Interaction between ATM and PARP-1 in response to DNA damage and sensitization of ATM deficient cells through PARP inhibition

ATM and PARP-1 are two of the most important players in the cell's response to DNA damage. PARP-1 and ATM recognize and bound to both single and double strand DNA breaks in response to different triggers. Here we report that ATM and PARP-1 form a molecular complex in vivo in undamaged cells and this association increases after gamma-irradiation. ATM is also modified by PARP-1 during DNA damage. We have also evaluated the impact of PARP-1 absence or inhibition on ATM-kinase activity and have found that while PARP-1 deficient cells display a defective ATM-kinase activity and reduced gamma-H2AX foci formation in response to gamma-irradiation, PARP inhibition on itself is able to activate ATM-kinase. PARP inhibition induced gamma H2AX foci accumulation, in an ATM-dependent manner. Inhibition of PARP also induces DNA double strand breaks which were dependent on the presence of ATM. As consequence ATM deficient cells display an increased sensitivity to PARP inhibition. In summary our results show that while PARP-1 is needed in the response of ATM to gamma irradiation, the inhibition of PARP induces DNA double strand breaks (which are resolved in and ATM-dependent pathway) and activates ATM kinase.

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.

Prolonged cholestasis after raloxifene and fenofibrate interaction: A case report.

Assigning causality in drug-induced liver injury is challenging particularly when more than one drug could be responsible. We report a woman on long-term therapy with raloxifen who developed acute cholestasis shortly after starting fenofibrate. The picture evolved into chronic cholestasis. We hypothesized that an interaction at the metabolic level could have triggered the presentation of hepatotoxicity after a very short time of exposure to fenofibrate in this patient. The findings of an overexpression of vascular endothelial growth factor in the liver biopsy suggest that angiogenesis might play a role in the persistence of toxic cholestasis.

Nevirapine vs efavirenz in virologically supressed patients: Differences in lipoprotein subclasses and inflammatory biomarkers.

Background: The interaction between lipid disturbances and inflammatory markers is not well known in patients on antiretroviral therapy (ART). As nevirapine (NVP) is associated with a better lipid profile than efavirenz (EFV), we investigated the relationships between lipid profiles, lipoprotein subclasses and inflammatory biomarkers in patients with prolonged viral suppression with either NVP or EFV and no obvious clinical inflammation. Methods: 122 clinically stable HIV-infected patients with HIV-1 RNA <20 copies longer than 6 months on NNRTI therapy were studied. 72 (59%) were on EFV and 50 (41%) on NVP. Any potentially inflammatory co-morbid diseases (concurrent viral hepatitis, diabetes, hypertension, chronic liver or renal diseases), or statin treatment, were exclusion criteria. Inflammatory biomarkers included hsCRP, LpPLA2, sCD40L, IL-6, IL-8, t-PA, MCP-1, p-selectin and VCAM-1. Lipoprotein subclass measures (VLDL, LDL, IDL and HDL particle number and size) were obtained by the use of proton nuclear magnetic resonance spectroscopy. Results: 82% were male; median age 45 years. Median CD4 count 550/μL (IQR 324). Median time since HIV diagnosis 96 months (IQR 102) and accumulated time on ART 50 months (IQR 101). Patients on NVP had higher time since HIV diagnosis (126.9 [66.7] vs 91.3 [6.6] months, p=0.008) a prolonged time on ART (89.6 [54.6] vs 62.3 [52.2] months, p=0.01) and were older (47.7 vs 40.7 years, p=0.001) than those on EFV. NVP-treated patients presented increased HDL-c (55.8 [16] vs 48.8 [10.7] mg/dL, p=0.007) and apoA1 levels (153.4 [31.9] vs 141.5 [20.5] mg/dL, p=0.02), and reduced apoB/apoA1 ratio (0.68 [0.1] vs 0.61 [0.1], p=0.003) than EFV-treated patients. No differences in inflammatory markers or lipoprotein subclasses were found between NVP and EFV. In patients with extreme lipid values (less favorable: 75th percentiles of LDL, small/dense LDLp and small HDLp, or more favorable: HDL p75 and apoB/apoA1 ratio p25), no consistent differences in inflammatory biomarkers were found. Conclusions: Patients with prolonged viral suppression on NVP present significantly higher HDL and apoA1 levels and reduced apoB/apoA1 ratios than those on EFV, but no differences were found in lipoprotein particles nor inflammatory biomarkers. Relationships between lipid parameters and inflammatory biomarkers in NNRTItreated patients are complex and do not show a linear relationship in this study.

Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study.

BACKGROUND Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. METHODS AND FINDINGS The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10-4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10-3) and waist circumference (p for interaction  = 7.49×10-9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score. CONCLUSIONS The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.

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.

Changes in Body Composition in Anorexia Nervosa: Predictors of Recovery and Treatment Outcome.

The restoration of body composition (BC) parameters is considered to be one of the most important goals in the treatment of patients with anorexia nervosa (AN). However, little is known about differences between AN diagnostic subtypes [restricting (AN-R) and binge/purging (AN-BP)] and weekly changes in BC during refeeding treatment. Therefore, the main objectives of our study were twofold: 1) to assess the changes in BC throughout nutritional treatment in an AN sample and 2) to analyze predictors of BC changes during treatment, as well as predictors of treatment outcome. The whole sample comprised 261 participants [118 adult females with AN (70 AN-R vs. 48 AN-BP), and 143 healthy controls]. BC was measured weekly during 15 weeks of day-hospital treatment using bioelectrical impedance analysis (BIA). Assessment measures also included the Eating Disorders Inventory-2, as well as a number of other clinical indices. Overall, the results showed that AN-R and AN-BP patients statistically differed in all BC measures at admission. However, no significant time×group interaction was found for almost all BC parameters. Significant time×group interactions were only found for basal metabolic rate (p = .041) and body mass index (BMI) (p = .035). Multiple regression models showed that the best predictors of pre-post changes in BC parameters (namely fat-free mass, muscular mass, total body water and BMI) were the baseline values of BC parameters. Stepwise predictive logistic regressions showed that only BMI and age were significantly associated with outcome, but not with the percentage of body fat. In conclusion, these data suggest that although AN patients tended to restore all BC parameters during nutritional treatment, only AN-BP patients obtained the same fat mass values as healthy controls. Put succinctly, the best predictors of changes in BC were baseline BC values, which did not, however, seem to influence treatment outcome.