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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Ounoughene, Youcef, Elise Fourgous, Yvan Boublik, Estelle Saland, Nathan Guiraud, Christian Recher, Serge Urbach, et al. "SHED-Dependent Oncogenic Signaling of the PEAK3 Pseudo-Kinase." Cancers 13, no. 24 (December 17, 2021): 6344. http://dx.doi.org/10.3390/cancers13246344.

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The PEAK1 and Pragmin/PEAK2 pseudo-kinases have emerged as important components of the protein tyrosine kinase pathway implicated in cancer progression. They can signal using a scaffolding mechanism that involves a conserved split helical dimerization (SHED) module. We recently identified PEAK3 as a novel member of this family based on structural homology; however, its signaling mechanism remains unclear. In this study, we found that, although it can self-associate, PEAK3 shows higher evolutionary divergence than PEAK1/2. Moreover, the PEAK3 protein is strongly expressed in human hematopoietic cells and is upregulated in acute myeloid leukemia. Functionally, PEAK3 overexpression in U2OS sarcoma cells enhanced their growth and migratory properties, while its silencing in THP1 leukemic cells reduced these effects. Importantly, an intact SHED module was required for these PEAK3 oncogenic activities. Mechanistically, through a phosphokinase survey, we identified PEAK3 as a novel inducer of AKT signaling, independent of growth-factor stimulation. Then, proteomic analyses revealed that PEAK3 interacts with the signaling proteins GRB2 and ASAP1/2 and the protein kinase PYK2, and that these interactions require the SHED domain. Moreover, PEAK3 activated PYK2, which promoted PEAK3 tyrosine phosphorylation, its association with GRB2 and ASAP1, and AKT signaling. Thus, the PEAK1-3 pseudo-kinases may use a conserved SHED-dependent mechanism to activate specific signaling proteins to promote oncogenesis.

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2

Song, Jaewoo, Eunyoung Lee, Yula Jeon, Ji Eun Jang, Yundeok Kim, June-Won Cheong, and Yoo Hong Min. "Improved Sensitivity and Discriminative Power of Factor FVIII Assay By Applying Turbidimetric Clotting Curve Analysis." Blood 124, no. 21 (December 6, 2014): 2853. http://dx.doi.org/10.1182/blood.v124.21.2853.2853.

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Abstract Background: The need for sensitive detection of coagulation factor between the levels of 0.0 and1.0% is now growing continuously. The most popular method of measuring factor VIII (FVIII) activity is one stage clotting time (activated partial thromboplastin time, APTT)-based assay. From the first and the second derivatives of the original turbidimetric curve, the velocity and acceleration of clot formation can be followed and parameters like maximum velocity (peak1) and acceleration (peak2) of clot formation can also be derived. We examined the limit of detection of FVIII activities measured based on clotting time, peak1 and peak2, following the recommendations from Clinical and Laboratory Standards Institute guideline. Method: We performed APTT with sample/reagent volume and incubation time modified to be identical to those adopted for factor assay on factor deficient plasma as blank and plasmas prepared to have variable FVIII activities from 0.1 to 1.6%. Peak1 and Peak2 were also measured to determine the limit of blank (LoB) and limit of detection (LoD) corresponding to each parameter. Also, by modifying the method of LoB and LoD determination, we determined lower limit of 1.0% (lower Lo1) corresponding to each parameter. Results: The mean clotting time of blank sample (FVIII 0.0%) was 131.4 ± 1.90 seconds (mean ± SD). Thus, 95% of clotting times measured from a blank sample (FVIII 0.0%) are longer than 128.3 seconds, which was determined to be the LoB for clotting time. The pooled standard deviation (SD) of clotting times measured from low FVIII samples was 2.29 seconds and 124.49 seconds which was the theoretically minimum value that was statistically different from LoB was set as the clotting time corresponding to LoD of FVIII. The LoD clotting time was between the clotting times of FVIII 0.2% and FVIII 0.4% sample and 0.4% was safely determined to be LoD of FVIII. This implied that plasma sample with at least 0.4% FVIII level was guaranteed to be measured higher than blank (FVIII 0.0%) sample. The mean peak1 height for blank sample was 21.4 ± 0.68 and LoB peak height was determined to be 22.53. The pooled SD for peak1 height was 0.68 and peak1 height of LoD was calculated to be 23.63. Because the mean peak1 height for 0.2% sample was 24.27, the LoD FVIII activity could be safely determined to be 0.2%. Thus, by applying peak1 as primary measure to estimate FVIII activity, the sensitivity of FVIII assay was increased with lower LoD of 0.2% compared with clotting time based assay. For peak2 height, LoB and LoD peak2 height were 14.86 and 18.59 respectively and The LoD could be set at FVIII 0.4%. Next, we determined lower Lo1, which meant FVIII level that was guaranteed to be measured significantly lower than 1.0% sample. For clotting time, lower Lo1 was FVIII 0.2% and for peak1 and 2 FVIII, was 0.4%. These results implied that by conventional clotting time based FVIII assay FVIII activity between 0.0 and 1.0% could not be measured credibly. FVIII should be at least 0.4% to be ever detected but ironically FVIII should be less than 0.2% to be assuredly measured lower than 1.0%. However, with peak1 there was an interval of FVIII value that could be assured to be measured higher than 0.0% but lower than 1.0%. Conclusion: We concluded that the maximum clotting velocity derived from turbidimetric curve analysis can be applied to measure FVIII activity between 0.0 and 1.0% credibly. Disclosures No relevant conflicts of interest to declare.

