The Translational Landscape of the Human Heart.

Gene expression in human tissue has primarily been studied on the transcriptional level, largely neglecting translational regulation. Here, we analyze the translatomes of 80 human hearts to identify new translation events and quantify the effect of translational regulation. We show extensive translational control of cardiac gene expression, which is orchestrated in a process-specific manner. Translation downstream of predicted disease-causing protein-truncating variants appears to be frequent, suggesting inefficient translation termination. We identify hundreds of previously undetected microproteins, expressed from lncRNAs and circRNAs, for which we validate the protein products in vivo. The translation of microproteins is not restricted to the heart and prominent in the translatomes of human kidney and liver. We associate these microproteins with diverse cellular processes and compartments and find that many locate to the mitochondria. Importantly, dozens of microproteins are translated from lncRNAs with well-characterized noncoding functions, indicating previously unrecognized biology.

Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease.

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle.

RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time­dependent kinetic rates during dynamic processes. Here, we present SLAM­Drop­seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol­fixed cells with droplet­based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene­specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust­cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM­Drop­seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single­cell gene expression dynamics in continuous biological processes.

Deciphering signal transduction networks in the liver by mechanistic mathematical modelling.

In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.

Identification of Interleukin1ß as an Amplifier of Interferon alpha-induced Antiviral Responses.

The induction of an interferon-mediated response is the first line of defense against pathogens such as viruses. Yet, the dynamics and extent of interferon alpha (IFNα)-induced antiviral genes vary remarkably and comprise three expression clusters: early, intermediate and late. By mathematical modeling based on time-resolved quantitative data, we identified mRNA stability as well as a negative regulatory loop as key mechanisms endogenously controlling the expression dynamics of IFNα-induced antiviral genes in hepatocytes. Guided by the mathematical model, we uncovered that this regulatory loop is mediated by the transcription factor IRF2 and showed that knock-down of IRF2 results in enhanced expression of early, intermediate and late IFNα-induced antiviral genes. Co-stimulation experiments with different pro-inflammatory cytokines revealed that this amplified expression dynamics of the early, intermediate and late IFNα-induced antiviral genes can also be achieved by co-application of IFNα and interleukin1 beta (IL1ß). Consistently, we found that IL1ß enhances IFNα-mediated repression of viral replication. Conversely, we observed that in IL1ß receptor knock-out mice replication of viruses sensitive to IFNα is increased. Thus, IL1ß is capable to potentiate IFNα-induced antiviral responses and could be exploited to improve antiviral therapies.

Unambiguous identification of miRNA:target site interactions by different types of ligation reactions.

To exert regulatory function, miRNAs guide Argonaute (AGO) proteins to partially complementary sites on target RNAs. Crosslinking and immunoprecipitation (CLIP) assays are state-of-the-art to map AGO binding sites, but assigning the targeting miRNA to these sites relies on bioinformatics predictions and is therefore indirect. To directly and unambiguously identify miRNA:target site interactions, we modified our CLIP methodology in C. elegans to experimentally ligate miRNAs to their target sites. Unexpectedly, ligation reactions also occurred in the absence of the exogenous ligase. Our in vivo data set and reanalysis of published mammalian AGO-CLIP data for miRNA-chimeras yielded ∼17,000 miRNA:target site interactions. Analysis of interactions and extensive experimental validation of chimera-discovered targets of viral miRNAs suggest that our strategy identifies canonical, noncanonical, and nonconserved miRNA:targets. About 80% of miRNA interactions have perfect or partial seed complementarity. In summary, analysis of miRNA:target chimeras enables the systematic, context-specific, in vivo discovery of miRNA binding.

Disentangling molecular mechanisms regulating sensitization of interferon alpha signal transduction.

Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFNα) orchestrates antiviral responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFNα doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation model for IFNα signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model-based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFNα dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFNα leads to a dose-dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient-specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient-specific pathway desensitization, while the abundance of STAT2 predicts the patient-specific IFNα signal response.

Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation.

Signaling through the AKT and ERK pathways controls cell proliferation. However, the integrated regulation of this multistep process, involving signal processing, cell growth and cell cycle progression, is poorly understood. Here, we study different hematopoietic cell types, in which AKT and ERK signaling is triggered by erythropoietin (Epo). Although these cell types share the molecular network topology for pro-proliferative Epo signaling, they exhibit distinct proliferative responses. Iterating quantitative experiments and mathematical modeling, we identify two molecular sources for cell type-specific proliferation. First, cell type-specific protein abundance patterns cause differential signal flow along the AKT and ERK pathways. Second, downstream regulators of both pathways have differential effects on proliferation, suggesting that protein synthesis is rate-limiting for faster cycling cells while slower cell cycles are controlled at the G1-S progression. The integrated mathematical model of Epo-driven proliferation explains cell type-specific effects of targeted AKT and ERK inhibitors and faithfully predicts, based on the protein abundance, anti-proliferative effects of inhibitors in primary human erythroid progenitor cells. Our findings suggest that the effectiveness of targeted cancer therapy might become predictable from protein abundance.

