BatchFLEX: feature-level equalization of X-batch.

MOTIVATION: Integrative analysis of heterogeneous expression data remains challenging due to variations in platform, RNA quality, sample processing, and other unknown technical effects. Selecting the approach for removing unwanted batch effects can be a time-consuming and tedious process, especially for more biologically focused investigators. RESULTS: Here, we present BatchFLEX, a Shiny app that can facilitate visualization and correction of batch effects using several established methods. BatchFLEX can visualize the variance contribution of a factor before and after correction. As an example, we have analyzed ImmGen microarray data and enhanced its expression signals that distinguishes each immune cell type. Moreover, our analysis revealed the impact of the batch correction in altering the gene expression rank and single-sample GSEA pathway scores in immune cell types, highlighting the importance of real-time assessment of the batch correction for optimal downstream analysis. AVAILABILITY AND IMPLEMENTATION: Our tool is available through Github https://github.com/shawlab-moffitt/BATCH-FLEX-ShinyApp with an online example on Shiny.io https://shawlab-moffitt.shinyapps.io/batch_flex/.

Transgelin 2 guards T cell lipid metabolism and antitumour function.

Mounting effective immunity against pathogens and tumours relies on the successful metabolic programming of T cells by extracellular fatty acids1-3. Fatty-acid-binding protein 5 (FABP5) has a key role in this process by coordinating the efficient import and trafficking of lipids that fuel mitochondrial respiration to sustain the bioenergetic requirements of protective CD8+ T cells4,5. However, the mechanisms that govern this immunometabolic axis remain unexplored. Here we report that the cytoskeletal organizer transgelin 2 (TAGLN2) is necessary for optimal fatty acid uptake, mitochondrial respiration and anticancer function in CD8+ T cells. TAGLN2 interacts with FABP5 to facilitate its cell surface localization and function in activated CD8+ T cells. Analyses of ovarian cancer specimens revealed that endoplasmic reticulum (ER) stress responses induced by the tumour microenvironment repress TAGLN2 in infiltrating CD8+ T cells, thereby enforcing their dysfunctional state. Restoring TAGLN2 expression in ER-stressed CD8+ T cells increased their lipid uptake, mitochondrial respiration and cytotoxic capacity. Accordingly, chimeric antigen receptor T cells overexpressing TAGLN2 bypassed the detrimental effects of tumour-induced ER stress and demonstrated therapeutic efficacy in mice with metastatic ovarian cancer. Our study establishes the role of cytoskeletal TAGLN2 in T cell lipid metabolism and highlights the potential to enhance cellular immunotherapy in solid malignancies by preserving the TAGLN2-FABP5 axis.

Super multiple primary lung cancers harbor high-frequency BRAF and low-frequency EGFR mutations in the MAPK pathway.

The incidence of multiple primary lung cancer (MPLC) is increasing, with some of our surgical patients exhibiting numerous lesions. We defined lung cancer with five or more primary lesions as super MPLCs. Elucidating the genomic characteristics of this special MPLC subtype can help reduce disease burden and understand tumor evolution. In our cohort of synchronous super early-stage MPLCs (PUMCH-ssesMPLC), whole-exome sequencing on 130 resected malignant specimens from 18 patients provided comprehensive super-MPLC genomic landscapes. Mutations are enriched in PI3k-Akt and MAPK pathways. Their BRAF mutation frequency (31.5%) is significantly higher than MPLC with fewer lesions and early-stage single-lesion cancer, while EGFR mutations are significantly fewer (13.8%). As lesion counts increase, BRAF mutations gradually become dominant. Also, invasive lesions more tend to have classic super-MPLC mutation patterns. High-frequency BRAF mutations, especially Class II, and low-frequency EGFR mutations could be a reason for the limited effectiveness of targeted therapy in super-MPLC patients.

Pan-cancer γδ TCR analysis uncovers clonotype diversity and prognostic potential.

Gamma-delta T cells (γδ T cells) play a crucial role in both innate and adaptive immunity within tumors, yet their presence and prognostic value in cancer remain underexplored. This study presents a large-scale analysis of γδ T cell receptor (γδ TCR) reads from 11,000 tumor samples spanning 33 cancer types, utilizing the TRUST4 algorithm. Our findings reveal extensive diversity in γδ TCR clonality and gene expression, underscoring the potential of γδ T cells as prognostic biomarkers in various cancers. We further demonstrate the utility of TCR gamma (TRG) and delta (TRD) gene expression from standard RNA-sequencing (RNA-seq) data. This comprehensive dataset offers a valuable resource for advancing γδ T cell research, with implications for enhanced immunotherapy approaches or alternative therapeutic strategies. Additionally, our centralized database supports translational research into the therapeutic significance of γδ T cells.

