Spatially resolved analysis of microenvironmental gradient impact on cancer cell phenotypes

Despite the physiological and pathophysiological significance of microenvironmental gradients, e.g., for diseases such as cancer, tools for generating such gradients and analyzing their impact are lacking. Here, we present an integrated microfluidic-based workflow that mimics extracellular pH gradients characteristic of solid tumors while enabling high-resolution live imaging of, e.g., cell motility and chemotaxis, and preserving the capacity to capture the spatial transcriptome. Our microfluidic device generates a pH gradient that can be rapidly controlled to mimic spatiotemporal microenvironmental changes over cancer cells embedded in a 3D matrix. The device can be reopened allowing immunofluorescence analysis of selected phenotypes, as well as the transfer of cells and matrix to a Visium slide for spatially resolved analysis of transcriptional changes across the pH gradient. This workflow is easily adaptable to other gradients and multiple cell types and can therefore prove invaluable for integrated analysis of roles of microenvironmental gradients in biology.

Crosstalk between tumor acidosis, p53 and extracellular matrix regulates pancreatic cancer aggressiveness

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive malignancy with minimal treatment options and a global rise in prevalence. PDAC is characterized by frequent driver mutations including KRAS and TP53 (p53), and a dense, acidic tumor microenvironment (TME). The relation between genotype and TME in PDAC development is unknown. Strikingly, when wild type (WT) Panc02 PDAC cells were adapted to growth in an acidic TME and returned to normal pH to mimic invasive cells escaping acidic regions, they displayed a strong increase of aggressive traits such as increased growth in 3-dimensional (3D) culture, adhesion-independent colony formation and invasive outgrowth. This pattern of acidosis-induced aggressiveness was observed in 3D spheroid culture as well as upon organotypic growth in matrigel, collagen-I and combination thereof, mimicking early and later stages of PDAC development. Acid-adaptation-induced gain of cancerous traits was further increased by p53 knockout (KO), but only in specific extracellular matrix (ECM) compositions. Akt- and Transforming growth factor-β (TGFβ) signaling, as well as expression of the Na⁺ /H⁺ exchanger NHE1, were increased by acid adaptation. Whereas Akt inhibition decreased spheroid growth regardless of treatment and genotype, stimulation with TGFβI increased growth of WT control spheroids, and inhibition of TGFβ signaling tended to limit growth under acidic conditions only. Our results indicate that a complex crosstalk between tumor acidosis, ECM composition and genotype contributes to PDAC development. The findings may guide future strategies for acidosis-targeted therapies.

The LEAFY floral regulator displays pioneer transcription factor properties

Pioneer transcription factors (TFs) are a special category of TFs with the capacity to bind to closed chromatin regions in which DNA is wrapped around histones and may be highly methylated. Subsequently, pioneer TFs are able to modify the chromatin state to initiate gene expression. In plants, LEAFY (LFY) is a master floral regulator and has been suggested to act as a pioneer TF in Arabidopsis. Here, we demonstrate that LFY is able to bind both methylated and non-methylated DNA using a combination of in vitro genome-wide binding experiments and structural modeling. Comparisons between regions bound by LFY in vivo and chromatin accessibility data suggest that a subset of LFY bound regions is occupied by nucleosomes. We confirm that LFY is able to bind nucleosomal DNA in vitro using reconstituted nucleosomes. Finally, we show that constitutive LFY expression in seedling tissues is sufficient to induce chromatin accessibility in the LFY direct target genes APETALA1 and AGAMOUS. Taken together, our study suggests that LFY possesses key pioneer TF features that contribute to launching the floral gene expression program.

Genome-wide binding of SEPALLATA3 and AGAMOUS complexes determined by sequential DNA-affinity purification sequencing

The MADS transcription factors (TF), SEPALLATA3 (SEP3) and AGAMOUS (AG) are required for floral organ identity and floral meristem determinacy. While dimerization is obligatory for DNA binding, SEP3 and SEP3–AG also form tetrameric complexes. How homo and hetero-dimerization and tetramerization of MADS TFs affect genome-wide DNA-binding and gene regulation is not known. Using sequential DNA affinity purification sequencing (seq-DAP-seq), we determined genome-wide binding of SEP3 homomeric and SEP3–AG heteromeric complexes, including SEP3^Δtet-AG, a complex with a SEP3 splice variant, SEP3^Δtet, which is largely dimeric and SEP3–AG tetramer. SEP3 and SEP3–AG share numerous bound regions, however each complex bound unique sites, demonstrating that protein identity plays a role in DNA-binding. SEP3–AG and SEP3^Δtet-AG share a similar genome-wide binding pattern; however the tetrameric form could access new sites and demonstrated a global increase in DNA-binding affinity. Tetramerization exhibited significant cooperative binding with preferential distances between two sites, allowing efficient binding to regions that are poorly recognized by dimeric SEP3^Δtet-AG. By intersecting seq-DAP-seq with ChIP-seq and expression data, we identified unique target genes bound either in SEP3–AG seq-DAP-seq or in SEP3/AG ChIP-seq. Seq-DAP-seq is a versatile genome-wide technique and complements in vivo methods to identify putative direct regulatory targets.

