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.

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.

The antipsychotic drug penfluridol inhibits N-linked glycoprotein processing and enhances T-cell-mediated tumor immunity.

Aberrant N-linked glycosylation is a prominent feature of cancers. Perturbance of oligosaccharide structure on cell surfaces directly affects key processes in tumor development and progression. In spite of the critical role played by N-linked glycans in tumor biology, the discovery of small molecules that specifically disturbs the N-linked glycans is still under investigation. To identify more saccharide-structure-perturbing compounds, a repurposed drug screen by using a library consisting of 1530 FDA-approved drugs was performed. Interestingly, an antipsychotic drug, penfluridol, was identified as being able to decrease cell surface Wheat germ agglutinin (WGA) staining. In the presence of penfluridol, cell membrane glycoproteins PD-L1 shifted to a lower molecular weight. Further studies demonstrated that penfluridol treatment caused an accumulation of high-mannose oligosaccharides, especially Man5-7GlcNAc2 glycan structures. Mechanistically, this effect is due to direct targeting of MAN1A1 mannosidase, a Golgi enzyme involved in N-glycan maturation. Moreover, we found that altered glycosylation of PD-L1 caused by penfluridol disrupted interactions between PD-1 and PD-L1, resulting in activation of T-cell tumor immunity. In a mouse xenograft and glioma model, penfluridol enhanced the anti-tumor effect of the anti-PD-L1 antibody in vivo. Overall, these findings revealed an important biological activity of the antipsychotic drug penfluridol as an inhibitor of glycan processing and proposed a repurposed use of penfluridol in anti-tumor therapy through activation of T-cell immunity.

Natural product P57 induces hypothermia through targeting pyridoxal kinase.

Induction of hypothermia during hibernation/torpor enables certain mammals to survive under extreme environmental conditions. However, pharmacological induction of hypothermia in most mammals remains a huge challenge. Here we show that a natural product P57 promptly induces hypothermia and decreases energy expenditure in mice. Mechanistically, P57 inhibits the kinase activity of pyridoxal kinase (PDXK), a key metabolic enzyme of vitamin B6 catalyzing phosphorylation of pyridoxal (PL), resulting in the accumulation of PL in hypothalamus to cause hypothermia. The hypothermia induced by P57 is significantly blunted in the mice with knockout of PDXK in the preoptic area (POA) of hypothalamus. We further found that P57 and PL have consistent effects on gene expression regulation in hypothalamus, and they may activate medial preoptic area (MPA) neurons in POA to induce hypothermia. Taken together, our findings demonstrate that P57 has a potential application in therapeutic hypothermia through regulation of vitamin B6 metabolism and PDXK serves as a previously unknown target of P57 in thermoregulation. In addition, P57 may serve as a chemical probe for exploring the neuron circuitry related to hypothermia state in mice.

A novel in vitro prognostic model of bladder cancer based on urine-derived living tumor cells.

Bladder cancer (BLCA) remains a difficult malignancy to manage because of its high recurrence, intense follow-up, and invasive diagnostic and treatment techniques. Immune checkpoint inhibitors (ICIs) have forged a new direction for the treatment of BLCA, but it is currently challenging to predict whether an individual patient will be sensitive to ICIs. We collected 43 urine/tumor samples from BLCA patients for primary bladder cancer cells (BCCs) culturing using our previously reported BCC culture platform. We used flow cytometry (FCM) to measure the expression levels of Programmed Death-Ligand 1 (PD-L1) on BCCs before and after interferon-gamma (IFN-γ) treatment and found that PD-L1 expression and the sensitivities to IFN-γ varied among patients. RNA-sequencing, western blotting, and programmed death-1 (PD-1) binding assays confirmed that the BCC FCM-based PD-L1 detection platform (BC-PD-L1) was reliable and was not hindered by the  glycosylation of PD-L1. In the subsequent retrospective study, we found that IFN-γ-stimulated PD-L1 (sPD-L1) expression on BCCs detected by BC-PD-L1 could predict the prognosis of BLCA patients. Importantly, the prognostic value was similar or even better in urine-derived BC-PD-L1 (UBC-PD-L1). Transcriptome analysis showed that BCCs with high sPD-L1 tended to enrich genes associated with the collagen-containing extracellular matrix, cell-cell adhesion, and positive regulation of the immune system. In addition, the UBC-PD-L1 also exhibited predictive value for ICI response in BLCA patients. In conclusion, as a novel personalized urine-detection method, UBC-PD-L1 may provide a rapid, accurate, and non-invasive tool for monitoring tumor progression, predicting therapeutic responses, and helping improve BLCA clinical treatment in future.

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.

Integrative multi-omics and drug-response characterization of patient-derived prostate cancer primary cells.

Prostate cancer (PCa) is the second most prevalent malignancy in males across the world. A greater knowledge of the relationship between protein abundance and drug responses would benefit precision treatment for PCa. Herein, we establish 35 Chinese PCa primary cell models to capture specific characteristics among PCa patients, including gene mutations, mRNA/protein/surface protein distributions, and pharmaceutical responses. The multi-omics analyses identify Anterior Gradient 2 (AGR2) as a pre-operative prognostic biomarker in PCa. Through the drug library screening, we describe crizotinib as a selective compound for malignant PCa primary cells. We further perform the pharmacoproteome analysis and identify 14,372 significant protein-drug correlations. Surprisingly, the diminished AGR2 enhances the inhibition activity of crizotinib via ALK/c-MET-AKT axis activation which is validated by PC3 and xenograft model. Our integrated multi-omics approach yields a comprehensive understanding of PCa biomarkers and pharmacological responses, allowing for more precise diagnosis and therapies.

