Proteogenomic characterization of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.

CellCommuNet: an atlas of cell-cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states.

Cell-cell communication, as a basic feature of multicellular organisms, is crucial for maintaining the biological functions and microenvironmental homeostasis of cells, organs, and whole organisms. Alterations in cell-cell communication contribute to many diseases, including cancers. Single-cell RNA sequencing (scRNA-seq) provides a powerful method for studying cell-cell communication by enabling the analysis of ligand-receptor interactions. Here, we introduce CellCommuNet (http://www.inbirg.com/cellcommunet/), a comprehensive data resource for exploring cell-cell communication networks in scRNA-seq data from human and mouse tissues in normal and disease states. CellCommuNet currently includes 376 single datasets from multiple sources, and 118 comparison datasets between disease and normal samples originating from the same study. CellCommuNet provides information on the strength of communication between cells and related signalling pathways and facilitates the exploration of differences in cell-cell communication between healthy and disease states. Users can also search for specific signalling pathways, ligand-receptor pairs, and cell types of interest. CellCommuNet provides interactive graphics illustrating cell-cell communication in different states, enabling differential analysis of communication strength between disease and control samples. This comprehensive database aims to be a valuable resource for biologists studying cell-cell communication networks.

DiSignAtlas: an atlas of human and mouse disease signatures based on bulk and single-cell transcriptomics.

Molecular signatures are usually sets of biomolecules that can serve as diagnostic, prognostic, predictive, or therapeutic markers for a specific disease. Omics data derived from various high-throughput molecular biology technologies offer global, unbiased and appropriately comparable data, which can be used to identify such molecular signatures. To address the need for comprehensive disease signatures, DiSignAtlas (http://www.inbirg.com/disignatlas/) was developed to provide transcriptomics-based signatures for a wide range of diseases. A total of 181 434 transcriptome profiles were manually curated from studies involving 1836 nonredundant disease types in humans and mice. Then, 10 306 comparison datasets comprising both disease and control samples, including 328 single-cell RNA sequencing datasets, were established. Furthermore, a total of 3 775 317 differentially expressed genes in humans and 1 723 674 in mice were identified as disease signatures by analysing transcriptome profiles using commonly used pipelines. In addition to providing multiple methods for the retrieval of disease signatures, DiSignAtlas provides downstream functional enrichment analysis, cell type analysis and signature correlation analysis between diseases or species when available. Moreover, multiple analytical and comparison tools for disease signatures are available. DiSignAtlas is expected to become a valuable resource for both bioscientists and bioinformaticians engaged in translational research.

CrossFuse-XGBoost: accurate prediction of the maximum recommended daily dose through multi-feature fusion, cross-validation screening and extreme gradient boosting.

In the drug development process, approximately 30% of failures are attributed to drug safety issues. In particular, the first-in-human (FIH) trial of a new drug represents one of the highest safety risks, and initial dose selection is crucial for ensuring safety in clinical trials. With traditional dose estimation methods, which extrapolate data from animals to humans, catastrophic events have occurred during Phase I clinical trials due to interspecies differences in compound sensitivity and unknown molecular mechanisms. To address this issue, this study proposes a CrossFuse-extreme gradient boosting (XGBoost) method that can directly predict the maximum recommended daily dose of a compound based on existing human research data, providing a reference for FIH dose selection. This method not only integrates multiple features, including molecular representations, physicochemical properties and compound-protein interactions, but also improves feature selection based on cross-validation. The results demonstrate that the CrossFuse-XGBoost method not only improves prediction accuracy compared to that of existing local weighted methods [k-nearest neighbor (k-NN) and variable k-NN (v-NN)] but also solves the low prediction coverage issue of v-NN, achieving full coverage of the external validation set and enabling more reliable predictions. Furthermore, this study offers a high level of interpretability by identifying the importance of different features in model construction. The 241 features with the most significant impact on the maximum recommended daily dose were selected, providing references for optimizing the structure of new compounds and guiding experimental research. The datasets and source code are freely available at https://github.com/cqmu-lq/CrossFuse-XGBoost.

OrganoidDB: a comprehensive organoid database for the multi-perspective exploration of bulk and single-cell transcriptomic profiles of organoids.

