confidence data in kidney and liver perfusate. Key pathways identified by gene ontology analysis included complement, coagulation and antioxidant pathways, and a number of biomarkers previously linked to ischemia-reperfusion injury were also observed in perfusate.</p><p><strong>Conclusions: </strong>This study showed that in-solution digestion is a more efficient method for LC-MS/MS analysis of kidney and liver organ perfusion solutions. This method is also quicker and easier than in-gel digestion, allowing for greater sample throughput, with fewer opportunities for experimental error or peptide loss.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"51"},"PeriodicalIF":3.8,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134648625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pathway activity-based proteomic classifier stratifies prostate tumors into two subtypes.","authors":"Rui Sun, Lingling Tan, Xuan Ding, Jun A, Zhangzhi Xue, Xue Cai, Sainan Li, Tiannan Guo","doi":"10.1186/s12014-023-09441-w","DOIUrl":"10.1186/s12014-023-09441-w","url":null,"abstract":"<p><p>Prostate cancer (PCa) is the second most common cancer in males worldwide. The risk stratification of PCa is mainly based on morphological examination. Here we analyzed the proteome of 667 tumor samples from 487 Chinese PCa patients and characterized 9576 protein groups by PulseDIA mass spectrometry. Then we developed a pathway activity-based classifier concerning 13 proteins from seven pathways, and dichotomized the PCa patients into two subtypes, namely PPS1 and PPS2. PPS1 is featured with enhanced innate immunity, while PPS2 with suppressed innate immunity. This classifier exhibited a correlation with PCa progression in our cohort and was further validated by two published transcriptome datasets. Notably, PPS2 was significantly correlated with poor biochemical recurrence (BCR)/metastasis-free survival (log-rank P-value < 0.05). The PPS2 was also featured with cell proliferation activation. Together, our study presents a novel pathway activity-based stratification scheme for PCa.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"50"},"PeriodicalIF":3.8,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72208756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}