Citations

Background: Glioblastoma is a highly malignant brain tumor with limited treatment options and a poor prognosis, largely driven by its complex immune microenvironment. This study aimed to identify and characterize an immune-related gene signature associated with prognosis and immune regulation in glioblastoma. Methods: We performed integrative analyses using bulk and single-cell RNA sequencing data to identify prognostically significant immune-related genes. A five-gene signature (THEMIS2, SIGLEC9, CSTA, LILRB3, and MS4A6A) was derived and its expression patterns were analyzed in association with immune cell infiltration and macrophage subtypes. Functional enrichment and pathway analyses were conducted, followed by drug sensitivity profiling to explore potential therapeutic implications. Results: The five-gene signature was significantly associated with worse survival outcomes and increased immune cell infiltration. Functional analyses revealed involvement in key immune pathways, including antigen presentation, cytokine signaling, and immune cell activation. Single-cell RNA sequencing demonstrated high expression of the signature in tumor-associated macrophages, particularly immune-suppressive and proliferation-associated subtypes. The high expression in proliferation TAMs suggests a role in promoting tumor angiogenesis and growth. Drug sensitivity analysis revealed distinct vulnerabilities between high- and low-risk groups based on signature expression. Conclusions: This Macrophage-Associated Prognostic Signature (MAPS) provides new insights into glioblastoma immunobiology and identifies potential biomarkers and therapeutic targets. It may serve as a valuable tool to guide personalized immunotherapy-based strategies for glioblastoma patients.

Abstract Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A significant issue hindering GBM therapeutic development is intratumor heterogeneity. GBM tumors contain neoplastic cells within a spectrum of different transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we developed a novel framework, ISOSCELES ( I nferred cell S ensitivity O perating on the integration of S ingle- C ell E xpression and L 1000 E xpression S ignatures), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identified compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states. We validated the significance of these findings in vitro, ex vivo, and in vivo , and identified a novel combination of an OLIG2 inhibitor and Depatux-M for GBM. Our studies suggest that ISOSCELES identifies cell states sensitive and resistant to targeted therapies in GBM and that it can be applied to identify new synergistic combinations. Highlights Integration of GBM single-cell RNA sequencing data with L1000-derived drug response signatures facilitates clustering of tumor cells and small molecules on cell-drug connectivity. Cell-drug connectivity predicts the identities of drug-sensitive and resistant cell states. In silico perturbation analysis using cell-drug connectivity predicts drug-induced changes in the cell-drug connectivity landscape in vivo. In silico perturbation analysis to predict drug-induced changes in the tumor cell-drug connectivity landscape predicts drug combinations that synergize in vivo to extend survival.

Background: Currently, biomarkers do not have a role in diagnosis or outcome prediction models for patients with recurrent anterior shoulder instability. Purpose/Hypothesis: The purpose of this study was to compare gene expression differences in the blood and tissue of patients with anterior shoulder instability with and without significant glenoid bone loss (GBL). The hypothesis was that the severity of GBL would be associated with the expression level of genes in the blood and capsular tissue of patients with anterior shoulder instability. Study Design: Descriptive laboratory study. Methods: Consecutive patients with anterior shoulder instability undergoing arthroscopic and open shoulder stabilization were prospectively enrolled. Blood and anterior capsular tissue specimens obtained during surgery were compared between patients with significant GBL (≥10%) and patients without significant GBL (<10%). RNA was extracted, and a panel of 277 inflammatory genes was utilized to quantify gene expression at the RNA level using a probe-based RNA quantification platform. Differential expression analysis was performed to identify genes expressed at different levels between patients with and without significant GBL. The expression levels of the subset of genes identified were used to generate a ridge regression model to predict the severity of GBL. Quantitative polymerase chain reaction was performed to confirm probe-based RNA findings. Results: A total of 17 patients were included, with a mean age of 26 years. Overall, 7 patients had <10% GBL (mean, 2.3%), and 10 patients had ≥10% GBL (mean, 16.4%). There were 9 genes that were identified as significantly differentially expressed in the blood, and 5 of these ( IFIT1 , IFIT3 , IFI44 , PRKCB , and OAS2 with P values of 1 × 10 −5 , 1 × 10 −4 , 1 × 10 −4 , 1 × 10 −4 , and 6 × 10 −4 , respectively) were confirmed using nonparametric tests. The authors developed a model using the 5 genes to predict the severity of GBL, and this had an accuracy of 88% (95% CI, 64%-99%). By quantitative polymerase chain reaction, IFIT3 was confirmed to be significantly differentially expressed ( P = .012). Conclusion: There were significant gene expression differences in the blood of patients with (≥10%) and without (<10%) significant GBL. The differential expression of 5 genes allowed for the development of an accurate predictive model and transcriptomic biomarker to predict the severity of GBL. Clinical Relevance: The addition of a blood biomarker to current outcome prediction models may provide increased accuracy in identifying those at risk of failure from arthroscopic Bankart repair.

