HiChIP-Based Epigenomic Footprinting Identifies a Promoter Variant of UXS1 That Confers Genetic Susceptibility to Gastroesophageal Cancer.

Genome-wide association studies (GWAS) have identified more than a hundred single nucleotide variants (SNV) associated with the risk of gastroesophageal cancer (GEC). The majority of the identified SNVs map to noncoding regions of the genome. Uncovering the causal SNVs and genes they modulate could help improve GEC prevention and treatment. Herein, we used HiChIP against histone 3 lysine 27 acetylation (H3K27ac) to simultaneously annotate active promoters and enhancers, identify the interactions between them, and detect nucleosome-free regions (NFR) harboring potential causal SNVs in a single assay. The application of H3K27ac HiChIP in GEC relevant models identified 61 potential functional SNVs that reside in NFRs and interact with 49 genes at 17 loci. The approach led to a 67% reduction in the number of SNVs in linkage disequilibrium at these 17 loci, and at 7 loci, a single putative causal SNV was identified. One SNV, rs147518036, located within the promoter of the UDP-glucuronate decarboxylase 1 (UXS1) gene, seemed to underlie the GEC risk association captured by the rs75460256 index SNV. The rs147518036 SNV creates a GABPA DNA recognition motif, resulting in increased promoter activity, and CRISPR-mediated inhibition of the UXS1 promoter reduced the viability of the GEC cells. These findings provide a framework that simplifies the identification of potentially functional regulatory SNVs and target genes underlying risk-associated loci. In addition, the study implicates increased expression of the enzyme UXS1 and activation of its metabolic pathway as a predisposition to gastric cancer, which highlights potential therapeutic avenues to treat this disease. Significance: Epigenomic footprinting using a histone posttranslational modification targeted 3D genomics methodology elucidates functional noncoding sequence variants and their target genes at cancer risk loci.

The Adr1 transcription factor directs regulation of the ergosterol pathway and azole resistance in Candida albicans.

IMPORTANCE: Research often relies on well-studied orthologs within related species, with researchers using a well-studied gene or protein to allow prediction of the function of the ortholog. In the opportunistic pathogen Candida albicans, orthologs are usually compared with Saccharomyces cerevisiae, and this approach has been very fruitful. Many transcription factors (TFs) do similar jobs in the two species, but many do not, and typically changes in function are driven not by modifications in the structures of the TFs themselves but in the connections between the transcription factors and their regulated genes. This strategy of changing TF function has been termed transcription factor rewiring. In this study, we specifically looked for rewired transcription factors, or Candida-specific TFs, that might play a role in drug resistance. We investigated 30 transcription factors that were potentially rewired or were specific to the Candida clade. We found that the Adr1 transcription factor conferred resistance to drugs like fluconazole, amphotericin B, and terbinafine when activated. Adr1 is known for fatty acid and glycerol utilization in Saccharomyces, but our study reveals that it has been rewired and is connected to ergosterol biosynthesis in Candida albicans.

A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies.

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen Candida albicans. Our system, entitled Candescence, automatically detects C. albicans cells from differential image contrast microscopy and labels each detected cell with one of nine morphologies. This ranges from yeast white and opaque forms to hyphal and pseudohyphal filamentous morphologies. The software is based upon a fully convolutional one-stage (FCOS) object detector, a deep learning technique that uses an extensive set of images that we manually annotated with the location and morphology of each cell. We developed a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple yeast forms to complex filamentous architectures. Candescence achieves very good performance (~85% recall; 81% precision) on this difficult learning set, where some images contain hundreds of cells with substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology and how they intermix, we used a second technique from deep learning entitled generative adversarial networks. The resultant models allow us to identify and explore technical variables, developmental trajectories, and morphological switches. Importantly, the model allows us to quantitatively capture morphological plasticity observed with genetically modified strains or strains grown in different media and environments. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology. IMPORTANCE The fungus Candida albicans can "shape shift" between 12 morphologies in response to environmental variables. The cytoprotective capacity provided by this polymorphism makes C. albicans a formidable pathogen to treat clinically. Microscopy images of C. albicans colonies can contain hundreds of cells in different morphological states. Manual annotation of images can be difficult, especially as a result of densely packed and filamentous colonies and of technical artifacts from the microscopy itself. Manual annotation is inherently subjective, depending on the experience and opinion of annotators. Here, we built a deep learning approach entitled Candescence to parse images in an automated, quantitative, and objective fashion: each cell in an image is located and labeled with its morphology. Candescence effectively replaces simple rules based on visual phenotypes (size, shape, and shading) with neural circuitry capable of capturing subtle but salient features in images that may be too complex for human annotators.