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.

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.

14q32 rearrangements deregulating BCL11B mark a distinct subgroup of T-lymphoid and myeloid immature acute leukemia.

Acute leukemias (ALs) of ambiguous lineage are a heterogeneous group of high-risk leukemias characterized by coexpression of myeloid and lymphoid markers. In this study, we identified a distinct subgroup of immature acute leukemias characterized by a broadly variable phenotype, covering acute myeloid leukemia (AML, M0 or M1), T/myeloid mixed-phenotype acute leukemia (T/M MPAL), and early T-cell precursor acute lymphoblastic leukemia (ETP-ALL). Rearrangements at 14q32/BCL11B are the cytogenetic hallmark of this entity.

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.

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 -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.

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.

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.

Mapping gene regulatory networks from single-cell omics data.

Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes.

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).

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks.

The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce μ-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying "personal" gene regulatory network. We validated μ-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma.

Decoding transcriptional states in cancer.

Gene regulatory networks determine cellular identity. In cancer, aberrations of gene networks are caused by driver mutations that often affect transcription factors and chromatin modifiers. Nevertheless, gene transcription in cancer follows the same cis-regulatory rules as normal cells, and cancer cells have served as convenient model systems to study transcriptional regulation.

Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic.

Transcription factors regulate their target genes by binding to regulatory regions in the genome. Although the binding preferences of TP53 are known, it remains unclear what distinguishes functional enhancers from nonfunctional binding. In addition, the genome is scattered with recognition sequences that remain unoccupied.

Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models.

Cancer genomes contain vast amounts of somatic mutations, many of which are passenger mutations not involved in oncogenesis. Whereas driver mutations in protein-coding genes can be distinguished from passenger mutations based on their recurrence, non-coding mutations are usually not recurrent at the same position. Therefore, it is still unclear how to identify cis-regulatory driver mutations, particularly when chromatin data from the same patient is not available, thus relying only on sequence and expression information.

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.

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.

iRegulon: from a gene list to a gene regulatory network using large motif and track collections.

Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets.

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.

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.

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.

High accuracy mutation detection in leukemia on a selected panel of cancer genes.

With the advent of whole-genome and whole-exome sequencing, high-quality catalogs of recurrently mutated cancer genes are becoming available for many cancer types. Increasing access to sequencing technology, including bench-top sequencers, provide the opportunity to re-sequence a limited set of cancer genes across a patient cohort with limited processing time. Here, we re-sequenced a set of cancer genes in T-cell acute lymphoblastic leukemia (T-ALL) using Nimblegen sequence capture coupled with Roche/454 technology.

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).

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.

Mutation analysis of the tyrosine phosphatase PTPN2 in Hodgkin's lymphoma and T-cell non-Hodgkin's lymphoma.

We recently reported deletion of the protein tyrosine phosphatase gene PTPN2 in T-cell acute lymphoblastic leukemia. Functional analyses confirmed that PTPN2 acts as classical tumor suppressor repressing the proliferation of T cells, in part through inhibition of JAK/STAT signaling. We investigated the expression of PTPN2 in leukemia as well as lymphoma cell lines.