MACC: a visual interactive knowledgebase of metabolite-associated cell communications.

Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported, no relevant database has become available so far. To address this gap, we here developed the first knowledgebase (MACC), to comprehensively describe human metabolite-associated cell communications through curation of experimental literatures. MACC currently contains: (a) 4206 carefully curated metabolite-associated cell communications pairs involving 244 human endogenous metabolites and reported biological effects in vivo and in vitro; (b) 226 comprehensive cell subtypes and 296 disease states, such as cancers, autoimmune diseases, and pathogenic infections; (c) 4508 metabolite-related enzymes and transporters, involving 542 pathways; (d) an interactive tool with user-friendly interface to visualize networks of multiple metabolite-cell interactions. (e) overall expression landscape of metabolite-associated gene sets derived from over 1500 single-cell expression profiles to infer metabolites variations across different cells in the sample. Also, MACC enables cross-links to well-known databases, such as HMDB, DrugBank, TTD and PubMed etc. In complement to ligand-receptor databases, MACC may give new perspectives of alternative communication between cells via metabolite secretion and adsorption, together with the resulting biological functions. MACC is publicly accessible at: http://macc.badd-cao.net/.

SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs.

Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.

Function of unconventional T cells in oral lichen planus revealed by single-cell RNA sequencing.

OBJECTIVE: We intended to map the single-cell profile of OLP, explore the molecular characteristics of unconventional T cells in OLP tissues. METHODS: Buccal mucosa samples from OLP patients and healthy individuals were used to prepare single-cell suspension. Single-cell RNA sequencing was used to analyze the proportion of all the cells, and the molecular characteristics of unconventional T cells. Immunohistochemical staining was used to detect the expression of unconventional T cells marker genes. RESULTS: The cell clusters from buccal mucosa were categorized into immune cells, fibroblasts, endothelial cells, and epithelial cells. Unconventional T cells with phenotype of CD247+TRDC+NCAM1+ were identified. Immunohistochemical staining revealed higher expression of unconventional T cell marker genes in OLP tissue, predominantly in the lamina propria. In OLP, unconventional T cells are in a unique stress response state, exhibited enhanced NF-κB signaling and apoptosis inhibition, enhanced heat shock protein genes expression, weakened cytotoxic function. A large number of ligand-receptor pairs were found between unconventional T cells and other cells, particularly with fibroblasts and endothelial cells. CONCLUSIONS: This study mapped the single-cell profile of OLP, delineated the molecular characteristics of unconventional T cells in OLP, and uncovered that these unconventional T cells are in a stress response state.

HIT 2.0: an enhanced platform for Herbal Ingredients' Targets.

Literature-described targets of herbal ingredients have been explored to facilitate the mechanistic study of herbs, as well as the new drug discovery. Though several databases provided similar information, the majority of them are limited to literatures before 2010 and need to be updated urgently. HIT 2.0 was here constructed as the latest curated dataset focusing on Herbal Ingredients' Targets covering PubMed literatures 2000-2020. Currently, HIT 2.0 hosts 10 031 compound-target activity pairs with quality indicators between 2208 targets and 1237 ingredients from more than 1250 reputable herbs. The molecular targets cover those genes/proteins being directly/indirectly activated/inhibited, protein binders, and enzymes substrates or products. Also included are those genes regulated under the treatment of individual ingredient. Crosslinks were made to databases of TTD, DrugBank, KEGG, PDB, UniProt, Pfam, NCBI, TCM-ID and others. More importantly, HIT enables automatic Target-mining and My-target curation from daily released PubMed literatures. Thus, users can retrieve and download the latest abstracts containing potential targets for interested compounds, even for those not yet covered in HIT. Further, users can log into 'My-target' system, to curate personal target-profiling on line based on retrieved abstracts. HIT can be accessible at http://hit2.badd-cao.net.

A comparative study on laparoscopic and open surgical approaches for perforated peptic ulcer repair: efficacy and outcomes analysis.

