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Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition. Author summary: Spatial transcriptome sequencing can not only reveal the mechanisms of disease development, but can also be used to explore the structure of biological tissues, which are widely used in a variety of fields such as developmental biology and oncology. To utilize spatial transcriptome data to understand how different cells work together to perform complex functions, spatially relevant groups of cells must be identified, which leads to the task of dissecting tissues into spatial domains. However, most of the currently available spatial domain identification tools have yet to fully utilize the information embedded in spatial transcriptomic data, and their identification remains to be improved for the fine-grained exploration of tissue structure. Here, we developed a tool for accurate spatial domain identification, and our tool makes full use of the various types of spatial transcriptome data from multiple perspectives, identifying the real grouping information in spatial transcriptome data as much as possible. Our results show that our spatial domain identification tool can identify spatial domains more accurately and provide effective help for biological organization structure exploration. [ABSTRACT FROM AUTHOR]

The off-target activities within the CRISPR-Cas9 system remains a formidable barrier to its broader application and development. Recent advancements have highlighted the potential of deep learning models in predicting these off-target effects, yet they encounter significant hurdles including imbalances within datasets and the intricacies associated with encoding schemes and model architectures. To surmount these challenges, our study innovatively introduces an Efficiency and Specificity-Based (ESB) class rebalancing strategy, specifically devised for datasets featuring mismatches-only off-target instances, marking a pioneering approach in this realm. Furthermore, through a meticulous evaluation of various One-hot encoding schemes alongside numerous hybrid neural network models, we discern that encoding and models of moderate complexity ideally balance performance and efficiency. On this foundation, we advance a novel hybrid model, the CRISPR-MCA, which capitalizes on multi-feature extraction to enhance predictive accuracy. The empirical results affirm that the ESB class rebalancing strategy surpasses five conventional methods in addressing extreme dataset imbalances, demonstrating superior efficacy and broader applicability across diverse models. Notably, the CRISPR-MCA model excels in off-target effect prediction across four distinct mismatches-only datasets and significantly outperforms contemporary state-of-the-art models in datasets comprising both mismatches and indels. In summation, the CRISPR-MCA model, coupled with the ESB rebalancing strategy, offers profound insights and a robust framework for future explorations in this field. Author summary: In the field of gene editing, the application of deep learning technologies holds significant promise for predicting off-target effects in the CRISPR-Cas9 system. Nevertheless, one of the primary challenges encountered is the extreme imbalance among classes within the off-target datasets, which severely hampers the predictive accuracy for certain classes. Furthermore, as an array of sequence encoding methods continue to evolve, there has been a corresponding increase in model complexity. Addressing these issues, we introduce a novel Efficiency and Specificity-Based (ESB) class rebalancing strategy designed to mitigate the impact of class imbalance. Additionally, we assess the influence of six encoding schemes and four distinct architectural approaches on the prediction performance, employing four benchmark datasets for validation. Building upon these insights, we have developed a new hybrid model, termed CRISPR-MCA. Our experimental results demonstrate that the ESB strategy significantly surpasses the performance of existing baseline methods across multiple models. Moreover, the CRISPR-MCA model exhibits robust performance on two distinct types of datasets, affirming its effectiveness in enhancing the accuracy of deep learning predictions for off-target activities. [ABSTRACT FROM AUTHOR]

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification. Author summary: Cancer has become a major disease threatening human life and health. Cancer usually originates from abnormal gene activities, such as mutations and copy number variations. Mutations in cancer driver genes are crucial for the selective growth of tumor cells. Identifying cancer driver genes is crucial in cancer-related research and treatment strategies, as it helps understand cancer occurrence and development. However, the complex gene-gene interactions, measurement errors, and the prevalence of unlabeled data significantly complicate the identification of these driver genes. We developed a new method that integrates an energy-constrained diffusion mechanism with an attention mechanism to uncover implicit gene dependencies in biomolecular networks and generate robust gene representations. Extensive experiments demonstrated that our model accurately identifies known cancer driver genes and effectively discovers potential ones. Furthermore, we analyzed and predicted patient-specific mutated genes, enhancing our understanding of their pathogenesis and advancing precision medicine. In summary, our method offers a promising tool for advancing the identification of cancer driver genes. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data. Author summary: It is still difficult to analyze scRNA-seq data because a significant portion of expressed genes have zeros. Gene expression levels can be restored through the imputation of scRNA-seq data, facilitating downstream analysis. To overcome this challenge, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. Through the use of several simulated and real datasets, we perform an comprehensively evaluation of scRNMF against existing methods. scRNMF can enhance various aspects of downstream analysis, including gene expression data recovery, cell clustering analysis, gene differential expression analysis, and cellular trajectory reconstruction. The results of our study demonstrate that scRNMF is a powerful tool that can improve the accuracy of single-cell data analysis. [ABSTRACT FROM AUTHOR]