Whole-Transcriptome Analysis Reveals Potential CeRNA Regulatory Mechanism in Takifugu rubripes against Cryptocaryon irritans Infection.

Cryptocaryon irritans (C. irritans) is a proto-ciliate parasite that infects marine fishes, including the cultured species Takifugu rubripes (T. rubripes), causing disease and potential mortality. In host organisms, infection by parasites triggers an immune response that is modulated by regulatory elements including proteins and non-coding RNAs. In this study, the whole transcriptome RNA sequencing of T. rubripes gill tissue before and after infection with C. irritans was performed to reveal the competitive endogenous RNA (ceRNA) regulatory network. Histomorphology revealed gill segment swelling and parasitic invasion in the infected group. The analysis identified 18 differentially expressed miRNAs (DEMs), 214 lncRNAs (DELs), 2501 genes (DEGs), and 7 circRNAs (DECs) in the infected group. Gene Ontology (GO) enrichment analysis revealed that these genes were notably enriched in the Wnt signaling pathway and mTOR signaling pathway. The co-expression networks (lncRNA/circRNA-miRNA-mRNA) were constructed based on correlation analysis of the differentially expressed RNAs. Further analysis suggested that the LOC105418663-circ_0000361-fru-miR-204a-fzd3a ceRNA axis was potentially involved in the regulation of immune responses against C. irritans infection. Finally, the expression levels of DEG, DEL, and DEM were validated. This study reveals the regulatory mechanism of a candidate ceRNA network, providing insights into the potential mechanism of T. rubripes' infection with C. irritans.

The small MAF transcription factor MAFG co-opts MITF to promote melanoma progression.

Transcription factor deregulation potently drives melanoma progression by dynamically and reversibly controlling gene expression programs. We previously identified the small MAF family transcription factor MAFG as a putative driver of melanoma progression, prompting an in-depth evaluation of its role in melanoma. MAFG expression increases with human melanoma stages and ectopic MAFG expression enhances the malignant behavior of human melanoma cells in vitro, xenograft models, and genetic mouse models of spontaneous melanoma. Moreover, MAFG induces a melanoma phenotype switch from a melanocytic state to a more dedifferentiated state. Mechanistically, MAFG interacts with the lineage transcription factor MITF which is required for the pro-tumorigenic effects of MAFG. MAFG and MITF co-occupy numerous genomic sites and MAFG overexpression influences the expression of genes harboring binding sites for the MAFG~MITF complex. These results establish MAFG as a potent driver of melanomagenesis through dimerization with MITF and uncover an unappreciated mechanism of MITF regulation.

CGPA: multi-context insights from the cancer gene prognosis atlas.

Cancer transcriptomic data are used extensively to interrogate the prognostic value of targeted genes, yet basic scientists and clinicians have predominantly relied on univariable survival analysis for this purpose. This method often fails to capture the full prognostic potential and contextual relevance of the genes under study, inadvertently omitting a group of genes we term univariable missed-opportunity prognostic (UMOP) genes. Recognizing the complexity of revealing multifaceted prognostic implications, especially when extending the analysis to include various covariates and thresholds, we present the Cancer Gene Prognosis Atlas (CGPA). This platform greatly enhances gene-centric biomarker research across cancer types by offering an interactive and user-friendly interface for highly customized, in-depth prognostic analysis. CGPA notably supports data-driven exploration of gene pairs and gene-hallmark relationships, elucidating key composite biological mechanisms like synthetic lethality and immunosuppression. It further expands its capabilities to assess multi-gene panels using both public and user-provided data, facilitating a seamless mechanism-to-machine analysis. Additionally, CGPA features a designated portal for discovering prognostic gene modules using curated cancer immunotherapy data. Ultimately, CGPA's comprehensive, accessible tools allow cancer researchers, including those without statistical expertise, to precisely investigate the prognostic landscape of genes, customizing the model to fit specific research hypotheses and enhancing biomarker discovery and validation through a synergy of mechanistic and data-driven strategies.

A pan-cancer gamma delta T cell repertoire.

