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Metabolic profiling in diabetes

Karsten Suhre
2014 Journal of Endocrinology  
A recent GWAS for metabolic traits has also found a strong association of the GCKR locus with mannose-to-glucose ratios (Suhre et al. 2011b) .  ...  It is noteworthy herein that phosphatidylethanolamines were also found to be associated with diabetes in a subsequent study (Suhre et al. 2010) .  ... 
doi:10.1530/joe-14-0024 pmid:24868111 fatcat:xowj6mqxendqzi5s5yyjuv7qz4

Advancing Cancer Treatment by Targeting Glutamine Metabolism—A Roadmap

Anna Halama, Karsten Suhre
2022 Cancers  
Tumor growth and metastasis strongly depend on adapted cell metabolism. Cancer cells adjust their metabolic program to their specific energy needs and in response to an often challenging tumor microenvironment. Glutamine metabolism is one of the metabolic pathways that can be successfully targeted in cancer treatment. The dependence of many hematological and solid tumors on glutamine is associated with mitochondrial glutaminase (GLS) activity that enables channeling of glutamine into the
more » ... oxylic acid (TCA) cycle, generation of ATP and NADPH, and regulation of glutathione homeostasis and reactive oxygen species (ROS). Small molecules that target glutamine metabolism through inhibition of GLS therefore simultaneously limit energy availability and increase oxidative stress. However, some cancers can reprogram their metabolism to evade this metabolic trap. Therefore, the effectiveness of treatment strategies that rely solely on glutamine inhibition is limited. In this review, we discuss the metabolic and molecular pathways that are linked to dysregulated glutamine metabolism in multiple cancer types. We further summarize and review current clinical trials of glutaminolysis inhibition in cancer patients. Finally, we put into perspective strategies that deploy a combined treatment targeting glutamine metabolism along with other molecular or metabolic pathways and discuss their potential for clinical applications.
doi:10.3390/cancers14030553 pmid:35158820 pmcid:PMC8833671 fatcat:ic5dk5tt5fh3fdb4v7pexs4bfm

Comparative Evaluation Of Color-Based Video Signatures In The Presence Of Various Distortion Types

Aritz Sánchez De La Fuente, Patrick Ndjiki-Nya, Karsten Sühring, Tobias Hinz, Karsten Müller, Thomas Wiegand
2009 Zenodo  
Sühring, Tobias Hinz, Karsten Müller, and Thomas Wiegand T World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:3, No:10, 2009  ...  the Presence of Various DistortionTypes International Science Index, Computer and Information Engineering Vol:3, No:10, 2009 waset.org/Publication/15819 Aritz Sánchez de la Fuente, Patrick Ndjiki-Nya, Karsten  ... 
doi:10.5281/zenodo.1085764 fatcat:wxcfjg4lnffghok4yzeyxwrt7u

SGI: Automatic clinical subgroup identification in omics datasets

Mustafa Buyukozkan, Karsten Suhre, Jan Krumsiek, Alfonso Valencia
2021 Bioinformatics  
The 'Subgroup Identification' (SGI) toolbox provides an algorithm to automatically detect clinical subgroups of samples in large-scale omics datasets. It is based on hierarchical clustering trees in combination with a specifically designed association testing and visualization framework that can process an arbitrary number of clinical parameters and outcomes in a systematic fashion. A multi-block extension allows for the simultaneous use of multiple omics datasets on the same samples. In this
more » ... per, we first describe the functionality of the toolbox and then demonstrate its capabilities through application examples on a type 2 diabetes metabolomics study as well as two copy number variation datasets from The Cancer Genome Atlas. Availability SGI is an open-source package implemented in R. Package source codes and hands-on tutorials are available at https://github.com/krumsieklab/sgi. The QMdiab metabolomics data is included in the package and can be downloaded from https://doi.org/10.6084/m9.figshare.5904022. Supplementary information Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btab656 pmid:34529048 pmcid:PMC8723155 fatcat:bxvzsnqlxbhvxbf3tyr5f47vp4

