Clinical proteomics最新文献

{"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":"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":"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":"Candidate biomarkers for treatment benefit from sunitinib in patients with advanced renal cell carcinoma using mass spectrometry-based (phospho)proteomics.","authors":"Hanneke van der Wijngaart, Robin Beekhof, Jaco C Knol, Alex A Henneman, Richard de Goeij-de Haas, Sander R Piersma, Thang V Pham, Connie R Jimenez, Henk M W Verheul, Mariette Labots","doi":"10.1186/s12014-023-09437-6","DOIUrl":"10.1186/s12014-023-09437-6","url":null,"abstract":"<p><p>The tyrosine kinase inhibitor sunitinib is an effective first-line treatment for patients with advanced renal cell carcinoma (RCC). Hypothesizing that a functional read-out by mass spectrometry-based (phospho, p-)proteomics will identify predictive biomarkers for treatment outcome of sunitinib, tumor tissues of 26 RCC patients were analyzed. Eight patients had primary resistant (RES) and 18 sensitive (SENS) RCC. A 78 phosphosite signature (p < 0.05, fold-change > 2) was identified; 22 p-sites were upregulated in RES (unique in RES: BCAR3, NOP58, EIF4A2, GDI1) and 56 in SENS (35 unique). EIF4A1/EIF4A2 were differentially expressed in RES at the (p-)proteome and, in an independent cohort, transcriptome level. Inferred kinase activity of MAPK3 (p = 0.026) and EGFR (p = 0.045) as determined by INKA was higher in SENS. Posttranslational modifications signature enrichment analysis showed that different p-site-centric signatures were enriched (p < 0.05), of which FGF1 and prolactin pathways in RES and, in SENS, vanadate and thrombin treatment pathways, were most significant. In conclusion, the RCC (phospho)proteome revealed differential p-sites and kinase activities associated with sunitinib resistance and sensitivity. Independent validation is warranted to develop an assay for upfront identification of patients who are intrinsically resistant to sunitinib.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"49"},"PeriodicalIF":3.8,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71520691","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":"Mass spectrometry quantifies target engagement for a KRASG12C inhibitor in FFPE tumor tissue.","authors":"Andrew G Chambers, David C Chain, Steve M Sweet, Zifeng Song, Philip L Martin, Matthew J Ellis, Claire Rooney, Yeoun Jin Kim","doi":"10.1186/s12014-023-09435-8","DOIUrl":"10.1186/s12014-023-09435-8","url":null,"abstract":"<p><strong>Background: </strong>Quantification of drug-target binding is critical for confirming that drugs reach their intended protein targets, understanding the mechanism of action, and interpreting dose-response relationships. For covalent inhibitors, target engagement can be inferred by free target levels before and after treatment. Targeted mass spectrometry assays offer precise protein quantification in complex biological samples and have been routinely applied in pre-clinical studies to quantify target engagement in frozen tumor tissues for oncology drug development. However, frozen tissues are often not available from clinical trials so it is critical that assays are applicable to formalin-fixed, paraffin-embedded (FFPE) tissues in order to extend mass spectrometry-based target engagement studies into clinical settings.</p><p><strong>Methods: </strong>Wild-type RAS and RASG12C was quantified in FFPE tissues by a highly optimized targeted mass spectrometry assay that couples high-field asymmetric waveform ion mobility spectrometry (FAIMS) and parallel reaction monitoring (PRM) with internal standards. In a subset of samples, technical reproducibility was evaluated by analyzing consecutive tissue sections from the same tumor block and biological variation was accessed among adjacent tumor regions in the same tissue section.</p><p><strong>Results: </strong>Wild-type RAS protein was measured in 32 clinical non-small cell lung cancer tumors (622-2525 amol/µg) as measured by FAIMS-PRM mass spectrometry. Tumors with a known KRASG12C mutation (n = 17) expressed a wide range of RASG12C mutant protein (127-2012 amol/µg). The variation in wild-type RAS and RASG12C measurements ranged 0-18% CV across consecutive tissue sections and 5-20% CV among adjacent tissue regions. Quantitative target engagement was then demonstrated in FFPE tissues from 2 xenograft models (MIA PaCa-2 and NCI-H2122) treated with a RASG12C inhibitor (AZD4625).</p><p><strong>Conclusions: </strong>This work illustrates the potential to expand mass spectrometry-based proteomics in preclinical and clinical oncology drug development through analysis of FFPE tumor biopsies.