Tetramerization of MADS family transcription factors SEPALLATA3 and AGAMOUS is required for floral meristem determinacy in Arabidopsis

The MADS transcription factors (TF) constitute an ancient family of TF found in all eukaryotes that bind DNA as obligate dimers. Plants have dramatically expanded the functional diversity of the MADS family during evolution by adding protein-protein interaction domains to the core DNA-binding domain, allowing the formation of heterotetrameric complexes. Tetramerization of plant MADS TFs is believed to play a central role in the evolution of higher plants by acting as one of the main determinants of flower formation and floral organ specification. The MADS TF, SEPALLATA3 (SEP3), functions as a central protein-protein interaction hub, driving tetramerization with other MADS TFs. Here, we use a SEP3 splice variant, SEP3Δtet, which has dramatically abrogated tetramerization capacity to decouple SEP3 tetramerization and DNA-binding activities. We unexpectedly demonstrate that SEP3 heterotetramer formation is required for correct termination of the floral meristem, but plays a lesser role in floral organogenesis. The heterotetramer formed by SEP3 and the MADS protein, AGAMOUS, is necessary to activate two target genes, KNUCKLES and CRABSCLAW, which are required for meristem determinacy. These studies reveal unique and highly specific roles of tetramerization in flower development and suggest tetramerization may be required to activate only a subset of target genes in closed chromatin regions.

JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework

JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) and TF flexible models (TFFMs) for TFs across multiple species in six taxonomic groups. In the 2018 release of JASPAR, the CORE collection has been expanded with 322 new PFMs (60 for vertebrates and 262 for plants) and 33 PFMs were updated (24 for vertebrates, 8 for plants and 1 for insects). These new profiles represent a 30% expansion compared to the 2016 release. In addition, we have introduced 316 TFFMs (95 for vertebrates, 218 for plants and 3 for insects). This release incorporates clusters of similar PFMs in each taxon and each TF class per taxon. The JASPAR 2018 CORE vertebrate collection of PFMs was used to predict TF-binding sites in the human genome. The predictions are made available to the scientific community through a UCSC Genome Browser track data hub. Finally, this update comes with a new web framework with an interactive and responsive user-interface, along with new features. All the underlying data can be retrieved programmatically using a RESTful API and through the JASPAR 2018 R/Bioconductor package.

Capturing Auxin Response Factors Syntax Using DNA Binding Models

Auxin is a key hormone performing a wealth of functions throughout the life cycle of plants. It acts largely by regulating genes at the transcriptional level through a family of transcription factors called auxin response factors (ARFs). Even though all ARF monomers analyzed so far bind a similar DNA sequence, there is evidence that ARFs differ in their target genomic regions and regulated genes. Here, we report the use of position weight matrices (PWMs) to model ARF DNA binding specificity based on published DNA affinity purification sequencing (DAP-seq) data. We found that the genome binding of two ARFs (ARF2 and ARF5/Monopteros [MP]) differ largely because these two factors have different preferred ARF binding site (ARFbs) arrangements (orientation and spacing). We illustrated why PWMs are more versatile to reliably identify ARFbs than the widely used consensus sequences and demonstrated their power with biochemical experiments in the identification of the regulatory regions of IAA19, an well-characterized auxin-responsive gene. Finally, we combined gene regulation by auxin with ARF-bound regions and identified specific ARFbs configurations that are over-represented in auxin-upregulated genes, thus deciphering the ARFbs syntax functional for regulation. Our study provides a general method to exploit the potential of genome-wide DNA binding assays and to decode gene regulation.

Building Transcription Factor Binding Site Models to Understand Gene Regulation in Plants

Transcription factors (TFs) are key cellular components that control gene expression. They recognize specific DNA sequences, the TF binding sites (TFBSs), and thus are targeted to specific regions of the genome where they can recruit transcriptional co-factors and/or chromatin regulators to fine-tune spatiotemporal gene regulation. Therefore, the identification of TFBSs in genomic sequences and their subsequent quantitative modeling is of crucial importance for understanding and predicting gene expression. Here, we review how TFBSs can be determined experimentally, how the TFBS models can be constructed in silico, and how they can be optimized by taking into account features such as position interdependence within TFBSs, DNA shape, and/or by introducing state-of-the-art computational algorithms such as deep learning methods. In addition, we discuss the integration of context variables into the TFBS modeling, including nucleosome positioning, chromatin states, methylation patterns, 3D genome architectures, and TF cooperative binding, in order to better predict TF binding under cellular contexts. Finally, we explore the possibilities of combining the optimized TFBS model with technological advances, such as targeted TFBS perturbation by CRISPR, to better understand gene regulation, evolution, and plant diversity.