Accurate multiple sequence alignment (MSA) is imperative for the comprehensive analysis of biological sequences. However, a notable challenge arises as no single MSA tool consistently outperforms its counterparts across diverse datasets. Users often have to try multiple MSA tools to achieve optimal alignment results, which can be time-consuming and memory-intensive. While the overall accuracy of certain MSA results may be lower, there could be local regions with the highest alignment scores, prompting researchers to seek a tool capable of merging these locally optimal results from multiple initial alignments into a globally optimal alignment. In this study, we introduce Two Pointers Meta-Alignment (TPMA), a novel tool designed for the integration of nucleic acid sequence alignments. TPMA employs two pointers to partition the initial alignments into blocks containing identical sequence fragments. It selects blocks with the high sum of pairs (SP) scores to concatenate them into an alignment with an overall SP score superior to that of the initial alignments. Through tests on simulated and real datasets, the experimental results consistently demonstrate that TPMA outperforms M-Coffee in terms of aSP, Q, and total column (TC) scores across most datasets. Even in cases where TPMA's scores are comparable to M-Coffee, TPMA exhibits significantly lower running time and memory consumption. Furthermore, we comprehensively assessed all the MSA tools used in the experiments, considering accuracy, time, and memory consumption. We propose accurate and fast combination strategies for small and large datasets, which streamline the user tool selection process and facilitate large-scale dataset integration. The dataset and source code of TPMA are available on GitHub (https://github.com/malabz/TPMA). Author summary: Accurate multiple sequence alignment (MSA) is vital for comprehensive biological sequence analysis. However, as no single MSA tool consistently outperforms others across diverse datasets, researchers must invest significant time exploring multiple tools to identify the most suitable one for their specific dataset. To address this, researchers seek tools that can merge locally optimal results from diverse initial alignments into a globally optimal alignment. Our novel approach, Two Pointers Meta-Alignment (TPMA), employs a two-pointer to partition initial alignments into blocks, selecting those with the higher sum of pairs (SP) scores for integration into a globally optimal alignment. TPMA consistently outperforms M-Coffee, demonstrating superior aSP, Q, and total column (TC) scores, coupled with faster running times and lower memory consumption. We present comprehensive assessments of various MSA tools, proposing efficient combination strategies for diverse datasets. Our tool, TPMA, and associated resources are publicly available on GitHub (https://github.com/malabz/TPMA), offering a valuable contribution to the field of evolutionary biology and streamlining the selection process for users dealing with large-scale datasets. [ABSTRACT FROM AUTHOR]

Background: To analyze vault effects of crystalline lens rise (CLR) and anterior chamber parameters (recorded by Pentacam) in highly myopic patients receiving implantable collamer lenses (ICLs), which may avoid subsequent complications such as glaucoma and cataract caused by the abnormal vault. Methods: We collected clinical data of 137 patients with highly myopic vision, who were all subsequent recipients of V4c ICLs between June 2020 and January 2021. Horizontal ciliary sulcus-to-sulcus diameter (hSTS) and CLR were measured by ultrasonic biomicroscopy (UBM), and a Pentacam anterior segment analyzer was used to measure horizontal white-to-white diameter (hWTW), anterior chamber depth (ACD), anterior chamber angle (ACA), anterior chamber volume (ACV), CLR, and postoperative vault (Year 1 and Month 1). The lens thickness (LT) was determined by optical biometry (IOL Master instrument). The predictive model was generated through multiple linear regression analyses of influential factors, such as hSTS, CLR, hWTW, ACD, ACA, ACV, ICL size, and LT. The predictive performance of the multivariate model on vault after ICL was assessed using the receiver operating characteristic (ROC) curve with area under the curve (AUC) as well as the point of tangency. Results: Average CLR assessed by UBM was lower than the average value obtained by Pentacam (0.561 vs. 0.683). Bland-Altman analysis showed a good consistency in the two measurement methods and substantial correlation (r = 0.316; P = 0.000). The ROC curve of Model 1 (postoperative Year 1) displayed an AUC of 0.847 (95% confidence interval [CI]: 74.19–95.27), with optimal threshold of 0.581 (sensitivity, 0.857; specificity, 0.724). In addition, respective values for Model 2 (postoperative Month 1) were 0.783 (95% CI: 64.94–91.64) and 0.522 (sensitivity, 0.917; specificity, 0.605). Conclusion: CLR and anterior chamber parameters are important determinants of postoperative vault after ICL placement. The multivariate regression model we constructed may serve in large part as a predictive gauge, effectively avoid postoperative complication. [ABSTRACT FROM AUTHOR]

