bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.09.08.556783

Dissecting Metabolic Landscape of Alveolar Macrophage

Abstract

The highly plastic nature of Alveolar Macrophage (AM) plays a crucial role in the defense against inhaled particulates and pathogens in the lungs. Depending upon the signal, AM acquires either classically activated M1 phenotype or alternatively activated M2 phenotype. These phenotypes have specific functions and unique metabolic traits such as upregulated glycolysis and pentose phosphate pathway in M1 phase and enhanced oxidative phosphorylation and tricarboxylic acid cycle during M2 phase that help maintain the sterility of the lungs. In this study, we investigate the metabolic shift in the activated phases of AM (M1 and M2 phase) and highlight the roles of pathways other than the typical players of central carbon metabolism. Pathogenesis is a complex and elongated process where the heightened requirement for energy is matched by metabolic shifts that supplement immune response and maintain homeostasis. The first step of pathogenesis is fever; however, analyzing the role of physical parameters such as temperature is challenging. Here, we observe the effect of an increase in temperature on pathways such as glycolysis, pentose phosphate pathway, oxidative phosphorylation, tricarboxylic acid cycle, amino acid metabolism, and leukotriene metabolism. We report the role of temperature as a catalyst to the immune response of the cell. The activity of pathways such as pyruvate metabolism, arachidonic acid metabolism, chondroitin/heparan sulfate biosynthesis, and heparan sulfate degradation are found to be important driving forces in the M1/M2 phenotype. We have also identified a list of 34 reactions such as nitric oxide production from arginine and the conversion of glycogenin to UDP which play major roles in the metabolic models and prompt the shift of the M2 phenotype to M1 and vice versa. In future, these reactions could further be probed as major contributors in designing effective therapeutic targets against severe respiratory diseases. Author SummaryAlveolar macrophage (AM) is highly plastic in nature and has a wide range of functions including invasion/killing of bacteria to maintaining the homeostasis in the lungs. The regulatory mechanism involved in the alveolar macrophage polarization is essential to fight against severe respiratory conditions (pathogens and particulates). Over the years, experiments on mouse/rat models have been used to draw insightful inferences. However, recent advances have highlighted the lack of transmission from non-human models to successful in vivo human experiments. Hence using genome-scale metabolic (GSM) models to understand the unique metabolic traits of human alveolar macrophages and comprehend the complex metabolic underpinnings that govern the polarization can lead to novel therapeutic strategies. The GSM models of AMs thus far, has not incorporated the activated phases of AM. Here, we aim to exhaustively dissect the metabolic landscape and capabilities of AM in its healthy and activated stages. We carefully explore the changes in reaction fluxes under each of the conditions to understand the role and function of all the pathways with special attention to pathways away from central carbon metabolism. Understanding the characteristics of each phase of AM has applications that could help improve the therapeutic approaches against respiratory conditions.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Malla, S., Sajeevan, K. A., Acharya, B., Chowdhury, R., Saha, R.. 2023-09-12. Dissecting Metabolic Landscape of Alveolar Macrophage. https://doi.org/10.1101/2023.09.08.556783

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

KEEP EXPLORING

Related preprints

Low-fidelity simulator selection for multi-fidelity inference of epithelial vertex models

Vertex models link cell division, neighbour exchange, and intercellular forces to epithelial tissue form through parameters such as junctional line tension and perimeter contractility. Bayesian inference of these parameters from data quantifies their uncertainty, but for stochastic models of proliferating tissues it requires many costly simulations. Multi-fidelity simulation-based inference reduces this cost by training on many runs of a cheaper, less accurate ('low-fidelity') simulator before refining with a few full ('high-fidelity') runs. Low-fidelity versions can be constructed in several ways, for example by stopping simulations early, coarsening the timestep, or averaging fewer replicates, but it is unclear which to choose before committing to costly training. To address this, we introduce PRISM (Paired Ranking Index for Simulator Matching), a screen that compares each candidate with the full simulator on paired runs, scoring agreement in cell-packing statistics against cost. For a vertex model of a proliferating tissue, PRISM flags stopping early as unreliable before training; once trained, this choice can produce confidently wrong parameter estimates. By contrast, a coarser timestep, within the range tested, is cheap and accurate; while fewer replicates are accurate but too costly. Using a screened low-fidelity simulator, multi-fidelity inference outperforms approximate Bayesian computation and neural-network inference trained on full simulations alone, at matched cost and with well-calibrated uncertainty. The same pattern recurs for a second vertex model fitted to published Drosophila pupal-wing imaging data, although there stopping halfway proves safe. Together, our findings suggest that which approximations are safe depends on the model, and that screening them before training makes multi-fidelity inference of tissue mechanics practical.

systems biology↗

Calibrating developmental dynamics from single-cell atlases: lineage paths outrun least-action routes

Single-cell atlases infer developmental dynamics from destructive snapshots, yet how far realized lineage paths depart from such inferred dynamics has never been measured with controls. We introduce LADD (Least-Action Developmental Dynamics), a measurement framework that learns a stochastic developmental vector field from time-series single-cell RNA-seq and computes minimum-action and minimum-time paths, together with a three-layer calibration protocol that separates genuine path deviation from the generic cost of centroid measurement, and we compare its forecasting component with optimal-transport and flow-matching methods. In the lineage-resolved Caenorhabditis elegans atlas, the raw action ratio of observed lineage-centroid trajectories to optimal paths is 3.79, yet forward-simulated ensembles of the learned model itself score 2.07, leaving a genuine excess of about two-fold. In zebrafish (Danio rerio), the observed ratio sits entirely at the model's floor. Three mechanisms that could explain the residual deviation (detours, flow-opposition, and fate-map complexity) are each excluded; what remains is speed, with lineages moving 30-43% faster along the learned flow. We report validity boundaries for temporal resolution, cross-species comparison, and energetic interpretation, delimiting where action-based trajectory comparisons are meaningful.

systems biology↗

Choice of annotation data significantly impacts automated reconstruction of genome-scale metabolic networks

Genome-scale metabolic models are valuable tools for studying metabolism in many applications, such as metabolic engineering or understanding human disease. However, curated models are available for only a limited number of organisms, and generating high-quality models for additional organisms remains a challenge. To help address this challenge, several software tools have been developed that can produce draft models in an automated fashion, but the quality of these draft models lags behind manually curated ones. Thus, there is still a need to improve these tools. Previous work has focused primarily on developing better methods for each step in the reconstruction process, such as gene annotation and gap-filling. Here, we focus on the data that underlies these methods, such as known connections between protein sequence and function. We find that changing the source of reconstruction data has a substantial impact on the contents and predictions of generated models. In fact, the source of data can have a bigger impact than the choice of reconstruction method. Additionally, we show that the reconstruction process is sensitive to the accuracy of gene annotations in the reconstruction data. Finally, we demonstrate the importance of having functional annotation data from an organism that is closely related to the one for which a model is being built. These observations highlight the importance of reconstruction data and identify principles that should be considered when selecting data for reconstruction tools.

systems biology↗