Unveiling the potential of Rumex hanus diets on chicken meat quality: Insights from metabolomics and molecular dynamics approach

Abstract

In order to solve the problem of loose meat quality and single flavor level of broiler chicken, the present study was conducted to improve the quality characteristics of chicken by incorporating unfermented (URH) and fermented Rumex hanus (FRH) in basal diets. URH supplementation significantly improved meat textural properties, evidenced by increased hardness, gumminess and chewiness. Histomorphological analysis demonstrated denser myofibrillar structures in URH-treated samples, concomitant with elevated immobilized water content and reduced free water fraction, contributing to enhanced tenderness and juiciness. Compared to CON, supplementation of FRH increased volatile organic compounds (48.97 %), notably increasing the content of nitrogen oxides and sulfur-containing compounds, as well as saltiness, umami and richness, with higher alcohol (wine aroma) and ester (fruity) contents. Liquid chromatography-mass spectrometry (LC-MS) detected the coclaurine and L-aspartic acid as molecular mimetic candidates. Molecular simulations revealed stable binding of flavor compounds to myosin without conformational perturbation, primarily mediated by intermolecular interactions with residues Lys273 and Tyr135. These findings highlight the potential of Rumex hanus to improve meat quality and flavor and also provide a theoretical framework for innovation in the field of feed.

Keywords: Meat quality; Molecular docking analysis; Molecular dynamics simulations; Rumex hanus; Volatile flavor compounds.

Fig. 1

Texture profile analysis (TPA) of chicken left breast chicken meat (A-E). Scanning electron microscopy (SEM) under 400× and 1000× (F). Hematoxylin-eosin (HE) staining under 40× and 200× (G).

Fig. 2

Low-field nuclear magnetic resonance relaxation time T₂ spectrum 3D diagram and moisture ratio diagram (A). Protein content and glutathione (GSH), malondialdehyde (MDA), NADPH oxidation index (B). Catalase (CAT), superoxide dismutase (SOD) and reactive oxygen species (ROS) (C). Radar map and PCA score map of electronic nose (D). Radar map and PCA score map of electronic tongue (E).

Fig. 3

Analysis of GC–MS. Types and number of volatile flavor compounds (A). The volatile proportion chord plot of the CON, URH, and FRH groups and the chord plot of the proportion of key volatile substances after screening (B). Sankey plots of the proportion of key volatile flavor compounds screened for each group (C). GC–MS heat map of all differential flavor compounds and key volatile substances (D). Filter criteria: VIP > 1 as well as P < 0.05. CON: Basic diet (BD). URH: BD + 10 % unfermented rumex hanu. FRH = BD + 10 %fermented rumex hanu.

Fig. 4

Analysis of LC-MS. Major differential metabolites after screening (VIP > 1, P < 0.05) (A). The contents of various types of substances in the CON, URH, FRH groups (B). PCA plot (C). Cluster heat map of major differential metabolites (D). Correlation analysis of electronic nose, electronic tongue, important volatile compounds and differential metabolites as well as oxidation indicators (E). *: P < 0.05, **: P < 0.01, ***: P < 0.001. Red represents a positive correlation, blue represents a negative correlation, and the shade of color represents the strength of the correlation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Molecular docking of myosin protein (6YSY) with different volatile compounds and differential metabolites.

Fig. 6

Analysis of molecular dynamics simulations. Root mean square deviation (RMSD) (A). Root mean square fluctuation (RMSF) (B). Radius of gyration (Rg) (C). Number of hydrogen bond (D). Total solvent accessible surface area (SASA) (E). The energy contribution of hot resides of 4 complexes (F–I).