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool. Author summary: CircRNAs serve as crucial biomarkers and drug targets in cancer therapy. Predicting cancer-associated circRNAs and drugs contributes to uncover intricate molecular mechanisms driving tumorigenesis, thus offering novel insights into cancer diagnosis, treatment, and research. However, prevailing predictive methods often neglect the comprehensive interactions within circRNAs, drugs, and cancer, leading to an incomplete understanding of their complex interplay. In response, we introduce AutoEdge-CCP, a framework that models circRNA-cancer-drug interactions within a multi-source heterogeneous network. Each molecule combines intrinsic attribute information describing molecular features with interaction information derived through autoGNN, revealing pivotal circRNAs and drugs associated with cancer. Experimental results across multi-scenario attest to AutoEdge-CCP's superior performance compared to competing methods, particularly in predicting novel circRNAs and drugs associated with cancer. Additionally, visualization of edge embeddings and case studies provide interpretable insights into the prediction outcomes. [ABSTRACT FROM AUTHOR]

The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition. Author summary: Although many methods have been proposed to infer the gene regulatory network of a single cell, they only focus on the regulatory relationships of pairs of genes and ignore the global regulatory structure. Here, we present a deep learning-based model to learn the global regulatory structure and reconstruct the gene regulatory networks from single-cell RNA sequencing data with a graph view. We utilize the weighted gene co-expression analysis to build a prior regulatory graph of gene and a graph autoencoder to deconstruct the latent regulatory structure among genes. We performed extensive experiments on varieties of single-cell RNA sequencing datasets and compared our method with 9 stat-of-the-art gene regulatory network inference method. The results show that our method can significantly improve the accuracy of gene regulatory network inference and can be applied to identify key regulators in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

DNA methylation is an important regulator of gene transcription. WGBS is the gold-standard approach for base-pair resolution quantitative of DNA methylation. It requires high sequencing depth. Many CpG sites with insufficient coverage in the WGBS data, resulting in inaccurate DNA methylation levels of individual sites. Many state-of-arts computation methods were proposed to predict the missing value. However, many methods required either other omics datasets or other cross-sample data. And most of them only predicted the state of DNA methylation. In this study, we proposed the RcWGBS, which can impute the missing (or low coverage) values from the DNA methylation levels on the adjacent sides. Deep learning techniques were employed for the accurate prediction. The WGBS datasets of H1-hESC and GM12878 were down-sampled. The average difference between the DNA methylation level at 12× depth predicted by RcWGBS and that at >50× depth in the H1-hESC and GM2878 cells are less than 0.03 and 0.01, respectively. RcWGBS performed better than METHimpute even though the sequencing depth was as low as 12×. Our work would help to process methylation data of low sequencing depth. It is beneficial for researchers to save sequencing costs and improve data utilization through computational methods. Author summary: DNA methylation has a major impact on gene regulation. WGBS is the gold standard for investigating the DNA methylation. The DNA methylation level of the sites with low coverage are often not accurate in WGBS datasets. Therefore, we proposed a method based on the CNN model to perform DNA methylation level interpolation for specific sites and named this method as RcWGBS. RcWGBS did not rely on other omics data or other cross-sample data. It only used the sites with sufficient coverage contained in the target WGBS dataset for model training to obtain parameters. Then, the trained model can be used to predict the DNA methylation level of sites with low coverage. Our analyses showed that RcWGBS could recalibrate the methylation level of some CpGs with insufficient coverage. It is suggested that our research could benefit the WGBS datasets with insufficient sequencing coverage. RcWGBS is implemented as an R-packages. It is efficient and convenient and does not need other WGBS or omics data. [ABSTRACT FROM AUTHOR]

