An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality.

BACKGROUND: Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality. METHODS: CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis. RESULTS: A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]). CONCLUSION: We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

DNA G-Quadruplex in NRP1 Promoter Facilitates SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to raise concerns worldwide. Numerous host factors involved in SARS-CoV-2 infection have been identified, but the regulatory mechanisms of these host factor remain unclear. Here, we report the role of G-quadruplexes (G4s) located in the host factor promoter region in SARS-CoV-2 infection. Using bioinformatics, biochemical, and biological assays, we provide evidence for the presence of G4 structures in the promoter regions of SARS-CoV-2 host factors NRP1. Specifically, we focus on two representative G4s in the NRP1 promoter and highlight its importance in SARS-CoV-2 pathogenesis. The presence of the G4 structure greatly increases NRP1 expression, facilitating SARS-CoV-2 entry into cells. Utilizing published single-cell RNA sequencing data obtained from simulated SARS-CoV-2 infection in human bronchial epithelial cells (HBECs), we found that ciliated cells with high levels of NRP1 are prominently targeted by the virus during infection. Furthermore, our study identifies E2F1 act as a transcription factor that binds to G4s. These findings uncover a previously unknown mechanism underlying SARS-CoV-2 infection and suggest that targeting G4 structures could be a potential strategy for COVID-19 prevention and treatment.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

BACKGROUND: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. METHODS: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. RESULTS: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. CONCLUSIONS: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1,453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

Targeting LEF1-mediated epithelial-mesenchymal transition reverses lenvatinib resistance in hepatocellular carcinoma.

Acquired resistance is a significant hindrance to clinical application of lenvatinib in unresectable hepatocellular carcinoma (HCC). Further in-depth investigation of resistance mechanisms can help to develop additional therapeutic strategies to overcome or delay resistance. In our study, two lenvatinib-resistant (LR) HCC cell lines were established by treatment with gradient increasing concentration of lenvatinib, named Hep3B-LR and HepG2-LR. Interestingly, continuous lenvatinib treatment reinforced epithelial-mesenchymal transition (EMT), cell migration, and cell invasion. Gene set enrichment analysis (GSEA) enrichment analysis of RNA-sequencing from Hep3B-LR and corresponding parental cells revealed that activation of Wnt signaling pathway was involved in this adaptive process. Active ß-catenin and its downstream target lymphoid enhancer binding factor 1 (LEF1) were significantly elevated in LR HCC cells, which promoted lenvatinib resistance through mediating EMT-related genes. Data analysis based on Gene Expression Omnibus (GEO) and the Cancer Genome Atlas Program (TCGA) databases suggests that LEF1, as a key regulator of EMT, was a novel molecular target linked to lenvatinib resistance and poor prognosis in HCC. Using a small-molecule specific inhibitor ICG001 and knocking down LEF1 showed that targeting LEF1 restored the sensitivity of LR HCC cells to lenvatinib. Our results uncover upregulation of LEF1 confers lenvatinib resistance by facilitating EMT, cell migration, and invasion of LR HCC cells, indicating that LEF1 is a novel therapeutic target for overcoming acquired lenvatinib resistance.

Co-overexpression of BRD4 and CDK7 promotes cell proliferation and predicts poor prognosis in HCC.

Aberrant expression of critical components of the trans-acting super-enhancers (SE) complex contributes to the continuous and robust transcription of oncogenes in human cancers. Small-molecule inhibitors targeting core-transcriptional components such as transcriptional bromodomain protein 4 (BRD4) and cyclin-dependent kinase 7 (CDK7) have been developed and are currently undergoing preclinical and clinical testing in several malignant cancers. By analysis of TCGA data and clinical specimens, we demonstrated that BRD4 and CDK7 were frequently overexpressed in human HCCs and were associated with the poor prognosis. Shorter survival and poorly differentiated histology were linked to high BRD4 or CDK7 expression levels. Interestingly, co-overexpression of BRD4 and CDK7 was a more unfavorable prognostic factor in HCC. Treatment with JQ1 or THZ1 alone exhibited an inhibitory impact on the proliferation of HCC cells, while JQ1 synergized with THZ1 showed a more pronounced suppression. Concurrently, a combined JQ1 and THZ1 treatment abolished the transcription of oncogenes ETV4, MYC, NFE2L2. Our study suggested that BRD4 and CDK7 coupled can be a valuable biomarker in HCC diagnosis and the combination of JQ1 and THZ1 can be a promising therapeutic treatment against HCC.

