Journal articles: 'Proteomic pattern'

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Mischak, Harald, Eric Schiffer, Petra Zürbig, Mohammed Dakna, and Jochen Metzger. "Urinary Proteome Analysis using Capillary Electrophoresis Coupled to Mass Spectrometry: A Powerful Tool in Clinical Diagnosis, Prognosis and Therapy Evaluation." Journal of Medical Biochemistry 28, no. 4 (October 1, 2009): 223–34. http://dx.doi.org/10.2478/v10011-009-0020-0.

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Urinary Proteome Analysis using Capillary Electrophoresis Coupled to Mass Spectrometry: A Powerful Tool in Clinical Diagnosis, Prognosis and Therapy EvaluationProteome analysis has emerged as a powerful tool to decipher (patho) physiological processes, resulting in the establishment of the field of clinical proteomics. One of the main goals is to discover biomarkers for diseases from tissues and body fluids. Due to the enormous complexity of the proteome, a separation step is required for mass spectrometry (MS)-based proteome analysis. In this review, the advantages and limitations of protein separation by two-dimensional gel electrophoresis, liquid chromatography, surface-enhanced laser desorption/ionization and capillary electrophoresis (CE) for proteomic analysis are described, focusing on CE-MS. CE-MS enables separation and detection of the small molecular weight proteome in biological fluids with high reproducibility and accuracy in one single processing step and in a short time. As sensitive and specific single biomarkers generally may not exist, a strategy to overcome this diagnostic void is shifting from single analyte detection to simultaneous analysis of multiple analytes that together form a disease-specific pattern. Such approaches, however, are accompanied with additional challenges, which we will outline in this review. Besides the choice of adequate technological platforms, a high level of standardization of proteomic measurements and data processing is also necessary to establish proteomic profiling. In this regard, demands concerning study design, choice of specimens, sample preparation, proteomic data mining, and clinical evaluation should be considered before performing a proteomic study.

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Baumann, Sven, Uta Ceglarek, Georg Martin Fiedler, Jan Lembcke, Alexander Leichtle, and Joachim Thiery. "Standardized Approach to Proteome Profiling of Human Serum Based on Magnetic Bead Separation and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry." Clinical Chemistry 51, no. 6 (June 1, 2005): 973–80. http://dx.doi.org/10.1373/clinchem.2004.047308.

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Abstract Background: Magnetic bead purification for the analysis of low-abundance proteins in body fluids facilitates the identification of potential new biomarkers by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The aims of our study were to establish a proteome fractionation technique and to validate a standardized blood sampling, processing, and storage procedure for proteomic pattern analysis. Methods: We used magnetic bead separation for proteome profiling of human blood by MALDI-TOF MS (mass range, 1000–10 000 Da) and studied the effects on the quality and reproducibility of the proteome analysis of anticoagulants, blood clotting, time and temperature of sample storage, and the number of freeze–thaw cycles of samples. Results: The proteome pattern of human serum was characterized by ∼350 signals in the mass range of 1000–10 000 Da. The proteome profile showed time-dependent dynamic changes before and after centrifugation of the blood samples. Serum mass patterns differed between native samples and samples frozen once. The best reproducibility of proteomic patterns was with a single thawing of frozen serum samples. Conclusion: Application of the standardized preanalytical blood sampling and storage procedure in combination with magnetic bead-based fractionation decreases variability of proteome patterns in human serum assessed by MALDI-TOF MS.

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Yu, Li-Rong, Ming Zhou, Thomas P. Conrads, and Timothy D. Veenstra. "Diagnostic Proteomics: Serum Proteomic Patterns for the Detection of Early Stage Cancers." Disease Markers 19, no. 4-5 (2004): 209–18. http://dx.doi.org/10.1155/2004/612071.

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The ability to interrogate thousands of proteins found in complex biological samples using proteomic technologies has brought the hope of discovering novel disease-specific biomarkers. While most proteomic technologies used to discover diagnostic biomarkers are quite sophisticated, "proteomic pattern analysis" has emerged as a simple, yet potentially revolutionary, method for the early diagnosis of diseases. Utilizing this technology, hundreds of clinical samples can be analyzed per day and several preliminary studies suggest proteomic pattern analysis has the potential to be a novel, highly sensitive diagnostic tool for the early detection of cancer.

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Zhan, Xianquan, Biao Li, Xiaohan Zhan, Hartmut Schlüter, Peter R. Jungblut, and Jens R. Coorssen. "Innovating the Concept and Practice of Two-Dimensional Gel Electrophoresis in the Analysis of Proteomes at the Proteoform Level." Proteomes 7, no. 4 (October 30, 2019): 36. http://dx.doi.org/10.3390/proteomes7040036.

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Two-dimensional gel electrophoresis (2DE) is an important and well-established technical platform enabling extensive top-down proteomic analysis. However, the long-held but now largely outdated conventional concepts of 2DE have clearly impacted its application to in-depth investigations of proteomes at the level of protein species/proteoforms. It is time to popularize a new concept of 2DE for proteomics. With the development and enrichment of the proteome concept, any given “protein” is now recognized to consist of a series of proteoforms. Thus, it is the proteoform, rather than the canonical protein, that is the basic unit of a proteome, and each proteoform has a specific isoelectric point (pI) and relative mass (Mr). Accordingly, using 2DE, each proteoform can routinely be resolved and arrayed according to its different pI and Mr. Each detectable spot contains multiple proteoforms derived from the same gene, as well as from different genes. Proteoforms derived from the same gene are distributed into different spots in a 2DE pattern. High-resolution 2DE is thus actually an initial level of separation to address proteome complexity and is effectively a pre-fractionation method prior to analysis using mass spectrometry (MS). Furthermore, stable isotope-labeled 2DE coupled with high-sensitivity liquid chromatography-tandem MS (LC-MS/MS) has tremendous potential for the large-scale detection, identification, and quantification of the proteoforms that constitute proteomes.

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Walker, Maura E., Rebecca J. Song, Xiang Xu, Robert E. Gerszten, Debby Ngo, Clary B. Clish, Laura Corlin, et al. "Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study." Nutrients 12, no. 5 (May 19, 2020): 1476. http://dx.doi.org/10.3390/nu12051476.

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Data on proteomic and metabolomic signatures of healthy dietary patterns are limited. We evaluated the cross-sectional association of serum proteomic and metabolomic markers with three dietary patterns: the Alternative Healthy Eating Index (AHEI), the Dietary Approaches to Stop Hypertension (DASH) diet; and a Mediterranean-style (MDS) diet. We examined participants from the Framingham Offspring Study (mean age; 55 years; 52% women) who had complete proteomic (n = 1713) and metabolomic (n = 2284) data; using food frequency questionnaires to derive dietary pattern indices. Proteins and metabolites were quantified using the SomaScan platform and liquid chromatography/tandem mass spectrometry; respectively. We used multivariable-adjusted linear regression models to relate each dietary pattern index (independent variables) to each proteomic and metabolomic marker (dependent variables). Of the 1373 proteins; 103 were associated with at least one dietary pattern (48 with AHEI; 83 with DASH; and 8 with MDS; all false discovery rate [FDR] ≤ 0.05). We identified unique associations between dietary patterns and proteins (17 with AHEI; 52 with DASH; and 3 with MDS; all FDR ≤ 0.05). Significant proteins enriched biological pathways involved in cellular metabolism/proliferation and immune response/inflammation. Of the 216 metabolites; 65 were associated with at least one dietary pattern (38 with AHEI; 43 with DASH; and 50 with MDS; all FDR ≤ 0.05). All three dietary patterns were associated with a common signature of 24 metabolites (63% lipids). Proteins and metabolites associated with dietary patterns may help characterize intermediate phenotypes that provide insights into the molecular mechanisms mediating diet-related disease. Our findings warrant replication in independent populations

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Mischak-Weissinger, Eva M., Jochen Metzger, Annika Krons, Julia Kontsendorn, Jürgen Krauter, Michael Stadler, Harald Mischak, and Arnold Ganser. "Prospective Evaluation of Proteomic Screening with An Agvhd-Specific Proteomic Pattern MS-17." Blood 114, no. 22 (November 20, 2009): 2246. http://dx.doi.org/10.1182/blood.v114.22.2246.2246.