PURPOSE: This study aimed to compare the clinical outcomes of the clinical outcomes of laparoscopic and open sutures for peptic ulcer perforation (PPU). MATERIALS AND METHODS: PubMed, EMBASE, and Cochrane Library databases were searched for eligible studies from inception to March 31, 2023. Odds ratios (OR) and 95% confidence intervals (Cl) were also calculated. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the included studies. This study was performed using the Stata (V.16.0) software. RESULTS: A total of 29 studies involving 17,228 patients were included in this study. In terms of postoperative outcomes, the laparoscopic group had a shorter postoperative hospital stay (MD = -0.29, 95%CI = -0.44 to -0.13, P = 0.00), less blood loss (MD = -0.45, 95%CI = -0.82 to -0.08, P = 0.02), fewer wound infection (OR = 0.20, 95%CI = 0.17 to 0.24, P = 0.00), fewer pneumonia (OR = 0.59, 95%CI = 0.41 to 0.87, P = 0.01), fewer respiratory complications (OR = 0.26, 95%CI = 0.13 to 0.55, P = 0.00) and lower postoperative morbidity (OR = 0.51, 95%CI = 0.33 to 0.78, P = 0.00). The laparoscopic group had a lower mortality rate (OR = 0.36, 95%CI = 0.27 to 0.49, P = 0.00) than the open group. We also found that the laparoscopic group had a higher overall complication rate than the open group (OR = 0.45, 95%CI = 0.34 to 0.60, P = 0.00). CONCLUSION: Laparoscopic repair was associated with a lower risk of mortality than open repair in patients with PPU. Laparoscopic repair may be a better option in patients with PPU.

Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer.

Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html.

Function of normal oral mucosa revealed by single-cell RNA sequencing.

The functions of oral mucosa include barrier, sensation, and secretion. The barrier protection function is particularly important, which includes physical barrier and immunological barrier. Few studies have revealed the function of oral mucosa by displaying the map of normal oral mucosal cells from the perspective of single cells. Here, single-cell transcriptome sequencing was used to bring a relatively comprehensive map of the normal oral mucosal cells. In total, 26,398 cells from three cases of normal oral mucosa were analyzed by single-cell RNA-sequencing and 14 distinct cell groups were defined, 7 of which were immune cells. We performed subgroup classification and heterogeneity analysis of epithelial cells, T cells, and macrophagocytes, which found a subpopulation of epithelial cells with high expression of major histocompatibility complex class II molecules, a subpopulation CD8+ GZMK+ T cells, and two kinds of active macrophagocytes. Meanwhile, we identified ligand-receptor pairs among the major cell types to explore the interactions and how they maintain the homeostasis of normal oral mucosa. Based on these results, the epithelial barrier function, immunological barrier function, and potential maintenance function of stromal cells in the oral mucosa were described at the single-cell level, which provides basic data resources for further studies of oral mucosal diseases.

SEPPA 3.0-enhanced spatial epitope prediction enabling glycoprotein antigens.

B-cell epitope information is critical to immune therapy and vaccine design. Protein epitopes can be significantly affected by glycosylation, while no methods have considered this till now. Based on previous versions of Spatial Epitope Prediction of Protein Antigens (SEPPA), we here present an enhanced tool SEPPA 3.0, enabling glycoprotein antigens. Parameters were updated based on the latest and largest dataset. Then, additional micro-environmental features of glycosylation triangles and glycosylation-related amino acid indexes were added as important classifiers, coupled with final calibration based on neighboring antigenicity. Logistic regression model was retained as SEPPA 2.0. The AUC value of 0.794 was obtained through 10-fold cross-validation on internal validation. Independent testing on general protein antigens resulted in AUC of 0.740 with BA (balanced accuracy) of 0.657 as baseline of SEPPA 3.0. Most importantly, when tested on independent glycoprotein antigens only, SEPPA 3.0 gave an AUC of 0.749 and BA of 0.665, leading the top performance among peers. As the first server enabling accurate epitope prediction for glycoproteins, SEPPA 3.0 shows significant advantages over popular peers on both general protein and glycoprotein antigens. It can be accessed at http://bidd2.nus.edu.sg/SEPPA3/ or at http://www.badd-cao.net/seppa3/index.html. Batch query is supported.

Advances in computational approaches in identifying synergistic drug combinations.

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.

DSab-origin: a novel IGHD sensitive VDJ mapping method and its application on antibody response after influenza vaccination.

BACKGROUND: Functional antibody genes are often assembled by VDJ recombination and then diversified by somatic hypermutation. Identifying the combination of sourcing germline genes is critical to understand the process of antibody maturation, which may facilitate the diagnostics and rapid generation of human monoclonal antibodies in therapeutics. Despite of successful efforts in V and J fragment assignment, method in D segment tracing remains weak for immunoglobulin heavy diversity (IGHD). RESULTS: In this paper, we presented a D-sensitive mapping method called DSab-origin with accuracies around 90% in human monoclonal antibody data and average 95.8% in mouse data. Besides, DSab-origin achieved the best performance in holistic prediction of VDJ segments assignment comparing with other methods commonly used in simulation data. After that, an application example was explored on the antibody response based on a time-series antibody sequencing data after influenza vaccination. The result indicated that, despite the personal response among different donors, IGHV3-7 and IGHD4-17 were likely to be dominated gene segments in these three donors. CONCLUSIONS: This work filled in a computational gap in D segment assignment for VDJ germline gene identification in antibody research. And it offered an application example of DSab-origin for studying the antibody maturation process after influenza vaccination.

