Center for Molecular Medicine Cologne

Antczak, Philipp - assoc. JRG 06 and CAP 20

Understanding the molecular basis of stress, longevity, and aging

Introduction

The Computational Biology Aging group aims to apply state-of-the-art computational approaches to address challenges in aging, stress (including disease), and regulatory acceptance. Central to our approach is the integration of multiple levels of data to better understand the specific biological system.

Here we are able to make use of available complex datasets, including OMICs, chemistry, or other metadata to, for example, develop novel multivariate biomarkers or develop new adverse outcome pathways.

The advancement in OMICs technologies has brought in a new era of understanding of human and environmental health. Larger and larger projects are being thought up to establish the molecular basis of a multitude of societal challenges. From the fight against cancer, establishment of early detection systems for (preventable) diseases, development of new and repurposing of drugs for novel therapies, to the understanding of the impact of human action on the environment – OMICs are being used as key tools to establish this knowledge.

Data generated by OMICs technologies are rarely simple and can contain any number of features that represent the molecular building blocks. In transcriptomics, for example, working on the transcript level can yield up to 300.000 molecular features, while in another technology such as mass-spec based proteomics up to 6.000 proteins can be measured simultaneously. Analysing these datasets therefore requires a very specific skillset starting with an understanding of the peculiarities of the technology, knowledge on applying the correct statistical/computational methodology, and means to interpret and understand the results.

Even more complex and exciting is the integration of multiple datasets both vertical (multiple datasets from a single individual) and horizontal (multiple datasets from several independent individuals). This can allow for the development of more accurate multivariate biomarkers, identify novel pathways linked to the challenge, and improve our general understanding of the biological system being worked on. Here we present a few active projects that utilise OMICs and metadata to highlight the effectiveness of applied computational biology.

Understanding the impact of chemical stress on early development

An important aspect of the aging process is accrued DNA damage, particularly those that have occurred over the lifetime of an organism. Human actions, including the release of chemicals, climate change, or habitat loss can contribute to the increase in DNA damage across all organisms subjected to the environment. In addition, early developmental stages are particularly sensitive to external signals and can lead to numerous adverse outcomes including early death, increased disease risk, or reduced fecundity.

To better understand the impact of external influence on developing organisms we exposed Danio rerio (zebrafish) embryos to a suite of industrial, pharmaceutical, and other chemical agents known to be in the environment. For a total of 158 compounds in up to 5 concentrations we developed transcriptomic and phenotypic readouts including death, deformities, behaviour, and cardiac output. Changes in heart rate were observed in three different states upregulation (Figure 1B), downregulation (Figure 1C and D), and no change (Figure 1A).

Using the transcriptomics data we then build a model able to predict these changes in heart rate and achieved rates with up to 90% cross-validated accuracy. In parallel predicting heart from chemical structure only achieved up to 60% accuracy, suggesting that chemical structure alone is not sufficient for predicting heart rate. To further explore the chemical structural relationships we developed a PCA and associated clustering to identify the number of chemical structure groups in our dataset (Figure 2A).

Similarly, we clustered the molecular responses which showed a very different response and clustering pattern (Figure 2B). A closer examination of the cluster memberships found that only 1 out of the 4 groups in each approach led to the same membership suggesting that only in very specific structures the molecular response is equivalent. We are continuing to work on the development of further predictive models describing this data.

Antitumour activity of Apigenin on Ovarian Cancer cells

For thousands of years humans have used bee products in numerous applications such as honey as sweetener, beeswax as waterproof coating, or even medicine. The medical applications are particularly interesting and possibly every bee product appears to have a medicinal benefit. Several bee products, honey, propolis and venom had shown antimicrobial, antioxidative, antiaging, anti-inflammatory and anticarcinogenic activity. Moreover, wound healing and gastroprotective effect were observed in propolis, honey and royal jelly. Honey, in particular, has been shown to contain many anti-cancer relevant compounds.

