Prediction Of Protein-protein Interactions Research Articles

Genome-wide identification and expression analysis of ARF gene family in embryonic development of Korean pine (Pinus koraiensis)

BackgroundThe Auxin Responsive Factor (ARF) family plays a crucial role in mediating auxin signal transduction and is vital for plant growth and development. However, the function of ARF genes in Korean pine (Pinus koraiensis), a conifer species of significant economic value, remains unclear.ResultsThis study utilized the whole genome of Korean pine to conduct bioinformatics analysis, resulting in the identification of 13 ARF genes. A phylogenetic analysis revealed that these 13 PkorARF genes can be classified into 4 subfamilies, indicating the presence of conserved structural characteristics within each subfamily. Protein interaction prediction indicated that Pkor01G00962.1 and Pkor07G00704.1 may have a significant role in regulating plant growth and development as core components of the PkorARFs family. Additionally, the analysis of RNA-seq and RT-qPCR expression patterns suggested that PkorARF genes play a crucial role in the development process of Korean pine.ConclusionPkor01G00962.1 and Pkor07G00704.1, which are core genes of the PkorARFs family, play a potentially crucial role in regulating the fertilization and developmental process of Korean pine. This study provides a valuable reference for investigating the molecular mechanism of embryonic development in Korean pine and establishes a foundation for cultivating high-quality Korean pine.

Expressional vagaries of OsCAMTA genes under differential abiotic stresses supported with protein–protein interaction study and prediction of miRNA target sites

Expressional vagaries of OsCAMTA genes under differential abiotic stresses supported with protein–protein interaction study and prediction of miRNA target sites

Decoding the Arsenal: Protist Effectors and Their Impact on Photosynthetic Hosts.

Interactions between various microbial pathogens including viruses, bacteria, fungi, oomycetes, and their plant hosts have traditionally been the focus of phytopathology. In recent years, a significant and growing interest in the study of eukaryotic microorganisms not classified among fungi or oomycetes has emerged. Many of these protists establish complex interactions with photosynthetic hosts, and understanding these interactions is crucial in understanding the dynamics of these parasites within traditional and emerging types of farming, including marine aquaculture. Many phytopathogenic protists are biotrophs with complex polyphasic life cycles, which makes them difficult or impossible to culture, a fact reflected in a wide gap in the availability of comprehensive genomic data when compared to fungal and oomycete plant pathogens. Furthermore, our ability to use available genomic resources for these protists is limited by the broad taxonomic distance that these organisms span, which makes comparisons with other genomic datasets difficult. The current rapid progress in genomics and computational tools for the prediction of protein functions and interactions is revolutionizing the landscape in plant pathology. This is also opening novel possibilities, specifically for a deeper understanding of protist effectors. Tools like AlphaFold2 enable structure-based function prediction of effector candidates with divergent protein sequences. In turn, this allows us to ask better biological questions and, coupled with innovative experimental strategies, will lead into a new era of effector research, especially for protists, to expand our knowledge on these elusive pathogens and their interactions with photosynthetic hosts. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Xuefu Zhuyu decoction promotes synaptic plasticity by targeting miR-191a-5p/BDNF-TrkB axis in severe traumatic brain injury

