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Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss-of-function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity., Molecular Systems Biology, 19 (3), ISSN:1744-4292

Macrophages exhibit a spectrum of activation states ranging from classical to alternative activation1. Alternatively, activated macrophages are involved in diverse pathophysiological processes such as confining tissue parasites2, improving insulin sensitivity3 or promoting an immune-tolerant microenvironment that facilitates tumour growth and metastasis4. Recently, the metabolic regulation of macrophage function has come into focus as both the classical and alternative activation programmes require specific regulated metabolic reprogramming5. While most of the studies regarding immunometabolism have focussed on the catabolic pathways activated to provide energy, little is known about the anabolic pathways mediating macrophage alternative activation. In this study, we show that the anabolic transcription factor sterol regulatory element binding protein 1 (SREBP1) is activated in response to the canonical T helper 2 cell cytokine interleukin-4 to trigger the de novo lipogenesis (DNL) programme, as a necessary step for macrophage alternative activation. Mechanistically, DNL consumes NADPH, partitioning it away from cellular antioxidant defences and raising reactive oxygen species levels. Reactive oxygen species serves as a second messenger, signalling sufficient DNL, and promoting macrophage alternative activation. The pathophysiological relevance of this mechanism is validated by showing that SREBP1/DNL is essential for macrophage alternative activation in vivo in a helminth infection model.

Over 50% of human tumors display hyperactivation of the serine/threonine kinase AKT. Despite evidence of clinical efficacy, there remains scope to improve upon the therapeutic window of the current generation of AKT inhibitors. Here we report the development of a second-generation AKT degrader, INY-05-040, which outperformed catalytic AKT inhibition with respect to cellular suppression of AKT-driven phenotypes in breast cancer cell lines. A systematic growth inhibition screen across 288 cancer cell lines confirmed a substantially higher potency for INY-05-040 (median GI50adj= 1.1 µM) compared to our first-generation AKT degrader (INY-03-041; median GI50adj= 3.1 µM), with both compounds outperforming catalytic AKT inhibition with GDC-0068 (median GI50adj> 10 µM). Using multi-omic profiling and causal network integration in breast cancer cells, we demonstrate that the enhanced efficacy of INY-05-040 is associated with sustained suppression of AKT signaling, followed by a potent induction of the stress mitogen activated protein kinase (MAPK) c-Jun N-terminal kinase (JNK). Further integration of growth inhibition assays with publicly available transcriptomic, proteomic, and reverse phase protein array (RPPA) measurements established low baseline JNK signaling as a biomarker for breast cancer sensitivity to AKT degradation. Collectively, our study presents a systematic framework for mapping the network-wide signaling effects of therapeutically relevant compounds, and identifies INY-05-040 as a potent pharmacological suppressor of AKT signaling.

The biguanide drug metformin is a safe and widely prescribed drug for type 2 diabetes. Interestingly, hundreds of clinical trials were set to evaluate the potential role of metformin in the prevention and treatment of cancer including colorectal cancer (CRC). To interrogate cell signaling events and networks in CRC and explore the druggability of the metformin-rewired phosphorylation network, we performed a proteomic and phosphoproteomic analysis on a panel of 12 molecularly heterogeneous CRC cell lines. Using in-depth data-independent analysis mass spectrometry (DIA-MS), we profiled a total of 10,142 proteins and 56,080 phosphosites (P-sites) in CRC cells treated with metformin for 30 minutes and 24 hours. Our results indicate that metformin does not directly trigger or inhibit any immediate phosphorylation events. Instead, it primarily remodels cell signaling in the long-term. Strikingly, the phosphorylation response to metformin was highly heterogeneous in the CRC panel, uncovering four groups of metformin responsivity. We further performed a network analysis to systematically estimate kinase/phosphatase activities and reconstruct signaling cascades in each cell line. We created a “MetScore” which catalogs the most consistently perturbed P-sites among CRC cells for future studies. Finally, we leveraged the metformin P-site signature to identify pharmacodynamic interactions revealing a number of candidate metformin-interacting drugs. Together, we provide a data resource using state-of-the-art phosphoproteomics to understand the metformin-induced cell signaling for potential cancer therapeutics.

