Developmental transcriptional control of mitochondrial homeostasis is required for activity-dependent synaptic connectivity.

During neuronal circuit formation, local control of axonal organelles ensures proper synaptic connectivity. Whether this process is genetically encoded is unclear and if so, its developmental regulatory mechanisms remain to be identified. We hypothesized that developmental transcription factors regulate critical parameters of organelle homeostasis that contribute to circuit wiring. We combined cell type-specific transcriptomics with a genetic screen to discover such factors. We identified Telomeric Zinc finger-Associated Protein (TZAP) as a temporal developmental regulator of neuronal mitochondrial homeostasis genes, including Pink1 . In Drosophila , loss of dTzap function during visual circuit development leads to loss of activity-dependent synaptic connectivity, that can be rescued by Pink1 expression. At the cellular level, loss of dTzap/TZAP leads to defects in mitochondrial morphology, attenuated calcium uptake and reduced synaptic vesicle release in fly and mammalian neurons. Our findings highlight developmental transcriptional regulation of mitochondrial homeostasis as a key factor in activity-dependent synaptic connectivity.

Reduced Levels of Misfolded and Aggregated Mutant p53 by Proteostatic Activation.

In malignant cancer, excessive amounts of mutant p53 often lead to its aggregation, a feature that was recently identified as druggable. Here, we describe that induction of a heat shock-related stress response mediated by Foldlin, a small-molecule tool compound, reduces the protein levels of misfolded/aggregated mutant p53, while contact mutants or wild-type p53 remain largely unaffected. Foldlin also prevented the formation of stress-induced p53 nuclear inclusion bodies. Despite our inability to identify a specific molecular target, Foldlin also reduced protein levels of aggregating SOD1 variants. Finally, by screening a library of 778 FDA-approved compounds for their ability to reduce misfolded mutant p53, we identified the proteasome inhibitor Bortezomib with similar cellular effects as Foldlin. Overall, the induction of a cellular heat shock response seems to be an effective strategy to deal with pathological protein aggregation. It remains to be seen however, how this strategy can be translated to a clinical setting.

A Multi-Omics Analysis of Metastatic Melanoma Identifies a Germinal Center-Like Tumor Microenvironment in HLA-DR-Positive Tumor Areas.

The emergence of immune checkpoint inhibitors has dramatically changed the therapeutic landscape for patients with advanced melanoma. However, relatively low response rates and a high incidence of severe immune-related adverse events have prompted the search for predictive biomarkers. A positive predictive value has been attributed to the aberrant expression of Human Leukocyte Antigen-DR (HLA-DR) by melanoma cells, but it remains unknown why this is the case. In this study, we have examined the microenvironment of HLA-DR positive metastatic melanoma samples using a multi-omics approach. First, using spatial, single-cell mapping by multiplexed immunohistochemistry, we found that the microenvironment of HLA-DR positive melanoma regions was enriched by professional antigen presenting cells, including classical dendritic cells and macrophages, while a more general cytotoxic T cell exhaustion phenotype was present in these regions. In parallel, transcriptomic analysis on micro dissected tissue from HLA-DR positive and HLA-DR negative areas showed increased IFNγ signaling, enhanced leukocyte adhesion and mononuclear cell proliferation in HLA-DR positive areas. Finally, multiplexed cytokine profiling identified an increased expression of germinal center cytokines CXCL12, CXCL13 and CCL19 in HLA-DR positive metastatic lesions, which, together with IFNγ and IL4 could serve as biomarkers to discriminate tumor samples containing HLA-DR overexpressing tumor cells from HLA-DR negative samples. Overall, this suggests that HLA-DR positive areas in melanoma attract the anti-tumor immune cell infiltration by creating a dystrophic germinal center-like microenvironment where an enhanced antigen presentation leads to an exhausted microenvironment, nevertheless representing a fertile ground for a better efficacy of anti-PD-1 inhibitors due to simultaneous higher levels of PD-1 in the immune cells and PD-L1 in the HLA-DR positive melanoma cells.

Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning.

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%-20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

Yin Yang 1 sustains biosynthetic demands during brain development in a stage-specific manner.

The transcription factor Yin Yang 1 (YY1) plays an important role in human disease. It is often overexpressed in cancers and mutations can lead to a congenital haploinsufficiency syndrome characterized by craniofacial dysmorphisms and neurological dysfunctions, consistent with a role in brain development. Here, we show that Yy1 controls murine cerebral cortex development in a stage-dependent manner. By regulating a wide range of metabolic pathways and protein translation, Yy1 maintains proliferation and survival of neural progenitor cells (NPCs) at early stages of brain development. Despite its constitutive expression, however, the dependence on Yy1 declines over the course of corticogenesis. This is associated with decreasing importance of processes controlled by Yy1 during development, as reflected by diminished protein synthesis rates at later developmental stages. Thus, our study unravels a novel role for Yy1 as a stage-dependent regulator of brain development and shows that biosynthetic demands of NPCs dynamically change throughout development.

