Translational insights from single-cell technologies across the cardiovascular disease continuum

Cardiovascular disease is the leading cause of death worldwide. The societal health burden it represents can be reduced by taking preventive measures and developing more effective therapies. Reaching these goals, however, requires a better understanding of the pathophysiological processes leading to and occurring in the diseased heart. In the last 5 years, several biological advances applying single-cell technologies have enabled researchers to study cardiovascular diseases with unprecedented resolution. This has produced many new insights into how specific cell types change their gene expression level, activation status and potential cellular interactions with the development of cardiovascular disease, but a comprehensive overview of the clinical implications of these findings is lacking. In this review, we summarize and discuss these recent advances and the promise of single-cell technologies from a translational perspective across the cardiovascular disease continuum, covering both animal and human studies, and explore the future directions of the field

Importance of Metal-Ion Exchange for the Biological Activity of Coordination Complexes of the Biomimetic Ligand N4Py

Metal coordination complexes can display interesting biological activity, as illustrated by the bleomycins (BLMs), a family of natural antibiotics that when coordinated to a redox-active metal ion, show antitumor activity. Yet, which metal ion is required for the activity in cells is still subject to debate. In this study, we described how different metal ions affect the intracellular behavior and activity of the synthetic BLM-mimic N, N-bis(2-pyridylmethyl)- N-bis(2-pyridyl)methylamine (N4Py). Our study shows that a mixture of iron(II), copper(II), and zinc(II) complexes can be generated when N4Py is added to cell cultures but that the metal ion can also be exchanged by other metal ions present in cells. Moreover, the combination of chemical data, together with the performed biological experiments, shows that the active complex causing oxidative damage to cells is the FeII-N4Py complex and not per se the metal complex that was initially added to the cell culture medium. Finally, it is proposed that the high activity observed upon the addition of the free N4Py ligand is the result of a combination of scavenging of biologically relevant metals and oxidative damage caused by the iron(II) complex

Targeting Nrf2 in healthy and malignant ovarian epithelial cells:Protection versus promotion

Risk factors indicate the importance of oxidative stress during ovarian carcinogenesis. To tolerate oxidative stress, cells activate the transcription factor Nrf2 (Nfe2l2), the master regulator of antioxidant and cytoprotective genes. Indeed, for most cancers, hyperactivity of Nrf2 is observed, and siRNA studies assigned Nrf2 as therapeutic target. However, the cancer-protective role of Nrf2 in healthy cells highlights the requirement for an adequate therapeutic window. We engineered artificial transcription factors to assess the role of Nrf2 in healthy (OSE-C2) and malignant ovarian cells (A2780). Successful NRF2 up- and downregulation correlated with decreased, respectively increased, sensitivity toward oxidative stress. Inhibition of NRF2 reduced the colony forming potential to the same extent in wild-type and BRCA1 knockdown A2780 cells. Only in BRCA1 knockdown A2780 cells, the effect of Nrf2 inhibition could be enhanced when combined with PARP inhibitors. Therefore, we propose that this combination therapy of PARP inhibitors and Nrf2 inhibition can further improve treatment efficacy specifically in BRCA1 mutant cancer cells without acquiring the side-effects associated with previously studied Nrf2 inhibition combinations with either chemotherapy or radiation. Our findings stress the dual role of Nrf2 in carcinogenesis, while offering approaches to exploit Nrf2 as a potent therapeutic target in ovarian cancer. (C) 2015 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved

Critical changes in hypothalamic gene networks in response to pancreatic cancer as found by single-cell RNA sequencing

OBJECTIVE: Cancer cachexia is a devastating chronic condition characterized by involuntary weight loss, muscle wasting, abnormal fat metabolism, anorexia, and fatigue. However, the molecular mechanisms underlying this syndrome remain poorly understood. In particular, the hypothalamus may play a central role in cachexia, given that it has direct access to peripheral signals because of its anatomical location and attenuated blood–brain barrier. Furthermore, this region has a critical role in regulating appetite and metabolism. METHODS: To provide a detailed analysis of the hypothalamic response to cachexia, we performed single-cell RNA-seq combined with RNA-seq of the medial basal hypothalamus (MBH) in a mouse model for pancreatic cancer. RESULTS: We found many cell type-specific changes, such as inflamed endothelial cells, stressed oligodendrocyes and both inflammatory and moderating microglia. Lcn2, a newly discovered hunger suppressing hormone, was the highest induced gene. Interestingly, cerebral treatment with LCN2 not only induced many of the observed molecular changes in cachexia but also affected gene expression in food-intake decreasing POMC neurons. In addition, we found that many of the cachexia-induced molecular changes found in the hypothalamus mimic those at the primary tumor site. CONCLUSION: Our data reveal that multiple cell types in the MBH are affected by tumor-derived factors or host factors that are induced by tumor growth, leading to a marked change in the microenvironment of neurons critical for behavioral, metabolic, and neuroendocrine outputs dysregulated during cachexia. The mechanistic insights provided in this study explain many of the clinical features of cachexia and will be useful for future therapeutic development

