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Abstract (Expand)

Prerequisite for a successful proteomics experiment is a high-quality lysis of the sample of interest, resulting in a large number of identified proteins as well as a high coverage of protein sequences. Therefore, the choice of suitable lysis conditions is crucial. Many buffers were previously employed in proteomics studies, yet a comprehensive comparison of lysate preparation conditions was so far missing. In this study, we compared the efficiency of four commonly used lysis buffers, containing the agents NP40, SDS, urea or GdnHCl, in four different types of biological samples (suspension and adherent cell lines, primary mouse cells and mouse liver tissue). After liquid chromatography-mass spectrometry (LC-MS) measurement and MaxQuant analysis, we compared chromatograms, intensities, number of identified proteins and the localization of the identified proteins. Overall, SDS emerged as the most reliable reagent, ensuring stable performance and reproducibility across diverse samples. Furthermore, our data advocated for a dual-sample lysis approach, including that the resulting pellet is lysed again after the initial lysis with a urea lysis buffer and subsequently both lysates are combined for a single LC-MS run to maximize the proteome coverage. However, none of the investigated lysis buffers proved to be superior in every category, indicating that the lysis buffer of choice depends on the proteins of interest and on the biological question. Further, we demonstrated with our systematic studies the establishment of conditions that allows to perform global proteomics and affinity purification-based interactome characterization from the same lysate. In sum our results provide guidance for the best-suited lysis buffer for mass spectrometry-based proteomics depending on the question of interest.

Authors: Barbara Helm, Pauline Hansen, Li Lai, Luisa Schwarzmüller, Simone M. Clas, Annika Richter, Max Ruwolt, Fan Liu, Dario Frey, Lorenza A. D’Alessandro, Wolf-Dieter Lehmann, Marcel Schilling, Dominic Helm, Dorothea Fiedler, Ursula Klingmüller

Date Published: 21st Feb 2024

Publication Type: Journal

Abstract

Not specified

Authors: Chaowen Zheng, Siyuan Li, Huanran Lyu, Cheng Chen, Johannes Mueller, Anne Dropmann, Seddik Hammad, Steven Dooley, Songqing He, Sebastian Mueller

Date Published: 2024

Publication Type: Journal

Abstract (Expand)

Abstract Type I interferons (IFNs) play a central role not only in innate immunity against viral infection, but also in the antitumour response, e.g. through a direct impact on cell proliferation.act on cell proliferation. Particularly for cancer arising in the context of chronic inflammation, constant exposure to IFNs may constitute a strong selective pressure during tumour evolution. Expansion of neoplastic subclones resistant to the antiproliferative effects of IFNs may contribute to immunoediting of tumours, leading to more aggressive disease. Experimental evidence for this development of IFN-insensitivity has been scarce and its molecular mechanism is unclear. In this study we demonstrate that six weeks exposure of cells to IFN-β in vitro reduces their sensitivity to its antiproliferative effects, and that this phenotype was stable for up to four weeks. Furthermore, we observed substantial differences in cellular sensitivity to growth inhibition by IFN-β in a panel of ten different liver cancer cell lines, most prominently in a pair of highly dedifferentiated cell lines, and least in cells from well-differentiated tumours. In both, long-term IFN selection and in dedifferentiated tumour cell lines, we found IFNAR2 expression to be substantially reduced, suggesting the receptor complex to be a sensitive target amenable to immunoediting. Beyond new insights into possible molecular processes in tumour evolution, these findings might prove valuable for the development of biomarkers allowing to stratify tumours for their sensitivity to IFN treatment in the context of patient tailored therapies.