One of these compounds, Apigenin, has been firstly described by Birt et al where they observed anti-mutagenic and anti-promotion of carcinogenesis activity on mutagenesis inducing in Salmonella typhimurium and mouse skin epidermis. This led to Apigenin being tested on several cancer cell lines including colorectal, breast, lung, prostate, cervical, ovarian, glioblastoma, leukaemia, melanoma, pancreatic and osteosarcoma cancer cell lines to establish its potency surrounding anti-cancer and cancer preventing functions. Apigenin was found to trigger diverse mechanisms in each of the cancer cell lines exhibiting anticancer activity, induction of apoptosis, cell cycle arrest, metastasis inhibition and anti-angiogenesis. It also demonstrated low toxicity against non-cancerous cells in comparison with cancerous alternatives, which is one of requirements for becoming a clinical candidate.

The mechanism by which Apigenin functions however has remained elusive. Due to the effect of Apigenin on cell cycle arrest, suggesting an impact on protein phosphorylation in and around the cell cycle, we employed a phosphoproteomics approach. For 3 concentrations (IC10, IC20, and IC30) as well as over time (30, 60, and 90 min) we extracted and analysed the impact of Apigenin on the phosphoproteomics state. A modified geneset enrichment analysis approach we developed for phosphoproteomics datasets identified a number of pathways associated with concentration and the interaction between concentration and time. Interestingly, epigenetic regulation of gene expression was identified as one of the most significant functions impacted by Apigenin exposure over time and concentration. To further understand the potential mechanisms a NetworKIN analysis of the data, which looks to assign potential kinases to the observed phosphoproteomics data, identified that CDK1 is a key kinase linked to the observed changes. In addition, 60% of the directly connected proteins in this analysis are related to epigenetic function further strengthening our observations (Figure 3).

A literature search of the identified functions and kinases identified that CDK1 is known to be upregated in ovarian cancers compared to normal cells. The disruption of CDK1 activity and expression resulted in apoptosis and cell cycle arrest at G2/M phase. Apigenin was reported to inhibit the activation and expression of cyclin B and cdc25c, both working consequently as a regulator of CDK1. This could explain on how Apigenin exhibit more antiproliferative activity in CDK1 overexpressing cancer cells than normal cells. Moreover, Apigenin has also been implicated in inhibition of DNA topoisomerase I and II resulting in increased DNA stress and fragmentation which related directly to the epigenetic function identified. Lastly, the strongest interaction in this NetworKIN analysis was identified between GSK3A and PDHK1. GSK3A is involved with a wide range of cellular processes. The deactivation of GSK3A caused inhibition of its downstream pathway, which are involved with multiple cellular mechanisms e.g. cell progression, proliferation, RNA translation, etc; again providing a direct link to the epigenetic impact observed. In summary, it is likely that Apigenin targets multiple cellular activities to express its antiproliferative activity (Figure 4). Further experiments into the impact of Apigenin on cell continue.

Understanding the molecular responses in respect to aging and longevity

Aging, and in particular, longevity have always been a hallmark of scientific research. With the advance of molecular technologies, it is now possible to study aging on multiple levels of biological hierarchy and, even more excitingly, in an integrated manner to establish the molecular interactome leading to longer life.

One may envision comparisons between older persons with kidney failure as rapidly aging persons (and tissues) such as in cohorts collected at CECAD, that can be compared to individuals aging at an average rate and individuals with an exceptionally healthy aging trajectory (collected at LUMC).

This project will focus on establishing a collaborative exchange with world-leading researchers in the aging field in and around the Cologne campus across the CECAD, University Hospital Cologne, MPI-AGE, CMMC, and LUMC.

LUMC currently holds the Leiden Longevity Study (LLS) on long-lived individuals and their siblings, their middle-aged offspring and the partners thereof as including clinical, molecular, and demographic data. Relevant for this project are the participants of IOP2 and 3 and an intervention study GOTO. In parallel, the Dept. 2 of Internal Medicine (UHC/CECAD) holds several cohorts suffering from chronic kidney disease (CKD) including intervention studies with similar data depth (Figure 5).

CKD is one of the key morbidities leading to premature aging and increased risk of aging-associated diseases in humans. The dietary and physical exercise intervention substudies of the LLS and the CKD cohorts will complement the knowledge on beneficial and adverse molecular profiles in the circulation related to kidney aging with a specific focus on proteomic responses. Dietary interventions are among the most powerful tools to increase lifespan and organismal fitness and are conserved in evolution.