This report presents the largest collection of gamma-delta T cell receptor (γδ TCR) reads in human cancer to date, analyzing about 11,000 patient tumor samples across 33 cancer types using the TRUST4 algorithm. Despite γδ T cells being a small fraction of the T cell population, they play a key role in both innate and adaptive immunity. Our comprehensive analysis reveals their significant presence across all cancer types, specifically highlighting the diverse spectrum and clonality patterns of their γδ receptors. This research highlights the complex roles of γδ T cells in tumor tissues and their potential as prognostic biomarkers. We also demonstrate the utility of T cell receptor gamma (TRG) and delta (TRD) gene expression values from standard RNA-seq data. Ultimately, our work establishes a fundamental resource for future tumor-infiltrating γδ T cell research and may facilitate the development of novel γδ-T-cell-based therapeutic strategies. Together, we demonstrate the strong diversity and prognostic potential of γδ T cells in multiple cancer types.

Rapid Autopsy to Define Dendritic Cell Spatial Distribution and T Cell Association in Lung Adenocarcinoma.

Immunotherapy response is associated with the presence of conventional dendritic cells (cDCs). cDC type 1 (cDC1) is critically important for CD8+ T cell activation, cDC type 2 (cDC2) regulates CD4+ T cell responses, and mature regulatory cDCs may dampen T cell responses in the tumor microenvironment (TME). However, we lack a clear understanding of cDC distribution in the human TME, cDC prevalence in metastatic sites, and cDC differences in early- versus late-stage disease. Rapid autopsy specimens of 10 patients with lung adenocarcinoma were evaluated to detect cDCs and immune cells via multiplex immunofluorescence using 18 markers and 42 tumors. First, we found that T cells, cDC1, and cDC2 were confined to stroma, whereas mature regulatory DCs were enriched in tumor, suggesting unique localization-specific functions. Second, lung and lymph node tumors were more enriched in T cells and cDCs than liver tumors, underscoring differences in the TME of metastatic sites. Third, although the proportion of T cells and cDC1 did not differ in different stages, an increase in the proportion of cDC2 and macrophages in late stage suggests potential differences in regulation of T cell responses in different stages. Collectively, these findings provide new, to our knowledge, insights into cDC biology in human cancer that may have important therapeutic implications.

Licochalcone A mitigates aflatoxin B1-induced immunotoxicity via ferroptosis in bursa of broilers and macrophages.

Aflatoxin B1 (AFB1) is a mycotoxin which is responsible for severe damage to the immune system of humans and livestock. Licochalcone A (Lico A), a polyphenol derived from turmeric, has attracted great attention due to its wonderful antioxidant properties. Ferroptosis, an iron-dependent cell death related to oxidative stress, which plays a crucial role in the resistance of phytochemical to immune-associated injury. Nevertheless, effects of Lico A on the bursa of broilers exposed to AFB1 remain unclear. In this work, broilers were fed diets supplemented with 2 mg/kg of AFB1 and 50 mg/kg of Lico A. Meanwhile, various concentrations of Lico A and AFB1 (15 µM) were used to stimulate macrophages. These results revealed that AFB1 resulted in more severe bursa atrophy and relative weight reduction; the expression of pro-ferroptosis protein ACSL4 and the content of malondialdehyde (MDA) were significantly elevated, while the expression of anti-ferroptosis proteins GPX4, xCT, FSP1 and the content of Glutathione (GSH) was obviously reduced. However, Lico A treatment effectively reversed these effects in the bursa of broilers. Meanwhile, in bursa and macrophages, Lico A mitigated the expression of AFB1-induced apoptosis-associated protein (Caspase-3, Bax, Bcl-2) as well as antioxidant protein (Nrf2, GCLM, HO-1). Importantly, ferroptosis was also observed in macrophages induced by AFB1. Lico A efficaciously alleviated AFB1-induced mitochondrial membrane potential decrease and reactive oxygen species (ROS) production in macrophages; in contrast, Lico A evidently inhibited AFB1-triggered ROS generation and cytotoxicity, which was disabled by the addition of Erastin. Moreover, Liproxstatin-1 significantly inhibited ROS generation induced by AFB1. In summary, the present study elucidates that the main mechanism by which Lico A attenuates AFB1-induced immunotoxicity is through the suppression of ferroptosis, apoptosis, mitochondrial damage and oxidative stress, which is promising for the improvement of immunotoxic effects of AFB1.

Spotlight on the treatment of non-small cell lung cancer with rare genetic alterations and brain metastasis: Current status and future perspectives.