During breast cancer metastasis, the developmental process epithelial-mesenchymal (EM) transition is abnormally activated. Transcriptional regulatory networks controlling EM transition are well-studied; however, alternative RNA splicing also plays a critical regulatory role during this process. Alternative splicing was proved to control the EM transition process, and RNA-binding proteins were determined to regulate alternative splicing. A comprehensive understanding of alternative splicing and the RNA-binding proteins that regulate it during EM transition and their dynamic impact on breast cancer remains largely unknown. To accurately study the dynamic regulatory relationships, time-series data of the EM transition process are essential. However, only cross-sectional data of epithelial and mesenchymal specimens are available. Therefore, we developed a pseudotemporal causality-based Bayesian (PCB) approach to infer the dynamic regulatory relationships between alternative splicing events and RNA-binding proteins. Our study sheds light on facilitating the regulatory network-based approach to identify key RNA-binding proteins or target alternative splicing events for the diagnosis or treatment of cancers. The data and code for PCB are available at: http://hkumath.hku.hk/~wkc/PCB(data+code).zip. Author summary: In this paper, pseudotime causality-based Bayesian (PCB) model is proposed to detect the regulatory relationships between alternative splicing events and RNA-binding proteins in the EM transition process of breast cancer. It is the first time to reveal the global role of alternative splicing events and RNA-binding proteins during EM transition, in which RNA-binding proteins can dynamically regulate alternative splicing. Furthermore, to address the challenges that lack of temporal information in sample-based transcriptomic data, we propose to decode the latent temporal information underlying cancer progression through ordering patient sample based on transcriptomic similarity, and design a latent-temporal progression-based Bayesian method to infer alternative splicing regulatory network from sample based transcriptomic data of cancer patients. We inferred dynamic regulatory relationships between EM transition-associated alternative splicing events and RNA-binding proteins. Overall, we determined that ZCCHC24, PIWIL4, and TDRD10 may be key RNA-binding proteins in the EM transition process of breast cancer. In addition, we studied the dynamic regulatory relationships between alternative splicing events of the CD44 gene and RNA-binding proteins (hnRNPM, ESRP1 and ESRP2). Our study suggests that inferring dynamic progression trajectory from static expression data of tumor samples helps to uncover regulatory mechanism underlying cancer progression. In addition, our study proposes a new and effective way to characterize cancer progression by clinical transcriptomic data modeling and facilitate the incorporation of a regulatory network-based approach into precision medicine. [ABSTRACT FROM AUTHOR]

Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. It is of great significance to identify drug-induced target-organ toxicity, especially the detailed pathological findings, which are crucial for toxicity assessment, in the early stage of drug development process. A large variety of studies have devoted to identify drug toxicity. However, most of them are limited to single organ or only binary toxicity. Here we proposed a novel multi-label learning model named Att-RethinkNet, for predicting drug-induced pathological findings targeted on liver and kidney based on toxicogenomics data. The Att-RethinkNet is equipped with a memory structure and can effectively use the label association information. Besides, attention mechanism is embedded to focus on the important features and obtain better feature presentation. Our Att-RethinkNet is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized. And more importantly, it predicts multiple pathological findings at the same time, instead of predicting each pathology separately as the previous model did. To demonstrate the effectiveness of the proposed model, we compared the proposed method with a series of state-of-the-arts methods. Our model shows competitive performance and can predict potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way. The implementation of the proposed method is available at https://github.com/RanSuLab/Drug-Toxicity-Prediction-MultiLabel. Author summary: Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. Hence, to fully assess drug-induced toxicity, it is important to predict the detailed pathological findings, which are also crucial for toxicity mechanism understanding. However, most of the existing toxicity studies only predict binary toxicity (the toxicity or non-toxicity) or only predict the toxicity targeting single organ. The pathological findings of multiple organs are not well explored. Here we show, through the proposed Att-RethinkNet, it is possible to predict drug-induced pathological findings on both liver and kidney. Our results suggest that the Att-RethinkNet predicts potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way, and it is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized than the existing methods. The accurate prediction of pathological findings on multiple organs may benefit drug development. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git. Author summary: More and more evidences show that circular RNA can directly bind to proteins and participate in countless different biological processes. The calculation method can quickly and accurately predict the binding site of circular RNA and RBP. In order to identify the interaction of circRNA with 37 different types of circRNA binding proteins, we developed an integrated deep learning network based on hierarchical network, called CRBPDL. It can effectively learn high-level feature representations. The performance of the model was verified through comparative experiments of different feature extraction algorithms, different deep learning models and classifier models. Moreover, the CRBPDL model was applied to 31 linear RNAs, and the effectiveness of our method was proved by comparison with the results of current excellent algorithms. It is expected that the CRBPDL model can effectively predict the binding site of circular RNA-RBP and provide reliable candidates for further biological experiments. [ABSTRACT FROM AUTHOR]