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3

Hamalian, Sarkis, Robert Güth, Farhana Runa, Francesca Sanchez, Eric Vickers, Megan Agajanian, Justin Molnar, et al. "A SNAI2-PEAK1-INHBA stromal axis drives progression and lapatinib resistance in HER2-positive breast cancer by supporting subpopulations of tumor cells positive for antiapoptotic and stress signaling markers." Oncogene 40, no. 33 (July 8, 2021): 5224–35. http://dx.doi.org/10.1038/s41388-021-01906-2.

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AbstractIntercellular mechanisms by which the stromal microenvironment contributes to solid tumor progression and targeted therapy resistance remain poorly understood, presenting significant clinical hurdles. PEAK1 (Pseudopodium-Enriched Atypical Kinase One) is an actin cytoskeleton- and focal adhesion-associated pseudokinase that promotes cell state plasticity and cancer metastasis by mediating growth factor-integrin signaling crosstalk. Here, we determined that stromal PEAK1 expression predicts poor outcomes in HER2-positive breast cancers high in SNAI2 expression and enriched for MSC content. Specifically, we identified that the fibroblastic stroma in HER2-positive breast cancer patient tissue stains positive for both nuclear SNAI2 and cytoplasmic PEAK1. Furthermore, mesenchymal stem cells (MSCs) and cancer-associated fibroblasts (CAFs) express high PEAK1 protein levels and potentiate tumorigenesis, lapatinib resistance and metastasis of HER2-positive breast cancer cells in a PEAK1-dependent manner. Analysis of PEAK1-dependent secreted factors from MSCs revealed INHBA/activin-A as a necessary factor in the conditioned media of PEAK1-expressing MSCs that promotes lapatinib resistance. Single-cell CycIF analysis of MSC-breast cancer cell co-cultures identified enrichment of p-Akthigh/p-gH2AXlow, MCL1high/p-gH2AXlow and GRP78high/VIMhigh breast cancer cell subpopulations by the presence of PEAK1-expressing MSCs and lapatinib treatment. Bioinformatic analyses on a PEAK1-centric stroma-tumor cell gene set and follow-up immunostaining of co-cultures predict targeting antiapoptotic and stress pathways as a means to improve targeted therapy responses and patient outcomes in HER2-positive breast cancer and other stroma-rich malignancies. These data provide the first evidence that PEAK1 promotes tumorigenic phenotypes through a previously unrecognized SNAI2-PEAK1-INHBA stromal cell axis.

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4

Suzuki, Y., A. Mitsui, Y. Yamamoto, K. Noda, and A. Nakajima. "SAT0235 THE EFFECT OF ANTIPHOSPHOLIPID ANTIBODIES ON APTT WAVEFORM PATTERNS." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 1060. http://dx.doi.org/10.1136/annrheumdis-2020-eular.1833.

