miR-302b promotes bovine preadipocyte differentiation and inhibits proliferation by targeting CDK2.

MicroRNAs have been recently reported to act as key regulators of adipogenesis, a multifactorial complex process. One miRNA, miR-302b, is an important regulator of cell proliferation and differentiation and controls cancer development, but we speculate that miR-302b may also regulate bovine adipogenesis. Herein we have evaluated the role of this miRNA in bovine adipocyte differentiation using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), Oil Red O staining, a dual-luciferase reporter. CDK2 was identified as the target gene of miR-302b, and miR-302b agomir promoted mRNA and protein expression levels of adipocyte-specific genes. In addition, a CCK-8 kit was used to show that miR-302b agomir, but not the negative control, inhibits preadipocyte proliferation. In conclusion, miR-302b promotes bovine preadipocyte differentiation and inhibits proliferation by targeting CDK2.

Bta-miR-484 Targets SFRP1 and Affects Preadipocytes Proliferation, Differentiation, and Apoptosis.

MicroRNAs (miRNAs) are essential regulators of numerous biological processes in animals, including adipogenesis. Despite the abundance of miRNAs associated with adipogenesis, their exact mechanisms of action remain largely unknown. Our study highlights the role of bta-miR-484 as a major regulator of adipocyte proliferation, apoptosis, and differentiation. Here, we demonstrated that the expression of bta-miR-484 initially increased during adipogenesis before decreasing. Overexpression of bta-miR-484 in adipocytes ultimately inhibited cell proliferation and differentiation, reduced the number of EdU fluorescence-stained cells, increased the number of G1 phase cells, reduced the number of G2 and S phase cells, and downregulated the expression of proliferation markers (CDK2 and PCNA) and differentiation markers (CEBPA, FABP4, and LPL). Additionally, overexpression of bta-miR-484 promoted the expression of apoptosis-related genes (Caspase 3, Caspase 9, and BAX), and increased the number of apoptotic cells observed via flow cytometry. In contrast, bta-miR-484 inhibition in adipocytes yielded opposite effects to those observed during bta-miR-484 overexpression. Moreover, luciferase reporter assays confirmed SFRP1 as a target gene of bta-miR-484, and revealed that bta-miR-484 downregulates SFRP1 mRNA expression. These findings offer compelling evidence that bta-miR-484 targets SFRP1, inhibits proliferation and differentiation, and promotes apoptosis. Therefore, these results offer novel insights into the bta-miR-484 regulation of adipocyte growth and development.

Incorporating NOE-Derived Distances in Conformer Generation of Cyclic Peptides with Distance Geometry.

Nuclear magnetic resonance (NMR) data from NOESY (nuclear Overhauser enhancement spectroscopy) and ROESY (rotating frame Overhauser enhancement spectroscopy) experiments can easily be combined with distance geometry (DG) based conformer generators by modifying the molecular distance bounds matrix. In this work, we extend the modern DG based conformer generator ETKDG, which has been shown to reproduce experimental crystal structures from small molecules to large macrocycles well, to include NOE-derived interproton distances. In noeETKDG, the experimentally derived interproton distances are incorporated into the distance bounds matrix as loose upper (or lower) bounds to generate large conformer sets. Various subselection techniques can subsequently be applied to yield a conformer bundle that best reproduces the NOE data. The approach is benchmarked using a set of 24 (mostly) cyclic peptides for which NOE-derived distances as well as reference solution structures obtained by other software are available. With respect to other packages currently available, the advantages of noeETKDG are its speed and that no prior force-field parametrization is required, which is especially useful for peptides with unnatural amino acids. The resulting conformer bundles can be further processed with the use of structural refinement techniques to improve the modeling of the intramolecular nonbonded interactions. The noeETKDG code is released as a fully open-source software package available at www.github.com/rinikerlab/customETKDG.

Bta-miR-6517 promotes proliferation and inhibits differentiation of pre-adipocytes by targeting PFKL.