Authors: Felix Hiebinger, Aiste Kudulyte, Huanting Chi, Sebastian Burbano De Lara, Doroteja Ilic, Barbara Helm, Hendrik Welsch, Viet Loan Dao Thi, Ursula Klingmüller, Marco Binder

Date Published: 1st Dec 2023

Publication Type: Journal

Abstract (Expand)

Type I interferons (IFNs) play a central role not only in innate immunity against viral infection, but also in the antitumour response. Apart from indirect immune-modulatory and anti-angiogenic effects, they have direct impact on cell proliferation. Particularly for cancer arising in the context of chronic inflammation, constant exposure to IFNs may constitute a strong selective pressure during tumour evolution. Expansion of neoplastic subclones or -populations that developed resistance to the antiproliferative effects of IFNs might constitute an important contribution to immunoediting of the cancer cells leading to more aggressive and metastasising disease. Experimental evidence for this development of IFN-insensitivity has been scarce and its molecular mechanism is unclear. In this study we demonstrate that prolonged (six weeks) exposure of cells to IFN-β in vitro reduces their sensitivity to its antiproliferative effects, and that this phenotype was stable for up to four weeks. Furthermore, we observed substantial differences in cellular sensitivity to growth inhibition by IFN-β in a panel of ten different liver cancer cell lines of varying malignity. IFN-resistance was most prominent in a pair of highly dedifferentiated cell lines, and least in cells from well-differentiated tumours, fostering the hypothesis of IFN-driven immunoediting in advanced cancers. In both settings, long-term IFN selection in vitro as well as in dedifferentiated tumour cell lines, we found IFNAR expression to be substantially reduced, suggesting the receptor complex, in particular IFNAR2, to be a sensitive target amenable to immunoediting. Beyond new insights into possible molecular processes in tumour evolution, these findings might prove valuable for the development of biomarkers allowing to stratify tumours for their sensitivity to IFN treatment in the context of patient tailored therapies.

Authors: Felix Hiebinger, Aiste Kudulyte, Huanting Chi, Sebastian Burbano De Lara, Barbara Helm, Hendrik Welsch, Viet Loan Dao Thi, Ursula Klingmüller, Marco Binder

Date Published: 24th Aug 2023

Publication Type: Journal

Abstract (Expand)

Abstract The human liver has a remarkable capacity to regenerate and thus compensate over decades for fibrosis caused by toxic chemicals, drugs, alcohol, or malnutrition. To date, no protective mechanismsrition. To date, no protective mechanisms have been identified that help the liver tolerate these repeated injuries. In this study, we revealed dysregulation of lipid metabolism and mild inflammation as protective mechanisms by studying longitudinal multi-omic measurements of liver fibrosis induced by repeated CCl 4 injections in mice ( n  = 45). Based on comprehensive proteomics, transcriptomics, blood- and tissue-level profiling, we uncovered three phases of early disease development—initiation, progression, and tolerance. Using novel multi-omic network analysis, we identified multi-level mechanisms that are significantly dysregulated in the injury-tolerant response. Public data analysis shows that these profiles are altered in human liver diseases, including fibrosis and early cirrhosis stages. Our findings mark the beginning of the tolerance phase as the critical switching point in liver response to repetitive toxic doses. After fostering extracellular matrix accumulation as an acute response, we observe a deposition of tiny lipid droplets in hepatocytes only in the Tolerant phase. Our comprehensive study shows that lipid metabolism and mild inflammation may serve as biomarkers and are putative functional requirements to resist further disease progression.

Authors: Seddik Hammad, Christoph Ogris, Amnah Othman, Pia Erdoesi, Wolfgang Schmidt-Heck, Ina Biermayer, Barbara Helm, Yan Gao, Weronika Piorońska, Christian H. Holland, Lorenza A. D’Alessandro, Carolina de la Torre, Carsten Sticht, Sherin Al Aoua, Fabian J. Theis, Heike Bantel, Matthias P. Ebert, Ursula Klingmüller, Jan G. Hengstler, Steven Dooley, Nikola S. Mueller

