Proteomic-Based Analysis of Hypoxia- and Physioxia-Responsive Proteins and Pathways in Diffuse Large B-Cell Lymphoma

Supervised learning of high-confidence phenotypic subpopulations from single-cell data

Accurately identifying phenotype-relevant cell subsets from heterogeneous cell populations is crucial for delineating the underlying mechanisms driving biological or clinical phenotypes. Here, by deploying a learning with rejection strategy, we developed a novel supervised learning framework called PENCIL to identify subpopulations associated with categorical or continuous phenotypes from single-cell data. By embedding a feature selection function into this flexible framework, for the first time, we were able to select informative features and identify cell subpopulations simultaneously, which enables the accurate identification of phenotypic subpopulations otherwise missed by methods incapable of concurrent gene selection. Furthermore, the regression mode of PENCIL presents a novel ability for supervised phenotypic trajectory learning of subpopulations from single-cell data. We conducted comprehensive simulations to evaluate PENCIĽs versatility in simultaneous gene selection, subpopulation identification and phenotypic trajectory prediction. PENCIL is fast and scalable to analyze 1 million cells within 1 hour. Using the classification mode, PENCIL detected T-cell subpopulations associated with melanoma immunotherapy outcomes. Moreover, when applied to scRNA-seq of a mantle cell lymphoma patient with drug treatment across multiple time points, the regression mode of PENCIL revealed a transcriptional treatment response trajectory. Collectively, our work introduces a scalable and flexible infrastructure to accurately identify phenotype-associated subpopulations from single-cell data.

HIF-1α mediates CXCR4 transcription to activate the AKT/mTOR signaling pathway and augment the viability and migration of activated B cell-like diffuse large B-cell lymphoma cells

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid malignancy with a high relapse rate. We previously found that C-X-C motif chemokine receptor 4 (CXCR4) was highly expressed in DLBCL and associated with poor prognosis. This study focused on the effect of hypoxia-inducible factor-1α (HIF-1α) on CXCR4 expression and the DLBCL progression. Two activated B cell-like DLBCL cell lines Ly-3 and SUDHL2 were transfected with overexpression and knockdown plasmids or HIF-1α. The viability and migration of DLBCL cells were significantly increased under hypoxic conditions, or upon HIF-1α overexpression under normoxic conditions, but the HIF-1α downregulation led to inverse trends. However, the promoting effects of HIF-1α overexpression on DLBCL cells were suppressed by Plerixafor (a CXCR4 inhibitor). The luciferase and chromatin immunoprecipitation assays revealed that HIF-1α bound to the functional site HRE1 on CXCR4 promoter to activate its transcription. HIF-1α-mediated CXCR4 activation further led to increased phosphorylation of AKT/mTOR under hypoxic conditions. Taken together, this work reports that HIF-1α promotes viability and migration of activated B cell-like cells under hypoxia, which might involve the transcription of CXCR4 and the activation of the AKT/mTOR pathway. The finding may provide novel lights in the management of DCBCL.

Modeling Preclinical Cancer Studies under Physioxia to Enhance Clinical Translation

Oxygen (O2) plays a key role in cellular homeostasis. O2 levels are tightly regulated in vivo such that each tissue receives an optimal amount to maintain physiologic status. Physiologic O2 levels in various organs range between 2% and 9% in vivo, with the highest levels of 9% in the kidneys and the lowest of 0.5% in parts of the brain. This physiologic range of O2 tensions is disrupted in pathologic conditions such as cancer, where it can reach as low as 0.5%. Regardless of the state, O2 tension in vivo is maintained at significantly lower levels than ambient O2, which is approximately 21%. Yet, routine in vitro cellular manipulations are carried out in ambient air, regardless of whether or not they are eventually transferred to hypoxic conditions for subsequent studies. Even brief exposure of hematopoietic stem cells to ambient air can cause detrimental effects through a mechanism termed extraphysiologic oxygen shock/stress (EPHOSS), leading to reduced engraftment capabilities. Here, we provide an overview of the effects of ambient air exposure on stem and non-stem cell subtypes, with a focus on recent findings that reveal the impact of EPHOSS on cancer cells.

Culture of Cancer Cells at Physiological Oxygen Levels Affects Gene Expression in a Cell-Type Specific Manner

Standard cell culture is routinely performed at supraphysiological oxygen levels (~18% O2). Conversely, O2 levels in most mammalian tissues range from 1-6% (physioxia). Such hyperoxic conditions in cell culture can alter reactive oxygen species (ROS) production, metabolism, mitochondrial networks, and response to drugs and hormones. The aim of this study was to investigate the transcriptional response to different O2 levels and determine whether it is similar across cell lines, or cell line-specific. Using RNA-seq, we performed differential gene expression and functional enrichment analyses in four human cancer cell lines, LNCaP, Huh-7, PC-3, and SH-SY5Y cultured at either 5% or 18% O2 for 14 days. We found that O2 levels affected transcript abundance of thousands of genes, with the affected genes having little overlap between cell lines. Functional enrichment analysis also revealed different processes and pathways being affected by O2 in each cell line. Interestingly, most of the top differentially expressed genes are involved in cancer biology, which highlights the importance of O2 levels in cancer cell research. Further, we observed several hypoxia-inducible factor (HIF) targets, HIF-2α targets particularly, upregulated at 5% O2, consistent with a role for HIFs in physioxia. O2 levels also differentially induced the transcription of mitochondria-encoded genes in most cell lines. Finally, by comparing our transcriptomic data from LNCaP and PC-3 with datasets from the Prostate Cancer Transcriptome Atlas, a correlation between genes upregulated at 5% O2 in LNCaP cells and the in vivo prostate cancer transcriptome was found. We conclude that the transcriptional response to O2 over the range from 5-18% is robust and highly cell-type specific. This latter finding indicates that the effects of O2 levels are difficult to predict and thus highlights the importance of regulating O2 in cell culture.

Hub Gene and Its Key Effects on Diffuse Large B-Cell Lymphoma by Weighted Gene Coexpression Network Analysis

Diffuse large B-cell lymphoma (DLBC) is a kind of tumor with rapid progress and poor prognosis. Therefore, it is necessary to explore new biomarkers or therapeutic targets to assist in diagnosis or treatment. This study is aimed at screening hub genes by weighted gene coexpression network analysis (WGCNA) and exploring the significance of overall survival (OS) in DLBC patients. Statistical data using WGCNA to analyze mRNA expression in DLBC patients came from The Cancer Genome Atlas (TCGA) dataset. After analyzing with clinical information, the biological functions of hub genes were detected. Survival analysis, Cox regression detection, and correlation analysis of the hub genes were carried out. The potential function of the hub gene related to prognosis was predicted by gene set enrichment analysis (GSEA). The results showed that APOE, CTSD, LGALS2, and TMEM176B expression in normal tissues was significantly higher than that in cancer tissues (P < 0.01). Survival analysis showed that patients with high APOE and CTSD were associated with better OS (P < 0.01). APOE and CTSD genes were mainly enriched in the regulation of ROS and oxidative stress. The two hub genes related to the prognosis of DLBC were identified and verified based on WGCNA. Survival analysis showed that the overexpression of APOE and CTSD in DLBC might be beneficial to the prognosis. These findings identified vital pathways and genes that may become new therapeutic targets and contribute to prognostic indicators.