Use of such approaches in elderly individuals and patients suffering from CKD is expected to revert aging-associated changes. Inclusion of data before and after these interventions allows for a dynamic view in a longitudinal fashion. Taken together, the combination of these large datasets is a unique asset and allows for a deep molecular phenotyping of aging combined with clinical characteristics in several conditions of human aging and longevity. One approach to utilise this large cohort of data is to develop a network representation of the interactions between every possible combination of features (Figure 6).

In its simplest form this can be represented by a correlation network. However, correlation does not always perform well when data is zero-inflated for example - a hallmark of count based datasets. For this reason, we will use data property relevant modelling techniques to link features with each other. For zero-inflated data this could employ hurdle or negative binomial, for demographics binomial, and for normally distributed data gaussian models. Such an approach then allows us to develop a better understanding of the relationships between molecular responses and the aging processes.

In addition, more accurate biomarkers predicting age and disease could highlight key biological processes that need to be further studied in the context of longevity and general health or detect confounding factors associated with disease.

Perspectives

The approaches underlying computational biology can be utilised in numerous challenges presented in biomedical and environmental fields. OMICs analyses benefit from the unbiased approach that these technologies represent and can yield novel insight into the underlying biological mechanisms at a fraction of the time required for more classical molecular biology approaches. Data integration approaches further help in developing more robust biomarkers and associated knowledge leading to better patient stratification and more personalised medicine.   

Lab Website

For further information please check the Antczak Lab - Computational Biology of Aging webpage.