The proliferation and differentiation of pre-adipocytes are regulated by microRNAs (miRNAs) and other factors. In this study, the potential functions of bta-miR-6517 in the regulation of pre-adipocyte proliferation and differentiation were explored. The qRT-PCR, oil red O staining and CCK-8 assay were used to evaluate the role of bta-miR-6517. Further, the target gene of bta-miR-6517 was identified using bioinformatics analysis, dual-luciferase reporter system and qRT-PCR system. The results found that the overexpression of bta-miR-6517 promoted the expression of proliferation marker genes and substantially increased the adipocyte proliferation vitality in the CCK-8 assay, whereas suppressing of bta-miR-6517 had the opposite effect. Overexpression bta-miR-6517 suppressed the expression of adipogenic genes, which inhibited lipid accumulation, whereas suppressing of bta-miR-6517 had the opposite effect. Furthermore, the dual-fluorescent reporter experiment results demonstrated that bta-miR-6517 directly targeted phosphofructokinase, liver type (PFKL). When bta-miR-6517 was either overexpressed or suppressed, it negatively regulated PFKL. In conclusion, we observed that bta-miR-6517 promoted adipocyte proliferation and inhibited differentiation by targeting PFKL.

Dual-Regulation of Defect Sites and Vertical Conduction by Spiral Domain for Electrocatalytic Hydrogen Evolution.

Insufficient active sites and weak vertical conduction are the intrinsic factors that restrict the electrocatalytic HER for transition-metal dichalcogenides. As a prototype, we proposed a model of spiral MoTe2 to optimize collectively the above issues. The conductive atomic force microscopy of an individual spiral reveals that the retentive vertical conduction irrespective of layer thickness benefits from the connected screw dislocation lines between interlayers. Theoretical calculations uncover that the regions near the edge step of the spiral structures more easily form Te vacancies and have lower ΔGH * as extra active sites. A single spiral MoTe2 -based on-chip microcell was fabricated to extract HER activity and achieved an ultrahigh current density of 3000 mA cm-2 at an overpotential of 0.4 V, which is about two orders of magnitude higher than the exfoliated counterpart. Profoundly, this unusual spiral model will initiate a new pathway for triggering other inert catalytic reactions.

Genome-wide identification of cyclin-dependent kinase (CDK) genes affecting adipocyte differentiation in cattle.

BACKGROUND: Cyclin-dependent kinases (CDKs) are protein kinases regulating important cellular processes such as cell cycle and transcription. Many CDK genes also play a critical role during adipogenic differentiation, but the role of CDK gene family in regulating bovine adipocyte differentiation has not been studied. Therefore, the present study aims to characterize the CDK gene family in bovine and study their expression pattern during adipocyte differentiation. RESULTS: We performed a genome-wide analysis and identified a number of CDK genes in several bovine species. The CDK genes were classified into 8 subfamilies through phylogenetic analysis. We found that 25 bovine CDK genes were distributed in 16 different chromosomes. Collinearity analysis revealed that the CDK gene family in Bos taurus is homologous with Bos indicus, Hybrid-Bos taurus, Hybrid Bos indicus, Bos grunniens and Bubalus bubalis. Several CDK genes had higher expression levels in preadipocytes than in differentiated adipocytes, as shown by RNA-seq analysis and qPCR, suggesting a role in the growth of emerging lipid droplets. CONCLUSION: In this research, 185 CDK genes were identified and grouped into eight distinct clades in Bovidae, showing extensively homology. Global expression analysis of different bovine tissues and specific expression analysis during adipocytes differentiation revealed CDK4, CDK7, CDK8, CDK9 and CDK14 may be involved in bovine adipocyte differentiation. The results provide a basis for further study to determine the roles of CDK gene family in regulating adipocyte differentiation, which is beneficial for beef quality improvement.

Evaluation of nine condensed-phase force fields of the GROMOS, CHARMM, OPLS, AMBER, and OpenFF families against experimental cross-solvation free energies.

