Abstract Background and Aims The endogenous repair process of the mammalian kidney allows rapid recovery after acute kidney injury (AKI) through robust proliferation of tubular epithelial cells. There is currently limited understanding of which transcriptional regulators activate these repair programs and how transcriptional dysregulation leads to maladaptive repair. Here we investigate the existence of enhancer dynamics in the regenerating mouse kidney. Method RNA-seq and ChIP-seq (H3K27ac, H3K4m3, BRD4, POL2 and selected transcription factors) were performed on samples from repairing kidney cortex 2 days after ischemia/reperfusion injury (IRI) to identify activated genes, transcription factors, enhancer and super-enhancers associated with kidney repair. Further we investigated the role of super-enhancer activation in kidney repair through pharmacological BET inhibition using the small molecule JQ1 in vitro and in acute kidney injury models in vivo. Results Response to kidney injury leads to genome-wide alteration in enhancer repertoire in-vivo. We identified 16,781 enhancer sites (H3K27ac and BRD4 positive, H3K4me3 negative binding) active in SHAM and IRI samples; 6,512 lost and 9,774 gained after IRI. The lost and gained enhancer sites can be annotated to 62% and 63% of down- and up-regulated transcripts at day 2 after kidney injury, respectively. Super-enhancer analysis revealed 164 lost and 216 gained super-enhancer sites at IRI day 2. 385 super-enhancers maintain activity before and after injury. ChIP-seq profiles of selected transcription factors based on motif analysis show specific binding at corresponding enhancer sites. We observed lost enhancer binding of HNF4A and GR mainly at kidney related enhancer elements. In contrast, STAT3 showed increased binding at injury induces enhancer elements. No dynamic was observed for STAT5. Both transcription factor groups show corresponding mRNA changes after injury. Pharmacological inhibition of enhancer and super-enhancer activity by BRD4 inhibition (JQ1: 50mg/kg/day) before IRI leads to suppression of 40% of injury-induced transcripts associated with cell cycle regulation and significantly increased mortality between days 2 and 3 after AKI. Conclusion This is the first demonstration of enhancer and super-enhancer function in the repairing kidney. In addition, our data call attention to potential caveats for use of small molecule inhibitors of BET proteins that are currently being tested in clinical trials in cancer patients who are at risk for AKI. Our analyses of enhancer dynamics after kidney injury in vivo have the potential to identify new targets for therapeutic intervention.
A bstract The Kidney Precision Medicine Project (KPMP) plans to construct a spatially specified tissue atlas of the human kidney at a cellular resolution with near comprehensive molecular details. The atlas will have maps of healthy, acute kidney injury and chronic kidney disease tissues. To construct such maps, we integrate different data sets that profile mRNAs, proteins and metabolites collected by five KPMP Tissue Interrogation Sites. Here, we describe a set of hierarchical analytical methods to process, combine, and harmonize single-cell, single-nucleus and subsegmental laser microdissection (LMD) transcriptomics with LMD and near single-cell proteomics, 3-D nondestructive and immunofluorescence-based Codex imaging and spatial metabolomics datasets. We use nephrectomy, healthy living donor and surveillance transplant biopsy tissues to create a harmonized reference tissue map. Our results demonstrate that different assays produce reliable and coherent identification of cell types and tissue subsegments. They further show that the molecular profiles and pathways are partially overlapping yet complementary for cell type-specific and subsegmental physiological processes. Focusing on the proximal tubules, we find that our integrated systems biologybased analyses identify different subtypes of tubular cells with potential for different levels of lipid oxidation and energy generation. Integration of our omics data with pathways from the literature, enables us to construct predictive computational models to develop a smart kidney atlas. These integrated models can describe physiological capabilities of the tissues based on the underlying cell types and pathways in health and disease.
The zygote is a single cell, a fertilized egg, capable of giving rise to a complete organism. This is a truly remarkable transformation, with a single cell becoming trillions of cells or more, with a microscopic mass becoming in some cases tens or hundreds of kilograms, with a single amorphous cell making a number of complex organs, including the brain with its uncountable precise synaptic connections. For mammals, which generally appear to lack localized cytoplasmic determinants, this process is guided by a genetic program encoded in the DNA of the zygote.
After significant injury, the liver must maintain homeostasis during the regenerative process. We hypothesized the existence of mechanisms to limit hepatocyte proliferation after injury to maintain metabolic and synthetic function. A screen for candidates revealed suppressor of cytokine signaling 2 (SOCS2), an inhibitor of growth hormone (GH) signaling, was strongly induced after partial hepatectomy. Using genetic deletion and administration of various factors we investigated the role of SOCS2 during liver regeneration. SOCS2 preserves liver function by restraining the first round of hepatocyte proliferation after partial hepatectomy by preventing increases in growth hormone receptor (GHR) via ubiquitination, suppressing GH pathway activity. At later times, SOCS2 enhances hepatocyte proliferation by modulating a decrease in serum insulin-like growth factor 1 (IGF-1) that allows GH release from the pituitary. SOCS2, therefore, plays a dual role in modulating the rate of hepatocyte proliferation. In particular, this is the first demonstration of an endogenous mechanism to limit hepatocyte proliferation after injury.
Abstract Background Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods We have developed Histo-Cloud , a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. Results By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions Histo-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain.