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Abstract Abstract 2246 Poster Board II-223 Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the only curative treatment for many hematologic malignancies or hematopoietic dysfunction syndromes in adults, but the application is still limited due to major complications, such as severe graft versus host disease (GvHD) and infectious complications. Diagnosis GvHD is based on clinical features and biopsies, a non-invasive, unbiased laboratory test using proteomics was published previously and evaluated in a multicenter setting (Weissinger et al., 2007). In order to maintain good quality data of the proteomic pattern data and due to the fact that the electrospray ionization time of flight mass spectrometer (ESI-TOF) from ABI (mariner) is no longer produced, we applied a more sensitive mass spectrometer (MS; ESI-TOF/Bruker). In order to validate the quality of the data produced with the Bruker, ESI-TOF was used since December 2007 in parallel to the mariner. Our first and previously published “aGvHD-specific pattern” (Weissinger et al., 2007) was used to evaluate and improve the pattern with the more sensitive decreased (6) or increased (11) signal intensity in patients with GvHD grade II to IV. Patients post HSCT and patients with GvHD grade I have the same pattern. In addition, the MS-17-pattern yielded at least comparable if not superior results to the previous one yielding sensitivities around 95% and specificity about 80%. To date the proteomic pattern specific for aGvHD MS17 was evaluated blindly on 400 samples collected from about 90 patients undergoing allo-HSCT at MHH since Dec. 2007. The peptide pattern MS-17 consists of 17 peptides - either absent/decreased (6) or present /increased (11) in aGvHD grade II to IV when compared to patients with clinical aGvHD grade I or without GvHD. Prospective and blinded evaluation of the patients included in this parallel diagnostic study revealed the correct classification of patients developing aGvHD about 7 days prior to the development of clinical signs and symptoms for aGVHD with a sensitivity and specificity of about 85%. Sequencing 3 of the 17 markers yielded the following results: Peptide No 5: Albumine fragment (N-terminus); Peptide No 7: ψ2 microglobulin; Peptide No 10: fragment of CD99 (MIC2; a single-chain type-1 glycoprotein). CD99 and β-2 microglobulin are particulary interesting because of their involvement in immune responses, both being expressed on leukocytes and CD 99 especially on thymocytes with involvement in apoptosis of double positive T-cells and binding cyclosporin A. Data on the pattern, signal intensity (3 dimension, mass and charge are shown in Figure 1) Disclosures: Metzger: mosaiques-diagnostics GmbH: Employment. Krons:mosaiques-diagnostics GmbH: Employment. Mischak:mosaiques-diagnostics GmbH: Patents & Royalties, coowner and founder of mosaiques-diagnostics.

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Gillette, Michael A., D. R. Mani, and Steven A. Carr. "Place of Pattern in Proteomic Biomarker Discovery†." Journal of Proteome Research 4, no. 4 (August 2005): 1143–54. http://dx.doi.org/10.1021/pr0500962.

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Müller, Ute, Günther Ernst, Christian Melle, Reinhard Guthke, and Ferdinand von Eggeling. "Convergence of the proteomic pattern in cancer." Bioinformatics 22, no. 11 (March 7, 2006): 1293–96. http://dx.doi.org/10.1093/bioinformatics/btl077.

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Conrads, T. P., V. A. Fusaro, S. Ross, D. Johann, V. Rajapakse, B. A. Hitt, S. M. Steinberg, et al. "High-resolution serum proteomic features for ovarian cancer detection." Endocrine-related cancer 11, no. 2 (June 2004): 163–78. http://dx.doi.org/10.1677/erc.0.0110163.

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Serum proteomic pattern diagnostics is an emerging paradigm employing low-resolution mass spectrometry (MS) to generate a set of biomarker classifiers. In the present study, we utilized a well-controlled ovarian cancer serum study set to compare the sensitivity and specificity of serum proteomic diagnostic patterns acquired using a high-resolution versus a low-resolution MS platform. In blinded testing sets, the high-resolution mass spectral data contained multiple diagnostic signatures that were superior to the low-resolution spectra in terms of sensitivity and specificity (P<0.00001) throughout the range of modeling conditions. Four mass spectral feature set patterns acquired from data obtained exclusively with the high-resolution mass spectrometer were 100% specific and sensitive in their diagnosis of serum samples as being acquired from either unaffected patients or those suffering from ovarian cancer. Important to the future of proteomic pattern diagnostics is the ability to recognize inferior spectra statistically, so that those resulting from a specific process error are recognized prior to their potentially incorrect (and damaging) diagnosis. To meet this need, we have developed a series of quality-assurance and in-process control procedures to (a) globally evaluate sources of sample variability, (b) identify outlying mass spectra, and (c) develop quality-control release specifications. From these quality-assurance and control (QA/QC) specifications, we identified 32 mass spectra out of the total 248 that showed statistically significant differences from the norm. Hence, 216 of the initial 248 high-resolution mass spectra were determined to be of high quality and were remodeled by pattern-recognition analysis. Again, we obtained four mass spectral feature set patterns that also exhibited 100% sensitivity and specificity in blinded validation tests (68/68 cancer: including 18/18 stage I, and 43/43 healthy). We conclude that (a) the use of high-resolution MS yields superior classification patterns as compared with those obtained with lower resolution instrumentation; (b) although the process error that we discovered did not have a deleterious impact on the present results obtained from proteomic pattern analysis, the major source of spectral variability emanated from mass spectral acquisition, and not bias at the clinical collection site; (c) this variability can be reduced and monitored through the use of QA/QC statistical procedures; (d) multiple and distinct proteomic patterns, comprising low molecular weight biomarkers, detected by high-resolution MS achieve accuracies surpassing individual biomarkers, warranting validation in a large clinical study.

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Pouliquen, Daniel L., Alice Boissard, Cécile Henry, Stéphanie Blandin, Olivier Coqueret, and Catherine Guette. "Lymphoid Organ Proteomes Identify Therapeutic Efficacy Biomarkers following the Intracavitary Administration of Curcumin in a Highly Invasive Rat Model of Peritoneal Mesothelioma." International Journal of Molecular Sciences 22, no. 16 (August 9, 2021): 8566. http://dx.doi.org/10.3390/ijms22168566.

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This study aimed to identify the proteomic changes produced by curcumin treatment following stimulation of the host immune system in a rat model of malignant mesothelioma. We analyzed the proteomes of secondary lymphoid organs from four normal rats, four untreated tumor-bearing rats, and four tumor-bearing rats receiving repeated intraperitoneal administrations of curcumin. Cross-comparing proteome analyses of histological sections of the spleen from the three groups first identified a list of eighty-three biomarkers of interest, thirteen of which corresponded to proteins already reported in the literature and involved in the anticancer therapeutic effects of curcumin. In a second step, comparing these data with proteomic analyses of histological sections of mesenteric lymph nodes revealed eight common biomarkers showing a similar pattern of changes in both lymphoid organs. Additional findings included a partial reduction of the increase in spleen-circulating biomarkers, a decrease in C-reactive protein and complement C3 in the spleen and lymph nodes, and an increase in lymph node purine nucleoside phosphorylase previously associated with liver immunodeficiency. Our results suggest some protein abundance changes could be related to the systemic, distant non-target antitumor effects produced by this phytochemical.

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Künzel, Steffen E., Leonie T. M. Flesch, Dominik P. Frentzel, Vitus A. Knecht, Anne Rübsam, Felix Dreher, Moritz Schütte, et al. "Systemic Blood Proteome Patterns Reflect Disease Phenotypes in Neovascular Age-Related Macular Degeneration." International Journal of Molecular Sciences 24, no. 12 (June 19, 2023): 10327. http://dx.doi.org/10.3390/ijms241210327.

