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

INFORME: coupling information-theoretic experimental design with nonlinear mixed-effects modeling for efficient observation scheduling

Mathematical models of treatment response can inform individualized therapy, but their calibration often requires longitudinal measurements that are costly, burdensome, and collected on fixed schedules. Such schedules may be inefficient, over-sampling patients whose response is already well characterized while delaying informative measurements for those whose model parameters remain uncertain. We present INFORME (INFORmation-theoretic design with Mixed Effects), a framework that combines Bayesian information-theoretic experimental design with nonlinear mixed-effects modeling to adaptively select each patients next measurement time. Population and response-subgroup parameter distributions learned from an existing cohort provide informative priors, allowing candidate measurement times to be ranked by their expected reduction in patient-specific parameter uncertainty. As observations accumulate, priors can be updated to reflect the response subgroup most consistent with the patients data. We evaluate INFORME in two radiotherapy datasets: 150 synthetic tumor volume trajectories from a hybrid cellular automaton model of prostate cancer spheroids (HD1) and longitudinal tumor volumes from 39 patients with head-and-neck cancer (HD2). In HD1, population priors allowed omission of both pretreatment scans, while adaptive scheduling reduced the protocol from nine scans to three or four, with the response group identified from a single post-treatment scan on day 27. In HD2, the adaptive schedule used three scans instead of six and improved prediction by delaying the first on-treatment scan from week 1 to week 2, avoiding transient dynamics that produced false-positive and false-negative response projections. Across both datasets, the adaptive schedules used a mean of 2.7 scans in stead of seven and advanced completion of the patient-specific prediction by a mean of 15.5 days (95% CI, 6.7-24.3) relative to the equidistant protocol, while treatment duration remained unchanged. INFORME therefore reduces measurement burden and accelerates patient-specific prediction by concentrating observations at times that are most informative for model calibration.

systems biology↗

Sobetirome, a thyroid hormone receptor beta agonist, is a potential therapeutic agent for pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease with limited treatment options. Our group previously identified the antifibrotic potential of thyroid hormone, triiodothyronine (T3); however, clinical translation of thyroid hormone therapy is limited by its systemic adverse effects. In this study, we investigate whether sobetirome, a selective and well tolerated thyroid hormone receptor beta (THRB) agonist, offers antifibrotic benefits of thyroid hormone while minimizing systemic toxicity. Our study reveals that sobetirome, administered via intraperitoneal or inhalational routes, effectively mitigates bleomycin-induced pulmonary fibrosis in mice, with no evidence of toxicity. We identified that sobetirome restores mitochondrial homeostasis via activating the THRB-PPARGC1a axis. This protects alveolar type II epithelial cells from injury-induced apoptosis while selectively inducing apoptosis and metabolic reprogramming in apoptosis resistant IPF fibroblasts. Cell-specific deletion of Ppargc1a in either alveolar epithelial cells or fibroblasts abolishes sobetirome-mediated protection, establishing PPARGC1a as an essential mediator of therapeutic response. Importantly, sobetirome reverses fibrosis-associated transcriptional programs in human IPF lung tissue, reducing expression of key fibrosis-associated genes, including collagen I alpha 1 (COL1A1), collagen III alpha 1 (COL3A1), periostin (POSTN), cathepsin K (CTSK), and Chitinase 3 Like 1 (CHI3L1), while promoting extracellular matrix remodeling, epithelial restoration, and tissue homeostasis. Collectively, our findings identify THRB activation as a novel metabolic strategy for reversing pulmonary fibrosis. Across complementary in vitro, in vivo, and human ex vivo models, sobetirome restores mitochondrial function, modulates apoptotic pathways in pathogenic cells, and promotes fibrosis resolution, highlighting its potential as a lung-targeted therapeutic approach for IPF and other fibrotic lung diseases.

systems biology↗

Mechanistic modeling of bacterial translation initiation across growth conditions

Translation frequency in bacteria depends on how ribosomes, mRNAs, and initiation factors are allocated across growth conditions. Here, we developed a mechanistic ODE-based model of Escherichia coli translation that represents initiation, elongation, termination, and coupled auxiliary processes. Growth-dependent abundances were derived from physiological relationships and reprocessed omics data, and simulated outputs were compared with translation-frequency and active-ribosome references. The model predicts a continuous shift from complex-formation-limited toward ribosome-limited behavior as growth increases. This shift is characterized by a decline in free-ribosome abundance, whereas initiation-factor pools remain largely unbound and do not become depleted in parallel. Together with the implemented IF-dependent kinetic term, this preserved availability provides a model-internal route through which productive initiation can be maintained despite increasing ribosome utilization. Consistently, transcript-wide ribosome loading remains below its theoretical maximum, while COG-level simulations reveal distinct sector-specific translation-frequency trajectories. The study therefore provides a resource-allocation framework for interpreting how mRNA--ribosome interactions shape bacterial translation across growth conditions.

systems biology↗