Highly immunogenic exotoxins are used as carrier proteins because they efficiently improve the immunogenicity of polysaccharides. However, their efficiency with protein antigens remains unclear. In the current study, the candidate antigen PA0833 from Pseudomonas aeruginosa was fused to the α-hemolysin mutant HlaH35A from Staphylococcus aureus to form a HlaH35A-PA0833 fusion protein (HPF). Immunization with HPF resulted in increased PA0833-specific antibody titers, higher protective efficacy, and decreased bacterial burden and pro-inflammatory cytokine secretion compared with PA0833 immunization alone. Using fluorescently labeled antigens to track antigen uptake and delivery, we found that HlaH35A fusion significantly improved antigen uptake in injected muscles and antigen delivery to draining lymph nodes. Both in vivo and in vitro studies demonstrated that the increased antigen uptake after immunization with HPF was mainly due to monocyte- and macrophage-dependent macropinocytosis, which was probably the result of HPF binding to ADAM10, the Hla host receptor. Furthermore, a transcriptome analysis showed that several immune signaling pathways were activated by HPF, shedding light on the mechanism whereby HlaH35A fusion improves immunogenicity. Finally, the improvement in immunogenicity by HlaH35A fusion was also confirmed with two other antigens, GlnH from Klebsiella pneumoniae and the model antigen OVA, indicating that HlaH35A could serve as a universal carrier protein to improve the immunogenicity of protein antigens. Author summary: Pore-forming toxins, a kind of exotoxins utilized by many pathogens as immune escaping weapons that targeting the immune cells and disturbing the immune system, are conventionally deemed as perfect antigens for vaccine development against infectious diseases. In this study, we reported that fusion of HlaH35A, a typical pore-forming toxin toxoid, to candidate protein antigens from different species resulted in improved immunogenicity and protective efficacy. The improvement was mainly due to the increased antigen uptake and activating of immune-associated signaling pathways, probably by targeting ADAM10, the receptor of Hla on host immune cells. The importance of this work was to demonstrate the possible mechanisms of that pore-forming toxin function as atypical carrier protein to improve the immunogenicity of other proteins and confirm the potential of non-conservative but highly immunogenic exotoxins derived from hyper-virulent clinical strains for application in rational antigen design and vaccines development. [ABSTRACT FROM AUTHOR]

In their comment, Chen and Chuang [] pointed out several weak points of our recent paper []. Here we respond in detail to clarify the dataset we used in our work. We also discuss the three confounding factors they listed in their comment. [ABSTRACT FROM AUTHOR]

Several ecological hypotheses (e.g., specific plaque, non-specific plaque and keystone pathogen) regarding the etiology of periodontitis have been proposed since the 1990s, most of which have been centered on the concept of dysbiosis associated with periodontitis. Nevertheless, none of the existing hypotheses have presented mechanistic interpretations on how and why dysbiosis actually occurs. Hubbell’s neutral theory of biodiversity offers a powerful null model to test hypothesis regarding the mechanism of community assembly and diversity maintenance from the metagenomic sequencing data, which can help to understand the forces that shape the community dynamics such as dysbiosis. Here we reanalyze the dataset from Abusleme et al.’s comparative study of the oral microbial communities from periodontitis patients and healthy individuals. Our study demonstrates that 14 out of 61 communities (23%) passed the neutrality test, a percentage significantly higher than the previous reported neutrality rate of 1% in human microbiome (Li & Ma 2016, Scientific Reports). This suggests that, while the niche selection may play a predominant role in the assembly and diversity maintenance in oral microbiome, the effect of neutral dynamics may not be ignored. However, no statistically significant differences in the neutrality passing rates were detected between the periodontitis and healthy treatments with Fisher’s exact probability test and multiple testing corrections, suggesting that the mechanism of community assembly is robust against disturbances such as periodontitis. In addition, our study confirmed previous finding that periodontitis patients exhibited higher biodiversity. These findings suggest that while periodontitis may significantly change the community composition measured by diversity (i.e., the exhibition or ‘phenotype’ of community assembly), it does not seem to cause the ‘mutation’ of the ‘genotype” (mechanism) of community assembly. We argue that the ‘phenotypic’ changes explain the observed link (not necessarily causal) between periodontitis and community dysbiosis, which is certainly worthy of further investigation. [ABSTRACT FROM AUTHOR]

Circular RNA (circRNA) is mainly generated by the splice donor of a downstream exon joining to an upstream splice acceptor, a phenomenon known as backsplicing. It has been reported that circRNA can function as microRNA (miRNA) sponges, transcriptional regulators, or potential biomarkers. The availability of massive non-polyadenylated transcriptomes data has facilitated the genome-wide identification of thousands of circRNAs. Several circRNA detection tools or pipelines have recently been developed, and it is essential to provide useful guidelines on these pipelines for users, including a comprehensive and unbiased comparison. Here, we provide an improved and easy-to-use circRNA read simulator that can produce mimicking backsplicing reads supporting circRNAs deposited in CircBase. Moreover, we compared the performance of 11 circRNA detection tools on both simulated and real datasets. We assessed their performance regarding metrics such as precision, sensitivity, F1 score, and Area under Curve. It is concluded that no single method dominated on all of these metrics. Among all of the state-of-the-art tools, CIRI, CIRCexplorer, and KNIFE, which achieved better balanced performance between their precision and sensitivity, compared favorably to the other methods. [ABSTRACT FROM AUTHOR]