Analysis of PM2.5 pollution in Urumqi City from 2016 to 2023 and construction of a prediction model / 预防医学

Objective@#To analyze the characteristics of fine particulate matter (PM2.5) pollution in Urumqi City, Xinjiang Uygur Autonomous Region from 2016 to 2023 and establish a prediction model, so as to provide the reference for air pollution prevention and control.@*Methods@#PM2.5 monitoring data of Urumqi City from 2016 to 2023 were collected through the website of Ministry of Ecology and Environment of China. The changing trend of PM2.5 concentration was analyzed using temporal chart and seasonal index. PM2.5 monthly average concentrations from 2016 to 2023 were used to establish an autoregressive integrated moving average (ARIMA) model, and the data in 2023 was fitted and compared with the actual values, using mean absolute percentage error (MAPE) to evaluate the effectiveness of the model, and PM2.5 monthly average concentration from 2024 to 2025 was predicted.@*Results@#PM2.5 daily average concentration in Urumqi City showed a decreasing trend from 2016 to 2023 (rs=-0.239, P<0.001), with high seasonal indexes in January, February and December, indicating certain seasonal characteristics. The optional model was ARIMA (1, 0, 0) (1, 1, 0)12, with the value of Akaike information criterion, corrected Akaike information criterion, and Bayesian information criterion being 727.38, 727.88 and 737.10, respectively. PM2.5 monthly average concentration in 2023 was fitted and compared with the actual values, with an absolute error range of 0.31-7.45 μg/m3, a relative error range of 0.01-0.53, and MAPE of 14.42%. PM2.5 monthly average concentration in Urumqi City from 2024 to 2025 was predicted to be consistent with the trend from 2016 to 2023.@*Conclusions@#PM2.5 concentration in Urumqi City showed a tendency towards a decline from 2016 to 2023, and was relatively high in winter. ARIMA (1, 0, 0) (1, 1, 0)12 can be used for short-term prediction of PM2.5 pollution in Urumqi City.

Construction of Mn doped Cu7S4 nanozymes for synergistic tumor therapy in NIR-I/II bio-windows.

In photothermal therapy (PTT) and chemodynamic therapy (CDT) of cancer, poor performance of nanoagents severely impaired the therapeutic effect of cancer. To solve the problem, we proposed and constructed a novel Mn doped Cu7S4 phothermal nanoagent both in the first near-infrared (NIR-I) and the second near- infrared (NIR-II) windows in this work, which exhibited high photothermal conversion efficiency of 40.3% at 808 nm (NIR-I window) and 33.4% at 1064 nm (NIR-II window), as well as outstanding pH-sensitive catalytic performance (peroxidase-like catalytic activity and Fenton-like catalytic activities). The as-prepared Mn doped Cu7S4 could be used to load chemotherapy drug doxorubicin (DOX) after modified by folic acid. Both in vitro and in vivo studies indicated that it could be used as nanoagent for chemodynamic therapy (CDT)/photothermal therapy (PTT)/ chemotherapy of cervical carcinoma. This study thus provided an NIR-I/NIR-II/pH responsive nanoagent for potential synergistic therapy of deep-seated tumors.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

Background: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Methods: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. Results: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Conclusion: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases.

Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory diseases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single-cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted molecular interactions within and between organs. That model supports that inflammation is switched on or off by altered balance between pro- and anti-inflammatory upstream regulators (URs) and downstream pathways. Meta-analyses of human IMIDs show a similar, but graded, on/off switch system. This system has the potential to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients. That potential is supported by UR analyses in more than 600 sera from patients with systemic lupus erythematosus.

