Biology subjects

Marine, J.-C.

Publications and source records attributed to Marine, J.-C..

23 records · Page 2Linked to original sources

bioRxiv2019

Cross-species analysis of melanoma enhancer logic using deep learning

Genomic enhancers form the central nodes of gene regulatory networks by harbouring combinations of transcription factor binding sites. Deciphering the combinatorial code by which these binding sites are assembled within enhancers is indispensable to understand their regulatory involvement in establishing a cells phenotype, especially within biological systems with dysregulated gene regulatory networks, such as melanoma. In order to unravel the enhancer logic of the two most common melanoma cell states, namely the melanocytic and mesenchymal-like state, we combined comparative epigenomics with machine learning. By profiling chromatin accessibility using ATAC-seq on a cohort of 27 melanoma cell lines across six different species, we demonstrate the conservation of the two main melanoma states and their underlying master regulators. To perform an in-depth analysis of the enhancer architecture, we trained a deep neural network, called DeepMEL, to classify melanoma enhancers not only in the human genome, but also in other species. DeepMEL revealed the presence, organisation and positional specificity of important transcription factor binding sites. Together, this extensive analysis of the melanoma enhancer code allowed us to propose the concept of a core regulatory complex binding to melanocytic enhancers, consisting of SOX10, TFAP2A, MITF and RUNX, and to disentangle their individual roles in regulating enhancer accessibility and activity.

genomics↗

bioRxiv2019

Single-cell gene regulatory network analysis reveals new melanoma cell states and transition trajectories during phenotype switching

Melanoma is notorious for its cellular heterogeneity, which is at least partly due to its ability to transition between alternate cell states. Similarly to EMT, melanoma cells with a melanocytic phenotype can switch to a mesenchymal-like phenotype. However, scattered emerging evidence indicates that additional, intermediate state(s) may exist. In order to search for such new melanoma states and decipher their underlying gene regulatory network (GRN), we extensively studied ten patient-derived melanoma cultures by single-cell RNA-seq of >39,000 cells. Although each culture exhibited a unique transcriptome, we identified shared gene regulatory networks that underlie the extreme melanocytic and mesenchymal cell states, as well as one (stable) intermediate state. The intermediate state was corroborated by a distinct open chromatin landscape and governed by the transcription factors EGR3, NFATC2, and RXRG. Single-cell migration assays established that this \"transition\" state exhibits an intermediate migratory phenotype. Through a dense time-series sampling of single cells and dynamic GRN inference, we unraveled the sequential and recurrent arrangement of transcriptional programs at play during phenotype switching that ultimately lead to the mesenchymal cell state. We provide the scRNA-Seq data with 39,263 melanoma cells on our SCope platform and the ATAC-seq data on a UCSC hub to jointly serve as a resource for the melanoma field. Together, this exhaustive analysis of melanoma cell state diversity indicates that additional states exists between the two extreme melanocytic and mesenchymal-like states. The GRN we identified may serve as a new putative target to prevent the switch to mesenchymal cell state and thereby, acquisition of metastatic and drug resistant potential.

genomics↗

bioRxiv2019

The lncRNA NEAT1_1 is dispensable for normal tissue homeostasis and cancer cell growth

NEAT1 is one of the most studied lncRNAs, in part because its silencing in mice causes defects in mammary gland development and corpus luteum formation, and protects them from skin cancer development. Moreover, depleting NEAT1 in established cancer cell lines reduces growth and sensitizes cells to DNA damaging agents. However, NEAT1 produces two isoforms and because the short isoform, NEAT1_1, completely overlaps the 5 part of the long NEAT1_2 isoform, the respective contributions of each of the isoforms to these phenotypes has remained unclear. Whereas NEAT1_1 is highly expressed in most tissues, NEAT1_2 is the central architectural component of paraspeckles, which are nuclear bodies that assemble in specific tissues and cells exposed to various forms of stress. Using dual RNA-FISH to detect both NEAT1_1 outside of paraspeckles and NEAT1_2/NEAT1 inside this nuclear body, we report herein that NEAT1_1 levels are dynamically regulated during the cell cycle and targeted for degradation by the nuclear RNA exosome. Unexpectedly, however, cancer cells engineered to lack NEAT1_1, but not NEAT1_2, do not exhibit cell cycle defects. Moreover, Neat1_1-specific knockout mice do not exhibit the phenotypes observed in Neat1-deficient mice. We propose that NEAT1 functions are mainly, if not exclusively, attributable to NEAT1_2 and, by extension, to paraspeckles.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/574582_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (54K):\norg.highwire.dtl.DTLVardef@1ceaf78org.highwire.dtl.DTLVardef@6203b2org.highwire.dtl.DTLVardef@e0d5c9org.highwire.dtl.DTLVardef@f37f92_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

molecular biology↗

bioRxiv2019

Dynamic Erasure of Random X-Chromosome Inactivation during iPSC Reprogramming

BackgroundInduction and reversal of chromatin silencing is critical for successful development, tissue homeostasis and the derivation of induced pluripotent stem cells (iPSCs). X-chromosome inactivation (XCI) and reactivation (XCR) in female cells represent chromosome-wide transitions between active and inactive chromatin states. While XCI has long been studied and provided important insights into gene regulation, the dynamics and mechanisms underlying the reversal of stable chromatin silencing of X-linked genes are much less understood. Here, we use allele-specific transcriptomic approaches to study XCR during mouse iPSC reprogramming in order to elucidate the timing and mechanisms of chromosome-wide reversal of gene silencing. ResultsWe show that XCR is hierarchical, with subsets of genes reactivating early, late and very late. Early genes are activated before the onset of late pluripotency genes activation and the complete silencing of the long non-coding RNA (lncRNA) Xist. These genes are located genomically closer to genes that escape XCI, unlike those reactivating late. Interestingly, early genes also show increased pluripotency transcription factor (TF) binding. We also reveal that histone deacetylases (HDACs) restrict XCR in reprogramming intermediates and that the severe hypoacetylation state of the Xi persists until late reprogramming stages. ConclusionsAltogether, these results reveal the timing of transcriptional activation of mono-allelically repressed genes during iPSC reprogramming, and suggest that allelic activation involves the combined action of chromatin topology, pluripotency transcription factors and chromatin regulators. These findings are important for our understanding of gene silencing, maintenance of cell identity, reprogramming and disease.

developmental biology↗

bioRxiv2017

SCENIC: Single-Cell Regulatory Network Inference And Clustering

Single-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

bioinformatics↗