Organoids, three-dimensional in vitro tissue cultures derived from pluripotent (embryonic or induced) or adult stem cells, are promising models for the study of human processes and structures, disease onset and preclinical drug development. An increasing amount of omics data has been generated for organoid studies. Here, we introduce OrganoidDB (http://www.inbirg.com/organoid_db/), a comprehensive resource for the multi-perspective exploration of the transcriptomes of organoids. The current release of OrganoidDB includes curated bulk and single-cell transcriptome profiles of 16 218 organoid samples from both human and mouse. Other types of samples, such as primary tissue and cell line samples, are also integrated to enable comparisons with organoids. OrganoidDB enables queries of gene expression under different modes, e.g. across different organoid types, between different organoids from different sources or protocols, between organoids and other sample types, across different development stages, and via correlation analysis. Datasets and organoid samples can also be browsed for detailed information, including organoid information, differentially expressed genes, enriched pathways and single-cell clustering. OrganoidDB will facilitate a better understanding of organoids and help improve organoid culture protocols to yield organoids that are highly similar to living organs in terms of composition, architecture and function.

PertOrg 1.0: a comprehensive resource of multilevel alterations induced in model organisms by in vivo genetic perturbation.

Genetically modified organisms (GMOs) can be generated to model human genetic disease or plant disease resistance, and they have contributed to the exploration and understanding of gene function, physiology, disease onset and drug target discovery. Here, PertOrg (http://www.inbirg.com/pertorg/) was introduced to provide multilevel alterations in GMOs. Raw data of 58 707 transcriptome profiles and associated information, such as phenotypic alterations, were collected and curated from studies involving in vivo genetic perturbation (e.g. knockdown, knockout and overexpression) in eight model organisms, including mouse, rat and zebrafish. The transcriptome profiles from before and after perturbation were organized into 10 116 comparison datasets, including 122 single-cell RNA-seq datasets. The raw data were checked and analysed using widely accepted and standardized pipelines to identify differentially expressed genes (DEGs) in perturbed organisms. As a result, 8 644 148 DEGs were identified and deposited as signatures of gene perturbations. Downstream functional enrichment analysis, cell type analysis and phenotypic alterations were also provided when available. Multiple search methods and analytical tools were created and implemented. Furthermore, case studies were presented to demonstrate how users can utilize the database. PertOrg 1.0 will be a valuable resource aiding in the exploration of gene functions, biological processes and disease models.

Importin 7 enhances protective autophagy induced by nuclear translocation of homeobox A10 in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is a common malignant tumor. Importin7 (IPO7) is responsible for nucleoplasmic transport of RNAs and proteins, and it has been confirmed to be involved in the development of human cancers. This study aimed to explore the function and mechanism of IPO7 in OSCC. IPO7 expression in tissues and cells was determined by RT-qPCR. Cell proliferative, migratory, and invasive capabilities were detected through transwell assay and colony formation assay. Mice xenograft models were established for evaluating tumor growth. Autophagy was estimated by the LC3 levels in cells through western blot and immunofluorescence (IF). Western blot was utilized to detect the key proteins in PERK/EIF2AK3/ATF4 pathway for assessing the endoplasmic reticulum stress (ERS). The interaction of IPO7 and homeobox A10 (HOXA10) was tested by GST pull-down assay and Co-IP assay. ChIP assay and luciferase reporter assay were utilized to determine the combination of HOXA10 and EIF2AK3. We proved that IPO7 was upregulated in OSCC tissues and cells, and its depletion reduced cell proliferation, migration, invasion and tumor growth. Furthermore, LC3 expression in cells was found to be reduced by IPO7 knockdown. IPO7 promoted OSCC tumor metastasis by activating autophagy. Additionally, we discovered that IPO7 could regulate ERS by activating the PERK/ATF4 pathway. EIF2AK3 upregulation can promote cell autophagy. Furthermore, IPO7 was proven to promote nuclear translocation of HOXA10 in cells. EIF2AK3 promoter can bind to HOXA10. Rescue assay confirmed that HOXA10 upregulation can reverse the effect of IPO7 silencing on OSCC progression. IPO7 can enhance proliferation, migration, invasion, and autophagy by nuclear translocation of HOXA10 and the activation of EIF2AK3/ATF4 pathway in OSCC.