Hazardous alcohol use remains a major contributor to acute and chronic liver disease, while alcohol-associated liver disease (ALD) is a leading indication for liver transplantation. In recent years, embedded, interprofessional ALD clinics have improved access to alcohol use disorder care within hepatology and liver transplantation, but more work is needed to meet this challenge. The literature is lacking regarding scaling procedures to provide services for increasingly large ill patient populations. This article begins to fill this gap by describing “expanded ALD care”: broad, innovative, longitudinal, interprofessional care delivery strategies surpassing standalone clinics. Drawing from analogous patient populations served by collaborative models in primary care and comprehensive eating disorder treatment, the expanded ALD care framework proposes practical strategies toward specific innovations: equipoise between biomedical and psychosocial care elements, increased clinician number and reach, long-term patient relationships, harm reduction and palliative care, outreach to external agencies and clinicians, and enhanced support for patients and families. The article also defines attributes of innovative healthcare systems that support expanded ALD care.

Abstract Introduction Resilient smokers, who maintain respiratory health despite smoking, remain poorly characterized. Airway epithelial cell gene expression in resilient smokers can provide insight into mechanisms and pathways that may protect smokers from developing smoking-related lung disease. The objective of this study is to identify genes and potential pathways associated with lung-related resilience. Methods Resilient smokers were defined based on a previously published multi-dimensional definition which includes the absence of: 1) spirometric airflow limitation, 2) symptoms (cough/sputum, dyspnea), 3) radiographic (HRCT) abnormalities, 4) exacerbations, and 5) an accelerated FEV1 decline. “Non-resilient” smokers had preserved spirometry but showed abnormalities in at least one domain of resilience. We analyzed airway epithelial brush samples from 21 resilient and 34 non-resilient smokers from SPIROMICS. Differential gene expression was performed using DESeq2 package in R, which estimates differential expression using generalized linear models. We adjusted for sequencing batch, age (continuous), race, gender, and current smoking status. While controlling for these covariates we evaluated differential gene expression between resilient and non-resilient smokers, the interaction between resilience and smoking status, and in each sub-category of resilient (absence of symptoms, radiographic abnormalities, exacerbations, and accelerated decline in FEV1). Results We identified 98 genes that were differentially expressed (FDR ≤0.1) when comparing resilient to non-resilient smokers, 20 of those genes were up regulated in resilient smokers (Figure 1). Notable genes included cadherin-12 (CDH12), aspartate beta-hydroxylase (ASPH), and protein kinase AMP-activated alpha 2 (PRKAA2). CDH12 is involved in cell adhesion and epithelial integrity. ASPH influences cell adhesion and signaling. PRKAA2 plays role in cellular homeostasis and stress response, which may be altered in non-resilient smokers, affecting cellular metabolism and inflammation. BPI folding containing family A member (BPIFA1) was significantly associated with smoking status in the interaction analysis, suggesting its role in modulating the impact of smoking on resilience. BPIFA1 has anti-microbial properties, regulates immune responses, and contributes mucosal surface protection. Sub-category analysis identified 17 genes associated with resilience to symptoms and 2 genes related to physiologic resilience (FEV1 decline), though no significant genes were associated with radiographic or exacerbation resilience. Conclusion Differential gene expression of airway epithelial cells identified distinct expression patterns between resilient and non-resilient smokers. Specifically, genes associated with epithelial cell adhesion, signaling, and cellular homeostasis were up regulated in resilient smokers. These findings suggest that resilient smokers may have transcriptomic profiles and potential mechanisms that mitigate the effects of cigarette smoke.

