IL-31 levels correlate with pruritus in patients with cholestatic and metabolic liver diseases and is farnesoid X receptor responsive in NASH.

BACKGROUND AND AIMS: Pruritus is associated with multiple liver diseases, particularly those with cholestasis, but the mechanism remains incompletely understood. Our aim was to evaluate serum IL-31 as a putative biomarker of pruritus in clinical trials of an farnesoid X receptor (FXR) agonist, cilofexor, in patients with NASH, primary sclerosing cholangitis (PSC), and primary biliary cholangitis (PBC). APPROACH AND RESULTS: Serum IL-31 was measured in clinical studies of cilofexor in NASH, PSC, and PBC. In patients with PSC or PBC, baseline IL-31 was elevated compared to patients with NASH and healthy volunteers (HVs). IL-31 correlated with serum bile acids among patients with NASH, PBC, and PSC. Baseline IL-31 levels in PSC and PBC were positively correlated with Visual Analog Scale for pruritus and 5-D itch scores. In patients with NASH, cilofexor dose-dependently increased IL-31 from Week (W)1 to W24. In patients with NASH receiving cilofexor 100 mg, IL-31 was higher in those with Grade 2-3 pruritus adverse events (AEs) than those with Grade 0-1 pruritus AEs. IL-31 weakly correlated with C4 at baseline in patients with NASH, and among those receiving cilofexor 100 mg, changes in IL-31 and C4 from baseline to W24 were negatively correlated. IL-31 messenger RNA (mRNA) was elevated in hepatocytes from patients with PSC and NASH compared to HVs. In a humanized liver murine model, obeticholic acid increased IL-31 mRNA expression in human hepatocytes and serum levels of human IL-31. CONCLUSIONS: IL-31 levels correlate with pruritus in patients with cholestatic disease and NASH, with FXR agonist therapy resulting in higher serum levels in the latter group. IL-31 appears to derive in part from increased hepatocyte expression. These findings have therapeutic implications for patients with liver disease and pruritus.

Overexpression of CD36 in mammary fibroblasts suppresses colony growth in breast cancer cell lines.

Human breast tumors are not fully autonomous. They are dependent on nutrients and growth-promoting signals provided by the supporting stromal cells. Within the tumor microenvironment, one of the secreted macromolecules by tumor cells is activin A, where we show to downregulate CD36 in fibroblasts. Downregulation of CD36 in fibroblasts also increases the secretion of activin A by fibroblasts. We hypothesize that overexpression of CD36 in fibroblasts inhibits the formation of solid tumors in subtypes of breast cancer models. For the first time, we show that co-culturing organoid models of breast cancer cell lines of MDA-MB-231 (e.g., a triple-negative line) or MCF7 (e.g., a luminal-A line) with CD36+ fibroblasts inhibit the growth and normalizes basal and lateral polarities, respectively. In the long-term anchorage-independent growth assay, the rate of colony formation is also reduced for MDA-MB-231. These observations are consistent with the mechanism of tumor suppression involving the downregulation of pSMAD2/3 and YY1 expression levels. Our integrated analytical methods leverage and extend quantitative assays at cell- and colony-scales in both short- and long-term cultures using brightfield or immunofluorescent microscopy and robust image analysis. Conditioned media are profiled with the ELISA assay.

YY1 is a cis-regulator in the organoid models of high mammographic density.

MOTIVATION: Our previous study has shown that ERBB2 is overexpressed in the organoid model of MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density (MD). We now aim to identify key transcription factors (TFs) and functional enhancers that regulate processes associated with increased stiffness of the microenvironment in the organoid models of premalignant human mammary cell lines. RESULTS: 3D colony organizations and the cis-regulatory networks of two human mammary epithelial cell lines (184A1 and MCF10A) are investigated as a function of the increased stiffness of the microenvironment within the range of MD. The 3D colonies are imaged using confocal microscopy, and the morphometries of colony organizations and heterogeneity are quantified as a function of the stiffness of the microenvironment using BioSig3D. In a surrogate assay, colony organizations are profiled by transcriptomics. Transcriptome data are enriched by correlative analysis with the computed morphometric indices. Next, a subset of enriched data are processed against publicly available ChIP-Seq data using Model-based Analysis of Regulation of Gene Expression to predict regulatory transcription factors. This integrative analysis of morphometric and transcriptomic data predicted YY1 as one of the cis-regulators in both cell lines as a result of the increased stiffness of the microenvironment. Subsequent experiments validated that YY1 is expressed at protein and mRNA levels for MCF10A and 184A1, respectively. Also, there is a causal relationship between activation of YY1 and ERBB2 when YY1 is overexpressed at the protein level in MCF10A. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep fusion of contextual and object-based representations for delineation of multiple nuclear phenotypes.

