TY  - JOUR
AB  - Spatial proteomics offers unprecedented insights into the localization, quantity, and interactions of proteins within cells and tissues, thereby enabling researchers and clinicians to map protein networks with high precision. This powerful approach is currently revolutionizing our understanding of cellular organization and function (Method of the Year 2024: spatial proteomics, 2024), and providing deeper insights into how protein distribution and dynamics contribute to physiological and pathological processes. Here, we discuss recent advances in targeted and untargeted (exploratory) spatial proteomics, shed light on emerging multiscale approaches for tissue profiling, and highlight their translational potential.
AU  - Horvath, P.
AU  - Coscia, F.*
C1  - 74095
C2  - 57310
CY  - Campus, 4 Crinan St, London, N1 9xw, England
SP  - 526 - 530
TI  - Spatial proteomics in translational and clinical research.
JO  - Mol. Syst. Biol.
VL  - 21
PB  - Springernature
PY  - 2025
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Proteomic techniques now measure thousands of proteins circulating in blood at population scale, but successful translation into clinically useful protein biomarkers is hampered by our limited understanding of their origins. Here, we use machine learning to systematically identify a median of 20 factors (range: 1-37) out of >1800 participant and sample charateristics that jointly explained an average of 19.4% (max. 100.0%) of the variance in plasma levels of ~3000 protein targets among 43,240 individuals. Proteins segregated into distinct clusters according to their explanatory factors, with modifiable characteristics explaining more variance compared to genetic variation (median: 10.0% vs 3.9%), and factors being largely consistent across the sexes and ancestral groups. We establish a knowledge graph that integrates our findings with genetic studies and drug characteristics to guide identification of potential drug target engagement markers. We demonstrate the value of our resource by identifying disease-specific biomarkers, like matrix metalloproteinase 12 for abdominal aortic aneurysm, and by developing a widely applicable framework for phenotype enrichment (R package: https://github.com/comp-med/r-prodente ). All results are explorable via an interactive web portal ( https://omicscience.org/apps/prot_foundation ).
AU  - Pietzner, M.*
AU  - Beuchel, C.*
AU  - Demircan, K.*
AU  - Hoffmann Anton, J.*
AU  - Zeng, W.*
AU  - Römisch-Margl, W.
AU  - Yasmeen, S.*
AU  - Uluvar, B.*
AU  - Zoodsma, M.*
AU  - Koprulu, M.*
AU  - Kastenmüller, G.
AU  - Carrasco-Zanini, J.*
AU  - Langenberg, C.*
C1  - 75747
C2  - 58123
CY  - Campus, 4 Crinan St, London, N1 9xw, England
TI  - Machine learning-guided deconvolution of plasma protein levels.
JO  - Mol. Syst. Biol.
PB  - Springernature
PY  - 2025
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Many studies have used single-cell RNA sequencing (scRNA-seq) to infer gene regulatory networks (GRNs), which are crucial for understanding complex cellular regulation. However, the inherent noise and sparsity of scRNA-seq data present significant challenges to accurate GRN inference. This review explores one promising approach that has been proposed to address these challenges: integrating prior knowledge into the inference process to enhance the reliability of the inferred networks. We categorize common types of prior knowledge, such as experimental data and curated databases, and discuss methods for representing priors, particularly through graph structures. In addition, we classify recent GRN inference algorithms based on their ability to incorporate these priors and assess their performance in different contexts. Finally, we propose a standardized benchmarking framework to evaluate algorithms more fairly, ensuring biologically meaningful comparisons. This review provides guidance for researchers selecting GRN inference methods and offers insights for developers looking to improve current approaches and foster innovation in the field.
AU  - Stock, M.
AU  - Losert, C.
AU  - Zambon, M.
AU  - Popp, N.
AU  - Lubatti, G.
AU  - Hörmanseder, E.
AU  - Heinig, M.
AU  - Scialdone, A.
C1  - 73382
C2  - 57023
CY  - Campus, 4 Crinan St, London, N1 9xw, England
SP  - 214-230
TI  - Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.
JO  - Mol. Syst. Biol.
VL  - 21
IS  - 3
PB  - Springernature
PY  - 2025
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Correlation is not causation: this simple and uncontroversial statement has far-reaching implications. Defining and applying causality in biomedical research has posed significant challenges to the scientific community. In this perspective, we attempt to connect the partly disparate fields of systems biology, causal reasoning, and machine learning to inform future approaches in the field of systems biology and molecular medicine.
AU  - Lobentanzer, S.*
AU  - Rodriguez-Mier, P.*
AU  - Bauer, S.
AU  - Saez-Rodriguez, J.*
C1  - 70868
C2  - 55777
CY  - Campus, 4 Crinan St, London, N1 9xw, England
TI  - Molecular causality in the advent of foundation models.
JO  - Mol. Syst. Biol.
PB  - Springernature
PY  - 2024
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here, we dissected the diversity of neural stem cells in the adult zebrafish brain, an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single-cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze the regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells, with some subtypes being restricted to a single brain region, while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation, with different states being involved in proliferative and non-proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for the functional manipulation of neural stem cell subtypes.
AU  - Mitic, N.*
AU  - Neuschulz, A.*
AU  - Spanjaard, B.*
AU  - Schneider, J.