Prostaglandin E

Prostaglandin E2 (PGE2) promotes an immunosuppressive microenvironment in cancer, partly by signaling through four receptors (EP1, EP2, EP3, and EP4) on T cells. Here, we comprehensively characterized PGE2 signaling networks in helper, cytotoxic, and regulatory T cells using a phosphoproteomics and phosphoflow cytometry approach. We identified ~1500 PGE2-regulated phosphosites and several important EP1–4 signaling nodes, including PKC, CK2, PKA, PI3K, and Src. T cell subtypes exhibited distinct signaling pathways, with the strongest signaling in EP2-stimulated CD8+ cells. EP2 and EP4, both of which signal through Gαs, induced similar signaling outputs, but with distinct kinetics and intensity. Functional predictions from the observed phosphosite changes revealed PGE2 regulation of key cellular and immunological processes. Last, network modeling suggested signal integration between the receptors and a substantial contribution from G protein–independent signaling. This study offers a comprehensive view of the different PGE2-regulated phosphoproteomes in T cell subsets, providing a valuable resource for further research on this physiologically and pathophysiologically important signaling system.

SUMMARYMetabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we combined multi-omics datasets of primary and metastatic clonally related clear cell renal cancer cells (ccRCC) and generated a computational tool to explore the metabolic landscape during cancer progression. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism is required to maintain the aspartate pool in cancer cells across all tumor stages. We also provide evidence that metastatic renal cancer cells reactivate argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, to enable invasion in vitro and metastasis in vivo. Overall, our study provides the first comprehensive elucidation of the molecular mechanisms responsible for metabolic flexibility in ccRCC, paving the way to the development of therapeutic strategies based on the specific metabolism that characterizes each tumor stage.HighlightsBranched-chain amino acids catabolism is reprogrammed in ccRCC tumorsBCAT-dependent transamination supplies nitrogen for de novo biosynthesis of amino acids including aspartate and asparagine in ccRCCAspartate produced downstream of BCAT is used specifically by metastatic cells through argininosuccinate synthase (ASS1) and argininosuccinate lyase (ASL) to generate arginine, providing a survival advantage in the presence of microenvironments with rate limiting levels of arginineASS1 is re-expressed in metastatic 786-M1A through epigenetic remodeling and it is sensitive to arginine levelsSilencing of ASS1 impairs the metastatic potential in vitro and in vivo of ccRCC cells

In diabetic patients, dyslipidemia frequently contributes to organ damage such as diabetic kidney disease (DKD). DKD is associated with excessive renal deposition of triacylglycerol (TAG) in lipid droplets (LD). Yet, it is unclear whether LDs play a protective or damaging role and how this might be influenced by dietary patterns. By using a diabetes mouse model, we find here that high fat diet enriched in the unsaturated oleic acid (OA) caused more lipid storage in LDs in renal proximal tubular cells (PTC) but less tubular damage than a corresponding butter diet with the saturated palmitic acid (PA). Mechanistically, we identify endoplasmic reticulum (ER) stress as the main cause of PA-induced PTC injury. ER stress is caused by elevated cellular levels of saturated TAG precursors and to higher membrane order in the ER. The resulting cell death is preceded by a transcriptional rewiring of phospholipid metabolism. Simultaneous addition of OA rescues the cytotoxic effects by normalizing membrane order and by increasing the total TAG amount. The latter also stimulates the formation of LDs that in turn can release unsaturated lipids upon demand by lipolysis. Our study thus clarifies mechanisms underlying PA-induced cell stress in PTCs and emphasizes the importance of olive oil for the prevention of DKD.

The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/liana/tree/biorxiv-clean, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods.