Experimental solvation free energies are nowadays commonly included as target properties in the validation of condensed-phase force fields, sometimes even in their calibration. In a previous article [Kashefolgheta et al., J. Chem. Theory. Comput., 2020, 16, 7556-7580], we showed how the involved comparison between experimental and simulation results could be made more systematic by considering a full matrix of cross-solvation free energies . For a set of N molecules that are all in the liquid state under ambient conditions, such a matrix encompasses N×N entries for considering each of the N molecules either as solute (A) or as solvent (B). In the quoted study, a cross-solvation matrix of 25 × 25 experimental value was introduced, considering 25 small molecules representative for alkanes, chloroalkanes, ethers, ketones, esters, alcohols, amines, and amides. This experimental data was used to compare the relative accuracies of four popular condensed-phase force fields, namely GROMOS-2016H66, OPLS-AA, AMBER-GAFF, and CHARMM-CGenFF. In the present work, the comparison is extended to five additional force fields, namely GROMOS-54A7, GROMOS-ATB, OPLS-LBCC, AMBER-GAFF2, and OpenFF. Considering these nine force fields, the correlation coefficients between experimental values and simulation results range from 0.76 to 0.88, the root-mean-square errors (RMSEs) from 2.9 to 4.8 kJ mol-1, and average errors (AVEEs) from -1.5 to +1.0 kJ mol-1. In terms of RMSEs, GROMOS-2016H66 and OPLS-AA present the best accuracy (2.9 kJ mol-1), followed by OPLS-LBCC, AMBER-GAFF2, AMBER-GAFF, and OpenFF (3.3 to 3.6 kJ mol-1), and then by GROMOS-54A7, CHARM-CGenFF, and GROMOS-ATB (4.0 to 4.8 kJ mol-1). These differences are statistically significant but not very pronounced, and are distributed rather heterogeneously over the set of compounds within the different force fields.

Volume-scaled common nearest neighbor clustering algorithm with free-energy hierarchy.

The combination of Markov state modeling (MSM) and molecular dynamics (MD) simulations has been shown in recent years to be a valuable approach to unravel the slow processes of molecular systems with increasing complexity. While the algorithms for intermediate steps in the MSM workflow such as featurization and dimensionality reduction have been specifically adapted to MD datasets, conventional clustering methods are generally applied to the discretization step. This work adds to recent efforts to develop specialized density-based clustering algorithms for the Boltzmann-weighted data from MD simulations. We introduce the volume-scaled common nearest neighbor (vs-CNN) clustering that is an adapted version of the common nearest neighbor (CNN) algorithm. A major advantage of the proposed algorithm is that the introduced density-based criterion directly links to a free-energy notion via Boltzmann inversion. Such a free-energy perspective allows a straightforward hierarchical scheme to identify conformational clusters at different levels of a generally rugged free-energy landscape of complex molecular systems.

Passing the Barrier - How Computer Simulations Can Help to Understand and Improve the Passive Membrane Permeability of Cyclic Peptides.

Proteins with large and flat binding sites as well as protein-protein interactions are considered ' undruggable ' with conventional small-molecule drugs. Cyclic peptides have been found to be capable of binding to such targets with high affinity, making this class of compounds an interesting source for possible therapeutics. However, the oftentimes poor passive membrane permeability of cyclic peptides still imposes restrictions on the applicability of cyclic peptide drugs. Here, we describe how computational methods in combination with experimental data can be used to improve our understanding of the structure-permeability relationship. Especially the conformational dynamic and chameleonic nature of cyclic peptides, which we investigate by a combination of MD simulations and kinetic modeling, is important for their ability to permeate passively through the membrane. The insights from such studies may enable the formulation of design principles for the rational design of permeable cyclic peptides.

Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates.

The efflux transporter P-glycoprotein (P-gp) is responsible for the extrusion of a wide variety of molecules, including drug molecules, from the cell. Therefore, P-gp-mediated efflux transport limits the bioavailability of drugs. To identify potential P-gp substrates early in the drug discovery process, in silico models have been developed based on structural and physicochemical descriptors. In this study, we investigate the use of molecular dynamics fingerprints (MDFPs) as an orthogonal descriptor for the training of machine learning (ML) models to classify small molecules into substrates and nonsubstrates of P-gp. MDFPs encode the information from short MD simulations of the molecules in different environments (water, membrane, or protein pocket). The performance of the MDFPs, evaluated on both an in-house dataset (3930 compounds) and a public dataset from ChEMBL (1114 compounds), is compared to that of commonly used 2D molecular descriptors, including structure-based and property-based descriptors. We find that all tested classifiers interpolate well, achieving high accuracy on chemically diverse subsets. However, by challenging the models with external validation and prospective analysis, we show that only tree-based ML models trained on MDFPs or property-based descriptors generalize well to regions of the chemical space not covered by the training set.

Improving Conformer Generation for Small Rings and Macrocycles Based on Distance Geometry and Experimental Torsional-Angle Preferences.