Light transport through a magneto-optical medium: simple theory revealing fruitful phenomena.

Electromagnetic wave transmission in a magneto-optical (MO) medium is a basic and old topic but has raised new interest in recent years, because MO medium plays a vital role in optical isolator, topological optics, electromagnetic field regulation, microwave engineering, and many other technological applications. Here, we describe several fascinating physical images and classical physical variables in MO medium by using a simple and rigorous electromagnetic field solution approach. We can easily obtain explicit formulations for all relevant physical quantities, such as the electromagnetic field distribution, energy flux, reflection/transmission phase, reflection/transmission coefficients, and Goos-Hänchen (GH) shift in MO medium. This theory can help to deepen and broaden our physical understanding of basic electromagnetics, optics, and electrodynamics in application to gyromagnetic and MO homogeneous medium and microstructures, and might help to disclose and develop new ways and routes to high technologies in optics and microwave.

Human umbilical cord mesenchymal stem cell exosome-derived miR-874-3p targeting RIPK1/PGAM5 attenuates kidney tubular epithelial cell damage.

BACKGROUND: Kidney insults due to various pathogenic factors, such as trauma, infection, and inflammation, can cause tubular epithelial cell injury and death, leading to acute kidney injury and the transformation of acute kidney injury to chronic kidney disease. There is no definitive treatment available. In previous studies, human umbilical cord mesenchymal stem cells have been shown to promote kidney injury. In this preclinical study, we investigate the role and mechanism of human umbilical cord mesenchymal stem cell exosomes (HucMSC-Exos) on the repair of renal tubular epithelial cells after injury. METHODS: C57BL/6 mice underwent unilateral ureteral obstruction, and epithelial cell injury was induced in HK-2 cells by cisplatin. HucMSC-Exos were assessed in vivo and in vitro. The extent of renal cell injury, activation of necroptosis pathway, and mitochondrial quality-control-related factors were determined in different groups. We also analyzed the possible regulatory effector molecules in HucMSC-Exos by transcriptomics. RESULTS: HucMSC-Exo inhibited necroptosis after renal tubular epithelial cell injury and promoted the dephosphorylation of the S637 site of the Drp1 gene by reducing the expression of PGAM5. This subsequently inhibited mitochondrial fission and maintained mitochondrial functional homeostasis, mitigating renal injury and promoting repair. In addition, HucMSC-Exo displayed a regulatory role by targeting RIPK1 through miR-874-3p. CONCLUSION: The collective findings of the present study demonstrate that HucMSC-Exos can regulate necroptosis through miR-874-3p to attenuate renal tubular epithelial cell injury and enhance repair, providing new therapeutic modalities and ideas for the treatment of AKI and the process of AKI to CKD transformation to mitigate renal damage.

Identification of Novel Serological Autoantibodies in Takayasu Arteritis Patients Using HuProt Arrays.

To identify novel autoantibodies of Takayasu arteritis (TAK) using HuProt array-based approach, a two-phase approach was adopted. In Phase I, serum samples collected from 40 TAK patients, 15 autoimmune disease patients, and 20 healthy subjects were screened to identify TAK-specific autoantibodies using human protein (HuProt) arrays. In phase II, the identified candidate autoantibodies were validated with TAK-focused arrays using an additional cohort comprised of 109 TAK patients, 110 autoimmune disease patients, and 96 healthy subjects. Subsequently, the TAK-specific autoantibodies validated in phase II were further confirmed using western blot analysis. We identified and validated eight autoantibodies as potential TAK-specific diagnostic biomarkers, including anti-SPATA7, -QDPR, -SLC25A2, -PRH2, -DIXDC1, -IL17RB, -ZFAND4, and -NOLC1 antibodies, with AUC of 0.803, 0.801, 0.780, 0.696, 0.695, 0.678, 0.635, and 0.613, respectively. SPATA7 could distinguish TAK from healthy and disease controls with 73.4% sensitivity at 85.4% specificity, while QDPR showed 71.6% sensitivity at 86.4% specificity. SLC25A22 showed the highest sensitivity of 80.7%, but at lower specificity of 67.0%. In addition, PRH2, IL17RB, and NOLC1 showed good specificities of 88.3%, 85.9%, and 86.9%, respectively, but at lower sensitivities (<50%). Finally, DIXDC1 and ZFAND4 showed moderate performance as compared with the other autoantibodies. Using a decision tree model, we could reach a specificity of 94.2% with AUC of 0.843, a significantly improved performance as compared with that by each individual biomarker. The performances of three autoantibodies, namely anti-SPATA7, -QDPR, and -PRH2, were successfully confirmed with western blot analysis. Using this two-phase strategy, we identified and validated eight novel autoantibodies as TAK-specific biomarker candidates, three of which could be readily adopted in a clinical setting.

