Gene expressions changes in bronchial epithelial cells: Markers for respiratory sensitizers and exploration of the NRF2 pathway

The transcriptomic signature of respiratory sensitizers using an alveolar model

Environmental contaminants are ubiquitous in the air we breathe and can potentially cause adverse immunological outcomes such as respiratory sensitization, a type of immune-driven allergic response in the lungs. Wood dust, latex, pet dander, oils, fragrances, paints, and glues have all been implicated as possible respiratory sensitizers. With the increased incidence of exposure to chemical mixtures and the rapid production of novel materials, it is paramount that testing regimes accounting for sensitization are incorporated into development cycles. However, no validated assay exists that is universally accepted to measure a substance's respiratory sensitizing potential. The lungs comprise various cell types and regions where sensitization can occur, with the gas-exchange interface being especially important due to implications for overall lung function. As such, an assay that can mimic the alveolar compartment and assess sensitization would be an important advance for inhalation toxicology. Some such models are under development, but in-depth transcriptomic analyses have yet to be reported. Understanding the transcriptome after sensitizer exposure would greatly advance hazard assessment and sustainability. We tested two known sensitizers (i.e., isophorone diisocyanate and ethylenediamine) and two known non-sensitizers (i.e., chlorobenzene and dimethylformamide). RNA sequencing was performed in our in vitro alveolar model, consisting of a 3D co-culture of epithelial, macrophage, and dendritic cells. Sensitizers were readily distinguishable from non-sensitizers by principal component analysis. However, few differentially regulated genes were common across all pair-wise comparisons (i.e., upregulation of genes SOX9, UACA, CCDC88A, FOSL1, KIF20B). While the model utilized in this study can differentiate the sensitizers from the non-sensitizers tested, further studies will be required to robustly identify critical pathways inducing respiratory sensitization.

Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study

BACKGROUND

Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This study aimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals.

METHODS

In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set.

FINDINGS

Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission.

INTERPRETATION

These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection.

FUNDING

Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.

Analysis of signature genes and association with immune cells infiltration in pediatric septic shock

Background

Early diagnosis of septic shock in children is critical for prognosis. This study committed to investigate the signature genes and their connection with immune cells in pediatric septic shock.

Methods

We screened a dataset of children with septic shock from the GEO database and analyzed differentially expressed genes (DEGs). Functional enrichment analysis was performed for these DEGs. Weighted gene co-expression network analysis (WCGNA) was used to screen the key modules. Least absolute shrinkage and selection operator (LASSO) and random forest analysis were finally applied to identify the signature genes. Then gene set enrichment analysis (GSEA) was exerted to explore the signaling pathways related to the hub genes. And the immune cells infiltration was subsequently classified via using CIBERSORT.

Results

A total of 534 DEGs were screened from GSE26440. The data then was clustered into 17 modules via WGCNA, which MEgrey module was significantly related to pediatric septic shock (cor=-0.62, p<0.0001). LASSO and random forest algorithms were applied to select the signature genes, containing UPP1, S100A9, KIF1B, S100A12, SLC26A8. The receiver operating characteristic curve (ROC) of these signature genes was 0.965, 0.977, 0.984, 0.991 and 0.989, respectively, which were verified in the external dataset from GSE13904. GSEA analysis showed these signature genes involve in positively correlated fructose and mannose metabolism and starch and sucrose metabolism signaling pathway. CIBERSORT suggested these signature genes may participate in immune cells infiltration.

Conclusion

UPP1, S100A9, KIF1B, S100A12, SLC26A8 emerge remarkable diagnostic performance in pediatric septic shock and involved in immune cells infiltration.

Identification of Neoantigens and Construction of Immune Subtypes in Prostate Adenocarcinoma

Background: Messenger ribonucleic acid (mRNA) vaccine has been considered as a potential therapeutic strategy and the next research hotspot, but their efficacy against prostate adenocarcinoma (PRAD) remains undefined. This study aimed to find potential antigens of PRAD for mRNA vaccine development and identify suitable patients for vaccination through immunophenotyping.

Methods: Gene expression profiles and clinical information were obtained from TCGA and ICGC. GEPIA2 was used to calculate the prognostic index of the selected antigens. The genetic alterations were compared on cBioPortal and the correlation between potential antigen and immune infiltrating cells was explored by TIMER. ConsensusClusterPlus was used to construct a consistency matrix, and identify the immune subtypes. Graph learning-based dimensional reduction was performed to depict immune landscape. Boruta algorithm and LASSO logistic analysis were used to screen PRAD patients who may benefit from mRNA vaccine.

Results: Seven potential tumor antigens selected were significantly positively associated with poor prognosis and the antigen-presenting immune cells (APCs) in PRAD, including ADA, FYN, HDC, NFKBIZ, RASSF4, SLC6A3, and UPP1. Five immune subtypes of PRAD were identified by differential molecular, cellular, and clinical characteristics in both cohorts. C3 and C5 had immune “hot” and immunosuppressive phenotype, On the contrary, C1&C2 had immune “cold” phenotype. Finally, the immune landscape characterization showed the immune heterogeneity among patients with PRAD.

