BACKGROUND\nMETHODS\nRESULTS\nCONCLUSION\nPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest types of cancer and the chemotherapies such as gemcitabine/nab-paclitaxel are confronted... Show moreBACKGROUND\nMETHODS\nRESULTS\nCONCLUSION\nPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest types of cancer and the chemotherapies such as gemcitabine/nab-paclitaxel are confronted with intrinsic or acquired resistance. The aim of this study was to investigate mechanisms underlying paclitaxel resistance in PDAC and explore strategies to overcome it.\nThree paclitaxel (PR) and gemcitabine resistant (GR) PDAC models were established. Transcriptomics and proteomics were used to identify conserved mechanisms of drug resistance. Genetic and pharmacological approaches were used to overcome paclitaxel resistance.\nUpregulation of ABCB1 through locus amplification was identified as a conserved feature unique to PR cells. ABCB1 was not affected in any of the GR models and no cross resistance was observed. The ABCB1 inhibitor verapamil or siRNA-mediated ABCB1 depletion sensitized PR cells to paclitaxel and prevented efflux of ABCB1 substrates in all models. ABCB1 expression was associated with a trend towards shorter survival in patients who had received gemcitabine/nab-paclitaxel treatment. A pharmacological screen identified known and novel kinase inhibitors that attenuate efflux of ABCB1 substrates and sensitize PR PDAC cells to paclitaxel.\nUpregulation of ABCB1 through locus amplification represents a novel, conserved mechanism of PDAC paclitaxel resistance. Kinase inhibitors identified in this study can be further (pre) clinically explored as therapeutic strategies to overcome paclitaxel resistance in PDAC. Show less
Duranti, C.; Iorio, J.; Bagni, G.; Altadonna, G.C.; Fillion, T.; Lulli, M.; ... ; Arcangeli, A. 2023
BackgroundTakotsubo syndrome (TTS) is a reversible form of heart failure with incompletely understood pathophysiology.ObjectivesThis study analyzed altered cardiac hemodynamics during TTS to... Show moreBackgroundTakotsubo syndrome (TTS) is a reversible form of heart failure with incompletely understood pathophysiology.ObjectivesThis study analyzed altered cardiac hemodynamics during TTS to elucidate underlying disease mechanisms.MethodsLeft ventricular (LV) pressure–volume loops were recorded in 24 consecutive patients with TTS and a control population of 20 participants without cardiovascular diseases.ResultsTTS was associated with impaired LV contractility (end-systolic elastance 1.74 mm Hg/mL vs 2.35 mm Hg/mL [P = 0.024]; maximal rate of change in systolic pressure over time 1,533 mm Hg/s vs 1,763 mm Hg/s [P = 0.031]; end-systolic volume at a pressure of 150 mm Hg, 77.3 mL vs 46.4 mL [P = 0.002]); and a shortened systolic period (286 ms vs 343 ms [P < 0.001]). In response, the pressure–volume diagram was shifted rightward with significantly increased LV end-diastolic (P = 0.031) and end-systolic (P < 0.001) volumes, which preserved LV stroke volume (P = 0.370) despite a lower LV ejection fraction (P < 0.001). Diastolic function was characterized by prolonged active relaxation (relaxation constant 69.5 ms vs 45.9 ms [P < 0.001]; minimal rate of change in diastolic pressure –1,457 mm Hg/s vs –2,192 mm Hg/s [P < 0.001]), whereas diastolic stiffness (1/compliance) was not affected during TTS (end-diastolic volume at a pressure of 15 mm Hg, 96.7 mL vs 109.0 mL [P = 0.942]). Mechanical efficiency was significantly reduced in TTS (P < 0.001) considering reduced stroke work (P = 0.001), increased potential energy (P = 0.036), and a similar total pressure–volume area compared with that of control subjects (P = 0.357).ConclusionsTTS is characterized by reduced cardiac contractility, a shortened systolic period, inefficient energetics, and prolonged active relaxation but unaltered diastolic passive stiffness. These findings may suggest decreased phosphorylation of myofilament proteins, which represents a potential therapeutic target in TTS. (Optimized Characterization of Takotsubo Syndrome by Obtaining Pressure Volume Loops [OCTOPUS]; NCT03726528) Show less
In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine... Show moreIn recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research. Show less
Data set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC)... Show moreData set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC) coupled to mass spectrometry (MS) data sets, due to the high levels of data reduction that occur between raw data and machine learning-ready data. Since predictive proteomics is an emerging field, when predicting peptide behavior in LC-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based data sets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides data sets that are useful for comparing state-of-the-art machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available at https://www.proteomicsml.org/, and we welcome the entire proteomics community to contribute to the project at https://github.com/ProteomicsML/ProteomicsML. Show less
Gregori, A.; Bergonzini, C.; Capula, M.; Mantini, G.; Khojasteh-Leylakoohi, F.; Comandatore, A.; ... ; Giovannetti, E. 2023
In cell-matrix adhesions, integrin receptors and associated proteins provide a dynamic coupling of the extracellular matrix (ECM) to the cytoskeleton. This allows bidirectional transmission of... Show moreIn cell-matrix adhesions, integrin receptors and associated proteins provide a dynamic coupling of the extracellular matrix (ECM) to the cytoskeleton. This allows bidirectional transmission of forces between the ECM and the cytoskeleton, which tunes intracellular signaling cascades that control survival, proliferation, differentiation, and motility. The quantitative relationships between recruitment of distinct cell-matrix adhesion proteins and local cellular traction forces are not known. Here, we applied quantitative super-resolution microscopy to cell-matrix adhesions formed on fibronectin-stamped elastomeric pillars and developed an approach to relate the number of talin, vinculin, paxillin, and focal adhesion kinase (FAK) molecules to the local cellular traction force. We find that FAK recruitment does not show an association with traction-force application, whereas a ∼60 pN force increase is associated with the recruitment of one talin, two vinculin, and two paxillin molecules on a substrate with an effective stiffness of 47 kPa. On a substrate with a fourfold lower effective stiffness, the stoichiometry of talin:vinculin:paxillin changes to 2:12:6 for the same ∼60 pN traction force. The relative change in force-related vinculin recruitment indicates a stiffness-dependent switch in vinculin function in cell-matrix adhesions. Our results reveal a substrate-stiffness-dependent modulation of the relationship between cellular traction-force and the molecular stoichiometry of cell-matrix adhesions. Show less
Mytiliniou, M.; Wondergem, A.J.; Schmidt, T.; Heinrich, D.M. 2022