Identification of Cell Type-Specific Differences in Erythropoietin Receptor Signaling in Primary Erythroid and Lung Cancer Cells.

Lung cancer, with its most prevalent form non-small-cell lung carcinoma (NSCLC), is one of the leading causes of cancer-related deaths worldwide, and is commonly treated with chemotherapeutic drugs such as cisplatin. Lung cancer patients frequently suffer from chemotherapy-induced anemia, which can be treated with erythropoietin (EPO). However, studies have indicated that EPO not only promotes erythropoiesis in hematopoietic cells, but may also enhance survival of NSCLC cells. Here, we verified that the NSCLC cell line H838 expresses functional erythropoietin receptors (EPOR) and that treatment with EPO reduces cisplatin-induced apoptosis. To pinpoint differences in EPO-induced survival signaling in erythroid progenitor cells (CFU-E, colony forming unit-erythroid) and H838 cells, we combined mathematical modeling with a method for feature selection, the L1 regularization. Utilizing an example model and simulated data, we demonstrated that this approach enables the accurate identification and quantification of cell type-specific parameters. We applied our strategy to quantitative time-resolved data of EPO-induced JAK/STAT signaling generated by quantitative immunoblotting, mass spectrometry and quantitative real-time PCR (qRT-PCR) in CFU-E and H838 cells as well as H838 cells overexpressing human EPOR (H838-HA-hEPOR). The established parsimonious mathematical model was able to simultaneously describe the data sets of CFU-E, H838 and H838-HA-hEPOR cells. Seven cell type-specific parameters were identified that included for example parameters for nuclear translocation of STAT5 and target gene induction. Cell type-specific differences in target gene induction were experimentally validated by qRT-PCR experiments. The systematic identification of pathway differences and sensitivities of EPOR signaling in CFU-E and H838 cells revealed potential targets for intervention to selectively inhibit EPO-induced signaling in the tumor cells but leave the responses in erythroid progenitor cells unaffected. Thus, the proposed modeling strategy can be employed as a general procedure to identify cell type-specific parameters and to recommend treatment strategies for the selective targeting of specific cell types.

T160­phosphorylated CDK2 defines threshold for HGF dependent proliferation in primary hepatocytes.

Liver regeneration is a tightly controlled process mainly achieved by proliferation of usually quiescent hepatocytes. The specific molecular mechanisms ensuring cell division only in response to proliferative signals such as hepatocyte growth factor (HGF) are not fully understood. Here, we combined quantitative time-resolved analysis of primary mouse hepatocyte proliferation at the single cell and at the population level with mathematical modeling. We showed that numerous G1/S transition components are activated upon hepatocyte isolation whereas DNA replication only occurs upon additional HGF stimulation. In response to HGF, Cyclin:CDK complex formation was increased, p21 rather than p27 was regulated, and Rb expression was enhanced. Quantification of protein levels at the restriction point showed an excess of CDK2 over CDK4 and limiting amounts of the transcription factor E2F-1. Analysis with our mathematical model revealed that T160 phosphorylation of CDK2 correlated best with growth factor-dependent proliferation, which we validated experimentally on both the population and the single cell level. In conclusion, we identified CDK2 phosphorylation as a gate-keeping mechanism to maintain hepatocyte quiescence in the absence of HGF.

Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling.

Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex and cell-context specific signaling networks. Dissecting the underlying relations is crucial to predict the impact of targeted perturbations. However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions. Here, we report a novel hybrid mathematical modeling strategy to systematically unravel hepatocyte growth factor (HGF) stimulated phosphoinositide-3-kinase (PI3K) and mitogen activated protein kinase (MAPK) signaling, which critically contribute to liver regeneration. By combining time-resolved quantitative experimental data generated in primary mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and experimentally validate a network structure that represents the experimental data best and indicates specific crosstalk mechanisms. Whereas the identified network is robust against single perturbations, combinatorial inhibition strategies are predicted that result in strong reduction of Akt and ERK activation. Thus, by capitalizing on the advantages of the two modeling approaches, we reduce the high combinatorial complexity and identify cell-context specific signaling networks.

Comprehensive research synopsis and systematic meta-analyses in Parkinson's disease genetics: The PDGene database.

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of -27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P < 5 × 10(-8)) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P  =  1.3 × 10(-8)). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies.

Identification of isoform-specific dynamics in phosphorylation-dependent STAT5 dimerization by quantitative mass spectrometry and mathematical modeling.