BackgroundXuefu Zhuyu decoction (XFZYD) is a traditional Chinese herbal formula known for its ability to eliminate blood stasis and improve blood circulation, providing neuroprotection against severe traumatic brain injury (sTBI). However, the underlying mechanism is still unclear. PurposeWe aim to investigate the neuroprotective effects of XFZYD in sTBI from a novel mechanistic perspective of miRNA-mRNA. Additionally, we sought to elucidate a potential specific mechanism by integrating transcriptomics, bioinformatics, and conducting both in vitro and in vivo experiments. MethodsThe sTBI rat model was established, and the rats were treated with XFZYD for 14 days. The neuroprotective effects of XFZYD were evaluated using a modified neurological severity score, hematoxylin and eosin staining, as well as Nissl staining. The anti-inflammatory effects of XFZYD were explored using quantitative real-time PCR (qRT-PCR), Western blot analysis, and immunofluorescence. Next, miRNA sequencing of the hippocampus was performed to determine which miRNAs were differentially expressed. Subsequently, qRT-PCR was used to validate the differentially expressed miRNAs. Target core mRNAs were determined using various methods, including miRNA prediction targets, mRNA sequencing, miRNA-mRNA network, and protein-protein interaction (PPI) analysis. The miRNA/mRNA regulatory axis were verified through qRT-PCR or Western blot analysis. Finally, morphological changes in the neural synapses were observed using transmission electron microscopy and immunofluorescence. ResultsXFZYD exhibited significant neuroprotective and anti-inflammatory effects on subacute sTBI rats' hippocampus. The analyses of miRNA/mRNA sequences combined with the PPI network revealed that the therapeutic effects of XFZYD on sTBI were associated with the regulation of the rno-miR-191a-5p/BDNF axis. Subsequently, qRT-PCR and Western blot analysis confirmed XFZYD reversed the decrease of BDNF and TrkB in the hippocampus caused by sTBI. Additionally, XFZYD treatment potentially increased the number of synaptic connections, and the expression of the synapse-related protein PSD95, axon-related protein GAP43 and neuron-specific protein TUBB3. ConclusionsXFZYD exerts neuroprotective effects by promoting hippocampal synaptic remodeling and improving cognition during the subacute phase of sTBI through downregulating of rno-miR-191a-5p/BDNF axis, further activating BDNF-TrkB signaling.

Abstract 550: Identification of isoform switching events linked with esophageal adenocarcinoma patient survival informs novel prognostic and therapeutic targets

Abstract Esophageal adenocarcinoma (EAC) represents a growing health problem characterized by rising incidence and poor prognosis. Although it is a cancer with a high mutational burden, it lacks highly prevalent oncogenic drivers that can be therapeutically targeted. Recently, our group characterized isoform switching events, defined by the changing of dominant gene isoforms between conditions, associated with EAC progression and identified 75 switching events comparing Barrett’s low-grade dysplasia (BE.LGD) and Barrett’s high-grade dysplasia (BE.HGD) patient samples. Herein, we further investigated whether isoform-switching events may offer prognostic or therapeutic value in EAC. Isoform switching analysis was performed using RNA-seq data derived from patient samples between BE.LGD and BE.HGD combined with EAC (BE.HGD + EAC) alone or in combination with TP53 mutations. Patients were further stratified into tertiles based on isoform expression level followed by survival analysis. Thirty isoforms were significantly (P≤0.05) linked with all-cause mortality, comparing BE.LGD with BE.HGD + EAC, whereas 20 isoforms were identified when TP53 mutation status was included. In terms of cancer-specific mortality, 10 isoforms were significantly linked with survival comparing BE.LGD with BE.HGD+EAC. With the inclusion of TP53 mutation, 16 isoforms were also significantly linked with cancer-specific mortality. Isoform-specific genetic knockdown experiments were designed targeting the top isoforms identified in the analysis, including TTLL12 and HM13 isoforms. Knockdown of TTLL12 and HM13 isoforms led to significant inhibition of cell viability and migration in two EAC cell lines (OE19 and OE33). Chemotherapeutic agents (Paclitaxel + Carboplatin) further decreased cell viability with synergistic effects observed in both siRNA-treated cell lines suggesting a role for specific isoform switches in therapeutic sensitization. Protein-interaction prediction using STRING and immunoblot analysis suggest different mechanisms leading to the inhibition of cell viability and migration. TTLL12 is linked with the activation of chaperon-medicated autophagy (LAMP2A and HSC70) and protein translation, whereas HM13 is linked with unfolded protein response (IRE1α, PERK, and ATF6) and may be linked with immunotherapy outcome. An antigen-dependent cell-mediated cytotoxicity assay is currently underway to investigate whether the knockdown of HM13 isoforms can sensitize EAC cells to PD-1/PD-L1 immunotherapy agents. Future direction includes using FDA-approved agents to target the two major pathways identified above. In conclusion, isoform switching may provide fresh insight for the identification of prognostic markers and inform new potential therapeutic targets for EAC treatment or prevention. Citation Format: Yun Zhang, Rachel Israel, Bryce Vandenburg, Shari Barnett, Jean-Jack Riethoven, Jennifer L. Clarke, Michelle P. Lee, Kiran Lagisetty, Jules Lin, Rishindra Reddy, Andrew Chang, Laura A. Kresty. Identification of isoform switching events linked with esophageal adenocarcinoma patient survival informs novel prognostic and therapeutic targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 550.