Although microRNAs (miRNAs) have been widely studied as epigenetic regulation molecules, fewer studies focus on the gender difference at the miRNA and isomiR expression levels. In this study, we aim to understand the potential relationships between gender difference and miRNA/isomiR expression through a comprehensive analysis of small RNA-sequencing datasets based on different human diseases and tissues. Based on specific samples from males and females, we determined that some miRNAs may be diversely expressed between different tissues and genders. Thus, these miRNAs may exhibit inconsistent and even opposite expression between males and females. According to deregulated miRNA expression profiles, some dominantly expressed miRNA loci were selected to analyze isomiR expression patterns using rates of dominant isomiRs. In some miRNA loci, isomiRs showed statistical significance between tumor and normal samples and between males and females samples, suggesting that isomiR expression patterns are not always invariable but may vary between males and females, as well as among different tissues, tumors, and normal samples. The divergence implicates the fluctuation in the expression of miRNA and its detailed expression at the isomiR levels. The divergence also indicates that gender difference may be an important factor that affects the screening of disease-associated miRNAs and isomiRs. This study suggests that miRNA/isomiR expression and gender difference may be more complex than previously assumed and should be further studied according to specific samples from males or females. [ABSTRACT FROM AUTHOR]

Vaccination strategies for Staphylococcus aureus, particularly methicillin-resistant S. aureus (MRSA) infections have attracted much research attention. Recent efforts have been made to select manganese transport protein C, or manganese binding surface lipoprotein C (MntC), which is a metal ion associated with pathogen nutrition uptake, as potential candidates for an S. aureus vaccine. Although protective humoral immune responses to MntC are well-characterised, much less is known about detailed MntC-specific B cell epitope mapping and particularly epitope vaccines, which are less-time consuming and more convenient. In this study, we generated a recombinant protein rMntC which induced strong antibody response when used for immunisation with CFA/IFA adjuvant. On the basis of the results, linear B cell epitopes within MntC were finely mapped using a series of overlapping synthetic peptides. Further studies indicate that MntC113-136, MntC209-232, and MntC263-286 might be the original linear B-cell immune dominant epitope of MntC, furthermore, three-dimensional (3-d) crystal structure results indicate that the three immunodominant epitopes were displayed on the surface of the MntC antigen. On the basis of immunodominant MntC113-136, MntC209-232, and MntC263-286 peptides, the epitope vaccine for S. aureus induces a high antibody level which is biased to TH2 and provides effective immune protection and strong opsonophagocytic killing activity in vitro against MRSA infection. In summary, the study provides strong proof of the optimisation of MRSA B cell epitope vaccine designs and their use, which was based on the MntC antigen in the development of an MRSA vaccine. [ABSTRACT FROM AUTHOR]

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: . [ABSTRACT FROM AUTHOR]

Background: Gastric cancer is one of the most common malignant diseases worldwide. Emerging evidence has shown that microRNAs (miRNAs) are associated with tumor development and progression. Our previous studies have revealed that H. pylori infection was able to induce the altered expression of miR-30b in gastric epithelial cells. However, little is known about the potential role of miR-30b in gastric cancer. Methods: We analyzed the expression of miR-30b in gastric cancer cell lines and human gastric cancer tissues. We examined the effect of miR-30b mimics on the apoptosis of gastric cancer cells in vitro by flow cytometry (FCM) and caspase-3/7 activity assays. Nude mouse xenograft model was used to determine whether miR-30b is involved in tumorigenesis of gastric cancer. The target of miR-30b was identified by bioinformatics analysis, luciferase assay and Western blot. Finally, we performed the correlation analysis between miR-30b and its target expression in gastric cancer. Results: miR-30b was significantly down-regulated in gastric cancer cells and human gastric cancer tissues. Enforced expression of miR-30b promoted the apoptosis of gastric cancer cells in vitro, and miR-30b could significantly inhibit tumorigenicity of gastric cancer by increasing the apoptosis proportion of cancer cells in vivo. Moreover, plasminogen activator inhibitor-1 (PAI-1) was identified as the potential target of miR-30b, and miR-30b level was inversely correlated with PAI-1 expression in gastric cancer. In addition, silencing of PAI-1 was able to phenocopy the effect of miR-30b overexpression on apoptosis regulation of cancer cells, and overexpression of PAI-1 could suppressed the effect of promoting cell apoptosis by miR-30b, indicating PAI-1 is potentially involved in miR-30b-induced apoptosis on cancer cells. Conclusion: miR-30b may function as a novel tumor suppressor gene in gastric cancer by targeting PAI-1 and regulating the apoptosis of cancer cells. miR-30b could serve as a potential biomarker and therapeutic target against gastric cancer. [ABSTRACT FROM AUTHOR]