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets.

BACKGROUND: Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. METHODS: We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. RESULTS: Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. CONCLUSIONS: We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

Edaravone Plays Protective Effects on LPS-Induced Microglia by Switching M1/M2 Phenotypes and Regulating NLRP3 Inflammasome Activation.

Parkinson's disease is a neurodegenerative disorder in which activated microglia may appear prior to motor symptoms, but the specific therapeutic mechanisms remain unclear. This study investigated the potential effects of Edaravone (EDA) on M1/M2 polarization of microglia in rats with dopaminergic neurons damage induced by lipopolysaccharide (LPS) and its mechanism. Rats were randomly grouped as the following (n = 10): Control, EDA alone (10 mg/kg), LPS-model (LPS 5 µg), LPS + EDA (5 mg/kg) and LPS + EDA (10 mg/kg). After intragastric administration of EDA once a day for seven consecutive days, LPS was injected into SN pars unilaterally. Rotarod test, pole test, and traction test were used to analyze the intervention effect of EDA on neurobehavioral function in rats. Protein expression levels of TH, TNF-α, Arg-1, Iba-1, NLRP3 and caspase-1 were measured by immunofluorescence staining and western blot. In vitro, BV-2 cells were treated with LPS (100 ng/ml) before adding different doses of EDA. Levels of inflammatory cytokines in culture medium were detected by ELISA. Western blot and immunofluorescence were used to evaluate microglial activation and polarization. First, rotarod test, pole test, and traction test all showed that EDA mitigated motor dysfunction of PD rats. Second, pathological analysis suggested that EDA inhibited LPS-induced microglial activation and remitted declines of dopaminergic neurons. In addition, EDA shifted M1 pro-inflammatory phenotype of microglia to M2 anti-inflammatory state, while decreased expression of M1 markers (TNF-α and IL-1ß) and facilitated expression of M2 markers (Arg-1 and IL-10). EDA suppressed inflammatory responses through inhibiting the expression of pro-inflammatory factors (IL-1ß, IL-18 and NO), but the neuroprotective effects were invalid while siRNA NLRP3 existed. In conclusion, these results indicated that EDA could improve neurobehavioral functions and play anti-neuroinflammatory roles in PD rats, possibly by inhibiting NLPR3 inflammasome activation and regulating microglia M1/M2 polarization.

TRERNA1 upregulation mediated by HBx promotes sorafenib resistance and cell proliferation in HCC via targeting NRAS by sponging miR-22-3p.

Hepatocellular carcinoma (HCC) is among the most common malignancies and has an unfavorable prognosis. The hepatitis B virus-encoded X (HBx) protein is closely associated with hepatocarcinogenesis. Sorafenib is a unique targeted oral kinase inhibitor for advanced HCC. Long noncoding RNAs (lncRNAs) mediate HCC progression and therapeutic resistance by acting as competing endogenous RNAs (ceRNAs). However, the ceRNA regulatory mechanisms underlying sorafenib resistance in HBx-associated HCC remain largely unknown. In this study, we found that translation regulatory lncRNA 1 (TRERNA1) upregulation by HBx not only promoted HCC cell proliferation by regulating the cell cycle in vitro and in vivo but also correlated positively with poor prognosis in HCC. Importantly, TRERNA1 enhanced sorafenib resistance in HCC cells. RNA sequencing (RNA-seq) analysis indicated that NRAS proto-oncogene (NRAS) is a potential target of TRERNA1 that mediates aspects of hepatocellular carcinogenesis. TRERNA1 acts as a ceRNA to regulate NRAS expression by sponging microRNA (miR)-22-3p. In summary, we show that increased TRERNA1 expression induced by HBx reduces HCC cell sensitivity to sorafenib by activating the RAS/Raf/MEK/ERK signaling pathway. We reveal a novel regulatory mode by which the TRERNA1/miR-22-3p/NRAS axis mediates HCC progression and indicates that TRERNA1 might constitute a powerful tumor biomarker and therapeutic target in HCC.

