bioRxiv Science⌕ Search

bioRxiv · 10.1101/564922

Operating regimes in a single enzymatic cascade at ensemble-level

Abstract

Operating regime of a single enzymatic cascade such as ubiquitously conserved MAPK building block provides insights into the nature of the sensitivity of the steady-state dose response that maps the upstream kinase and the downstream activated substrate concentrations. Steady-state response can be viewed either at a single-cell level in an ensemble of cells or at population-average level. Four operating regimes, viz., hyperbolic, threshold-hyperbolic, signal transducing and saturated, have been identified using the population-average level dose response curve. However, cell-to-cell variability exists in enzymatic cascades. This variability is captured using reporter based experimentation at ensemble level which permits detection of snapshot(s) of ensemble-level distribution of phosphorylated proteins. As a result, often the corresponding underlying steady-state dose response curve may not be available. We consider the question if the underyling operating regime can be directly inferred from ensemble level snapshot upstream kinase (input) and downstream phosphorylated substrates (response) distributions. In order to address this question, we use mathematical model of a single enzymatic cascade based on quasi-steady state approximation superimposed with an input distribution constrained by single-cell level experimental measurements of the MAPK cascade in Jurkat E6.1 cells stimulated with Phorbol Myristate Acetate (PMA). We prove that, under steady-state conditions, a monotonic relationship between the RIQR (=ratio of the inter-quartile range of the response and input distributions) and Rm (=ratio of medians of the two distributions), both of which are experimental observables, can be used to identify the underlying operating regime. We also show that the identification of the unimodal vs bimodal nature of the response distributions can further lead to identification of the potential parameter range in the planes of Michaelis-Menten constants K1 and K2, the two key parameters that dictate the operating regimes. We implement the proposed method on the stimulus strength dependent steady-state single-cell level pMEK (input) and pERK (response) distributions in Jurkat E6.1 cells treated with PMA. While cells stimulated using low concentrations of PMA are likely to operate in hyperbolic regime, those exposed to higher concentrations may lie in signal-transducing regime. Authors summarySingle enzymatic cascade, a ubiquitously found key building block in biological signaling networks, exhibits different steady-state behaviour at population (ensemble) level compared to population-averaged response. Detection at ensemble level typically achieved by non-plasmid based reporter permits snapshot fluorescence distribution measurement. We ask if there are signatures of fluorescence distribution of input kinase and response activated protein that can help decipher the underlying dose-response belonging to four distinct operating regimes a single enzymatic cascade exhibits. Based on simultaneously measured snapshot input (pMEK) and response (pERK) protein levels in an ensemble of PMA stimulated immortalized cancer (Jurkat E6.1) cells, we superimpose pMEK data-guided upstream kinase distribution capturing cell-to-cell variability on the steady-state Michaelis-Menten (MM) kinetic model. Following extensive MM constants sampling and simulations, we systematically identify that monotonicity between RIQR, the ratio of the inter-quartile range of response and input distributions, and RM, the median ratio of the two distributions, enables regime identification of the measured pERK distribution. We further show qualitative assessment of the modality of the response distribution can constrain the parameter range within an operating regime. Both RIQR and RM being experimental observables makes the proposed method suitable for assessing signal modulation capability of an enzymatic cascade of interest.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Parundekar, A., Kalantre, G., Khadpekar, A., Viswanathan, G. A.. 2019-03-01. Operating regimes in a single enzymatic cascade at ensemble-level. https://doi.org/10.1101/564922

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

A single-cell transcriptomic atlas of the pigtail macaque lung

Background: The lungs undergo substantial physiological and immunological changes during respiratory illness to meet the host's increased metabolic and oxygen demands. These responses require coordination among epithelial, endothelial, stromal, and immune cells throughout the respiratory zone, interstitium, and vasculature. Nonhuman primates (NHPs) are a valuable model for studying pulmonary biology as they have similar anatomic, physiologic, and immunologic correspondence to humans. Despite the widespread use of NHPs in pulmonary research, the cellular diversity and molecular characteristics of the pigtail macaque lung remain incompletely defined. Methods: Parenchymal lung tissue from five adult female pigtail macaques (Macaca nemestrina) was dissociated into single cells and profiled using 10x Genomics 3' single-cell RNA sequencing (scRNA-Seq). Next, ambient RNA was removed and quality control analyses performed. Samples were then integrated using scVI and scANVI. Cell types were labeled after unsupervised clustering and subclustering, followed by manual annotation using canonical marker genes. We compared lineages and cell types with the Level 4 Human Lung Cell Atlas (HLCA) using scArches reference mapping and with the Rhesus Immune Reference Atlas (RIRA) using Seurat label transfer. Results: The atlas includes 61,323 cells representing 37 cell types in epithelial, mesenchymal, endothelial, lymphoid, and myeloid populations. Annotated populations included both major and rare lung cell types, including alveolar type I and II cells, club and multiciliated cells, aerocyte capillary, and lymphatic endothelial cells, pre-terminal bronchiole secretory cells, CD4+ and CD8+ T cells, and several innate-like lymphocyte populations (gamma delta T cells, MAIT-like cells, innate-like CD8+ T cells, and group 3 innate lymphoid cells). There was strong concordance at HLCA annotation level 4, including AT1 and AT2 cells (96% and 93%), B cells (94 to 97%), CD4+ T cells (99 to 100%), classical and non-classical monocytes (84% and 85%), alveolar macrophages (80%), and mast cells (96%). Immune populations mapped to the RIRA with similar fidelity (alveolar macrophages and B cells, 99%). Conclusions: While studies of pandemic respiratory infections, vaccines, and therapeutics rely on NHP disease models, this atlas provides a unique reference for epithelial, endothelial, stromal, and immune cell states in the pigtail macaque lung. We found that single-cell populations from the pigtail macaque lung are highly similar to those in the human lung at the lineage level. This reference will enable studies of pathogenesis and vaccine and drug efficacy in a highly translational model of the human lung.

