Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

A vector projection similarity-based method for miRNA-disease association prediction.

[S U M M A R Y] Many miRNA-disease association prediction models incorporate Gaussian interaction profile kernel similarity (GIPS). However, the GIPS fails to consider the specificity of the miRNA-disease association matrix, where matrix elements with a value of 0 represent miRNA and disease relationships that have not been discovered yet. To address this issue and better account for the impact of known and unknown miRNA-disease associations on similarity, we propose a method called vector projection similarity-based method for miRNA-disease association prediction (VPSMDA). In VPSMDA, we introduce three projection rules and combined with logistic functions for the miRNA-disease association matrix and propose a vector projection similarity measure for miRNAs and diseases. By integrating the vector projection similarity matrix with the original one, we obtain the improved miRNA and disease similarity matrix. Additionally, we construct a weight matrix using different numbers of neighbors to reduce the noise in the similarity matrix. In performance evaluation, both LOOCV and 5-fold CV experiments demonstrate that VPSMDA outperforms seven other state-of-the-art methods in AUC. Furthermore, in a case study, VPSMDA successfully predicted 10, 9, and 10 out of the top 10 associations for three important human diseases, respectively, and these predictions were confirmed by recent biomedical resources.

4 mC site recognition algorithm based on pruned pre-trained DNABert-Pruning model and fused artificial feature encoding.

DNA 4 mC plays a crucial role in the genetic expression process of organisms. However, existing deep learning algorithms have shortcomings in the ability to represent DNA sequence features. In this paper, we propose a 4 mC site identification algorithm, DNABert-4mC, based on a fusion of the pruned pre-training DNABert-Pruning model and artificial feature encoding to identify 4 mC sites. The algorithm prunes and compresses the DNABert model, resulting in the pruned pre-training model DNABert-Pruning. This model reduces the number of parameters and removes redundancy from output features, yielding more precise feature representations while upholding accuracy.Simultaneously, the algorithm constructs an artificial feature encoding module to assist the DNABert-Pruning model in feature representation, effectively supplementing the information that is missing from the pre-trained features. The algorithm also introduces the AFF-4mC fusion strategy, which combines artificial feature encoding with the DNABert-Pruning model, to improve the feature representation capability of DNA sequences in multi-semantic spaces and better extract 4 mC sites and the distribution of nucleotide importance within the sequence. In experiments on six independent test sets, the DNABert-4mC algorithm achieved an average AUC value of 93.81%, outperforming seven other advanced algorithms with improvements of 2.05%, 5.02%, 11.32%, 5.90%, 12.02%, 2.42% and 2.34%, respectively.

Elucidation of the Reversible Self-Association Interface of a Diabody-Interleukin Fusion Protein Using Hydrogen-Exchange Mass Spectrometry and In Silico Modeling.

Reversible self-association (RSA) of therapeutic proteins presents major challenges in the development of high-concentration formulations, especially those intended for subcutaneous administration. Understanding self-association mechanisms is therefore critical to the design and selection of candidates with acceptable developability to advance to clinical trials. The combination of experiments and in silico modeling presents a powerful tool to elucidate the interface of self-association. RSA of monoclonal antibodies has been studied extensively under different solution conditions and have been shown to involve interactions for both the antigen-binding fragment and the crystallizable fragment. Novel modalities such as bispecific antibodies, antigen-binding fragments, single-chain-variable fragments, and diabodies constitute a fast-growing class of antibody-based therapeutics that have unique physiochemical properties compared to monoclonal antibodies. In this study, the RSA interface of a diabody-interleukin 22 fusion protein (FP-1) was studied using hydrogen-deuterium exchange coupled with mass spectrometry (HDX-MS) in combination with in silico modeling. Taken together, the results show that a complex solution behavior underlies the self-association of FP-1 and that the interface thereof can be attributed to a specific segment in the variable light chain of the diabody. These findings also demonstrate that the combination of HDX-MS with in silico modeling is a powerful tool to guide the design and candidate selection of novel biotherapeutic modalities.