Abstract RATIONALE: Mesenchymal progenitor cells (MPCs) in the lungs of individuals with Idiopathic Pulmonary Fibrosis (IPF) show intrinsic fibrogenic properties, possessing a distinct transcriptomic profile and maintaining fibrogenic activity, which contributes to the fibroblast population in IPF. We hypothesize that IPF MPCs are distinguished from control MPCs by a unique gene interaction network. METHODS: We analyzed bulk RNA-seq data, available on the Gene Expression Omnibus (GEO) under accession numbers GSE97038 and GSE276568. In these experiments, CD44hi MPCs were isolated through flow cytometry from mesenchymal cell lines derived from IPF patients (explanted lungs from live IPF patients after lung transplant) and control subjects (lobectomy for clinical reasons) using antibodies against CD44 and SSEA4. The pooled dataset comprises nine IPF and nine control MPC lines. We identified differentially expressed genes (DEGs) in IPF MPCs compared to controls using DESeq and performed pathway enrichment analysis on DEGs using Gene Ontology (GO). We also conducted Gene Set Enrichment Analysis (GSEA) with gene sets related to stemness, pluripotency, and self-renewal. Weighted gene co-expression network analysis (WGCNA) identified functional modules specifically associated with IPF MPCs. RESULTS: Applying an adjusted p-value threshold of 0.05, we identified 134 DEGs in IPF MPCs compared to controls. GO analysis showed significant enrichment in pathways associated with leukocyte migration, extracellular matrix organization, and cell differentiation and growth. GSEA highlighted significant enrichment in two gene sets related to stemness, with adjusted p-values of 0.002 and 0.048. Using WGCNA, we identified 16 functional modules, one of which reached significance based on an adjusted p-value threshold of 0.05 and a correlation coefficient cutoff of 0.45. This significant module demonstrated a positive correlation with IPF status (correlation coefficient of 0.69, adjusted p-value 0.0085). CONCLUSION: This study is the first to characterize a distinct gene expression profile in IPF MPCs compared to controls, highlighting enriched biological processes relevant to IPF pathogenesis. GSEA results further support the hypothesis that IPF MPCs display stem cell-like gene expression patterns, including genes associated with pluripotency and self-renewal. Additionally, we identified functional gene modules that significantly correlate with the fibrogenic properties of IPF MPCs, including hub genes that may play critical roles in promoting fibrosis in IPF. These findings deepen our understanding of IPF MPC biology and identify potential targets for future research. We plan to expand this approach by increasing the sample size to enhance statistical power, aiming to identify more robust functional modules and their highly connected hub genes.

Abstract Rationale. Bronchoscopy can directly assess cells in airway epithelium in participants with COPD, but it is invasive, expensive, difficult to perform at scale, and contraindicated in severe disease. Blood is much more easily available but contains only a limited subset of cell types, many of which are less relevant to lung biology. Recent advances in RNA-sequencing technology have allowed for reliable measurement of transcriptomics in a wider variety of more accessible tissues, including hair follicle cells. We collected and performed bulk RNA-seq on hair follicles as an ancillary study within the NIH-funded SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) and investigated their cell type composition to better understand the opportunities and limitations of this tissue for the study of lung disease. Methods. SPIROMICS recruited individuals with and without COPD aged 40-80 years with ≥ 20 pack-years smoking history. Participant at several sites underwent collection of approximately 10 hair follicles from the head, which were immediately frozen at -80ºC in 2018-21. mRNA in hair follicles was measured by bulk RNA-seq, and cell type deconvolution was performed using GEDIT v1.7 with the provided BlueCodeV1.0.tsv reference matrix. Cell types were collapsed into broader categories by summing the estimated cell type proportions. Results. The cell type deconvolution of the hair follicle bulk RNA-seq from 80 participants with and without COPD estimated that the tissue was largely composed of immune cells (median 36%, including B cells, T cells, neutrophils, macrophages, NK cells, and monocytes) and epithelial cell types (median 31%), with the largest subgroup of epithelial cell types identified as hair follicular keratinocytes (Figure 1). The next largest component was fibroblasts (median 9%), followed by endothelial cells (median 8%). Signals for myocytes (median 5%), melanocytes (median 5%), and chondrocytes (median 5%) were also present. Conclusion. This initial study demonstrated promising signals for a variety of lung-relevant cell types in hair follicles in SPIROMICS, including immune and epithelial cells in addition to fibroblasts and endothelial cells that are not otherwise easily accessible. Hair follicle gene expression profiling shows promise for understanding numerous facets of lung biology and disease that both complements and exceeds that of other noninvasive tissues such as blood.