    • Myall, Ashleight C., Simon Perkins, David Rushton, David Jonathan, Phillippa Spencer, Andrew R. Jones, and Philipp Antczak. (2020). “Identifying Robust Biomarkers of Infection through an Omics-Based Meta-Analysis.” MedRxiv 2020.07.28.20163329
    • Perkins, Edward J., Kalyan Gayen, Jason E. Shoemaker, Philipp Antczak, Lyle Burgoon, Francesco Falciani, Steve Gutsell, Geoff Hodges, Aude Kienzler, Dries Knapen, Mary McBride, Catherine Willett, Francis J. Doyle, and Natàlia Garcia-Reyero. 2019. “Chemical Hazard Prediction and Hypothesis Testing Using Quantitative Adverse Outcome Pathways.” Altex 36(1):91–102.
    • Basili, Danilo, Ji Liang Zhang, John Herbert, Kevin Kroll, Nancy D. Denslow, Christopher J. Martyniuk, Francesco Falciani, and Philipp Antczak. 2018. “In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass (Micropterus Salmoides).” Environmental Science and Technology 52(13):7553–65.
    • Murphy, Cheryl A., Roger M. Nisbet, Philipp Antczak, Natàlia Garcia-Reyero, Andre Gergs, Konstadia Lika, Teresa Mathews, Erik B. Muller, Diane Nacci, Angela Peace, Christopher H. Remien, Irvin R. Schultz, Louise M. Stevenson, and Karen H. Watanabe. 2018. “Incorporating Suborganismal Processes into Dynamic Energy Budget Models for Ecological Risk Assessment.” Integrated Environmental Assessment and Management 14(5):615–24.
    • Antczak, Philipp, Thomas Andrew White, Anirudha Giri, Francesco Frank Michelangeli, Mark R. Viant, Mark T. D. D. Cronin, Chris Vulpe, Francesco Falciani, Francesco Frank Michelangeli, Mark R. Viant, Chris Vulpe, and Francesco Falciani. 2014. “A Systems Biology Approach Reveals a Novel Calcium-Dependent Mechanism for Basal Toxicity in Daphnia Magna.” Environmental Science & Technology 49(18):11132–40.
    • Brooker, Rachel C., Philipp Antczak, Triantafillos Liloglou, Janet M. Risk, Joseph J. Sacco, Andrew G. Schache, and Richard J. Shaw. ‘Genetic Variants Associated with Mandibular Osteoradionecrosis Following Radiotherapy for Head and Neck Malignancy’. Radiotherapy and Oncology 165 (December 2021): 87–93. https://doi.org/10.1016/j.radonc.2021.10.020.
    • Katsiadaki I, Ellis T, Andersen L, Antczak P, Blaker E, Burden N, Fisher T, Green C, Labram B, Pearson A, Petersen K, Pickford D, Ramsden C, Ronneseth A, Ryder K, Sacker D, Stevens C, Watanabe H, Yamamoto H, Sewell F, Hawkins P, Rufli H, Handy RD, Maynard SK, and Jacobs MN (2021). Dying for change: A roadmap to refine the fish acute toxicity test after 40 years of applying a lethal endpoint. Ecotoxicol Environ Saf223, 112585. doi:10.1016/j.ecoenv.2021.112585.
    • Lawton E, Antczak P, Walker S, Germain-Cripps E, Falciani F, and Routledge EJ (2021). An investigation into the biological effects of indirect potable reuse water using zebrafish embryos. Sci Total Environ789, 147981. doi:10.1016/j.scitotenv.2021.147981.
    • Myall AC, Perkins S, Rushton D, David J, Spencer P, Jones AR, and Antczak P (2021). An OMICs based meta-analysis to support infection state stratification. Bioinformatics. doi:10.1093/bioinformatics/btab089.
    • Takeshita LY, Davidsen PK, Herbert JM, Antczak P, Hesselink MKC, Schrauwen P, Weisnagel SJ, Robbins JM, Gerszten RE, Ghosh S, Sarzynski MA, Bouchard C, and Falciani F (2021). Genomics and transcriptomics landscapes associated to changes in insulin sensitivity in response to endurance exercise training. Sci Rep11, 23314. doi:10.1038/s41598-021-98792-1.
    • Takeshita, Louise Y., Peter K. Davidsen, John M. Herbert, Philipp Antczak, Matthijs K. C. Hesselink, Patrick Schrauwen, S. John Weisnagel, et al. ‘Genomics and Transcriptomics Landscapes Associated to Changes in Insulin Sensitivity in Response to Endurance Exercise Training’. Scientific Reports 11, no. 1 (2 December 2021): 23314. doi.org/10.1038/s41598-021-98792-1.