STAT5A and STAT5B are important transcription factors that dimerize and transduce activation signals of cytokine receptors directly to the nucleus. A typical cytokine that mediates STAT5 activation is erythropoietin (Epo). Differential functions of STAT5A and STAT5B have been reported. However, the extent to which phosphorylated STAT5A and STAT5B (pSTAT5A, pSTAT5B) form homo- or heterodimers is not understood, nor is how this might influence the signal transmission to the nucleus. To study this, we designed a concept to investigate the isoform-specific dimerization behavior of pSTAT5A and pSTAT5B that comprises isoform-specific immunoprecipitation (IP), measurement of the degree of phosphorylation, and isoform ratio determination between STAT5A and STAT5B. For the main analytical method, we employed quantitative label-free and -based mass spectrometry. For the cellular model system, we used Epo receptor (EpoR)-expressing BaF3 cells (BaF3-EpoR) stimulated with Epo. Three hypotheses of dimer formation between pSTAT5A and pSTAT5B were used to explain the analytical results by a static mathematical model: formation of (i) homodimers only, (ii) heterodimers only, and (iii) random formation of homo- and heterodimers. The best agreement between experimental data and model simulations was found for the last case. Dynamics of cytoplasmic STAT5 dimerization could be explained by distinct nuclear import rates and individual nuclear retention for homo- and heterodimers of phosphorylated STAT5.

Assessment of microRNA-related SNP effects in the 3' untranslated region of the IL22RA2 risk locus in multiple sclerosis.

Recent large-scale association studies have identified over 100 MS risk loci. One of these MS risk variants is single-nucleotide polymorphism (SNP) rs17066096, located ~14 kb downstream of IL22RA2. IL22RA2 represents a compelling MS candidate gene due to the role of IL-22 in autoimmunity; however, rs17066096 does not map into any known functional element. We assessed whether rs17066096 or a nearby proxy SNP may exert pathogenic effects by affecting microRNA-to-mRNA binding and thus IL22RA2 expression using comprehensive in silico predictions, in vitro reporter assays, and genotyping experiments in 6,722 individuals. In silico screening identified two predicted microRNA binding sites in the 3'UTR of IL22RA2 (for hsa-miR-2278 and hsa-miR-411-5p) encompassing a SNP (rs28366) in moderate linkage disequilibrium with rs17066096 (r (2) = 0.4). The binding of both microRNAs to the IL22RA2 3'UTR was confirmed in vitro, but their binding affinities were not significantly affected by rs28366. Association analyses revealed significant association of rs17066096 and MS risk in our independent German dataset (odds ratio = 1.15, P = 3.48 × 10(-4)), but did not indicate rs28366 to be the cause of this signal. While our study provides independent validation of the association between rs17066096 and MS risk, this signal does not appear to be caused by sequence variants affecting microRNA function.

MANBA, CXCR5, SOX8, RPS6KB1 and ZBTB46 are genetic risk loci for multiple sclerosis.

A recent genome-wide association study reported five loci for which there was strong, but sub-genome-wide significant evidence for association with multiple sclerosis risk. The aim of this study was to evaluate the role of these potential risk loci in a large and independent data set of ≈ 20,000 subjects. We tested five single nucleotide polymorphisms rs228614 (MANBA), rs630923 (CXCR5), rs2744148 (SOX8), rs180515 (RPS6KB1), and rs6062314 (ZBTB46) for association with multiple sclerosis risk in a total of 8499 cases with multiple sclerosis, 8765 unrelated control subjects and 958 trios of European descent. In addition, we assessed the overall evidence for association by combining these newly generated data with the results from the original genome-wide association study by meta-analysis. All five tested single nucleotide polymorphisms showed consistent and statistically significant evidence for association with multiple sclerosis in our validation data sets (rs228614: odds ratio = 0.91, P = 2.4 × 10(-6); rs630923: odds ratio = 0.89, P = 1.2 × 10(-4); rs2744148: odds ratio = 1.14, P = 1.8 × 10(-6); rs180515: odds ratio = 1.12, P = 5.2 × 10(-7); rs6062314: odds ratio = 0.90, P = 4.3 × 10(-3)). Combining our data with results from the previous genome-wide association study by meta-analysis, the evidence for association was strengthened further, surpassing the threshold for genome-wide significance (P < 5 × 10(-8)) in each case. Our study provides compelling evidence that these five loci are genuine multiple sclerosis susceptibility loci. These results may eventually lead to a better understanding of the underlying disease pathophysiology.

Short-term information processing, long-term responses: Insights by mathematical modeling of signal transduction. Early activation dynamics of key signaling mediators can be predictive for cell fate decisions.

