Clinical proteomics最新文献

{"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}

{"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":"Comment on \"Cerebrospinal fluid camk2a levels at baseline predict long-term progression in multiple sclerosis. Clinical Proteomics\".","authors":"Fereshteh Behdarvand Dehkordi, Mohammad Chehelgerdi","doi":"10.1186/s12014-023-09433-w","DOIUrl":"10.1186/s12014-023-09433-w","url":null,"abstract":"","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"46"},"PeriodicalIF":3.8,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50160888","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}