    • Braun F, Rinschen M, Buchner D, Bohl K, Plagmann I, Bachurski D, Richard Späth M, Antczak P, Göbel H, Klein C, Lackmann JW, Kretz O, Puelles VG, Wahba R, Hallek M, Schermer B, Benzing T, Huber TB, Beyer A, Stippel D, Kurschat CE, Müller RU. (2020). "The proteomic landscape of small urinary extracellular vesicles during kidney transplantation." J Extracell Vesicles.
    • Mathews, Teresa, Louise Stevenson, Paul C. Pickhardt, Cheryl A. Murphy, Roger M. Nisbet, Philipp Antczak, Natàlia Garcia‐Reyero, and Andre Gergs. (2020). “The Effect of Dietary Exposure to Coal Ash Contaminants Within Food Ration on Growth and Reproduction in Daphnia Magna.” Environmental Toxicology and Chemistry etc.4819.
    • Mathews TJ, Stevenson LM, Pickhardt PC, Murphy CA, Nisbet RM, Antczak P, Garcia-Reyero N, and Gergs A (2020). The Effect of Dietary Exposure to Coal Ash Contaminants within Food Ration on Growth and Reproduction in Daphnia magna. Environmental toxicology and chemistry 10.1002/etc.4819.
    • Myall, Ashleight C., Simon Perkins, David Rushton, David Jonathan, Phillippa Spencer, Andrew R. Jones, and Philipp Antczak. 2020. “Identifying Robust Biomarkers of Infection through an Omics-Based Meta-Analysis.” MedRxiv 2020.07.28.20163329.
    • Sleight, Victoria A., Philipp Antczak, Francesco Falciani, Melody S. Clark, and Lenore Cowen. 2020. “Computationally Predicted Gene Regulatory Networks in Molluscan Biomineralization Identify Extracellular Matrix Production and Ion Transportation Pathways” edited by L. Cowen. Bioinformatics 36(5):1326–32.
    • Perkins, Edward J., Kalyan Gayen, Jason E. Shoemaker, Philipp Antczak, Lyle Burgoon, Francesco Falciani, Steve Gutsell, Geoff Hodges, Aude Kienzler, Dries Knapen, Mary McBride, Catherine Willett, Francis J. Doyle, and Natàlia Garcia-Reyero. 2019. “Chemical Hazard Prediction and Hypothesis Testing Using Quantitative Adverse Outcome Pathways.” Altex 36(1):91–102.
    • Salem, Shebl E., Rachael Hough, Chris Probert, Thomas W. Maddox, Philipp Antczak, Julian M. Ketley, Nicola J. Williams, Sarah J. Stoneham, and Debra C. Archer. 2019. “A Longitudinal Study of the Faecal Microbiome and Metabolome of Periparturient Mares.” PeerJ 7:e6687.
    • Salem, Shebl E., Thomas W. Maddox, Philipp Antczak, Julian M. Ketley, Nicola J. Williams, and Debra C. Archer. 2019. “Acute Changes in the Colonic Microbiota Are Associated with Large Intestinal Forms of Surgical Colic.” Veterinary Research.
    • Basili, Danilo, Ji Liang Zhang, John Herbert, Kevin Kroll, Nancy D. Denslow, Christopher J. Martyniuk, Francesco Falciani, and Philipp Antczak. 2018. “In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass (Micropterus Salmoides).” Environmental Science and Technology 52(13):7553–65.
    • Flamini, Valentina, Rachel S. Ghadiali, Philipp Antczak, Amy Rothwell, Jeremy E. Turnbull, and Addolorata Pisconti. 2018. “The Satellite Cell Niche Regulates the Balance between Myoblast Differentiation and Self-Renewal via P53.” Stem Cell Reports 10(3):970–83.
    • Murphy, Cheryl A., Roger M. Nisbet, Philipp Antczak, Natàlia Garcia-Reyero, Andre Gergs, Konstadia Lika, Teresa Mathews, Erik B. Muller, Diane Nacci, Angela Peace, Christopher H. Remien, Irvin R. Schultz, Louise M. Stevenson, and Karen H. Watanabe. 2018. “Incorporating Suborganismal Processes into Dynamic Energy Budget Models for Ecological Risk Assessment.” Integrated Environmental Assessment and Management 14(5):615–24.
    • Murphy, Cheryl A., Roger M. Nisbet, Philipp Antczak, Natàlia Garcia-Reyero, Andre Gergs, Konstadia Lika, Teresa Mathews, Erik B. Muller, Diane Nacci, Angela Peace, Christopher H. Remien, Irvin R. Schultz, and Karen H. Watanabe. 2018. “Linking Adverse Outcome Pathways to Dynamic Energy Budgets: A Conceptual Model.” Pp. 281–302 in A Systems Biology Approach to Advancing Adverse Outcome Pathways for Risk Assessment. Cham: Springer International Publishing.
