Adequate lipid secretion by mammary glands during lactation is essential for the survival of mammalian offspring. However, the mechanism governing this process is poorly understood. Here we show that Cidea is expressed at high levels in lactating mammary glands and its deficiency leads to premature pup death as a result of severely reduced milk lipids. Furthermore, the expression of xanthine oxidoreductase (XOR), an essential factor for milk lipid secretion, is markedly lower in Cidea-deficient mammary glands. Conversely, ectopic Cidea expression induces the expression of XOR and enhances lipid secretion in vivo. Unexpectedly, as Cidea has heretofore been thought of as a cytoplasmic protein, we detected it in the nucleus and found it to physically interact with transcription factor CCAAT/enhancer-binding protein β (C/EBPβ) in mammary epithelial cells. We also observed that Cidea induces XOR expression by promoting the association of C/EBPβ onto, and the dissociation of HDAC1 from, the promoter of the Xdh gene encoding XOR. Finally, we found that Fsp27, another CIDE family protein, is detected in the nucleus and interacts with C/EBPβ to regulate expression of a subset of C/EBPβ downstream genes in adipocytes. Thus, Cidea acts as a previously unknown transcriptional coactivator of C/EBPβ in mammary glands to control lipid secretion and pup survival.

P-glycoprotein (P-gp) is one of the major ABC transporters and involved in many essential processes such as lipid and steroid transport across cell membranes but also in the uptake of drugs such as HIV protease and reverse transcriptase inhibitors. Despite its importance, reliable models predicting substrates of P-gp are scarce. In this study, we have built several computational models to predict whether or not a compound is a P-gp substrate, based on the largest data set yet published, employing 332 distinct structures. Each molecule is represented by ADRIANA.Code, MOE, and ECFP_4 fingerprint descriptors. The models are computed using a support vector machine based on a training set which includes 131 substrates and 81 nonsubstrates that were evaluated by 5-, 10-fold, and leave-one-out (LOO) cross-validation. The best model gives a Matthews Correlation Coefficient of 0.73 and a prediction accuracy of 0.88 on the test set. Examination of the model based on ECFP_4 fingerprints revealed several substructures which could have significance in separating substrates and nonsubstrates of P-gp, such as the nitrile and sulfoxide functional groups which have a higher frequency in nonsubstrates than in substrates. In addition structural isomerism in sugars was found to result in remarkable differences regarding the likelihood of a compound to be a substrate for P-gp.

Platelets respond to vascular injury via surface receptor stimulation and signaling events to trigger aggregation, procoagulant activation, and granule secretion during hemostasis, thrombosis, and vascular remodeling. Platelets contain three major types of secretory granules including dense granules (or δ-granules, DGs), α-granules (AGs), and lysosomes. The contents of platelet granules are specific. Platelet DGs store polyphosphate and small molecules such as ADP, ATP, Ca2+, and serotonin, while AGs package most of the proteins that platelets release. The platelet DGs and AGs are regarded as being budded from the endosomes and the trans-Golgi network (TGN), respectively, and then matured from multivesicular bodies (MVBs). However, the sorting machineries between DGs and AGs are different. Inherited platelet disorders are associated with deficiency of DGs and AGs, leading to bleeding diathesis in patients with Hermansky–Pudlak syndrome (HPS), gray platelet syndrome (GPS), and arthrogryposis, renal dysfunction, and cholestasis syndrome (ARC). Here, we reviewed the current understanding about how DGs differ from AGs in structure, biogenesis, and function. In particular, we focus on the sorting machineries that are involved in the formation of these two types of granules to provide insights into their diverse biological functions.