Experimental mitochondria-targeted DNA methylation identifies GpC methylation, not CpG methylation, as potential regulator of mitochondrial gene expression

Like the nucleus, mitochondria contain their own DNA and recent reports provide accumulating evidence that also the mitochondrial DNA (mtDNA) is subjective to DNA methylation. This evidence includes the demonstration of mitochondria-localised DNA methyltransferases and demethylases, and the detection of mtDNA methylation as well as hydroxymethylation. Importantly, differential mtDNA methylation has been linked to aging and diseases, including cancer and diabetes. However, functionality of mtDNA methylation has not been demonstrated. Therefore, we targeted DNA methylating enzymes (modifying cytosine in the CpG or GpC context) to the mtDNA. Unexpectedly, mtDNA gene expression remained unchanged upon induction of CpG mtDNA methylation, whereas induction of C-methylation in the GpC context decreased mtDNA gene expression. Intriguingly, in the latter case, the three mtDNA promoters were differentially affected in each cell line, while cellular function seemed undisturbed. In conclusion, this is the first study which directly addresses the potential functionality of mtDNA methylation. Giving the important role of mitochondria in health and disease, unravelling the impact of mtDNA methylation adds to our understanding of the role of mitochondria in physiological and pathophysiological processes

Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response

Contains fulltext : 220669.pdf (publisher's version ) (Open Access)Candida bloodstream infection, i.e. candidemia, is the most frequently encountered life-threatening fungal infection worldwide, with mortality rates up to almost 50%. In the majority of candidemia cases, Candida albicans is responsible. Worryingly, a global increase in the number of patients who are susceptible to infection (e.g. immunocompromised patients), has led to a rise in the incidence of candidemia in the last few decades. Therefore, a better understanding of the anti-Candida host response is essential to overcome this poor prognosis and to lower disease incidence. Here, we integrated genome-wide association studies with bulk and single-cell transcriptomic analyses of immune cells stimulated with Candida albicans to further our understanding of the anti-Candida host response. We show that differential expression analysis upon Candida stimulation in single-cell expression data can reveal the important cell types involved in the host response against Candida. This confirmed the known major role of monocytes, but more interestingly, also uncovered an important role for NK cells. Moreover, combining the power of bulk RNA-seq with the high resolution of single-cell RNA-seq data led to the identification of 27 Candida-response QTLs and revealed the cell types potentially involved herein. Integration of these response QTLs with a GWAS on candidemia susceptibility uncovered a potential new role for LY86 in candidemia susceptibility. Finally, experimental follow-up confirmed that LY86 knockdown results in reduced monocyte migration towards the chemokine MCP-1, thereby implying that this reduced migration may underlie the increased susceptibility to candidemia. Altogether, our integrative systems genetics approach identifies previously unknown mechanisms underlying the immune response to Candida infection

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Background: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. Results: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. Conclusion: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.</p

Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure

Not just differential gene expression but also differential gene regulation in immune cells account for individual differences in the immune response. Authors show here by single-cell RNA-sequencing of peripheral blood mononuclear cells from a large cohort of genetically diverse individuals that gene expression and regulatory changes in these cells depend on the context of and interactions between cell types, genetics, type of pathogen and time after exposure. The host's gene expression and gene regulatory response to pathogen exposure can be influenced by a combination of the host's genetic background, the type of and exposure time to pathogens. Here we provide a detailed dissection of this using single-cell RNA-sequencing of 1.3M peripheral blood mononuclear cells from 120 individuals, longitudinally exposed to three different pathogens. These analyses indicate that cell-type-specificity is a more prominent factor than pathogen-specificity regarding contexts that affect how genetics influences gene expression (i.e., eQTL) and co-expression (i.e., co-expression QTL). In monocytes, the strongest responder to pathogen stimulations, 71.4% of the genetic variants whose effect on gene expression is influenced by pathogen exposure (i.e., response QTL) also affect the co-expression between genes. This indicates widespread, context-specific changes in gene expression level and its regulation that are driven by genetics. Pathway analysis on the CLEC12A gene that exemplifies cell-type-, exposure-time- and genetic-background-dependent co-expression interactions, shows enrichment of the interferon (IFN) pathway specifically at 3-h post-exposure in monocytes. Similar genetic background-dependent association between IFN activity and CLEC12A co-expression patterns is confirmed in systemic lupus erythematosus by in silico analysis, which implies that CLEC12A might be an IFN-regulated gene. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Background: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. Results: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. Conclusion: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.</p

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Background: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. Results: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. Conclusion: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.</p