The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope.

As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.

SEPPA 2.0--more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen.

Spatial Epitope Prediction server for Protein Antigens (SEPPA) has received lots of feedback since being published in 2009. In this improved version, relative ASA preference of unit patch and consolidated amino acid index were added as further classification parameters in addition to unit-triangle propensity and clustering coefficient which were previously reported. Then logistic regression model was adopted instead of the previous simple additive one. Most importantly, subcellular localization of protein antigen and species of immune host were fully taken account to improve prediction. The result shows that AUC of 0.745 (5-fold cross-validation) is almost the baseline performance with no differentiation like all the other tools. Specifying subcellular localization of protein antigen and species of immune host will generally push the AUC up. Secretory protein immunized to mouse can push AUC to 0.823. In this version, the false positive rate has been largely decreased as well. As the first method which has considered the subcellular localization of protein antigen and species of immune host, SEPPA 2.0 shows obvious advantages over the other popular servers like SEPPA, PEPITO, DiscoTope-2, B-pred, Bpredictor and Epitopia in supporting more specific biological needs. SEPPA 2.0 can be accessed at http://badd.tongji.edu.cn/seppa/. Batch query is also supported.

In Silico Insight into Potential Anti-Alzheimer's Disease Mechanisms of Icariin.

Herbal compounds that have notable therapeutic effect upon Alzheimer's disease (AD) have frequently been found, despite the recent failure of late-stage clinical drugs. Icariin, which is isolated from Epimedium brevicornum, is widely reported to exhibit significant anti-AD effects in in vitro and in vivo studies. However, the molecular mechanism remains thus far unclear. In this work, the anti-AD mechanisms of icariin were investigated at a target network level assisted by an in silico target identification program (INVDOCK). The results suggested that the anti-AD effects of icariin may be contributed by: attenuation of hyperphosphorylation of tau protein, anti-inflammation and regulation of Ca(2+) homeostasis. Our results may provide assistance in understanding the molecular mechanism and further developing icariin into promising anti-AD agents.

Identification of Toxic Pyrrolizidine Alkaloids and Their Common Hepatotoxicity Mechanism.

Pyrrolizidine Alkaloids (PAs) are currently one of the most important botanical hepatotoxic ingredients. Glutathion (GSH) metabolism is the most reported pathway involved in hepatotoxicity mechanism of PAs. We speculate that, for different PAs, there should be a common mechanism underlying their hepatotoxicity in GSH metabolism. Computational methods were adopted to test our hypothesis in consideration of the limitations of current experimental approaches. Firstly, the potential targets of 22 PAs (from three major PA types) in GSH metabolism were identified by reverse docking; Secondly, glutathione S-transferase A1 (GSTA1) and glutathione peroxidase 1 (GPX1) targets pattern was found to be a special characteristic of toxic PAs with stepwise multiple linear regressions; Furthermore, the molecular mechanism underlying the interactions within toxic PAs and these two targets was demonstrated with the ligand-protein interaction analysis; Finally, GSTA1 and GPX1 were proved to be significant nodes in GSH metabolism. Overall, toxic PAs could be identified by GSTA1 and GPX1 targets pattern, which suggests their common hepatotoxicity mechanism: the interfering of detoxication in GSH metabolism. In addition, all the strategies developed here could be extended to studies on toxicity mechanism of other toxins.

Towards a bioinformatics analysis of anti-Alzheimer's herbal medicines from a target network perspective.

With the growth of aging population all over the world, a rising incidence of Alzheimer's disease (AD) has been recently observed. In contrast to FDA-approved western drugs, herbal medicines, featured as abundant ingredients and multi-targeting, have been acknowledged with notable anti-AD effects although the mechanism of action (MOA) is unknown. Investigating the possible MOA for these herbs can not only refresh but also extend the current knowledge of AD pathogenesis. In this study, clinically tested anti-AD herbs, their ingredients as well as their corresponding target proteins were systematically reviewed together with applicable bioinformatics resources and methodologies. Based on above information and resources, we present a systematically target network analysis framework to explore the mechanism of anti-AD herb ingredients. Our results indicated that, in addition to the binding of those symptom-relieving targets as the FDA-approved drugs usually do, ingredients of anti-AD herbs also interact closely with a variety of successful therapeutic targets related to other diseases, such as inflammation, cancer and diabetes, suggesting the possible cross-talks between these complicated diseases. Furthermore, pathways of Ca(2+) equilibrium maintaining upstream of cell proliferation and inflammation were densely targeted by the anti-AD herbal ingredients with rigorous statistic evaluation. In addition to the holistic understanding of the pathogenesis of AD, the integrated network analysis on the MOA of herbal ingredients may also suggest new clues for the future disease modifying strategies.

iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis.

