The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were 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 systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods' predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods.
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.
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•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.
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.
Synopsis
Estimation of activity changes for over 500 kinases and transcription factors in more than 1,000 cancer samples and cell lines identifies signalling regulators often dysregulated in cancer associated with patient survival.
Matched data for changes in DNA, RNA and (phospho)proteins are compiled from publicly available data across 1,000 tumour samples.
Changes in activity for over 500 kinases and transcription factors identify often dysregulated activities in cancer.
Recurrent mutations in kinases and transcription factors are not often associated with significant changes in their known targets
Signalling regulators within the same pathway tend to be co‐regulated, and activities can be used as predictors of patient survival.
Estimation of activity changes for over 500 kinases and transcription factors in more than 1,000 cancer samples and cell lines identifies signalling regulators often dysregulated in cancer associated with patient survival.
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.
SYNOPSIS
A new approach integrates multi‐omics datasets with a prior knowledge network spanning signaling, metabolism and allosteric regulations. Application to a kidney cancer patient cohort captures relevant cross‐talks among deregulated processes.
A causal multi‐omics network is built by integrating multiple ressources spanning signaling, metabolism and allosteric regulations.
Transcriptomics, phosphoproteomics and metabolomics data are integrated in a set of coherent mechanistic hypotheses using CARNIVAL, a tool contextualizing causal networks.
This set of coherent mechanistic hypotheses can be mined to identify disease mechanisms and therapeutic targets.
A network built for a cohort of kidney cancer patients shows coherence with other studies and known therapeutic targets.
A new approach integrates multi‐omics datasets with a prior knowledge network spanning signaling, metabolism and allosteric regulations. Application to a kidney cancer patient cohort captures relevant cross‐talks among deregulated processes.
In diabetic patients, dyslipidemia frequently contributes to organ damage such as diabetic kidney disease (DKD). Dyslipidemia is associated with both excessive deposition of triacylglycerol (TAG) in ...lipid droplets (LD) and lipotoxicity. Yet, it is unclear how these two effects correlate with each other in the kidney and how they are influenced by dietary patterns. By using a diabetes mouse model, we find here that high fat diet enriched in the monounsaturated 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). This effect was particularly evident S2/S3 but not S1 segments of the proximal tubule. Combining transcriptomics, lipidomics and functional studies, we identify endoplasmic reticulum (ER) stress as the main cause of PA-induced PTC injury. Mechanistically, ER stress is caused by elevated levels of saturated TAG precursors, reduced LD formation and, consequently, higher membrane order in the ER. Simultaneous addition of OA rescues the cytotoxic effects by normalizing membrane order and by increasing both TAG and LD formation. Our study thus emphasizes the importance of monounsaturated fatty acids for the dietary management of DKD by preventing lipid bilayer stress in the ER and promoting TAG and LD formation in PTCs.
Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better ...interpretability. Here, we present decoupleR, a Bioconductor and Python package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions, which are not present in other frameworks. Moreover, it leverages OmniPath, a meta-resource comprising over 100 databases of prior knowledge. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across top methods perform better than other methods at predicting perturbed regulators.
decoupleR's open-source code is available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html) for R and in GitHub (https://github.com/saezlab/decoupler-py) for Python. The code to reproduce the results is in GitHub (https://github.com/saezlab/decoupleR_manuscript) and the data in Zenodo (https://zenodo.org/record/5645208).
Supplementary data are available at
online.
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.
We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer.
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.
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.
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.
SYNOPSIS
COVID‐19 Disease Map is a large‐scale collection of curated computational models and diagrams of molecular mechanisms involved in SARS‐CoV‐2 infection. The map supports the computational exploration of pathways affected by the virus.
COVID‐19 Disease Map was built by over 20 independent biocuration teams and harmonised using systems biology standards.
Biocuration efforts were assisted by the systematic use of text‐ and AI‐assisted mining of relevant bioinformatic databases and platforms.
Case studies illustrate the applications of the map for visual exploration and computational analysis of SARS‐CoV‐2 pathways in combination with omic data.
The map is an open‐access effort, with all content and code shared in public repositories.
COVID‐19 Disease Map is a large‐scale collection of curated computational models and diagrams of molecular mechanisms involved in SARS‐CoV‐2 infection. The map supports the computational exploration of pathways affected by the virus.
Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate ...multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.
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.
•Differential spatial expression of the chemokine CXCL4/platelet factor-4 marks the progression of fibrosis.•The absence of hematopoietic CXCL4 ameliorates the MPN phenotype and reduces stromal cell activation and BM fibrosis.
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