Multi-omic analysis tools for microbial metabolites prediction.

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

Identifying spatial domain by adapting transcriptomics with histology through contrastive learning.

Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histology images of the tissues. Accurately identifying the spatial domains of spots is a vital step for various downstream tasks in spatial transcriptomics analysis. To remove noises in gene expression, several methods have been developed to combine histopathological images for data analysis of spatial transcriptomics. However, these methods either use the image only for the spatial relations for spots, or individually learn the embeddings of the gene expression and image without fully coupling the information. Here, we propose a novel method ConGI to accurately exploit spatial domains by adapting gene expression with histopathological images through contrastive learning. Specifically, we designed three contrastive loss functions within and between two modalities (the gene expression and image data) to learn the common representations. The learned representations are then used to cluster the spatial domains on both tumor and normal spatial transcriptomics datasets. ConGI was shown to outperform existing methods for the spatial domain identification. In addition, the learned representations have also been shown powerful for various downstream tasks, including trajectory inference, clustering, and visualization.

A parameter-free deep embedded clustering method for single-cell RNA-seq data.

Clustering analysis is widely used in single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data to discover cell heterogeneity and cell states. While many clustering methods have been developed for scRNA-seq analysis, most of these methods require to provide the number of clusters. However, it is not easy to know the exact number of cell types in advance, and experienced determination is not always reliable. Here, we have developed ADClust, an automatic deep embedding clustering method for scRNA-seq data, which can accurately cluster cells without requiring a predefined number of clusters. Specifically, ADClust first obtains low-dimensional representation through pre-trained autoencoder and uses the representations to cluster cells into initial micro-clusters. The clusters are then compared in between by a statistical test, and similar micro-clusters are merged into larger clusters. According to the clustering, cell representations are updated so that each cell will be pulled toward centers of its assigned cluster and similar clusters, while cells are separated to keep distances between clusters. This is accomplished through jointly optimizing the carefully designed clustering and autoencoder loss functions. This merging process continues until convergence. ADClust was tested on 11 real scRNA-seq datasets and was shown to outperform existing methods in terms of both clustering performance and the accuracy on the number of the determined clusters. More importantly, our model provides high speed and scalability for large datasets.

A robust and scalable graph neural network for accurate single-cell classification.

Single-cell RNA sequencing (scRNA-seq) techniques provide high-resolution data on cellular heterogeneity in diverse tissues, and a critical step for the data analysis is cell type identification. Traditional methods usually cluster the cells and manually identify cell clusters through marker genes, which is time-consuming and subjective. With the launch of several large-scale single-cell projects, millions of sequenced cells have been annotated and it is promising to transfer labels from the annotated datasets to newly generated datasets. One powerful way for the transferring is to learn cell relations through the graph neural network (GNN), but traditional GNNs are difficult to process millions of cells due to the expensive costs of the message-passing procedure at each training epoch. Here, we have developed a robust and scalable GNN-based method for accurate single-cell classification (GraphCS), where the graph is constructed to connect similar cells within and between labelled and unlabeled scRNA-seq datasets for propagation of shared information. To overcome the slow information propagation of GNN at each training epoch, the diffused information is pre-calculated via the approximate Generalized PageRank algorithm, enabling sublinear complexity over cell numbers. Compared with existing methods, GraphCS demonstrates better performance on simulated, cross-platform, cross-species and cross-omics scRNA-seq datasets. More importantly, our model provides a high speed and scalability on large datasets, and can achieve superior performance for 1 million cells within 50 min.

Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks.