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Background:Patients with antiphospholipid antibody (aPL) are said to be at increased risk for thrombosis, however it is difficult to predict whether they will develop thrombosis. In recent years, it has been revealed that the characteristics of the second derivative curve of APTT waveform with aPL positive patient is biphasic changes1,2. As first step in predicting the risk of thrombosis, we sought to understand the effect of aPL on APTT waveform patterns.Objectives:To analyze the characteristics of APTT waveforms according to the background diseases and the presence of aPLMethods:Patients who underwent coagulation function tests from 2017 to 2019 were analyzed. A coagulation waveform (Clot waveform: CW) was drawn using a fully automatic coagulation time measuring device manufactured by Instrumentation Laboratory From the APTT waveform, the 1st derivative curve (DC) indicating the coagulation speed and the 2nd DC indicating the coagulation acceleration were depicted to measure the 1st DC height, 2nd DC peak 1 time, and 2nd DC peak 1 height (Figure12). Patients were devided into CTD with aPL-negative patients (group A), aPL-positive patients with no prior thrombosis (group B), and antiphospholipid antibody syndrome (APS) (group C). Patients characteristics and aPL (anti-cardiolipin [CL] antibody IgM, anti-CL antibody IgG, anti-CLβ2GP1 complex antibody, LA-APTT, and LA- DRVVT) status were examind. A further analysis was performed according to the numbers of positive aPL. Comparison between the three groups were made by the one-way ANOVA method, with significant differences set as p-values <0.1. Factors with significant differences were analyzed by Steel-Dwass test. APTT waveforms was analyzed according to the numbers of positive aPL by least squares methods. Furthermore, to determine the cut off value of APTT, 1st DC height, 2nd DC peak 1 time, and 2nd DC peak 1 height for each case with 2 or more positive aPLs and 3 positive aPLs, area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity and specificity were caliculated.Figure 1.Results:The APTT waveform was analyzed in 61 patients (51 women, 83.6%) with average age of 54.1 ± 17.1 years. Group A was 26 cases, Group B was 18 cases, and Group C was 17 cases. APTT, 2nd DC peak1 time, 2nd DC peak1 height, 1st DC peak time were significantly different among A, B, and C groups (p <0.01). APTT, 1st DC peak height, 2nd DC peak 1 time, and 2nd DC peak 1 height differed among the number of aPL (p < 0.01, respectively). APTT and 2nd DC peak1 time prolonged by 9.43 (seconds) and 16.3 (seconds) respectively according to the number of aPLs increased, and 1st DC peak height (mabs/s) and 2nd DC peak1 height (mabs/s2) decreased by 56.4 (mabs/s) and 223.9 (mabs/s2) respectively according to the number of aPLs decreased (Table 1). APTT> 35.2 (seconds) (sensitivity 80%, specificity 80.4%), 2nd DC peak1 height> 302 (mabs/s2) (sensitivity 80%, specificity 91.3%) were relevant to the presence of two or more aPLs and APTT> 35.2 (seconds) (sensitivity 100%, specificity 80%), 2nd DC peak1 height> 302 (mabs/s2) (sensitivity 100%, specificity 90%) were relevant to the presence of three aPLs.Table 1.The number of positive aPL0123p valueThe number of cases2719411APTT(seconds)28.9[26.8, 31.4]30.9[29.1, 38.2]31.1[27.3, 54.2]60.7[45.9, 73.7]0.00012nd DC peak time (seconds)29.2[26.8, 30.7]33.7[31, 41.7]36.1[31.5, 99]75.8[50.5, 102.4]0.00012nd DC peak height (mabs/s2)839.9[666.1, 962.2]669.6[346.4, 946]608.4[137.8, 956.7]119.3[30.6, 196]0.00011st DC peak height (mabs/s)309.6[260.6, 355.1]271.5[168, 353]241.8[96.3, 364.0]135.6[76.8, 163]0.0001Conclusion:The presence of aPL was more related to the 2nd DC peak1 height of APTT waveform than APTT. A detailed review of the APTT waveform may further predict future thrombosis risk.References:[1]Tokunaga N, et al. Blood Coagul Fibrinolysis. 2016;27:474-476.[2]Matsumoto T, et al. Int J Hematol. 2017;105:174-183.Disclosure of Interests:YASUO SUZUKI: None declared, Asako Mitsui: None declared, Yoshiki Yamamoto: None declared, Kentaro Noda: None declared, Ayako Nakajima Grant/research support from: AN has received research grants from Chugai Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Co., Pfizer Japan Inc., Consultant of: AN has consultant fee from Nippon Kayaku Co. Ltd., Speakers bureau: AN has received speaker’s fee from AbbVie Japan GK, Actelion Pharmaceuticals Japan LTD., Asahi Kasei Pharma Co., Astellas Pharma Inc., Ayumi Pharmaceutical Co., Bristol Myers Squibb Co., Ltd., Chugai Pharmaceutical Co., Ltd., Eisai Co., Ltd., Eli Lilly Japan K.K., GlaxoSmithKline K.K., Hisamitsu Pharmaceutical Co. Inc., Kyorin Pharmaceutical Co. Ltd., Mitsubishi Tanabe Pharma Co., Otsuka Pharmaceutical Co. Ltd., Pfizer Japan Inc., and Teijin Pharma Ltd.

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5

Kelber, Jonathan A., and Richard L. Klemke. "PEAK1, a Novel Kinase Target in the Fight Against Cancer." Oncotarget 1, no. 3 (July 8, 2010): 219–23. http://dx.doi.org/10.18632/oncotarget.128.

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6

Prasad, K. Sudhakara, Yousef Abugalyon, Chunqiang Li, Feng Xu, and XiuJun Li. "A new method to amplify colorimetric signals of paper-based nanobiosensors for simple and sensitive pancreatic cancer biomarker detection." Analyst 145, no. 15 (2020): 5113–17. http://dx.doi.org/10.1039/d0an00704h.

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Catalytic properties of gold nanoparticles in colour dye degradation are utilized to amplify colorimetric detection signals of a low-cost paper-based immunosensor for instrument-free detection of pancreatic cancer biomarker PEAK1.

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7

Fujimura, Ken, Tracy Wright, Jan Strnadel, Sharmeela Kaushal, Cristina Metildi, Andrew M. Lowy, Michael Bouvet, Jonathan A. Kelber, and Richard L. Klemke. "A Hypusine–eIF5A–PEAK1 Switch Regulates the Pathogenesis of Pancreatic Cancer." Cancer Research 74, no. 22 (September 26, 2014): 6671–81. http://dx.doi.org/10.1158/0008-5472.can-14-1031.

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8

Strnadel, Jan, Sunkyu Choi, Ken Fujimura, Huawei Wang, Wei Zhang, Meghan Wyse, Tracy Wright, et al. "eIF5A-PEAK1 Signaling Regulates YAP1/TAZ Protein Expression and Pancreatic Cancer Cell Growth." Cancer Research 77, no. 8 (April 5, 2017): 1997–2007. http://dx.doi.org/10.1158/0008-5472.can-16-2594.

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9

Liu, Ling, Yu Wei Phua, Rachel S. Lee, Xiuquan Ma, Yiping Jenkins, Karel Novy, Emily S. Humphrey, et al. "Homo- and Heterotypic Association Regulates Signaling by the SgK269/PEAK1 and SgK223 Pseudokinases." Journal of Biological Chemistry 291, no. 41 (August 16, 2016): 21571–83. http://dx.doi.org/10.1074/jbc.m116.748897.

