Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning.

Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction. Therefore, we established survival curve-based CRC survival predictors and benchmarked them against standard binary survival predictors, comparing their performance extensively on the clinical high and low risk subsets of one internal and three external cohorts. Survival curve-based risk prediction achieved a very similar risk stratification to binary risk prediction for this task. Exchanging other components of the pipeline, namely input tissue and feature extractor, had largely identical effects on model performance independently of the type of risk prediction. An ensemble of all survival curve-based models exhibited a more robust performance, as did a similar ensemble based on binary risk prediction. Patients could be further stratified within clinical risk groups. However, performance still varied across cohorts, indicating limited generalization of all investigated image analysis pipelines, whereas models using clinical data performed robustly on all cohorts.

Hepatic farnesoid X receptor is necessary to facilitate ductular reaction and expression of heme biosynthetic genes.

BACKGROUND: Bile, which contains bile acids, the natural ligands for farnesoid x receptor (FXR), moves from the liver to the intestine through bile ducts. Ductular reaction often occurs during biliary obstruction. A subset of patients with erythropoietic protoporphyria, an inherited genetic mutation in heme biosynthetic enzyme ferrochelatase, accumulate porphyrin-containing bile plugs, leading to cholestasis. Here, we examined the link between FXR, bile plug formation, and how heme biosynthesis relates to this connection. METHODS: We treated female and male wild-type and global and tissue-specific Fxr knockout mice with a diet containing 3,5-diethoxycarbonyl-1,4-dihydrocollidine, an inhibitor of ferrochelatase, and examined the expression of heme biosynthetic genes. We mined FXR mouse ChIP-Seq data, performed biochemical and histological analysis, and tested HepG2 and primary human hepatocytes after treatment with obeticholic acid, an FXR agonist. RESULTS: We observed that hepatic but not intestinal Fxr loss resulted in reduced bile plugs and ductular reaction in the liver. Then, we examined if FXR plays a regulatory role in heme biosynthesis and found significantly lower porphyrin accumulation in 3,5-diethoxycarbonyl-1, 4-dihydrocollidine-fed Fxr knockout mice. Gene expression and FXR mouse ChIP-Seq atlas analysis revealed that FXR orchestrates the expression of multiple heme biosynthetic enzymes. Finally, human HepG2 cells and primary human hepatocytes treated with obeticholic acid, showed increased expression of several heme biosynthetic genes. CONCLUSIONS: Overall, our data show that hepatic Fxr is necessary to maintain ductular reaction and accumulation of bile plugs. FXR can direct the expression of multiple heme biosynthetic genes. Thus, modulating FXR activity in EPP patients may help alleviate its associated liver disease.

BIBBOX, a FAIR toolbox and App Store for life science research.

Data quality has recently become a critical topic for the research community. European guidelines recommend that scientific data should be made FAIR: findable, accessible, interoperable and reusable. However, as FAIR guidelines do not specify how the stated principles should be implemented, it might not be straightforward for researchers to know how actually to make their data FAIR. This can prevent life-science researchers from sharing their datasets and pipelines, ultimately hindering the progress of research. To address this difficulty, we developed the BIBBOX, which is a platform that supports researchers publishing their datasets and the associated software in a FAIR manner.

Understanding and Explaining Diagnostic Paths: Toward Augmented Decision Making.

The process of finding a diagnosis in the medical domain relies on implicit knowledge and the experience of a human expert. In this article, we report on the observation of human decision making, shown by the example of pathology. By tracking the diagnostic steps, individual building blocks are identified, which not only contribute to a diagnostic finding, but can also be used in the future to train and develop artificial intelligence (AI) algorithms. This work also provides insights into the interaction of human experts regarding the observation time of so-called "hot spots," the magnification used for specific findings, and the overall observation and decision path followed. The documentation scheme yields a standardized examination procedure that shows the concept the pathologist is actually looking for as well as the possible features of findings that can be identified. This contribution indicates how important visualization is for human-centered AI, and specifically for enabling human oversight with respect to AI implementation in high-stake areas, such as medicine.

Meta-analysis and Consolidation of Farnesoid X Receptor Chromatin Immunoprecipitation Sequencing Data Across Different Species and Conditions.