Integration of IgA and IgG Autoantigens Improves Performance of Biomarker Panels for Early Diagnosis of Lung Cancer.

Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. In our previous study, we surveyed both IgG and IgM-bound serological biomarkers and validated a panel of IgG-bound autoantigens for early LC diagnosis with 50% sensitivity at 90% specificity. To further improve the performance of these serological biomarkers, we surveyed HuProt arrays, comprised of 20,240 human proteins, for IgA-bound autoantigens because IgAs are a major immunoglobulin isotype in the lung. Integrating with IgG-bound autoantigens, we discovered and validated a combined biomarker panel using ELISA-format tests. Specifically, in Phase I, we obtained IgA-based autoimmune profiles of 69 early stage LC patients, 30 healthy subjects and 25 patients with lung benign lesions (LBL) on HuProt arrays and identified 28 proteins as candidate autoantigens that were significantly associated with early stage LC. In Phase II, we re-purified the autoantigens and converted them into an ELISA-format testing to profile an additional large cohort, comprised of 136 early stage LC patients, 58 healthy individuals, and 29 LBL patients. Integration of IgG autoimmune profiles allowed us to identify and validate a biomarker panel of three IgA autoantigens (i.e. BCL7A, and TRIM33 and MTERF4) and three IgG autoantigens (i.e. CTAG1A, DDX4 and MAGEC2) for diagnosis of early stage LC with 73.5% sensitivity at >85% specificity. In Phase III, the performance of this biomarker panel was confirmed with an independent cohort, comprised of 88 early stage LC patients, 18 LBL patients, and 36 healthy subjects. Finally, a blind test on 178 serum samples was conducted to confirm the performance of the biomarker panel. In summary, this study demonstrates for the first time that an integrated panel of IgA/IgG autoantigens can serve as valuable biomarkers to further improve the performance of early diagnosis of LC.

Multiplexed Biomarker Panels Discriminate Zika and Dengue Virus Infection in Humans.

Zika virus (ZIKV) and dengue virus (DENV) are closely related flaviviruses that cause widespread, acute febrile illnesses, notably microcephaly for fetuses of infected pregnant women. Detecting the viral cause of these illnesses is paramount to determine risks to patients, counsel pregnant women, and help fight outbreaks. A combined diagnostic algorithm for ZIKV and DENV requires Reverse transcription polymerase chain reaction (RT-PCR) and IgM antibody detection. RT-PCR differentiates between DENV and ZIKV infections during the acute phases of infection, but differentiation based on IgM antibodies is currently nearly impossible in endemic areas. We have developed a ZIKV/DENV protein array and tested it with serum samples collected from ZIKV- and DENV-infected patients and healthy subjects in Puerto Rico. Our analyses reveal a biomarker panel that are capable of discriminating ZIKV and DENV infections with high accuracy, including Capsid protein from African ZIKV strain MR766, and other 5 pair of proteins (NS1, NS2A, NS3, NS4B and NS5) from ZIKV and DENV respectively. Both sensitivity and specificity of the test for ZIKV from DENV are around 90%. We propose that the ZIKV/DENV protein array will be used in future studies to discriminate patients infected with ZIKV from DENV.

AAgMarker 1.0: a resource of serological autoantigen biomarkers for clinical diagnosis and prognosis of various human diseases.