Chalkiness and premature controlled by energy homeostasis in OsNAC02 Ko-mutant during vegetative endosperm development.

Chalkiness is a common phenotype induced by various reasons, such as abiotic stress or the imbalance of starch synthesis and metabolism during the development period. However, the reason mainly for one gene losing its function such as NAC (TFs has a large family in rice) which may cause premature is rarely known to us. The Ko-Osnac02 mutant demonstrated an obviously early maturation stage compared to the wild type (WT) with 15days earlier. The result showed that the mature endosperm of Ko-Osnac02 mutant exhibited chalkiness, characterized by white-core and white-belly in mature endosperm. As grain filling rate is a crucial factor in determining the yield and quality of rice (Oryza sativa, ssp. japonica), it's significant that mutant has a lower amylose content (AC) and higher soluble sugar content in the mature endosperm. Interestingly among the top DEGs in the RNA sequencing of N2 (3DAP) and WT seeds revealed that the OsBAM2 (LOC_Os10g32810) expressed significantly high in N2 mutant, which involved in Maltose up-regulated by the starch degradation. As Prediction of Protein interaction showed in the chalky endosperm formation in N2 seeds (3 DAP), seven genes were expressed at a lower-level which should be verified by a heatmap diagrams based on DEGs of N2 versus WT. The Tubulin genes controlling cell cycle are downregulated together with the MCM family genes MCM4 ( ↓), MCM7 ( ↑), which may cause white-core in the early endosperm development. In conclusion, the developing period drastically decreased in the Ko-Osnac02 mutants, which might cause the chalkiness in seeds during the early endosperm development. The gene OsNAC02 which controls a great genetic co-network for cell cycle regulation in early development, and KO-Osnac02 mutant shows prematurity and white-core in endosperm.

SpatialPPI: Three-dimensional space protein-protein interaction prediction with AlphaFold Multimer

Rapid advancements in protein sequencing technology have resulted in gaps between proteins with identified sequences and those with mapped structures. Although sequence-based predictions offer insights, they can be incomplete due to the absence of structural details. Conversely, structure-based methods face challenges with respect to newly sequenced proteins. The AlphaFold Multimer has remarkable accuracy in predicting the structure of protein complexes. However, it cannot distinguish whether the input protein sequences can interact. Nonetheless, by analyzing the information in the models predicted by the AlphaFold Multimer, we propose a highly accurate method for predicting protein interactions. This study focuses on the use of deep neural networks, specifically to analyze protein complex structures predicted by the AlphaFold Multimer. By transforming atomic coordinates and utilizing sophisticated image-processing techniques, vital 3D structural details were extracted from protein complexes. Recognizing the significance of evaluating residue distances in protein interactions, this study leveraged image recognition approaches by integrating Densely Connected Convolutional Networks (DenseNet) and Deep Residual Network (ResNet) within 3D convolutional networks for protein 3D structure analysis. When benchmarked against leading protein-protein interaction prediction methods, such as SpeedPPI, D-script, DeepTrio, and PEPPI, our proposed method, named SpatialPPI, exhibited notable efficacy, emphasizing the promising role of 3D spatial processing in advancing the realm of structural biology. The SpatialPPI code is available at: https://github.com/ohuelab/SpatialPPI.