AU  - Fresmann, N.*
AU  - Novoselc, K.T.
AU  - Strunk, T.*
AU  - Münster, L.*
AU  - Olivares-Chauvet, P.*
AU  - Ninkovic, J.
AU  - Junker, J.P.*
C1  - 69974
C2  - 55345
CY  - Campus, 4 Crinan St, London, N1 9xw, England
SP  - 321-337
TI  - Dissecting the spatiotemporal diversity of adult neural stem cells.
JO  - Mol. Syst. Biol.
VL  - 20
IS  - 4
PB  - Springernature
PY  - 2024
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Codon optimality is a major determinant of mRNA translation and degradation rates. However, whether and through which mechanisms its effects are regulated remains poorly understood. Here we show that codon optimality associates with up to 2-fold change in mRNA stability variations between human tissues, and that its effect is attenuated in tissues with high energy metabolism and amplifies with age. Mathematical modeling and perturbation data through oxygen deprivation and ATP synthesis inhibition reveal that cellular energy variations non-uniformly alter the effect of codon usage. This new mode of codon effect regulation, independent of tRNA regulation, provides a fundamental mechanistic link between cellular energy metabolism and eukaryotic gene expression.
AU  - Tomaz da Silva, P.*
AU  - Zhang, Y.*
AU  - Theodorakis, E.*
AU  - Martens, L.D.
AU  - Yépez, V.A.*
AU  - Pelechano, V.*
AU  - Gagneur, J.
C1  - 70238
C2  - 55454
CY  - Campus, 4 Crinan St, London, N1 9xw, England
SP  - 506-520
TI  - Cellular energy regulates mRNA degradation in a codon-specific manner.
JO  - Mol. Syst. Biol.
VL  - 20
IS  - 5
PB  - Springernature
PY  - 2024
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.
AU  - Lotfollahi, M.
AU  - Klimovskaia Susmelj, A.*
AU  - De Donno, C.
AU  - Hetzel, L.
AU  - Ji, Y.
AU  - Ibarra Del Rio, I.A.
AU  - Srivatsan, S.R.*
AU  - Naghipourfar, M.*
AU  - Daza, R.M.*
AU  - Martin, B.*
AU  - Shendure, J.*
AU  - McFaline-Figueroa, J.L.*
AU  - Boyeau, P.*
AU  - Wolf, F.A.
AU  - Yakubova, N.*
AU  - Günnemann, S.*
AU  - Trapnell, C.*
AU  - Lopez-Paz, D.*
AU  - Theis, F.J.
C1  - 67764
C2  - 54242
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - Predicting cellular responses to complex perturbations in high-throughput screens.
JO  - Mol. Syst. Biol.
VL  - 19
IS  - 6
PB  - Wiley
PY  - 2023
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - In this Editorial, our Chief Editor and members of our Advisory Editorial Board discuss recent breakthroughs, current challenges, and emerging opportunities in single-cell biology and share their vision of "where the field is headed."
AU  - Polychronidou, M.*
AU  - Hou, J.*
AU  - Babu, M.M.*
AU  - Liberali, P.*
AU  - Amit, I.*
AU  - Deplancke, B.*
AU  - Lahav, G.*
AU  - Itzkovitz, S.*
AU  - Mann, M.*
AU  - Saez-Rodriguez, J.*
AU  - Theis, F.J.
AU  - Eils, R.*
C1  - 68476
C2  - 54692
TI  - Single-cell biology: What does the future hold?
JO  - Mol. Syst. Biol.
VL  - 19
IS  - 7
PY  - 2023
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.
AU  - Thielert, M.*
AU  - Itang, E.C.*
AU  - Ammar, C.*
AU  - Rosenberger, F.A.*
AU  - Bludau, I.*
AU  - Schweizer, L.*
AU  - Nordmann, T.M.*
AU  - Skowronek, P.*
AU  - Wahle, M.*
AU  - Zeng, W.F.*
AU  - Zhou, X.X.*
AU  - Brunner, A.D.*
AU  - Richter, S.
AU  - Levesque, M.P.*
AU  - Theis, F.J.
AU  - Steger, M.*
AU  - Mann, M.*
C1  - 67980
C2  - 54458
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel.
JO  - Mol. Syst. Biol.
VL  - 19
IS  - 9
PB  - Wiley
PY  - 2023
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - The ongoing degradation of natural systems and other environmental changes has put our society at a crossroad with respect to our future relationship with our planet. While the concept of One Health describes how human health is inextricably linked with environmental health, many of these complex interdependencies are still not well-understood. Here, we describe how the advent of real-time genomic analyses can benefit One Health and how it can enable timely, in-depth ecosystem health assessments. We introduce nanopore sequencing as the only disruptive technology that currently allows for real-time genomic analyses and that is already being used worldwide to improve the accessibility and versatility of genomic sequencing. We showcase real-time genomic studies on zoonotic disease, food security, environmental microbiome, emerging pathogens, and their antimicrobial resistances, and on environmental health itself – from genomic resource creation for wildlife conservation to the monitoring of biodiversity, invasive species, and wildlife trafficking. We stress why equitable access to real-time genomics in the context of One Health will be paramount and discuss related practical, legal, and ethical limitations.
AU  - Urban, L.
AU  - Perlas Puente,A.
