Interferon-induced transmembrane protein-1 competitively blocks Ephrin receptor A2-mediated Epstein-Barr virus entry into epithelial cells.

Epstein-Barr virus (EBV) can infect both B cells and epithelial cells (ECs), causing diseases such as mononucleosis and cancer. It enters ECs via Ephrin receptor A2 (EphA2). The function of interferon-induced transmembrane protein-1 (IFITM1) in EBV infection of ECs remains elusive. Here we report that IFITM1 inhibits EphA2-mediated EBV entry into ECs. RNA-sequencing and clinical sample analysis show reduced IFITM1 in EBV-positive ECs and a negative correlation between IFITM1 level and EBV copy number. IFITM1 depletion increases EBV infection and vice versa. Exogenous soluble IFITM1 effectively prevents EBV infection in vitro and in vivo. Furthermore, three-dimensional structure prediction and site-directed mutagenesis demonstrate that IFITM1 interacts with EphA2 via its two specific residues, competitively blocking EphA2 binding to EBV glycoproteins. Finally, YTHDF3, an m6A reader, suppresses IFITM1 via degradation-related DEAD-box protein 5 (DDX5). Thus, this study underscores IFITM1's crucial role in blocking EphA2-mediated EBV entry into ECs, indicating its potential in preventing EBV infection.

ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction.

Cancer has become a major factor threatening human life and health. Under the circumstance that traditional treatment methods such as chemotherapy and radiotherapy are not highly specific and often cause severe side effects and toxicity, new treatment methods are urgently needed. Anticancer peptide drugs have low toxicity, stronger efficacy and specificity, and have emerged as a new type of cancer treatment drugs. However, experimental identification of anticancer peptides is time-consuming and expensive, and difficult to perform in a high-throughput manner. Computational identification of anticancer peptides can make up for the shortcomings of experimental identification. In this study, a deep learning-based predictor named ACPred-BMF is proposed for the prediction of anticancer peptides. This method uses the quantitative and qualitative properties of amino acids, binary profile feature to numerical representation for the peptide sequences. The Bidirectional LSTM network architecture is used in the model, and the attention mechanism is also considered. To alleviate the black-box problem of deep learning model prediction, we visualized the automatically extracted features and used the Shapley additive explanations algorithm to determine the importance of features to further understand the anticancer peptide mechanism. The results show that our method is one of the state-of-the-art anticancer peptide predictors. A web server as the implementation of ACPred-BMF that can be accessed via: http://mialab.ruc.edu.cn/ACPredBMFServer/ .

Protein-protein interaction and non-interaction predictions using gene sequence natural vector.

Predicting protein-protein interaction and non-interaction are two important different aspects of multi-body structure predictions, which provide vital information about protein function. Some computational methods have recently been developed to complement experimental methods, but still cannot effectively detect real non-interacting protein pairs. We proposed a gene sequence-based method, named NVDT (Natural Vector combine with Dinucleotide and Triplet nucleotide), for the prediction of interaction and non-interaction. For protein-protein non-interactions (PPNIs), the proposed method obtained accuracies of 86.23% for Homo sapiens and 85.34% for Mus musculus, and it performed well on three types of non-interaction networks. For protein-protein interactions (PPIs), we obtained accuracies of 99.20, 94.94, 98.56, 95.41, and 94.83% for Saccharomyces cerevisiae, Drosophila melanogaster, Helicobacter pylori, Homo sapiens, and Mus musculus, respectively. Furthermore, NVDT outperformed established sequence-based methods and demonstrated high prediction results for cross-species interactions. NVDT is expected to be an effective approach for predicting PPIs and PPNIs.

Heterogeneous multiple kernel learning for breast cancer outcome evaluation.