In patients with non-small cell lung cancer (NSCLC), oncogenic variants present in <5% of cases are considered rare, the predominant of which include human epidermal growth factor receptor 2 (HER2) mutations, mesenchymal-epithelial transition (MET) alterations, c-ros oncogene 1 (ROS1) rearrangements, rearrangement during transfection (RET) fusions, v-raf mouse sarcoma virus oncogene homolog B1 (BRAF) mutations, and neurotrophic troponin receptor kinase (NTRK) fusions. Brain metastases (BMs) occur in approximately 10%-50% of patients with NSCLC harboring rare genetic variants. The recent advent of small-molecule tyrosine kinase inhibitors and macromolecular antibody-drug conjugates (ADCs) has conferred marked survival benefits to patients with NSCLC harboring rare driver alterations. Despite effective brain lesion control for most targeted agents and promising reports of intracranial remission associated with novel ADCs, BM continues to be a major therapeutic challenge. This review discusses the recent advances in the treatment of NSCLC with rare genetic variants and BM, with a particular focus on intracranial efficacy, and explores future perspectives on how best to treat these patients.

SmartImpute: A Targeted Imputation Framework for Single-cell Transcriptome Data.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and tissue transcriptomic complexity. However, the high frequency of dropout events in scRNA-seq data complicates downstream analyses such as cell type identification and trajectory inference. Existing imputation methods address the dropout problem but face limitations such as high computational cost and risk of over-imputation. We present SmartImpute, a novel computational framework designed for targeted imputation of scRNA-seq data. SmartImpute focuses on a predefined set of marker genes, enhancing the biological relevance and computational efficiency of the imputation process while minimizing the risk of model misspecification. Utilizing a modified Generative Adversarial Imputation Network architecture, SmartImpute accurately imputes the missing gene expression and distinguishes between true biological zeros and missing values, preventing overfitting and preserving biologically relevant zeros. To ensure reproducibility, we also provide a function based on the GPT4 model to create target gene panels depending on the tissue types and research context. Our results, based on scRNA-seq data from head and neck squamous cell carcinoma and human bone marrow, demonstrate that SmartImpute significantly enhances cell type annotation and clustering accuracy while reducing computational burden. Benchmarking against other imputation methods highlights SmartImpute's superior performance in terms of both accuracy and efficiency. Overall, SmartImpute provides a lightweight, efficient, and biologically relevant solution for addressing dropout events in scRNA-seq data, facilitating deeper insights into cellular heterogeneity and disease progression. Furthermore, SmartImpute's targeted approach can be extended to spatial omics data, which also contain many missing values.

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven for precision genomics.

spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis.

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.

Targeting antioxidant factor Nrf2 by raffinose ameliorates lipid dysmetabolism-induced pyroptosis, inflammation and fibrosis in NAFLD.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a persistent liver condition that affects both human health and animal productive efficiency on a global scale. A number of naturally occurring compounds activate nuclear factor erythroid 2-related factor 2 (Nrf2) as a transcription factor with important protective effects against many liver diseases, including NAFLD. Raffinose (Ra), an oligosaccharide extracted from several plants, exhibits diverse biological functions. However, the uncertainty lies in determining whether the activation of Nrf2 by Ra can provide a preventive effect on liver lipotoxicity. PURPOSE: The aim of this study was to shed light on the molecular pathways by which Ra possesses its protective benefits against NAFLD. METHODS: Experimental protocols were established using WT and Nrf2-null (Nrf2-/-) mice. Liver samples from each group were collected for Western blot, RT-qPCR, H & E, Sirius red and Oil red O staining. Additionally, serums were processed for ELISA. ALM12 cells were gathered for Western blot and immunofluorescence. Moreover, to elucidate the molecular mechanism of Ra, molecular docking was performed. RESULTS: Our results indicated that Ra remarkably alleviated liver lipotoxic in vivo and in vitro. Ra treatment effectively corrected hepatic steatosis, the release of AST, ALT, TG, and TC, as well as the depletion of HDL and LDL. Meanwhile, Ra efficiently prevented inflammation by inhibiting the TLR4-MyD88-NF-κB pathway and pyroptosis. Additionally, these findings implied that Ra reduced the production of fibrosis-related proteins, which enhanced collagen deposition. Molecular docking revealed that Ra possessed the ability to bind specific regions of Nrf2, resulting in the enhancement of Nrf2 activation and nuclear translocation. Ra treatment restored serum redox factors and antioxidant enzymes to normal levels; however, these alterations were clearly reversed in Nrf2-/- mice. CONCLUSION: This study reveals novel information on Ra's protective benefits against liver injury caused by abnormal lipid metabolism; these effects are mostly mediated by Nrf2 activation, suggesting a potential new medicine or treatment strategy for NAFLD.

