StanDep: Capturing transcriptomic variability improves context-specific metabolic models

Joshi, Chintan; Schinn, Song‐Min; Richelle, Anne; Shamie, Isaac; O’Rourke, Eyleen J.; Lewis, Nathan E.

doi:10.1371/journal.pcbi.1007764

Cited by 30 publications

(52 citation statements)

References 58 publications

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“…Several interferon-related genes ( IFI6 , IFI27 ) follow a similar pattern in plasmablasts in the scRNA-seq data from cohort 1 and 2, corroborating the suppression of IFN signalling by severe SARS-CoV-2 infection in plasmablasts themselves. Of note, we found that the COVID-19 plasmablasts were predicted to be highly metabolically active in a systems biology modelling approach 77 , beyond their expected upregulation of an unfolded protein response 92 . Among the pathways from the transcriptional pattern-derived model, we identified glycolysis and amino acid catabolism to be transiently activated, while NAD + energy metabolism, vitamin B2 and several transport processes were specifically inhibited at the critical peak of the disease in plasmablasts, when compared to other B cell subpopulations.…”

Section: Discussionmentioning

confidence: 83%

“…As metabolically active cells, plasmablasts have been reported to modulate immune responses by serving as a nutrient sink 76 . Thus, we next applied our cell-specific metabolic model to the expression data of cell-types of interest, using a constraint-based method through the reconstruction of the metabolic state of individual cells from scRNA-seq data core reactions 77 . Plasmablasts found in inflammatory states of the disease trajectory (pseudotimes 1-4) were associated with metabolic hyperactivity, which was reduced only upon recovery to the state of healthy subjects (Supplementary Figure 6a).…”

Section: Resultsmentioning

confidence: 99%

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Longitudinal multi-omics analysis identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories

Bernardes

Mishra

Tran

et al. 2020

Preprint

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The pandemic spread of the potentially life-threatening disease COVID-19 requires a thorough understanding of the longitudinal dynamics of host responses. Temporal resolution of cellular features associated with a severe disease trajectory will be a pre-requisite for finding disease outcome predictors. Here, we performed a longitudinal multi-omics study using a two-centre German cohort of 13 patients (from Cologne and Kiel, cohort 1). We analysed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. The results from single-cell and bulk transcriptome analyses were validated in two independent cohorts of COVID-19 patients from Bonn (18 patients, cohort 2) and Nijmegen (40 patients, cohort 3), respectively. We observed an increase of proliferating, activated plasmablasts in severe COVID-19, and show a distinct expression pattern related to a hyperactive cellular metabolism of these cells. We further identified a notable expansion of type I IFN-activated circulating megakaryocytes and their progenitors, indicative of emergency megakaryopoiesis, which was confirmed in cohort 2. These changes were accompanied by increased erythropoiesis in the critical phase of the disease with features of hypoxic signalling. Finally, projecting megakaryocyte- and erythroid cell-derived co-expression modules to longitudinal blood transcriptome samples from cohort 3 confirmed an association of early temporal changes of these features with fatal COVID-19 disease outcome. In sum, our longitudinal multi-omics study demonstrates distinct cellular and gene expression dynamics upon SARS-CoV-2 infection, which point to metabolic shifts of circulating immune cells, and reveals changes in megakaryocytes and increased erythropoiesis as important outcome indicators in severe COVID-19 patients.

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Section: Discussionmentioning

confidence: 83%

Section: Resultsmentioning

confidence: 99%

Longitudinal multi-omics analysis identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories

Bernardes

Mishra

Tran

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…For instance, selecting a threshold value to consider ligands and receptors as expressed can affect the number of false positives and negatives 9 . In this regard, different values could be explored to infer the presence of biologically active protein, as previously addressed 92,93 . Moreover, other approaches such as using expression products to compute the usage of a ligand-receptor pair may also help 10 , but further adaptations should be done to use it with our Bray-Curtis like score.…”

Section: Discussionmentioning

confidence: 99%

Inferring a spatial code of cell-cell interactions across a whole animal body

Armingol

Ghaddar

Joshi

et al. 2020

Preprint

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Cell-cell interactions are crucial for multicellular organisms as they shape cellular function and ultimately organismal phenotype. However, the spatial code embedded in the molecular interactions that drive and sustain spatial organization, and in the organization that in turns drives intercellular interactions across a living animal remains to be elucidated. Here we use the expression of ligand-receptor pairs obtained from a whole-body single-cell transcriptome of Caenorhabditis elegans larvae to compute the potential for intercellular interactions through a Bray-Curtis-like metric. Leveraging a 3D atlas of C. elegans’ cells, we implement a genetic algorithm to select the ligand-receptor pairs most informative of the spatial organization of cells. Validating the strategy, the selected ligand-receptor pairs are involved in known cell-migration and morphogenesis processes and we confirm a negative correlation between cell-cell distances and interactions. Thus, our computational framework helps identify cell-cell interactions and their relationship with intercellular distances, and decipher molecular bases encoding spatial information in a whole animal. Furthermore, it can also be used to elucidate associations with any other intercellular phenotype and applied to other multicellular organisms.Graphical abstract

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“…We use this threshold-based method for the classification of reactions based on gene expression levels because of its simplicity and widespreadness, but other methods could be used instead, for example StanDep [ 44 ] or Barcode [ 45 ] (for Affymetrix microarray data). Changing the method changes the set of optimal solutions to the problem, but does not eliminate the problem associated with the enumeration.…”

Section: Methodsmentioning

confidence: 99%

DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

et al. 2021

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The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom.

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StanDep: Capturing transcriptomic variability improves context-specific metabolic models

Cited by 30 publications

References 58 publications

Longitudinal multi-omics analysis identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories

Longitudinal multi-omics analysis identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories

Inferring a spatial code of cell-cell interactions across a whole animal body

DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

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