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Abstract Liver homeostasis requires the presence of both parenchymal and non-parenchymal cells (NPCs). However, systems biology studies of the liver have primarily focused on hepatocytes. Using an organotypic three-dimensional (3D) hepatic culture, we report the first transcriptomic study of liver sinusoidal endothelial cells (LSECs) and Kupffer cells (KCs) cultured with hepatocytes. Through computational pathway and interaction network analyses, we demonstrate that hepatocytes, LSECs and KCs have distinct expression profiles and functional characteristics. Our results show that LSECs in the presence of KCs exhibit decreased expression of focal adhesion kinase (FAK) signaling, a pathway linked to LSEC dedifferentiation. We report the novel result that peroxisome proliferator-activated receptor alpha (PPARα) is transcribed in LSECs. The expression of downstream processes corroborates active PPARα signaling in LSECs. We uncover transcriptional evidence in LSECs for a feedback mechanism between PPARα and farnesoid X-activated receptor (FXR) that maintains bile acid homeostasis; previously, this feedback was known occur only in HepG2 cells. We demonstrate that KCs in 3D liver models display expression patterns consistent with an anti-inflammatory phenotype when compared to monocultures. These results highlight the distinct roles of LSECs and KCs in maintaining liver function and emphasize the need for additional mechanistic studies of NPCs in addition to hepatocytes in liver-mimetic microenvironments.
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Abstract The jejunum is the segment of the small intestine responsible for several metabolism and biotransformation functions. In this report, we have cultured rat jejunum explants
in vitro and integrated them with hepatocyte cultures. We have also investigated the changes in jejunum function at different locations since spatial variations in intestinal functions have been reported previously. We divided the length of the rat jejunum into three distinct regions of approximately 9 cm each. We defined the regions as proximal (adjacent to the duodenum), medial, and distal (adjacent to the ileum). Spatiotemporal variations in functions were observed between these regions within the jejunum. Alkaline phosphatase activity (a marker of enterocyte function), decreased twofold between the proximal and distal regions at 4 hr. Lysozyme activity (a marker of Paneth cell function) increased from the proximal to the distal jejunum by 40% at 24 hr. Mucin‐covered areas, a marker of goblet cell function, increased by twofold between the proximal and distal segments of the jejunum at 24 hr. When hepatocytes were integrated with proximal jejunum explants, statistically higher urea (~2.4‐fold) and mucin (57%) production were observed in the jejunum explants. The integrated intestine‐liver cultures can be used as a platform for future investigations. -
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Abstract Background Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction.
Results We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents.
Conclusions We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.