The rapid development of spatial transcriptomics allows the measurement of RNA abundance at a high spatial resolution, making it possible to simultaneously profile gene expression, spatial locations of cells or spots, and the corresponding hematoxylin and eosin-stained histology images. It turns promising to predict gene expression from histology images that are relatively easy and cheap to obtain. For this purpose, several methods are devised, but they have not fully captured the internal relations of the 2D vision features or spatial dependency between spots. Here, we developed Hist2ST, a deep learning-based model to predict RNA-seq expression from histology images. Around each sequenced spot, the corresponding histology image is cropped into an image patch and fed into a convolutional module to extract 2D vision features. Meanwhile, the spatial relations with the whole image and neighbored patches are captured through Transformer and graph neural network modules, respectively. These learned features are then used to predict the gene expression by following the zero-inflated negative binomial distribution. To alleviate the impact by the small spatial transcriptomics data, a self-distillation mechanism is employed for efficient learning of the model. By comprehensive tests on cancer and normal datasets, Hist2ST was shown to outperform existing methods in terms of both gene expression prediction and spatial region identification. Further pathway analyses indicated that our model could reserve biological information. Thus, Hist2ST enables generating spatial transcriptomics data from histology images for elucidating molecular signatures of tissues.

VSTH: a user-friendly web server for structure-based virtual screening on Tianhe-2.

SUMMARY: VSTH is a user-friendly web server with the complete workflow for virtual screening. By self-customized visualization software, users can interactively prepare protein files, set docking sites as well as view binding conformers in a target protein in a few clicks. We provide serval purchasable ligand libraries for selection. And, we integrate six open-source docking programs as computing engine, or as conformational sampling tools for DLIGAND2. Users can select various docking methods simultaneously and personalize computing parameters. After docking processing, user can filter docking conformations by ranked scores, or cluster-based molecular similarity to find highly populated clusters of low-energy conformations. AVAILABILITY AND IMPLEMENTATION: The VSTH web server is free and open to all users at https://matgen.nscc-gz.cn/VirtualScreening.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model.

MOTIVATION: Identifying the B-cell epitopes is an essential step for guiding rational vaccine development and immunotherapies. Since experimental approaches are expensive and time-consuming, many computational methods have been designed to assist B-cell epitope prediction. However, existing sequence-based methods have limited performance since they only use contextual features of the sequential neighbors while neglecting structural information. RESULTS: Based on the recent breakthrough of AlphaFold2 in protein structure prediction, we propose GraphBepi, a novel graph-based model for accurate B-cell epitope prediction. For one protein, the predicted structure from AlphaFold2 is used to construct the protein graph, where the nodes/residues are encoded by ESM-2 learning representations. The graph is input into the edge-enhanced deep graph neural network (EGNN) to capture the spatial information in the predicted 3D structures. In parallel, a bidirectional long short-term memory neural networks (BiLSTM) are employed to capture long-range dependencies in the sequence. The learned low-dimensional representations by EGNN and BiLSTM are then combined into a multilayer perceptron for predicting B-cell epitopes. Through comprehensive tests on the curated epitope dataset, GraphBepi was shown to outperform the state-of-the-art methods by more than 5.5% and 44.0% in terms of AUC and AUPR, respectively. A web server is freely available at http://bio-web1.nscc-gz.cn/app/graphbepi. AVAILABILITY AND IMPLEMENTATION: The datasets, pre-computed features, source codes, and the trained model are available at https://github.com/biomed-AI/GraphBepi.

Tislelizumab augment the efficacy of CD19/22 dual-targeted chimeric antigen receptor T cell in advanced stage relapsed or refractory B-cell non-Hodgkin lymphoma.