AU  - Francino, O.*
AU  - Martí-Carreras, J.*
AU  - Muga, B.A.*
AU  - Mwangi, J.W.*
AU  - Boykin Okalebo, L.*
AU  - Stanton, J.A.L.*
AU  - Black, A.*
AU  - Waipara, N.*
AU  - Fontsere, C.*
AU  - Eccles, D.*
AU  - Urel, H.
AU  - Reska, T.T.M.
AU  - Morales, H.E.*
AU  - Palmada-Flores, M.*
AU  - Marques-Bonet, T.*
AU  - Watsa, M.*
AU  - Libke, Z.*
AU  - Erkenswick, G.*
AU  - van Oosterhout, C.*
C1  - 68515
C2  - 54686
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - Real-time genomics for One Health.
JO  - Mol. Syst. Biol.
VL  - 19
IS  - 8
PB  - Wiley
PY  - 2023
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10-fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable-core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease.
AU  - Brunner, A.D.*
AU  - Thielert, M.*
AU  - Vasilopoulou, C.G.*
AU  - Ammar, C.*
AU  - Coscia, F.*
AU  - Mund, A.*
AU  - Hoerning, O.B.*
AU  - Bache, N.*
AU  - Apalategui, A.*
AU  - Lubeck, M.*
AU  - Richter, S.
AU  - Fischer, D.S.
AU  - Raether, O.*
AU  - Park, M.A.*
AU  - Meier, F.*
AU  - Theis, F.J.
AU  - Mann, M.*
C1  - 64499
C2  - 51953
TI  - Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation.
JO  - Mol. Syst. Biol.
VL  - 18
IS  - 3
PY  - 2022
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Despite the therapeutic promise of direct reprogramming, basic principles concerning fate erasure and the mechanisms to resolve cell identity conflicts remain unclear. To tackle these fundamental questions, we established a single-cell protocol for the simultaneous analysis of multiple cell fate conversion events based on combinatorial and traceable reprogramming factor expression: Collide-seq. Collide-seq revealed the lack of a common mechanism through which fibroblast-specific gene expression loss is initiated. Moreover, we found that the transcriptome of converting cells abruptly changes when a critical level of each reprogramming factor is attained, with higher or lower levels not contributing to major changes. By simultaneously inducing multiple competing reprogramming factors, we also found a deterministic system, in which titration of fates against each other yields dominant or colliding fates. By investigating one collision in detail, we show that reprogramming factors can disturb cell identity programs independent of their ability to bind their target genes. Taken together, Collide-seq has shed light on several fundamental principles of fate conversion that may aid in improving current reprogramming paradigms.
AU  - Hersbach, B.A.
AU  - Fischer, D.S.
AU  - Masserdotti, G.
AU  - Deeksha
AU  - Mojžišová, K.
AU  - Waltzhöni, T.
AU  - Rodriguez-Terro, D.
AU  - Heinig, M.
AU  - Theis, F.J.
AU  - Götz, M.
AU  - Stricker, S.H.
C1  - 66226
C2  - 52947
TI  - Probing cell identity hierarchies by fate titration and collision during direct reprogramming.
JO  - Mol. Syst. Biol.
VL  - 18
IS  - 9
PY  - 2022
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Neuronal stimulation induced by the brain-derived neurotrophic factor (BDNF) triggers gene expression, which is crucial for neuronal survival, differentiation, synaptic plasticity, memory formation, and neurocognitive health. However, its role in chromatin regulation is unclear. Here, using temporal profiling of chromatin accessibility and transcription in mouse primary cortical neurons upon either BDNF stimulation or depolarization (KCl), we identify features that define BDNF-specific chromatin-to-gene expression programs. Enhancer activation is an early event in the regulatory control of BDNF-treated neurons, where the bZIP motif-binding Fos protein pioneered chromatin opening and cooperated with co-regulatory transcription factors (Homeobox, EGRs, and CTCF) to induce transcription. Deleting cis-regulatory sequences affect BDNF-mediated Arc expression, a regulator of synaptic plasticity. BDNF-induced accessible regions are linked to preferential exon usage by neurodevelopmental disorder-related genes and the heritability of neuronal complex traits, which were validated in human iPSC-derived neurons. Thus, we provide a comprehensive view of BDNF-mediated genome regulatory features using comparative genomic approaches to dissect mammalian neuronal stimulation.
AU  - Ibarra Del Rio, I.A.
AU  - Ratnu, V.S.*
AU  - Gordillo, L.*
AU  - Hwang, I.Y.*
AU  - Mariani, L.*
AU  - Weinand, K.*
AU  - Hammarén, H.M.*
AU  - Heck, J.*
AU  - Bulyk, M.L.*
AU  - Savitski, M.M.*
AU  - Zaugg, J.B.*
AU  - Noh, K.M.*
C1  - 65949
C2  - 52997
TI  - Comparative chromatin accessibility upon BDNF stimulation delineates neuronal regulatory elements.
JO  - Mol. Syst. Biol.
VL  - 18
IS  - 8
PY  - 2022
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - RNA velocity has enabled the recovery of directed dynamic information from single-cell transcriptomics by connecting measurements to the underlying kinetics of gene expression. This approach has opened up new ways of studying cellular dynamics. Here, we review the current state of RNA velocity modeling approaches, discuss various examples illustrating limitations and potential pitfalls, and provide guidance on how the ensuing challenges may be addressed. We then outline future directions on how to generalize the concept of RNA velocity to a wider variety of biological systems and modalities.