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There is early evidence of extraocular systemic signals effecting function and morphology in neovascular age-related macular degeneration (nAMD). The prospective, cross-sectional BIOMAC study is an explorative investigation of peripheral blood proteome profiles and matched clinical features to uncover systemic determinacy in nAMD under anti-vascular endothelial growth factor intravitreal therapy (anti-VEGF IVT). It includes 46 nAMD patients stratified by the level of disease control under ongoing anti-VEGF treatment. Proteomic profiles in peripheral blood samples of every patient were detected with LC-MS/MS mass spectrometry. The patients underwent extensive clinical examination with a focus on macular function and morphology. In silico analysis includes unbiased dimensionality reduction and clustering, a subsequent annotation of clinical features, and non-linear models for recognition of underlying patterns. The model assessment was performed using leave-one-out cross validation. The findings provide an exploratory demonstration of the link between systemic proteomic signals and macular disease pattern using and validating non-linear classification models. Three main results were obtained: (1) Proteome-based clustering identifies two distinct patient subclusters with the smaller one (n = 10) exhibiting a strong signature for oxidative stress response. Matching the relevant meta-features on the individual patient’s level identifies pulmonary dysfunction as an underlying health condition in these patients. (2) We identify biomarkers for nAMD disease features with Aldolase C as a putative factor associated with superior disease control under ongoing anti-VEGF treatment. (3) Apart from this, isolated protein markers are only weakly correlated with nAMD disease expression. In contrast, applying a non-linear classification model identifies complex molecular patterns hidden in a high number of proteomic dimensions determining macular disease expression. In conclusion, so far unconsidered systemic signals in the peripheral blood proteome contribute to the clinically observed phenotype of nAMD, which should be examined in future translational research on AMD.

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Yang, Xiao Li, and Qiong He. "Biomimetic Pattern Recognition for Classification of Proteomic Profile." Advanced Materials Research 791-793 (September 2013): 1961–64. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1961.

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We propose a biomimetic pattern recognition (BPR) approach for classification of proteomic profile. The proposed approach preprocess profile using iterative minimum in adaptive setting window (IMASW) method for baseline correction, discrete wavelet transform (DWT) for fitting and smoothing, and average total ion normalization (ATIN) for remove the influence of vary amount of sample and degradation over time. Then principal component analysis (PCA) and BPR build classification model. With an optimization of the parameters involved in the modeling, we obtain a satisfactory model for cancer diagnosis in three proteomic profile datasets. The predicted results show that BPR technique is more reliable and efficient than support vector machine (SVM) method.

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Hazem Radwan Ahmed, Hazem Radwan Ahmed, and Janice Glasgow. "Pattern Discovery in Protein Networks Reveals High-Confidence Predictions of Novel Interactions." Proceedings of the AAAI Conference on Artificial Intelligence 28, no. 2 (July 27, 2014): 2938–45. http://dx.doi.org/10.1609/aaai.v28i2.19035.

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Pattern discovery in protein interaction networks can reveal crucial biological knowledge on the inner workings of cellular machinery. Although far from complete, extracting meaningful patterns from proteomic networks is a nontrivial task due to their size-complexity. This paper proposes a computational framework to efficiently discover topologically-similar patterns from large proteomic networks using Particle Swarm Optimization (PSO). PSO is a robust and low-cost optimization technique that demonstrated to work effectively on the complex, mostly sparse proteomic networks. The resulting topologicallysimilar patterns of close proximity are utilized to systematically predict new high-confidence protein-protein interactions (PPIs). The proposed PSO-based PPI prediction method (3PI) managed to predict high-confidence PPIs, validated by more than one computational/experimental source, through a proposed PPI knowledge transfer process between topologically-similar interaction patterns of close proximity. In three case studies, over 50% of the predicted interactions for EFGR, ERBB2, ERBB3, GRB2 and UBC are overlapped with publically available interaction databases, ~80% of the predictions are found among the Top 1% results of another PPI prediction method and their genes are significantly co-expressed across different tissues. Moreover, the only single prediction example that did not overlap with any of our validation sources was recently experimentally supported by two PubMed publications.

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Jaffe, Jacob D., D. R. Mani, Kyriacos C. Leptos, George M. Church, Michael A. Gillette, and Steven A. Carr. "PEPPeR: A Platform for Experimental Proteomic Pattern Recognition." Molecular & Cellular Proteomics 8, no. 3 (March 2009): 584. http://dx.doi.org/10.1016/s1535-9476(20)30621-6.

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Jaffe, Jacob D., D. R. Mani, Kyriacos C. Leptos, George M. Church, Michael A. Gillette, and Steven A. Carr. "PEPPeR, a Platform for Experimental Proteomic Pattern Recognition." Molecular & Cellular Proteomics 5, no. 10 (July 19, 2006): 1927–41. http://dx.doi.org/10.1074/mcp.m600222-mcp200.

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Liu, Y., Y. Guo, N. Song, Y. Fan, K. Li, X. Teng, Q. Guo, and Z. Ding. "Proteomic pattern changes associated with obesity-induced asthenozoospermia." Andrology 3, no. 2 (October 8, 2014): 247–59. http://dx.doi.org/10.1111/andr.289.

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Htike, Zaw Zaw, and Shoon Lei Win. "Premalignant Pancreatic Cancer Diagnosis Using Proteomic Pattern Analysis." Journal of Medical and Bioengineering 4, no. 4 (2015): 288–92. http://dx.doi.org/10.12720/jomb.4.4.288-292.

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LIN, Y. W., C. Y. LIN, H. C. LAI, J. Y. CHIOU, C. C. CHANG, M. H. YU, and T. Y. CHU. "Plasma proteomic pattern as biomarkers for ovarian cancer." International Journal of Gynecological Cancer 16, S1 (February 2006): 139–46. http://dx.doi.org/10.1111/j.1525-1438.2006.00475.x.

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Minafra, Luigi, Gianluca Di Cara, Nadia Ninfa Albanese, and Patrizia Cancemi. "Proteomic differentiation pattern in the U937 cell line." Leukemia Research 35, no. 2 (February 2011): 226–36. http://dx.doi.org/10.1016/j.leukres.2010.07.040.

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Jaffe, Jacob D., D. R. Mani, Kyriacos C. Leptos, George M. Church, Michael A. Gillette, and Steven A. Carr. "PEPPeR: A Platform for Experimental Proteomic Pattern Recognition." Molecular & Cellular Proteomics 8, no. 3 (March 2009): 584. http://dx.doi.org/10.1016/s1535-9476(20)30621-6.

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Lin, Y. W., C. Y. Lin, H. C. Lai, J. Y. Chiou, C. C. Chang, M. H. Yu, and T. Y. Chu. "Plasma proteomic pattern as biomarkers for ovarian cancer." International Journal of Gynecologic Cancer 16, Suppl 1 (January 2006): 139–46. http://dx.doi.org/10.1136/ijgc-00009577-200602001-00023.

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Early detection of ovarian cancer remains a challenge. Pathologic changes within an organ might be reflected in proteomic patterns in serum or plasma. The objective of this study was to identify new plasma biomarkers in ovarian cancer patients using mass spectrometry (MS) protein profiling and artificial intelligence. The study included 35 women with ovarian cancer and 30 age-matched disease-free controls. For plasma protein signature analysis, the protein chip array surface-enhanced laser desorption/ionization (SELDI) analysis was performed. The strong anion exchange (SAX) and weak cation exchange (WCX) chips were used for analysis. After a training analysis by SAX and WCX protein chips, learning algorithm and clustering analysis was performed to reach a discriminate pattern of protein signature. SELDI mass spectroscopy was highly reproducible in detecting ovarian tumor-specific protein profiles. Four specific protein peaks were identified in plasma of women with ovarian cancer, but not in controls, with relative molecular masses of 6190.48, 5147.06, 11522.6, and 11537.7 d. Two peaks, with Mr 5295.5 and 8780.48 d, were present in plasma of control but not in women with ovarian cancer. A sensitivity of 90–96.3% and specificity of 100% for this studied cases and controls were reached. This study clearly demonstrates that the combined technology of SELDI-MS and artificial intelligence is effective in distinguishing protein expression between normal and ovary cancer plasma. The identified gained and lost protein peaks in plasma may provide as candidate proteins to be used for the detection or monitoring ovarian cancer.