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10

Guo, Qingqu, Wenjie Qin, Baozhong Li, Haijun Yang, Jianyun Guan, Zhiqiang Liu, and Shoumiao Li. "Analysis of a cytoskeleton-associated kinase PEAK1 and E-cadherin in gastric cancer." Pathology - Research and Practice 210, no. 12 (December 2014): 793–98. http://dx.doi.org/10.1016/j.prp.2014.09.013.

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11

Lopez, Mitchell L., Megan Lo, Jennifer E. Kung, Małgorzata Dudkiewicz, Gwendolyn M. Jang, John Von Dollen, Jeffrey R. Johnson, Nevan J. Krogan, Krzysztof Pawłowski, and Natalia Jura. "PEAK3/C19orf35 pseudokinase, a new NFK3 kinase family member, inhibits CrkII through dimerization." Proceedings of the National Academy of Sciences 116, no. 31 (July 16, 2019): 15495–504. http://dx.doi.org/10.1073/pnas.1906360116.

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Members of the New Kinase Family 3 (NKF3), PEAK1/SgK269 and Pragmin/SgK223 pseudokinases, have emerged as important regulators of cell motility and cancer progression. Here, we demonstrate that C19orf35 (PEAK3), a newly identified member of the NKF3 family, is a kinase-like protein evolutionarily conserved across mammals and birds and a regulator of cell motility. In contrast to its family members, which promote cell elongation when overexpressed in cells, PEAK3 overexpression does not have an elongating effect on cell shape but instead is associated with loss of actin filaments. Through an unbiased search for PEAK3 binding partners, we identified several regulators of cell motility, including the adaptor protein CrkII. We show that by binding to CrkII, PEAK3 prevents the formation of CrkII-dependent membrane ruffling. This function of PEAK3 is reliant upon its dimerization, which is mediated through a split helical dimerization domain conserved among all NKF3 family members. Disruption of the conserved DFG motif in the PEAK3 pseudokinase domain also interferes with its ability to dimerize and subsequently bind CrkII, suggesting that the conformation of the pseudokinase domain might play an important role in PEAK3 signaling. Hence, our data identify PEAK3 as an NKF3 family member with a unique role in cell motility driven by dimerization of its pseudokinase domain.

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12

Uçkun, Ezgi, Joachim T. Siaw, Jikui Guan, Vimala Anthonydhason, Johannes Fuchs, Georg Wolfstetter, Bengt Hallberg, and Ruth H. Palmer. "BioID-Screening Identifies PEAK1 and SHP2 as Components of the ALK Proximitome in Neuroblastoma Cells." Journal of Molecular Biology 433, no. 19 (September 2021): 167158. http://dx.doi.org/10.1016/j.jmb.2021.167158.

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13

Fujimura, Ken, Huawei Wang, Felicia Watson, and Richard L. Klemke. "KRAS Oncoprotein Expression Is Regulated by a Self-Governing eIF5A-PEAK1 Feed-Forward Regulatory Loop." Cancer Research 78, no. 6 (January 10, 2018): 1444–56. http://dx.doi.org/10.1158/0008-5472.can-17-2873.

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14

Agajanian, Megan, Farhana Runa та Jonathan A. Kelber. "Identification of a PEAK1/ZEB1 signaling axis during TGFβ/fibronectin-induced EMT in breast cancer". Biochemical and Biophysical Research Communications 465, № 3 (вересень 2015): 606–12. http://dx.doi.org/10.1016/j.bbrc.2015.08.071.

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15

Agajanian, Megan, Anaamika Campeau, Malachia Hoover, Alexander Hou, Daniel Brambilla, Sa La Kim, Richard L. Klemke та Jonathan A. Kelber. "PEAK1 Acts as a Molecular Switch to Regulate Context-Dependent TGFβ Responses in Breast Cancer". PLOS ONE 10, № 8 (12 серпня 2015): e0135748. http://dx.doi.org/10.1371/journal.pone.0135748.

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16

Runa, Farhana, Luke Tomaneng, Yvess Adamian, Nathan Cox, Albert-Fred Aquino, Matthew Wallace, Carolina Gonzalez, et al. "Abstract 2414: Retinoic acid induced 14 drives pancreatic cancer progression and metastasis." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2414. http://dx.doi.org/10.1158/1538-7445.am2022-2414.