Autoantibodies are produced to target an individual's own antigens (e.g. proteins). They can trigger autoimmune responses and inflammation, and thus, cause many types of diseases. Many high-throughput autoantibody profiling projects have been reported for unbiased identification of serological autoantigen-based biomarkers. However, a lack of centralized data portal for these published assays has been a major obstacle to further data mining and cross-evaluate the quality of these datasets generated from different diseases. Here, we introduce a user-friendly database, AAgMarker 1.0, which collects many published raw datasets obtained from serum profiling assays on the proteome microarrays, and provides a toolbox for mining these data. The current version of AAgMarker 1.0 contains 854 serum samples, involving 136 092 proteins. A total of 7803 (4470 non-redundant) candidate autoantigen biomarkers were identified and collected for 12 diseases, such as Alzheimer's disease, Bechet's disease and Parkinson's disease. Seven statistical parameters are introduced to quantitatively assess these biomarkers. Users can retrieve, analyse and compare the datasets through basic search, advanced search and browse. These biomarkers are also downloadable by disease terms. The AAgMarker 1.0 is now freely accessible at http://bioinfo.wilmer.jhu.edu/AAgMarker/. We believe this database will be a valuable resource for the community of both biomedical and clinical research.

Identification of Serological Biomarkers for Early Diagnosis of Lung Cancer Using a Protein Array-Based Approach.

Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. Currently, a lack of serological biomarkers for early LC diagnosis is a major roadblock for early intervention and prevention of LC. To undertake this challenge, we employed a two-phase strategy to discover and validate a biomarker panel using a protein array-based approach. In Phase I, we obtained serological autoimmune profiles of 80 LC patients and 20 healthy subjects on HuProt arrays, and identified 170 candidate proteins significantly associated with LC. In Phase II, we constructed a LC focused array with the 170 proteins, and profiled a large cohort, comprised of 352 LC patients, 93 healthy individuals, and 101 patients with lung benign lesions (LBL). The comparison of autoimmune profiles between the early stage LC and the combined group of healthy and LBL allowed us to identify and validate a biomarker panel of p53, HRas, and ETHE1 for diagnosis of early stage LC with 50% sensitivity at >90% specificity. Finally, the performance of this biomarker panel was confirmed in ELISA tests. In summary, this study represents one of the most comprehensive proteome-wide surveys with one of the largest (i.e. 1,101 unique samples) and most diverse (i.e. nine disease groups) cohorts, resulting in a biomarker panel with good performance.

Identification of Novel Biomarkers for Behcet Disease Diagnosis Using Human Proteome Microarray Approach.

Behcet disease (BD) is a chronic systemic vasculitis and considered as an autoimmune disease. Although rare, BD can be fatal due to ruptured vascular aneurysms or severe neurological complications. To date, no known biomarker has been reported for this disease, making it difficult to diagnosis in the clinics. To undertake this challenge, we employed the HuProt arrays, each comprised of ∼20,000 unique human proteins, to identify BD-specific autoantibodies using a Two-Phase strategy established previously. In Phase I, we profiled the autoimmunity on the HuProt arrays with 75 serum samples collected from 40 BD patients, 15 diagnosed autoimmune patients who suffer from Takayasu arteritis (TA; n = 5)), ANCA associated vasculitis (AAV; n = 5), and Sjogren's syndrome (SS; n = 5), and 20 healthy subjects, and identified 20 candidate autoantigens that were significantly associated with BD. To validate these candidates, in Phase II we constructed a focused array with these 20 candidate BD-associated antigens, and use it to profile a much larger cohort, comprised of serum samples collected from 130 BD patients, 103 autoimmune patients (i.e. 40TA, 40 AAV and 23 SS), and 110 healthy controls. This allowed us to validate CTDP1 (RNA polymerase II subunit A C-terminal domain phosphatase)as a BD-specific autoantigen. The association of anti-CTDP1 with BD patients was further validated using the traditional Western blotting analysis. In conclusion, anti-CTDP1 antibody serves a novel autoantibody for Behcet disease and is expected to help more accurate clinical diagnosis.

Anatomical differences of the protein profile in the rabbit sclera during growth.