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Liu, Ying. "Serum Proteomic Pattern Analysis for Early Cancer Detection." Technology in Cancer Research & Treatment 5, no. 1 (February 2006): 61–66. http://dx.doi.org/10.1177/153303460600500108.

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Hammoud, Zane T., Lacey Dobrolecki, Kenneth A. Kesler, Emad Rahmani, Karen Rieger, Linda H. Malkas, and Robert J. Hickey. "Diagnosis of Esophageal Adenocarcinoma by Serum Proteomic Pattern." Annals of Thoracic Surgery 84, no. 2 (August 2007): 384–92. http://dx.doi.org/10.1016/j.athoracsur.2007.03.088.

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Koch, Marianne, Wolfgang Umek, Engelbert Hanzal, Thomas Mohr, Sonja Seyfert, Heinz Koelbl, and Goran Mitulović. "Serum proteomic pattern in female stress urinary incontinence." ELECTROPHORESIS 39, no. 8 (February 6, 2018): 1071–78. http://dx.doi.org/10.1002/elps.201700423.

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Barbé, Caroline, Jérôme Salles, Christophe Chambon, Christophe Giraudet, Phelipe Sanchez, Véronique Patrac, Philippe Denis, Yves Boirie, Stéphane Walrand, and Marine Gueugneau. "Characterization of the Skeletal Muscle Proteome in Undernourished Old Rats." International Journal of Molecular Sciences 23, no. 9 (April 26, 2022): 4762. http://dx.doi.org/10.3390/ijms23094762.

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Aging is associated with a progressive loss of skeletal muscle mass and function termed sarcopenia. Various metabolic alterations that occur with aging also increase the risk of undernutrition, which can worsen age-related sarcopenia. However, the impact of undernutrition on aged skeletal muscle remains largely under-researched. To build a deeper understanding of the cellular and molecular mechanisms underlying age-related sarcopenia, we characterized the undernutrition-induced changes in the skeletal muscle proteome in old rats. For this study, 20-month-old male rats were fed 50% or 100% of their spontaneous intake for 12 weeks, and proteomic analysis was performed on both slow- and fast-twitch muscles. Proteomic profiling of undernourished aged skeletal muscle revealed that undernutrition has profound effects on muscle proteome independently of its effect on muscle mass. Undernutrition-induced changes in muscle proteome appear to be muscle-type-specific: slow-twitch muscle showed a broad pattern of differential expression in proteins important for energy metabolism, whereas fast-twitch muscle mainly showed changes in protein turnover between undernourished and control rats. This first proteomic analysis of undernourished aged skeletal muscle provides new molecular-level insight to explain phenotypic changes in undernourished aged muscle. We anticipate this work as a starting point to define new biomarkers associated with undernutrition-induced muscle loss in the elderly.

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Barbé, Caroline, Jérôme Salles, Christophe Chambon, Christophe Giraudet, Phelipe Sanchez, Véronique Patrac, Philippe Denis, Yves Boirie, Stéphane Walrand, and Marine Gueugneau. "Characterization of the Skeletal Muscle Proteome in Undernourished Old Rats." International Journal of Molecular Sciences 23, no. 9 (April 26, 2022): 4762. http://dx.doi.org/10.3390/ijms23094762.

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Aging is associated with a progressive loss of skeletal muscle mass and function termed sarcopenia. Various metabolic alterations that occur with aging also increase the risk of undernutrition, which can worsen age-related sarcopenia. However, the impact of undernutrition on aged skeletal muscle remains largely under-researched. To build a deeper understanding of the cellular and molecular mechanisms underlying age-related sarcopenia, we characterized the undernutrition-induced changes in the skeletal muscle proteome in old rats. For this study, 20-month-old male rats were fed 50% or 100% of their spontaneous intake for 12 weeks, and proteomic analysis was performed on both slow- and fast-twitch muscles. Proteomic profiling of undernourished aged skeletal muscle revealed that undernutrition has profound effects on muscle proteome independently of its effect on muscle mass. Undernutrition-induced changes in muscle proteome appear to be muscle-type-specific: slow-twitch muscle showed a broad pattern of differential expression in proteins important for energy metabolism, whereas fast-twitch muscle mainly showed changes in protein turnover between undernourished and control rats. This first proteomic analysis of undernourished aged skeletal muscle provides new molecular-level insight to explain phenotypic changes in undernourished aged muscle. We anticipate this work as a starting point to define new biomarkers associated with undernutrition-induced muscle loss in the elderly.

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MISCHAK, Harald, Thorsten KAISER, Michael WALDEN, Meike HILLMANN, Stefan WITTKE, Alena HERRMANN, Stefan KNUEPPEL, Hermann HALLER, and Danilo FLISER. "Proteomic analysis for the assessment of diabetic renal damage in humans." Clinical Science 107, no. 5 (October 26, 2004): 485–95. http://dx.doi.org/10.1042/cs20040103.

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Renal disease in patients with Type II diabetes is the leading cause of terminal renal failure and a major healthcare problem. Hence early identification of patients prone to develop this complication is important. Diabetic renal damage should be reflected by a change in urinary polypeptide excretion at a very early stage. To analyse these changes, we used an online combination of CE/MS (capillary electrophoresis coupled with MS), allowing fast and accurate evaluation of up to 2000 polypeptides in urine. Employing this technology, we have examined urine samples from 39 healthy individuals and from 112 patients with Type II diabetes mellitus and different degrees of albumin excretion rate. We established a ‘normal’ polypeptide pattern in the urine of healthy subjects. In patients with Type II diabetes and normal albumin excretion rate, the polypeptide pattern in urine differed significantly from normal, indicating a specific ‘diabetic’ pattern of polypeptide excretion. In patients with higher grade albuminuria, we were able to detect a polypeptide pattern indicative of ‘diabetic renal damage’. We also found this pattern in 35% of those patients who had low-grade albuminuria and in 4% of patients with normal albumin excretion. Moreover, we could identify several of the indicative polypeptides using MS/MS sequencing. We conclude that proteomic analysis with CE/MS permits fast and accurate identification and differentiation of polypeptide patterns in urine. Longitudinal studies should explore the potential of this powerful diagnostic tool for early detection of diabetic renal damage.

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XIAO, Xueyuan. "Discovery of laryngeal carcinoma by serum proteomic pattern analysis." Science in China Series C 47, no. 3 (2004): 219. http://dx.doi.org/10.1360/03yc0105.

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Xiao, Xueyuan, Xiaodong Zhao, Jiankai Liu, Fuzheng Guo, Danhui Liu, and Dacheng He. "Discovery of laryngeal carcinoma by serum proteomic pattern analysis." Science in China Series C: Life Sciences 47, no. 3 (May 2004): 219–23. http://dx.doi.org/10.1007/bf03182766.

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Hu, Jin-yu, Chang-Lin Li, and Ying-Wei Wang. "Altered proteomic pattern in platelets of rats with sepsis." Blood Cells, Molecules, and Diseases 48, no. 1 (January 2012): 30–35. http://dx.doi.org/10.1016/j.bcmd.2011.09.010.

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Komori, Mika, Yumiko Matsuyama, Takashi Nirasawa, Herbert Thiele, Michael Becker, Theodore Alexandrov, Takahiko Saida, et al. "Proteomic pattern analysis discriminates among multiple sclerosis-related disorders." Annals of Neurology 71, no. 5 (April 20, 2012): 614–23. http://dx.doi.org/10.1002/ana.22633.

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Kim, Young Bun, Chin-Rang Yang, and Jean Gao. "Functional proteomic pattern identification under low dose ionizing radiation." Artificial Intelligence in Medicine 49, no. 3 (July 2010): 177–85. http://dx.doi.org/10.1016/j.artmed.2010.04.001.

