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Despite remarkable advances in therapeutic interventions, malignant melanoma (MM) remains a life-threating disease. Following high initial response rates to targeted kinase-inhibition metastases quickly acquire resistance and present with enhanced tumor progression and invasion, demanding alternative treatment options. We show 2nd generation hexameric TRAIL-receptor-agonist IZI1551 (IZI) to effectively induce apoptosis in MM cells irrespective of the intrinsic BRAF/NRAS mutation status. Conditioning to the EC50 dose of IZI converted the phenotype of IZI-sensitive parental MM cells into a fast proliferating and invasive, IZI-resistant metastasis. Mechanistically, we identified focal adhesion kinase (FAK) to play a dual role in phenotype-switching. In the cytosol, activated FAK triggers survival pathways in a PI3K- and MAPK-dependent manner. In the nucleus, the FERM domain of FAK prevents activation of wtp53, as being expressed in the majority of MM, and consequently intrinsic apoptosis. Caspase-8-mediated cleavage of FAK as well as FAK knockdown, and pharmacological inhibition, respectively, reverted the metastatic phenotype-switch and restored IZI responsiveness. FAK inhibition also re-sensitized MM cells isolated from patient metastasis that had relapsed from targeted kinase inhibition to cell death, irrespective of the intrinsic BRAF/NRAS mutation status. Hence, FAK-inhibition alone or in combination with 2nd generation TRAIL-receptor agonists may be recommended for treatment of initially resistant and relapsed MM, respectively.

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.

The nuclear factor erythroid 2-related factor 2 (NRF2) was originally described as a master regulator of antioxidant cellular response, but in the time since, numerous important biological functions linked to cell survival, cellular detoxification, metabolism, autophagy, proteostasis, inflammation, immunity, and differentiation have been attributed to this pleiotropic transcription factor that regulates hundreds of genes. After 40 years of in-depth research and key discoveries, NRF2 is now at the center of a vast regulatory network, revealing NRF2 signalling as increasingly complex. It is widely recognized that reactive oxygen species (ROS) play a key role in human physiological and pathological processes such as ageing, obesity, diabetes, cancer, and neurodegenerative diseases. The high oxygen consumption associated with high levels of free iron and oxidizable unsaturated lipids make the brain particularly vulnerable to oxidative stress. A good stability of NRF2 activity is thus crucial to maintain the redox balance and therefore brain homeostasis. In this review, we have gathered recent data about the contribution of the NRF2 pathway in the healthy brain as well as during metabolic diseases, cancer, ageing, and ageing-related neurodegenerative diseases. We also discuss promising therapeutic strategies and the need for better understanding of cell-type-specific functions of NRF2 in these different fields.

Dopaminergic neurons (DA neurons) are controlled by multiple factors, many involved in neurological disease. Parkinson's disease motor symptoms are caused by the demise of nigral DA neurons, leading to loss of striatal dopamine (DA). Here, we measured DA concentration in the dorsal striatum of 32 members of Collaborative Cross (CC) family and their eight founder strains. Striatal DA varied greatly in founders, and differences were highly heritable in the inbred CC progeny. We identified a locus, containing 164 genes, linked to DA concentration in the dorsal striatum on chromosome X. We used RNAseq profiling of the ventral midbrain of two founders with substantial difference in striatal DA–C56BL/6 J and A/J—to highlight potential protein-coding candidates modulating this trait. Among the five differentially expressed genes within the locus, we found that the gene coding for the collagen IV alpha 6 chain (Col4a6) was expressed nine times less in A/J than in C57BL/6J. Using single cell RNA-seq data from developing human midbrain, we found that COL4A6 is highly expressed in radial glia-like cells and neuronal progenitors, indicating a role in neuronal development. Collagen IV alpha-6 chain (COL4A6) controls axogenesis in simple model organisms. Consistent with these findings, A/J mice had less striatal axonal branching than C57BL/6J mice. We tentatively conclude that DA concentration and axonal branching in dorsal striatum are modulated by COL4A6, possibly during development. Our study shows that genetic mapping based on an easily measured Central Nervous System (CNS) trait, using the CC population, combined with follow-up observations, can parse heritability of such a trait, and nominate novel functions for commonly expressed proteins.