Staphylococcus aureus is an opportunistic bacterial pathogen responsible for a diverse spectrum of human diseases and a leading cause of nosocomial and community-acquired infections. Development of a vaccine against this pathogen is an important goal. The fibronectin binding protein A (FnBPA) of S. aureus is one of multifunctional ‘microbial surface components recognizing adhesive matrix molecules' (MSCRAMMs). It is one of the most important adhesin molecules involved in the initial adhesion steps of S. aureus infection. It has been studied as potential vaccine candidates. However, FnBPA is a high-molecular-weight protein of 106 kDa and difficulties in achieving its high-level expression in vitro limit its vaccine application in S. aureus infection diseases control. Therefore, mapping the immunodominant regions of FnBPA is important for developing polyvalent subunit fusion vaccines against S. aureus infections. In the present study, we cloned and expressed the N-terminal and C-terminal of FnBPA. We evaluated the immunogenicity of the two sections of FnBPA and the protective efficacy of the two truncated fragments vaccines in a murine model of systemic S. aureus infection. The results showed recombinant truncated fragment F130-500 had a strong immunogenicity property and survival rates significantly increased in the group of mice immunized with F130-500 than the control group. We futher identified the immunodominant regions of FnBPA. The mouse antisera reactions suggest that the region covering residues 110 to 263 (F1B110-263) is highly immunogenic and is the immunodominant regions of FnBPA. Moreover, vaccination with F1B110-263 can generate partial protection against lethal challenge with two different S. aureus strains and reduced bacterial burdens against non-lethal challenge as well as that immunization with F130-500. This information will be important for further developing anti- S. aureus polyvalent subunit fusion vaccines. [ABSTRACT FROM AUTHOR]

Staphylococcal enterotoxin B (SEB) is one of the most potent Staphylococcus aureus exotoxins (SEs). Due to its conserved sequence and stable structure, SEB might be a good candidate antigen for MRSA vaccines. Although cellular immune responses to SEB are well-characterized, much less is known regarding SEB-specific humoral immune responses, particularly regarding detailed epitope mapping. In this study, we utilized a recombinant nontoxic mutant of SEB (rSEB) and an AlPO4 adjuvant to immunize BALB/c mice and confirmed that rSEB can induce a high antibody level and effective immune protection against MRSA infection. Next, the antisera of immunized mice were collected, and linear B cell epitopes within SEB were finely mapped using a series of overlapping synthetic peptides. Three immunodominant B cell epitopes of SEB were screened by ELISA, including a novel epitope, SEB205-222, and two known epitopes, SEB97–114 and SEB247-261. Using truncated peptides, an ELISA was performed with peptide-KLH antisera, and the core sequence of the three immunodominant B cell epitopes were verified as SEB97-112, SEB207-222, and SEB247-257. In vitro, all of the immunodominant epitope-specific antisera (anti-SEB97-112, anti-SEB207-222 and anti-SEB247-257) were observed to inhibit SEB-induced T cell mitogenesis and cytokine production from splenic lymphocytes of BALB/c mice. The homology analysis indicated that SEB97–112 and SEB207-222 were well-conserved among different Staphylococcus aureus strains. The 3D crystal structure of SEB indicated that SEB97–112 was in the loop region inside SEB, whereas SEB207-222 and SEB247-257 were in the β-slice region outside SEB. In summary, the fine-mapping of linear B-cell epitopes of the SEB antigen in this study will be useful to understand anti-SEB immunity against MRSA infection further and will be helpful to optimize MRSA vaccine designs that are based on the SEB antigen. [ABSTRACT FROM AUTHOR]

Background: Interleukin-32 (IL-32) is a recently discovered proinflammatory cytokine involved in inflammatory diseases. We investigated the expression of IL-32 and its regulation mechanism in the inflammatory response of patients with Helicobacter pylori (H. pylori) infection. Design and Methods: IL-32 mRNA and protein expression in gastric tissues was detected by quantitative real-time PCR and immunohistochemistry. The regulation of IL-32 in human gastric epithelia cell line AGS was investigated by different cytokine stimulation and different H. pylori strain infection. Results: Gastric IL-32 mRNA and protein expression were elevated in patients with H. pylori infection and positively correlated with gastritis. In H. pylori-infected patients, the mRNA level of IL-32 was also correlated with that of proinflammatory cytokines IL-1β and TNF-α. In vitro IL-1β and TNF-α could upregulate IL-32 mRNA and protein level in AGS cells, which was dependent on NF-κB signal pathway. The regulation of IL-32 expression in response to H. pylori-infection could be weakened by using neutralizing antibodies to block IL-1β and TNF-α. Moreover, H. pylori-infected AGS cells also induced IL-32 mRNA and protein expression, which was dependent on CagA. Conclusions: IL-32 level is elevated in patients with H. pylori infection and its expression is regulated by proinflammatory stimuli, suggesting that IL-32 may play a role in the pathogenesis of H. pylori-related gastritis. [ABSTRACT FROM AUTHOR]