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Abstract Pancreatic ductal adenocarcinoma (PDAC) is associated with very poor outcomes - fewer than 10 percent of patients survive beyond five years after diagnosis. The primary hurdles facing PDAC patients and clinicians are early dissemination, a lack of therapeutic targets and desmoplasia that renders the primary tumor refractory to chemotherapy. In this regard, we have previously reported that pseudopodium-enriched atypical kinase 1 (PEAK1) and integrin α1 (ITGA1) mediate gemcitabine resistance and metastasis in PDAC. To identify new mechanisms of PDAC progression, we mined the Cancer BioPortal and Human Cell Map BioID databases for additional pseudopodium-enriched (PDE) proteins that predict poor patient outcomes, correlate with PEAK1 and ITGA1 expression in PDAC, and interact with PEAK1 and ITGA1. Here, we identify Retinoic Acid Induced 14 (RAI14, Ankycorbin or NORPEG) as a new candidate driver of PDAC that is a constituent of the KRasG12D PDAC cell autonomous phosphoproteome, localizes to cytoskeleton/adhesion domains in PDAC cells and correlates in expression with ITGA1-binding collagens in PDAC. Knockdown or knockout of RAI14 in KRas mutant PDAC cells impaired adhesion-dependent proliferation/survival in vitro and tumor growth and metastasis in vivo. Single cell cyclic immunofluorescence (CycIF) further revealed that RAI14 supports a subpopulation of PDAC cells positive for proliferation, epithelial to mesenchymal transition (EMT) and antiapoptotic programs. By using a RAI14-focused bioinformatics pipeline in combination with proteomic and immunofluorescence data on the composition of ITGA1-dependent adhesion complexes in PDAC cells, we identified Polo-Like Kinase 1 (PLK1) as a candidate that may control RAI14 function and adhesion-regulated mitosis during PDAC progression. Notably, the potency of volasertib, a PLK1-specific inhibitor, was reduced in RAI14 knockout cells, supporting a model in which RAI14 mediates adhesion-dependent PLK1 functions in PDAC. Taken together, these studies uncover a mechanism for RAI14-driven PDAC progression and the development of strategies to increase chemotherapy sensitivity, reduce primary/metastatic tumor burden and improve patient outcomes. Citation Format: Farhana Runa, Luke Tomaneng, Yvess Adamian, Nathan Cox, Albert-Fred Aquino, Matthew Wallace, Carolina Gonzalez, Kishan Bhakta, Malachia Hoover, Laurelin Wolfenden, Jonathan D. Humphries, Martin J. Humphries, Michael Boyce, Jonathan A. Kelber. Retinoic acid induced 14 drives pancreatic cancer progression and metastasis [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 2414.

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17

Hinz, Stefan, Masaru Miyano, Antigoni Manousopoulou, Rosalyn W. Sayaman, Kristina Y. Aguilera, Michael E. Todhunter, Jennifer C. Lopez, Leo D. Wang, Lydia L. Sohn, and Mark A. LaBarge. "Abstract A016: Aging-dependent emergent mechanical properties of single epithelial cells exploited for detection of breast cancer susceptibility." Cancer Research 83, no. 2_Supplement_1 (January 15, 2023): A016. http://dx.doi.org/10.1158/1538-7445.agca22-a016.

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Abstract Age is the major risk factor in most carcinomas, yet, little is known about the specific reasons aging increases cancer susceptibility. In the mammary gland, luminal epithelial cells rank high as the putative breast cancer cell of origin. Dysregulation of keratin intermediate filament proteins exemplifies a hallmark age-dependent change in luminal cells, which implicates mechanical states unique to cancer susceptible cells. We implemented mechano-node-pore sensing (mechano-NPS), a multi-parametric single-cell analysis that simultaneously measures cell diameter, resistance to compressive deformation, transverse deformation under constant strain, and recovery time after deformation. We demonstrated that the epithelial lineages, chronological ages, and stages of cancer progression of primary human mammary epithelial cells (HMEC) exhibited discrete mechanical phenotypes. We trained a machine learning model that accurately predicted the chronological age of average risk HMEC cells based exclusively on mechanical properties. Application of the model to cells from women who are germline carriers of high-risk cancer-causing mutations showed that they are mechanically old irrespective of their chronological age, suggesting that mechanical states could be a window into detection and prevention of cancer susceptible states. Indeed, this mechano-age model detected high-risk women with >90% accuracy. Mass spectrometry and cell-based functional assays in mammary epithelia revealed that cytoskeleton related proteins keratin 14 (KRT14) and pseudopodium enriched atypical kinase 1 (PEAK1) were key drivers of age-dependent mechanical signatures. Pharmacological and gene silencing approaches that targeted KRT14 and PEAK1 modulated the mechanical age of HMEC and, in the case of PEAK1 modulation, ablated luminal epithelial cells in an age- and lineage- dependent manner. We define an intersection between mechanical phenotypes and novel age-dependent changes in cytoskeleton-related proteins that we hypothesize can be exploited to assess an individual’s breast cancer susceptibility and provide new targets for cancer-prevention strategies. Citation Format: Stefan Hinz, Masaru Miyano, Antigoni Manousopoulou, Rosalyn W. Sayaman, Kristina Y. Aguilera, Michael E. Todhunter, Jennifer C. Lopez, Leo D. Wang, Lydia L. Sohn, Mark A. LaBarge. Aging-dependent emergent mechanical properties of single epithelial cells exploited for detection of breast cancer susceptibility [abstract]. In: Proceedings of the AACR Special Conference: Aging and Cancer; 2022 Nov 17-20; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_1):Abstract nr A016.

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18

Ding, Chenbo, Wendong Tang, and Guoqiu Wu. "The PEAK1-PPP1R12B axis inhibits the development of colorectal cancer via regulating Grb2/PI3K/Akt signaling." Annals of Oncology 29 (October 2018): vii63. http://dx.doi.org/10.1093/annonc/mdy374.054.

