Redox signalling regulates breast cancer metastasis via phenotypic and metabolic reprogramming due to p63 activation by HIF1α.

BACKGROUND: Redox signaling caused by knockdown (KD) of Glutathione Peroxidase 2 (GPx2) in the PyMT mammary tumour model promotes metastasis via phenotypic and metabolic reprogramming. However, the tumour cell subpopulations and transcriptional regulators governing these processes remained unknown. METHODS: We used single-cell transcriptomics to decipher the tumour cell subpopulations stimulated by GPx2 KD in the PyMT mammary tumour and paired pulmonary metastases. We analyzed the EMT spectrum across the various tumour cell clusters using pseudotime trajectory analysis and elucidated the transcriptional and metabolic regulation of the hybrid EMT state. RESULTS: Integration of single-cell transcriptomics between the PyMT/GPx2 KD primary tumour and paired lung metastases unraveled a basal/mesenchymal-like cluster and several luminal-like clusters spanning an EMT spectrum. Interestingly, the luminal clusters at the primary tumour gained mesenchymal gene expression, resulting in epithelial/mesenchymal subpopulations fueled by oxidative phosphorylation (OXPHOS) and glycolysis. By contrast, at distant metastasis, the basal/mesenchymal-like cluster gained luminal and mesenchymal gene expression, resulting in a hybrid subpopulation using OXPHOS, supporting adaptive plasticity. Furthermore, p63 was dramatically upregulated in all hybrid clusters, implying a role in regulating partial EMT and MET at primary and distant sites, respectively. Importantly, these effects were reversed by HIF1α loss or GPx2 gain of function, resulting in metastasis suppression. CONCLUSIONS: Collectively, these results underscored a dramatic effect of redox signaling on p63 activation by HIF1α, underlying phenotypic and metabolic plasticity leading to mammary tumour metastasis.

Deconstructing heterogeneity of replicative senescence in human mesenchymal stem cells at single cell resolution.

Following prolonged cell division, mesenchymal stem cells enter replicative senescence, a state of permanent cell cycle arrest that constrains the use of this cell type in regenerative medicine applications and that in vivo substantially contributes to organismal ageing. Multiple cellular processes such as telomere dysfunction, DNA damage and oncogene activation are implicated in promoting replicative senescence, but whether mesenchymal stem cells enter different pre-senescent and senescent states has remained unclear. To address this knowledge gap, we subjected serially passaged human ESC-derived mesenchymal stem cells (esMSCs) to single cell profiling and single cell RNA-sequencing during their progressive entry into replicative senescence. We found that esMSC transitioned through newly identified pre-senescent cell states before entering into three different senescent cell states. By deconstructing this heterogeneity and temporally ordering these pre-senescent and senescent esMSC subpopulations into developmental trajectories, we identified markers and predicted drivers of these cell states. Regulatory networks that capture connections between genes at each timepoint demonstrated a loss of connectivity, and specific genes altered their gene expression distributions as cells entered senescence. Collectively, this data reconciles previous observations that identified different senescence programs within an individual cell type and should enable the design of novel senotherapeutic regimes that can overcome in vitro MSC expansion constraints or that can perhaps slow organismal ageing.

Senolytic therapy alleviates physiological human brain aging and COVID-19 neuropathology.

Aging is a major risk factor for neurodegenerative diseases, and coronavirus disease 2019 (COVID-19) is linked to severe neurological manifestations. Senescent cells contribute to brain aging, but the impact of virus-induced senescence on neuropathologies is unknown. Here we show that senescent cells accumulate in aged human brain organoids and that senolytics reduce age-related inflammation and rejuvenate transcriptomic aging clocks. In postmortem brains of patients with severe COVID-19 we observed increased senescent cell accumulation compared with age-matched controls. Exposure of human brain organoids to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induced cellular senescence, and transcriptomic analysis revealed a unique SARS-CoV-2 inflammatory signature. Senolytic treatment of infected brain organoids blocked viral replication and prevented senescence in distinct neuronal populations. In human-ACE2-overexpressing mice, senolytics improved COVID-19 clinical outcomes, promoted dopaminergic neuron survival and alleviated viral and proinflammatory gene expression. Collectively our results demonstrate an important role for cellular senescence in driving brain aging and SARS-CoV-2-induced neuropathology, and a therapeutic benefit of senolytic treatments.

Impaired signaling for neuromuscular synaptic maintenance is a feature of Motor Neuron Disease.