BACKGROUND: Breast cancer is one of the common kinds of cancer among women, and it ranks second among all cancers in terms of incidence, after lung cancer. Therefore, it is of great necessity to study the detection methods of breast cancer. Recent research has focused on using gene expression data to predict outcomes, and kernel methods have received a lot of attention regarding the cancer outcome evaluation. However, selecting the appropriate kernels and their parameters still needs further investigation. RESULTS: We utilized heterogeneous kernels from a specific kernel set including the Hadamard, RBF and linear kernels. The mixed coefficients of the heterogeneous kernel were computed by solving the standard convex quadratic programming problem of the quadratic constraints. The algorithm is named the heterogeneous multiple kernel learning (HMKL). Using the particle swarm optimization (PSO) in HMKL, we selected the kernel parameters, then we employed HMKL to perform the breast cancer outcome evaluation. By testing real-world microarray datasets, the HMKL method outperforms the methods of the random forest, decision tree, GA with Rotation Forest, BFA + RF, SVM and MKL. CONCLUSIONS: On one hand, HMKL is effective for the breast cancer evaluation and can be utilized by physicians to better understand the patient's condition. On the other hand, HMKL can choose the function and parameters of the kernel. At the same time, this study proves that the Hadamard kernel is effective in HMKL. We hope that HMKL could be applied as a new method to more actual problems.

Cideb controls sterol-regulated ER export of SREBP/SCAP by promoting cargo loading at ER exit sites.

SREBPs are master regulators of lipid homeostasis and undergo sterol-regulated export from ER to Golgi apparatus for processing and activation via COPII-coated vesicles. While COPII recognizes SREBP through its escort protein SCAP, factor(s) specifically promoting SREBP/SCAP loading to the COPII machinery remains unknown. Here, we show that the ER/lipid droplet-associated protein Cideb selectively promotes the loading of SREBP/SCAP into COPII vesicles. Sterol deprivation releases SCAP from Insig and enhances ER export of SREBP/SCAP by inducing SCAP-Cideb interaction, thereby modulating sterol sensitivity. Moreover, Cideb binds to the guanine nucleotide exchange factor Sec12 to enrich SCAP/SREBP at ER exit sites, where assembling of COPII complex initiates. Loss of Cideb inhibits the cargo loading of SREBP/SCAP, reduces SREBP activation, and alleviates diet-induced hepatic steatosis. Our data point to a linchpin role of Cideb in regulated ER export of SREBP and lipid homeostasis.

Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images.

BACKGROUND: Efficient computational recognition and segmentation of target organ from medical images are foundational in diagnosis and treatment, especially about pancreas cancer. In practice, the diversity in appearance of pancreas and organs in abdomen, makes detailed texture information of objects important in segmentation algorithm. According to our observations, however, the structures of previous networks, such as the Richer Feature Convolutional Network (RCF), are too coarse to segment the object (pancreas) accurately, especially the edge. METHOD: In this paper, we extend the RCF, proposed to the field of edge detection, for the challenging pancreas segmentation, and put forward a novel pancreas segmentation network. By employing multi-layer up-sampling structure replacing the simple up-sampling operation in all stages, the proposed network fully considers the multi-scale detailed contexture information of object (pancreas) to perform per-pixel segmentation. Additionally, using the CT scans, we supply and train our network, thus get an effective pipeline. RESULT: Working with our pipeline with multi-layer up-sampling model, we achieve better performance than RCF in the task of single object (pancreas) segmentation. Besides, combining with multi scale input, we achieve the 76.36% DSC (Dice Similarity Coefficient) value in testing data. CONCLUSION: The results of our experiments show that our advanced model works better than previous networks in our dataset. On the other words, it has better ability in catching detailed contexture information. Therefore, our new single object segmentation model has practical meaning in computational automatic diagnosis.

Structural basis for specific self-incompatibility response in Brassica.