LUNCRW: Prediction of potential lncRNA-disease associations based on unbalanced neighborhood constraint random walk.

Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are associated with various complex human diseases. They can serve as disease biomarkers and hold considerable promise for the prevention and treatment of various diseases. The traditional random walk algorithms generally exclude the effect of non-neighboring nodes on random walking. In order to overcome the issue, the neighborhood constraint (NC) approach is proposed in this study for regulating the direction of the random walk by computing the effects of both neighboring nodes and non-neighboring nodes. Then the association matrix is updated by matrix multiplication for minimizing the effect of the false negative data. The heterogeneous lncRNA-disease network is finally analyzed using an unbalanced random walk method for predicting the potential lncRNA-disease associations. The LUNCRW model is therefore developed for predicting potential lncRNA-disease associations. The area under the curve (AUC) values of the LUNCRW model in leave-one-out cross-validation and five-fold cross-validation were 0.951 and 0.9486 ± 0.0011, respectively. Data from published case studies on three diseases, including squamous cell carcinoma, hepatocellular carcinoma, and renal cell carcinoma, confirmed the predictive potential of the LUNCRW model. Altogether, the findings indicated that the performance of the LUNCRW method is superior to that of existing methods in predicting potential lncRNA-disease associations.

Image Adversarial Example Generation Method Based on Adaptive Parameter Adjustable Differential Evolution.

Adversarial example generation techniques for neural network models have exploded in recent years. In the adversarial attack scheme for image recognition models, it is challenging to achieve a high attack success rate with very few pixel modifications. To address this issue, this paper proposes an adversarial example generation method based on adaptive parameter adjustable differential evolution. The method realizes the dynamic adjustment of the algorithm performance by adjusting the control parameters and operation strategies of the adaptive differential evolution algorithm, while searching for the optimal perturbation. Finally, the method generates adversarial examples with a high success rate, modifying just a very few pixels. The attack effectiveness of the method is confirmed in CIFAR10 and MNIST datasets. The experimental results show that our method has a greater attack success rate than the One Pixel Attack based on the conventional differential evolution. In addition, it requires significantly less perturbation to be successful compared to global or local perturbation attacks, and is more resistant to perception and detection.

HBRWRLDA: predicting potential lncRNA-disease associations based on hypergraph bi-random walk with restart.

Accumulating evidence indicates that the regulation of long non-coding RNAs (lncRNAs) is closely related to a variety of diseases. Identifying meaningful lncRNA-disease associations will help to contribute to the understanding of the molecular mechanisms underlying these diseases. However, only a limited number of associations between lncRNAs and diseases have been inferred from traditional biological experiments due to the high cost and highly specialized. Therefore, computational methods are increasingly used to reduce time of biological experiments and complement biological research. In this paper, a computational method called HBRWRLDA is proposed to predict lncRNA-disease associations. First, HBRWRLDA models the relationships between multiple nodes using hypergraphs, which allows HBRWRLDA to integrate the expression similarity of lncRNAs and the semantic similarity of diseases to construct hypergraphs. Then, a bi-random walk on hypergraphs is used to predict potential lncRNA-disease associations. HBRWRLDA achieves a higher area under the curve value of 0.9551 and [Formula: see text], respectively, compared with the other five advanced methods under the framework of one-leave cross validation (LOOCV) and five-fold cross-validation (5-fold CV). In addition, the prediction effect of HBRWRLDA was confirmed case studies of three diseases: renal cell carcinoma, gastric cancer, and hepatocellular carcinoma. Case studies demonstrates the capacity of HBRWRLDA to identify potentially disease-associated lncRNAs. Overall, HBRWRLDA is excellent at predicting potential lncRNA-disease associations and could be useful in conducting further biological experiments by helping researchers identify candidates of lncRNA-disease association.

DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.

Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity. Furthermore, multiple types of similarity information are fused by a CKA-MKL algorithm. Finally, a compressed sensing algorithm is used to predict the potential drug-disease (virus) association scores. Experimental results show that DRPADC has superior performance than several competitive methods in terms of AUC values and case studies. DRPADC achieved the AUC value of 0.941, 0.955 and 0.876 in Fdataset, Cdataset and HDVD dataset, respectively. In addition, the conducted case studies of COVID-19 show that DRPADC can predict drug candidates accurately.

Genome-Wide Identification of MYB Transcription Factors and Screening of Members Involved in Stress Response in Actinidia.

MYB transcription factors (TFs) play an active role in plant responses to abiotic stresses, but they have not been systematically studied in kiwifruit (Actinidia chinensis). In this study, 181 AcMYB TFs were identified from the kiwifruit genome, unevenly distributed on 29 chromosomes. The high proportion (97.53%) of segmental duplication events (Ka/Ks values less than 1) indicated that AcMYB TFs underwent strong purification selection during evolution. According to the conservative structure, 91 AcR2R3-MYB TFs could be divided into 34 subgroups. A combination of transcriptomic data under drought and high temperature from four AcMYB TFs (AcMYB2, AcMYB60, AcMYB61 and AcMYB102) was screened out in response to stress and involvement in the phenylpropanoid pathway. They were highly correlated with the expression of genes related to lignin biosynthesis. qRT-PCR analysis showed that they were highly correlated with the expression of genes related to lignin biosynthesis in different tissues or under stress, which was consistent with the results of lignin fluorescence detection. The above results laid a foundation for further clarifying the role of MYB in stress.

Ferrous iron-induced increases in capitate glandular trichome density and upregulation of CbHO-1 contributes to increases in blinin content in Conyza blinii.

MAIN CONCLUSION: Ferrous iron can promote the development of glandular trichomes and increase the content of blinin, which depends on CbHO-1 expression. Conyza blinii (C. blinii) is a unique Chinese herbal medicine that grows in Sichuan Province, China. Because the habitat of C. blinii is an iron ore mining area with abundant iron content, this species can be used as one of the best materials to study the mechanism of plant tolerance to iron. In this study, C. blinii was treated with ferrous-EDTA solutions at different concentrations, and it was found that the tolerance value of C. blinii to iron was 200 µM. Under this concentration, the plant height, root length, biomass, and iron content of C. blinii increased to the maximum values, and the effect was dependent on the upregulated expression of CbHO-1. At the same time, under ferrous iron, the photosynthetic capacity and capitate glandular trichome density of C. blinii also significantly increased, providing precursors and sites for the synthesis of blinin, thus significantly increasing the content of blinin. These processes were also dependent on the high expression of CbHO-1. Correlation analysis showed that there were strong positive correlations between iron content, capitate glandular trichome density, CbHO-1 gene expression, and blinin content. This study explored the effects of ferrous iron on the physiology and biochemistry of C. blinii, greatly improving our understanding of the mechanism of iron tolerance in C. blinii.

Synthesis and Characterization of Long-Acting Darunavir Prodrugs.

Antiretroviral therapy (ART) has improved the quality of life in patients infected with HIV-1. However, complete viral suppression within anatomical compartments remains unattainable. This is complicated by adverse side effects and poor adherence to lifelong therapy leading to the emergence of viral drug resistance. Thus, there is an immediate need for cellular and tissue-targeted long-acting (LA) ART formulations. Herein, we describe two LA prodrug formulations of darunavir (DRV), a potent antiretroviral protease inhibitor. Two classes of DRV prodrugs, M1DRV and M2DRV, were synthesized as lipophilic and hydrophobic prodrugs and stabilized into aqueous suspensions designated NM1DRV and NM2DRV. The formulations demonstrated enhanced intracellular prodrug levels with sustained drug retention and antiretroviral activities for 15 and 30 days compared to native DRV formulation in human monocyte-derived macrophages. Pharmacokinetics tests of NM1DRV and NM2DRV administered to mice demonstrated sustained drug levels in blood and tissues for 30 days. These data, taken together, support the idea that LA DRV with sustained antiretroviral responses through prodrug nanoformulations is achievable.