The conformer generator ETKDG is a stochastic search method that utilizes distance geometry together with knowledge derived from experimental crystal structures. It has been shown to generate good conformers for acyclic, flexible molecules. This work builds on ETKDG to improve conformer generation of molecules containing small or large aliphatic (i.e., non-aromatic) rings. For one, we devise additional torsional-angle potentials to describe small aliphatic rings and adapt the previously developed potentials for acyclic bonds to facilitate the sampling of macrocycles. However, due to the larger number of degrees of freedom of macrocycles, the conformational space to sample is much broader than for small molecules, creating a challenge for conformer generators. We therefore introduce different heuristics to restrict the search space of macrocycles and bias the sampling toward more experimentally relevant structures. Specifically, we show the usage of elliptical geometry and customizable Coulombic interactions as heuristics. The performance of the improved ETKDG is demonstrated on test sets of diverse macrocycles and cyclic peptides. The code developed here will be incorporated into the 2020.03 release of the open-source cheminformatics library RDKit.

Use of molecular dynamics fingerprints (MDFPs) in SAMPL6 octanol-water log P blind challenge.

The in silico prediction of partition coefficients is an important task in computer-aided drug discovery. In particular the octanol-water partition coefficient is used as surrogate for lipophilicity. Various computational approaches have been proposed, ranging from simple group-contribution techniques based on the 2D topology of a molecule to rigorous methods based molecular dynamics (MD) or quantum chemistry. In order to balance accuracy and computational cost, we recently developed the MD fingerprints (MDFPs), where the information in MD simulations is encoded in a floating-point vector, which can be used as input for machine learning (ML). The MDFP-ML approach was shown to perform similarly to rigorous methods while being substantially more efficient. Here, we present the application of MDFP-ML for the prediction of octanol-water partition coefficients in the SAMPL6 blind challenge. The underlying computational pipeline is made freely available in form of the MDFPtools package.

miRNA transcriptome comparison between muscle and adipose tissues indicates potential miRNAs associated with intramuscular fat in Chinese swamp buffalo.

The amount of intramuscular fat (IMF) affects the tenderness and juiciness of beef and is an important indicator of beef quality. A few miRNAs involved in IMF deposition have been identified in other livestock. However, in the buffalo, the association between miRNA and IMF has not been reported and the miRNA expression profile remains poorly understood. In this study, small RNA sequencing was performed to characterize the miRNA expression pattern in muscle and adipose tissues using the Illumina platform. A total of 108 differentially expressed (DE) miRNAs were identified, including 98 known miRNAs and 10 novel miRNAs. A qRT-PCR experiment confirmed the quality of the DE analysis. Eight DE miRNAs showed high expression in adipose tissue and a considerable expression level in muscle tissue. Functional enrichment indicated that bta-miR-148a, bta-miR-143, bta-miR-10b, bta-let-7i, bta-let-7f, bta-let-7b, bta-miR-30a-5p, and bta-miR-100 were significantly associated with adipogenesis, suggesting these as candidate regulators for IMF deposition in buffalo. However, further functional validation is required. This is the first characterization of the miRNA expression profile in the muscle and adipose tissues of buffalo. These results provide information for the identification of miRNAs with potential effects on IMF deposition in buffalo.

Rationalization of the Membrane Permeability Differences in a Series of Analogue Cyclic Decapeptides.

Cyclization and selected backbone N-methylations are found to be often necessary but not sufficient conditions for peptidic drugs to have a good bioavailability. Thus, the design of cyclic peptides with good passive membrane permeability and good solubility remains a challenge. The backbone scaffold of a recently published series of cyclic decapeptides with six selected backbone N-methylations was designed to favor the adoption of a closed conformation with ß-turns and four transannular hydrogen bonds. Although this conformation was indeed adopted by the peptides as determined by NMR measurements, substantial differences in the membrane permeability were observed. In this work, we aim to rationalize the impact of discrete side chain modifications on membrane permeability for six of these cyclic decapeptides. The thermodynamic and kinetic properties were investigated using molecular dynamics simulations and Markov state modeling in water and chloroform. The study highlights the influence that side-chain modifications can have on the backbone conformation. Peptides with a d-proline in the ß-turns were more likely to adopt, even in water, the closed conformation with transannular hydrogen bonds, which facilitates transition through the membrane. The population of the closed conformation in water was found to correlate positively with PAMPA log Pe.