AU  - Bergen, V.
AU  - Soldatov, R.A.*
AU  - Kharchenko, P.V.*
AU  - Theis, F.J.
C1  - 62876
C2  - 51142
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - RNA velocity-current challenges and future perspectives.
JO  - Mol. Syst. Biol.
VL  - 17
IS  - 8
PB  - Wiley
PY  - 2021
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Histological analysis of biological tissues by mechanical sectioning is significantly time-consuming and error-prone due to loss of important information during sample slicing. In the recent years, the development of tissue clearing methods overcame several of these limitations and allowed exploring intact biological specimens by rendering tissues transparent and subsequently imaging them by laser scanning fluorescence microscopy. In this review, we provide a guide for scientists who would like to perform a clearing protocol from scratch without any prior knowledge, with an emphasis on DISCO clearing protocols, which have been widely used not only due to their robustness, but also owing to their relatively straightforward application. We discuss diverse tissue-clearing options and propose solutions for several possible pitfalls. Moreover, after surveying more than 30 researchers that employ tissue clearing techniques in their laboratories, we compiled the most frequently encountered issues and propose solutions. Overall, this review offers an informative and detailed guide through the growing literature of tissue clearing and can help with finding the easiest way for hands-on implementation.
AU  - Molbay, M.
AU  - Kolabas, Z.I.
AU  - Todorov, M.I.
AU  - Ohn, T.-L.
AU  - Ertürk, A.
C1  - 61681
C2  - 50390
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - A guidebook for DISCO tissue clearing.
JO  - Mol. Syst. Biol.
VL  - 17
IS  - 3
PB  - Wiley
PY  - 2021
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
AU  - Ostaszewski, M.*
AU  - Niarakis, A.*
AU  - Mazein, A.*
AU  - Kuperstein, I.*
AU  - Phair, R.*
AU  - Orta-Resendiz, A.*
AU  - Singh, V.*
AU  - Aghamiri, S.S.*
AU  - Acencio, M.L.*
AU  - Glaab, E.*
AU  - Ruepp, A.
AU  - Fobo, G.
AU  - Montrone, C.
AU  - Brauner, B.
AU  - Frishman, G.
AU  - Monraz Gómez, L.C.*
AU  - Somers, J.*
AU  - Hoch, M.*
AU  - Kumar Gupta, S.*
AU  - Scheel, J.*
AU  - Borlinghaus, H.*
AU  - Czauderna, T.*
AU  - Schreiber, F.*
AU  - Montagud, A.*
AU  - Ponce de Leon, M.*
AU  - Funahashi, A.*
AU  - Hiki, Y.*
AU  - Hiroi, N.*
AU  - Yamada, T.G.*
AU  - Dräger, A.*
AU  - Renz, A.*
AU  - Naveez, M.*
AU  - Bocskei, Z.*
AU  - Messina, F.*
AU  - Börnigen, D.*
AU  - Fergusson, L.*
AU  - Conti, M.*
AU  - Rameil, M.*
AU  - Nakonecnij, V.*
AU  - Vanhoefer, J.*
AU  - Schmiester, L.*
AU  - Wang, M.*
AU  - Ackerman, E.E.*
AU  - Shoemaker, J.E.*
AU  - Zucker, J.*
AU  - Oxford, K.*
AU  - Teuton, J.*
AU  - Kocakaya, E.*
AU  - Summak, G.Y.*
AU  - Hanspers, K.*
AU  - Kutmon, M.*
AU  - Coort, S.*
AU  - Eijssen, L.*
AU  - Ehrhart, F.*
AU  - Rex, D.A.B.*
AU  - Slenter, D.*
AU  - Martens, M.*
AU  - Pham, N.*
AU  - Haw, R.*
AU  - Jassal, B.*
AU  - Matthews, L.*
AU  - Orlic-Milacic, M.*
AU  - Senff Ribeiro, A.*
AU  - Rothfels, K.*
AU  - Shamovsky, V.*
AU  - Stephan, R.*
AU  - Sevilla, C.*
AU  - Varusai, T.*
AU  - Ravel, J.M.*
AU  - Fraser, R.*
AU  - Ortseifen, V.*
AU  - Marchesi, S.*
AU  - Gawron, P.*
AU  - Smula, E.*
AU  - Heirendt, L.*
AU  - Satagopam, V.P.*
AU  - Wu, G.*
AU  - Riutta, A.*
AU  - Golebiewski, M.*
AU  - Owen, S.*
AU  - Goble, C.*
AU  - Hu, X.*
AU  - Overall, R.W.*
AU  - Maier, D.*
AU  - Bauch, A.*
AU  - Gyori, B.M.*
AU  - Bachman, J.A.*
AU  - Vega, C.*
AU  - Grouès, V.*
AU  - Vázquez, M.J.*
AU  - Porras, P.*
AU  - Licata, L.*
AU  - Iannuccelli, M.*
AU  - Sacco, F.*
AU  - Nesterova, A.*
AU  - Yuryev, A.*
AU  - de Waard, A.*
AU  - Turei, D.*
AU  - Luna, A.*
AU  - Babur, O.*
AU  - Soliman, S.*
AU  - Valdeolivas, A.*
AU  - Esteban-Medina, M.*
AU  - Peña-Chilet, M.*
AU  - Rian, K.*
AU  - Helikar, T.*
AU  - Lal Puniya, B.*
AU  - Módos, D.*
AU  - Treveil, A.*
AU  - Olbei, M.*
AU  - De Meulder, B.*
AU  - Ballereau, S.*
AU  - Dugourd, A.*
AU  - Naldi, A.*
AU  - Noël, V.*
AU  - Calzone, L.*
AU  - Sander, C.*
AU  - Demir, E.*
AU  - Korcsmáros, T.*
AU  - Freeman, T.C.*
AU  - Augé, F.*
AU  - Beckmann, J.S.*
AU  - Hasenauer, J.