Objective Neuroimmune interactions between the sympathetic nervous system (SNS) and macrophages are required for the homeostasis of multiple tissues, including the adipose tissue. It has been proposed that the SNS maintains adipose tissue macrophages (ATMs) in an anti-inflammatory state via direct norepinephrine (NE) signaling to macrophages. This study aimed to investigate the physiological importance of this paradigm by utilizing a mouse model in which the adrenergic signaling from the SNS to macrophages, but not to other adipose tissue cells, was disrupted. Methods We generated a macrophage-specific B2AR knockout mouse (Adrb2ΔLyz2) by crossing Adrb2fl/fl and Lyz2Cre/+ mice. We have previously shown that macrophages isolated from Adrb2ΔLyz2 animals do not respond to NE stimulation in vitro. Herein we performed a metabolic phenotyping of Adrb2ΔLyz2 mice on either chow or high-fat diet (HFD). We also assessed the adipose tissue function of Adrb2ΔLyz2 animals during fasting and cold exposure. Finally, we transplanted Adrb2ΔLyz2 bone marrow to low-density lipoprotein receptor (LDLR) knockout mice and investigated the development of atherosclerosis during Western diet feeding. Results We demonstrated that SNS-associated ATMs have a transcriptional profile indicative of activated beta-2 adrenergic receptor (B2AR), the main adrenergic receptor isoform in myeloid cells. However, Adrb2ΔLyz2 mice have unaltered energy balance on a chow or HFD. Furthermore, Adrb2ΔLyz2 mice show similar levels of adipose tissue inflammation and function during feeding, fasting, or cold exposure, and develop insulin resistance during HFD at the same rate as controls. Finally, macrophage-specific B2AR deletion does not affect the development of atherosclerosis on an LDL receptor-null genetic background. Conclusions Overall, our data suggest that the SNS does not directly modulate the phenotype of adipose tissue macrophages in either lean mice or mouse models of cardiometabolic disease. Instead, sympathetic nerve activity exerts an indirect effect on adipose tissue macrophages through the modulation of adipocyte function., Graphical abstract Image 1, Highlights • Sympathetic nerve-associated macrophages in white adipose tissues exhibit a transcriptional signature of B2AR activation. • Macrophage-specific B2AR knockout does not affect the phenotype of adipose tissue macrophages. • Macrophage B2AR signaling does not affect adipose tissue function during fasting or cold exposure. • Macrophage B2AR is dispensable for the development of insulin resistance and atherosclerosis in obesity.

The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods.FindingsBuilt a framework to systematically combine 15 resources and 6 methods to estimate cell-cell communication from single-cell RNA dataCell-cell communication resources are often built from the same original databases and very few interactions are unique to a single resource. Yet overlap varies among resources and certain biological terms are unevenly representedDifferent methods and resources provided notably different resultsThe observed disagreement among the methods could have a considerable impact on the interpretation of results

Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We focus on methods developed to recover functional insights using footprints. Footprints are signatures defined by the effect of molecules or processes of interest. They integrate information from multiple measurements whose abundances are under the influence of a common regulator. For example, transcripts controlled by a transcription factor or peptides phosphorylated by a kinase. Footprints can also be generalised across multiple types of omic data. Thus, we also present methods to integrate multiple types of omic data and features (such as the ones derived from footprints) together. We highlight some examples of studies that leverage such approaches to discover new biological mechanisms., Graphical abstract Image 1, Highlights • Functional information on signalling pathways, metabolism and gene regulation can be found across multiple types of omic data. • One way to extract such information is to consider these data as the footprint of the activity of enzymes and pathways. • Information on enzyme/pathway activities and omic data can be integrated together to contextualise multi-scale networks. • Such an approach can lead to the discovery of regulatory events spanning across multiple biological processes.

Post-translational modifications of proteins play an important role in the regulation of cellular processes. The mass spectrometry analysis of proteome modifications offers huge potential for the study of how protein inhibitors affect the phosphosignaling mechanisms inside the cells. We have recently proposed PHONEMeS, a method that uses high-content shotgun phosphoproteomic data to build logical network models of signal perturbation flow. However, in its original implementation, PHONEMeS was computationally demanding and was only used to model signaling in a perturbation context. We have reformulated PHONEMeS as an Integer Linear Program (ILP) that is orders of magnitude more efficient than the original one. We have also expanded the scenarios that can be analyzed. PHONEMeS can model data upon perturbation on not only a known target but also deregulated pathways upstream and downstream of any set of deregulated kinases. Finally, PHONEMeS can now analyze data sets with multiple time points, which helps us to obtain better insight into the dynamics of the propagation of signals. We illustrate the value of the new approach on various data sets of medical relevance, where we shed light on signaling mechanisms and drug modes of action.

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. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.; 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. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

Contains fulltext : 232477.pdf (Publisher’s version ) (Open Access) Tissue-resident macrophages are required for homeostasis, but also contribute to tissue dysfunction in pathophysiological states. The sympathetic neurotransmitter norepinephrine (NE) induces an anti-inflammatory and tissue-reparative phenotype in macrophages. As NE has a well-established role in promoting triglyceride lipolysis in adipocytes, and macrophages accumulate triglyceride droplets in various physiological and disease states, we investigated the effect of NE on primary mouse bone marrow-derived macrophage triglyceride metabolism. Surprisingly, our data show that in contrast to the canonical role of NE in stimulating lipolysis, NE acting via beta2-adrenergic receptors (B2ARs) in macrophages promotes extracellular fatty acid uptake and their storage as triglycerides and reduces free fatty acid release from triglyceride-laden macrophages. We demonstrate that these responses are mediated by a B2AR activation-dependent increase in Hilpda and Dgat1 gene expression and activity. We further show that B2AR activation favors the storage of extracellular polyunsaturated fatty acids. Finally, we present evidence that macrophages isolated from hearts after myocardial injury, for which survival critically depends on leukocyte B2ARs, have a transcriptional signature indicative of a transient triglyceride accumulation. Overall, we describe a novel and unexpected role of NE in promoting triglyceride storage in macrophages that could have potential implications in multiple diseases.