Objective: To establish a workflow for mitochondrial DNA (mtDNA) CpG methylation using Nanopore whole-genome sequencing and perform first pilot experiments on affected Parkin biallelic mutation carriers (Parkin-PD) and healthy controls. Background: Mitochondria, including mtDNA, are established key players in Parkinson's disease (PD) pathogenesis. Mutations in Parkin, essential for degradation of damaged mitochondria, cause early-onset PD. However, mtDNA methylation and its implication in PD is understudied. Herein, we establish a workflow using Nanopore sequencing to directly detect mtDNA CpG methylation and compare mtDNA methylation between Parkin-related PD and healthy individuals. Methods: To obtain mtDNA, whole-genome Nanopore sequencing was performed on blood-derived from five Parkin-PD and three control subjects. In addition, induced pluripotent stem cell (iPSC)-derived midbrain neurons from four of these patients with PD and the three control subjects were investigated. The workflow was validated, using methylated and unmethylated 897 bp synthetic DNA samples at different dilution ratios (0, 50, 100% methylation) and mtDNA without methylation. MtDNA CpG methylation frequency (MF) was detected using Nanopolish and Megalodon. Results: Across all blood-derived samples, we obtained a mean coverage of 250.3X (SD ± 80.5X) and across all neuron-derived samples 830X (SD ± 465X) of the mitochondrial genome. We detected overall low-level CpG methylation from the blood-derived DNA (mean MF ± SD = 0.029 ± 0.041) and neuron-derived DNA (mean MF ± SD = 0.019 ± 0.035). Validation of the workflow, using synthetic DNA samples showed that highly methylated DNA molecules were prone to lower Guppy Phred quality scores and thereby more likely to fail Guppy base-calling. CpG methylation in blood- and neuron-derived DNA was significantly lower in Parkin-PD compared to controls (Mann-Whitney U-test p < 0.05). Conclusion: Nanopore sequencing is a useful method to investigate mtDNA m

Background: Cell types in ventral midbrain are involved in diseases with variable genetic susceptibility, such as Parkinson’s disease and schizophrenia. Many genetic variants affect regulatory regions and alter gene expression in a cell-type-specific manner depending on the chromatin structure and accessibility. Results: We report 20,658 single-nuclei chromatin accessibility profiles of ventral midbrain from two genetically and phenotypically distinct mouse strains. We distinguish ten cell types based on chromatin profiles and analysis of accessible regions controlling cell identity genes highlights cell-type-specific key transcription factors. Regulatory variation segregating the mouse strains manifests more on transcriptome than chromatin level. However, cell-type-level data reveals changes not captured at tissue level. To discover the scope and cell-type specificity of cis-acting variation in midbrain gene expression, we identify putative regulatory variants and show them to be enriched at differentially expressed loci. Finally, we find TCF7L2 to mediate trans-acting variation selectively in midbrain neurons. Conclusions: Our data set provides an extensive resource to study gene regulation in mesencephalon and provides insights into control of cell identity in the midbrain and identifies cell-type-specific regulatory variation possibly underlying phenotypic and behavioural differences between mouse strains.

Sialic acid-binding receptors (Siglecs) are linked to neurodegenerative processes, but the role of sialic acids in physiological aging is still not fully understood. We investigated the impact of reduced sialylation in the brain of mice heterozygous for the enzyme glucosamine-2-epimerase/N-acetylmannosamine kinase (GNE+/-) that is essential for sialic acid biosynthesis. We demonstrate that GNE+/- mice have hyposialylation in different brain regions, less synapses in the hippocampus and reduced microglial arborization already at 6 months followed by increased loss of neurons at 12 months. A transcriptomic analysis revealed no pro-inflammatory changes indicating an innate homeostatic immune process leading to the removal of synapses and neurons in GNE+/- mice during aging. Crossbreeding with complement C3-deficient mice rescued the earlier onset of neuronal and synaptic loss as well as the changes in microglial arborization. Thus, sialic acids of the glycocalyx contribute to brain homeostasis and act as a recognition system for the innate immune system in the brain.

Parkinson's disease (PD) is a neurodegenerative disorder caused by a complex interplay of genetic and environmental factors. For the stratification of PD patients and the development of advanced clinical trials, including causative treatments, a better understanding of the underlying genetic architecture of PD is required. Despite substantial efforts, genome-wide association studies have not been able to explain most of the observed heritability. The majority of PD-associated genetic variants are located in non-coding regions of the genome. A systematic assessment of their functional role is hampered by our incomplete understanding of genotype-phenotype correlations, for example through differential regulation of gene expression. Here, the recent progress and remaining challenges for the elucidation of the role of non-coding genetic variants is reviewed with a focus on PD as a complex disease with multifactorial origins. The function of gene regulatory elements and the impact of non-coding variants on them, and the means to map these elements on a genome-wide level, will be delineated. Moreover, examples of how the integration of functional genomic annotations can serve to identify disease-associated pathways and to prioritize disease- and cell type-specific regulatory variants will be given. Finally, strategies for functional validation and considerations for suitable model systems are outlined. Together this emphasizes the contribution of rare and common genetic variants to the complex pathogenesis of PD and points to remaining challenges for the dissection of genetic complexity that may allow for better stratification, improved diagnostics and more targeted treatments for PD in the future.