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Rüetschi, Ulla, Martin Stenson, Sverker Hasselblom, Herman Nilsson-Ehle, Ulrika Hansson, Henrik Fagman, and Per-Ola Andersson. "SILAC-Based Quantitative Proteomic Analysis of Diffuse Large B-Cell Lymphoma Patients." International Journal of Proteomics 2015 (April 28, 2015): 1–12. http://dx.doi.org/10.1155/2015/841769.

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Diffuse large B-cell lymphoma (DLBCL), the most common lymphoma, is a heterogeneous disease where the outcome for patients with early relapse or refractory disease is very poor, even in the era of immunochemotherapy. In order to describe possible differences in global protein expression and network patterns, we performed a SILAC-based shotgun (LC-MS/MS) quantitative proteomic analysis in fresh-frozen tumor tissue from two groups of DLBCL patients with totally different clinical outcome: (i) early relapsed or refractory and (ii) long-term progression-free patients. We could identify over 3,500 proteins; more than 1,300 were quantified in all patients and 87 were significantly differentially expressed. By functional annotation analysis on the 66 proteins overexpressed in the progression-free patient group, we found an enrichment of proteins involved in the regulation and organization of the actin cytoskeleton. Also, five proteins from actin cytoskeleton regulation, applied in a supervised regression analysis, could discriminate the two patient groups. In conclusion, SILAC-based shotgun quantitative proteomic analysis appears to be a powerful tool to explore the proteome in DLBCL tumor tissue. Also, as progression-free patients had a higher expression of proteins involved in the actin cytoskeleton protein network, such a pattern indicates a functional role in the sustained response to immunochemotherapy.

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Lannert, Heinrich, Thomas Franz, Volker Eckstein, Reiner Hofmann, Angela Lenze, Kerstin Horsch, Katrin Miesala, and Anthony D. Ho. "Quantitative and Qualitative Protein Expression Mapping of Highly Enriched G-CSF Mobilized CD34+ Stem Cells from Peripheral Blood." Blood 104, no. 11 (November 16, 2004): 4130. http://dx.doi.org/10.1182/blood.v104.11.4130.4130.

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Abstract Background: Proteome analysis is a direct measurement of proteins in terms of their presence and relative abundance in a defined system. The overall aim of a proteomic study is characterization of the complex network of cell regulation. Different states of a cell can be compared and specific qualitative and quantitative protein changes can be identified. We focused our first proteom-investigations on G-CSF mobilized CD34+ stem cells from peripheral blood (PB). Methods: Mononuclear cells from healthy donors were isolated by a standard Ficoll-Hypaque gradient separation method after leucapheresis from PB. An Auto-MACS (Miltenyi) and FACS Vantage SE cell sorter (Becton Dickinson) was used to highly enrich (>99%) CD34+ cells fractions. Sample preparation, determination of protein concentrations, 2D-gel-electrophoresis with a 1D-separation isoelectric focusing (IEF) with immobilised pH gradient (IPG) strips (17cm), 2D-separation with SDS-PAGE were performed and described in Proteome Works System (BioRad), pH range from 3 until 10 and molecular weight from 5 until 200kDa. Sypro ruby stained gels were used for protein identification by peptide mass fingerprint analysis with matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS). Results: A.) The pattern of the G-CSF mobilized CD34+ fraction from PB cells is complex and shows >1000 protein spots (dependent on protein concentration) in silver stain using PDQuest. B.) The most dominant 150 protein spots were characterized by MALDI-TOF analyses. A sample of identified spots and their functional groups are specified: 1. Cytosketeletal proteins: tubulin, actin, profilin, tropomyosin. 2. Signaling proteins: enolase, rho GDP dissociation inhibitor 2, glutathione S transferase, nucleophosmin. 3. Metabolism: phosphoglycerate kinase I, glyceraldehyde-3P-dehydrogenase. 4. Protein folding: heat shock proteins (HSP 60, HSP70c), molecular chaperones (GRP78). Conclusions: Proteomics is a highly effective method to describe the gen-expression and the cell biology of stem cells on real protein transcription. Highly purified CD34+ cells from PB demonstrate a complex proteom. Our preliminary results show that cell cycle determining chaperones (folding of other protein and responsible for their functional activation or deactivation) are dominantly expressed. Thus cytoskeletal proteins dominate the protein pattern of CB stem cells. Further Proteom-comparison with other subsets of stem cells from different origins (e.g. fetal liver, cord blood, bone marrow) are currently achieved. Figure Figure

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Azri, Wassim, Zouhaier Barhoumi, Farhat Chibani, Manel Borji, Mouna Bessrour, and Ahmed Mliki. "Proteomic responses in shoots of the facultative halophyte Aeluropus littoralis (Poaceae) under NaCl salt stress." Functional Plant Biology 43, no. 11 (2016): 1028. http://dx.doi.org/10.1071/fp16114.

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Salinity is an environmental constraint that limits agricultural productivity worldwide. Studies on the halophytes provide valuable information to describe the physiological and molecular mechanisms of salinity tolerance. Therefore, because of genetic relationships of Aeluropus littoralis (Willd) Parl. with rice, wheat and barley, the present study was conducted to investigate changes in shoot proteome patterns in response to different salt treatments using proteomic methods. To examine the effect of salinity on A. littoralis proteome pattern, salt treatments (0, 200 and 400 mM NaCl) were applied for 24 h and 7 and 30 days. After 24 h and 7 days exposure to salt treatments, seedlings were fresh and green, but after 30 days, severe chlorosis was established in old leaves of 400 mM NaCl-salt treated plants. Comparative proteomic analysis of the leaves revealed that the relative abundance of 95 and 120 proteins was significantly altered in 200 and 400 mM NaCl treated plants respectively. Mass spectrometry-based identification was successful for 66 out of 98 selected protein spots. These proteins were mainly involved in carbohydrate, energy, amino acids and protein metabolisms, photosynthesis, detoxification, oxidative stress, translation, transcription and signal transduction. These results suggest that the reduction of proteins related to photosynthesis and induction of proteins involved in glycolysis, tricarboxylic acid (TCA) cycle, and energy metabolism could be the main mechanisms for salt tolerance in A. littoralis. This study provides important information about salt tolerance, and a framework for further functional studies on the identified proteins in A. littoralis.

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Foratori-Junior, Gerson Aparecido, Talita Mendes Oliveira Ventura, Larissa Tercilia Grizzo, Guy Howard Carpenter, Marília Afonso Rabelo Buzalaf, and Silvia Helena de Carvalho Sales-Peres. "Label-Free Quantitative Proteomic Analysis Reveals Inflammatory Pattern Associated with Obesity and Periodontitis in Pregnant Women." Metabolites 12, no. 11 (November 10, 2022): 1091. http://dx.doi.org/10.3390/metabo12111091.

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Obesity and pregnancy may have synergistic effects on periodontal condition, and proteomics could be an ideal approach to highlight the pathophysiological mechanisms associated with these outcomes. This study analyzed the salivary proteomics related to obesity and periodontitis in women during pregnancy (T1) and after delivery (T2). Initially, 126 women were recruited and forty were allocated into groups: with obesity and periodontitis (OP); with obesity, but without periodontitis (OWP); with normal BMI, but with periodontitis (NP); with normal BMI and without periodontitis (NWP). Whole-mouth saliva was collected in T1 and T2, and proteins were extracted and individually processed by label-free proteomics (nLC-ESI-MS/MS). The up-regulations of Heat shock 70 kDa protein 1A, 1B, and 1-like were related to both obesity and periodontitis, separately. Albumin and Thioredoxin were up-regulated in periodontitis cases, while Cystatins (mainly S, SA, SN) and Lactotransferrin were down-regulated. The high abundances of Submaxillary gland androgen-regulated protein 3B, Protein S100-A8, Matrix metalloproteinase-9, Heat shock 70 kDa protein 2 and 6, Putative Heat shock 70 kDa protein 7, Heat shock 71 kDa protein, Haptoglobin and Plastin-1 were significant in the combination of obesity and periodontitis. Obesity and periodontitis remarkably altered the proteome of the saliva during pregnancy with substantial alterations after delivery.