A central event in the pathogenesis of motor neuron disease (MND) is the loss of neuromuscular junctions (NMJs), yet the mechanisms that lead to this event in MND remain to be fully elucidated. Maintenance of the NMJ relies upon neural agrin (n-agrin) which, when released from the nerve terminal, activates the postsynaptic Muscle Specific Kinase (MuSK) signaling complex to stabilize clusters of acetylcholine receptors. Here, we report that muscle from MND patients has an increased proportion of slow fibers and muscle fibers with smaller diameter. Muscle cells cultured from MND biopsies failed to form large clusters of acetylcholine receptors in response to either non-MND human motor axons or n-agrin. Furthermore, levels of expression of MuSK, and MuSK-complex components: LRP4, Caveolin-3, and Dok7 differed between muscle cells cultured from MND patients compared to those from non-MND controls. To our knowledge, this is the first time a fault in the n-agrin-LRP4-MuSK signaling pathway has been identified in muscle from MND patients. Our results highlight the n-agrin-LRP4-MuSK signaling pathway as a potential therapeutic target to prolong muscle function in MND.

Redox signaling by glutathione peroxidase 2 links vascular modulation to metabolic plasticity of breast cancer.

In search of redox mechanisms in breast cancer, we uncovered a striking role for glutathione peroxidase 2 (GPx2) in oncogenic signaling and patient survival. GPx2 loss stimulates malignant progression due to reactive oxygen species/hypoxia inducible factor-α (HIF1α)/VEGFA (vascular endothelial growth factor A) signaling, causing poor perfusion and hypoxia, which were reversed by GPx2 reexpression or HIF1α inhibition. Ingenuity Pathway Analysis revealed a link between GPx2 loss, tumor angiogenesis, metabolic modulation, and HIF1α signaling. Single-cell RNA analysis and bioenergetic profiling revealed that GPx2 loss stimulated the Warburg effect in most tumor cell subpopulations, except for one cluster, which was capable of oxidative phosphorylation and glycolysis, as confirmed by coexpression of phosphorylated-AMPK and GLUT1. These findings underscore a unique role for redox signaling by GPx2 dysregulation in breast cancer, underlying tumor heterogeneity, leading to metabolic plasticity and malignant progression.

scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell-cell variability therefore are useful for going beyond changes based on average expression and, instead, identifying genes with homogeneous expression versus those that vary widely from cell to cell. RESULTS: We present a novel statistical framework, scShapes, for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single-cell data are driven by overdispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that scShapes identifies subtle variations that are independent of altered mean expression and detects biologically relevant genes that were not discovered through standard approaches. CONCLUSIONS: This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from scShapes help to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework scShapes is incorporated into a Bioconductor R package (https://www.bioconductor.org/packages/release/bioc/html/scShapes.html).

The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data.

BACKGROUND: In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression distributions in three different tumor types from the Cancer Genome Atlas (TCGA). RESULTS: Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the shape of the distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of distributions were permitted, i.e. the shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression distribution. CONCLUSIONS: Our results highlight the value of studying a gene's distribution shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of distribution in a patient cohort.

Single-cell analysis of transcriptome and DNA methylome in human oocyte maturation.

Oocyte maturation is a coordinated process that is tightly linked to reproductive potential. A better understanding of gene regulation during human oocyte maturation will not only answer an important question in biology, but also facilitate the development of in vitro maturation technology as a fertility treatment. We generated single-cell transcriptome and used our previously published single-cell methylome data from human oocytes at different maturation stages to investigate how genes are regulated during oocyte maturation, focusing on the potential regulatory role of non-CpG methylation. DNMT3B, a gene encoding a key non-CpG methylation enzyme, is one of the 1,077 genes upregulated in mature oocytes, which may be at least partially responsible for the increased non-CpG methylation as oocytes mature. Non-CpG differentially methylated regions (DMRs) between mature and immature oocytes have multiple binding motifs for transcription factors, some of which bind with DNMT3B and may be important regulators of oocyte maturation through non-CpG methylation. Over 98% of non-CpG DMRs locate in transposable elements, and these DMRs are correlated with expression changes of the nearby genes. Taken together, this data indicates that global non-CpG hypermethylation during oocyte maturation may play an active role in gene expression regulation, potentially through the interaction with transcription factors.

Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types.

BACKGROUND: It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level. METHODS: Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism. RESULTS: For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF. CONCLUSIONS: We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.

Metformin alters skeletal muscle transcriptome adaptations to resistance training in older adults.

Evidence from clinical trials and observational studies suggests that both progressive resistance exercise training (PRT) and metformin delay a variety of age-related morbidities. Previously, we completed a clinical trial testing the effects of 14 weeks of PRT + metformin (metPRT) compared to PRT with placebo (plaPRT) on muscle hypertrophy in older adults. We found that metformin blunted PRT-induced muscle hypertrophic response. To understand potential mechanisms underlying the inhibitory effect of metformin on PRT, we analyzed the muscle transcriptome in 23 metPRT and 24 plaPRT participants. PRT significantly increased expression of genes involved in extracellular matrix remodeling pathways, and downregulated RNA processing pathways in both groups, however, metformin attenuated the number of differentially expressed genes within these pathways compared to plaPRT. Pathway analysis showed that genes unique to metPRT modulated aging-relevant pathways, such as cellular senescence and autophagy. Differentially expressed genes from baseline biopsies in older adults compared to resting muscle from young volunteers were reduced following PRT in plaPRT and were further reduced in metPRT. We suggest that although metformin may blunt pathways induced by PRT to promote muscle hypertrophy, adjunctive metformin during PRT may have beneficial effects on aging-associated pathways in muscle from older adults.