    • Salem, Shebl E., Thomas W. Maddox, Adam Berg, Philipp Antczak, Julian M. Ketley, Nicola J. Williams, and Debra C. Archer. 2018. “Variation in Faecal Microbiota in a Group of Horses Managed at Pasture over a 12-Month Period.” Scientific Reports 8(1).
    • Brockmeier, Erica K., Geoff Hodges, Philipp Antczak, Thomas H. Hutchinson, Emma Butler, Markus Hecker, Knut Erik Tollefsen, Natalia Garcia-Reyero, Peter Kille, Dörthe Becker, Kevin Chipman, John Colbourne, Timothy W. Collette, Andrew Cossins, Mark Cronin, Peter Graystock, Steve Gutsell, Dries Knapen, Ioanna Katsiadaki, Anke Lange, Stuart Marshall, Stewart F. Owen, Edward J. Perkins, Stewart Plaistow, Anthony Schroeder, Daisy Taylor, Mark Viant, Gerald Ankley, and Francesco Falciani. 2017. “The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment.” Toxicological Sciences 158(2):252–62.
    • Feswick, April, Meghan Isaacs, Adam Biales, Robert W. Flick, David C. Bencic, Rong Lin Wang, Chris Vulpe, Marianna Brown-Augustine, Alex Loguinov, Francesco Falciani, Philipp Antczak, John Herbert, Lorraine Brown, Nancy D. Denslow, Kevin J. Kroll, Candice Lavelle, Viet Dang, Lynn Escalon, Natàlia Garcia-Reyero, Christopher J. Martyniuk, and Kelly R. Munkittrick. 2017. “How Consistent Are We? Interlaboratory Comparison Study in Fathead Minnows Using the Model Estrogen 17Α-Ethinylestradiol to Develop Recommendations for Environmental Transcriptomics.” Environmental Toxicology and Chemistry 36(10):2614–23.
    • Trevino, Victor, Alberto Cassese, Zsuzsanna Nagy, Xiaodong Zhuang, John Herbert, Philipp Antczak, Kim Clarke, Nicholas Davies, Ayesha Rahman, Moray J. Campbell, Michele Guindani, Roy Bicknell, Marina Vannucci, and Francesco Falciani. 2016. “A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.” PLoS Computational Biology 12(4).
    • Altenburger, Rolf, Selim Ait-Aissa, Philipp Antczak, Thomas Backhaus, Damià Barceló, Thomas Benjamin Seiler, Francois Brion, Wibke Busch, Kevin Chipman, Miren López de Alda, Gisela de Aragão Umbuzeiro, Beate I. Escher, Francesco Falciani, Michael Faust, Andreas Focks, Klara Hilscherova, Juliane Hollender, Henner Hollert, Felix Jäger, Annika Jahnke, Andreas Kortenkamp, Martin Krauss, Gregory F. Lemkine, John Munthe, Steffen Neumann, Emma L. Schymanski, Mark Scrimshaw, Helmut Segner, Jaroslav Slobodnik, Foppe Smedes, Subramaniam Kughathas, Ivana Teodorovic, Andrew J. Tindall, Knut Erik Tollefsen, Karl Heinz Walz, Tim D. Williams, Paul J. Van den Brink, Jos van Gils, Branislav Vrana, Xiaowei Zhang, and Werner Brack. 2015. “Future Water Quality Monitoring - Adapting Tools to Deal with Mixtures of Pollutants in Water Resource Management.” Science of the Total Environment 512–513:540–51.
    • Antczak, Philipp, Andrew Filer, Greg N. Parsonage, Holly M. Legault, Margot O’Toole, Mark J. Pearson, Andrew M. Thomas, Dagmar Scheel-Toellner, Karim Raza, Christopher D. Buckley, and Francesco Falciani. 2015. “Stromal Transcriptional Profiles Reveal Hierarchies of Anatomical Site, Serum Response and Disease and Identify Disease Specific Pathways.” Plos One 10(3):e0120917.
    • Cassese, Alberto, Michele Guindani, Philipp Antczak, Francesco Falciani, and Marina Vannucci. 2015. “A Bayesian Model for the Identification of Differentially Expressed Genes in Daphnia Magna Exposed to Munition Pollutants.” Biometrics 71(3):803–11.