Self-incompatibility (SI) is a widespread mechanism in flowering plants which prevents self-fertilization and inbreeding. In Brassica, recognition of the highly polymorphic S-locus cysteine-rich protein (SCR; or S-locus protein 11) by the similarly polymorphic S-locus receptor kinase (SRK) dictates the SI specificity. Here, we report the crystal structure of the extracellular domain of SRK9 (eSRK9) in complex with SCR9 from Brassica rapa. SCR9 binding induces eSRK9 homodimerization, forming a 2:2 eSRK:SCR heterotetramer with a shape like the letter "A". Specific recognition of SCR9 is mediated through three hyper-variable (hv) regions of eSRK9. Each SCR9 simultaneously interacts with hvI and one-half of hvII from one eSRK9 monomer and the other half of hvII from the second eSRK9 monomer, playing a major role in mediating SRK9 homodimerization without involving interaction between the two SCR9 molecules. Single mutations of residues critical for the eSRK9-SCR9 interaction disrupt their binding in vitro. Our study rationalizes a body of data on specific recognition of SCR by SRK and provides a structural template for understanding the co-evolution between SRK and SCR.

Targeted next-generation sequencing of dedifferentiated chondrosarcoma in the skull base reveals combined TP53 and PTEN mutations with increased proliferation index, an implication for pathogenesis.

Dedifferentiated chondrosarcoma (DDCS) is a rare disease with a dismal prognosis. DDCS consists of two morphologically distinct components: the cartilaginous and noncartilaginous components. Whether the two components originate from the same progenitor cells has been controversial. Recurrent DDCS commonly displays increased proliferation compared with the primary tumor. However, there is no conclusive explanation for this mechanism. In this paper, we present two DDCSs in the sellar region. Patient 1 exclusively exhibited a noncartilaginous component with a TP53 frameshift mutation in the pathological specimens from the first surgery. The tumor recurred after radiation therapy with an exceedingly increased proliferation index. Targeted next-generation sequencing (NGS) revealed the presence of both a TP53 mutation and a PTEN deletion in the cartilaginous and the noncartilaginous components of the recurrent tumor. Fluorescence in situ hybridization and immunostaining confirmed reduced DNA copy number and protein levels of the PTEN gene as a result of the PTEN deletion. Patient 2 exhibited both cartilaginous and noncartilaginous components in the surgical specimens. Targeted NGS of cells from both components showed neither TP53 nor PTEN mutations, making Patient 2 a naïve TP53 and PTEN control for comparison. In conclusion, additional PTEN loss in the background of the TP53 mutation could be the cause of increased proliferation capacity in the recurrent tumor.

Molecular Mechanism for Fungal Cell Wall Recognition by Rice Chitin Receptor OsCEBiP.

Chitin is the major component of fungal cell wall and serves as a molecular pattern that can be recognized by the receptor OsCEBiP in rice, a lysine motif (LysM) receptor-like protein (RLP), to trigger immune responses. The molecular mechanisms underlying chitin recognition remain elusive. Here we report the crystal structures of the ectodomain of OsCEBiP (OsCEBiP-ECD) in free and chitin-bound forms. The structures reveal that OsCEBiP-ECD contains three tandem LysMs followed by a novel structure fold of cysteine-rich domain. The structures showed that chitin binding induces no striking conformational changes in OsCEBiP. Structural comparison among N-acetylglucosamine (NAG) oligomer-bound LysMs revealed a highly conserved recognition mechanism, which is expected to facilitate study of other LysM-containing proteins for their NAG binding. Modeling study showed that chitin induces OsCEBiP homodimerization in a "sliding mode". Our data provide insights into rice chitin receptor-mediated immunity triggered by fungal cell wall.

Structural basis for recognition of an endogenous peptide by the plant receptor kinase PEPR1.

The endogenous peptides AtPep1-8 in Arabidopsis mature from the conserved C-terminal portions of their precursor proteins PROPEP1-8, respectively. The two homologous leucine-rich repeat-receptor kinases (LRR-RKs) PEPR1 and PEPR2 act as receptors of AtPeps. AtPep binding leads to stable association of PEPR1,2 with the shared receptor LRR-RK BAK1, eliciting immune responses similar to those induced by pathogens. Here we report a crystal structure of the extracellular LRR domain of PEPR1 (PEPR1LRR) in complex with AtPep1. The structure reveals that AtPep1 adopts a fully extended conformation and binds to the inner surface of the superhelical PEPR1LRR. Biochemical assays showed that AtPep1 is capable of inducing PEPR1LRR-BAK1LRR heterodimerization. The conserved C-terminal portion of AtPep1 dominates AtPep1 binding to PEPR1LRR, with the last amino acid of AtPep1 Asn23 forming extensive interactions with PEPR1LRR. Deletion of the last residue of AtPep1 significantly compromised AtPep1 interaction with PEPR1LRR. Together, our data reveal a conserved structural mechanism of AtPep1 recognition by PEPR1, providing significant insight into prediction of recognition of other peptides by their cognate LRR-RKs.