AU  - Wolkenhauer, O.*
AU  - Wilighagen, E.L.*
AU  - Pico, A.R.*
AU  - Evelo, C.T.*
AU  - Gillespie, M.E.*
AU  - Stein, L.D.*
AU  - Hermjakob, H.*
AU  - D'Eustachio, P.*
AU  - Saez-Rodriguez, J.*
AU  - Dopazo, J.*
AU  - Valencia, A.*
AU  - Kitano, H.*
AU  - Barillot, E.*
AU  - Auffray, C.*
AU  - Balling, R.*
AU  - Schneider, R.*
C1  - 63392
C2  - 51418
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms.
JO  - Mol. Syst. Biol.
VL  - 17
IS  - 10
PB  - Wiley
PY  - 2021
SN  - 1744-4292
ER  - 

TY  - JOUR
AU  - Ostaszewski, M.*
AU  - Niarakis, A.*
AU  - Mazein, A.*
AU  - Kuperstein, I.*
AU  - Phair, R.*
AU  - Orta-Resendiz, A.*
AU  - Singh, V.*
AU  - Aghamiri, S.S.*
AU  - Acencio, M.L.*
AU  - Glaab, E.*
AU  - Ruepp, A.
AU  - Fobo, G.
AU  - Montrone, C.
AU  - Brauner, B.
AU  - Frishman, G.
AU  - Monraz Gómez, L.C.*
AU  - Somers, J.*
AU  - Hoch, M.*
AU  - Kumar Gupta, S.*
AU  - Scheel, J.*
AU  - Borlinghaus, H.*
AU  - Czauderna, T.*
AU  - Schreiber, F.*
AU  - Montagud, A.*
AU  - Ponce de Leon, M.*
AU  - Funahashi, A.*
AU  - Hiki, Y.*
AU  - Hiroi, N.*
AU  - Yamada, T.G.*
AU  - Dräger, A.*
AU  - Renz, A.*
AU  - Naveez, M.*
AU  - Bocskei, Z.*
AU  - Messina, F.*
AU  - Börnigen, D.*
AU  - Fergusson, L.*
AU  - Conti, M.*
AU  - Rameil, M.*
AU  - Nakonecnij, V.*
AU  - Vanhoefer, J.*
AU  - Schmiester, L.
AU  - Wang, M.*
AU  - Ackerman, E.E.*
AU  - Shoemaker, J.E.*
AU  - Zucker, J.*
AU  - Oxford, K.*
AU  - Teuton, J.*
AU  - Kocakaya, E.*
AU  - Summak, G.Y.*
AU  - Hanspers, K.*
AU  - Kutmon, M.*
AU  - Coort, S.*
AU  - Eijssen, L.*
AU  - Ehrhart, F.*
AU  - Rex, D.A.B.*
AU  - Slenter, D.*
AU  - Martens, M.*
AU  - Pham, N.*
AU  - Haw, R.*
AU  - Jassal, B.*
AU  - Matthews, L.*
AU  - Orlic-Milacic, M.*
AU  - Senff-Ribeiro, A.*
AU  - Rothfels, K.*
AU  - Shamovsky, V.*
AU  - Stephan, R.*
AU  - Sevilla, C.*
AU  - Varusai, T.*
AU  - Ravel, J.*
AU  - Fraser, R.*
AU  - Ortseifen, V.*
AU  - Marchesi, S.*
AU  - Gawron, P.*
AU  - Smula, E.*
AU  - Heirendt, L.*
AU  - Satagopam, V.*
AU  - Wu, G.*
AU  - Riutta, A.*
AU  - Golebiewski, M.*
AU  - Owen, S.*
AU  - Goble, C.*
AU  - Hu, X.*
AU  - Overall, R.W.*
AU  - Maier, D.*
AU  - Bauch, A.*
AU  - Gyori, B.M.*
AU  - Bachman, J.A.*
AU  - Vega, C.*
AU  - Grouès, V.*
AU  - Vázquez, M.J.*
AU  - Porras, P.*
AU  - Licata, L.*
AU  - Iannuccelli, M.*
AU  - Sacco, F.*
AU  - Nesterova, A.*
AU  - Yuryev, A.*
AU  - de Waard, A.*
AU  - Türei, D.*
AU  - Luna, A.*
AU  - Babur, O.*
AU  - Soliman, S.*
AU  - Valdeolivas, A.*
AU  - Esteban-Medina, M.*
AU  - Peña-Chilet, M.*
AU  - Rian, K.*
AU  - Helikar, T.*
AU  - Puniya, B.L.*
AU  - Módos, D.*
AU  - Treveil, A.*
AU  - Ölbei, M.*
AU  - De Meulder, B.*
AU  - Ballereau, S.*
AU  - Dugourd, A.*
AU  - Naldi, A.*
AU  - Noël, V.*
AU  - Calzone, L.*
AU  - Sander, C.*
AU  - Demir, E.*
AU  - Korcsmáros, T.*
AU  - Freeman, T.C.*
AU  - Augé, F.*
AU  - Beckmann, J.S.*
AU  - Hasenauer, J.
