Targeting metabolic sensing switch GPR84 on macrophages for cancer immunotherapy.

INTRODUCTION: As one of the major components of the tumor microenvironment, tumor-associated macrophages (TAMs) possess profound inhibitory activity against T cells and facilitate tumor escape from immune checkpoint blockade therapy. Converting this pro-tumorigenic toward the anti-tumorigenic phenotype thus is an important strategy for enhancing adaptive immunity against cancer. However, a plethora of mechanisms have been described for pro-tumorigenic differentiation in cancer, metabolic switches to program the anti-tumorigenic property of TAMs are elusive. MATERIALS AND METHODS: From an unbiased analysis of single-cell transcriptome data from multiple tumor models, we discovered that anti-tumorigenic TAMs uniquely express elevated levels of a specific fatty acid receptor, G-protein-coupled receptor 84 (GPR84). Genetic ablation of GPR84 in mice leads to impaired pro-inflammatory polarization of macrophages, while enhancing their anti-inflammatory phenotype. By contrast, GPR84 activation by its agonist, 6-n-octylaminouracil (6-OAU), potentiates pro-inflammatory phenotype via the enhanced STAT1 pathway. Moreover, 6-OAU treatment significantly retards tumor growth and increases the anti-tumor efficacy of anti-PD-1 therapy. CONCLUSION: Overall, we report a previously unappreciated fatty acid receptor, GPR84, that serves as an important metabolic sensing switch for orchestrating anti-tumorigenic macrophage polarization. Pharmacological agonists of GPR84 hold promise to reshape and reverse the immunosuppressive TME, and thereby restore responsiveness of cancer to overcome resistance to immune checkpoint blockade.

A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset.

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCE: Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.

Distinctive patterns of sulfide- and butyrate-metabolizing bacteria after bariatric surgery: potential implications for colorectal cancer risk.

Despite improved cardiometabolic outcomes following bariatric surgery, its long-term impact on colorectal cancer (CRC) risk remains uncertain. In parallel, the influence of bariatric surgery on the host microbiome and relationships with disease outcomes is beginning to be appreciated. Therefore, we investigated the impact of Roux-en-Y gastric bypass (RYGB) and vertical sleeve gastrectomy (VSG) on the patterns of sulfide-reducing and butyrate-producing bacteria, which are hypothesized to modulate CRC risk after bariatric surgery. In this single-center, cross-sectional study, we included 15 pre-surgery subjects with severe obesity and patients who are at a median (range) of 25.6 (9.9-46.5) months after RYGB (n = 16) or VSG (n = 10). The DNA abundance of fecal bacteria and enzymes involved in butyrate and sulfide metabolism were identified using metagenomic sequencing. Differences between pre-surgery and post-RYGB or post-VSG cohorts were quantified using the linear discriminant analysis (LDA) effect size (LEfSe) method. Our sample was predominantly female (87%) with a median (range) age of 46 (23-71) years. Post-RYGB and post-VSG patients had a higher DNA abundance of fecal sulfide-reducing bacteria than pre-surgery controls (LDA = 1.3-4.4, p < .05). The most significant enrichments were for fecal E. coli, Acidaminococcus and A. finegoldii after RYGB, and for A. finegoldii, S. vestibularis, V. parvula after VSG. As for butyrate-producing bacteria, R. faecis was more abundant, whereas B. dentium and A. hardus were lower post-RYGB vs. pre-surgery. B. dentium was also lower in post-VSG vs. pre-surgery. Consistent with these findings, our analysis showed a greater enrichment of sulfide-reducing enzymes after bariatric surgery, especially RYGB, vs. pre-surgery. The DNA abundance of butyrate-producing enzymes was lower post-RYGB. In conclusion, the two most used bariatric surgeries, RYGB and VSG, are associated with microbiome patterns that are potentially implicated in CRC risk. Future studies are needed to validate and understand the impact of these microbiome changes on CRC risk after bariatric surgery.

Identification of HPV oncogene and host cell differentiation associated cellular heterogeneity in cervical cancer via single-cell transcriptomic analysis.