Yin Yang 1 Orchestrates a Metabolic Program Required for Both Neural Crest Development and Melanoma Formation.

Increasing evidence suggests that cancer cells highjack developmental programs for disease initiation and progression. Melanoma arises from melanocytes that originate during development from neural crest stem cells (NCSCs). Here, we identified the transcription factor Yin Yang 1 (Yy1) as an NCSCs regulator. Conditional deletion of Yy1 in NCSCs resulted in stage-dependent hypoplasia of all major neural crest derivatives due to decreased proliferation and increased cell death. Moreover, conditional ablation of one Yy1 allele in a melanoma mouse model prevented tumorigenesis, indicating a particular susceptibility of melanoma cells to reduced Yy1 levels. Combined RNA sequencing (RNA-seq), chromatin immunoprecipitation (ChIP)-seq, and untargeted metabolomics demonstrated that YY1 governs multiple metabolic pathways and protein synthesis in both NCSCs and melanoma. In addition to directly regulating a metabolic gene set, YY1 can act upstream of MITF/c-MYC as part of a gene regulatory network controlling metabolism. Thus, both NCSC development and melanoma formation depend on an intricate YY1-controlled metabolic program.

SCENIC: single-cell regulatory network inference and clustering.

We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.

i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly.

i-cisTarget is a web tool to predict regulators of a set of genomic regions, such as ChIP-seq peaks or co-regulated/similar enhancers. i-cisTarget can also be used to identify upstream regulators and their target enhancers starting from a set of co-expressed genes. Whereas the original version of i-cisTarget was focused on Drosophila data, the 2015 update also provides support for human and mouse data. i-cisTarget detects transcription factor motifs (position weight matrices) and experimental data tracks (e.g. from ENCODE, Roadmap Epigenomics) that are enriched in the input set of regions. As experimental data tracks we include transcription factor ChIP-seq data, histone modification ChIP-seq data and open chromatin data. The underlying processing method is based on a ranking-and-recovery procedure, allowing accurate determination of enrichment across heterogeneous datasets, while also discriminating direct from indirect target regions through a 'leading edge' analysis. We illustrate i-cisTarget on various Ewing sarcoma datasets to identify EWS-FLI1 targets starting from ChIP-seq, differential ATAC-seq, differential H3K27ac and differential gene expression data. Use of i-cisTarget is free and open to all, and there is no login requirement. Address: http://gbiomed.kuleuven.be/apps/lcb/i-cisTarget.

Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state.

Transcriptional reprogramming of proliferative melanoma cells into a phenotypically distinct invasive cell subpopulation is a critical event at the origin of metastatic spreading. Here we generate transcriptome, open chromatin and histone modification maps of melanoma cultures; and integrate this data with existing transcriptome and DNA methylation profiles from tumour biopsies to gain insight into the mechanisms underlying this key reprogramming event. This shows thousands of genomic regulatory regions underlying the proliferative and invasive states, identifying SOX10/MITF and AP-1/TEAD as regulators, respectively. Knockdown of TEADs shows a previously unrecognized role in the invasive gene network and establishes a causative link between these transcription factors, cell invasion and sensitivity to MAPK inhibitors. Using regulatory landscapes and in silico analysis, we show that transcriptional reprogramming underlies the distinct cellular states present in melanoma. Furthermore, it reveals an essential role for the TEADs, linking it to clinically relevant mechanisms such as invasion and resistance.

Comprehensive analysis of transcriptome variation uncovers known and novel driver events in T-cell acute lymphoblastic leukemia.

RNA-seq is a promising technology to re-sequence protein coding genes for the identification of single nucleotide variants (SNV), while simultaneously obtaining information on structural variations and gene expression perturbations. We asked whether RNA-seq is suitable for the detection of driver mutations in T-cell acute lymphoblastic leukemia (T-ALL). These leukemias are caused by a combination of gene fusions, over-expression of transcription factors and cooperative point mutations in oncogenes and tumor suppressor genes. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq. First, we optimized the detection of SNVs in RNA-seq data by comparing the results with exome re-sequencing data. We identified known driver genes with recurrent protein altering variations, as well as several new candidates including H3F3A, PTK2B, and STAT5B. Next, we determined accurate gene expression levels from the RNA-seq data through normalizations and batch effect removal, and used these to classify patients into T-ALL subtypes. Finally, we detected gene fusions, of which several can explain the over-expression of key driver genes such as TLX1, PLAG1, LMO1, or NKX2-1; and others result in novel fusion transcripts encoding activated kinases (SSBP2-FER and TPM3-JAK2) or involving MLLT10. In conclusion, we present novel analysis pipelines for variant calling, variant filtering, and expression normalization on RNA-seq data, and successfully applied these for the detection of translocations, point mutations, INDELs, exon-skipping events, and expression perturbations in T-ALL.

The Drosophila homologue of the amyloid precursor protein is a conserved modulator of Wnt PCP signaling.