Dual-targeted chimeric antigen receptor T (CAR-T) cell is an important strategy to improve the efficacy of CD19 CAR-T cell against refractory or relapsed B cell non-Hodgkin lymphoma (R/R B-NHL). However, durable responses are not achieved in most patients, in part owing CAR-T cell exhaustion caused by PD-1/PD-L1 pathway. We conducted a prospective, single-arm study of dual-targeted CD19/22 CAR-T cell combined with anti-PD-1 antibody, tislelizumab, in R/R B-NHL (NCT04539444). Tislelizumab was administrated on +1 day after patients received infusion of CD19/22 CAR-T cell. Responses, survival and safety were evaluated. From 1 August 2020 to 30 March 2023, 16 patients were enrolled. The median follow-up time is 16.0 (range: 5.0-32.0 months) months. Overall response was achieved in 14 of 16 (87.5%) patients, and the complete response (CR) was achieved in 11 of 16 (68.8%) patients. The 1-year progression-free survival and overall survival rates were 68.8% and 81.3%, respectively. Of the 14 patients responded, 9 patients maintained their response until the end of follow-up. Among the 15 out of 16 (93.8%) patients who had extranodal involvement, 14 (93.3%) patients achieved overall response rate with 11 (73.3%) patients achieving CR. Eight (50%) patients experienced cytokine release syndrome. No neurologic adverse events were reported. Gene Ontology-Biological Process enrichment analysis showed that immune response-related signaling pathways were enriched in CR patients. Our results suggest that CD19/22 CAR-T cell combined with tislelizumab elicit a safe and durable response in R/R B-NHL and may improve the prognosis of those patients.

iProX in 2021: connecting proteomics data sharing with big data.

The rapid development of proteomics studies has resulted in large volumes of experimental data. The emergence of big data platform provides the opportunity to handle these large amounts of data. The integrated proteome resource, iProX (https://www.iprox.cn), which was initiated in 2017, has been greatly improved with an up-to-date big data platform implemented in 2021. Here, we describe the main iProX developments since its first publication in Nucleic Acids Research in 2019. First, a hyper-converged architecture with high scalability supports the submission process. A hadoop cluster can store large amounts of proteomics datasets, and a distributed, RESTful-styled Elastic Search engine can query millions of records within one second. Also, several new features, including the Universal Spectrum Identifier (USI) mechanism proposed by ProteomeXchange, RESTful Web Service API, and a high-efficiency reanalysis pipeline, have been added to iProX for better open data sharing. By the end of August 2021, 1526 datasets had been submitted to iProX, reaching a total data volume of 92.42TB. With the implementation of the big data platform, iProX can support PB-level data storage, hundreds of billions of spectra records, and second-level latency service capabilities that meet the requirements of the fast growing field of proteomics.

Roles of Integrin in Cardiovascular Diseases: From Basic Research to Clinical Implications.

Cardiovascular diseases (CVDs) pose a significant global health threat due to their complex pathogenesis and high incidence, imposing a substantial burden on global healthcare systems. Integrins, a group of heterodimers consisting of α and ß subunits that are located on the cell membrane, have emerged as key players in mediating the occurrence and progression of CVDs by regulating the physiological activities of endothelial cells, vascular smooth muscle cells, platelets, fibroblasts, cardiomyocytes, and various immune cells. The crucial role of integrins in the progression of CVDs has valuable implications for targeted therapies. In this context, the development and application of various integrin antibodies and antagonists have been explored for antiplatelet therapy and anti-inflammatory-mediated tissue damage. Additionally, the rise of nanomedicine has enhanced the specificity and bioavailability of precision therapy targeting integrins. Nevertheless, the complexity of the pathogenesis of CVDs presents tremendous challenges for monoclonal targeted treatment. This paper reviews the mechanisms of integrins in the development of atherosclerosis, cardiac fibrosis, hypertension, and arrhythmias, which may pave the way for future innovations in the diagnosis and treatment of CVDs.

Cell-Based Drug Delivery Systems Participate in the Cancer Immunity Cycle for Improved Cancer Immunotherapy.