LLCLPLDA: a novel model for predicting lncRNA-disease associations.

Long noncoding RNAs play a significant role in the occurrence of diseases. Thus, studying the relationship prediction between lncRNAs and disease is becoming more popular. Researchers hope to determine effective treatments by revealing the occurrence and development of diseases at the molecular level. However, the traditional biological experimental way to verify the association between lncRNAs and disease is very time-consuming and expensive. Therefore, we developed a method called LLCLPLDA to predict potential lncRNA-disease associations. First, locality-constrained linear coding (LLC) is leveraged to project the features of lncRNAs and diseases to local-constraint features, and then, a label propagation (LP) strategy is used to mix up the initial association matrix and the obtained features of lncRNAs and diseases. To demonstrate the performance of our method, we compared LLCLPLDA with five methods in the leave-one-out cross-validation and fivefold cross-validation scheme, and the experimental results show that the proposed method outperforms the other five methods. Additionally, we conducted case studies on three diseases: cervical cancer, gliomas, and breast cancer. The top five predicted lncRNAs for cervical cancer and gliomas were verified, and four of the five lncRNAs for breast cancer were also confirmed.

Antiretroviral Drug Metabolism in Humanized PXR-CAR-CYP3A-NOG Mice.

Antiretroviral drug (ARV) metabolism is linked largely to hepatic cytochrome P450 activity. One ARV drug class known to be metabolized by intestinal and hepatic CYP3A are the protease inhibitors (PIs). Plasma drug concentrations are boosted by CYP3A inhibitors such as cobisistat and ritonavir (RTV). Studies of such drug-drug interactions are limited since the enzyme pathways are human specific. While immune-deficient mice reconstituted with human cells are an excellent model to study ARVs during human immunodeficiency virus type 1 (HIV-1) infection, they cannot reflect human drug metabolism. Thus, we created a mouse strain with the human pregnane X receptor, constitutive androstane receptor, and CYP3A4/7 genes on a NOD.Cg-Prkdcscid Il2rgtm1Sug /JicTac background (hCYP3A-NOG) and used them to evaluate the impact of human CYP3A metabolism on ARV pharmacokinetics. In proof-of-concept studies we used nanoformulated atazanavir (nanoATV) with or without RTV. NOG and hCYP3A-NOG mice were treated weekly with 50 mg/kg nanoATV alone or boosted with nanoformulated ritonavir (nanoATV/r). Plasma was collected weekly and liver was collected at 28 days post-treatment. Plasma and liver atazanavir (ATV) concentrations in nanoATV/r-treated hCYP3A-NOG mice were 2- to 4-fold higher than in replicate NOG mice. RTV enhanced plasma and liver ATV concentrations 3-fold in hCYP3A-NOG mice and 1.7-fold in NOG mice. The results indicate that human CYP3A-mediated drug metabolism is reduced compared with mouse and that RTV differentially affects human gene activity. These differences can affect responses to PIs in humanized mouse models of HIV-1 infection. Importantly, hCYP3A-NOG mice reconstituted with human immune cells can be used for bench-to-bedside translation.

Self-immolative polycations as gene delivery vectors and prodrugs targeting polyamine metabolism in cancer.

Polycations are explored as carriers to deliver therapeutic nucleic acids. Polycations are conventionally pharmacological inert with the sole function of delivering therapeutic cargo. This study reports synthesis of a self-immolative polycation (DSS-BEN) based on a polyamine analogue drug N(1),N(11)-bisethylnorspermine (BENSpm). The polycation was designed to function dually as a gene delivery carrier and a prodrug targeting dysregulated polyamine metabolism in cancer. Using a combination of NMR and HPLC, we confirm that the self-immolative polycation undergoes intracellular degradation into the parent drug BENSpm. The released BENSpm depletes cellular levels of spermidine and spermine and upregulates polyamine catabolic enzymes spermine/spermidine N(1)-acetyltransferase (SSAT) and spermine oxidase (SMO). The synthesized polycations form polyplexes with DNA and facilitate efficient transfection. Taking advantage of the ability of BENSpm to sensitize cancer cells to TNFα-induced apoptosis, we show that DSS-BEN enhances the cell killing activity of TNFα gene therapy. The reported findings validate DSS-BEN as a dual-function delivery system that can deliver a therapeutic gene and improve the outcome of gene therapy as a result of the intracellular degradation of DSS-BEN to BENSpm and the subsequent beneficial effect of BENSpm on dysregulated polyamine metabolism in cancer.