systems biology↗

Cell cycling throttles epithelial--mesenchymal transition

Cell cycle phase is an understudied source of variation in gene expression. EMT is a case in point: this cell state transition that drives metastasis has been studied widely across tissue, stimulus, and microenvironmental conditions, yet cell cycle effects are routinely regressed out of single-cell EMT analyses. Here, we compare EMT between G1 and cycling (S/G2/M) cell states in two independent scRNA-seq datasets of TGF-{beta}-stimulated MCF10A mammary epithelial cells (varying dose and timepoint). G1 cells consistently show higher expression of EMT genes and upregulated RNA velocities than cycling cells, with the strongest upregulation in G1 EMT intermediate states. Consistent with the go-or-grow hypothesis, EMT and cell cycle programs inversely correlate; moreover, this anticorrelation extends to RNA velocity and strengthens with TGF-{beta} dose. We identify a 77-gene G1-specific EMT signature enriched for actin cytoskeletal and extracellular matrix genes that would otherwise be lost by regressing out cell cycle effects. This gene set prompts the hypothesis that, unconstrained by the cytoskeletal demands of mitosis, G1 cells have greater capacity to execute EMT. Further, we developed an EMT gene regulatory model coupled with a cell cycle module, and found through simulation that the model recapitulated this inverse EMT--cell cycle relationship. Using this model, we predict that cycle-selective chemotherapy enriches for the G1 intermediate state, which has high metastatic potential. Cell cycle phase thus ought not be regressed out of EMT states, given its influence on EMT.

systems biology↗

Multi-Enzyme Data-Independent Acquisition Mass Spectrometry for Proteoform-Aware Single-shot Proteomics

Biological regulation does not operate at the level of the protein but across ensembles of protein variants shaped by alternative splicing, post-translational modification and endogenous proteolytic processing. Yet most conventional bottom-up discovery proteomics workflows remain optimized for a canonical protein census, leaving much of the sequence-level variation invisible. Multi-enzyme digestion has been recognized as an effective strategy to expand sequence coverage and proteoform sensitivity, but existing implementations rely on resource-heavy per-enzyme liquid chromatography coupled to mass spectrometry (LC-MS) analysis, dedicated measurements for spectral library construction, and semi-manual reconciliation across multiple software tools, restricting the use to specialized laboratories and limited sample cohorts. Here we introduce mE-DIA, a multi-enzyme, proteoform-resolved data independent acquisition (DIA)-MS workflow that multiplexes complementary protease digested samples into a single DIA injection and processes them through a library-free pipeline within a single analysis engine, without supplemental data dependent acquisition (DDA)-MS measurements. The mE-DIA Pipeline graphical user interface orchestrates the analysis, and a dedicated proteoform inference algorithm and the interactive mE-DIA Peptide Mapper viewer make the sequence-level evidence explorable. Comparing the workflow to prevailing DDA-library-based multi-enzyme DIA on the same raw data and within shared search space showed increased identified protein groups by 30 % and protein groups quantified at [≤] 20 % CV by 47 %. A three-enzyme multiplex of the human pathogen Streptococcus pyogenes resolved 990 protein-backbone variants among 309 canonical protein entries, 1.8-fold more than the most productive single-enzyme analysis, including an H69 C-terminal proteoform of the ribosomal protein uS19 that the trypsin-only workflow did not resolve. By consolidating multi-enzyme acquisition, analysis and proteoform interpretation into one accessible toolset, mE-DIA brings sub-gene-resolved evidence within reach of routine single-shot discovery DIA proteomics.

systems biology↗