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Zhu, L. R., W. Y. Zhang, L. Yu, Y. H. Zheng, J. Z. Zhang, and Q. P. Liao. "Serum proteomic features for detection of endometrial cancer." International Journal of Gynecologic Cancer 16, no. 3 (2006): 1374–78. http://dx.doi.org/10.1136/ijgc-00009577-200605000-00065.

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To find new potential biomarkers for detection of endometrial cancer (EC), 70 serum samples including 40 from EC patients and 30 from normal healthy females were detected by surface-enhanced laser desorption–ionization time-of-flight mass spectrometry (SELDI-TOF-MS) using WCX2 (weak cation exchange) protein chip. Mass spectra were then assessed with three powerful data-mining tools: a tree classifier, Biomarker Wizard software, and Biomarker Patterns System. The diagnostic pattern combined with 13 potential biomarkers could differentiate EC patients from healthy persons, with a specificity of 100%, sensitivity of 92.5%, and total coincidence of 95.7%. The combination of surface-enhanced laser desorption–ionization with bioinformatics tools could help find new biomarkers and establish with high sensitivity and specificity for the detection of EC.

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Weissinger, Eva M., Daniel Wolff, Jochen Metzger, Christiane E. Dobbelstein, Stefanie Buchholz, Elke Dammann, Uwe Borchert, et al. "Prospective Validation of a Chronic GvHD-Specific Proteome Pattern (cGvHD-MS14) Post Allogeneic Hematopoietic Stem Cell Transplantation." Blood 118, no. 21 (November 18, 2011): 1970. http://dx.doi.org/10.1182/blood.v118.21.1970.1970.

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Abstract Abstract 1970 Introduction: Allogeneic hematopoietic stem cell transplantation (HSCT) is the only curative treatment for many hematologic malignancies and non-malignant hematopoietic disorders, but is associated with significant morbidity and mortality with focus on acute and chronic graft-versus-host disease (GvHD). Chronic GvHD (cGvHD) occurs with increasing frequency, hampering quality of life of patients post-allogeneic HSCT and leading to increased morbidity and mortality even years after allogeneic HSCT. Diagnosis of chronic GvHD is based on clinical features and histology. Here we present the generation of cGvHD-specific proteomic pattern (cGvHD-MS14) using capillary electrophoreses and mass spectrometry to analyze urine sample collected prospectively after allogeneic HSCT. Methods: A proteomic pattern (cGvHD-MS14) was developed in order to diagnose cGvHD, to differentiate acute versus cGvHD, and to predict onset and severity of cGvHD prior to clinical diagnosis of cGVHD as a non-invasive, unbiased laboratory test for diagnosis of cGvHD. This pattern was prospectively evaluated on 329 patients (1034 urine samples) after allogeneic HSCT at MHH and 3 collaborating transplant centers. The majority of the patients had acute leukemias prior to transplantation (n=210) and were transplanted from matched unrelated or related donors (MUD n=134; MRD n=125). Reduced intensity conditioning regimens were used in about 75% of all patients and the majority (80%) received ATG (anti-thymocyte globulin) as GVHD-prophylaxis prior to transplantation and a calcineurin-inhibitor based prophylaxis afterwards. Results: Prospective and blinded evaluation revealed the correct classification of patients developing cGvHD with a sensitivity 78% and specificity of about 71% at time of diagnosis. Differentiation between late onset acute GvHD and chronic GvHD was achieved in 3 patients in this validation set. Acute GvHD prior to day 100 is not recognized by cGvHD-MS14, since aGvHD-specific peptides had been excluded during cGvHD-pattern generation. The pattern consists of 14 differentially excreted peptides, differentiating chronic GvHD from tolerant patients. Four of 14 peptides have been sequenced to date, 2 are fragments from collagen 1, 1 is from inter-alpha trypsin inhibitor heavy chain 4 and 1 is a fragment from the fibrinogen ß-chain. Conclusions: The proteomic pattern of urine proteomics enables diagnosis of cGvHD as well as differentiation of acute versus chronic GvHD. Further prospective evaluation of the cGvHD-specific pattern cGvHD-MS14 for organ specificity as well as severity prediction is currently ongoing. Taken together our results indicate that diagnosis of cGvHD is possible using CE/MS analysis of prospectively collected urine samples with high sensitivity and specificity. Disclosures: Metzger: mosaiques-diagnostics GmbH: Employment. Krons:mosaiques-diagnostics GmbH: Employment.

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Stevens, E. V., L. A. Liotta, and E. C. Kohn. "Proteomic analysis for early detection of ovarian cancer: A realistic approach?" International Journal of Gynecologic Cancer 13, Suppl 2 (2003): 133–39. http://dx.doi.org/10.1136/ijgc-00009577-200311001-00001.

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Ovarian cancer is a multifaceted disease wherein most women are diagnosed with advanced stage disease. One of the most imperative issues in ovarian cancer is early detection. Biomarkers that allow cancer detection at stage I, a time when the disease is amenable to surgical and chemotherapeutic cure in over 90% of patients, can dramatically alter the horizon for women with this disease. Recent developments in mass spectroscopy and protein chip technology coupled with bioinformatics have been applied to biomarker discovery. The complexity of the proteome is a rich resource from which the patterns can be gleaned; the pattern rather than its component parts is the diagnostic. Serum is a key source of putative protein biomarkers, and, by its nature, can reflect organ-confined events. Pioneering use of mass spectroscopy coupled with bioinformatics has been demonstrated as being capable of distinguishing serum protein pattern signatures of ovarian cancer in patients with early- and late-stage disease. This is a sensitive, precise, and promising tool for which further validation is needed to confirm that ovarian cancer serum protein signature patterns can be a robust biomarker approach for ovarian cancer diagnosis, yielding improved patient outcome and reducing the death and suffering from ovarian cancer.

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Zhang, Qing, Ayumu Taguchi, Mark Schliekelman, Chee-Hong Wong, Alice Chin, Rork Kuick, David E. Misek, and Samir Hanash. "Comprehensive Proteomic Profiling of Aldehyde Dehydrogenases in Lung Adenocarcinoma Cell Lines." International Journal of Proteomics 2011 (October 29, 2011): 1–8. http://dx.doi.org/10.1155/2011/145010.

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We have explored the potential of proteomic profiling to contribute to the delineation of the range of expression and subcellular localization of aldehyde dehydrogenases (ALDHs) in lung adenocarcinoma. In-depth quantitative proteomics was applied to 40 lung adenocarcinoma cell lines resulting in the identification of the known members of the ALDH family. Substantial heterogeneity in the level and occurrence of ALDHs in total lysates and on the cell surface and in their release into the culture media was observed based on mass spectrometry counts. A distinct pattern of expression of ALDHs was observed in cells exhibiting epithelial features relative to cells exhibiting mesenchymal features. Strikingly elevated levels of ALDH1A1 were observed in two cell lines. We also report on the occurrence of an immune response to ALDH1A1 in lung cancer.

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Liu, You-Pi, Weng Man Chong, Harry Huang, Yi-De Chen, Chia-Wen Chung, Hsiao-Jen Chang, Chih-Wei Chang, and Jung-Chi Liao. "Abstract 3875: De novo spatial proteomic profiling of immune synapses using machine learning-guided microscoop." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3875. http://dx.doi.org/10.1158/1538-7445.am2022-3875.

