A time-resolved multi-omic atlas of the developing mouse liver

Abstract

Liver organogenesis and development are composed of a series of complex, well-orchestrated events. Identifying key factors and pathways governing liver development will help elucidate the physiological and pathological processes including those of cancer. We conducted multidimensional omics measurements including protein, mRNA, and transcription factor (TF) DNA-binding activity for mouse liver tissues collected from embryonic day 12.5 (E12.5) to postnatal week 8 (W8), encompassing major developmental stages. These data sets reveal dynamic changes of core liver functions and canonical signaling pathways governing development at both mRNA and protein levels. The TF DNA-binding activity data set highlights the importance of TF activity in early embryonic development. A comparison between mouse liver development and human hepatocellular carcinoma (HCC) proteomic profiles reveal that more aggressive tumors are characterized with the activation of early embryonic development pathways, whereas less aggressive ones maintain liver function–related pathways that are elevated in the mature liver. This work offers a panoramic view of mouse liver development and provides a rich resource to explore in-depth functional characterization.

In silico analysis suggests disruption of interactions between HAMP from hepatocytes and SLC40A1 from macrophages in hepatocellular carcinoma

“…Gong et al [ 19 ] profiled the gene expression of mouse liver tissues collected from embryonic day 12.5 to postnatal week 8. Expression of genes from the Hep and S1 gene signatures at different time points is illustrated in Fig.…”

A MicroRNA-Based Network Provides Potential Predictive Signatures and Reveals the Crucial Role of PI3K/AKT Signaling for Hepatic Lineage Maturation

“…*P < 0.05, **P < 0.01, and ***P < 0.001. Gong et al, 2020). However, whether these 23 genes obtained from our study could show the lineage-specific characteristics during fetal liver development remains ambiguous.…”

“…However, with stages going on after postnatal 1 or 3 weeks, genes began to be gradually downregulated (Figures 6B,H), showing a similar pattern as the metabolismrelated module_3 and module_4 which was exhibited in the work of Gong et al's (2020) team. In addition, other cell types in the whole liver organ might contribute to this different peak of score with the weight of liver growing fast after the postnatal weeks as Gong et al (2020) has reported. In addition, a similar result was obtained by analyzing the expression profiling between E13 hepatoblasts and adult hepatocytes in another dataset (GSE56734) (Figure 6D).…”

“…Nine datasets from other teams were all used to validate the results in this work. The datasets are as follows: GSE57833, GSE57878, GSE90047 (Yang et al, 2017), GSE132034 (Gong et al, 2020), GSE28892 (Shin et al, 2011), GSE56734 (Ito et al, 2014), GSE25048 (Kim et al, 2011), GSE112330 (Xie et al, 2019), and GSE124528 (Wang et al, 2019). All the data have been normalized and exhibited in a heatmap by using the pheatmap R package 2 .…”

Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

“…Time-course experiments using a parallel sequencer or mass spectrometry capture dynamic changes during the development or perturbation of a cellular system over time ( 1 , 2 ). Although specific issues in biological data, such as low sampling frequency, exist ( 3 ), recent advancements in modern high-throughput techniques have enabled description of the regulatory molecular circuits that drive differentiation processes and adaptation to the environment in greater detail ( 4 , 5 ).…”