Select α-arrestins control cell-surface abundance of the mammalian Kir2.1 potassium channel in a yeast model.

Protein composition at the plasma membrane is tightly regulated, with rapid protein internalization and selective targeting to the cell surface occurring in response to environmental changes. For example, ion channels are dynamically relocalized to or from the plasma membrane in response to physiological alterations, allowing cells and organisms to maintain osmotic and salt homeostasis. To identify additional factors that regulate the selective trafficking of a specific ion channel, we used a yeast model for a mammalian potassium channel, the K+ inward rectifying channel Kir2.1. Kir2.1 maintains potassium homeostasis in heart muscle cells, and Kir2.1 defects lead to human disease. By examining the ability of Kir2.1 to rescue the growth of yeast cells lacking endogenous potassium channels, we discovered that specific α-arrestins regulate Kir2.1 localization. Specifically, we found that the Ldb19/Art1, Aly1/Art6, and Aly2/Art3 α-arrestin adaptor proteins promote Kir2.1 trafficking to the cell surface, increase Kir2.1 activity at the plasma membrane, and raise intracellular potassium levels. To better quantify the intracellular and cell-surface populations of Kir2.1, we created fluorogen-activating protein fusions and for the first time used this technique to measure the cell-surface residency of a plasma membrane protein in yeast. Our experiments revealed that two α-arrestin effectors also control Kir2.1 localization. In particular, both the Rsp5 ubiquitin ligase and the protein phosphatase calcineurin facilitated the α-arrestin-mediated trafficking of Kir2.1. Together, our findings implicate α-arrestins in regulating an additional class of plasma membrane proteins and establish a new tool for dissecting the trafficking itinerary of any membrane protein in yeast.

Gynecological organ involvement at robot-assisted radical cystectomy in females: Is anterior exenteration necessary?

INTRODUCTION: We aimed to investigate patient and disease variables associated with gynecological organ invasion in females with bladder cancer at the time of robot-assisted radical cystectomy (RARC). METHODS: We conducted a retrospective review of female patients who underwent robot-assisted anterior pelvic exenteration (RAAE) between 2005 and 2016. Patients were divided into two groups: those with gynecological organ involvement at RAAE and those without. Data were reviewed for perioperative and pathological outcomes. Kaplan-Meier method was used to depict survival outcomes. Multivariable stepwise regression analysis was performed to identify predictors of gynecological organ involvement. RESULTS: A total of 118 female patients were identified; 17 (14%) showed evidence of gynecological organ invasion at RAAE. Patients with gynecological organ invasion had more lymphovascular invasion at transurethral resection of bladder tumour (TURBT) (82% vs. 46%; p=0.006), trigonal tumours at TURBT (59% vs. 18%; p=0.001), multifocal disease (65% vs. 33%; p=0.01), pN+ (71% vs. 22%; p<0.001), positive surgical margins (24% vs. 4%; p=0.02), and they less commonly demonstrated pure urothelial carcinoma at TURBT (18% vs. 66%; p<0.001). On multivariate analysis, significant predictors of gynecological organ invasion were pN positive disease (odds ratio [OR] 6.48; 95% confidence interval [CI] 1.64-25.51; p=0.008), trigonal tumour location (OR 5.72; 95% CI 1.39-23.61; p=0.02), and presence of variant histology (OR18.52; 95% CI 3.32-103.4; p=0.001). CONCLUSIONS: Patients with trigonal tumours, variant histology, and nodal involvement are more likely to have gynecological organs invasion at RAAE. This information may help improve counselling of patients and better identify candidates for gynecological organ-sparing cystectomy.

Development of a Patient-Based Model for Estimating Operative Times for Robot-Assisted Radical Prostatectomy.

OBJECTIVES: To develop a methodology for predicting operative times for robot-assisted radical prostatectomy (RARP) using preoperative patient, disease, procedural, and surgeon variables to facilitate operating room (OR) scheduling. METHODS: The model included preoperative metrics: body mass index (BMI), American Society of Anesthesiologists score, clinical stage, National Comprehensive Cancer Network risk, prostate weight, nerve-sparing status, extent and laterality of lymph node dissection, and operating surgeon (six surgeons were included in the study). A binary decision tree was fit using a conditional inference tree method to predict operative times. The variables most associated with operative time were determined using permutation tests. Data were split at the value of the variable that results in the largest difference in mean for surgical time across the split. This process was repeated recursively on the resultant data. RESULTS: A total of 1709 RARPs were included. The variable most strongly associated with operative time was the surgeon (surgeons 2 and 4-102 minutes shorter than surgeons 1, 3, 5, and 6, p < 0.001). Among surgeons 2 and 4, BMI had the strongest association with surgical time (p < 0.001). Among patients operated by surgeons 1, 3, 5, and 6, RARP time was again most strongly associated with the surgeon performing RARP. Surgeons 1, 3, and 6 were on average 76 minutes faster than surgeon 5 (p < 0.001). The regression tree output in the form of box plots showed operative time median and ranges according to patient, disease, procedural, and surgeon metrics. CONCLUSION: We developed a methodology that can predict operative times for RARP based on patient, disease and surgeon variables. This methodology can be utilized for quality control, facilitate OR scheduling, and maximize OR efficiency.