Controversies exist as to whether the genetic polymorphisms of the enzymes responsible for the metabolism of tamoxifen can predict breast cancer outcome in patients using adjuvant tamoxifen. Direct measurement of concentrations of active tamoxifen metabolites in serum may be a more biological plausible and robust approach. We have investigated the association between CYP2D6 genotypes, serum concentrations of active tamoxifen metabolites, and long-term outcome in tamoxifen treated breast cancer patients. From an original observational study comprising 817 breast cancer patients, 99 women with operable breast cancer were retrospectively included in the present study. This cohort of patients were adjuvantly treated with tamoxifen, had provided serum samples suitable for measuring tamoxifen metabolites, and were relapse-free at 3 years after the primary treatment commenced. The median follow-up time from this entry point to breast cancer death was 13.9 years. Patients were CYP2D6 genotyped and grouped into four CYP2D6 phenotype groups (Ultra rapid, extensive, intermediate, and poor metabolizers). Tamoxifen and nine metabolites were quantified in serum (n = 86) and compared with CYP2D6 phenotype groups and outcome. Breast cancer patients with low concentrations of Z-4-hydroxy-tamoxifen (Z-4OHtam; ≤ 3.26 nM) had a breast cancer-specific survival (BCSS) of 60% compared to 84% in patients with Z-4OHtam concentrations > 3.26 nM (p = 0.020, log-rank hazard ratio (HR) = 3.56, 95% confidence interval (CI) = 1.14–11.07). For patients with Z-4-hydroxy-N-desmethyl-tamoxifen (Z-endoxifen) levels ≤ 9.00 nM BCSS was 57% compared to 84% for patients with concentrations > 9.00 nM (p = 0.029, HR = 3.73, 95% CI = 1.05–13.22). Low concentrations of Z-4OHtam and Z-endoxifen were associated with poorer survival also after adjusting for clinically relevant variables (HR = 4.27, 95% CI = 1.35–13.58, and HR = 3.70, 95% CI = 1.03–13.25, respectively). Overall survival analysis showed similar survival differences for both active metabolites. The Antiestrogen Activity Score showed comparable effects, but did not improve the prognostic information. Patients with Z-4OHtam and Z-endoxifen concentrations lower than 3.26 nM or 9.00 nM, respectively, showed an adverse outcome. Our results suggest that direct measurement of active tamoxifen metabolite concentrations could be of clinical value. Validation in larger study cohorts is warranted.

DJ-1 is a redox sensitive protein with a wide range of functions related to oxidative stress protection. Mutations in the park7 gene, which codes for DJ-1 are associated with early onset familial Parkinson’s disease and increased astrocytic DJ-1 levels are found in pathologic tissues from idiopathic Parkinson’s disease. We have previously established a DJ-1 knockout zebrafish line that developed normally, but with aging the DJ-1 null fish had a lowered level of tyrosine hydroxylase, respiratory mitochondrial failure and a lower body mass. Here we have examined the DJ-1 knockout from the early adult stage and show that loss of DJ-1 results in a progressive, age-dependent increase in both motoric and non-motoric symptoms associated to Parkinson’s disease. These changes coincide with changes in mitochondrial and mitochondrial associated proteins. Recent studies have suggested that a decline in NAD+ can contribute to Parkinson’s disease and that supplementation of NAD+ precursors may delay disease progression. We found that the brain NAD+/NADH ratio decreased in aging zebrafish but did not correlate with DJ-1 induced altered behavior. Differences were first observed at the late adult stage in which NAD+ and NADPH levels were decreased in DJ-1 knockouts. Considering the experimental power of zebrafish and the development of Parkinson’s disease-related symptoms in the DJ-1 null fish, this model can serve as a useful tool both to understand the progression of the disease and the effect of suggested treatments.

Age-dependent loss of muscle mass and function is associated with oxidative stress. DJ-1/Park7 acts as an antioxidant through multiple signalling pathways. DJ-1-knockout zebrafish show a decline in swimming performance and loss of weight gain between 6 and 9 months of age. Here, we address the degree to which this is associated with muscle degeneration and identify molecular changes preceding dysregulation of muscle performance. Loss of DJ-1 reduced the skeletal muscle fibre cross-section area. The highly mitochondrial-dependent red slow muscle was more affected than the white muscle, and degeneration of sub-sarcolemma red muscle mitochondria was observed. Using TandemMassTag-based quantitative proteomics, we identified a total of 3721 proteins in the multiplex sample of 4 and 12-month-old muscles. A total of 68 proteins, mainly associated with inflammation and mitochondrial function, were dysregulated in the young DJ-1-null adults, with Annexin A3, Sphingomyelin phosphodiesterase acid-like 3B, Complement C3a, and 2,4-dienoyl CoA reductase 1 being the most affected. The loss of DJ-1 also accelerated molecular features associated with sarcopenia, such as a decrease in the NAD⁺/NADH ratio and a reduction in Prostaglandin reductase 2 and Cytosolic glycerol-3-phosphate dehydrogenase levels. In view of the experimental power of zebrafish, the DJ-1-null zebrafish makes a valuable model for understanding the connection between oxidative stress and age-dependent muscle loss and function.