MOTIVATION: Nuclear delineation and phenotypic profiling are important steps in the automated analysis of histology sections. However, these are challenging problems due to (i) technical variations (e.g. fixation, staining) that originate as a result of sample preparation; (ii) biological heterogeneity (e.g. vesicular versus high chromatin phenotypes, nuclear atypia) and (iii) overlapping nuclei. This Application-Note couples contextual information about the cellular organization with the individual signature of nuclei to improve performance. As a result, routine delineation of nuclei in H&E stained histology sections is enabled for either computer-aided pathology or integration with genome-wide molecular data. RESULTS: The method has been evaluated on two independent datasets. One dataset originates from our lab and includes H&E stained sections of brain and breast samples. The second dataset is publicly available through IEEE with a focus on gland-based tissue architecture. We report an approximate AJI of 0.592 and an F1-score 0.93 on both datasets. AVAILABILITY AND IMPLEMENTATION: The code-base, modified dataset and results are publicly available. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep Learning Models Differentiate Tumor Grades from H&E Stained Histology Sections.

Aberration in tissue architecture is an essential index for cancer diagnosis and tumor grading. Therefore, extracting features of aberrant phenotypes and classification of the histology tissue can provide a model for computer-aided pathology (CAP). As a case study, we investigate the application of convolutional neural networks (CNN)s for tumor grading and decomposing tumor architecture from hematoxylin and eosin (H&E) stained histology sections of kidney. The former and latter contribute to CAP and the role of the tumor architecture on the outcome (e.g., survival), respectively. A training set is constructed and sample images are classified into six categories of normal, fat, blood, stroma, low-grade granular tumor, and high-grade clear cell carcinoma. We have compared the performances of a deep versus shallow networks, and shown that the deeper model outperforms the shallow network.

Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes.

BACKGROUND: Nuclear segmentation is an important step for profiling aberrant regions of histology sections. If nuclear segmentation can be resolved, then new biomarkers of nuclear phenotypes and their organization can be predicted for the application of precision medicine. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), nuclear phenotypes (e.g., vesicular, aneuploidy), and overlapping nuclei. The problem is further complicated as a result of variations in sample preparation (e.g., fixation, staining). Our hypothesis is that (i) deep learning techniques can learn complex phenotypic signatures that rise in tumor sections, and (ii) fusion of different representations (e.g., regions, boundaries) contributes to improved nuclear segmentation. RESULTS: We have demonstrated that training of deep encoder-decoder convolutional networks overcomes complexities associated with multiple nuclear phenotypes, where we evaluate alternative architecture of deep learning for an improved performance against the simplicity of the design. In addition, improved nuclear segmentation is achieved by color decomposition and combining region- and boundary-based features through a fusion network. The trained models have been evaluated against approximately 19,000 manually annotated nuclei, and object-level Precision, Recall, F1-score and Standard Error are reported with the best F1-score being 0.91. Raw training images, annotated images, processed images, and source codes are released as a part of the Additional file 1. CONCLUSIONS: There are two intrinsic barriers in nuclear segmentation to H&E stained images, which correspond to the diversity of nuclear phenotypes and perceptual boundaries between adjacent cells. We demonstrate that (i) the encoder-decoder architecture can learn complex phenotypes that include the vesicular type; (ii) delineation of overlapping nuclei is enhanced by fusion of region- and edge-based networks; (iii) fusion of ENets produces an improved result over the fusion of UNets; and (iv) fusion of networks is better than multitask learning. We suggest that our protocol enables processing a large cohort of whole slide images for applications in precision medicine.

Feature-Based Representation Improves Color Decomposition and Nuclear Detection Using a Convolutional Neural Network.

Detection of nuclei is an important step in phenotypic profiling of 1) histology sections imaged in bright field; and 2) colony formation of the 3-D cell culture models that are imaged using confocal microscopy. It is shown that feature-based representation of the original image improves color decomposition (CD) and subsequent nuclear detection using convolutional neural networks independent of the imaging modality. The feature-based representation utilizes the Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Moreover, in the case of samples imaged in bright field, the LoG response also provides the necessary initial statistics for CD using nonnegative matrix factorization. Several permutations of input data representations and network architectures are evaluated to show that by coupling improved CD and the LoG response of this representation, detection of nuclei is advanced. In particular, the frequencies of detection of nuclei with the vesicular or necrotic phenotypes, or poor staining, are improved. The overall system has been evaluated against manually annotated images, and the F-scores for alternative representations and architectures are reported.

Detection of Nuclei in H&E Stained Sections Using Convolutional Neural Networks.

Detection of nuclei is an important step in phenotypic profiling of histology sections that are usually imaged in bright field. However, nuclei can have multiple phenotypes, which are difficult to model. It is shown that convolutional neural networks (CNN)s can learn different phenotypic signatures for nuclear detection, and that the performance is improved with the feature-based representation of the original image. The feature-based representation utilizes Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Several combinations of input data representations are evaluated to show that by LoG representation, detection of nuclei is advanced. In addition, the efficacy of CNN for vesicular and hyperchromatic nuclei is evaluated. In particular, the frequency of detection of nuclei with the vesicular and apoptotic phenotypes is increased. The overall system has been evaluated against manually annotated nuclei and the F-Scores for alternative representations have been reported.