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"47"},"PeriodicalIF":3.8,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50160889","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":"Change of histone H3 lysine 14 acetylation stoichiometry in human monocyte derived macrophages as determined by MS-based absolute targeted quantitative proteomic approach: HIV infection and methamphetamine exposure.","authors":"Katarzyna Macur, Andrew Schissel, Fang Yu, Shulei Lei, Brenda Morsey, Howard S Fox, Pawel Ciborowski","doi":"10.1186/s12014-023-09438-5","DOIUrl":"10.1186/s12014-023-09438-5","url":null,"abstract":"<p><strong>Background: </strong>Histones posttranslational modification represent an epigenetic mechanism that regulate gene expression and other cellular processes. Quantitative mass spectrometry used for the absolute quantification of such modifications provides further insight into cellular responses to extracellular insults such as infections or toxins. Methamphetamine (Meth), a drug of abuse, is affecting the overall function of the immune system. In this report, we developed, validated and applied a targeted, MS-based quantification assay to measure changes in histone H3 lysine 14 acetylation (H3K14Ac) during exposure of human primary macrophages to HIV-1 infection and/or Meth.</p><p><strong>Methods: </strong>The quantification assay was developed and validated to determine H3K14Ac stoichiometry in histones that were isolated from the nuclei of control (CIC) and exposed to Meth before (CIM) or/and after (MIM) HIV-infection human monocyte-derived macrophages (hMDM) of six donors. It was based on LC-MS/MS measurement using multiple reaction monitoring (MRM) acquisition of the unmodified and acetylated form of lysine K14 of histone H3 ⁹KSTGGKAPR¹⁷ peptides and the corresponding stable isotope labeled (SIL) heavy peptide standards of the same sequences. The histone samples were propionylated (Poy) pre- and post- trypsin digestion so that the sequences of the monitored peptides were: K[Poy]STGGK[1Ac]APR, K[Poy]STGGK[1Ac]APR-heavy, K[Poy]STGGK[Poy]APR and K[Poy]STGGK[Poy]APR-heavy. The absolute amounts of the acetylated and unmodified peptides were determined by comparing to the abundances of their SIL standards, that were added to the samples in the known concentrations, and, then used for calculation of H3K14Ac stoichiometry in CIC, CIM and MIM hMDM.</p><p><strong>Results: </strong>The assay was characterized by LLOD of 0.106 fmol/µL and 0.204 fmol/µL for unmodified and acetylated H3 ⁹KSTGGKAPR¹⁷ peptides, respectively. The LLOQ was 0.5 fmol/µL and the linear range of the assay was from 0.5 to 2500 fmol/µL. The absolute abundances of the quantified peptides varied between the donors and conditions, and so did the H3K14Ac stoichiometry. This was rather attributed to the samples nature itself, as the variability of their triplicate measurements was low.</p><p><strong>Conclusions: </strong>The developed LC-MS/MS assay enabled absolute quantification of H3K14Ac in exposed to Meth HIV-infected hMDM. It can be further applied determination of this PTM stoichiometry in other studies on human primary macrophages.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"48"},"PeriodicalIF":3.8,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50160887","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":"Glial cell proteome using targeted quantitative methods for potential multi-diagnostic biomarkers.","authors":"Narae Kang, Hyun Jeong Oh, Ji Hye Hong, Hyo Eun Moon, Yona Kim, Hyeon-Jeong Lee, Hophil Min, Hyeonji Park, Sang Hun Lee, Sun Ha Paek, Jonghwa Jin","doi":"10.1186/s12014-023-09432-x","DOIUrl":"10.1186/s12014-023-09432-x","url":null,"abstract":"<p><p>Glioblastoma is one of the most malignant primary brain cancer. Despite surgical resection with modern technology followed by chemo-radiation therapy with temozolomide, resistance to the treatment and recurrence is common due to its aggressive and infiltrating nature of the tumor with high proliferation index. The median survival time of the patients with glioblastomas is less than 15 months. Till now there has been no report of molecular target specific for glioblastomas. Early diagnosis and development of molecular target specific for glioblastomas are essential for longer survival of the patients with glioblastomas. Development of biomarkers specific for glioblastomas is most important for early diagnosis, estimation of the prognosis, and molecular target therapy of glioblastomas. To that end, in this study, we have conducted a comprehensive proteome study using primary cells and tissues from patients with glioblastoma. In the discovery stage, we have identified 7429 glioblastoma-specific proteins, where 476 proteins were quantitated using Tandem Mass Tag (TMT) method; 228 and 248 proteins showed up and down-regulated pattern, respectively. In the validation stage (20 selected target proteins), we developed quantitative targeted method (MRM: Multiple reaction monitoring) using stable isotope standards (SIS) peptide. In this study, five proteins (CCT3, PCMT1, TKT, TOMM34, UBA1) showed the significantly different protein levels (t-test: p value ≤ 0.05, AUC ≥ 0.7) between control and cancer groups and the result of multiplex assay using logistic regression showed the 5-marker panel showed better sensitivity (0.80 and 0.90), specificity (0.92 and 1.00), error rate (10 and 2%), and AUC value (0.94 and 0.98) than the best single marker (TOMM34) in primary cells and tissues, respectively. Although we acknowledge that the model requires further validation in a large sample size, the 5 protein marker panel can be used as baseline data for the discovery of novel biomarkers of the glioblastoma.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"45"},"PeriodicalIF":2.8,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50157233","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}