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19

Croucher, David R., Falko Hochgräfe, Luxi Zhang, Ling Liu, Ruth J. Lyons, Danny Rickwood, Carole M. Tactacan, et al. "Involvement of Lyn and the Atypical Kinase SgK269/PEAK1 in a Basal Breast Cancer Signaling Pathway." Cancer Research 73, no. 6 (February 1, 2013): 1969–80. http://dx.doi.org/10.1158/0008-5472.can-12-1472.

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20

Pan, Min, Xiaohui Yin, and Yi-chuan Huang. "Pseudopodium enriched atypical kinase 1(PEAK1) promotes invasion and of melanoma cells by activating JAK/STAT3 signals." Bioengineered 12, no. 1 (January 1, 2021): 5045–55. http://dx.doi.org/10.1080/21655979.2021.1961661.

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21

Zhu, Qingli, Fengyun Hao, Han Zhao, Dong Chen, Liang Ning, and Kejun Zhang. "PEAK1 attenuation sensitizes anaplastic thyroid carcinoma cells in vitro to BRAF^V600E inhibitor Vemurafenib." International Journal of Medical Sciences 19, no. 10 (2022): 1525–38. http://dx.doi.org/10.7150/ijms.58754.

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22

Ding, Chenbo, Wendong Tang, Hailu Wu, Xiaobo Fan, Junmin Luo, Jihong Feng, Kunming Wen, and Guoqiu Wu. "The PEAK1–PPP1R12B axis inhibits tumor growth and metastasis by regulating Grb2/PI3K/Akt signalling in colorectal cancer." Cancer Letters 442 (February 2019): 383–95. http://dx.doi.org/10.1016/j.canlet.2018.11.014.

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23

Kelber, Jonathan A., Theresa Reno, Sharmeela Kaushal, Cristina Metildi, Tracy Wright, Konstantin Stoletov, Jessica M. Weems, et al. "KRas Induces a Src/PEAK1/ErbB2 Kinase Amplification Loop That Drives Metastatic Growth and Therapy Resistance in Pancreatic Cancer." Cancer Research 72, no. 10 (May 14, 2012): 2554–64. http://dx.doi.org/10.1158/0008-5472.can-11-3552.

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24

Zhang, Chunmei, Yang Li, Wancheng Zhao, Guipeng Liu, and Qing Yang. "Circ‐PGAM1 promotes malignant progression of epithelial ovarian cancer through regulation of the miR‐542‐3p/CDC5L/PEAK1 pathway." Cancer Medicine 9, no. 10 (March 13, 2020): 3500–3521. http://dx.doi.org/10.1002/cam4.2929.

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25

Geng, Qianqian, Zhubin Li, Xintao Li, Yunhua Wu, and Nanzheng Chen. "LncRNA NORAD, sponging miR-363-3p, promotes invasion and EMT by upregulating PEAK1 and activating the ERK signaling pathway in NSCLC cells." Journal of Bioenergetics and Biomembranes 53, no. 3 (March 19, 2021): 321–32. http://dx.doi.org/10.1007/s10863-021-09892-6.

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26

Ha, Byung Hak, and Titus J. Boggon. "The crystal structure of pseudokinase PEAK1 (Sugen kinase 269) reveals an unusual catalytic cleft and a novel mode of kinase fold dimerization." Journal of Biological Chemistry 293, no. 5 (December 6, 2017): 1642–50. http://dx.doi.org/10.1074/jbc.ra117.000751.

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27

Metildi, C. A., S. Kaushal, J. Strnadel, T. Wright, J. A. Kelber, R. L. Klemke, R. M. Hoffman, and M. Bouvet. "Serial In Vivo Passaging of Human Pancreatic Tumors in Nude Mice Results in Aggressive Variants Enriched in Stem Cell Markers and PEAK1 Expression." Journal of Surgical Research 179, no. 2 (February 2013): 240. http://dx.doi.org/10.1016/j.jss.2012.10.447.

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28

Kang, G., and O. P. Gandhi. "Effect of Dielectric Properties on the Peak1-and 10-g SAR for 802.11 a/b/g Frequencies 2.45 and 5.15 to 5.85 GHz." IEEE Transactions on Electromagnetic Compatibility 46, no. 2 (May 2004): 268–74. http://dx.doi.org/10.1109/temc.2004.826875.

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29

Bristow, Jeanne M., Theresa A. Reno, Minji Jo, Steven L. Gonias, and Richard L. Klemke. "Dynamic Phosphorylation of Tyrosine 665 in Pseudopodium-enriched Atypical Kinase 1 (PEAK1) Is Essential for the Regulation of Cell Migration and Focal Adhesion Turnover." Journal of Biological Chemistry 288, no. 1 (October 26, 2012): 123–31. http://dx.doi.org/10.1074/jbc.m112.410910.

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30

Romanenko, S. V., A. G. Stromberg, and T. N. Pushkareva. "Modeling of analytical peaks: Peaks properties and basic peak functions." Analytica Chimica Acta 580, no. 1 (October 2006): 99–106. http://dx.doi.org/10.1016/j.aca.2006.07.050.

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31

Shi, Chenfu, Magnus Rattray, and Gisela Orozco. "HiChIP-Peaks: a HiChIP peak calling algorithm." Bioinformatics 36, no. 12 (March 24, 2020): 3625–31. http://dx.doi.org/10.1093/bioinformatics/btaa202.