Computational fluid dynamics simulation of time-resolved blood flow in Budd-Chiari syndrome with inferior vena cava stenosis and its implication for postoperative efficacy assessment.

BACKGROUND: This study aimed to adopt computational fluid dynamics to simulate the blood flow dynamics in inferior vena cava stenosis based on time-dependent patient-specific models of Budd-Chiari syndrome as well as a normal model. It could offer valuable references for a retrospective insight into the underlying mechanisms of Budd-Chiari syndrome pathogenesis as well as more accurate evaluation of postoperative efficacy. METHODS: Three-dimensional inferior vena cava models of Budd-Chiari syndrome patient-specific (preoperative and postoperative) and normal morphology model were reconstructed as per magnetic resonance images using Simpleware. Moreover, computational fluid dynamics of time-resolved inferior vena cava blood flow were simulated using actual patient-specific measurements to reflect time-dependent flow rates. FINDINGS: The assessment of the preoperative model revealed the dramatic variations of hemodynamic parameters of the stenotic inferior vena cava. Moreover, the comparison of the preoperative and postoperative models with the normal model as benchmark showed that postoperative hemodynamic parameters were markedly ameliorated via stenting, with the attenuation of overall velocity and wall shear stress, and the increase of pressure. However, the comparative analysis of the patient-specific simulations revealed that some postoperative hemodynamic profiles still bore some resemblance to the preoperative ones, indicating potential risks of restenosis. INTERPRETATION: Computational fluid dynamics simulation of time-resolved blood flow could reveal the tight correlation between the hemodynamic characteristics and the pathological mechanisms of inferior vena cava stenosis. Furthermore, such time-resolved hemodynamic profiles could provide a quantitative approach to diagnosis, operative regimen and postoperative evaluation of Budd-Chiari syndrome with inferior vena cava stenosis.

Meta-Analysis of Expression Profiling Data Indicates Need for Combinatorial Biomarkers in Pediatric Ulcerative Colitis.

BACKGROUND: Unbiased studies using different genome-wide methods have identified a great number of candidate biomarkers for diagnosis and treatment response in pediatric ulcerative colitis (UC). However, clinical translation has been proven difficult. Here, we hypothesized that one reason could be differences between inflammatory responses in an inflamed gut and in peripheral blood cells. METHODS: We performed meta-analysis of gene expression microarray data from intestinal biopsies and whole blood cells (WBC) from pediatric patients with UC and healthy controls in order to identify overlapping pathways, predicted upstream regulators, and potential biomarkers. RESULTS: Analyses of profiling datasets from colonic biopsies showed good agreement between different studies regarding pathways and predicted upstream regulators. The most activated predicted upstream regulators included TNF, which is known to have a key pathogenic and therapeutic role in pediatric UC. Despite this, the expression levels of TNF were increased in neither colonic biopsies nor WBC. A potential explanation was increased expression of TNFR2, one of the membrane-bound receptors of TNF in the inflamed colon. Further analyses showed a similar pattern of complex relations between the expression levels of the regulators and their receptors. We also found limited overlap between pathways and predicted upstream regulators in colonic biopsies and WBC. An extended search including all differentially expressed genes that overlapped between colonic biopsies and WBC only resulted in identification of three potential biomarkers involved in the regulation of intestinal inflammation. However, two had been previously proposed in adult inflammatory bowel diseases (IBD), namely, MMP9 and PROK2. CONCLUSIONS: Our findings indicate that biomarker identification in pediatric UC is complicated by the involvement of multiple pathways, each of which includes many different types of genes in the blood or inflamed intestine. Therefore, further studies for identification of combinatorial biomarkers are warranted. Our study may provide candidate biomarkers for such studies.

Correction to: A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases.

An amendment to this paper has been published and can be accessed via the original article.