Liver epigenomic signature associated with chronic oxidative stress in a mouse model of glutathione deficiency.

Oxidative stress is intimately involved in the pathogenesis of fatty liver disease (FLD). A major factor contributing to oxidative stress is the depletion of the ubiquitous antioxidant glutathione (GSH). Unexpectedly, chronic GSH deficiency renders glutamate-cysteine ligase modifier subunit (Gclm)-null mice protected from fatty liver injuries. Epigenetic regulation serves as an important cellular mechanism in modulating gene expression and disease outcome in FLD, although it is not well understood how systemic redox imbalance modifies the liver epigenome. In the current study, utilizing the Gclm-null mouse model, we aimed to elucidate redox-associated epigenomic changes and their implications in liver stress response. We performed high-throughput array-based DNA methylation profiling (MeDIP array) in 22,327 gene promoter regions (from -1300 bp to +500 bp of the Transcription Start Sites) in the liver and peripheral blood cells. Results from the MeDIP array demonstrate that, although global methylation enrichment in gene promoters did not change, low GSH resulted in prevalent demethylation at the individual promoter level. Such an effect likely attributed to a declined availability of the methyl donor S-adenosyl methionine (SAM) in Gclm-null liver. Functional enrichment analysis of liver target genes is suggestive of a potential role of epigenetic mechanisms in promoting cellular survival and lipid homeostasis in Gclm-null liver. In comparison with the liver tissue, MeDIP array in peripheral blood cells revealed a panel of 19 gene promoters that are candidate circulating biomarkers for hepatic epigenomic changes associated with chronic GSH deficiency. Collectively, our results provided new insights into the in vivo interplay between liver redox state and DNA methylation status. The current study laid the groundwork for future epigenetic/epigenomic investigations in experimental settings or human populations under conditions of liver oxidative stress induced by environmental or dietary challenges.

A novel efficient strategy to generate liver sinusoidal endothelial cells from human pluripotent stem cells.

Liver sinusoidal endothelial cells (LSECs) are highly specialized endothelial cells (ECs) that play an important role in liver development and regeneration. Additionally, it is involved in various pathological processes, including steatosis, inflammation, fibrosis and hepatocellular carcinoma. However, the rapid dedifferentiation of LSECs after culture greatly limits their use in vitro modeling for biomedical applications. In this study, we developed a highly efficient protocol to induce LSEC-like cells from human induced pluripotent stem cells (hiPSCs) in only 8 days. Using single-cell transcriptomic analysis, we identified several novel LSEC-specific markers, such as EPAS1, LIFR, and NID1, as well as several previously revealed markers, such as CLEC4M, CLEC1B, CRHBP and FCN3. These LSEC markers are specifically expressed in our LSEC-like cells. Furthermore, hiPSC-derived cells expressed LSEC-specific proteins and exhibited LSEC-related functions, such as the uptake of acetylated low density lipoprotein (ac-LDL) and immune complex endocytosis. Overall, this study confirmed that our novel protocol allowed hiPSCs to rapidly acquire an LSEC-like phenotype and function in vitro. The ability to generate LSECs efficiently and rapidly may help to more precisely mimic liver development and disease progression in a liver-specific multicellular microenvironment, offering new insights into the development of novel therapeutic strategies.

From Detection to Cure - Emerging Roles for Urinary Tumor DNA (utDNA) in Bladder Cancer.

PURPOSE OF REVIEW: This review sought to define the emerging roles of urinary tumor DNA (utDNA) for diagnosis, monitoring, and treatment of bladder cancer. Building from early landmark studies the focus is on recent studies, highlighting how utDNA could aid personalized care. RECENT FINDINGS: Recent research underscores the potential for utDNA to be the premiere biomarker in bladder cancer due to the constant interface between urine and tumor. Many studies find utDNA to be more informative than other biomarkers in bladder cancer, especially in early stages of disease. Points of emphasis include superior sensitivity over traditional urine cytology, broad genomic and epigenetic insights, and the potential for non-invasive, real-time analysis of tumor biology. utDNA shows promise for improving all phases of bladder cancer care, paving the way for personalized treatment strategies. Building from current research, future comprehensive clinical trials will validate utDNA's clinical utility, potentially revolutionizing bladder cancer management.

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models.

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).