    • Perkins, Edward J., Philipp Antczak, Lyle Burgoon, Francesco Falciani, Natàlia Garcia-Reyero, Steve Gutsell, Geoff Hodges, Aude Kienzler, Dries Knapen, Mary McBride, and Catherine Willett. 2015. “Adverse Outcome Pathways for Regulatory Applications: Examination of Four Case Studies with Different Degrees of Completeness and Scientific Confidence.” Toxicological Sciences 148(1):14–25.
    • Scanlan, Leona D., Alexandre V. Loguinov, Quincy Teng, Philipp Antczak, Kathleen P. Dailey, Daniel T. Nowinski, Jonah Kornbluh, Xin Xin Lin, Erica Lachenauer, Audrey Arai, Nora K. Douglas, Francesco Falciani, Heather M. Stapleton, and Chris D. Vulpe. 2015. “Gene Transcription, Metabolite and Lipid Profiling in Eco-Indicator Daphnia Magna Indicate Diverse Mechanisms of Toxicity by Legacy and Emerging Flame-Retardants.” Environmental Science and Technology 49(12):7400–7410.
    • Antczak, Philipp, Thomas Andrew White, Anirudha Giri, Francesco Frank Michelangeli, Mark R. Viant, Mark T. D. D. Cronin, Chris Vulpe, Francesco Falciani, Francesco Frank Michelangeli, Mark R. Viant, Chris Vulpe, and Francesco Falciani. 2014. “A Systems Biology Approach Reveals a Novel Calcium-Dependent Mechanism for Basal Toxicity in Daphnia Magna.” Environmental Science & Technology 49(18):11132–40.
    • Cano, Isaac, Ákos Tényi, Christine Schueller, Martin Wolff, M. Mercedes Huertas Migueláñez, David Gomez-Cabrero, Philipp Antczak, Josep Roca, Marta Cascante, Francesco Falciani, and Dieter Maier. 2014. “The COPD Knowledge Base: Enabling Data Analysis and Computational Simulation in Translational COPD Research.” Journal of Translational Medicine 12(Suppl 2):S6.
    • Davidsen, Peter K., John M. Herbert, Philipp Antczak, Kim Clarke, Elisabet Ferrer, Victor I. Peinado, Constancio Gonzalez, Josep Roca, Stuart Egginton, Joan A. Barberá, and Francesco Falciani. 2014. “A Systems Biology Approach Reveals a Link between Systemic Cytokines and Skeletal Muscle Energy Metabolism in a Rodent Smoking Model and Human COPD.” Genome Medicine 6(8):59.
    • Antczak, Philipp, HJ Hun Je HJ Jo, Seonock Woo, Leona Scanlan, Helen Poynton, Alex Loguinov, Sarah Chan, Francesco Falciani, and Chris Vulpe. 2013. “The Molecular Toxicity Identification Evaluation (MTIE) Approach Predicts Chemical Exposure in Daphnia Magna.” Environmental Science & Technology 1–20.
    • Fergelot, Patricia, Jean Christophe Bernhard, Fabienne Soulet, Witold W. Kilarski, Céline Léon, Nathalie Courtois, Colette Deminière, John M. J. Herbert, Philipp Antczak, Francesco Falciani, Nathalie Rioux-Leclercq, Jean Jacques Patard, Jean Marie Ferrière, Alain Ravaud, Martin Hagedorn, and Andreas Bikfalvi. 2013. “The Experimental Renal Cell Carcinoma Model in the Chick Embryo.” Angiogenesis 16(1):181–94.
    • Martyniuk, Christopher J., Melinda S. Prucha, Nicholas J. Doperalski, Philipp Antczak, Kevin J. Kroll, Francesco Falciani, David S. Barber, and Nancy D. Denslow. 2013. “Gene Expression Networks Underlying Ovarian Development in Wild Largemouth Bass (Micropterus Salmoides).” PloS One 8(3):e59093.
    • Scanlan, Leona D., Robert B. Reed, Alexandre V. Loguinov, Philipp Antczak, Abderrahmane Tagmount, Shaul Aloni, Daniel Thomas Nowinski, Pauline Luong, Christine Tran, Nadeeka Karunaratne, Don Pham, Xin Xin Lin, Francesco Falciani, Christopher P. Higgins, James F. Ranville, Chris D. Vulpe, and Benjamin Gilbert. 2013. “Silver Nanowire Exposure Results in Internalization and Toxicity to Daphnia Magna.” ACS Nano 7(12):10681–94.
    • Moro, Sabrina, J. Kevin Chipman, Philipp Antczak, Nil Turan, Wolfgang Dekant, Francesco Falciani, and Angela Mally. 2012. “Identification and Pathway Mapping of Furan Target Proteins Reveal Mitochondrial Energy Production and Redox Regulation as Critical Targets of Furan Toxicity.” Toxicological Sciences 126(2):336–52.
