Transmembrane and Immunoglobulin Domain Containing 1, a Putative Tumor Suppressor, Induces G2/M Cell Cycle Checkpoint Arrest in Colon Cancer Cells.

Colorectal cancer (CRC) is a leading nonfamilial cause of cancer mortality among men and women. Although various genetic and epigenetic mechanisms have been identified, the full molecular mechanisms deriving CRC tumorigenesis are not fully understood. This study demonstrates that cell adhesion molecule transmembrane and immunoglobulin domain containing 1 (TMIGD1) are highly expressed in mouse and human normal intestinal epithelial cells. TMIGD1 knockout mice were developed, and the loss of TMIGD1 in mice was shown to result in the development of adenomas in small intestine and colon. In addition, the loss of TMIGD1 significantly impaired intestinal epithelium brush border membrane, junctional polarity, and maturation. Mechanistically, TMIGD1 inhibits tumor cell proliferation and cell migration, arrests cell cycle at the G2/M phase, and induces expression of p21CIP1 (cyclin-dependent kinase inhibitor 1), and p27KIP1 (cyclin-dependent kinase inhibitor 1B) expression, key cell cycle inhibitor proteins involved in the regulation of the cell cycle. Moreover, TMIGD1 is shown to be progressively down-regulated in sporadic human CRC, and its downregulation correlates with poor overall survival. The findings herein identify TMIGD1 as a novel tumor suppressor gene and provide new insights into the pathogenesis of colorectal cancer and a novel potential therapeutic target.

Temporal and tissue-specific activation of aryl hydrocarbon receptor in discrete mouse models of kidney disease.

Emerging evidence in animal models of chronic kidney disease (CKD) implicates Aryl Hydrocarbon Receptor (AHR) signaling as a mediator of uremic toxicity. However, details about its tissue-specific and time-dependent activation in response to various renal pathologies remain poorly defined. Here, a comprehensive analysis of AHR induction was conducted in response to discrete models of kidney diseases using a transgenic mouse line expressing the AHR responsive-promoter tethered to a ß-galactosidase reporter gene. Following validation using a canonical AHR ligand (a dioxin derivative), the transgenic mice were subjected to adenine-induced and ischemia/reperfusion-induced injury models representing CKD and acute kidney injury (AKI), respectively, in humans. Indoxyl sulfate was artificially increased in mice through the drinking water and by inhibiting its excretion into the urine. Adenine-fed mice showed a distinct and significant increase in ß-galactosidase in the proximal and distal renal tubules, cardiac myocytes, hepatocytes, and microvasculature in the cerebral cortex. The pattern of ß-galactosidase increase coincided with the changes in serum indoxyl sulfate levels. Machine-learning-based image quantification revealed positive correlations between indoxyl sulfate levels and ß-galactosidase expression in various tissues. This pattern of ß-galactosidase expression was recapitulated in the indoxyl sulfate-specific model. The ischemia/reperfusion injury model showed increase in ß-galactosidase in renal tubules that persisted despite reduction in serum indoxyl sulfate and blood urea nitrogen levels. Thus, our results demonstrate a relationship between AHR activation in various tissues of mice with CKD or AKI and the levels of indoxyl sulfate. This study demonstrates the use of a reporter gene mouse to probe tissue-specific manifestations of uremia in translationally relevant animal models and provide hypothesis-generating insights into the mechanism of uremic toxicity that warrant further investigation.

A computational solution to improve biomarker reproducibility during long-term projects.

Biomarkers are fundamental to basic and clinical research outcomes by reporting host responses and providing insight into disease pathophysiology. Measuring biomarkers with research-use ELISA kits is universal, yet lack of kit standardization and unexpected lot-to-lot variability presents analytic challenges for long-term projects. During an ongoing two-year project measuring plasma biomarkers in cancer patients, control concentrations for one biomarker (PF) decreased significantly after changes in ELISA kit lots. A comprehensive operations review pointed to standard curve shifts with the new kits, an analytic variable that jeopardized data already collected on hundreds of patient samples. After excluding other reasonable contributors to data variability, a computational solution was developed to provide a uniform platform for data analysis across multiple ELISA kit lots. The solution (ELISAtools) was developed within open-access R software in which variability between kits is treated as a batch effect. A defined best-fit Reference standard curve is modelled, a unique Shift factor "S" is calculated for every standard curve and data adjusted accordingly. The averaged S factors for PF ELISA kit lots #1-5 ranged from -0.086 to 0.735, and reduced control inter-assay variability from 62.4% to <9%, within quality control limits. S factors calculated for four other biomarkers provided a quantitative metric to monitor ELISAs over the 10 month study period for quality control purposes. Reproducible biomarker measurements are essential, particularly for long-term projects with valuable patient samples. Use of research-use ELISA kits is ubiquitous and judicious use of this computational solution maximizes biomarker reproducibility.