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Abstract The spatial proteome of the immune synapse (IS) between a target cell and a lymphocyte is fundamentally important to understand the mechanism of cell-mediated immunity for both immuno-oncology and therapeutic applications. In this research, we used Microscoop, a fully-automatic microscope system integrated with a machine learning-based algorithm, to best determine ISs for proteomic mapping. We used Raji B cells as antigen-presenting cells (APCs) and induced the formation of ISs by incubating with Jurkat T cells. Multiple IS images were applied to train our algorithm using convolution neural network-based deep learning. A sequential process including fluorescence imaging, deep learning-enabled pattern generation, and photochemical labeling was implemented to achieve spatial-specific biotinylation of the IS proteins. Moreover, Microscoop is capable to repeat the process automatically on thousands of fields of view to label sufficient immune-synaptic proteins with biotin-tag for further enrichment allowing protein identification with mass spectrometry. We have successfully labeled and isolated proteins from spatially reorganized interfaces between T cells and APCs. Following MS-based proteomic analysis, several hundreds of proteins were identified, including the proteins known to be specifically associated with T-cell receptor (TCR) activation such as LCK, one of the major factors involved in TCR signaling at ISs. More interestingly, we identified several proteins novel for ISs, including proteins involved in phosphatidylinositol signaling. Our data showcases the capability of subcellular de novo spatial proteomics of the Microscoop technology, revealing novel factors responsible for initiating the immune response of a lymphocyte and shedding light on immune checkpoint signaling and tumor immunotherapy. Citation Format: You-Pi Liu, Weng Man Chong, Harry Huang, Yi-De Chen, Chia-Wen Chung, Hsiao-Jen Chang, Chih-Wei Chang, Jung-Chi Liao. De novo spatial proteomic profiling of immune synapses using machine learning-guided microscoop [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3875.

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Gefaell, J., N. Varela, and E. Rolán-Alvarez. "Comparing shape along growth trajectories in two marine snail ecotypes of Littorina saxatilis: a test of evolution by paedomorphosis." Journal of Molluscan Studies 86, no. 4 (August 29, 2020): 382–88. http://dx.doi.org/10.1093/mollus/eyaa020.

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ABSTRACT Two sympatric ecotypes (‘crab’ and ‘wave’) of Littorina saxatilis are adapted to different microhabitats. It has been claimed, based on the comparison of proteomic differentiation across ontogeny, that the wave ecotype may have evolved by paedomorphosis from an ancestor more similar to the crab ecotype. Here, we test the paedomorphosis hypothesis at a morphological level by comparing crab and wave specimens from two localities using the pattern of shell shape differentiation across ontogeny. The results obtained show a pattern of shell shape differentiation similar to the one observed in previous proteomic studies, but such a pattern could be caused by different modes of evolution, and not necessarily by paedomorphosis. This study emphasizes that in addition to studying the pattern of differentiation, the direction of the evolutionary change across ontogeny has to be analysed before conclusions can be drawn on particular developmental modes of evolution.

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Kowalczewska, Malgorzata, Claude Villard, Daniel Lafitte, Florence Fenollar, and Didier Raoult. "Global proteomic pattern of Tropheryma whipplei: A Whipple's disease bacterium." PROTEOMICS 9, no. 6 (March 2009): 1593–616. http://dx.doi.org/10.1002/pmic.200700889.

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Kantawong, Fahsai, Richard Burchmore, Nikolaj Gadegaard, Richard O. C. Oreffo, and Matthew J. Dalby. "Proteomic analysis of human osteoprogenitor response to disordered nanotopography." Journal of The Royal Society Interface 6, no. 40 (December 9, 2008): 1075–86. http://dx.doi.org/10.1098/rsif.2008.0447.

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Previous studies have shown that microgroove-initiated contact guidance can induce bone formation in osteoprogenitor cells (OPGs) and produce changes in the cell proteome. For proteomic analysis, differential in-gel electrophoresis (DIGE) can be used as a powerful diagnostic method to provide comparable data between the proteomic profiles of cells cultured in different conditions. This study focuses on the response of OPGs to a novel nanoscale pit topography with osteoinductive properties compared with planar controls. Disordered near-square nanopits with 120 nm diameter and 100 nm depth with an average 300 nm centre-to-centre spacing (300 nm spaced pits in square pattern, but with ±50 nm disorder) were fabricated on 1×1 cm 2 polycaprolactone sheets. Human OPGs were seeded onto the test materials. DIGE analysis revealed changes in the expression of a number of distinct proteins, including upregulation of actin isoforms, beta-galectin1, vimentin and procollagen-proline, 2-oxoglutarate 4-dioxygenase and prolyl 4-hydroxylase. Downregulation of enolase, caldesmon, zyxin, GRASP55, Hsp70 (BiP/GRP78), RNH1, cathepsin D and Hsp27 was also observed. The differences in cell morphology and mineralization are also reported using histochemical techniques.

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Kim, Dong Kyu, Dohyun Han, Joonho Park, Hyunjung Choi, Jong-Chan Park, Moon-Yong Cha, Jongmin Woo, et al. "Deep proteome profiling of the hippocampus in the 5XFAD mouse model reveals biological process alterations and a novel biomarker of Alzheimer’s disease." Experimental & Molecular Medicine 51, no. 11 (November 2019): 1–17. http://dx.doi.org/10.1038/s12276-019-0326-z.

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AbstractAlzheimer’s disease (AD), which is the most common type of dementia, is characterized by the deposition of extracellular amyloid plaques. To understand the pathophysiology of the AD brain, the assessment of global proteomic dynamics is required. Since the hippocampus is a major region affected in the AD brain, we performed hippocampal analysis and identified proteins that are differentially expressed between wild-type and 5XFAD model mice via LC-MS methods. To reveal the relationship between proteomic changes and the progression of amyloid plaque deposition in the hippocampus, we analyzed the hippocampal proteome at two ages (5 and 10 months). We identified 9,313 total proteins and 1411 differentially expressed proteins (DEPs) in 5- and 10-month-old wild-type and 5XFAD mice. We designated a group of proteins showing the same pattern of changes as amyloid beta (Aβ) as the Aβ-responsive proteome. In addition, we examined potential biomarkers by investigating secretory proteins from the Aβ-responsive proteome. Consequently, we identified vitamin K-dependent protein S (PROS1) as a novel microglia-derived biomarker candidate in the hippocampus of 5XFAD mice. Moreover, we confirmed that the PROS1 level in the serum of 5XFAD mice increases as the disease progresses. An increase in PROS1 is also observed in the sera of AD patients and shows a close correlation with AD neuroimaging markers in humans. Therefore, our quantitative proteome data obtained from 5XFAD model mice successfully predicted AD-related biological alterations and suggested a novel protein biomarker for AD.

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Silvestri, Elena, Assunta Lombardi, Pieter de Lange, Daniela Glinni, Rosalba Senese, Federica Cioffi, Antonia Lanni, Fernando Goglia, and Maria Moreno. "Studies of Complex Biological Systems with Applications to Molecular Medicine: The Need to Integrate Transcriptomic and Proteomic Approaches." Journal of Biomedicine and Biotechnology 2011 (2011): 1–19. http://dx.doi.org/10.1155/2011/810242.

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Omics approaches to the study of complex biological systems with potential applications to molecular medicine are attracting great interest in clinical as well as in basic biological research. Genomics, transcriptomics and proteomics are characterized by the lack of ana prioridefinition of scope, and this gives sufficient leeway for investigators (a) to discern all at once a globally altered pattern of gene/protein expression and (b) to examine the complex interactions that regulate entire biological processes. Two popular platforms in “omics” are DNA microarrays, which measure messenger RNA transcript levels, and proteomic analyses, which identify and quantify proteins. Because of their intrinsic strengths and weaknesses, no single approach can fully unravel the complexities of fundamental biological events. However, an appropriate combination of different tools could lead to integrative analyses that would furnish new insights not accessible through one-dimensional datasets. In this review, we will outline some of the challenges associated with integrative analyses relating to the changes in metabolic pathways that occur in complex pathophysiological conditions (viz. ageing and altered thyroid state) in relevant metabolically active tissues. In addition, we discuss several new applications of proteomic analysis to the investigation of mitochondrial activity.

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Mischak-Weissinger, Eva M., Michael Stadler, Ernst Holler, Michael Schleuning, Hildegard Greinix, Hans-Jochem Kolb, Anne M. Dickinson, et al. "Proteomic Screening Applied to the Diagnosis of Chronic Graft-Versus-Host-Disease." Blood 114, no. 22 (November 20, 2009): 1164. http://dx.doi.org/10.1182/blood.v114.22.1164.1164.