    • Mura, M., R. K. Swain, X. Zhuang, H. Vorschmitt, G. Reynolds, S. Durant, J. F. J. J. Beesley, J. M. J. J. Herbert, H. Sheldon, M. Andre, S. Sanderson, K. Glen, N. T. T. Luu, H. M. McGettrick, P. Antczak, F. Falciani, G. B. Nash, Z. S. Nagy, and R. Bicknell. 2012. “Identification and Angiogenic Role of the Novel Tumor Endothelial Marker CLEC14A.” Oncogene 31(3):293–305.
    • Antczak, Philipp. 2011. “Moving Towards Predictive Toxicology - A Systems Biology Approach.” University of Birmingham.
    • Gupta, Rita, Anna Stincone, Philipp Antczak, Sarah Durant, Roy Bicknell, Andreas Bikfalvi, and Francesco Falciani. 2011. “A Computational Framework for Gene Regulatory Network Inference That Combines Multiple Methods and Datasets.” BMC Systems Biology 5(1):52.
    • Lin, Hong, John A. Halsall, Philipp Antczak, Laura P. O’Neill, Francesco Falciani, and Bryan M. Turner. 2011. “Relative Overexpression of X-Linked Genes in Mouse Embryonic Stem Cells Is Consistent with Ohno’s Hypothesis.” Nature Genetics 43(12):1169–70.
    • Perkins, Edward J., J. Kevin Chipman, Stephen Edwards, Tanwir Habib, Francesco Falciani, Ronald Taylor, Graham Van Aggelen, Chris Vulpe, Philipp Antczak, and Alexandre Loguinov. 2011. “Reverse Engineering Adverse Outcome Pathways.” Environmental Toxicology and Chemistry 30(1):22–38.
    • Stincone, Anna, Nazish Daudi, Ayesha S. Rahman, Philipp Antczak, Ian Henderson, Jeffrey Cole, Matthew D. Johnson, Peter Lund, and Francesco Falciani. 2011. “A Systems Biology Approach Sheds New Light on Escherichia Coli Acid Resistance.” Nucleic Acids Research 39(17):1–17.
    • Trevino, Victor, Mahlet G. Tadesse, Marina Vannucci, Fatima Al-Shahrour, Philipp Antczak, Sarah Durant, Andreas Bikfalvi, Joaquin Dopazo, Moray J. Campbell, and Francesco Falciani. 2011. “Analysis of Normal-Tumour Tissue Interaction in Tumours: Prediction of Prostate Cancer Features from the Molecular Profile of Adjacent Normal Cells” edited by D. Di Bernardo. PLoS ONE 6(3):13.
    • Antczak, Philipp, Fernando Ortega, J. Kevin Chipman, and Francesco Falciani. 2010. “Mapping Drug Physico-Chemical Features to Pathway Activity Reveals Molecular Networks Linked to Toxicity Outcome” edited by R. Khanin. PLoS ONE 5(8):e12385.
    • Antczak, Philipp, Fabienne Soulet, Witold W. Kilarski, John Herbert, Roy Bicknell, Francesco Falciani, Andreas Bikfalvi, Fabienne Soulet, Witold W. Kilarski, John Herbert, Roy Bicknell, Francesco Falciani, and Andreas Bikfalvi. 2010. “Gene Signatures in Wound Tissue as Evidenced by Molecular Profiling in the Chick Embryo Model.” BMC Genomics 11(1):495.
    • Burton, Neil A., Matthew D. Johnson, Philipp Antczak, Ashley Robinson, and Peter A. Lund. 2010. “Novel Aspects of the Acid Response Network of E. Coli K-12 Are Revealed by a Study of Transcriptional Dynamics.” Journal of Molecular Biology 401(5):726–42.
    • Sameith, Katrin, Philipp Antczak, Elliot Marston, Nil Turan, Dieter Maier, Tanja Stankovic, and Francesco Falciani. 2008. “Functional Modules Integrating Essential Cellular Functions Are Predictive of the Response of Leukaemia Cells to DNA Damage.” Bioinformatics 24(22):2602–7.
    Dr. Philipp Antczak CMMC Cologne
    Dr. Philipp Antczak

    Center for Molecular Medicine Cologne | Lab. of Computational Biology of Ageing - CMMC Research Building

    CMMC - PI - assoc. JRG 06 & CAP 20

    +49 221 478 42910

    Center for Molecular Medicine Cologne | Lab. of Computational Biology of Ageing - CMMC Research Building

    Robert-Koch-Str. 21

    50931 Cologne

    www.antczak-lab.cmmc-uni-koeln.de/

    CMMC Profile Page

    Curriculum Vitae (CV)

    Publications on PubMed

    Publications

    Link to PubMed

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    Group Members

    Hande Özge Aydogan (PhD student)
    Franziska Richter (Master student)