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.

Discovery of potential biomarkers for lung cancer classification based on human proteome microarrays using Stochastic Gradient Boosting approach.

PURPOSE: Early identification of lung cancer (LC) will considerably facilitate the intervention and prevention of LC. The human proteome micro-arrays approach can be used as a "liquid biopsy" to diagnose LC to complement conventional diagnosis, which needs advanced bioinformatics methods such as feature selection (FS) and refined machine learning models. METHODS: A two-stage FS methodology by infusing Pearson's Correlation (PC) with a univariate filter (SBF) or recursive feature elimination (RFE) was used to reduce the redundancy of the original dataset. The Stochastic Gradient Boosting (SGB), Random Forest (RF), and Support Vector Machine (SVM) techniques were applied to build ensemble classifiers based on four subsets. The synthetic minority oversampling technique (SMOTE) was used in the preprocessing of imbalanced data. RESULTS: FS approach with SBF and RFE extracted 25 and 55 features, respectively, with 14 overlapped ones. All three ensemble models demonstrate superior accuracy (ranging from 0.867 to 0.967) and sensitivity (0.917 to 1.00) in the test datasets with SGB of SBF subset outperforming others. The SMOTE technique has improved the model performance in the training process. Three of the top selected candidate biomarkers (LGR4, CDC34, and GHRHR) were highly suggested to play a role in lung tumorigenesis. CONCLUSION: A novel hybrid FS method with classical ensemble machine learning algorithms was first used in the classification of protein microarray data. The parsimony model constructed by the SGB algorithm with the appropriate FS and SMOTE approach performs well in the classification task with higher sensitivity and specificity. Standardization and innovation of bioinformatics approach for protein microarray analysis need further exploration and validation.

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.

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.

MK-2206 Alleviates Renal Fibrosis by Suppressing the Akt/mTOR Signaling Pathway In Vivo and In Vitro.

Renal fibrosis is a common pathological feature of various kidney diseases, leading to irreversible renal failure and end-stage renal disease. However, there are still no effective treatments to reverse renal fibrosis. This study aimed to explore the potential mechanism of a targeted drug for fibrosis. Here, unilateral ureteral obstruction (UUO)-treated mice and a TGF-ß1-treated human renal tubular epithelial cell line (HK-2 cells) were used as models of renal fibrosis. Based on the changes of mRNA in UUO kidneys detected by transcriptome sequencing, MK-2206, an Akt inhibitor, was predicted as a potential drug to alleviate renal fibrosis through bioinformatics. We dissolved UUO mice with MK-2206 by gastric gavage and cultured TGF-ß-induced HK-2 cells with MK-2206. Histopathological examinations were performed after MK-2206 intervention, and the degree of renal fibrosis, as well as the expression of Akt/mTOR pathway-related proteins, were evaluated by immunohistochemical staining, immunofluorescence staining, and Western blot. The results showed that MK-2206 significantly improved the pathological structure of the kidney. Furthermore, MK-2206 intervention effectively inhibited UUO- and TGF-ß1-induced epithelial-mesenchymal transition, fibroblast activation, and extracellular matrix deposition. Mechanistically, MK-2206 treatment attenuated the activation of the Akt/mTOR signaling pathway. Taken together, our study revealed for the first time that MK-2206 is a promising drug for the improvement of renal fibrosis.

DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development.

Drug-likeness is a vital consideration when selecting compounds in the early stage of drug discovery. A series of drug-like properties are needed to predict the drug-likeness of a given compound and provide useful guidelines to increase the likelihood of converting lead compounds into drugs. Experimental physicochemical properties, pharmacokinetic/toxicokinetic properties and maximum dosages of approved small-molecule drugs from multiple text-based unstructured data resources have been manually assembled, curated, further digitized and processed into structured data, which are deposited in the Database of Digital Properties of approved Drugs (DDPD). DDPD 1.0 contains 30 212 drug property entries, including 2250 approved drugs and 32 properties, in a standardized value/unit format. Moreover, two analysis tools are provided to examine the drug-likeness features of given molecules based on the collected property data of approved drugs. Additionally, three case studies are presented to demonstrate how users can utilize the database. We believe that this database will be a valuable resource for the drug discovery and development field. Database URL:  http://www.inbirg.com/ddpd.

Human retinal organoids release extracellular vesicles that regulate gene expression in target human retinal progenitor cells.

The mechanisms underlying retinal development have not been completely elucidated. Extracellular vesicles (EVs) are novel essential mediators of cell-to-cell communication with emerging roles in developmental processes. Nevertheless, the identification of EVs in human retinal tissue, characterization of their cargo, and analysis of their potential role in retina development has not been accomplished. Three-dimensional retinal tissue derived from human induced pluripotent stem cells (hiPSC) provide an ideal developmental system to achieve this goal. Here we report that hiPSC-derived retinal organoids release exosomes and microvesicles with small noncoding RNA cargo. EV miRNA cargo-predicted targetome correlates with Gene Ontology (GO) pathways involved in mechanisms of retinogenesis relevant to specific developmental stages corresponding to hallmarks of native human retina development. Furthermore, uptake of EVs by human retinal progenitor cells leads to changes in gene expression correlated with EV miRNA cargo predicted gene targets, and mechanisms involved in retinal development, ganglion cell and photoreceptor differentiation and function.

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.

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.