Immunotherapy aims to activate the cancer patient's immune system for cancer therapy. The whole process of the immune system against cancer referred to as the "cancer immunity cycle", gives insight into how drugs can be designed to affect every step of the anticancer immune response. Cancer immunotherapy such as immune checkpoint inhibitor (ICI) therapy, cancer vaccines, as well as small molecule modulators has been applied to fight various cancers. However, the effect of immunotherapy in clinical applications is still unsatisfactory due to the limited response rate and immune-related adverse events. Mounting evidence suggests that cell-based drug delivery systems (DDSs) with low immunogenicity, superior targeting, and prolonged circulation have great potential to improve the efficacy of cancer immunotherapy. Therefore, with the rapid development of cell-based DDSs, understanding their important roles in various stages of the cancer immunity cycle guides the better design of cell-based cancer immunotherapy. Herein, an overview of how cell-based DDSs participate in cancer immunotherapy at various stages is presented and an outlook on possible challenges of clinical translation and application in future development.

Monensin Enhanced Generation of Extracellular Vesicles as Transfersomes for Promoting Tumor Penetration of Pyropheophorbide-a from Fusogenic Liposome.

The current state of antitumor nanomedicines is severely restricted by poor penetration in solid tumors. It is indicated that extracellular vesicles (EVs) secreted by tumor cells can mediate the intercellular transport of antitumor drug molecules in the tumor microenvironment. However, the inefficient generation of EVs inhibits the application of this approach. Herein, we construct an EV-mediated self-propelled liposome containing monensin as the EV secretion stimulant and photosensitizer pyropheophorbide-a (PPa) as a therapeutic agent. Monensin and PPa are first transferred to the tumor plasma membrane with the help of membrane fusogenic liposomes. By hitchhiking EVs secreted by the outer tumor cells, both drugs are layer-by-layer transferred into the deep region of a solid tumor. Particularly, monensin, serving as a sustainable booster, significantly amplifies the EV-mediated PPa penetration by stimulating EV production. Our results show that this endogenous EV-driven nanoplatform leads to deep tumor penetration and enhanced phototherapeutic efficacy.

Bioengineered Platelets Combining Chemotherapy and Immunotherapy for Postsurgical Melanoma Treatment: Internal Core-Loaded Doxorubicin and External Surface-Anchored Anti-PD-L1 Antibody Backpacks.

The pivotal factors affecting the survival rate of patients include metastasis and tumor recurrence after the resection of the primary tumor. Anti-PD-L1 antibody (aPD-L1) has promising efficacy but with some side effects for the off-target binding between aPD-L1 and normal tissues. Here, inspired by the excellent targeting capability of platelets with respect to tumor cells, we propose bioengineered platelets (PDNGs) with inner-loaded doxorubicin (DOX) and outer-anchored aPD-L1-cross-linked nanogels to reduce tumor relapse and metastatic spread postoperation. The cargo does not impair the normal physiological functions of platelets. Free aPD-L1 is cross-linked to form nanogels with a higher drug-loading efficiency and is sustainably released to trigger the T-cell-mediated destruction of tumor cells, reversing the tumor immunosuppressive microenvironment. PDNGs can reduce the postoperative tumor recurrence and metastasis rate, prolonging the survival time of mice. Our findings indicate that bioengineered platelets are promising in postsurgical cancer treatment by the tumor-capturing and in situ microvesicle-secreting capabilities of platelets.

Interpretable Graph Transformer Network for Predicting Adsorption Isotherms of Metal-Organic Frameworks.

Predicting interactions between metal-organic frameworks (MOFs) and their adsorbates based on structures is critical to design high-performance porous materials. Many gas uptake prediction models have been proposed, but adsorption isotherm prediction is still challenging for most existing models. Here, we report a deep learning approach (MOFNet) that can predict adsorption isotherms for MOFs based on hierarchical representation and pressure adaptive mechanism. We elaborately design a hierarchical representation to encode the MOF structures. We adopt a graph transformer network to capture atomic-level information, which can help learn chemical features required under low-pressure conditions. A pressure adaptive mechanism is employed to interpolate and extrapolate the given limited data points by transfer learning, which can predict adsorption isotherms on a wider pressure range by only one model. We demonstrate that our predictor outperformed other traditional machine learning as well as graph neural network models on the challenging benchmarks and also achieves high performance on the real-world experimental observed adsorption isotherms. Finally, we interpret the models to discover and present potential structure-property relationships using the self-attention mechanism in the network. The proof-of-concept applications, such as disordered MOF predictions and missing data imputation of gas adsorption isotherms, showcase the generality and usability of our model to improve MOF material design.