AU  - Wolkenhauer, O.*
AU  - Willighagen, E.L.*
AU  - Pico, A.R.*
AU  - Evelo, C.T.*
AU  - Gillespie, M.E.*
AU  - Stein, L.D.*
AU  - Hermjakob, H.*
AU  - D'Eustachio, P.*
AU  - Saez-Rodriguez, J.*
AU  - Dopazo, J.*
AU  - Valencia, A.*
AU  - Kitano, H.*
AU  - Barillot, E.*
AU  - Auffray, C.*
AU  - Balling, R.*
AU  - Schneider, R.*
C1  - 63941
C2  - 52013
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - COVID-19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms (vol 17, e10387, 2021).
JO  - Mol. Syst. Biol.
VL  - 17
IS  - 12
PB  - Wiley
PY  - 2021
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter- and intracellular signaling, as well as transcriptional and post-transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath's web service (https://omnipathdb.org/), a Cytoscape plug-in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell-cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications studying SARS-CoV-2 infection and ulcerative colitis.
AU  - Türei, D.*
AU  - Valdeolivas, A.*
AU  - Gul, L.*
AU  - Palacio-Escat, N.*
AU  - Klein, M.
AU  - Ivanova, O.*
AU  - Ölbei, M.*
AU  - Gábor, A.*
AU  - Theis, F.J.
AU  - Módos, D.*
AU  - Korcsmáros, T.*
AU  - Saez-Rodriguez, J.*
C1  - 61700
C2  - 50402
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - Integrated intra- and intercellular signaling knowledge for multicellular omics analysis.
JO  - Mol. Syst. Biol.
VL  - 17
IS  - 3
PB  - Wiley
PY  - 2021
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - It has recently become possible to simultaneously assay T-cell specificity with respect to large sets of antigens and the T-cell receptor sequence in high-throughput single-cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T-cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell-to-dextramer binding strength and receptor-to-pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single-cell RNA-seq studies on T cells without the need for MHC staining.
AU  - Fischer, D.S.
AU  - Wu, Y.
AU  - Schubert, B.
AU  - Theis, F.J.
C1  - 59895
C2  - 49105
TI  - Predicting antigen specificity of single T cells based on TCR CDR3 regions.
JO  - Mol. Syst. Biol.
VL  - 16
IS  - 8
PY  - 2020
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Despite their importance in determining protein abundance, a comprehensive catalogue of sequence features controlling protein-to-mRNA (PTR) ratios and a quantification of their effects are still lacking. Here, we quantified PTR ratios for 11,575 proteins across 29 human tissues using matched transcriptomes and proteomes. We estimated by regression the contribution of known sequence determinants of protein synthesis and degradation in addition to 45 mRNA and 3 protein sequence motifs that we found by association testing. While PTR ratios span more than 2 orders of magnitude, our integrative model predicts PTR ratios at a median precision of 3.2-fold. A reporter assay provided functional support for two novel UTR motifs, and an immobilized mRNA affinity competition-binding assay identified motif-specific bound proteins for one motif. Moreover, our integrative model led to a new metric of codon optimality that captures the effects of codon frequency on protein synthesis and degradation. Altogether, this study shows that a large fraction of PTR ratio variation in human tissues can be predicted from sequence, and it identifies many new candidate post-transcriptional regulatory elements.
AU  - Eraslan, B.*
AU  - Wang, D.*
AU  - Gusic, M.
AU  - Prokisch, H.
AU  - Hallström, B.M.*
AU  - Uhlén, M.*
AU  - Asplund, A.*
AU  - Pontén, F.*
AU  - Wieland, T.*
AU  - Hopf, T.*
AU  - Hahne, H.*
AU  - Kuster, B.*
AU  - Gagneur, J.*
C1  - 55577
C2  - 46423
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - Quantification and discovery of sequence determinants of protein-per-mRNA amount in 29 human tissues.
JO  - Mol. Syst. Biol.
VL  - 15
IS  - 2
PB  - Wiley
PY  - 2019
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at . This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.
AU  - Luecken, M.
AU  - Theis, F.J.
C1  - 56374
C2  - 47050
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - Current best practices in single-cell RNA-seq analysis: A tutorial.
JO  - Mol. Syst. Biol.