Computational prediction of protein interactions in single cells by proximity sequencing.

Proximity sequencing (Prox-seq) simultaneously measures gene expression, protein expression and protein complexes on single cells. Using information from dual-antibody binding events, Prox-seq infers surface protein dimers at the single-cell level. Prox-seq provides multi-dimensional phenotyping of single cells in high throughput, and was recently used to track the formation of receptor complexes during cell signaling and discovered a novel interaction between CD9 and CD8 in naïve T cells. The distribution of protein abundance can affect identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model of Prox-seq and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq data, which resulted in more accurate and robust quantification of protein complexes. Finally, our Prox-seq model offers a simple way to investigate the behavior of Prox-seq data under various biological conditions and guide users toward selecting the best analysis method for their data.

A multi-source molecular network representation model for protein–protein interactions prediction

The prediction of potential protein–protein interactions (PPIs) is a critical step in decoding diseases and understanding cellular mechanisms. Traditional biological experiments have identified plenty of potential PPIs in recent years, but this problem is still far from being solved. Hence, there is urgent to develop computational models with good performance and high efficiency to predict potential PPIs. In this study, we propose a multi-source molecular network representation learning model (called MultiPPIs) to predict potential protein–protein interactions. Specifically, we first extract the protein sequence features according to the physicochemical properties of amino acids by utilizing the auto covariance method. Second, a multi-source association network is constructed by integrating the known associations among miRNAs, proteins, lncRNAs, drugs, and diseases. The graph representation learning method, DeepWalk, is adopted to extract the multisource association information of proteins with other biomolecules. In this way, the known protein–protein interaction pairs can be represented as a concatenation of the protein sequence and the multi-source association features of proteins. Finally, the Random Forest classifier and corresponding optimal parameters are used for training and prediction. In the results, MultiPPIs obtains an average 86.03% prediction accuracy with 82.69% sensitivity at the AUC of 93.03% under five-fold cross-validation. The experimental results indicate that MultiPPIs has a good prediction performance and provides valuable insights into the field of potential protein–protein interactions prediction. MultiPPIs is free available at https://github.com/jiboyalab/multiPPIs.

Advances in Computational Methods for Protein–Protein Interaction Prediction

Protein–protein interactions (PPIs) are pivotal in various physiological processes inside biological entities. Accurate identification of PPIs holds paramount significance for comprehending biological processes, deciphering disease mechanisms, and advancing medical research. Given the costly and labor-intensive nature of experimental approaches, a multitude of computational methods have been devised to enable swift and large-scale PPI prediction. This review offers a thorough examination of recent strides in computational methodologies for PPI prediction, with a particular focus on the utilization of deep learning techniques within this domain. Alongside a systematic classification and discussion of relevant databases, feature extraction strategies, and prominent computational approaches, we conclude with a thorough analysis of current challenges and prospects for the future of this field.

Enhancing coevolutionary signals in protein–protein interaction prediction through clade-wise alignment integration

Protein–protein interactions (PPIs) play essential roles in most biological processes. The binding interfaces between interacting proteins impose evolutionary constraints that have successfully been employed to predict PPIs from multiple sequence alignments (MSAs). To construct MSAs, critical choices have to be made: how to ensure the reliable identification of orthologs, and how to optimally balance the need for large alignments versus sufficient alignment quality. Here, we propose a divide-and-conquer strategy for MSA generation: instead of building a single, large alignment for each protein, multiple distinct alignments are constructed under distinct clades in the tree of life. Coevolutionary signals are searched separately within these clades, and are only subsequently integrated using machine learning techniques. We find that this strategy markedly improves overall prediction performance, concomitant with better alignment quality. Using the popular DCA algorithm to systematically search pairs of such alignments, a genome-wide all-against-all interaction scan in a bacterial genome is demonstrated. Given the recent successes of AlphaFold in predicting direct PPIs at atomic detail, a discover-and-refine approach is proposed: our method could provide a fast and accurate strategy for pre-screening the entire genome, submitting to AlphaFold only promising interaction candidates—thus reducing false positives as well as computation time.

XCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model

BackgroundPredicting protein–protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, which contain diverse information, including structural, evolutionary, and functional aspects, has not been fully exploited. Additionally, there is a significant need for a comprehensive neural network capable of efficiently extracting these multifaceted representations.ResultsAddressing this gap, we propose xCAPT5, a novel hybrid classifier that uniquely leverages the T5-XL-UniRef50 protein large language model for generating rich amino acid embeddings from protein sequences. The core of xCAPT5 is a multi-kernel deep convolutional siamese neural network, which effectively captures intricate interaction features at both micro and macro levels, integrated with the XGBoost algorithm, enhancing PPIs classification performance. By concatenating max and average pooling features in a depth-wise manner, xCAPT5 effectively learns crucial features with low computational cost.ConclusionThis study represents one of the initial efforts to extract informative amino acid embeddings from a large protein language model using a deep and wide convolutional network. Experimental results show that xCAPT5 outperforms recent state-of-the-art methods in binary PPI prediction, excelling in cross-validation on several benchmark datasets and demonstrating robust generalization across intra-species, cross-species, inter-species, and stringent similarity contexts.

Genome-Wide Identification and Expression Analysis of the PUB Gene Family in Zoysia japonica under Salt Stress.

The U-box protein family of ubiquitin ligases is important in the biological processes of plant growth, development, and biotic and abiotic stress responses. Plants in the genus Zoysia are recognized as excellent warm-season turfgrass species with drought, wear and salt tolerance. In this study, we conducted the genome-wide identification of plant U-box (PUB) genes in Zoysia japonica based on U-box domain searching. In total, 71 ZjPUB genes were identified, and a protein tree was constructed of AtPUBs, OsPUBs, and ZjPUBs, clustered into five groups. The gene structures, characteristics, cis-elements and protein interaction prediction network were analyzed. There were mainly ABRE, ERE, MYB and MYC cis-elements distributed in the promoter regions of ZjPUBs. ZjPUBs were predicted to interact with PDR1 and EXO70B1, related to the abscisic acid signaling pathway. To better understand the roles of ZjPUBs under salt stress, the expression levels of 18 ZjPUBs under salt stress were detected using transcriptome data and qRT-PCR analysis, revealing that 16 ZjPUBs were upregulated in the roots under salt treatment. This indicates that ZjPUBs might participate in the Z. japonica salt stress response. This research provides insight into the Z. japonica PUB gene family and may support the genetic improvement in the molecular breeding of salt-tolerant zoysiagrass varieties.

Kernel Bayesian nonlinear matrix factorization based on variational inference for human–virus protein–protein interaction prediction

Identification of potential human–virus protein–protein interactions (PPIs) contributes to the understanding of the mechanisms of viral infection and to the development of antiviral drugs. Existing computational models often have more hyperparameters that need to be adjusted manually, which limits their computational efficiency and generalization ability. Based on this, this study proposes a kernel Bayesian logistic matrix decomposition model with automatic rank determination, VKBNMF, for the prediction of human–virus PPIs. VKBNMF introduces auxiliary information into the logistic matrix decomposition and sets the prior probabilities of the latent variables to build a Bayesian framework for automatic parameter search. In addition, we construct the variational inference framework of VKBNMF to ensure the solution efficiency. The experimental results show that for the scenarios of paired PPIs, VKBNMF achieves an average AUPR of 0.9101, 0.9316, 0.8727, and 0.9517 on the four benchmark datasets, respectively, and for the scenarios of new human (viral) proteins, VKBNMF still achieves a higher hit rate. The case study also further demonstrated that VKBNMF can be used as an effective tool for the prediction of human–virus PPIs.