Dopaminergic neurons in the midbrain are of particular interest due to their role in diseases such as Parkinson’s disease and schizophrenia. Genetic variation between individuals can affect the integrity and function of dopaminergic neurons but the DNA variants and molecular cascades modulating dopaminergic neurons and other cells types of ventral midbrain remain poorly defined. Three genetically diverse inbred mouse strains – C57BL/6J, A/J, and DBA/2J – differ significantly in their genomes (∼7 million variants), motor and cognitive behavior, and susceptibility to neurotoxins. To further dissect the underlying molecular networks responsible for these variable phenotypes, we generated RNA-seq and ChIP-seq data from ventral midbrains of the 3 mouse strains. We defined 1000–1200 transcripts that are differentially expressed among them. These widespread differences may be due to altered activity or expression of upstream transcription factors. Interestingly, transcription factors were significantly underrepresented among the differentially expressed genes, and only one transcription factor, Pttg1, showed significant differences between all three strains. The changes in Pttg1 expression were accompanied by consistent alterations in histone H3 lysine 4 trimethylation at Pttg1 transcription start site. The ventral midbrain transcriptome of 3-month-old C57BL/6J congenic Pttg1–/– mutants was only modestly altered, but shifted toward that of A/J and DBA/2J in 9-month-old mice. Principle component analysis (PCA) identified the genes underlying the transcriptome shift and deconvolution of these bulk RNA-seq changes using midbrain single cell RNA-seq data suggested that the changes were occurring in several different cell types, including neurons, oligodendrocytes, and astrocytes. Taken together, our results show that Pttg1 contributes to gene regulatory variation between mouse strains and influences mouse midbrain transcriptome during aging.

Aim: Prediction of genes under dynamic post-transcriptional regulation from epigenomic data. Materials & methods: We used time-series profiles of chromatin immunoprecipitation-seq data of histone modifications from differentiation of mesenchymal progenitor cells toward adipocytes and osteoblasts to predict gene expression levels at five time points in both lineages and estimated the deviation of those predictions from the RNA-seq measured expression levels using linear regression. Results & conclusion: The genes with biggest changes in their estimated stability across the time series are enriched for noncoding RNAs and lineage-specific biological processes. Clustering mRNAs according to their stability dynamics allows identification of post-transcriptionally coregulated mRNAs and their shared regulators through sequence enrichment analysis. We identify miR-204 as an early induced adipogenic microRNA targeting Akr1c14 and Il1rl1.

Temporal data on gene expression and context-specific open chromatin states can improve identification of key transcription factors (TFs) and the gene regulatory networks (GRNs) controlling cellular differentiation. However, their integration remains challenging. Here, we delineate a general approach for data-driven and unbiased identification of key TFs and dynamic GRNs, called EPIC-DREM. We generated time-series transcriptomic and epigenomic profiles during differentiation of mouse multipotent bone marrow stromal cell line (ST2) toward adipocytes and osteoblasts. Using our novel approach we constructed time-resolved GRNs for both lineages and identifed the shared TFs involved in both differentiation processes. To take an alternative approach to prioritize the identified shared regulators, we mapped dynamic super-enhancers in both lineages and associated them to target genes with correlated expression profiles. The combination of the two approaches identified aryl hydrocarbon receptor (AHR) and Glis family zinc finger 1 (GLIS1) as mesenchymal key TFs controlled by dynamic cell type-specific super-enhancers that become repressed in both lineages. AHR and GLIS1 control differentiation-induced genes and their overexpression can inhibit the lineage commitment of the multipotent bone marrow-derived ST2 cells.

The microglial triggering receptor expressed on myeloid cells 2 (TREM2) signals via the activatory membrane adaptor molecule TYROBP. Genetic variants or mutations of TREM2 or TYROBP have been linked to inflammatory neurodegenerative diseases associated with aging. The typical aging process goes along with microglial changes and mild neuronal loss, but the exact contribution of TREM2 is still unclear. Aged TREM2 knock‐out mice showed decreased age‐related neuronal loss in the substantia nigra and the hippocampus. Transcriptomic analysis of the brains of 24 months old TREM2 knock‐out mice revealed 211 differentially expressed genes mostly downregulated and associated with complement activation and oxidative stress response pathways. Consistently, 24 months old TREM2 knock‐out mice showed lower transcription of microglial (Aif1 and Tmem119), oxidative stress markers (Inos, Cyba, and Cybb) and complement components (C1qa, C1qb, C1qc, C3, C4b, Itgam, and Itgb2), decreased microglial numbers and expression of the microglial activation marker Cd68, as well as accumulation of oxidized lipids. Cultured microglia of TREM2 knock‐out mice showed reduced phagocytosis and oxidative burst. Thus, microglial TREM2 contributes to age‐related microglial changes, phagocytic oxidative burst, and loss of neurons with possible detrimental effects during physiological aging.