Inhibition of post-surgery tumour recurrence via a sprayable chemo-immunotherapy gel releasing PD-L1 antibody and platelet-derived small EVs.

BACKGROUND: Melanoma is the most serious type of skin cancer, and surgery is an effective method to treat melanoma. Unfortunately, local residual micro-infiltrated tumour cells and systemic circulating tumour cells (CTCs) are significant causes of treatment failure, leading to tumour recurrence and metastasis. METHODS: Small EVs were isolated from platelets by differential centrifugation, and doxorubicin-loaded small EVs (PexD) was prepared by mixing small EVs with doxorubicin (DOX). PexD and an anti-PD-L1 monoclonal antibody (aPD-L1) were co-encapsulated in fibrin gel. The synergistic antitumour efficacy of the gel containing PexD and aPD-L1 was assessed both in vitro and in vivo. RESULTS: Herein, we developed an in situ-formed bioresponsive gel combined with chemoimmunotherapeutic agents as a drug reservoir that could effectively inhibit both local tumour recurrence and tumour metastasis. In comparison with a DOX solution, PexD could better bind to tumour cells, induce more tumour immunogenic cell death (ICD) and promote a stronger antitumour immune response. PexD could enter the blood circulation through damaged blood vessels to track and eliminate CTCs. The concurrent release of aPD-L1 at the tumour site could impair the PD-1/PD-L1 pathway and restore the tumour-killing effect of cytotoxic T cells. This chemoimmunotherapeutic strategy triggered relatively strong T cell immune responses, significantly improving the tumour immune microenvironment. CONCLUSION: Our findings indicated that the immunotherapeutic fibrin gel could "awaken" the host innate immune system to inhibit both local tumour recurrence post-surgery and metastatic potential, thus, it could serve as a promising approach to prevent tumour recurrence.

Accurate prediction of genome-wide RNA secondary structure profile based on extreme gradient boosting.

MOTIVATION: RNA secondary structure plays a vital role in fundamental cellular processes, and identification of RNA secondary structure is a key step to understand RNA functions. Recently, a few experimental methods were developed to profile genome-wide RNA secondary structure, i.e. the pairing probability of each nucleotide, through high-throughput sequencing techniques. However, these high-throughput methods have low precision and cannot cover all nucleotides due to limited sequencing coverage. RESULTS: Here, we have developed a new method for the prediction of genome-wide RNA secondary structure profile from RNA sequence based on the extreme gradient boosting technique. The method achieves predictions with areas under the receiver operating characteristic curve (AUC) >0.9 on three different datasets, and AUC of 0.888 by another independent test on the recently released Zika virus data. These AUCs are consistently >5% greater than those by the CROSS method recently developed based on a shallow neural network. Further analysis on the 1000 Genome Project data showed that our predicted unpaired probabilities are highly correlated (>0.8) with the minor allele frequencies at synonymous, non-synonymous mutations, and mutations in untranslated regions, which were higher than those generated by RNAplfold. Moreover, the prediction over all human mRNA indicated a consistent result with previous observation that there is a periodic distribution of unpaired probability on codons. The accurate predictions by our method indicate that such model trained on genome-wide experimental data might be an alternative for analytical methods. AVAILABILITY AND IMPLEMENTATION: The GRASP is available for academic use at https://github.com/sysu-yanglab/GRASP. SUPPLEMENTARY INFORMATION: Supplementary data are available online.

SPOT-Fold: Fragment-Free Protein Structure Prediction Guided by Predicted Backbone Structure and Contact Map.