Genomic Correlates of Hyperthermostability, an Update

Karsten Suhre, Jean-Michel Claverie
2003 Journal of Biological Chemistry  
It has been shown (Cambillau, C., and Claverie, J. M. (2000) J. Biol. Chem. 275, 32383-32386) that a large difference between the proportions of charged versus polar (non-charged) amino acids (CvP-bias) was an adequate, if empirical, signature of the proteome of hyperthermophilic organisms (T growth >80°C). Since that study, the number of available microbial genomes has more than doubled, raising the possibility that the simple CvP-bias rule might no longer hold. Taking advantage of the new
more » ... ence data, we re-analyzed the genomes of 9 fully sequenced thermophiles, 9 hyperthermophiles, and 53 mesothermophile microorganisms to identify the genomic correlates of hyperthermostability on a wider data set. Our new results confirm that the CvP-bias previously identified on a much smaller data set still holds. Moreover, we show that it is an optimal criterion, in the sense that it corresponds to the most discriminating factor between hyperthermophilic and mesothermophilic microorganisms in a principal component analysis. In parallel, we evaluated two other recently proposed correlates of hyperthermostability, the proteome average pI and the dinucleotide statistical index (We show that the CvP-bias is the sole criterion that is able to clearly discriminate hyperthermophile from mesothermophile microorganisms on a global genomic basis.
doi:10.1074/jbc.m301327200 pmid:12600994 fatcat:fpjv3lpmxrahzhukc2hxkce4za

Genetics of human metabolism: an update

Gabi Kastenmüller, Johannes Raffler, Christian Gieger, Karsten Suhre
2015 Human Molecular Genetics  
In particular, links to the Metabolomics GWAS server (http://www. gwas.eu) allow direct access to association data from the Suhre et al.  ...  et al. (9) Serum Non-targeted MS, knowns 276 + 37 179 ratios MS German, British 1768 + 1052 37 Suhre et al. (10) Urine and Plasma Urine: NMR peaks, plasma: mainly phospholipids Urine: 512  ... 
doi:10.1093/hmg/ddv263 pmid:26160913 pmcid:PMC4572003 fatcat:ndmq3hj52vf6bfozrf4zttk6m4

Metabolic dysregulations of cancer cells with metastatic potential [article]

Sara Abdul Kader, Shaima Dib, Iman W. Achka, Gaurav Thareja, Karsten Suhre, Arash Rafii, Anna Halama
2021 bioRxiv   pre-print
Metastasis is the primary cause of cancer related deaths due to the limited number of efficient druggable targets. Signatures of dysregulated cancer metabolism could serve as a roadmap for the determination of new treatment strategies, given their vital role in cancer cell responses to multiple challenges, including nutrient and oxygen availability. However, the metabolic signatures of metastatic cells remain vastly elusive. We conducted untargeted metabolic profiling of cells and growth media
more » ... f five selected triple negative breast cancer cell lines with high metastatic potential (HMP) (MDA-MB-231, MDA-MB-436, MDA-MB-468) and low metastatic potential (LMP) (BT549, HCC1143). We identified 92 metabolites in cells and 22 in growth medium that display significant differences between LMP and HMP. The HMP cell lines had elevated level of molecules involved in glycolysis, TCA cycle and lipid metabolism. We identified metabolic advantages of cell lines with HMP beyond enhanced glycolysis by pinpointing the role of branched chain amino acids (BCAA) catabolism as well as molecules supporting coagulation and platelet activation as important contributors to the metastatic cascade. The landscape of metabolic dysregulations, characterized in our study, could serve in the future as a roadmap for the identification of treatment strategies targeting cancer cells with enhanced metastatic potential.
doi:10.1101/2021.06.02.446725 fatcat:e3zfvibqergpdpg64f3c37elpm

Discovery of protein-coding palindromic repeats in Wolbachia

Hiroyuki Ogata, Karsten Suhre, Jean-Michel Claverie
2005 Trends in Microbiology  
doi:10.1016/j.tim.2005.03.013 pmid:15936655 fatcat:5lpxs2ueo5hqrflrmwu32623ny

