Women with endometriosis have higher comorbidities: Analysis of domestic data in Taiwan

AbstractEndometriosis, defined by the presence of viable extrauterine endometrial glands and stroma, can grow or bleed cyclically, and possesses characteristics including a destructive, invasive, and metastatic nature. Since endometriosis may result in pelvic inflammation, adhesion, chronic pain, and infertility, and can progress to biologically malignant tumors, it is a long-term major health issue in women of reproductive age. In this review, we analyze the Taiwan domestic research addressing associations between endometriosis and other diseases. Concerning malignant tumors, we identified four studies on the links between endometriosis and ovarian cancer, one on breast cancer, two on endometrial cancer, one on colorectal cancer, and one on other malignancies, as well as one on associations between endometriosis and irritable bowel syndrome, one on links with migraine headache, three on links with pelvic inflammatory diseases, four on links with infertility, four on links with obesity, four on links with chronic liver disease, four on links with rheumatoid arthritis, four on links with chronic renal disease, five on links with diabetes mellitus, and five on links with cardiovascular diseases (hypertension, hyperlipidemia, etc.). The data available to date support that women with endometriosis might be at risk of some chronic illnesses and certain malignancies, although we consider the evidence for some comorbidities to be of low quality, for example, the association between colon cancer and adenomyosis/endometriosis. We still believe that the risk of comorbidity might be higher in women with endometriosis than that we supposed before. More research is needed to determine whether women with endometriosis are really at risk of these comorbidities

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors

IL28B SNP rs12979860 Is a Critical Predictor for On-Treatment and Sustained Virologic Response in Patients with Hepatitis C Virus Genotype-1 Infection

Single nucleotide polymorphisms (SNPs) of interleukin-28B (IL28B) have received considerable interest for their association with sustained virological response (SVR) when treating patients of genotype-1 hepatitis C virus (GT1-HCV) chronic infection with pegylated interferon and ribavirin (PegIFN/RBV). This study was to investigate the predictive power of IL28B SNPs for on-treatment responses and SVR in treatment-naïve patients with GT1-HCV chronic infection.We analyzed ten SNPs of IL28B in 191 treatment-naïve patients with GT1-HCV chronic infection who received PegIFN/RBV. In these patients, rapid virological response (RVR), early virological response (EVR) and SVR were achieved in 69.6%, 95.8% and 68.6% of the patients, respectively. Multivariate analysis (odds ratio; 95% confidence interval; P value) indicated age (0.96; 0.93-0.99; 0.012), low baseline viral load (4.65; 2.23-9.66; <0.001) and CC genotype of rs12979860 (7.74; 2.55-23.53; <0.001) but no other SNPs were independent predictors for SVR. In addition, none of the ten SNPs examined were associated with baseline viral load and stages of liver fibrosis. Regarding RVR, low baseline viral load (2.83; 1.40-5.73; 0.004) and CC genotype of rs12979860 (10.52; 3.45-32.04; <0.001) were two critical predictors. As for EVR, only CC genotype of rs12979860 (36.21; 6.68-196.38; <0.001) was the predictor. Similarly, for end of treatment response (ETR), CC genotype of rs12979860 (15.42; 4.62-51.18; <0.001) was the only predictor. For patients with RVR, only low baseline viral load (3.90; 1.57-9.68; 0.003) could predict the SVR. For patients without RVR, only rs12979860 (4.60; 1.13-18.65; 0.033) was the predictor for SVR.rs12979860 is the critical predictor for RVR, EVR, ETR and SVR in treatment-naïve patients of GT1-HCV chronic infection. Furthermore, this SNP is the only predictor for SVR in patients without RVR. These results have provided evidence that rs12979860 is the ideal IL28B SNP for genetic testing in treating patients of GT1-HCV chronic infection

Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes

Structural Assembly of Q&beta; Virion and Its Diverse Forms of Virus-like Particles

The coat proteins (CPs) of single-stranded RNA bacteriophages (ssRNA phages) directly assemble around the genomic RNA (gRNA) to form a near-icosahedral capsid with a single maturation protein (Mat) that binds the gRNA and interacts with the retractile pilus during infection of the host. Understanding the assembly of ssRNA phages is essential for their use in biotechnology, such as RNA protection and delivery. Here, we present the complete gRNA model of the ssRNA phage Q&beta;, revealing that the 3&prime; untranslated region binds to the Mat and the 4127 nucleotides fold domain-by-domain, and is connected through long-range RNA&ndash;RNA interactions, such as kissing loops. Thirty-three operator-like RNA stem-loops are located and primarily interact with the asymmetric A/B CP-dimers, suggesting a pathway for the assembly of the virions. Additionally, we have discovered various forms of the virus-like particles (VLPs), including the canonical T = 3 icosahedral, larger T = 4 icosahedral, prolate, oblate forms, and a small prolate form elongated along the 3-fold axis. These particles are all produced during a normal infection, as well as when overexpressing the CPs. When overexpressing the shorter RNA fragments encoding only the CPs, we observed an increased percentage of the smaller VLPs, which may be sufficient to encapsidate a shorter RNA