Wnt Planar Cell Polarity (PCP) signaling is a universal regulator of polarity in epithelial cells, but it regulates axon outgrowth in neurons, suggesting the existence of axonal modulators of Wnt-PCP activity. The Amyloid precursor proteins (APPs) are intensely investigated because of their link to Alzheimer's disease (AD). APP's in vivo function in the brain and the mechanisms underlying it remain unclear and controversial. Drosophila possesses a single APP homologue called APP Like, or APPL. APPL is expressed in all neurons throughout development, but has no established function in neuronal development. We therefore investigated the role of Drosophila APPL during brain development. We find that APPL is involved in the development of the Mushroom Body αß neurons and, in particular, is required cell-autonomously for the ß-axons and non-cell autonomously for the α-axons growth. Moreover, we find that APPL is a modulator of the Wnt-PCP pathway required for axonal outgrowth, but not cell polarity. Molecularly, both human APP and fly APPL form complexes with PCP receptors, thus suggesting that APPs are part of the membrane protein complex upstream of PCP signaling. Moreover, we show that APPL regulates PCP pathway activation by modulating the phosphorylation of the Wnt adaptor protein Dishevelled (Dsh) by Abelson kinase (Abl). Taken together our data suggest that APPL is the first example of a modulator of the Wnt-PCP pathway specifically required for axon outgrowth.

Exome sequencing identifies mutation in CNOT3 and ribosomal genes RPL5 and RPL10 in T-cell acute lymphoblastic leukemia.

T-cell acute lymphoblastic leukemia (T-ALL) is caused by the cooperation of multiple oncogenic lesions. We used exome sequencing on 67 T-ALLs to gain insight into the mutational spectrum in these leukemias. We detected protein-altering mutations in 508 genes, with an average of 8.2 mutations in pediatric and 21.0 mutations in adult T-ALL. Using stringent filtering, we predict seven new oncogenic driver genes in T-ALL. We identify CNOT3 as a tumor suppressor mutated in 7 of 89 (7.9%) adult T-ALLs, and its knockdown causes tumors in a sensitized Drosophila melanogaster model. In addition, we identify mutations affecting the ribosomal proteins RPL5 and RPL10 in 12 of 122 (9.8%) pediatric T-ALLs, with recurrent alterations of Arg98 in RPL10. Yeast and lymphoid cells expressing the RPL10 Arg98Ser mutant showed a ribosome biogenesis defect. Our data provide insights into the mutational landscape of pediatric versus adult T-ALL and identify the ribosome as a potential oncogenic factor.

Variations in the exome of the LNCaP prostate cancer cell line.

BACKGROUND: The LNCaP cell line is widely used as a model for prostate cancer. However, information on protein-changing mutations, genetic heterogeneity and genetic (in)stability is largely lacking for these cells. METHODS: Next-generation sequencing of the LNCaP exome revealed many single nucleotide variants (SNVs). To help identify the mutations that are most likely drivers of the oncogenic process, we developed an in silico protocol, which can be adapted for other exome analyses. RESULTS: We detected 1,802 non-synonymous SNVs and 218 small insertions and deletions in the LNCaP exome. We confirm the known mutations in the androgen receptor and the PTEN gene, but most other mutations remained undescribed until now. The presence of 38 out of 42 SNVs was confirmed in monoclonal as well as in polyclonal LNCaP derivatives. Moreover, most variants were also detectable in LNCaP mRNA. CONCLUSIONS: We provide an extensive database of genetic variations in the protein-coding part of the genome of LNCaP cells, which should be taken into consideration when using LNCaP cells or its derivatives as models for prostate cancer. From the analysis of several LNCaP-derived cultures and clones, we can confirm that the cell line is heterozygous for a large number of variants and that both the variant and the wild-type allele can be simultaneously expressed as mRNA. The fact that the SNVs in the E-cadherin, CDK4, Notch1, and PlexinB1 genes are absent in some of the subclones strongly indicates a degree of genetic instability.

Using cisTargetX to predict transcriptional targets and networks in Drosophila.

Gene expression regulation is a fundamental biological process leading to complete organism development by controlling processes like cell type specification and differentiation. The accuracy of this process is -governed by transcription factors (TFs) acting within a complex gene regulatory network. CisTargetX has been developed to enable a user to predict TFs, enhancers, and target genes involved in the regulation of co-expressed genes. It uses a strategy that incorporates the genome-wide prediction of clusters of transcription factor binding sites (TFBSs), starting from a large, unbiased collection of position weight matrices (PWMs) and uses comparative genomics criteria to filter potential TFBS. We describe in this chapter, step-by-step, how to use cisTargetX starting from a set of genes or TF(s) to predict transcriptional targets with their putative binding sites and networks in Drosophila. Next, we illustrate this approach on a particular developmental system, namely, sensory organ development, and identify relevant TFs, DNA regions regulating gene expression, and TF/target gene interactions. CisTargetX is available at http://med.kuleuven.be/lcb/cisTargetX .