Endothelial progenitor cells as a possible component of stem cell niche to promote self-renewal of mesenchymal stem cells.

Stem cells dwell at the "stem cell niche" to accomplish a series of biological processes. The composition of the niche should be determined because the insufficient understanding of this feature limits the development in the study of stem cells. We showed in our study on mesenchymal stem cells (MSCs) that the MSCs first neighbored to CD31(+) cells, which proved to be endothelial progenitor cells (EPCs), and formed a group of cell colony before they exerted their biological functions. It was further proved that EPCs have close interactions with MSCs and promoted the self-renewal of the MSCs in vitro and in vivo. Together with these achievements, we hypothesized that EPCs may be a possible biological component of the MSC stem cell niche and affect the biological processes of MSCs.

Being a right parent: a narrative review of the theory and practice of parental involvement in sport parenting.

The family is the first classroom for children and adolescents to learn and grow, and parents' behavior plays an important role in influencing their children's development, which is also evident in the process of sport participation. The main purpose of this study is to summarise the specific theoretical and practical experiences of parents in sport parenting based on a comprehensive review of the types and functions that constitute parental involvement in sport parenting and the process of their practice. To this end, this study used narrative research as the main research method and searched the literature related to parents' involvement in parenting through sport using the Web of Science database. Using the theoretical underpinnings of parents' implementation of sport parenting and their role practice, studies were screened and 39 pieces of literature were finally obtained. The study found that in terms of theoretical underpinnings, the existing types of parental involvement in sport parenting can be broadly categorized into four types: authoritative, authoritarian, permissive and rejecting-neglecting. The functions of parental involvement in sport education have two dimensions: promoting sport development and promoting socialization. Based on a review of their theories, we further summarise and conclude the consequences of action and appropriate practices of parental practices in three scenarios: on the sports field, on the way home and in the private space. It is assumed that parents, when participating in sports parenting, need to: (I) regulate their own behavior in order to avoid psychological pressure on their children due to inappropriate behavior; (II) play different roles at different stages of their children's sports development; (III) should not put too much pressure on their children's performance. Based on these reviews of the theory and practice of parental involvement in sport parenting, this study further examines the theoretical limitations of the established research. It is argued that future research should pay attention to the differences between the identities and expectations of parents or children of different genders about their sport parenting, in addition to the differences in parental involvement in sport parenting and different practices in different cultural contexts.

Predicting Microbe-Disease Associations Based on a Linear Neighborhood Label Propagation Method with Multi-order Similarity Fusion Learning.

Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases. Therefore, it is important to obtain reliable similarity information by integrating multiple types of similarity information. However, existing similarity fusion methods do not consider multi-order fusion of similarity networks. To address this problem, a novel method of linear neighborhood label propagation with multi-order similarity fusion learning (MOSFL-LNP) is proposed to predict potential microbe-disease associations. Multi-order fusion learning comprises two parts: low-order global learning and high-order feature learning. Low-order global learning is used to obtain common latent features from multiple similarity sources. High-order feature learning relies on the interactions between neighboring nodes to identify high-order similarities and learn deeper interactive network structures. Coefficients are assigned to different high-order feature learning modules to balance the similarities learned from different orders and enhance the robustness of the fusion network. Overall, by combining low-order global learning with high-order feature learning, multi-order fusion learning can capture both the shared and unique features of different similarity networks, leading to more accurate predictions of microbe-disease associations. In comparison to six other advanced methods, MOSFL-LNP exhibits superior prediction performance in the leave-one-out cross-validation and 5-fold validation frameworks. In the case study, the predicted 10 microbes associated with asthma and type 1 diabetes have an accuracy rate of up to 90% and 100%, respectively.