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies., A.D. and E.G. were Marie-Curie Early Stage Researchers supported by the European Union’s Horizon 2020 research and innovation program (675585 Marie-Curie ITN “SymBioSys”) to J.S.R. A.D. was funded by German Federal Ministry of Education and Research (Bundesministerium fur Bildung und € Forschung BMBF) MSCoreSys research initiative research core SMART-CARE (031L0212A). This work was further supported by the JRC for Computational Biomedicine which was partially funded by Bayer AG, and the Medical Research Council (MC_UU_12022/6 to C.F. and M.S.). The Novo Nordisk Foundation Center for Protein Research is supported by Novo Nordisk Foundation grant number NNF14CC0001. J.V.O. was funded by a grant from Danish Council for Independent Research (8020-00100B) to partly support K.B.E. who was also supported in part by the Lundbeck Foundation (R193-2015-243). R.K. was supported by grants of the German Research Foundation (DFG: SFBTRR57, P30; SFBTRR219 C05, CRU344, P1), by a Grant of the European Research Council (ERC-StG 677448), a Grant of the State of North Rhine-Westphalia (Return to NRW), the BMBF eMed Consortia Fibromap, the ERA-CVD Consortia MEND-AGE, the Else Kroener Fresenius Foundation (EKFS) and the Interdisciplinary Centre for Clinical Research (IZKF) within the faculty of Medicine at the RWTH Aachen University (O3-11). C.K. was supported by the German Society of Internal Medicine (DGIM). Thanks to Hyojin Kim for her contribution to the original COSMOS logo design. Thanks to Denes Turei for his help with putting the meta PKN online. We thank E. Ruppin and R. Katzir for helping us with the breast cancer dataset from Katzir et al (2019). Open Access funding enabled and organized by ProjektDEAL., info:eu-repo/semantics/publishedVersion

Summary The causative agent of the coronavirus disease 2019 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected millions and killed hundreds of thousands of people worldwide, highlighting an urgent need to develop antiviral therapies. Here we present a quantitative mass spectrometry-based phosphoproteomics survey of SARS-CoV-2 infection in Vero E6 cells, revealing dramatic rewiring of phosphorylation on host and viral proteins. SARS-CoV-2 infection promoted casein kinase II (CK2) and p38 MAPK activation, production of diverse cytokines, and shutdown of mitotic kinases, resulting in cell cycle arrest. Infection also stimulated a marked induction of CK2-containing filopodial protrusions possessing budding viral particles. Eighty-seven drugs and compounds were identified by mapping global phosphorylation profiles to dysregulated kinases and pathways. We found pharmacologic inhibition of the p38, CK2, CDK, AXL, and PIKFYVE kinases to possess antiviral efficacy, representing potential COVID-19 therapies., Graphical Abstract, Highlights • Phosphoproteomics analysis of SARS-CoV-2-infected cells uncovers signaling rewiring • Infection promotes host p38 MAPK cascade activity and shutdown of mitotic kinases • Infection stimulates CK2-containing filopodial protrusions with budding virus • Kinase activity analysis identifies potent antiviral drugs and compounds, Phosphoproteomics analysis of SARS-CoV-2-infected Vero E6 cells reveals host cellular pathways hijacked by viral infection, leading to the identification of small molecules that target dysregulated pathways and elicit potent antiviral efficacy.

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Diverse tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from nine renal cell carcinoma patients. We used COSMOS to generate novel hypotheses such as the impact of Androgen Receptor on nucleoside metabolism and the influence of the JAK-STAT pathway on propionyl coenzyme A production. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.Abstract Figure

Background Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation. Methods We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer. Results We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing. Conclusions Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.

