The impact of ICAM-1, CCL2 and TGM2 gene polymorphisms on differentiation syndrome in acute promyelocytic leukemia

Factor VII R353Q (rs6046), FGA A6534G (rs6050), and FGG C10034T (rs2066865) Gene Polymorphisms and Risk of Recurrent Pregnancy Loss in Iranian Women

Recurrent pregnancy loss is a multi factorial and heterogeneous disorder defined as two or more consecutive pregnancy losses before 20 weeks' gestation. Gene polymorphisms including factor VII R353Q (rs6046), fibrinogen alpha chain A6534G (rs6050) and fibrinogen gamma chain C10034T (rs2066865) have potential role in thrombophilia and the relation between these three polymorphisms and an increased risk of venous thrombosis have been reported. As thrombophilia is associated with a considerable proportion of pregnancy loss and the association between these gene polymorphisms and recurrent pregnancy loss remains controversial, the aim of the present study was to evaluate the relation of these polymorphisms and recurrent pregnancy loss in Iranian women. A total of 144 women with a history of two or more consecutive miscarriages as the patient group and 150 healthy women with two live births and no history of pregnancy loss as the control group were included in the study. Polymerase chain reaction and restriction fragment length polymorphism were used for genotyping. The results were validated by DNA sequencing. The SPSS, SNPStats and Finch TV were used to analyze the results. Factor VII R353Q (rs6046) gene polymorphism showed a significant difference between RPL patients and the control group according to multiple logistic regression models [codominant (OR=0.38; 95% CI=0.23-0.63, P≤0.0001), dominant (OR=0.32; 95% CI=0.20-0.52, P≤0.0001), over dominant (OR=0.46; 95% CI=0.29-0.75, P=0.0017) and log-additive (OR=0.35; 95% CI=0.23-0.53, P≤0.0001)]. Fibrinogen alpha chain A6534G (rs6050) and fibrinogen gamma chain C10034T (rs2066865) gene polymorphisms showed no correlation with recurrent pregnancy loss. Factor VII R353Q (rs6046) gene polymorphism can be considered a risk factor for recurrent pregnancy loss. Further studies in larger populations are needed to confirm the findings.

The Role of Transglutaminase 2 in Cancer: An Update

Transglutaminase type 2 (TG2) is the most ubiquitously expressed and well characterized member of the transglutaminase family. It is a ubiquitous multifunctional enzyme implicated in the regulation of several cellular pathways that support the survival, death, and general homeostasis of eukaryotic cells. Due to its multiple localizations both inside and outside the cell, TG2 participates in the regulation of many crucial intracellular signaling cascades in a tissue- and cell-specific manner, making this enzyme an important player in disease development and progression. Moreover, TG2 is capable of modulating the tumor microenvironment, a process of dynamic tissue remodeling and biomechanical events, resulting in changes which influence tumor initiation, growth, and metastasis. Even if generally related to the Ca2+-dependent post-translational modification of proteins, a number of different biological functions have been ascribed to TG2, like those of a peptide isomerase, protein kinase, guanine nucleotide binder, and cytosolic-nuclear translocator. With respect to cancer, TG2's role is controversial and highly debated; it has been described both as an anti- and pro-apoptotic factor and is linked to all the processes of tumorigenesis. However, numerous pieces of evidence support a tissue-specific role of TG2 so that it can assume both oncogenic and tumor-suppressive roles.

Roles of PADI4 in the expression of cytokines involved in inflammation and adhesion in differentiated NB4 cells treated with ATRA

Differentiation syndrome (DS) is a common complication in patients with acute promyelocytic leukemia (APL) treated with all-trans-retinoic acid (ATRA). However, the target of ATRA during DS in patients with APL remains to be elucidated. Therefore, the current study aimed to investigate the role of peptidylarginine deiminase 4 (PADI4) in the differentiation of ATRA-induced NB4 APL cells. The results showed that PADI4 was significantly upregulated in peripheral blood samples derived from patients with APL DS compared with patients with APL only. In addition, whether ATRA could enhance the expression levels of PADI4 in NB4 cells in vitro was subsequently investigated. The results also showed that PADI4 overexpression promoted the differentiation of NB4 cells treated with ATRA, which was reversed after PADI4 silencing. To uncover the potential mechanisms underlying the above process, PADI4 overexpression induced the secretion of inflammation-related cytokines, such as TNF-α, IL-1β, IL-8, C-C motif chemokine (CCL)2, CCL4, CCR1 and intercellular adhesion molecule-1 in ATRA-treated NB4 cells. However, PADI4 knockdown in the same cells had the opposite effect. The above findings indicated that PADI4 could be involved in the differentiation of ATRA-induced NB4 cells and upregulation of cytokines.

M2PP: a novel computational model for predicting drug-targeted pathogenic proteins

BACKGROUND

Detecting pathogenic proteins is the origin way to understand the mechanism and resist the invasion of diseases, making pathogenic protein prediction develop into an urgent problem to be solved. Prediction for genome-wide proteins may be not necessarily conducive to rapidly cure diseases as developing new drugs specifically for the predicted pathogenic protein always need major expenditures on time and cost. In order to facilitate disease treatment, computational method to predict pathogenic proteins which are targeted by existing drugs should be exploited.

RESULTS

In this study, we proposed a novel computational model to predict drug-targeted pathogenic proteins, named as M2PP. Three types of features were presented on our constructed heterogeneous network (including target proteins, diseases and drugs), which were based on the neighborhood similarity information, drug-inferred information and path information. Then, a random forest regression model was trained to score unconfirmed target-disease pairs. Five-fold cross-validation experiment was implemented to evaluate model's prediction performance, where M2PP achieved advantageous results compared with other state-of-the-art methods. In addition, M2PP accurately predicted high ranked pathogenic proteins for common diseases with public biomedical literature as supporting evidence, indicating its excellent ability.

CONCLUSIONS

M2PP is an effective and accurate model to predict drug-targeted pathogenic proteins, which could provide convenience for the future biological researches.