VL  - 15
IS  - 6
PB  - Wiley
PY  - 2019
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Non-alcoholic fatty liver disease (NAFLD) affects 25% of the population and can progress to cirrhosis with limited treatment options. As the liver secretes most of the blood plasma proteins, liver disease may affect the plasma proteome. Plasma proteome profiling of 48 patients with and without cirrhosis or NAFLD revealed six statistically significantly changing proteins (ALDOB, APOM, LGALS3BP, PIGR, VTN, and AFM), two of which are already linked to liver disease. Polymeric immunoglobulin receptor (PICR) was significantly elevated in both cohorts by 170% in NAFLD and 298% in cirrhosis and was further validated in mouse models. Furthermore, a global correlation map of clinical and proteomic data strongly associated DPP4, ANPEP, TGFBI, PIGR, and APOE with NAFLD and cirrhosis. The prominent diabetic drug target DPP4 is an aminopeptidase like ANPEP, ENPEP, and LAP3, all of which are up-regulated in the human or mouse data. Furthermore, ANPEP and TGFBI have potential roles in extracellular matrix remodeling in fibrosis. Thus, plasma proteome profiling can identify potential biomarkers and drug targets in liver disease.
AU  - Niu, L.*
AU  - Geyer, P.E.*
AU  - Wewer Albrechtsen, N.J.*
AU  - Gluud, L.L.*
AU  - Santos, A.*
AU  - Doll, S.*
AU  - Treit, P.V.*
AU  - Holst, J.J.*
AU  - Knop, F.K.*
AU  - Vilsbøll, T.*
AU  - Junker, A.*
AU  - Sachs, S.
AU  - Stemmer, K.
AU  - Müller, T.D.
AU  - Tschöp, M.H.
AU  - Hofmann, S.M.
AU  - Mann, M.*
C1  - 55606
C2  - 46425
CY  - 111 River St, Hoboken 07030-5774, Nj Usa
TI  - Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease.
JO  - Mol. Syst. Biol.
VL  - 15
IS  - 3
PB  - Wiley
PY  - 2019
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Multi‐omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi‐Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi‐omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy‐chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single‐cell multi‐omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.
AU  - Argelaguet, R.*
AU  - Velten, B.*
AU  - Arnol, D.*
AU  - Dietrich, S.*
AU  - Zenz, T.*
AU  - Marioni, J.C.*
AU  - Buettner, F.
AU  - Huber, W.*
AU  - Stegle, O.*
C1  - 53911
C2  - 45094
CY  - Po Box 211, 1000 Ae Amsterdam, Netherlands
TI  - Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.
JO  - Mol. Syst. Biol.
VL  - 14
IS  - 6
PB  - Elsevier Science Bv
PY  - 2018
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - High-throughput -omics techniques have revolutionised biology, allowing for thorough and unbiased characterisation of the molecular states of biological systems. However, cellular decision-making is inherently a unicellular process to which “bulk” -omics techniques are poorly suited, as they capture ensemble averages of cell states. Recently developed single-cell methods bridge this gap, allowing high-throughput molecular surveys of individual cells. In this review, we cover core concepts of analysis of single-cell gene expression data and highlight areas of developmental biology where single-cell techniques have made important contributions. These include understanding of cell-to-cell heterogeneity, the tracing of differentiation pathways, quantification of gene expression from specific alleles, and the future directions of cell lineage tracing and spatial gene expression analysis.
AU  - Griffiths, J.A.*
AU  - Scialdone, A.
AU  - Marioni, J.C.*
C1  - 53823
C2  - 45062
CY  - 233 Spring St, New York, Ny 10013 Usa
TI  - Using single-cell genomics to understand developmental processes and cell fate decisions.
JO  - Mol. Syst. Biol.
VL  - 14
IS  - 4
PB  - Springer
PY  - 2018
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - The extracellular matrix (ECM) is a key regulator of tissue morphogenesis and repair. However, its composition and architecture are not well characterized. Here, we monitor remodeling of the extracellular niche in tissue repair in the bleomycin-induced lung injury mouse model. Mass spectrometry quantified 8,366 proteins from total tissue and bronchoalveolar lavage fluid (BALF) over the course of 8 weeks, surveying tissue composition from the onset of inflammation and fibrosis to its full recovery. Combined analysis of proteome, secretome, and transcriptome highlighted post-transcriptional events during tissue fibrogenesis and defined the composition of airway epithelial lining fluid. To comprehensively characterize the ECM, we developed a quantitative detergent solubility profiling (QDSP) method, which identified Emilin-2 and collagen-XXVIII as novel constituents of the provisional repair matrix. QDSP revealed which secreted proteins interact with the ECM, and showed drastically altered association of morphogens to the insoluble matrix upon injury. Thus, our proteomic systems biology study assigns proteins to tissue compartments and uncovers their dynamic regulation upon lung injury and repair, potentially contributing to the development of anti-fibrotic strategies.
AU  - Schiller, H. B.*
AU  - Fernandez, I.E.
AU  - Burgstaller, G.
AU  - Schaab, C.*
AU  - Scheltema, R.A.*
AU  - Schwarzmayr, T.
AU  - Strom, T.M.
AU  - Eickelberg, O.
AU  - Mann, M.*
C1  - 46367
C2  - 37565
TI  - Time- and compartment-resolved proteome profiling of the extracellular niche in lung injury and repair.
JO  - Mol. Syst. Biol.