How do cells interpret information from their environment and translate it into specific cell fate decisions? We propose that cell fate is already encoded in early signaling events and thus can be predicted from defined signal properties. Specifically, we hypothesize that the time integral of activated key signaling molecules can be correlated to cellular behavior such as proliferation or differentiation. The identification of these decisive key signal mediators and their connection to cell fate is facilitated by mathematical modeling. A possible mechanistic linkage between signaling dynamics and cellular function is the directed control of gene regulatory networks by defined signals. Targeted experiments in combination with mathematical modeling can increase our understanding of how cells process information and realize distinct cell fates.

Genome-wide meta-analysis of short-tandem repeats for Parkinson's disease risk using genotype imputation.

Idiopathic Parkinson's disease is determined by a combination of genetic and environmental factors. Recently, the first genome-wide association study on short-tandem repeats in Parkinson's disease reported on eight suggestive short-tandem repeat-based risk loci (α = 5.3 × 10-6), of which four were novel, i.e. they had not been implicated in Parkinson's disease risk by genome-wide association analyses of single-nucleotide polymorphisms before. Here, we tested these eight candidate short-tandem repeats in a large, independent Parkinson's disease case-control dataset (n = 4757). Furthermore, we combined the results from both studies by meta-analysis resulting in the largest Parkinson's disease genome-wide association study of short-tandem repeats to date (n = 43 844). Lastly, we investigated whether leading short-tandem repeat risk variants exert functional effects on gene expression regulation based on methylation quantitative trait locus data in human 'post-mortem' brain (n = 142). None of the eight previously reported short-tandem repeats were significantly associated with Parkinson's disease in our independent dataset after multiple testing correction (α = 6.25 × 10-3). However, we observed modest support for short-tandem repeats near CCAR2 and NCOR1 in the updated meta-analyses of all available data. While the genome-wide meta-analysis did not reveal additional study-wide significant (α = 6.3 × 10-7) short-tandem repeat signals, we identified seven novel suggestive Parkinson's disease short-tandem repeat risk loci (α = 5.3 × 10-6). Of these, especially a short-tandem repeat near MEIOSIN showed consistent evidence for association across datasets. CCAR2, NCOR1 and one novel suggestive locus identified here (LINC01012) emerged from colocalization analyses showing evidence for a shared causal short-tandem repeat variant affecting both Parkinson's disease risk and cis DNA methylation in brain. Larger studies, ideally using short-tandem repeats called from whole-sequencing data, are needed to more fully investigate their role in Parkinson's disease.

Cellular ERK phospho-form profiles with conserved preference for a switch-like pattern.

ERK is a member of the MAPK pathway with essential functions in cell proliferation, differentiation, and survival. Complete ERK activation by the kinase MEK requires dual phosphorylation at T and Y within the activation motif TEY. We show that exposure of primary mouse hepatocytes to hepatocyte growth factor (HGF) results in phosphorylation at the activation motif, but not of other residues nearby. To determine the relative abundances of unphosphorylated ERK and the three ERK phospho-forms pT, pY, and pTpY, we employed an extended one-source peptide/phosphopeptide standard method in combination with nanoUPLC-MS. This method enabled us to determine the abundances of phospho-forms with a relative variability of ≤5% (SD). We observed a switch-like preference of ERK phospho-form abundances toward the active, doubly phosphorylated and the inactive, unphosphorylated form. Interestingly, ERK phospho-form profiles were similar upon growth factor and cytokine stimulation. A screening of several murine and human cell systems revealed that the balance between TY- and pTpY-ERK is conserved while the abundances of pT- and pY-ERK are more variable within cell types. We show that the phospho-form profiles do not change by blocking MEK activity suggesting that cellular phosphatases determine the ERK phospho-form distribution. This study provides novel quantitative insights into multisite phosphorylation.

Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation.

OBJECTIVE: The scarcity of high-quality annotated data is omnipresent in machine learning. Especially in biomedical segmentation applications, experts need to spend a lot of their time into annotating due to the complexity. Hence, methods to reduce such efforts are desired. METHODS: Self-Supervised Learning (SSL) is an emerging field that increases performance when unannotated data is present. However, profound studies regarding segmentation tasks and small datasets are still absent. A comprehensive qualitative and quantitative evaluation is conducted, examining SSL's applicability with a focus on biomedical imaging. We consider various metrics and introduce multiple novel application-specific measures. All metrics and state-of-the-art methods are provided in a directly applicable software package (https://osf.io/gu2t8/). RESULTS: We show that SSL can lead to performance improvements of up to 10%, which is especially notable for methods designed for segmentation tasks. CONCLUSION: SSL is a sensible approach to data-efficient learning, especially for biomedical applications, where generating annotations requires much effort. Additionally, our extensive evaluation pipeline is vital since there are significant differences between the various approaches. SIGNIFICANCE: We provide biomedical practitioners with an overview of innovative data-efficient solutions and a novel toolbox for their own application of new approaches. Our pipeline for analyzing SSL methods is provided as a ready-to-use software package.