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Анотація:

Abstract Motivation HiChIP is a powerful tool to interrogate 3D chromatin organization. Current tools to analyse chromatin looping mechanisms using HiChIP data require the identification of loop anchors to work properly. However, current approaches to discover these anchors from HiChIP data are not satisfactory, having either a very high false discovery rate or strong dependence on sequencing depth. Moreover, these tools do not allow quantitative comparison of peaks across different samples, failing to fully exploit the information available from HiChIP datasets. Results We develop a new tool based on a representation of HiChIP data centred on the re-ligation sites to identify peaks from HiChIP datasets, which can subsequently be used in other tools for loop discovery. This increases the reliability of these tools and improves recall rate as sequencing depth is reduced. We also provide a method to count reads mapping to peaks across samples, which can be used for differential peak analysis using HiChIP data. Availability and implementation HiChIP-Peaks is freely available at https://github.com/ChenfuShi/HiChIP_peaks. Supplementary information Supplementary data are available at Bioinformatics online.

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32

Kalesse, Heike, Teresa Vogl, Cosmin Paduraru, and Edward Luke. "Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm." Atmospheric Measurement Techniques 12, no. 8 (August 30, 2019): 4591–617. http://dx.doi.org/10.5194/amt-12-4591-2019.

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Abstract. In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time–height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak-finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM zenith-pointing radar (KAZR) observations obtained in thick snowfall systems during the Atmospheric Radiation Measurement Program (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak-finding algorithms. The new algorithm consistently identifies Doppler spectra peaks and outperforms other algorithms by reducing noise and increasing temporal and height consistency in detected features. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.

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33

Folley, Joe P. "Systematic errors in the measurement of peak area and peak height for overlapping peaks." Journal of Chromatography A 384 (January 1987): 301–13. http://dx.doi.org/10.1016/s0021-9673(01)94679-5.

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34

Striegel, André M., and Deborah A. Striegel. "Peak Fraction Purity and Chromatographic Resolution: Gaussian Peaks Revisited." Chromatographia 85, no. 1 (January 2022): 65–72. http://dx.doi.org/10.1007/s10337-021-04112-0.

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35

Radetzki, Marian. "Peak Oil and other threatening peaks—Chimeras without substance." Energy Policy 38, no. 11 (November 2010): 6566–69. http://dx.doi.org/10.1016/j.enpol.2010.07.049.

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36

Alasmari, Fawaz, Sary Alsanea, Assim A. Alfadda, Ibrahim O. Alanazi, Mohthash Musambil, Afshan Masood, Faleh Alqahtani, Omer I. Fantoukh, Abdullah F. Alasmari, and Hicham Benabdelkamel. "Serum Proteomic Analysis of Cannabis Use Disorder in Male Patients." Molecules 26, no. 17 (September 1, 2021): 5311. http://dx.doi.org/10.3390/molecules26175311.

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Cannabis use has been growing recently and it is legally consumed in many countries. Cannabis has a variety of phytochemicals including cannabinoids, which might impair the peripheral systems responses affecting inflammatory and immunological pathways. However, the exact signaling pathways that induce these effects need further understanding. The objective of this study is to investigate the serum proteomic profiling in patients diagnosed with cannabis use disorder (CUD) as compared with healthy control subjects. The novelty of our study is to highlight the differentially changes proteins in the serum of CUD patients. Certain proteins can be targeted in the future to attenuate the toxicological effects of cannabis. Blood samples were collected from 20 male individuals: 10 healthy controls and 10 CUD patients. An untargeted proteomic technique employing two-dimensional difference in gel electrophoresis coupled with mass spectrometry was employed in this study to assess the differentially expressed proteins. The proteomic analysis identified a total of 121 proteins that showed significant changes in protein expression between CUD patients (experimental group) and healthy individuals (control group). For instance, the serum expression of inactive tyrosine protein kinase PEAK1 and tumor necrosis factor alpha-induced protein 3 were increased in CUD group. In contrast, the serum expression of transthyretin and serotransferrin were reduced in CUD group. Among these proteins, 55 proteins were significantly upregulated and 66 proteins significantly downregulated in CUD patients as compared with healthy control group. Ingenuity pathway analysis (IPA) found that these differentially expressed proteins are linked to p38MAPK, interleukin 12 complex, nuclear factor-κB, and other signaling pathways. Our work indicates that the differentially expressed serum proteins between CUD and control groups are correlated to liver X receptor/retinoid X receptor (RXR), farnesoid X receptor/RXR activation, and acute phase response signaling.

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37

Sunta, C. M., Ayta W. E. Feria, T. M. Piters, and S. Watanabe. "Limitation of peak fitting and peak shape methods for determination of activation energy of thermoluminescence glow peaks." Radiation Measurements 30, no. 2 (April 1999): 197–201. http://dx.doi.org/10.1016/s1350-4487(99)00033-5.

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38

OU, Linjun, and Jian CAO. "A peak recognition algorithm designed for chromatographic peaks of transformer oil." Chinese Journal of Chromatography 32, no. 9 (2014): 1019. http://dx.doi.org/10.3724/sp.j.1123.2014.05008.