Machine Learning with and for Molecular Dynamics Simulations.

From simple clustering techniques to more sophisticated neural networks, the use of machine learning has become a valuable tool in many fields of chemistry in the past decades. Here, we describe two different ways in which we explore the combination of machine learning (ML) and molecular dynamics (MD) simulations. One topic focuses on how the information in MD simulations can be encoded such that it can be used as input to train ML models for the quantitative understanding of molecular systems. The second topic addresses the utilization of machine learning to improve the set-up, interpretation, as well as accuracy of MD simulations.

Pharmacokinetic Study of Ultrasmall Superparamagnetic Iron Oxide Nanoparticles HY-088 in Rats.

BACKGROUND AND OBJECTIVE: HY-088 injection is an ultrasmall superparamagnetic iron oxide nanoparticle (USPIOs) composed of iron oxide crystals coated with polyacrylic acid (PAA) on the surface. The purpose of this study was to investigate the pharmacokinetics, tissue distribution, and mass balance of HY-088 injection. METHODS: The pharmacokinetics of [55Fe]-HY-088 and [14C]-HY-088 were investigated in 48 SD rats by intravenous injection of 8.5 (low-dose group), 25.5 (medium-dose group), and 85 (high-dose group) mg/100 µCi/kg. Tissue distribution was studied by intravenous injection of 35 mg/100 µCi/kg in 48 SD rats, and its tissue distribution in vivo was obtained by ex vivo tissue assay. At the same time, [14C]-HY-088 was injected intravenously at a dose of 25.5 mg/100 µCi/kg into 16 SD rats, and its tissue distribution in vivo was studied by quantitative whole-body autoradiography. [14C]-HY-088 and [55Fe]-HY-088 were injected intravenously into 24 SD rats at a dose of 35 mg/100 µCi/kg, and their metabolism was observed. RESULTS: In the pharmacokinetic study, [55Fe]-HY-088 reached the maximum observed concentration (Cmax) at 0.08 h in the low- and medium-dose groups of SD rats. [14C]-HY-088 reached Cmax at 0.08 h in the three groups of SD rats. The area under the concentration-time curve (AUC) of [55Fe]-HY-088 and [14C]-HY-088 increased with increasing dose. In the tissue distribution study, [55Fe]-HY-088 and [14C]-HY-088 were primarily distributed in the liver, spleen, and lymph nodes of both female and male rats. In the mass balance study conducted over 57 days, the radioactive content of 55Fe from [55Fe]-HY-088 was primarily found in the carcass, accounting for 86.42 ± 4.18% in females and 95.46 ± 6.42% in males. The radioactive recovery rates of [14C]-HY-088 in the urine of female and male rats were 52.99 ± 5.48% and 60.66 ± 2.23%, respectively. CONCLUSIONS: Following single intravenous administration of [55Fe]-HY-088 and [14C]-HY-088 in SD rats, rapid absorption was observed. Both [55Fe]-HY-088 and [14C]-HY-088 were primarily distributed in the liver, spleen, and lymph nodes. During metabolism, the radioactivity of [55Fe]-HY-088 is mainly present in the carcass, whereas the 14C-labeled [14C]-HY-088 shell PAA is eliminated from the body mainly through the urine.

Comparing Business, Innovation, and Platform Ecosystems: A Systematic Review of the Literature.

In recent decades, the term "ecosystem" has garnered substantial attention in scholarly and managerial discourse, featuring prominently in academic and applied contexts. While individual scholars have made significant contributions to the study of various types of ecosystem, there appears to be a research gap marked by a lack of comprehensive synthesis and refinement of findings across diverse ecosystems. This paper systematically addresses this gap through a hybrid methodology, employing bibliometric and content analyses to systematically review the literature from 1993 to 2023. The primary research aim is to critically examine theoretical studies on different ecosystem types, specifically focusing on business, innovation, and platform ecosystems. The methodology of this study involves a content review of the identified literature, combining quantitative bibliometric analyses to differentiate patterns and content analysis for in-depth exploration. The core findings center on refining and summarizing the definitions of business, innovation, and platform ecosystems, shedding light on both commonalities and distinctions. Notably, the research unveils shared characteristics such as openness and diversity across these ecosystems while highlighting significant differences in terms of participants and objectives. Furthermore, the paper delves into the interconnections within these three ecosystem types, offering insights into their dynamics and paving the way for discussions on future research directions. This comprehensive examination not only advances our understanding of business, innovation, and platform ecosystems but also lays the groundwork for future scholarly inquiries in this dynamic and evolving field.

Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.

We present a comprehensive study investigating the potential gain in accuracy for calculating absolute solvation free energies (ASFE) using a neural network potential to describe the intramolecular energy of the solute. We calculated the ASFE for most compounds from the FreeSolv database using the Open Force Field (OpenFF) and compared them to earlier results obtained with the CHARMM General Force Field (CGenFF). By applying a nonequilibrium (NEQ) switching approach between the molecular mechanics (MM) description (either OpenFF or CGenFF) and the neural net potential (NNP)/MM level of theory (using ANI-2x as the NNP potential), we attempted to improve the accuracy of the calculated ASFEs. The predictive performance of the results did not change when this approach was applied to all 589 small molecules in the FreeSolv database that ANI-2x can describe. When selecting a subset of 156 molecules, focusing on compounds where the force fields performed poorly, we saw a slight improvement in the root-mean-square error (RMSE) and mean absolute error (MAE). The majority of our calculations utilized unidirectional NEQ protocols based on Jarzynski's equation. Additionally, we conducted bidirectional NEQ switching for a subset of 156 solutes. Notably, only a small fraction (10 out of 156) exhibited statistically significant discrepancies between unidirectional and bidirectional NEQ switching free energy estimates.

Revolutionizing cattle breeding: Gene editing advancements for enhancing economic traits.

Beef and dairy products are rich in protein and amino acids, making them highly nutritious for human consumption. The increasing use of gene editing technology in agriculture has paved the way for genetic improvement in cattle breeding via the development of the CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein) system. Gene sequences are artificially altered and employed in the pursuit of improving bovine breeding research through targeted knockout, knock-in, substitution, and mutation methods. This review offers a comprehensive analysis of the advancements in gene editing technology and its diverse applications in enhancing both quantitative and qualitative traits across livestock. These applications encompass areas such as meat quality, milk quality, fertility, disease resistance, environmental adaptability, sex control, horn development, and coat colour. Furthermore, the review considers prospective ideas and insights that may be employed to refine breeding traits, enhance editing efficiency, and navigate the ethical considerations associated with these advancements. The review's focus on improving the quality of beef and milk is intended to enhance the economic viability of these products. Furthermore, it constitutes a valuable resource for scholars and researchers engaged in the fields of cattle genetic improvement and breeding.

Characterization of feed efficiency-related key signatures molecular in different cattle breeds.

Feed efficiency is a major constraint in the beef industry and has a significant negative correlation with residual feed intake (RFI). RFI is widely used as a measure of feed efficiency in beef cattle and is independent of economic traits such as body weight and average daily gain. However, key traits with commonality or specificity among beef cattle breeds at the same level of RFI have not been reported. Accordingly, the present study hypothesized that signatures associated with feed efficiency would have commonality or specificity in the liver of cattle breeds at the same RFI level. By comparing and integrating liver transcriptome data, we investigated the critical signatures closely associated with RFI in beef cattle using weighted co-expression network analysis, consensus module analysis, functional enrichment analysis and protein network interaction analysis. The results showed that the consensus modules in Angus and Charolais cattle were negatively correlated, and four (turquoise, red, tan, yellow) were significantly positively correlated in Angus liver, while (turquoise, red) were significantly negatively correlated in Charolais liver. These consensus modules were found to be primarily involved in biological processes such as substance metabolism, energy metabolism and gene transcription, which may be one of the possible explanations for the difference in feed efficiency between the two beef breeds. This research also identified five key candidate genes, PLA2G12B, LCAT, MTTP, LCAT, ABCA1 and FADS1, which are closely associated with hepatic lipid metabolism. The present study has identified some modules, genes and pathways that may be the major contributors to the variation in feed efficiency among different cattle breeds, providing a new perspective on the molecular mechanisms of feed efficiency in beef cattle and a research basis for investigating molecular markers associated with feed efficiency in beef cattle.