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning.

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.

MADS-Box Family Genes in Lagerstroemia indica and Their Involvement in Flower Development.

MADS-box is a key transcription factor regulating the transition to flowering and flower development. Lagerstroemia indica 'Xiang Yun' is a new cultivar of crape myrtle characterized by its non-fruiting nature. To study the molecular mechanism underlying the non-fruiting characteristics of 'Xiang Yun', 82 MADS-box genes were identified from the genome of L. indica. The physicochemical properties of these genes were examined using bioinformatics methods, and their expression as well as endogenous hormone levels at various stages of flower development were analyzed. The results showed that LiMADS genes were primarily classified into two types: type I and type II, with the majority being type II that contained an abundance of cis-acting elements in their promoters. By screening nine core proteins by predicted protein interactions and performing qRT-PCR analysis as well as in combination with transcriptome data, we found that the expression levels of most MADS genes involved in flower development were significantly lower in 'Xiang Yun' than in the wild type 'Hong Ye'. Hormonal analysis indicated that 'Xiang Yun' had higher levels of iP, IPR, TZR, and zeatin during its early stages of flower development than 'Hong Ye', whereas the MeJA content was substantially lower at the late stage of flower development of 'Hong Ye'. Finally, correlation analysis showed that JA, IAA, SA, and TZR were positively correlated with the expression levels of most type II genes. Based on these analyses, a working model for the non-fruiting 'Xiang Yun' was proposed. During the course of flower development, plant hormone response pathways may affect the expression of MADS genes, resulting in their low expression in flower development, which led to the abnormal development of the stamen and embryo sac and ultimately affected the fruiting process of 'Xiang Yun'.

ECA-PHV: Predicting human-virus protein-protein interactions through an interpretable model of effective channel attention mechanism

The prediction of human-virus protein-protein interactions (human-virus PPIs) is significant for exploring the mechanisms of viral infection, making their prediction a necessary and practically valuable research topic. Since conventional methods for the determination of human-virus protein-protein interactions are very complex and expensive, the construction of models plays a crucial role. In this paper, we construct an interpretable model, ECA-PHV, to predict human-virus protein-protein interactions based on an effective channel attention mechanism. First, we utilize five coding modalities, namely AAC, DDE, MMI, CT, and GTPC, to extract the hidden biological information in protein sequences. Individual feature weights are then learned by using a differential evolutionary algorithm that employs weighted combinations to adequately represent various protein sequence information. Next, irrelevant features in multi-information fusion are removed by Group Lasso. Finally, the prediction model is constructed by combining effective channel attention, BiGRU, and 1D-CNN. Compared with existing models, the interpretability framework ECA-PHV proposed in this paper has competitive and stable predictive performance. This shows that our model can efficiently focus on important information about protein sequences. In conclusion, this study accelerates the exploration of human-virus protein-protein interactions and provides some insights of practical value for probing human-virus relationships.

Multi-omics analysis reveals the biosynthesis of flavonoids during the browning process of Malus sieversii explants.