MetFlexo: An Automated Simulation of Realistic H1-NMR Spectra

Zeinab Atieh, Karsten Suhre, Halima Bensmail
2013 Procedia Computer Science  
The development of the 'omics' technologies such as transcriptomics, proteomics and metabolomics has made it possible to realize some of the goals of systems biology, where biological systems are interrogated at different levels of biochemical activity (such as gene expression, protein activity and/or metabolite concentration). Metabolomics deals with the metabolome that represents the complete set of small-molecule metabolites. Even though metabolomics can be thought of as a relatively young
more » ... thod, it is nevertheless a rapidly growing one that has the potential to reveal the molecular mechanism of certain diseases. H1 nuclear magnetic resonance (NMR) spectroscopy is commonly used in the metabolic profiling of biofluids as it has the potential to detect all proton-containing metabolites. Metabolites in biofluids are in dynamic equilibrium with those in cells and tissues, so their metabolic profile reflects changes in the state of an organism due to disease or environmental effects. Results: MetFlexo is as an easy-to-use C package that allows the simulation of datasets of 1 H-NMR spectra in order to test data analysis techniques, hypotheses and experimental designs. The idea is based on transforming statistical parameters of metabolites (shifts, couplings, concentrations and magnetic field) to an NMR spectrum using chemical-physics theory. Our method helps in the deconvolution of NMR spectra and in a better determination of metabolite concentrations, as these concentrations are key in detecting diseases and abnormalities. Unlike others, this program generates NMR spectrum of biofluids with no limit on magnetic field or pH. Thus, our approach is able to produce complex NMR profiles with flexible conditions. It is also simple to implement in C, requires small storage, is easy to compute and uses an independent platform. It will be available in R and MATLAB soon. The algorithm is freely available upon request to the corresponding author.
doi:10.1016/j.procs.2013.05.305 fatcat:nzukipf2ofd7vbwm5h32jhjjti

PopPAnTe: population and pedigree association testing for quantitative data [article]

Alessia Visconti, Mashael Al-Shafai, Wadha A Al Muftah, Shaza B Zaghlool, Massimo Mangino, Karsten Suhre, Mario Falchi
2016 bioRxiv   pre-print
Family-based designs, from twin studies to isolated populations with their complex genealogical data, are a valuable resource for genetic studies of heritable molecular biomarkers. Existing software for family-based studies have mainly focused on facilitating association between response phenotypes and genetic markers, and no user-friendly tools are at present available to straightforwardly extend association studies in related samples to large datasets of generic quantitative data, as those
more » ... erated by current -omics technologies. We developed PopPAnTe, a user-friendly Java program, which evaluates the association of quantitative data in related samples. Additionally, PopPAnTe implements data pre and post processing, region based testing, and empirical assessment of associations. PopPAnTe is an integrated and flexible framework for pairwise association testing in related samples with a large number of predictors and response variables. It works either with family data of any size and complexity, or, when the genealogical information is unknown, it uses genetic similarity information between individuals as those inferred from genome-wide genetic data. It can therefore be particularly useful in facilitating usage of biobank data collections from population isolates when extensive genealogical information is missing.
doi:10.1101/084871 fatcat:duskp7auk5cwpfr3vxh7tt2kve