Primary myelofibrosis (PMF) is a myeloproliferative neoplasm (MPN) that leads to progressive bone marrow (BM) fibrosis. Although the cellular mutations involved in the pathogenesis of PMF have been extensively investigated, the sequential events that drive stromal activation and fibrosis by hematopoietic–stromal cross-talk remain elusive. Using an unbiased approach and validation in patients with MPN, we determined that the differential spatial expression of the chemokine CXCL4/platelet factor-4 marks the progression of fibrosis. We show that the absence of hematopoietic CXCL4 ameliorates the MPN phenotype, reduces stromal cell activation and BM fibrosis, and decreases the activation of profibrotic pathways in megakaryocytes, inflammation in fibrosis-driving cells, and JAK/STAT activation in both megakaryocytes and stromal cells in 3 murine PMF models. Our data indicate that higher CXCL4 expression in MPN has profibrotic effects and is a mediator of the characteristic inflammation. Therefore, targeting CXCL4 might be a promising strategy to reduce inflammation in PMF.

Bioinformatics 36(16), 4523–4524 (2020). doi:10.1093/bioinformatics/btaa561, Published by Oxford Univ. Press, Oxford

Biological processes are regulated by intermolecular interactions and chemical modifications that do not affect protein levels, thus escaping detection in classical proteomic screens. We demonstrate here that a global protein structural readout based on limited proteolysis-mass spectrometry (LiP-MS) detects many such functional alterations, simultaneously and in situ, in bacteria undergoing nutrient adaptation and in yeast responding to acute stress. The structural readout, visualized as structural barcodes, captured enzyme activity changes, phosphorylation, protein aggregation, and complex formation, with the resolution of individual regulated functional sites such as binding and active sites. Comparison with prior knowledge, including other ‘omics data, showed that LiP-MS detects many known functional alterations within well-studied pathways. It suggested distinct metabolite-protein interactions and enabled identification of a fructose-1,6-bisphosphate-based regulatory mechanism of glucose uptake in E. coli. The structural readout dramatically increases classical proteomics coverage, generates mechanistic hypotheses, and paves the way for in situ structural systems biology., Cell, 184 (2), ISSN:0092-8674, ISSN:1097-4172

Bone marrow fibrosis (BMF) develops in various hematological and non-hematological conditions and is a central pathological feature of myelofibrosis. Effective cell-targeted therapeutics are needed, but the cellular origin of BMF remains elusive. Here, we show using genetic fate tracing in two murine models of BMF that Gli1(+) mesenchymal stromal cells (MSCs) are recruited from the endosteal and perivascular niche to become fibrosis-driving myofibroblasts in the bone marrow. Genetic ablation of Gli1(+) cells abolished BMF and rescued bone marrow failure. Pharmacological targeting of Gli proteins with GANT61 inhibited Gli1(+) cell expansion and myofibroblast differentiation, and attenuated fibrosis severity. The same pathway is also active in human BMF and Gli1 expression in BMF significantly correlates with the severity of the disease. In addition, GANT61 treatment reduced the myofibroblastic phenotype of human MSCs isolated from patients with BMF, suggesting that targeting of Gli proteins could be a relevant therapeutic strategy.

Motivation Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto-immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time-dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model. Results We present a new version of MaBoSS (2.0), including an updated version of the core software and an environment. With this environment, the needs for modeling signaling pathways are facilitated, including model construction, visualization, simulations of mutations, drug treatments and sensitivity analyses. It offers a framework for automated production of theoretical predictions. Availability and Implementation MaBoSS software can be found at https://maboss.curie.fr, including tutorials on existing models and examples of models. Supplementary information Supplementary data are available at Bioinformatics online.

International audience; Altered molecular responses to insulin and growth factors (GF) are responsible for late-life shortening diseases such as type-2 diabetes mellitus (T2DM) and cancers. We have built a network of the signaling pathways that control S-phase entry and a specific type of senescence called geroconversion. We have translated this network into a Boolean model to study possible cell phenotype outcomes under diverse molecular signaling conditions. In the context of insulin resistance, the model was able to reproduce the variations of the senescence level observed in tissues related to T2DM's main morbidity and mortality. Furthermore, by calibrating the pharmacodynamics of mTOR inhibitors, we have been able to reproduce the dose-dependent effect of rapamycin on liver degeneration and lifespan expansion in wild-type and HER2–neu mice. Using the model, we have finally performed an in silico prospective screen of the risk–benefit ratio of rapamycin dosage for healthy lifespan expansion strategies. We present here a comprehensive prognostic and predictive systems biology tool for human aging.