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39

Kitis, G., and V. Pagonis. "Peak shape methods for general order thermoluminescence glow-peaks: A reappraisal." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 262, no. 2 (September 2007): 313–22. http://dx.doi.org/10.1016/j.nimb.2007.05.027.

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40

Reh, E. "Peak-shape analysis for unresolved peaks in chromatography: comparison of algorithms." TrAC Trends in Analytical Chemistry 14, no. 1 (January 1995): 1–5. http://dx.doi.org/10.1016/0165-9936(95)91139-j.

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41

Li, Jianwei. "Development and Evaluation of Flexible Empirical Peak Functions for Processing Chromatographic Peaks." Analytical Chemistry 69, no. 21 (November 1997): 4452–62. http://dx.doi.org/10.1021/ac970481d.

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42

Zhu, Honghai, and Jun Dong. "An R-peak detection method based on peaks of Shannon energy envelope." Biomedical Signal Processing and Control 8, no. 5 (September 2013): 466–74. http://dx.doi.org/10.1016/j.bspc.2013.01.001.

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43

Reh, E. "An algorithm for peak-shape analysis for differentiating unresolved peaks in chromatography." TrAC Trends in Analytical Chemistry 12, no. 5 (May 1993): 192–94. http://dx.doi.org/10.1016/0165-9936(93)80019-g.

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44

Li, Jianwei. "Comparison of the capability of peak functions in describing real chromatographic peaks." Journal of Chromatography A 952, no. 1-2 (April 2002): 63–70. http://dx.doi.org/10.1016/s0021-9673(02)00090-0.

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45

Zhiyao Duan, B. Pardo, and Changshui Zhang. "Multiple Fundamental Frequency Estimation by Modeling Spectral Peaks and Non-Peak Regions." IEEE Transactions on Audio, Speech, and Language Processing 18, no. 8 (November 2010): 2121–33. http://dx.doi.org/10.1109/tasl.2010.2042119.

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46

Grytten, Ivar, Knut D. Rand, Alexander J. Nederbragt, Geir O. Storvik, Ingrid K. Glad, and Geir K. Sandve. "Graph Peak Caller: Calling ChIP-seq peaks on graph-based reference genomes." PLOS Computational Biology 15, no. 2 (February 19, 2019): e1006731. http://dx.doi.org/10.1371/journal.pcbi.1006731.

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47

Maghrabi, Mufeed. "Dependence of the peak shift, peak height and FWHM of thermoluminescence peaks on the heating rate and trap parameters." Journal of Luminescence 198 (June 2018): 54–58. http://dx.doi.org/10.1016/j.jlumin.2018.02.013.

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48

Turrigiano, G. G., A. Van Wormhoudt, L. Ogden, and A. I. Selverston. "Partial purification, tissue distribution and modulatory activity of a crustacean cholecystokinin-like peptide." Journal of Experimental Biology 187, no. 1 (February 1, 1994): 181–200. http://dx.doi.org/10.1242/jeb.187.1.181.

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Reversed-phase chromatography was used to separate several forms of cholecystokinin-like peptides (CCKLP) from the pericardial organs (PCOs) of the spiny lobster Panulirus interruptus. Fast protein liquid chromatography of PCOs, stomatogastric ganglia (STGs) and eyestalks revealed five peaks of CCKLP (peaks A-E) that were common to all three tissues, as well as two additional peaks (peaks F and G) in the STG. Peaks A-E were present in the hemolymph of fed, but not starved, lobsters. The bioactivity of peaks A-E was tested on the gastric mill rhythm of the isolated STG. Only peak E elicited activity. The effects of peak E included activating the gastric mill rhythm in quiescent preparations and strengthening existing rhythms in a dose-dependent manner. Further purification of peak E by high performance liquid chromatography resolved this peak into two immunoreactive peaks, one of which retained its bioactivity. The effects of peak E were blocked by the CCK antagonist proglumide. These results are consistent with a role for peak E in the feeding-induced activation of the gastric mill.

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49

Vecchio, K. S. "The Effect of Coherent Bremsstrahlung Peaks in AEM Studies of Grain Boundary Segregation." Proceedings, annual meeting, Electron Microscopy Society of America 43 (August 1985): 248–49. http://dx.doi.org/10.1017/s0424820100118163.

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Recently coherent bremsstrahlung (CB) peaks have been detected in x-ray spectra in the analytical electron microscope (AEM). It has been suggested that CB peaks, which are Gaussian, may either mask, or be misinterpreted as elemental peaks in x-ray spectra. A method for identifying and isolating these peaks has been presented, The problem of CB peaks is particularly severe in AEM grain boundary segregation studies, because the amount of segregant in the interaction volume is small (<∼3 wt%), the x-ray counting times are long, and as a result the CB peak intensities can approximate to the expected segregant peak intensity. The misleading effects of CB can be either to produce pseudo-element peaks close to true element peak positions, or to overestimate the true element peak intensity when the CB peaks are superimposed on the x-ray peak of the segregant. This article reports an investigation of the effects of CB on segregation studies in Cu and Fe.

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50

Kruczyk, Marcin, Husen M. Umer, Stefan Enroth, and Jan Komorowski. "Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data." BMC Bioinformatics 14, no. 1 (2013): 280. http://dx.doi.org/10.1186/1471-2105-14-280.

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