UNLABELLED: A challenge in biodata analysis is to understand the underlying phenomena among many interactions in signaling pathways. Such study is formulated as the pathway enrichment analysis, which identifies relevant pathways functional enriched in high-throughput data. The question faced here is how to analyze different data types in a unified and integrative way by characterizing pathways that these data simultaneously reveal. To this end, we developed integrative Pathway Enrichment Analysis Platform, iPEAP, which handles transcriptomics, proteomics, metabolomics and GWAS data under a unified aggregation schema. iPEAP emphasizes on the ability to aggregate various pathway enrichment results generated in different high-throughput experiments, as well as the quantitative measurements of different ranking results, thus providing the first benchmark platform for integration, comparison and evaluation of multiple types of data and enrichment methods. AVAILABILITY AND IMPLEMENTATION: iPEAP is freely available at http://www.tongji.edu.cn/∼qiliu/ipeap.html.

Target network differences between western drugs and Chinese herbal ingredients in treating cardiovascular disease.

BACKGROUND: Western drugs have achieved great successes in CVDs treatment. However, they may lead to some side effects and drug resistance. On the other hand, more and more studies found that Traditional Chinese herbs have efficient therapeutic effects for CVDs, while their therapeutic mechanism is still not very clear. It may be a good view towards molecules, targets and network to decipher whether difference exists between anti-CVD western drugs and Chinese herbal ingredients. RESULTS: Anti-CVD western drugs and Chinese herbal ingredients, as well as their targets were thoroughly collected in this work. The similarities and the differences between the herbal ingredients and the western drugs were deeply explored based on three target-based perspectives including biochemical property, regulated pathway and disease network. The biological function of herbal ingredients' targets is more complex than that of the western drugs' targets. The signal transduction and immune system associated signaling pathways, apoptosis associated pathways may be the most important pathway for herbal ingredients, however the western drugs incline to regulate vascular smooth muscle contraction associated pathways. Chinese herbal ingredients prefer to regulate the downstream proteins of apoptosis associated pathway; while the western drugs incline to regulate the upstream proteins of VECC (Vascular Epidermal Cells Contraction) related pathways. CONCLUSION: In summary, the characteristics identified in this study would be valuable for designing new network-based multi-target CVD drugs or vaccine adjuvants.

Recent Progress in Janus Nano-Objects with Asymmetric Polymer Brushes.

Over the past few decades, Janus materials have drawn much interest owing to the combination of two different functionalities on the opposite sides. Janus nano-objects (JNOs) with asymmetric polymer brushes are one unique material of them, which consist of a polymeric or inorganic core and asymmetric polymer chains coated on the core. Combining the properties of nanomaterials, asymmetric structures and flexible polymer brushes, hairy JNOs have shown great potential in interfacial compatibilization, interfacial catalysis, oil-water separation and drug delivery. This review summarizes recent progress in the preparation strategies of JNOs with asymmetric polymer brushes via self-assembly or grafting strategies, as well as their applications in interfacial engineering, biomedicine and other aspects. Finally, the outlook and challenges of this direction are discussed.

HIT: linking herbal active ingredients to targets.

The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients' Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user's reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.

Impact of preoperative type 2 diabetes mellitus on the outcomes of patients with gastric cancer following gastrectomy: Analysis of 834 patients using propensity score matching.

The purpose of the current study was to compare the outcomes of patients with gastric cancer (GC) between the type 2 diabetes mellitus (T2DM) group and the non-T2DM group. The PubMed, Embase and Cochrane Library databases were searched from inception to March 8, 2022, to identify propensity score matching (PSM) studies that analyzed the effect of T2DM on the outcomes of patients with GC. Total complications, overall survival (OS), disease-free survival (DFS) and cancer-specific survival (CSS) were compared between the T2DM group and the non-T2DM group. A total of four PSM studies with 834 patients were included in the current study. There were 311 and 523 patients in the T2DM group and the non-T2DM group, respectively. Baseline characteristics of the two groups were adjusted with PSM in all the four studies, however, no significant difference was found in baseline characteristics (P>0.05). DFS was significantly worse in the T2DM group compared with that in the non-T2DM group [hazard ratio (HR), 1.45; 95% confidence interval (CI), 1.10-1.90; P=0.007)]. However, after pooling up the data, there was no significant difference between the T2DM group and the non-T2DM group in terms of OS (HR, 1.41; 95% CI, 0.92-2.16; P=0.11), CSS (HR, 1.29; 95% CI, 0.92-1.81; P=0.14) and total complications (odds ratio, 1.01; 95% CI, 0.64-1.60; P=0.95). Patients with GC and T2DM are associated with poor DFS. However, there were no significant differences between the T2DM group and the non-T2DM group in terms of OS, CSS and total complications.