GdX/UBL4A specifically stabilizes the TC45/STAT3 association and promotes dephosphorylation of STAT3 to repress tumorigenesis.

Impaired phosphatase activity contributes to the persistent activation of STAT3 in tumors. Given that STAT family members with various or even opposite functions are often phosphorylated or dephosphorylated by the same enzymes, the mechanism for STAT3-specific dephosphorylation in cells remains largely unknown. Here, we report that GdX (UBL4A) promotes STAT3 dephosphorylation via mediating the interaction between TC45 (the nuclear isoform of TC-PTP) and STAT3 specifically. GdX stabilizes the TC45-STAT3 complex to bestow upon STAT3 an efficient dephosphorylation by TC45. Inasmuch, GdX suppresses tumorigenesis and tumor development by reducing the level of phospho-STAT3 (p-STAT3), whereas deletion of GdX results in a high level of p-STAT3 and accelerated colorectal tumorigenesis induced by AOM/DSS. Thus, GdX converts TC45, a nonspecific phosphatase, into a STAT3-specific phosphatase by bridging an association between TC45 and STAT3.

A small-molecule screen identifies L-kynurenine as a competitive inhibitor of TAA1/TAR activity in ethylene-directed auxin biosynthesis and root growth in Arabidopsis.

The interactions between phytohormones are crucial for plants to adapt to complex environmental changes. One example is the ethylene-regulated local auxin biosynthesis in roots, which partly contributes to ethylene-directed root development and gravitropism. Using a chemical biology approach, we identified a small molecule, l-kynurenine (Kyn), which effectively inhibited ethylene responses in Arabidopsis thaliana root tissues. Kyn application repressed nuclear accumulation of the ETHYLENE INSENSITIVE3 (EIN3) transcription factor. Moreover, Kyn application decreased ethylene-induced auxin biosynthesis in roots, and TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS1/TRYPTOPHAN AMINOTRANSFERASE RELATEDs (TAA1/TARs), the key enzymes in the indole-3-pyruvic acid pathway of auxin biosynthesis, were identified as the molecular targets of Kyn. Further biochemical and phenotypic analyses revealed that Kyn, being an alternate substrate, competitively inhibits TAA1/TAR activity, and Kyn treatment mimicked the loss of TAA1/TAR functions. Molecular modeling and sequence alignments suggested that Kyn effectively and selectively binds to the substrate pocket of TAA1/TAR proteins but not those of other families of aminotransferases. To elucidate the destabilizing effect of Kyn on EIN3, we further found that auxin enhanced EIN3 nuclear accumulation in an EIN3 BINDING F-BOX PROTEIN1 (EBF1)/EBF2-dependent manner, suggesting the existence of a positive feedback loop between auxin biosynthesis and ethylene signaling. Thus, our study not only reveals a new level of interactions between ethylene and auxin pathways but also offers an efficient method to explore and exploit TAA1/TAR-dependent auxin biosynthesis.

Evolving model of amino acid networks.

The three-dimensional structure of a protein can be treated as a complex network composed of amino acids, and the network properties can help us to understand the relationship between structure and function. Since the amino acid network of a protein is formed in the process of protein folding, it is difficult for general network models to explain its evolving mechanism. Based on the perspective of protein folding, we propose an evolving model for amino acid networks. In our model, the evolution starts from the amino acid sequence of a native protein and it is guided by two generic assumptions: i.e., the neighbor preferential rule and the energy preferential rule. We find that the neighbor preferential rule predominates the general network properties and the energy preferential rule predominates the specific biological structure characteristics. Applied to native proteins, our model mimics the features of amino acid networks well.