Identification of a novel subgroup of endometrial cancer patients with loss of thyroid hormone receptor beta expression and improved survival.

BACKGROUND: Endometrial cancer (EC) is the most common gynecologic cancer in women, and the incidence of EC has increased by about 1% per year in the U. S over the last 10 years. Although 5-year survival rates for early-stage EC are around 80%, certain subtypes of EC that lose nuclear hormone receptor (NHR) expression are associated with poor survival rates. For example, estrogen receptor (ER)-negative EC typically harbors a worse prognosis compared to ER-positive EC. The molecular basis for the loss of NHR expression in endometrial tumors and its contribution to poor survival is largely unknown. Furthermore, there are no tools to systematically identify tumors that lose NHR mRNA expression relative to normal tissue. The development of such an approach could identify sets of NHR-based biomarkers for classifying patients into subgroups with poor survival outcomes. METHODS: Here, a new computational method, termed receptLoss, was developed for identifying NHR expression loss in endometrial cancer relative to adjacent normal tissue. When applied to gene expression data from The Cancer Genome Atlas (TCGA), receptLoss identified 6 NHRs that were highly expressed in normal tissue and exhibited expression loss in a subset of endometrial tumors. RESULTS: Three of the six identified NHRs - estrogen, progesterone, and androgen receptors - that are known to lose expression in ECs were correctly identified by receptLoss. Additionally, a novel association was found between thyroid hormone receptor beta (THRB) expression loss, increased expression of miRNA-146a, and increased rates of 5-year survival in the EC TCGA patient cohort. THRB expression loss occurs independently of estrogen and progesterone expression loss, suggesting the discovery of a distinct, clinically-relevant molecular subgroup. CONCLUSION: ReceptLoss is a novel, open-source software tool to systematically identify NHR expression loss in cancer. The application of receptLoss to endometrial cancer gene expression data identified THRB, a previously undescribed biomarker of survival in endometrial cancer. Applying receptLoss to expression data from additional cancer types could lead to the development of biomarkers of disease progression for patients with any other tumor type. ReceptLoss can be applied to expression data from additional cancer types with the goal of identifying biomarkers of differential survival.

Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks.

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer's Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets-consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer-where we identify 109 candidate core genes.

Snai2 Maintains Bone Marrow Niche Cells by Repressing Osteopontin Expression.

Bone marrow (BM) mesenchymal stem and progenitor cells (MSPCs) are a critical constituent of the hematopoietic stem cell (HSC) niche. Previous studies have suggested that the zinc-finger epithelial-mesenchymal transition transcription factor Snai2 (also known as Slug) regulated HSCs autonomously. Here, we show that Snai2 expression in the BM is restricted to the BM stromal compartment where it regulates the HSC niche. Germline or MSPC-selective Snai2 deletion reduces the functional MSPC pool and their mesenchymal lineage output and impairs HSC niche function during homeostasis and after stress. RNA sequencing analysis revealed that Spp1 (osteopontin) expression is markedly upregulated in Snai2-deficient MSPCs. Genetic deletion of Spp1 in Snai2-deficient mice rescues MSPCs' functions. Thus, SNAI2 is a critical regulator of the transcriptional network maintaining MSPCs by the suppression of osteopontin expression.

Investigating skewness to understand gene expression heterogeneity in large patient cohorts.

BACKGROUND: Skewness is an under-utilized statistical measure that captures the degree of asymmetry in the distribution of any dataset. This study applied a new metric based on skewness to identify regulators or genes that have outlier expression in large patient cohorts. RESULTS: We investigated whether specific patterns of skewed expression were related to the enrichment of biological pathways or genomic properties like DNA methylation status. Our study used publicly available datasets that were generated using both RNA-sequencing and microarray technology platforms. For comparison, the datasets selected for this study also included different samples derived from control donors and cancer patients. When comparing the shift in expression skewness between cancer and control datasets, we observed an enrichment of pathways related to the immune function that reflects an increase towards positive skewness in the cancer relative to control datasets. A significant correlation was also detected between expression skewness and the top 500 genes corresponding to the most significant differential DNA methylation occurring in the promotor regions for four Cancer Genome Atlas cancer cohorts. CONCLUSIONS: Our results indicate that expression skewness can reveal new insights into transcription based on outlier and asymmetrical behaviour in large patient cohorts.