Abstract Rationale: The club cell secretory protein (CC16), encoded by the SCGB1A1 gene, has an anti-inflammatory role in airways diseases, including asthma (Li, AJRCCM 2023; Voraphani, AJRCCM 2023). We hypothesize that additional pathway genes regulated or co-expressed with CC16 have cumulative effects on asthma severity outcomes by modulating airway inflammation and maintaining lung homeostasis. Methods: As published previously, four genes were downregulated in CC16 knockout experiments and correlated with CC16 levels: BPIFA1, SFTPD, LTF, and LYZ (Iannuzo, Front. Immunol 2023). We developed a four-gene biomarker score in 94 patients with asthma from the NHLBI-sponsored Severe Asthma Research Program (SARP3) who underwent research bronchoscopy for lower airway epithelial brushings, RNA isolation, and bulk RNA sequencing. We log-transformed the raw values of each transcript; obtained standardized residuals from regression models that included sex, age, and batch effects; and summed the standardized residuals of all four proteins to estimate a standardized sum score (Zhai, JACI Allergy 2024). Logistic regression models tested the four-gene score, three-gene score without LTF, and individual transcripts for associations with exacerbations during 12-month follow-up and zero-inflated negative binomial regression for number of exacerbations. Results: 30.85% percent of 94 patients experienced at least one exacerbation at the end of follow-up. The CC16-associated four-gene biomarker score showed protective effects on asthma exacerbation risk with a 54% percent reduction in exacerbation risk for every one-fold higher score (OR=0.46, 95% CI: 0.27-0.78, p =0.0040, Figure 1). Transcript expression of BPIFA1 (OR=0.87, 95% CI:0.66-1.16, p=0.337), SFTPD (OR=0.33, 95% CI:0.04-2.52, p=0.283), LYZ (OR=0.62, 95%CI:0.28-1.39, p=0.248), and LTF (OR=0.36, 95% CI:0.18-0.74, p=0.0053) all indicated protective directionality with respect to exacerbation risk. However, only LTF demonstrated a statistically significant association. When we excluded LTF, the remaining three-gene score showed a trend toward association with risk (OR=0.64, 95% CI:0.40-1.03, p=0.068) and was significantly associated with number of exacerbations (RR=0.49, 95% CI:0.26-0.95, p=0.034). Compared to LTF alone (OR=0.48, PPV=0.50, NPV=0.71, Sensitivity=0.14, AUC=0.65), the combined four-gene score (OR=0.46, PPV=0.60, NPV= 0.73, Sensitivity=0.21, AUC=0.68) showed improved predictive performance. Conclusions: We identified a CC16-associated transcriptomic predictive score for asthma exacerbations, with LTF showing the strongest effect. Although the other three genes showed individually weak and non-significant associations with exacerbations, the three gene score was significant for number of exacerbations.We demonstrate the potential of this transcriptomic score as a predictive biomarker for asthma severity-related outcomes. Further research investigating these genes and their interaction with other CC16-associated immune pathways could provide insight into therapeutic targets.

Ebola virus (EBOV) causes severe disease outbreaks in humans with high case fatality rates. EBOV requires adaptation to cause lethal disease in mice by acquiring single mutations in both the nucleoprotein (NP) and VP24 genes. As an attempt to model mouse-adapted EBOV (MA-EBOV), we engineered novel pentacistronic minigenomes (5xMG) containing a reporter gene, VP40, and glycoprotein genes as well as the NP and VP24 genes from either EBOV or MA-EBOV. The 5xMGs were constructed and optimized, and the produced transcription- and replication-competent virus-like particles (trVLPs) were demonstrated to infect several cell lines. Introduction of the mouse-adaptation mutations did not significantly impact the replication and transcription of the 5xMG or the relative infectivity of the trVLPs in vitro. This work demonstrates the development of the 5xMG system as a new versatile tool to study EBOV biology.