VL  - 11
IS  - 7
PY  - 2015
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - In pharmacology, it is crucial to understand the complex biological responses that drugs elicit in the human organism and how well they can be inferred from model organisms. We therefore identified a large set of drug-induced transcriptional modules from genome-wide microarray data of drug-treated human cell lines and rat liver, and first characterized their conservation. Over 70% of these modules were common for multiple cell lines and 15% were conserved between the human in vitro and the rat in vivo system. We then illustrate the utility of conserved and cell-type-specific drug-induced modules by predicting and experimentally validating (i) gene functions, e.g., 10 novel regulators of cellular cholesterol homeostasis and (ii) new mechanisms of action for existing drugs, thereby providing a starting point for drug repositioning, e.g., novel cell cycle inhibitors and new modulators of α-adrenergic receptor, peroxisome proliferator-activated receptor and estrogen receptor. Taken together, the identified modules reveal the conservation of transcriptional responses towards drugs across cell types and organisms, and improve our understanding of both the molecular basis of drug action and human biology.
AU  - Iskar, M.*
AU  - Zeller, G.*
AU  - Blattmann, P.*
AU  - Campillos, M.
AU  - Kuhn, M.*
AU  - Kaminska, K.H.*
AU  - Runz, H.*
AU  - Gavin, A.C.*
AU  - Pepperkok, R.*
AU  - van Noort, V.*
AU  - Bork, P.*
C1  - 24226
C2  - 31345
TI  - Characterization of drug-induced transcriptional modules: Towards drug repositioning and functional understanding.
JO  - Mol. Syst. Biol.
VL  - 9
PB  - Nature Publishing
PY  - 2013
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large-scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug-target relations to identify overrepresented protein-side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.
AU  - Kuhn, M.*
AU  - Al Banchaabouchi, M.*
AU  - Campillos, M.
AU  - Jensen, L.J.*
AU  - Gross, C.*
AU  - Gavin, A.C.*
AU  - Bork, P.*
C1  - 25584
C2  - 31879
TI  - Systematic identification of proteins that elicit drug side effects.
JO  - Mol. Syst. Biol.
VL  - 9
PB  - Nature Publishing
PY  - 2013
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4 × 10(-4) to 2.1 × 10(-13). Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite-protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.
AU  - Wang-Sattler, R.
AU  - Yu, Z.
AU  - Herder, C.*
AU  - Messias, A.C.
AU  - Floegel, A.*
AU  - He, Y.*
AU  - Heim, K.
AU  - Campillos, M.J.
AU  - Holzapfel, C.
AU  - Thorand, B.
AU  - Grallert, H.
AU  - Xu, T.
AU  - Bader, E.
AU  - Huth, C.
AU  - Mittelstraß, K.
AU  - Döring, A.
AU  - Meisinger, C.
AU  - Gieger, C.
AU  - Prehn, C.
AU  - Römisch-Margl, W.
AU  - Carstensen, M.*
AU  - Xie, L.*
AU  - Yamanaka-Okumura, H.*
AU  - Xing, G.*
AU  - Ceglarek, U.*
AU  - Thiery, J.*
AU  - Giani, G.*
AU  - Lickert, H.
AU  - Lin, X.*
AU  - Li, Y.*
AU  - Boeing, H.*
AU  - Joost, H.-G.*
AU  - Hrabě de Angelis, M.
AU  - Rathmann, W.*
AU  - Suhre, K.
AU  - Prokisch, H.
AU  - Peters, A.
AU  - Meitinger, T.
AU  - Roden, M.*
AU  - Wichmann, H.-E.
AU  - Pischon, T.*
AU  - Adamski, J.
AU  - Illig, T.
C1  - 10426
C2  - 30232
TI  - Novel biomarkers for pre-diabetes identified by metabolomics.
JO  - Mol. Syst. Biol.
VL  - 8
PB  - Nature Publishing Group
PY  - 2012
SN  - 1744-4292
ER  - 

TY  - JOUR
AB  - Orchestration of signaling, photoreceptor structural integrity, and maintenance needed for mammalian vision remain enigmatic. By integrating three proteomic data sets, literature mining, computational analyses, and structural information, we have generated a multiscale signal transduction network linked to the visual G protein-coupled receptor (GPCR) rhodopsin, the major protein component of rod outer segments. This network was complemented by domain decomposition of protein-protein interactions and then qualified for mutually exclusive or mutually compatible interactions and ternary complex formation using structural data. The resulting information not only offers a comprehensive view of signal transduction induced by this GPCR but also suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking, predicting an important level of regulation through small GTPases. Further, it demonstrates a specific disease susceptibility of the core visual pathway due to the uniqueness of its components present mainly in the eye. As a comprehensive multiscale network, it can serve as a basis to elucidate the physiological principles of photoreceptor function, identify potential disease-associated genes and proteins, and guide the development of therapies that target specific branches of the signaling pathway.
AU  - Kiel, C.*
AU  - Vogt, A.
AU  - Campagna, A.*
AU  - Chatr-aryamontri, A.*
AU  - Swiatek-de Lange, M.
AU  - Beer, M.
AU  - Bolz, S.*
AU  - Mack, A.F.*
AU  - Kinkl, N.*
AU  - Cesareni, G.*
AU  - Serrano, L.*
AU  - Ueffing, M.
C1  - 6679
C2  - 29231
TI  - Structural and functional protein network analyses predict novel signaling functions for rhodopsin.
JO  - Mol. Syst. Biol.
VL  - 7
PB  - Nature Publishing Group
PY  - 2011
SN  - 1744-4292
ER  -