A direct comparison of interphase FISH versus low-coverage single cell sequencing to detect aneuploidy reveals respective strengths and weaknesses.

Aneuploidy has been reported to occur at remarkably high levels in normal somatic tissues using Fluorescence In Situ Hybridization (FISH). Recently, these reports were contradicted by single-cell low-coverage whole genome sequencing (scL-WGS) analyses, which showed aneuploidy frequencies at least an order of magnitude lower. To explain these seemingly contradictory findings, we used both techniques to analyze artificially generated mock aneuploid cells and cells with natural random aneuploidy. Our data indicate that while FISH tended to over-report aneuploidies, a modified 2-probe approach can accurately detect low levels of aneuploidy. Further, scL-WGS tends to underestimate aneuploidy levels, especially in a polyploid background.

A comparison of survival analysis methods for cancer gene expression RNA-Sequencing data.

Identifying genetic biomarkers of patient survival remains a major goal of large-scale cancer profiling studies. Using gene expression data to predict the outcome of a patient's tumor makes biomarker discovery a compelling tool for improving patient care. As genomic technologies expand, multiple data types may serve as informative biomarkers, and bioinformatic strategies have evolved around these different applications. For categorical variables such as a gene's mutation status, biomarker identification to predict survival time is straightforward. However, for continuous variables like gene expression, the available methods generate highly-variable results, and studies on best practices are lacking. We investigated the performance of eight methods that deal specifically with continuous data. K-means, Cox regression, concordance index, D-index, 25th-75th percentile split, median-split, distribution-based splitting, and KaplanScan were applied to four RNA-sequencing (RNA-seq) datasets from the Cancer Genome Atlas. The reliability of the eight methods was assessed by splitting each dataset into two groups and comparing the overlap of the results. Gene sets that had been identified from the literature for a specific tumor type served as positive controls to assess the accuracy of each biomarker using receiver operating characteristic (ROC) curves. Artificial RNA-Seq data were generated to test the robustness of these methods under fixed levels of gene expression noise. Our results show that methods based on dichotomizing tend to have consistently poor performance while C-index, D-index, and k-means perform well in most settings. Overall, the Cox regression method had the strongest performance based on tests of accuracy, reliability, and robustness.

Aneuvis: web-based exploration of numerical chromosomal variation in single cells.

BACKGROUND: Numerical chromosomal variation is a hallmark of populations of malignant cells. Identifying the factors that promote numerical chromosomal variation is important for understanding mechanisms of carcinogenesis. However, the ability to quantify and visualize differences in chromosome number between experimentally-defined groups (e.g. control vs treated) obtained from single-cell experiments is currently limited by the lack of user-friendly software. RESULTS: Aneuvis is a web application that allows users to determine whether numerical chromosomal variation exists between experimental treatment groups. The web interface allows users to upload molecular cytogenetic or processed single cell whole-genome sequencing data in a cell-by-chromosome matrix format and automatically generates visualizations and summary statistics that reflect the degree of numeric chromosomal variability. CONCLUSIONS: Aneuvis is the first user-friendly web application to help researchers identify the genetic and environmental perturbations that promote numerical chromosomal variation. Aneuvis is freely available as a web application at https://dpique.shinyapps.io/aneuvis/ and the source code for the application is available at https://github.com/dpique/aneuvis .

The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution.

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan.

A novel approach to modelling transcriptional heterogeneity identifies the oncogene candidate CBX2 in invasive breast carcinoma.

BACKGROUND: Oncogenes promote the development of therapeutic targets against subsets of cancers. Only several hundred oncogenes have been identified, primarily via mutation-based approaches, in the human genome. Transcriptional overexpression is a less-explored mechanism through which oncogenes can arise. METHODS: Here, a new statistical approach, termed oncomix, which captures transcriptional heterogeneity in tumour and adjacent normal (i.e., tumour-free) mRNA expression profiles, was developed to identify oncogene candidates that were overexpressed in a subset of breast tumours. RESULTS: Intronic DNA methylation was strongly associated with the overexpression of chromobox 2 (CBX2), an oncogene candidate that was identified using our method but not through prior analytical approaches. CBX2 overexpression in breast tumours was associated with the upregulation of genes involved in cell cycle progression and with poorer 5-year survival. The predicted function of CBX2 was confirmed in vitro, providing the first experimental evidence that CBX2 promotes breast cancer cell growth. CONCLUSIONS: Oncomix is a novel approach that captures transcriptional heterogeneity between tumour and adjacent normal tissue, and that has the potential to uncover therapeutic targets that benefit subsets of cancer patients. CBX2 is an oncogene candidate that should be further explored as a potential drug target for aggressive types of breast cancer.