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Viral infections have been linked to the expression of chemokines—small proteins that regulate the migration of immune cells. Inflammatory chemokines facilitate the chemotaxis of leukocytes to injured or inflamed areas of the body. These effects are expressed through the activation of chemokine receptors, which represent promising drug targets due to the role the play in inflammatory processes [1]. Among other chemoattractants, complement C3a and C5a anaphylatoxins mediate the chemotaxis of granulocytes, monocytes, and mast cells. Therefore, the complement system has been targeted in pharmacotherapy of inflammatory diseases [2]. Structural analysis was performed for various types of chemotaxis-related GPCR receptors. In the next step, structure-based virtual screening supported by machine learning [1] was carried out in order to provide novel scaffolds for drug discovery referring to inflammatory diseases, after which an analysis of the molecular switches was performed. The current study was supported by the National Science Centre in Poland (grant no. 2020/39/B/NZ2/00584).

[1] Dragan P, Merski M, Wiśniewski S, Sanmukh SG, Latek D. Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning. Pharmaceutics. 2023;15(2):516. doi: 10.3390/pharmaceutics15020516.
[2] Wisniewski S, Dragan P, Makal A, Latek D. Helix 8 in chemotactic receptors of the complement system. PLoS Comput Biol. 2022;18(7):e1009994. doi: 10.1371/journal.pcbi.1009994.

Collecting time-course data in vivo to study the dynamics of whole-body metabolism and gene expression can be ethically questionable and technically impractical. To explore the potential metabolic pathways involved in inter-organ communication and systemic metabolic homeostasis in the context of the circadian cycle, we have developed a lightweight framework based on Flux Balance Analysis and Metabolic Networks.
We utilized a modified version of the HGEM1 metabolic network as a scaffold and developed a new dynamic FBA algorithm that incorporates information on systemic metabolic concentrations and thermodynamics. Our optimization process considers critical phenomena like osmotic balance and allosteric interactions to maintain system homeostasis. To generate tissue-specific networks, we integrated data from multiple sources and obtained good approximations for the liver, skeletal muscle, and white adipose tissues. We also utilized published multi-omics datasets using mouse tissues to further refine and fine-tune the model parameters with a genetic algorithm.
Our goal is to provide a more in-depth study of inter-organ communication, considering also unperturbed states with a focus on dysfunctional circadian clocks and dietary challenges. By bridging the gap between sparse time points collected from in vivo samples, our method has the potential to elucidate the mechanisms underlying systemic metabolic regulation.