Conclusions: ADA, FYN, HDC, NFKBIZ, RASSF4, SLC6A3, and UPP1 are potential antigens for mRNA vaccine development against PRAD, and patients in type C1 and C2 are suitable for vaccination.

Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis

Background

Sepsis is an inflammatory response caused by infection with pathogenic microorganisms. The body shock caused by it is called septic shock. In view of this, we aimed to identify potential diagnostic gene biomarkers of the disease.

Material and methods

Firstly, mRNAs expression data sets of septic shock were retrieved and downloaded from the GEO (Gene Expression Omnibus) database for differential expression analysis. Functional enrichment analysis was then used to identify the biological function of DEmRNAs (differentially expressed mRNAs). Machine learning analysis was used to determine the diagnostic gene biomarkers for septic shock. Thirdly, RT-PCR (real-time polymerase chain reaction) verification was performed. Lastly, GSE65682 data set was utilized to further perform diagnostic and prognostic analysis of identified superlative diagnostic gene biomarkers.

Results

A total of 843 DEmRNAs, including 458 up-regulated and 385 down-regulated DEmRNAs were obtained in septic shock. 15 superlative diagnostic gene biomarkers (such as RAB13, KIF1B, CLEC5A, FCER1A, CACNA2D3, DUSP3, HMGN3, MGST1 and ARHGEF18) for septic shock were identified by machine learning analysis. RF (random forests), SVM (support vector machine) and DT (decision tree) models were used to construct classification models. The accuracy of the DT, SVM and RF models were very high. Interestingly, the RF model had the highest accuracy. It is worth mentioning that ARHGEF18 and FCER1A were related to survival. CACNA2D3 and DUSP3 participated in MAPK signaling pathway to regulate septic shock.

Conclusion

Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07056-4.

A new cytometry-based method reveals an accumulation of Nrf2 in dendritic cells exposed to two respiratory sensitizers

The mechanisms underlying chemical respiratory sensitization are incompletely understood. One of the major cell types involved in this pathology are dendritic cells. In this study, the mechanisms of the NRF2-Keap1 pathway were studied using a bone marrow-derived dendritic cell model exposed to two respiratory sensitizers: ammonium hexachloroplatinate (HCP) and ammonium tetrachloroplatinate (ATCP). Expression levels for two Nrf2-regulated genes, hmox1 and srxn1, were analyzed by real time-quantitative polymerase chain reaction. A flow cytometry-based method was also developed to measure intracellular Nrf2 accumulation in dendritic cells following exposure. Exposure to HCP and ATCP increased both hmox1 and srxn1 gene expression, and was associated with accumulation of Nrf2 protein in cells. Overall, these results show that the respiratory sensitizers, in addition to skin sensitizers, can also induced markers associated with NRF2-Keap1 pathway activation in dendritic cells. This study contributes to a better understanding of the adverse outcome of respiratory sensitization.

5-Fluorouracil Enhances the Antitumor Activity of the Glutaminase Inhibitor CB-839 against PIK3CA-Mutant Colorectal Cancers

PIK3CA encodes the p110α catalytic subunit of PI3K and is frequently mutated in human cancers, including ∼30% of colorectal cancer. Oncogenic mutations in PIK3CA render colorectal cancers more dependent on glutamine. Here we report that the glutaminase inhibitor CB-839 preferentially inhibits xenograft growth of PIK3CA-mutant, but not wild-type (WT), colorectal cancers. Moreover, the combination of CB-839 and 5-fluorouracil (5-FU) induces PIK3CA-mutant tumor regression in xenograft models. CB-839 treatment increased reactive oxygen species and caused nuclear translocation of Nrf2, which in turn upregulated mRNA expression of uridine phosphorylase 1 (UPP1). UPP1 facilitated the conversion of 5-FU to its active compound, thereby enhancing the inhibition of thymidylate synthase. Consistently, knockout of UPP1 abrogated the tumor inhibitory effect of combined CB-839 and 5-FU administration. A phase I clinical trial showed that the combination of CB-839 and capecitabine, a prodrug of 5-FU, was well tolerated at biologically-active doses. Although not designed to test efficacy, an exploratory analysis of the phase I data showed a trend that PIK3CA-mutant patients with colorectal cancer might derive greater benefit from this treatment strategy as compared with PIK3CA WT patients with colorectal cancer. These results effectively demonstrate that targeting glutamine metabolism may be an effective approach for treating patients with PIK3CA-mutant colorectal cancers and warrants further clinical evaluation. SIGNIFICANCE: Preclinical and clinical trial data suggest that the combination of CB-839 with capecitabine could serve as an effective treatment for PIK3CA-mutant colorectal cancers.