Inhibition of CXCL1-CXCR2 axis ameliorates cisplatin-induced acute kidney injury by mediating inflammatory response.

One of the limiting side effects of cisplatin use in cancer chemotherapy is nephrotoxicity. Inflammation is now believed to play a major role in the pathogenesis of cisplatin-induced acute kidney injury (AKI), and the mediators of inflammation contribute to it. CXCL1 was recently reported to be involved in renal physiology and pathology in ischemia mouse model; however, its roles and mechanisms in cisplatin-induced AKI are completely unknown. We observed that CXCL1 and CXCR2 expression in the kidney was markedly increased on day 7 after cisplatin treatment. Subsequently, we demonstrate that inhibition of CXCL1-CXCR2 signaling axis, using genetic and pharmacological approaches, reduces renal damage following cisplatin treatment as compared with control mice. Specifically, deficiency of CXCL1 or CXCR2 extensively preserved the renal histology and maintained the kidney functions after cisplatin treatment, which was associated with reduced expression of the pro-inflammatory cytokines and infiltration of neutrophils in the kidneys as compared. Furthermore, inhibition of CXCR2 by intragastric administration of repertaxin in mice with AKI reduces kidney injury associated with a reduction of inflammatory cytokines and neutrophils infiltration. Finally, we found that CXCL1/CXCR2 regulated cisplatin-induced inflammatory responses via the P38 and NF-κB signaling pathways in vitro and in vivo. In conclusion, our results indicate that CXCL1-CXCR2 signaling axis plays a crucial role in the pathogenesis of cisplatin-induced AKI through regulation of inflammatory response and maybe a novel therapeutic target for cisplatin-induced AKI.

Bulk and single cell transcriptomic data indicate that a dichotomy between inflammatory pathways in peripheral blood and arthritic joints complicates biomarker discovery.

BACKGROUND: Unbiased studies using different genome-wide methods have identified several novel biomarkers for diagnosis and treatment response in Rheumatoid Arthritis (RA). However, clinical translation has proven difficult. Here, we hypothesized that one reason could be that inflammatory responses in peripheral blood are different from those in the arthritic joint. METHODS: We performed meta-analysis of gene expression microarray data from synovium, whole blood cells (WBC), peripheral blood mononuclear cells (PBMC), and CD4+ T cells from patients with RA and healthy controls in order to identify overlapping pathways, upstream regulators and potential biomarkers. We also analyzed single cell RNA-sequencing (scRNA-seq) data from peripheral blood and whole joints from a mouse model of antigen-induced arthritis. RESULTS: Analyses of two profiling data sets from synovium from RA patients and healthy controls all showed significant activation of pathways with known pathogenic relevance, such as the Th1 pathway, the role of NFAT in regulation of the immune response, dendritic cell maturation, iCOS-iCOSL signaling in T helper cells, Fcγ receptor-mediated phagocytosis, interferon signaling, Cdc42 signaling, and cytotoxic T lymphocyte-mediated apoptosis. The most activated upstream regulators included TNF, an important drug target, as well as IFN-gamma and CD40LG, all of which are known to play important pathogenic roles in RA. The differentially expressed genes from synovium included several potential biomarkers, such as CCL5, CCL13, CCL18, CX3CL1, CXCL6, CXCL9, CXCL10, CXCL13, IL15, IL32, IL1RN, SPP1, and TNFSF11. By contrast, microarray studies of WBC, PBMC and CD4+ T cells showed variable pathways and limited pathway overlap with synovium. Similarly, scRNA-seq data from a mouse model of arthritis did not support that inflammatory responses in peripheral blood reflect those in the arthritic joints. These data showed pathway overlap between mouse joint cells and synovium from patients with RA, but not with cells in peripheral blood. CONCLUSIONS: Our findings indicate a dichotomy between gene expression changes, pathways, upstream regulators and biomarkers in synovium and cell types in peripheral blood, which complicates identification of biomarkers in blood.