Bcl-xL is overexpressed in hormone-resistant prostate cancer and promotes survival of LNCaP cells via interaction with proapoptotic Bak.

Androgen-sensitive prostate cancer cells turn androgen resistant through complex mechanisms that involve dysregulation of apoptosis. We investigated the role of antiapoptotic Bcl-xL in the progression of prostate cancer as well as the interactions of Bcl-xL with proapoptotic Bax and Bak in androgen-dependent and -independent prostate cancer cells. Immunohistochemical analysis was used to study the expression of Bcl-xL in a series of 139 prostate carcinomas and its association with Gleason grade and time to hormone resistance. Expression of Bcl-xL was more abundant in prostate carcinomas of higher Gleason grades and significantly associated with the onset of hormone-refractory disease. In vivo interactions of Bcl-xL with Bax or Bak in untreated and camptothecin-treated LNCaP and PC3 cells were investigated by means of coimmunoprecipitation. In the absence of any stimuli, Bcl-xL interacts with Bax and Bak in androgen-independent PC3 cells but only with Bak in androgen-dependent LNCaP cells. Interactions of Bcl-xL with Bax and Bak were also evidenced in lysates from high-grade prostate cancer tissues. In LNCaP cells treated with camptothecin, an inhibitor of topoisomerase I, the interaction between Bcl-xL and Bak was absent after 36 h, Bcl-xL decreased gradually and Bak increased coincidentally with the progress of apoptosis. These results support a model in which Bcl-xL would exert an inhibitory effect over Bak via heterodimerization. We propose that these interactions may provide mechanisms for suppressing the activity of proapoptotic Bax and Bak in prostate cancer cells and that Bcl-xL expression contributes to androgen resistance and progression of prostate cancer.

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.

Metabolomic insights into the intricate gut microbial-host interaction in the development of obesity and type 2 diabetes.

Gut microbiota has recently been proposed as a crucial environmental factor in the development of metabolic diseases such as obesity and type 2 diabetes, mainly due to its contribution in the modulation of several processes including host energy metabolism, gut epithelial permeability, gut peptide hormone secretion, and host inflammatory state. Since the symbiotic interaction between the gut microbiota and the host is essentially reflected in specific metabolic signatures, much expectation is placed on the application of metabolomic approaches to unveil the key mechanisms linking the gut microbiota composition and activity with disease development. The present review aims to summarize the gut microbial-host co-metabolites identified so far by targeted and untargeted metabolomic studies in humans, in association with impaired glucose homeostasis and/or obesity. An alteration of the co-metabolism of bile acids, branched fatty acids, choline, vitamins (i.e., niacin), purines, and phenolic compounds has been associated so far with the obese or diabese phenotype, in respect to healthy controls. Furthermore, anti-diabetic treatments such as metformin and sulfonylurea have been observed to modulate the gut microbiota or at least their metabolic profiles, thereby potentially affecting insulin resistance through indirect mechanisms still unknown. Despite the scarcity of the metabolomic studies currently available on the microbial-host crosstalk, the data-driven results largely confirmed findings independently obtained from in vitro and animal model studies, putting forward the mechanisms underlying the implication of a dysfunctional gut microbiota in the development of metabolic disorders.