Human Papillomaviruses (HPVs) are associated with around 5-10% of human cancer, notably nearly 99% of cervical cancer. The mechanisms HPV interacts with stratified epithelium (differentiated layers) during the viral life cycle, and oncogenesis remain unclear. In this study, we used single-cell transcriptome analysis to study viral gene and host cell differentiation-associated heterogeneity of HPV-positive cervical cancer tissue. We examined the HPV16 genes - E1, E6, and E7, and found they expressed differently across nine epithelial clusters. We found that three epithelial clusters had the highest proportion of HPV-positive cells (33.6%, 37.5%, and 32.4%, respectively), while two exhibited the lowest proportions (7.21% and 5.63%, respectively). Notably, the cluster with the most HPV-positive cells deviated significantly from normal epithelial layer markers, exhibiting functional heterogeneity and altered epithelial structuring, indicating that significant molecular heterogeneity existed in cancer tissues and that these cells exhibited unique/different gene signatures compared with normal epithelial cells. These HPV-positive cells, compared to HPV-negative, showed different gene expressions related to the extracellular matrix, cell adhesion, proliferation, and apoptosis. Further, the viral oncogenes E6 and E7 appeared to modify epithelial function via distinct pathways, thus contributing to cervical cancer progression. We investigated the HPV and host transcripts from a novel viewpoint focusing on layer heterogeneity. Our results indicated varied HPV expression across epithelial clusters and epithelial heterogeneity associated with viral oncogenes, contributing biological insights to this critical field of study.

Identification of HPV oncogene and host cell differentiation associated cellular heterogeneity in cervical cancer via single-cell transcriptomic analysis.

Human Papillomaviruses (HPVs) are associated with around 5%-10% of human cancer, notably nearly 99% of cervical cancer. The mechanisms HPV interacts with stratified epithelium (differentiated layers) during the viral life cycle, and oncogenesis remain unclear. In this study, we used single-cell transcriptome analysis to study viral gene and host cell differentiation-associated heterogeneity of HPV-positive cervical cancer tissue. We examined the HPV16 genes-E1, E6, and E7, and found they expressed differently across nine epithelial clusters. We found that three epithelial clusters had the highest proportion of HPV-positive cells (33.6%, 37.5%, and 32.4%, respectively), while two exhibited the lowest proportions (7.21% and 5.63%, respectively). Notably, the cluster with the most HPV-positive cells deviated significantly from normal epithelial layer markers, exhibiting functional heterogeneity and altered epithelial structuring, indicating that significant molecular heterogeneity existed in cancer tissues and that these cells exhibited unique/different gene signatures compared with normal epithelial cells. These HPV-positive cells, compared to HPV-negative, showed different gene expressions related to the extracellular matrix, cell adhesion, proliferation, and apoptosis. Further, the viral oncogenes E6 and E7 appeared to modify epithelial function via distinct pathways, thus contributing to cervical cancer progression. We investigated the HPV and host transcripts from a novel viewpoint focusing on layer heterogeneity. Our results indicated varied HPV expression across epithelial clusters and epithelial heterogeneity associated with viral oncogenes, contributing biological insights to this critical field of study.

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset.

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

Single-cell biological network inference using a heterogeneous graph transformer.

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.

Molecular Regulation of Heme Oxygenase-1 Expression by E2F Transcription Factor 2 in Lung Fibroblast Cells: Relevance to Idiopathic Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a fatal chronic lung disease. Heme oxygenase-1 (HMOX1/HO-1) is an enzyme that catalyzes the degradation of heme. The role of HO-1 in the pathogenesis of IPF has been studied; however, the molecular regulation of HO-1 and its role in IPF are still unclear. In this study, we found that HO-1 protein levels significantly increased in lung myofibroblasts in IPF patients and in lungs in a murine model of bleomycin-induced lung fibrosis. In addition, we observed that administration of a E2F transcription factor inhibitor elevated HO-1 mRNA and protein levels in lung fibroblasts. Downregulation of E2F2 by siRNA transfection increased HO-1 mRNA and protein levels, while overexpression of E2F2 reduced HO-1 levels. However, overexpression of E2F2 did not alter hemin-induced HO-1 protein levels. Furthermore, modulation of HO-1 levels regulated TGF-ß1-induced myofibroblast differentiation without altering the phosphorylation of Smad2/3 in lung fibroblast cells. Moreover, the phosphorylation of protein kinase B (Akt) was significantly upregulated in HO-1-depleted lung fibroblast cells. In summary, this study demonstrated that E2F2 regulates the baseline expression of HO-1, but has no effect on modulating HO-1 expression by hemin. Finally, elevated HO-1 expression contributes to the TGF-ß1-induced lung myofibroblast differentiation through the activation of the serine/threonine kinase AKT pathway. Overall, our findings suggest that targeting E2F2/HO-1 might be a new therapeutic strategy to treat fibrotic diseases such as IPF.