Protein structure determination has long been one of the most challenging problems in molecular biology for the past 60 years. Here we present an ab initio protein tertiary-structure prediction method assisted by predicted contact maps from SPOT-Contact and predicted dihedral angles from SPIDER 3. These predicted properties were then fed to the crystallography and NMR system (CNS) for restrained structure modeling. The resulted structures are first evaluated by the potential energy calculated by CNS, followed by dDFIRE energy function for model selections. The method called SPOT-Fold has been tested on 241 CASP targets between 67 and 670 amino acid residues, 60 randomly selected globular proteins under 100 amino acids. The method has a comparable accuracy to other contact-map-based modeling techniques. © 2019 Wiley Periodicals, Inc.

Accurately Predicting Mutation-Caused Stability Changes from Protein Sequences Using Extreme Gradient Boosting.

Accurately predicting the impact of point mutation on protein stability has crucial roles in protein design and engineering. In this study, we proposed a novel method (BoostDDG) to predict stability changes upon point mutations from protein sequences based on the extreme gradient boosting. We extracted features comprehensively from evolutional information and predicted structures and performed feature selection by a strategy of sequential forward selection. The features and parameters were optimized by homologue-based cross-validation to avoid overfitting. Finally, we found that 14 features from six groups led to the highest Pearson correlation coefficient (PCC) of 0.535, which is consistent with the 0.540 on an independent test. Our method was indicated to consistently outperform other sequence-based methods on three precompiled test sets, and 7363 variants on two proteins (PTEN and TPMT). These results highlighted that BoostDDG is a powerful tool for predicting stability changes upon point mutations from protein sequences.

To Improve Protein Sequence Profile Prediction through Image Captioning on Pairwise Residue Distance Map.

Protein sequence profile prediction aims to generate multiple sequences from structural information to advance the protein design. Protein sequence profile can be computationally predicted by energy-based or fragment-based methods. By integrating these methods with neural networks, our previous method, SPIN2, has achieved a sequence recovery rate of 34%. However, SPIN2 employed only one-dimensional (1D) structural properties that are not sufficient to represent three-dimensional (3D) structures. In this study, we represented 3D structures by 2D maps of pairwise residue distances and developed a new method (SPROF) to predict protein sequence profiles based on an image captioning learning frame. To our best knowledge, this is the first method to employ a 2D distance map for predicting protein properties. SPROF achieved 39.8% in sequence recovery of residues on the independent test set, representing a 5.2% improvement over SPIN2. We also found the sequence recovery increased with the number of their neighbored residues in 3D structural space, indicating that our method can effectively learn long-range information from the 2D distance map. Thus, such network architecture using a 2D distance map is expected to be useful for other 3D structure-based applications, such as binding site prediction, protein function prediction, and protein interaction prediction. The online server and the source code is available at http://biomed.nscc-gz.cn and https://github.com/biomed-AI/SPROF , respectively.

Validation of Fully Automatic Quantitative Software for Finger Joint Space Narrowing Progression for Rheumatoid Arthritis Patients.

In rheumatoid arthritis (RA), the radiographic progression of joint space narrowing (JSN) is evaluated using visual assessments. However, those methods are complicated and time-consuming. We developed an automatic system that can detect joint locations and compute the joint space difference index (JSDI), which was defined as the chronological change in JSN between two radiographs. The purpose of this study was to establish the validity of the software that automatically evaluates the temporal change of JSN. This study consisted of 39 patients with RA. All patients were treated with tocilizumab and underwent hand radiography (left and right hand separately) at 0, 6, and 12 months. The JSN was evaluated using mTSS (modified Total Sharp Score) by one musculoskeletal radiologist as well as our automatic system. Software measurement showed that JSDI between 0 and 12 months was significantly higher than that between 0 and 6 months (p < 0.01). While, there was no significant difference in mTSS between 0, 6, and 12 months. The group with higher disease activity at 0 months had significantly higher JSDI between 0 and 6 months than that with lower disease activity (p = 0.02). The automatic software can evaluate JSN progression of RA patients in the finger joint on X-ray.