Malus sieversii is a precious apple germplasm resource. Browning of explants is one of the most important factors limiting the survival rate of plant tissue culture. In order to explore the molecular mechanism of the browning degree of different strains of Malus sieversii, we compared the dynamic changes of Malus sieversii and Malus robusta Rehd. during the whole browning process using a multi-group method. A total of 44 048 differentially expressed genes (DEGs) were identified by transcriptome analysis on the DNBSEQ-T7 sequencing platform. KEGG enrichment analysis showed that the DEGs were significantly enriched in the flavonoid biosynthesis pathway. In addition, metabonomic analysis showed that (-)-epicatechin, astragalin, chrysin, irigenin, isoquercitrin, naringenin, neobavaisoflavone and prunin exhibited different degrees of free radical scavenging ability in the tissue culture browning process, and their accumulation in different varieties led to differences in the browning degree among varieties. Comprehensive transcriptome and metabonomics analysis of the data related to flavonoid biosynthesis showed that PAL, 4CL, F3H, CYP73A, CHS, CHI, ANS, DFR and PGT1 were the key genes for flavonoid accumulation during browning. In addition, WGCNA analysis revealed a strong correlation between the known flavonoid structure genes and the selected transcriptional genes. Protein interaction predictions demonstrated that 19 transcription factors (7 MYBs and 12 bHLHs) and 8 flavonoid structural genes had targeted relationships. The results show that the interspecific differential expression of flavonoid genes is the key influencing factor of the difference in browning degree between Malus sieversii and Malus robusta Rehd., providing a theoretical basis for further study on the regulation of flavonoid biosynthesis.

Asymmetric Learning for Graph Neural Network based Link Prediction

Link prediction is a fundamental problem in many graph-based applications, such as protein-protein interaction prediction. Recently, graph neural network (GNN) has been widely used for link prediction. However, existing GNN-based link prediction (GNN-LP) methods suffer from scalability problem during training for large-scale graphs, which has received little attention from researchers. In this paper, we first analyze the computation complexity of existing GNN-LP methods, revealing that one reason for the scalability problem stems from their symmetric learning strategy in applying the same class of GNN models to learn representation for both head nodes and tail nodes. We then propose a novel method, called a sym m etric l earning (AML), for GNN-LP. More specifically, AML applies a GNN model to learn head node representation while applying a multi-layer perceptron (MLP) model to learn tail node representation. To the best of our knowledge, AML is the first GNN-LP method to adopt an asymmetric learning strategy for node representation learning. Furthermore, we design a novel model architecture and apply a row-wise mini-batch sampling strategy to ensure promising model accuracy and training efficiency for AML. Experiments on three real large-scale datasets show that AML is 1.7×∼7.3× faster in training than baselines with a symmetric learning strategy while having almost no accuracy loss.

Genome-Wide Analysis of Aux/IAA Gene Family in Artemisia argyi: Identification, Phylogenetic Analysis, and Determination of Response to Various Phytohormones.

Artemisia argyi is a traditional herbal medicine plant, and its folium artemisia argyi is widely in demand due to moxibustion applications globally. The Auxin/indole-3-acetic acid (Aux/IAA, or IAA) gene family has critical roles in the primary auxin-response process, with extensive involvement in plant development and stresses, controlling various essential traits of plants. However, the systematic investigation of the Aux/IAA gene family in A. argyi remains limited. In this study, a total of 61 Aux/IAA genes were comprehensively identified and characterized. Gene structural analysis indicated that 46 Aux/IAA proteins contain the four typical domains, and 15 Aux/IAA proteins belong to non-canonical IAA proteins. Collinear prediction and phylogenetic relationship analyses suggested that Aux/IAA proteins were grouped into 13 distinct categories, and most Aux/IAA genes might experience gene loss during the tandem duplication process. Promoter cis-element investigation indicated that Aux/IAA promoters contain a variety of plant hormone response and stress response cis-elements. Protein interaction prediction analysis demonstrated that AaIAA26/29/7/34 proteins are possibly core members of the Aux/IAA family interaction. Expression analysis in roots and leaves via RNA-seq data indicated that the expression of some AaIAAs exhibited tissue-specific expression patterns, and some AaIAAs were involved in the regulation of salt and saline-alkali stresses. In addition, RT-qPCR results indicated that AaIAA genes have differential responses to auxin, with complex response patterns in response to other hormones, indicating that Aux/IAA may play a role in connecting auxin and other hormone signaling pathways. Overall, these findings shed more light on AaIAA genes and offer critical foundational knowledge toward the elucidation of their function during plant growth, stress response, and hormone networking of Aux/IAA family genes in A. argyi.