Suppressing Pitx2 inhibits proliferation and promotes differentiation of iHepSCs

Enamel defects of Axenfeld-Rieger syndrome and the role of PITX2 in its pathogenesis

OBJECTIVES

To investigate the detailed ultrastructural patterns of dental abnormalities affected by Axenfeld-Rieger syndrome (ARS) with a heterozygous microdeletion involving paired-like homeodomain 2 (PITX2) and explored the underlying molecular mechanisms driving enamel defects.

SUBJECTS AND METHODS

Sanger sequencing, genomic quantitative PCR analysis, and chromosomal microarray analysis (CMA) were used to screen the disease-causing mutation in one ARS proband. An exfoliated tooth from an ARS patient was analyzed with scanning electron microscopy and micro-computerized tomography. A stable Pitx2 knockdown cell line was generated to simulate PITX2 haploinsufficiency. Cell proliferation and ameloblast differentiation were analyzed, and the role of the Wnt/β-catenin pathway in proliferation of ameloblast precursor cells was investigated.

RESULTS

An approximately 0.216 Mb novel deletion encompassing PITX2 was identified. The affected tooth displayed a thinner and broken layer of enamel and abnormal enamel biomineralization. PITX2 downregulation inhibited the proliferation and differentiation of inner enamel epithelial cells, and LiCl stifmulation partially reversed the proliferation ability after Pitx2 knockdown.

CONCLUSIONS

Enamel formation is disturbed in some patients with ARS. Pitx2 knockdown can influence the proliferation and ameloblast differentiation of inner enamel epithelial cells, and PITX2 may regulate cell proliferation via Wnt/β-catenin signaling pathway.

PITX2C increases the stemness features of hepatocellular carcinoma cells by up-regulating key developmental factors in liver progenitor

Background

Tumor cells exhibited phenotypic and molecular characteristics similar to their lineage progenitor cells. Liver developmental signaling pathways are showed to be associated with HCC development and oncogenesis. The similarities of expression profiling between liver progenitors (LPs) and HCC suggest that understanding the molecular mechanism during liver development could provide insights into HCC.

Methods

To profile the dynamic gene expression during liver development, cells from an in vitro liver differentiation model and two paired hepatocellular carcinoma (HCC) samples were analyzed using deep RNA sequencing. The expression levels of selected genes were analyzed by qRT-PCR. Moreover, the role of a key transcription factor, pituitary homeobox 2 (PITX2), was characterized via in vitro and vivo functional assays. Furthermore, molecular mechanism studies were performed to unveil how PITX2C regulate the key developmental factors in LPs, thereby increasing the stemness of HCC.

Results

PITX2 was found to exhibit a similar expression pattern to specific markers of LPs. PITX2 consists of three isoforms (PITX2A/B/C). The expression of PITX2 is associated with tumor size and overall survival rate, whereas only PITX2C expression is associated with AFP and differentiation in clinical patients. PITX2A/B/C has distinct functions in HCC tumorigenicity. PITX2C promotes HCC metastasis, self-renewal and chemoresistance. Molecular mechanism studies showed that PITX2C could up-regulate RALYL which could enhance HCC stemness via the TGF-β pathway. Furthermore, ChIP assays confirmed the role of PITX2C in regulating key developmental factors in LP.

Conclusion

PITX2C is a newly discovered transcription factor involved in hepatic differentiation and could increase HCC stemness by upregulating key transcriptional factors related to liver development.

Inferring TF activation order in time series scRNA-Seq studies

Methods for the analysis of time series single cell expression data (scRNA-Seq) either do not utilize information about transcription factors (TFs) and their targets or only study these as a post-processing step. Using such information can both, improve the accuracy of the reconstructed model and cell assignments, while at the same time provide information on how and when the process is regulated. We developed the Continuous-State Hidden Markov Models TF (CSHMM-TF) method which integrates probabilistic modeling of scRNA-Seq data with the ability to assign TFs to specific activation points in the model. TFs are assumed to influence the emission probabilities for cells assigned to later time points allowing us to identify not just the TFs controlling each path but also their order of activation. We tested CSHMM-TF on several mouse and human datasets. As we show, the method was able to identify known and novel TFs for all processes, assigned time of activation agrees with both expression information and prior knowledge and combinatorial predictions are supported by known interactions. We also show that CSHMM-TF improves upon prior methods that do not utilize TF-gene interaction.

An important attribute of time series single cell RNA-Seq (scRNA-Seq) data, is the ability to infer continuous trajectories of genes based on orderings of the cells. While several methods have been developed for ordering cells and inferring such trajectories, to date it was not possible to use these to infer the temporal activity of several key TFs. These TFs are are only post-transcriptionally regulated and so their expression does not provide complete information on their activity. To address this we developed the Continuous-State Hidden Markov Models TF (CSHMM-TF) methods that assigns continuous activation time to TFs based on both, their expression and the expression of their targets. Applying our method to several time series scRNA-Seq datasets we show that it correctly identifies the key regulators for the processes being studied. We analyze the temporal assignments for these TFs and show that they provide new insights about combinatorial regulation and the ordering of TF activation. We used several complementary sources to validate some of these predictions and discuss a number of other novel suggestions based on the method. As we show, the method is able to scale to large and noisy datasets and so is appropriate for several studies utilizing time series scRNA-Seq data.