Staphylococcus aureus is a common commensal organism in humans and a major cause of bacteremia and hospital acquired infection. Because of the spread of strains resistant to antibiotics, these infections are becoming more difficult to treat. Therefore, exploration of anti-staphylococcal vaccines is currently a high priority. Iron surface determinant B (IsdB) is an iron-regulated cell wall-anchored surface protein of S. aureus. Alpha-toxin (Hla) is a secreted cytolytic pore-forming toxin. Previous studies reported that immunization with IsdB or Hla protected animals against S. aureus infection. To develop a broadly protective vaccine, we constructed chimeric vaccines based on IsdB and Hla. Immunization with the chimeric bivalent vaccine induced strong antibody and T cell responses. When the protective efficacy of the chimeric bivalent vaccine was compared to that of individual proteins in a murine model of systemic S. aureus infection, the bivalent vaccine showed a stronger protective immune response than the individual proteins (IsdB or Hla). Based on the results presented here, the chimeric bivalent vaccine affords higher levels of protection against S. aureus and has potential as a more effective candidate vaccine. [ABSTRACT FROM AUTHOR]

Pattern genes are a group of genes that have a modularized expression behavior under serial physiological conditions. The identification of pattern genes will provide a path toward a global and dynamic understanding of gene functions and their roles in particular biological processes or events, such as development and pathogenesis. In this study, we present PaGenBase, a novel repository for the collection of tissue- and time-specific pattern genes, including specific genes, selective genes, housekeeping genes and repressed genes. The PaGenBase database is now freely accessible at http://bioinf.xmu.edu.cn/PaGenBase/. In the current version (PaGenBase 1.0), the database contains 906,599 pattern genes derived from the literature or from data mining of more than 1,145,277 gene expression profiles in 1,062 distinct samples collected from 11 model organisms. Four statistical parameters were used to quantitatively evaluate the pattern genes. Moreover, three methods (quick search, advanced search and browse) were designed for rapid and customized data retrieval. The potential applications of PaGenBase are also briefly described. In summary, PaGenBase will serve as a resource for the global and dynamic understanding of gene function and will facilitate high-level investigations in a variety of fields, including the study of development, pathogenesis and novel drug discovery. [ABSTRACT FROM AUTHOR]

Nuclear hormone receptors respond to small molecules such as retinoids or steroids and regulate development. Signaling in the conserved p38/PMK-1 MAP kinase pathway regulates innate immunity. In this study, we show that the Caenorhabditis elegans nuclear receptor DAF-12 negatively regulates the defense against pathogens via the downstream let-7 family of microRNAs, which directly target SKN-1, a gene downstream of PMK-1. These findings identify nuclear hormone receptors as components of innate immunity that crosstalk with the p38/PMK-1 MAP kinase pathway. [ABSTRACT FROM AUTHOR]

The analysis of biological information from protein sequences is important for the study of cellular functions and interactions, and protein fold recognition plays a key role in the prediction of protein structures. Unfortunately, the prediction of protein fold patterns is challenging due to the existence of compound protein structures. Here, we processed the latest release of the Structural Classification of Proteins (SCOP, version 1.75) database and exploited novel techniques to impressively increase the accuracy of protein fold classification. The techniques proposed in this paper include ensemble classifying and a hierarchical framework, in the first layer of which similar or redundant sequences were deleted in two manners; a set of base classifiers, fused by various selection strategies, divides the input into seven classes; in the second layer of which, an analogous ensemble method is adopted to predict all protein folds. To our knowledge, it is the first time all protein folds can be intelligently detected hierarchically. Compared with prior studies, our experimental results demonstrated the efficiency and effectiveness of our proposed method, which achieved a success rate of 74.21%, which is much higher than results obtained with previous methods (ranging from 45.6% to 70.5%). When applied to the second layer of classification, the prediction accuracy was in the range between 23.13% and 46.05%. This value, which may not be remarkably high, is scientifically admirable and encouraging as compared to the relatively low counts of proteins from most fold recognition programs. The web server Hierarchical Protein Fold Prediction (HPFP) is available at http://datamining.xmu.edu.cn/software/hpfp. [ABSTRACT FROM AUTHOR]

Nuclear hormone receptors respond to small molecules such as retinoids or steroids and regulate development. Signaling in the conserved p38/PMK-1 MAP kinase pathway regulates innate immunity. In this study, we show that the Caenorhabditis elegans nuclear receptor DAF-12 negatively regulates the defense against pathogens via the downstream let-7 family of microRNAs, which directly target SKN-1, a gene downstream of PMK-1. These findings identify nuclear hormone receptors as components of innate immunity that crosstalk with the p38/PMK-1 MAP kinase pathway. [ABSTRACT FROM AUTHOR]