Cells continuously produce and degrade multiple small and large molecules, essential for maintaining homeostasis. The study of these dynamics has gained momentum since the development of pulse-chase and wash-in/out methods, utilizing fluorescent or isotopic labeling of cellular components to assess properties such as turnover rates or half-lives. However, standard analyses of these experiments often depend on simplifications such as the homogeneity of analyzed molecules or their immediate labeling, which do not always hold. Here, we present a rigorous analytical framework that interprets the readouts of dynamic labeling experiments as the distribution of metabolic ages, defined as the time that molecules have spent within a cell, and show that metabolic ages can be quantified by dynamic labeling with minimal assumptions. Using age-based interpretation, we demonstrate how the experimentally observed labeling dynamics is connected to a variety of dynamic parameters including half-lives, decay rates, and residence times and how these interpretations are affected by the conditions of delayed input or complex degradation patterns. To aid in the experimental quantification of dynamic parameters, we introduce a compartmental model framework including an open-source software package. We illustrate the framework’s practical utility by quantifying dynamic parameters and determining the kinetic pool structure of budding yeast proteins at optimal and suboptimal growth temperatures. Significance Statement: To be functional, cells must balance the production and degradation of biological molecules. This is often studied by labeling newly-made molecules with isotopic or fluorescent labels. However, determining parameters of these processes, such as degradation rates, is not easy and is challenged by non-instantaneous labeling and complex degradation patterns. We describe a generic framework for interpreting the results of dynamic labeling experiments based on the concept of metabolic age, defined as the time since a molecule entered the metabolic system. By analyzing the effects of heat stress on protein stability in yeast, we illustrate how this framework and its implementation in a custom opensource package enable us to standardize the determination of various dynamic parameters of metabolism.

Cells continuously produce and degrade molecules, essential for maintaining homeostasis. The study of these dynamics has gained momentum since the development of pulse–chase methods, utilizing fluorescent or isotopic labeling to assess properties such as turnover rates or half-lives. However, standard analyses of these experiments often depend on assumptions such as the homogeneity of analyzed molecules or their immediate labeling, which do not always hold. Here, we show that the readouts of steady-state dynamic labeling experiments can be interpreted as the distribution of metabolic ages, defined as the time since each molecule entered the metabolic system, and that metabolic ages can be quantified with minimal assumptions. Using this age-based interpretation, we demonstrate how the experimentally observed labeling dynamics is connected to a variety of dynamic parameters including half-lives, decay rates, and residence times and how these interpretations are affected by the conditions of delayed input, cell growth, or complex degradation patterns. To aid in the experimental quantification of dynamic parameters, we introduce a compartmental model framework as well as an open-source software package. We illustrate the framework’s practical utility by quantifying dynamic parameters and determining the kinetic pool structure of budding yeast proteins at optimal and suboptimal growth temperatures.

Background: Determination of steroid hormones by mass spectrometry (MS) offers several advantages over immunoassays. Modern MS instruments have high selectivity and sensitivity and permit measurements of several compounds at picomolar concentrations in one sample volume, which is advantageous when conducting biobank studies. Methods: We developed a LC-MS/MS method for the determination of 17β-estradiol, 17α-ethinylestradiol, estrone, estrone 3-sulfate, estriol, progesterone, testosterone, dehydroepiandrosterone sulfate, aldosterone, cortisone, and cortisol in 300 μL human serum. Samples were subjected to robotized protein precipitation with acetonitrile and liquid-liquid extraction with ethyl acetate/heptane, and the aqueous and organic phases were used to determine conjugated and unconjugated steroids, respectively. The steroids were separated from isobars and compounds with similar mass (M + 2) on a C18 reversed phase column. Ammonium hydroxide was infused post-column to enhance ionization, and ions were measured in multiple reaction monitoring negative and positive mode. Results: The lower limits of quantification for 17β-estradiol and estrone were 3.6 (0.98 pg/mL) and 2.1 pmol/L (0.57 pg/mL), respectively. For medium concentrations of steroid hormones, total CVs were in the range 2.3% to 10.5%, and accuracies were 97% to 109%. The method was validated in terms of linearity (r2 ≥ 0.9779) and assay recovery (83%–114%), and certified control sera were analyzed. Steroid profiles were obtained in 921 women with a mean age of 56 years (range 44–66 years) from the European Community Respiratory Health Survey. Conclusions: We developed a selective and sensitive LC-MS/MS method for 11 steroids in human serum. It is suitable for biobank studies.