Uridine phosphorylase 1 is a novel immune-related target and predicts worse survival in brain glioma

Uridine phosphorylase 1 (UPP1) has been reported as an oncogene in several malignancies. In glioma, the role of UPP1 remains unclear. This study was performed to explore its role in glioma at transcriptional level. Totally, 998 glioma patients with clinical data were enrolled, including 301 mRNA microarray data from Chinese Glioma Genome Atlas (CGGA) dataset and 697 RNAseq data from The Cancer Genome Atlas (TCGA) dataset. Statistical analysis was performed with R language. UPP1 expression level was positively correlated with WHO grade of glioma. UPP1 was significantly upregulated in mesenchymal subtype and could serve as a potential biomarker for this subtype. Based on most correlated genes of UPP1, Gene ontology analysis revealed that UPP1 was profoundly associated with immune and inflammatory response. Gene Sets Variation Analysis was further performed and showed that UPP1 was particularly correlated with MHC‐II and LCK, which were mainly associated with activities of antigen‐presenting cells and T cells. Moreover, UPP1 was found to be synergistic with various immune checkpoint members, especially with PD1 pathway and B7‐H3. Finally, Kaplan‐Meier curves revealed that higher UPP1 indicated significantly shorter survival for glioma patients. Taken together, UPP1 played an oncogenic role in glioma via suppressing tumor‐related immune response.

Identification of PM10 characteristics involved in cellular responses in human bronchial epithelial cells (Beas-2B)

Notwithstanding evidence is present that physicochemical characteristics of ambient particles attribute to adverse health effects, there is still some lack of understanding in this complex relationship. At this moment it is not clear which properties (such as particle size, chemical composition) or sources of the particles are most relevant for health effects. This study investigates the in vitro toxicity of PM10 in relation to PM chemical composition, black carbon (BC), endotoxin content and oxidative potential (OP). In 2013-2014 PM10 was sampled (24h sampling, 108 sampling days) in ambient air at three sites in Flanders (Belgium) with different pollution characteristics: an urban traffic site (Borgerhout), an industrial area (Zelzate) and a rural background location (Houtem). To characterize the toxic potential of PM10, airway epithelial cells (Beas-2B cells) have been exposed to particles in vitro. Different endpoints were studied including cell damage and death (cell viability) using the Neutral red Uptake assay, the production of pro-inflammatory molecules by interleukin 8 (IL-8) induction and DNA-damaging activity using the FPG-modified Comet assay. The endotoxin levels in the collected samples were analysed and the capacity of PM10 particles to produce reactive oxygen species (OP) was evaluated by electron paramagnetic resonance (EPR) spectroscopy. Chemical characteristics of PM10 (BC, As, Cd, Cr, Cu, Mn, Ni, Pb, Zn) and meteorological conditions were recorded on the sampling days. PM10 particles exhibited dose-dependent cytotoxicity in Beas-2B cells and were found to significantly induce the release of IL-8 in samples from the three locations. Oxidatively damaged DNA was observed in exposed Beas-2B cells. Endotoxin levels above the detection limit were detected in half of the samples. OP was measurable in all samples. Associations between PM10 characteristics and biological effects of PM10 were assessed by single and multiple regression analyses. The reduction in cell viability was significantly correlated with BC, Cd and Pb. The induction of IL-8 in Beas-2B cells was significantly associated with Cu, Ni and Zn and endotoxin. Endotoxin levels explained 33% of the variance in IL-8 induction. A significant interaction between ambient temperature and endotoxin on the pro-inflammatory activity was seen. No association was found between OP and the cellular responses. This study supports the hypothesis that, on an equal mass basis, PM10 induced biological effects differ due to differences in PM10 characteristics. Metals (Cd, Cu, Ni and Zn), BC, and endotoxin were among the main determinants for the observed biological responses.

Development of an in vitro test to identify respiratory sensitizers in bronchial epithelial cells using gene expression profiling

Chemicals that induce asthma at the workplace are substances of concern. At present, there are no widely accepted methods to identify respiratory sensitizers, and classification of these substances is based on human occupational data. Several studies have contributed to understanding the mechanisms involved in respiratory sensitization, although uncertainties remain. One point of interest for respiratory sensitization is the reaction of the epithelial lung barrier to respiratory sensitizers. To elucidate potential molecular effects of exposure of the epithelial lung barrier, a gene expression profile was created based on a DNA microarray experiment using the bronchial epithelial cell line 16 HBE14o(-). The cells were exposed to 12 respiratory sensitizers and 10 non-sensitizers. For statistical analysis, we used a class prediction approach that combined three machine learning algorithms, leave-one-compound-out cross validation, and majority voting per tested compound. This approach allowed for a prediction accuracy of 95%. Identified predictive genes were mainly associated with the cytoskeleton and barrier function of the epithelial cell. Several of these genes were reported to be associated with asthma as well. Taken together, this indicates that pulmonary barrier function is an important target for respiratory sensitizers and associated genes can be used to predict the respiratory sensitization potential of chemicals.