Date Published: 1st Jul 2023

Publication Type: Journal

Abstract

Not specified

Authors: Mihael Vucur, Ahmed Ghallab, Anne T. Schneider, Arlind Adili, Mingbo Cheng, Mirco Castoldi, Michael T. Singer, Veronika Büttner, Leonie S. Keysberg, Lena Küsgens, Marlene Kohlhepp, Boris Görg, Suchira Gallage, Jose Efren Barragan Avila, Kristian Unger, Claus Kordes, Anne-Laure Leblond, Wiebke Albrecht, Sven H. Loosen, Carolin Lohr, Markus S. Jördens, Anne Babler, Sikander Hayat, David Schumacher, Maria T. Koenen, Olivier Govaere, Mark V. Boekschoten, Simone Jörs, Carlos Villacorta-Martin, Vincenzo Mazzaferro, Josep M. Llovet, Ralf Weiskirchen, Jakob N. Kather, Patrick Starlinger, Michael Trauner, Mark Luedde, Lara R. Heij, Ulf P. Neumann, Verena Keitel, Johannes G. Bode, Rebekka K. Schneider, Frank Tacke, Bodo Levkau, Twan Lammers, Georg Fluegen, Theodore Alexandrov, Amy L. Collins, Glyn Nelson, Fiona Oakley, Derek A. Mann, Christoph Roderburg, Thomas Longerich, Achim Weber, Augusto Villanueva, Andre L. Samson, James M. Murphy, Rafael Kramann, Fabian Geisler, Ivan G. Costa, Jan G. Hengstler, Mathias Heikenwalder, Tom Luedde

Date Published: 1st Jul 2023

Publication Type: Journal

Abstract (Expand)

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

Authors: P. Badia-I-Mompel, L. Wessels, S. Muller-Dott, R. Trimbour, R. O. Ramirez Flores, R. Argelaguet, J. Saez-Rodriguez

Date Published: 26th Jun 2023

Publication Type: Journal

Abstract

Not specified

Authors: Stefan Hoehme, Seddik Hammad, Jan Boettger, Brigitte Begher-Tibbe, Petru Bucur, Eric Vibert, Rolf Gebhardt, Jan G. Hengstler, Dirk Drasdo

Date Published: 2023

Publication Type: Journal

Abstract (Expand)

MOTIVATION: Over the last decades, image processing and analysis have become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes in living systems is essential for understanding the complex underlying mechanisms and allows, i.e. the construction of spatio-temporal models that illuminate the interplay between architecture and function. Recently, deep learning significantly improved the performance of traditional image analysis in cases where imaging techniques provide large amounts of data. However, if only a few images are available or qualified annotations are expensive to produce, the applicability of deep learning is still limited. RESULTS: We present a novel approach that combines machine learning-based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large image sets which enables a guided reuse of interactively trained classifiers. Our approach solves the problem of deteriorated segmentation and quantification accuracy when reusing trained classifiers which is due to significant color variability prevalent and often unavoidable in biological and medical images. This increase in efficiency improves the suitability of interactive segmentation for larger image sets, enabling efficient quantification or the rapid generation of training data for deep learning with minimal effort. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general. AVAILABILITY AND IMPLEMENTATION: The presented methods are implemented in our image processing software TiQuant which is freely available at tiquant.hoehme.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authors: A. Friebel, T. Johann, D. Drasdo, S. Hoehme

Date Published: 30th Sep 2022

Publication Type: Journal

Abstract

Not specified

Authors: Yujia Li, Weiguo Fan, Frederik Link, Sai Wang, Steven Dooley

Date Published: 1st Feb 2022

Publication Type: Journal

Abstract (Expand)