Changes in mature microRNA (miRNA) levels that occur downstream of signaling cascades play an important role during human development and disease. However, the regulation of primary microRNA (pri-miRNA) genes remains to be dissected in detail. To address this, we followed a data-driven approach and developed a transcript identification, validation and quantification pipeline for characterizing the regulatory domains of pri-miRNAs. Integration of 92 nascent transcriptomes and multilevel data from cells arising from ecto-, endo- and mesoderm lineages reveals cell type-specific expression patterns, allows fine-resolution mapping of transcription start sites (TSS) and identification of candidate regulatory regions. We show that inter- and intragenic pri-miRNA transcripts span vast genomic regions and active TSS locations differ across cell types, exemplified by the mir-29a∼29b-1, mir-100∼let-7a-2∼125b-1 and miR-221∼222 clusters. Considering the presence of multiple TSS as an important regulatory feature at miRNA loci, we developed a strategy to quantify differential TSS usage. We demonstrate that the TSS activities associate with cell type-specific super-enhancers, differential stimulus responsiveness and higher-order chromatin structure. These results pave the way for building detailed regulatory maps of miRNA loci.

We previously showed that disease-linked metabolic genes are often under combinatorial regulation. Using the genome-wide ChIP-Seq binding profiles for 93 transcription factors in nine different cell lines, we show that genes under high regulatory load are significantly enriched for disease-association across cell types. We find that transcription factor load correlates with the enhancer load of the genes and thereby allows the identification of genes under high regulatory load by epigenomic mapping of active enhancers. Identification of the high enhancer load genes across 139 samples from 96 different cell and tissue types reveals a consistent enrichment for disease-associated genes in a cell type-selective manner. The underlying genes are not limited to super-enhancer genes and show several types of disease-association evidence beyond genetic variation (such as biomarkers). Interestingly, the high regulatory load genes are involved in more KEGG pathways than expected by chance, exhibit increased betweenness centrality in the interaction network of liver disease genes, and carry longer 3′ UTRs with more microRNA (miRNA) binding sites than genes on average, suggesting a role as hubs integrating signals within regulatory networks. In summary, epigenetic mapping of active enhancers presents a promising and unbiased approach for identification of novel disease genes in a cell type-selective manner

We previously showed that disease-linked metabolic genes are often under combinatorial regulation. Using the genome-wide ChIP-Seq binding profiles for 93 transcription factors in nine different cell lines, we show that genes under high regulatory load are significantly enriched for disease-association across cell types. We find that transcription factor load correlates with the enhancer load of the genes and thereby allows the identification of genes under high regulatory load by epigenomic mapping of active enhancers. Identification of the high enhancer load genes across 139 samples from 96 different cell and tissue types reveals a consistent enrichment for disease-associated genes in a cell type-selective manner. The underlying genes are not limited to super-enhancer genes and show several types of disease-association evidence beyond genetic variation (such as biomarkers). Interestingly, the high regulatory load genes are involved in more KEGG pathways than expected by chance, exhibit increased betweenness centrality in the interaction network of liver disease genes, and carry longer 3'UTRs with more microRNA (miRNA) binding sites than genes on average, suggesting a role as hubs integrating signals within regulatory networks. In summary, epigenetic mapping of active enhancers presents a promising and unbiased approach for identification of novel disease genes in a cell type-selective manner.

Background: The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied. Methods: Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks. Results: To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism. Conclusions: By integrating metabolic modelling and epigenomic analysis we have identified high regulat

Transcription factors (TFs) represent key factors to establish a cellular phenotype. It is known that several TFs could play a role in disease, yet less is known so far how their targets overlap. We focused here on identifying the most highly induced TFs and their putative targets during human adipogenesis. Applying chromatin immunoprecipitation coupled with deep sequencing (ChIP-Seq) in the human SGBS pre-adipocyte cell line, we identified genes with binding sites in their vicinity for the three TFs studied, PPARγ, CEBPα and LXR. Here we describe the experimental design and quality controls in detail for the deep sequencing data and related results published by Galhardo et al. in Nucleic Acids Research 2014 [1] associated with the data uploaded to NCBI Gene Expression Omnibus ().