Farnesoid X receptor (FXR) is a nuclear receptor that controls gene regulation of different metabolic pathways and represents an upcoming drug target for various liver diseases. Several data sets on genome-wide FXR binding in different species and conditions exist. We have previously reported that these data sets are heterogeneous and do not cover the full spectrum of potential FXR binding sites. Here, we report the first meta-analysis of all publicly available FXR chromatin immunoprecipitation sequencing (ChIP-seq) data sets from mouse, rat, and human across different conditions using a newly generated analysis pipeline. All publicly available single data sets were biocurated in a standardized manner and compared on every relevant level from raw reads to affected functional pathways. Individual murine data sets were then virtually merged into a single unique "FXR binding atlas" spanning all potential binding sites across various conditions. Comparison of the single biocurated data sets showed that the overlap of FXR binding sites between different species is modest and ranges from 48% (mouse-human) to 55% (mouse-rat). Moreover, in vivo data among different species are more similar than human in vivo data compared to human in vitro data. The consolidated murine global FXR binding atlas virtually increases sequencing depth and allows recovering more and novel potential binding sites and signaling pathways that were missed in the individual data sets. The FXR binding atlas is publicly searchable (https://fxratlas.tugraz.at). Conclusion: Published single FXR ChIP-seq data sets and large-scale integrated omics data sets do not cover the full spectrum of FXR binding. Combining different individual data sets and creating an "FXR super-binding atlas" enhances understanding of FXR signaling capacities across different conditions. This is important when considering the potential wide spectrum for drugs targeting FXR in liver diseases.

FXR-dependent Rubicon induction impairs autophagy in models of human cholestasis.

BACKGROUND & AIMS: Cholestasis comprises a spectrum of liver diseases characterized by the accumulation of bile acids. Bile acids and activation of the farnesoid X receptor (FXR) can inhibit autophagy, a cellular self-digestion process necessary for cellular homeostasis and regeneration. In mice, autophagy appears to be impaired in cholestasis and induction of autophagy may reduce liver injury. METHODS: Herein, we explored autophagy in human cholestasis in vivo and investigated the underlying molecular mechanisms in vitro. FXR chromatin immunoprecipitation-sequencing and qPCR were performed in combination with luciferase promoter studies to identify functional FXR binding targets in a human cholestatic liver sample. RESULTS: Autophagic processing appeared to be impaired in patients with cholestasis and in individuals treated with the FXR ligand obeticholic acid (OCA). In vitro, chenodeoxycholic acid and OCA inhibited autophagy at the level of autophagosome to lysosome fusion in an FXR-dependent manner. Rubicon, which inhibits autophago-lysosomal maturation, was identified as a direct FXR target that is induced in cholestasis and by FXR-agonistic bile acids. Genetic inhibition of Rubicon reversed the bile acid-induced impairment of autophagic flux. In contrast to OCA, ursodeoxycholic acid (UDCA), which is a non-FXR-agonistic bile acid, induced autophagolysosome formation independently of FXR, enhanced autophagic flux and was associated with reduced Rubicon levels. CONCLUSION: In models of human cholestasis, autophagic processing is impaired in an FXR-dependent manner, partly resulting from the induction of Rubicon. UDCA is a potent inducer of hepatic autophagy. Manipulating autophagy and Rubicon may represent a novel treatment concept for cholestatic liver diseases. LAY SUMMARY: Autophagy, a cellular self-cleansing process, is impaired in various forms of human cholestasis. Bile acids, which accumulate in cholestatic liver disease, induce Rubicon, a protein that inhibits proper execution of autophagy. Ursodeoxycholic acid, which is the first-line treatment option for many cholestatic liver diseases, induces hepatic autophagy along with reducing Rubicon.

A Comprehensive FXR Signaling Atlas Derived from Pooled ChIP-seq Data.

BACKGROUND: ChIP-seq is a method to identify genome-wide transcription factor (TF) binding sites. The TF FXR is a nuclear receptor that controls gene regulation of different metabolic pathways in the liver. OBJECTIVES: To re-analyze, standardize and combine all publicly available FXR ChIP-seq data sets to create a global FXR signaling atlas. METHODS: All data sets were (re-)analyzed in a standardized manner and compared on every relevant level from raw reads to affected functional pathways. RESULTS: Public FXR data sets were available for mouse, rat and primary human hepatocytes in different treatment conditions. Standardized re-analysis shows that the data sets are surprisingly heterogeneous concerning baseline quality criteria. Combining different data sets increased the depth of analysis and allowed to recover more peaks and functional pathways. CONCLUSION: Published single FXR ChIP-seq data sets do not cover the full spectrum of FXR signaling. Combining different data sets and creating a "FXR super-signaling atlas" enhances understanding of FXR signaling capacities.