We aimed to investigate the proteome changes in anatomical regions of sclera during growth and development of the rabbit. Sclera from New Zealand white rabbits of three ages (1 month, 2 months and 6 months) was dissected into three segments - anterior, equatorial, and posterior. A total of 36 samples were divided into groups by age and anatomical region. Tryptic digests of total proteins were analyzed by liquid chromatography followed by tandem mass spectrometry (LC-MS/MS). Label-free quantification based on spectral counts was used to determine the relative protein abundance and identify proteins with statistically significant differences between age groups or anatomical regions of the sclera. Western blotting was performed to validate some of the differentially expressed proteins. A total of 840 non-redundant proteins was identified in the sclera at different ages and regions with protein and peptide false discovery rate (FDR) at ≤1.0% and ≤0.1%, respectively. Differentially expressed proteins were identified by comparing age or anatomical region. Among these, periostin showed decreasing abundance with age, while myocilin, latent-transforming growth factor beta-binding protein 2, hyaluronan, proteoglycan link protein 1 and selenbp1 showed increasing abundance with age. In mature rabbits, alcohol dehydrogenase showed region-related differences in the sclera. Periostin showed an age-related decrease while selenbp1 showed an age-related increase in abundance in the anterior region. Vitronectin and extracellular superoxide dismutase had greater expression with age in the equatorial and posterior regions, respectively. The age related differential expression of periostin and selenbp1 was confirmed by western blotting. In conclusion, the protein profile of sclera showed age- and region-related differences. The differential protein profiles provide a baseline for understanding changes in the protein expression in the young and mature rabbit that appears to show regional changes. The changes observed in the present study add to the existing knowledge about regional alterations in biomechanical properties of sclera during growth.

Serum molecular signature for proliferative diabetic retinopathy in Saudi patients with type 2 diabetes.

PURPOSE: The risk of vision loss from proliferative diabetic retinopathy (PDR) can be reduced with timely detection and treatment. We aimed to identify serum molecular signatures that might help in the early detection of PDR in patients with diabetes. METHODS: A total of 40 patients with diabetes were recruited at King Khaled Eye Specialist Hospital in Riyadh, Saudi Arabia, 20 with extensive PDR and 20 with mild non-proliferative diabetic retinopathy (NPDR). The two groups were matched in age, gender, and known duration of diabetes. We examined the whole genome transcriptome of blood samples from the patients using RNA sequencing. We built a model using a support vector machine (SVM) approach to identify gene combinations that can classify the two groups. RESULTS: Differentially expressed genes were calculated from a total of 25,500 genes. Six genes (CCDC144NL, DYX1C1, KCNH3, LOC100506476, LOC285847, and ZNF80) were selected from the top 26 differentially expressed genes, and a combinatorial molecular signature was built based on the expression of the six genes. The mean area under receiver operating characteristic (ROC) curve was 0.978 in the cross validation. The corresponding sensitivity and specificity were 91.7% and 91.5%, respectively. CONCLUSIONS: Our preliminary study defined a combinatorial molecular signature that may be useful as a potential biomarker for early detection of proliferative diabetic retinopathy in patients with diabetes. A larger-scale study with an independent cohort of samples is necessary to validate and expand these findings.

High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics.

Drugs may induce adverse drug reactions (ADRs) when they unexpectedly bind to proteins other than their therapeutic targets. Identification of these undesired protein binding partners, called off-targets, can facilitate toxicity assessment in the early stages of drug development. In this study, a computational framework was introduced for the exploration of idiosyncratic mechanisms underlying analgesic-induced severe adverse drug reactions (SADRs). The putative analgesic-target interactions were predicted by performing reverse docking of analgesics or their active metabolites against human/mammal protein structures in a high-throughput manner. Subsequently, bioinformatics analyses were undertaken to identify ADR-associated proteins (ADRAPs) and pathways. Using the pathways and ADRAPs that this analysis identified, the mechanisms of SADRs such as cardiac disorders were explored. For instance, 53 putative ADRAPs and 24 pathways were linked with cardiac disorders, of which 10 ADRAPs were confirmed by previous experiments. Moreover, it was inferred that pathways such as base excision repair, glycolysis/glyconeogenesis, ErbB signaling, calcium signaling, and phosphatidyl inositol signaling likely play pivotal roles in drug-induced cardiac disorders. In conclusion, our framework offers an opportunity to globally understand SADRs at the molecular level, which has been difficult to realize through experiments. It also provides some valuable clues for drug repurposing.