Determination of strongly overlapping signaling activity from microarray data

Ghislain Bidaut, Karsten Suhre, Jean-Michel Claverie, Michael F Ochs
2006 BMC Bioinformatics  
As numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology.
more » ... ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups. Here we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly coupled signaling pathways and, in some cases, activity of specific signaling proteins. We demonstrate the technique using the Rosetta yeast deletion mutant data set, decompositional analysis by Bayesian Decomposition, and annotation analysis using ClutrFree. We determined from measurements of gene persistence in patterns across multiple potential dimensionalities that 15 basis vectors provides the correct dimensionality for interpreting the data. Using gene ontology and data on gene regulation in the Saccharomyces Genome Database, we identified the transcriptional signatures of several cellular processes in yeast, including cell wall creation, ribosomal disruption, chemical blocking of protein synthesis, and, critically, individual signatures of the strongly coupled mating and filamentation pathways. This works demonstrates that microarray data can provide downstream indicators of pathway activity either through use of gene ontology or transcription factor databases. This can be used to investigate the specificity and success of targeted therapeutics as well as to elucidate signaling activity in normal and disease processes.
doi:10.1186/1471-2105-7-99 pmid:16507110 pmcid:PMC1413561 fatcat:6z37c666jzhs7pbww3qg4goz4q

Variational autoencoders learn universal latent representations of metabolomics data [article]

Daniel P. Gomari, Annalise Schweickart, Leandro Cerchietti, Elisabeth Paietta, Hugo Fernandez, Hassen Al-Amin, Karsten Suhre, Jan Krumsiek
2021 bioRxiv   pre-print
Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. Here, we trained a VAE on a large-scale metabolomics population cohort of human
more » ... d samples consisting of over 4,500 individuals. We analyzed the pathway composition of the latent space using a global feature importance score, which showed that latent dimensions represent distinct cellular processes. To demonstrate model generalizability, we generated latent representations of unseen metabolomics datasets on type 2 diabetes, schizophrenia, and acute myeloid leukemia and found significant correlations with clinical patient groups. Taken together, we demonstrate for the first time that the VAE is a powerful method that learns biologically meaningful, nonlinear, and universal latent representations of metabolomics data.
doi:10.1101/2021.01.14.426721 fatcat:2454xc7wovck7oyz7uaod27fia

MoDentify: a tool for phenotype-driven module identification in multilevel metabolomics networks [article]

Kieu Trinh Do, David J.N.-P. Rasp, Gabi Kastenmueller, Karsten Suhre, Jan Krumsiek
2018 bioRxiv   pre-print
Summary: Metabolomics is an established tool to gain insights into (patho)physiological outcomes. Associations of metabolism with such outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites, which is an aspect that has not been addressed by previous methods. Here we present MoDentify, a freely available R package
more » ... o identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct visualization of results as interactive networks in Cytoscape. We present an application example using a complex, multifluid metabolomics dataset. Owing to its generic character, the method is widely applicable to any dataset with a phenotype variable, a data matrix, and optional pathway annotations. Availability and Implementation: MoDentify is freely available from GitHub: https://github.com/krumsiek/MoDentify. The package vignette contains a detailed tutorial of the analysis workflow. Contact: jan.krumsiek@helmholtz-muenchen.de
doi:10.1101/275057 fatcat:tw5bpozwxnhf7pzw4cmgmil734

Mimivirus and the emerging concept of "giant" virus

Jean-Michel Claverie, Hiroyuki Ogata, Stéphane Audic, Chantal Abergel, Karsten Suhre, Pierre-Edouard Fournier
2006 Virus Research  
On the contrary, Mimivirus exhibits some large families of paralogues originating from relatively recent multiplication/duplication events (Suhre, 2005) .  ...  indicates that they are not the result of a burst multiplication of the same ancestral gene, but that they were derived from distinct duplication events, and evolved independently following their creation (Suhre  ... 
doi:10.1016/j.virusres.2006.01.008 pmid:16469402 fatcat:fuyipzukebennax6f7ehrmoi6q

Connecting the epigenome, metabolome and proteome for a deeper understanding of disease

Karsten Suhre, Shaza Zaghlool
2021 Journal of Internal Medicine  
The Suhre Lab is presently also supported by Qatar National Research Fund (QNRF) grants NPRP11C-0115-180010, NPRP11S-0104-180191 and NPRP12S-0205-190042.  ...  Suhre and S.  ...  Suhre and S.  ... 
doi:10.1111/joim.13306 pmid:33904619 fatcat:4qiewtgix5apdovk5nbgaubs7i
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