Expression of AP-1 (c-fos/c-jun) in developing mouse corneal epithelium

Corneal epithelial development and homeostasis

The corneal epithelium (CE), the most anterior cellular structure of the eye, is a self-renewing stratified squamous tissue that protects the rest of the eye from external elements. Each cell in this exquisite three-dimensional structure needs to have proper polarity and positional awareness for the CE to serve as a transparent, refractive, and protective tissue. Recent studies have begun to elucidate the molecular and cellular events involved in the embryonic development, post-natal maturation, and homeostasis of the CE, and how they are regulated by a well-coordinated network of transcription factors. This review summarizes the status of related knowledge and aims to provide insight into the pathophysiology of disorders caused by disruption of CE development, and/or homeostasis.

Spatiotemporally Regulated Ablation of Klf4 in Adult Mouse Corneal Epithelial Cells Results in Altered Epithelial Cell Identity and Disrupted Homeostasis

PURPOSE

In previous studies, conditional disruption of Klf4 in the developing mouse ocular surface from embryonic day 10 resulted in corneal epithelial fragility, stromal edema, and loss of conjunctival goblet cells, revealing the importance of Klf4 in ocular surface maturation. Here, we use spatiotemporally regulated ablation of Klf4 to investigate its functions in maintenance of adult corneal epithelial homeostasis.

METHODS

Expression of Cre was induced in ternary transgenic (Klf4(LoxP/LoxP)/Krt12(rtTA/rtTA)/Tet-O-Cre) mouse corneal epithelium by doxycycline administered through intraperitoneal injections and drinking water, to generate corneal epithelium-specific deletion of Klf4 (Klf4(Δ/ΔCE)). Corneal epithelial barrier function was tested by fluorescein staining. Expression of selected Klf4-target genes was determined by quantitative PCR (QPCR), immunoblotting, and immunofluorescent staining.

RESULTS

Klf4 was efficiently ablated within 5 days of doxycycline administration in adult Klf4(Δ/ΔCE) corneal epithelium. The Klf4(Δ/ΔCE) corneal epithelial barrier function was disrupted, and the basal cells were swollen and rounded after 15 days of doxycycline treatment. Increased numbers of cell layers and Ki67-positive proliferating cells suggested deregulated Klf4(Δ/ΔCE) corneal epithelial homeostasis. Expression of tight junction proteins ZO-1 and occludin, desmosomal Dsg and Dsp, basement membrane laminin-332, and corneal epithelial-specific keratin-12 was decreased, while that of matrix metalloproteinase Mmp9 and noncorneal keratin-17 increased, suggesting altered Klf4(Δ/ΔCE) corneal epithelial cell identity.

CONCLUSIONS

Ablation of Klf4 in the adult mouse corneas resulted in the absence of characteristic corneal epithelial cell differentiation, disrupted barrier function, and squamous metaplasia, revealing that Klf4 is essential for maintenance of the adult corneal epithelial cell identity and homeostasis.

Ocular surface development and gene expression

The ocular surface-a continuous epithelial surface with regional specializations including the surface and glandular epithelia of the cornea, conjunctiva, and lacrimal and meibomian glands connected by the overlying tear film-plays a central role in vision. Molecular and cellular events involved in embryonic development, postnatal maturation, and maintenance of the ocular surface are precisely regulated at the level of gene expression by a well-coordinated network of transcription factors. A thorough appreciation of the biological characteristics of the ocular surface in terms of its gene expression profiles and their regulation provides us with a valuable insight into the pathophysiology of various blinding disorders that disrupt the normal development, maturation, and/or maintenance of the ocular surface. This paper summarizes the current status of our knowledge related to the ocular surface development and gene expression and the contribution of different transcription factors to this process.

A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data

BACKGROUND

Cluster analysis has become a standard computational method for gene function discovery as well as for more general explanatory data analysis. A number of different approaches have been proposed for that purpose, out of which different mixture models provide a principled probabilistic framework. Cluster analysis is increasingly often supplemented with multiple data sources nowadays, and these heterogeneous information sources should be made as efficient use of as possible.

RESULTS

This paper presents a novel Beta-Gaussian mixture model (BGMM) for clustering genes based on Gaussian distributed and beta distributed data. The proposed BGMM can be viewed as a natural extension of the beta mixture model (BMM) and the Gaussian mixture model (GMM). The proposed BGMM method differs from other mixture model based methods in its integration of two different data types into a single and unified probabilistic modeling framework, which provides a more efficient use of multiple data sources than methods that analyze different data sources separately. Moreover, BGMM provides an exceedingly flexible modeling framework since many data sources can be modeled as Gaussian or beta distributed random variables, and it can also be extended to integrate data that have other parametric distributions as well, which adds even more flexibility to this model-based clustering framework. We developed three types of estimation algorithms for BGMM, the standard expectation maximization (EM) algorithm, an approximated EM and a hybrid EM, and propose to tackle the model selection problem by well-known model selection criteria, for which we test the Akaike information criterion (AIC), a modified AIC (AIC3), the Bayesian information criterion (BIC), and the integrated classification likelihood-BIC (ICL-BIC).

CONCLUSION

Performance tests with simulated data show that combining two different data sources into a single mixture joint model greatly improves the clustering accuracy compared with either of its two extreme cases, GMM or BMM. Applications with real mouse gene expression data (modeled as Gaussian distribution) and protein-DNA binding probabilities (modeled as beta distribution) also demonstrate that BGMM can yield more biologically reasonable results compared with either of its two extreme cases. One of our applications has found three groups of genes that are likely to be involved in Myd88-dependent Toll-like receptor 3/4 (TLR-3/4) signaling cascades, which might be useful to better understand the TLR-3/4 signal transduction.