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Abstract Abstract 1164 Poster Board I-186 Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative treatment for many hematologic malignancies or hematopoietic dysfunction syndromes, but the application is still limited due to major complications, such as severe graft versus host disease (GvHD) and infectious complications. Diagnosis chronic GvHD is based on clinical features and biopsies, a non invasive, unbiased laboratory test does not exist. We used the urine collected from 20 patients (10 with limited cGvHD, 10 with extensive cGvHD) to establish a proteomic pattern that allowed the diagnosis of cGvHD development and tested the resulting set of polypeptide markers (27 differentially excreted peptides) on more than 200 patients prospectively and blinded for the correct classification of cGvHD samples. The majority of the patients included were transplanted for hematological malignancies (n=209), 6 for hematopoietic failure syndromes. Conditioning regimens included dose reduced conditioning regimens (FLAMSA and ClaraC for the majority of the patients of MHH), as well as standard conditioning regimens (TBI+Cy or Busulfan+Cy) for about 35% of the patients, with GvHD-prophylaxis including cyclosporine A and mycophenolate (MMF) or metothrexate (MTX) as appropriate. Eighty percent of the patients received ATG (antithymocyte globulin) prior to HSCT. A peptide pattern of 27 peptides, differentiating chronic from acute GvHD was developed. Controls were patients at least 100 days post HSCT, with no GvHD in the history, no infections and without relapse at the time of sampling. Prospective and blinded evaluation of the patients revealed the correct classification of patients developing cGvHD with a sensitivity of 85% and specificity of 95%.Further evaluation of the cGvHD patterns specific for particular organ manifestations of cGvHD are currently ongoing. Interestingly, the cGvHD pattern seems to be predictive for GvHD developing post DLI, while the aGvHD-specific proteomic pattern only predicts GvHD of the intestine, which may be more similar to “late acute GvHD”. Disclosures Krons: mosaiques-diagnostics GmbH: Employment. Metzger:mosaiques-diagnostics GmbH: Employment.

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Liu, Yu-Tsueng, Laura Z. Rassenti, Zhouxin Shen, Han-Yu Chuang, Steven P. Briggs, Thomas J. Kipps, and Dennis Carson. "Differential Expression Profile of the Proteome and Transcriptome in Aggressive and Indolent Chronic Lymphocytic Leukemia." Blood 106, no. 11 (November 16, 2005): 2101. http://dx.doi.org/10.1182/blood.v106.11.2101.2101.

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Abstract The course of chronic lymphocytic leukemia (CLL) is variable. In aggressive disease, the CLL cells usually express an unmutated immunoglobulin heavy-chain variable-region gene (IgVH) and the 70-kD zeta-associated protein (ZAP-70), whereas in indolent disease, the CLL cells usually express mutated IgVH but lack expression of ZAP-70. The reasons for the differences in clinical characteristics are unknown. Examination of microarray data has shown that these two subtypes of CLL share a common gene-expression pattern, suggesting that they constitute a single entity. However, the microarray data have also revealed some important differences between the two types of CLL in the expression of a small number of genes. While mRNA expression profiling by microarray is a useful and convenient way for signature gene recognition, systematic proteomic analysis may be more relevant to understanding the pathophysiology of disease. To our knowledge, few studies have performed systematic proteomic profiling on CLL and none have tried to identify proteins that are differentially expressed among patients with differing outcomes. We have applied multidimensional LC-ESI-tandem mass spectrometry to identify proteins that are differentially expressed between aggressive (5 ZAP-70 pos/IgVH unmutated) and indolent (5 ZAP-70 neg/IgVH mutated) purified B cell samples. A total of more than 3,000 proteins were identified in our proteomic analysis. We observed a positive correlation in expression of protein and mRNA of three genes (ZAP-70, gravin, and dystrophin); these genes were consistently associated with disease progression in CLL as reported by microarray analyses. This indicates that the proteomic data is of high quality. We also compared the proteomic and transcriptomic patterns between these two groups. In general, the correlation between mRNA and protein expression was poor. To identify the genes that appear coordinately regulated at the mRNA and protein level, we examined the mRNA expression pattern of about 200 proteins that were differentially expressed in our proteomic data between aggressive and indolent CLL. We found 37 genes were differentially regulated post-transcriptionally, perhaps through the influence of microRNA or protein stability. In addition, we found 117 genes to be differentially expressed in microarray but not proteomic analysis. This result raises the question of how reliable mRNA expression levels reflect the biological activity of protein function. In conclusion, we have identified a number of candidate proteins that are differentially expressed in CLL of distinctive clinical outcomes by comparing high quality proteomic and transcriptomic data. These proteins might serve as biomarkers or therapeutic targets. We also found genes that might be differentially regulated in CLL post-transcriptionally. Further studies of how these genes are regulated will advance our knowledge of CLL pathogenesis.

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Miranda-Galvis, Marisol, Carolina Carneiro Soares, Carolina Moretto Carnielli, Jaqueline Ramalho Buttura, Raisa Sales de Sá, Estela Kaminagakura, Fabio Albuquerque Marchi, et al. "New Insights into the Impact of Human Papillomavirus on Oral Cancer in Young Patients: Proteomic Approach Reveals a Novel Role for S100A8." Cells 12, no. 9 (May 5, 2023): 1323. http://dx.doi.org/10.3390/cells12091323.

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Human papillomavirus (HPV) infection has recently been linked to a subset of cancers affecting the oral cavity. However, the molecular mechanisms underlying HPV-driven oral squamous cell carcinoma (OSCC) onset and progression are poorly understood. Methods: We performed MS-based proteomics profiling based on HPV status in OSCC in young patients, following biological characterization and cell assays to explore the proteome functional landscape. Results: Thirty-nine proteins are differentially abundant between HPV (+) and HPV (−) OSCC. Among them, COPS3, DYHC1, and S100A8 are unfavorable for tumor recurrence and survival, in contrast to A2M and Serpine1, low levels of which show an association with better DFS. Remarkably, S100A8 is considered an independent prognostic factor for lower survival rates, and at high levels, it alters tumor-associated immune profiling, showing a lower proportion of M1 macrophages and dendritic cells. HPV (+) OSCC also displayed the pathogen-associated patterns receptor that, when activated, triggered the S100A8 and NFκB inflammatory responses. Conclusion: HPV (+) OSCC has a peculiar microenvironment pattern distinctive from HPV (−), involving the expression of pathogen-associated pattern receptors, S100A8 overexpression, and NFκB activation and responses, which has important consequences in prognosis and may guide therapeutic decisions.

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Kim, Geoffrey, Lucas Minig, and Elise C. Kohn. "Proteomic Profiling in Ovarian Cancer." International Journal of Gynecologic Cancer 19, Suppl 2 (November 2009): S2—S6. http://dx.doi.org/10.1111/igc.0b013e3181c03929.

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Objective:To describe the role of proteomic profiling in the diagnosis and treatment of ovarian cancer.Methods:We report a thorough review of the literature, ongoing trials, and our group's experience with proteomic profiling for early detection, recurrence, and treatment of ovarian cancer.Results/Conclusions:Ovarian cancer remains the deadliest gynecologic malignancy in the western world and is most often diagnosed at a rarely curable late stage. Novel applications of proteomic techniques, such as mass spectrometry, show promise in the quest for reliable multimodality screening programs for the early detection of ovarian cancer. Proteomic analysis of tissue samples has underscored the heterogeneity of this disease process. Development of validated assays that survey the genetic and/or proteomic makeup of an individual tumor will add greatly to the histological classification of the tumor and may lead to different treatment approaches tailored to the unique expression pattern of each individual patient. As novel agents that disrupt signal propagation develop, proteomic profiling by reverse-phase protein arrays can characterize the in-tumor efficacy of the agent by quantification of the changes in expression levels of activated proteins. Together, better understanding of the potential diagnostic and therapeutic targets followed with proof-of-target effect will lead to rational combinations of novel therapy and improve individual ovarian cancer patient outcome.

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Journal articles on the topic 'Proteomic pattern'

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