Is MTHFR 677 C>T Polymorphism Clinically Important in Polycystic Ovarian Syndrome (PCOS)? A Case-Control Study,Meta-Analysis and Trial Sequential Analysis

Background

Optimum efficiency of the folate pathway is considered essential for adequate ovarian function. 677 C>T substitution in the 5, 10-methylene tertrahydrofolatereductase (MTHFR) gene compromises activity of the MTHFR enzyme by about 50%. The significance of correlation between 677C>T substitution and PCOS remains dubious due to the low power of published studies.

Methods and Results

We analyzed MTHFR 677 C>T site in ethnically two different PCOS case-control groups (total 261 cases and 256 controls) from India. The data analysis revealed a lack of association between this polymorphism and PCOS [OR = 1.11 (95%CI = 0.71–1.72), P = 0.66]. Group-wise analysis on the basis of ethnicity also revealed no association in any of the ethnic groups [Indo-Europeans, P = 1; Dravidians, P = 0.70]. Homocysteine levels did not differ significantly between cases (15.51 μmol/L, SD = 2.89) and controls (15.89 μmol/L, SD = 2.23). We also undertook a meta-analysis on 960 cases and 1028 controls, which suggested a significant association of the substitution with PCOS in the dominant model of analysis (OR = 1.47 (95%CI = 1.04–2.09), P = 0.032]. Trial sequential analysis corroborated findings of the traditional meta-analysis. However, we found that the conclusions of meta-analysis were strongly influenced by studies that deviated from the Hardy Weinberg equilibrium. A careful investigation of each study and a trial sequential analysis suggested that 677 C>T substitution holds no clinical significance in PCOS in most of the populations.

Conclusion

In conclusion, MTHFR 677 C>T polymorphism does not affect PCOS risk in India. The association seen in the meta-analysis is due to an outlier study and studies showing deviation from the Hardy Weinberg equilibrium.     

Endoplasmic Reticulum Stress Induced Synthesis of a Novel Viral Factor Mediates Efficient Replication of Genotype-1 Hepatitis E Virus

Hepatitis E virus (HEV) causes acute hepatitis in many parts of the world including Asia, Africa and Latin America. Though self-limiting in normal individuals, it results in ~30% mortality in infected pregnant women. It has also been reported to cause acute and chronic hepatitis in organ transplant patients. Of the seven viral genotypes, genotype-1 virus infects humans and is a major public health concern in South Asian countries. Sporadic cases of genotype-3 and 4 infection in human and animals such as pigs, deer, mongeese have been reported primarily from industrialized countries. Genotype-5, 6 and 7 viruses are known to infect animals such as wild boar and camel, respectively. Genotype-3 and 4 viruses have been successfully propagated in the laboratory in mammalian cell culture. However, genotype-1 virus replicates poorly in mammalian cell culture and no other efficient model exists to study its life cycle. Here, we report that endoplasmic reticulum (ER) stress promotes genotype-1 HEV replication by inducing cap-independent, internal initiation mediated translation of a novel viral protein (named ORF4). Importantly, ORF4 expression and stimulatory effect of ER stress inducers on viral replication is specific to genotype-1. ORF4 protein sequence is mostly conserved among genotype-1 HEV isolates and ORF4 specific antibodies were detected in genotype-1 HEV patient serum. ORF4 interacted with multiple viral and host proteins and assembled a protein complex consisting of viral helicase, RNA dependent RNA polymerase (RdRp), X, host eEF1α1 (eukaryotic elongation factor 1 isoform-1) and tubulinβ. In association with eEF1α1, ORF4 stimulated viral RdRp activity. Furthermore, human hepatoma cells that stably express ORF4 or engineered proteasome resistant ORF4 mutant genome permitted enhanced viral replication. These findings reveal a positive role of ER stress in promoting genotype-1 HEV replication and pave the way towards development of an efficient model of the virus.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Xanthones and 4-phenylcoumarins of Mesua thwaitesii

The constituents of the bark, timber and seeds of Mesua thwaitesii were examined by column chromatography and GC-MS. 1,5-Dihydroxyxanthone, 1,7-dihydroxyxanthone (euxanthone), 1,3-dimethoxy-5-hydroxyxanthone, 1,5,6-trihydroxyxanthone, mammeisin, (4-phenylcoumarin) and sitosterol have been characterized. Nine 4-phenylcoumarins including mammeigin, mesuagin, mammeisin, mesuol, or its isomers, have been identified in the seed extract. One coumarin has not been previously reported.