Prediction of miRNA-disease associations based on strengthened hypergraph convolutional autoencoder.

Most existing graph neural network-based methods for predicting miRNA-disease associations rely on initial association matrices to pass messages, but the sparsity of these matrices greatly limits performance. To address this issue and predict potential associations between miRNAs and diseases, we propose a method called strengthened hypergraph convolutional autoencoder (SHGAE). SHGAE leverages multiple layers of strengthened hypergraph neural networks (SHGNN) to obtain robust node embeddings. Within SHGNN, we design a strengthened hypergraph convolutional network module (SHGCN) that enhances original graph associations and reduces matrix sparsity. Additionally, SHGCN expands node receptive fields by utilizing hyperedge features as intermediaries to obtain high-order neighbor embeddings. To improve performance, we also incorporate attention-based fusion of self-embeddings and SHGCN embeddings. SHGAE predicts potential miRNA-disease associations using a multilayer perceptron as the decoder. Across multiple metrics, SHGAE outperforms other state-of-the-art methods in five-fold cross-validation. Furthermore, we evaluate SHGAE on colon and lung neoplasms cases to demonstrate its ability to predict potential associations. Notably, SHGAE also performs well in the analysis of gastric neoplasms without miRNA associations.

RNA m6A modification regulates cell fate transition between pluripotent stem cells and 2-cell-like cells.

N6-methyladenosine (m6A) exerts essential roles in early embryos, especially in the maternal-to-zygotic transition stage. However, the landscape and roles of RNA m6A modification during the transition between pluripotent stem cells and 2-cell-like (2C-like) cells remain elusive. Here, we utilised ultralow-input RNA m6A immunoprecipitation to depict the dynamic picture of transcriptome-wide m6A modifications during 2C-like transitions. We found that RNA m6A modification was preferentially enriched in zygotic genome activation (ZGA) transcripts and MERVL with high expression levels in 2C-like cells. During the exit of the 2C-like state, m6A facilitated the silencing of ZGA genes and MERVL. Notably, inhibition of m6A methyltransferase METTL3 and m6A reader protein IGF2BP2 is capable of significantly delaying 2C-like state exit and expanding 2C-like cells population. Together, our study reveals the critical roles of RNA m6A modification in the transition between 2C-like and pluripotent states, facilitating the study of totipotency and cell fate decision in the future.

Research on a collaborative evolution model of multi-airport route network considering subsidy strategies.

To efficiently utilize subsidy strategies for optimizing multi-airport route networks and promoting collaborative development among multiple airports, we delve into the tripartite strategic interactions between passengers, airlines and airports. A dual-layer game-theoretic model is constructed to optimize subsidy strategies, facilitating a synergistic alignment between multi-airport positioning and route networks. In the upper-layer game-theoretic model, Fermi rules are employed to analyze the interplay between pricing strategies of distinct airline brands and passenger travel preferences, aiding in determining optimal pricing strategies for airlines. The lower-layer game-theoretic model introduces an asymmetric stochastic best response equilibrium (QRE) model, drawing insights from optimal airline pricing and the impact of airport subsidies on airline route adjustments to formulate effective multi-airport subsidy strategies. The results reveal that: (ⅰ) Airline revenues display varying peaks based on travel distances, with optimal fare discount intervals clustering between 0.6 and 0.9, contingent upon travel distances and passenger rationality; (ⅱ) dynamic monopolistic intervals and inefficient ranges characterize airport subsidy strategies due to diverse competitive strategies employed by rivals; (ⅲ) targeted airport subsidy strategies can enhance inter-airport route coordination in alignment with their functional positioning. This research provides decision-making insights into collaborative airport group development, encompassing airport subsidy strategies and considerations for airline pricing.