Abstract Chronic alcohol consumption induces stress and damage in alcohol metabolising hepatocytes, which leads to inflammatory and fibrogenic responses. Besides these direct effects, alcohol disruptsffects, alcohol disrupts intestinal barrier functions and induces gut microbial dysbiosis, causing translocation of bacteria or microbial products through the gut mucosa to the liver and, which induce inflammation indirectly. Inflammation is one of the key drivers of alcohol-associated liver disease progression from steatosis to severe alcoholic hepatitis. The current standard of care for the treatment of severe alcoholic hepatitis is prednisolone, aiming to reduce inflammation. Prednisolone, however improves only short-term but not long-term survival rates in those patients, and even increases the risk for bacterial infections. Thus, recent studies focus on the exploration of more specific inflammatory targets for the treatment of severe alcoholic hepatitis. These comprise, among others interference with inflammatory cytokines, modulation of macrophage phenotypes or targeting of immune cell communication, as summarized in the present overview. Although several approaches give promising results in preclinical studies, data robustness and ability to transfer experimental results to human disease is still not sufficient for effective clinical translation.

Authors: Sophie Lotersztajn, Antonio Riva, Sai Wang, Steven Dooley, Shilpa Chokshi, Bin Gao

Date Published: 18th Jan 2022

Publication Type: Journal

Abstract (Expand)

Abstract Alcohol-related liver disease (ALD) impacts millions of patients worldwide each year and the numbers are increasing. Disease stages range from steatosis via steatohepatitis and fibrosis toepatitis and fibrosis to cirrhosis, severe alcohol-associated hepatitis and liver cancer. ALD is usually diagnosed at an advanced stage of progression with no effective therapies. A major research goal is to improve diagnosis, prognosis and also treatments for early ALD. This however needs prioritization of this disease for financial investment in basic and clinical research to more deeply investigate mechanisms and identify biomarkers and therapeutic targets for early detection and intervention. Topics of interest are communication of the liver with other organs of the body, especially the gut microbiome, the individual genetic constitution, systemic and liver innate inflammation, including bacterial infections, as well as fate and number of hepatic stellate cells and the composition of the extracellular matrix in the liver. Additionally, mechanical forces and damaging stresses towards the sophisticated vessel system of the liver, including the especially equipped sinusoidal endothelium and the biliary tract, work together to mediate hepatocytic import and export of nutritional and toxic substances, adapting to chronic liver disease by morphological and functional changes. All the aforementioned parameters contribute to the outcome of alcohol use disorder and the risk to develop advanced disease stages including cirrhosis, severe alcoholic hepatitis and liver cancer. In the present collection, we summarize current knowledge on these alcohol-related liver disease parameters, excluding the aspect of inflammation, which is presented in the accompanying review article by Lotersztajn and colleagues.

Authors: Bernd Schnabl, Gavin E. Arteel, Felix Stickel, Jan Hengstler, Nachiket Vartak, Ahmed Ghallab, Steven Dooley, Yujia Li, Robert F. Schwabe

Date Published: 18th Jan 2022

Publication Type: Journal

Abstract (Expand)

Abstract Summary Mass spectrometry-based proteomics is increasingly employed in biology and medicine. To generate reliable information from large datasets and ensure comparability of results, it isrge datasets and ensure comparability of results, it is crucial to implement and standardize the quality control of the raw data, the data processing steps and the statistical analyses. MSPypeline provides a platform for importing MaxQuant output tables, generating quality control reports, data preprocessing including normalization and performing exploratory analyses by statistical inference plots. These standardized steps assess data quality, provide customizable figures and enable the identification of differentially expressed proteins to reach biologically relevant conclusions. Availability and implementation The source code is available under the MIT license at https://github.com/siheming/mspypeline with documentation at https://mspypeline.readthedocs.io. Benchmark mass spectrometry data are available on ProteomeXchange (PXD025792). Supplementary information Supplementary data are available at Bioinformatics Advances online.

Authors: Simon Heming, Pauline Hansen, Artyom Vlasov, Florian Schwörer, Stephen Schaumann, Paulina Frolovaitė, Wolf-Dieter Lehmann, Jens Timmer, Marcel Schilling, Barbara Helm, Ursula Klingmüller

Date Published: 2022

Publication Type: Journal

Abstract

Not specified

Authors: A. Schmoldt, H. F. Benthe, G. Haberland

Date Published: 1st Sep 1975

Publication Type: Journal

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