DESSO-DB: A web database for sequence and shape motif analyses and identification.

Cis-regulatory motif (motif for short) identification and analyses are essential steps in detecting gene regulatory mechanisms. Deep learning (DL) models have shown substantial advances in motif prediction. In parallel, intuitive and integrative web databases are needed to make effective use of DL models and ensure easy access to the identified motifs. Here, we present DESSO-DB, a web database developed to allow efficient access to the identified motifs and diverse motif analyses. DESSO-DB provides motif prediction results and visualizations of 690 ENCODE human Chromatin Immunoprecipitation sequencing (ChIP-seq) data (including 161 transcription factors (TFs) in 91 cell lines) and 1,677 human ChIP-seq data (including 547 TFs in 359 cell lines) from Cistrome DB using DESSO, which is an in-house developed DL tool for motif prediction. It also provides online motif finding and scanning functions for new ChIP-seq/ATAC-seq datasets and downloadable motif results of the above 690 DECODE datasets, 126 cancer ChIP-seq, 55 RNA Crosslinking-Immunoprecipitation and high-throughput sequencing (CLIP-seq) data. DESSO-DB is deployed on the Google Cloud Platform, providing stabilized and efficient resources freely to the public. DESSO-DB is free and available at http://cloud.osubmi.com/DESSO/.

Microglia coordinate cellular interactions during spinal cord repair in mice.

Traumatic spinal cord injury (SCI) triggers a neuro-inflammatory response dominated by tissue-resident microglia and monocyte derived macrophages (MDMs). Since activated microglia and MDMs are morphologically identical and express similar phenotypic markers in vivo, identifying injury responses specifically coordinated by microglia has historically been challenging. Here, we pharmacologically depleted microglia and use anatomical, histopathological, tract tracing, bulk and single cell RNA sequencing to reveal the cellular and molecular responses to SCI controlled by microglia. We show that microglia are vital for SCI recovery and coordinate injury responses in CNS-resident glia and infiltrating leukocytes. Depleting microglia exacerbates tissue damage and worsens functional recovery. Conversely, restoring select microglia-dependent signaling axes, identified through sequencing data, in microglia depleted mice prevents secondary damage and promotes recovery. Additional bioinformatics analyses reveal that optimal repair after SCI might be achieved by co-opting key ligand-receptor interactions between microglia, astrocytes and MDMs.

Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model.

Measuring conditional relatedness between a pair of genes is a fundamental technique and still a significant challenge in computational biology. Such relatedness can be assessed by gene expression similarities while suffering high false discovery rates. Meanwhile, other types of features, e.g., prior-knowledge based similarities, is only viable for measuring global relatedness. In this paper, we propose a novel machine learning model, named Multi-Features Relatedness (MFR), for accurately measuring conditional relatedness between a pair of genes by incorporating expression similarities with prior-knowledge based similarities in an assessment criterion. MFR is used to predict gene-gene interactions extracted from the COXPRESdb, KEGG, HPRD, and TRRUST databases by the 10-fold cross validation and test verification, and to identify gene-gene interactions collected from the GeneFriends and DIP databases for further verification. The results show that MFR achieves the highest area under curve (AUC) values for identifying gene-gene interactions in the development, test, and DIP datasets. Specifically, it obtains an improvement of 1.1% on average of precision for detecting gene pairs with both high expression similarities and high prior-knowledge based similarities in all datasets, comparing to other linear models and coexpression analysis methods. Regarding cancer gene networks construction and gene function prediction, MFR also obtains the results with more biological significances and higher average prediction accuracy, than other compared models and methods. A website of the MFR model and relevant datasets can be accessed from http://bmbl.sdstate.edu/MFR .