Common congenital anomalies: Environmental causes and prevention with folic acid containing multivitamins

Congenital anomalies, congenital defects, or birth defects are significant causes of death in infants. The most common congenital defects are congenital heart defects (CHDs) and neural tube defects (NTDs). Defects induced by genetic mutations, environmental exposure to toxins, or a combination of these effects can result in congenital malformations, leading to infant death or long‐term disabilities. These defects produce significant mortality and morbidity in the affected individuals, and families are affected emotional and financially. Also, society is impacted on many levels. Congenital anomalies may be reduced by dietary supplements of folic acid and other vitamins. Here, we review the evidence for specific roles of toxins (alcohol, cigarette smoke) in causing common severe congenital anomalies like CHDs, NTDs, and ocular defects. We also review the evidence for beneficial effects for dietary supplementation, and highlight gaps in our knowledge, where research may contribute to additional benefits of intervention that can reduce birth defects. Extensive discussion of common severe congenital anomalies (CHDs, NTDs, and ocular defects) illustrates the effects of diet on the frequency and severity of these defects. Birth Defects Research (Part C) 108:274–286, 2016. © 2016 Wiley Periodicals, Inc.     

Modelling and Characterization of Glial Fibrillary Acidic Protein

Glial Fibrillary Acidic Protein (GFAP) is an intermediate-filament (IF) protein that maintains the astrocytes of the Central Nervous System in Human. This is differentially expressed during serological studies in inflamed condition such as Rheumatoid Arthritis (RA). Therefore, it is of interest to glean molecular insight using a model of GFAP (49.88 kDa) due to its crystallographic nonavailability. The present study has been taken into consideration to construct computational protein model using Modeller 9.11. The structural relevance of the protein was verified using Gromacs 4.5 followed by validation through PROCHECK, Verify 3D, WHAT-IF, ERRAT and PROVE for reliability. The constructed three dimensional (3D) model of GFAP protein had been scrutinized to reveal the associated functions by identifying ligand binding sites and active sites. Molecular level interaction study revealed five possible surface cavities as active sites. The model finds application in further computational analysis towards drug discovery in order to minimize the effect of inflammation.     

Photochemistry of dyes and fluorochromes used in biology and medicine: some physicochemical background and current applications

An overview of the basic principles of photochemistry is presented to facilitate discussion of fluorescence, quenching and quantum yields. These topics in turn provide the foundation for an account of fluorescence spectroscopy and its application to microscopy. A brief overview of light microscopy and the application of fluorescence microscopy is given. The influences of molecular features, such as aromatic character and substitution patterns, on color and fluorescence are described. The concept of color fading is considered with particular reference to its effect on microscopic preparations. A survey of representative fluorescent probes is provided, and their sensitivity, application, and limitations are described. The phototoxicity of fluorescent molecules is discussed using biomembranes and DNA as examples of targets of toxicity. Photodynamic therapy, a relatively new clinical application of phototoxicity, is described. Both anticancer and antimicrobial applications are noted, and an assessment is given of the current ideas on the ideal physicochemical properties of the sensitizing agents for such applications.     

A common neighbor based technique to detect protein complexes in PPI networks

Detection of protein complexes by analyzing and understanding PPI networks is an important task and critical to all aspects of cell biology. We present a technique called PROtein COmplex DEtection based on common neighborhood (PROCODE) that considers the inherent organization of protein complexes as well as the regions with heavy interactions in PPI networks to detect protein complexes. Initially, the core of the protein complexes is detected based on the neighborhood of PPI network. Then a merging strategy based on density is used to attach proteins and protein complexes to the core-protein complexes to form biologically meaningful structures. The predicted protein complexes of PROCODE was evaluated and analyzed using four PPI network datasets out of which three were from budding yeast and one from human. Our proposed technique is compared with some of the existing techniques using standard benchmark complexes and PROCODE was found to match very well with actual protein complexes in the benchmark data. The detected complexes were at par with existing biological evidence and knowledge.     

Extracting proteins involved in disease progression using temporally connected networks

Background

Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time in an individual and through the perturbations of genes. Systematic experiments tracking disease progression at gene level are usually conducted giving a temporal microarray data. There is a need for developing methods to analyze such complex data and extract important proteins which could be involved in temporal progression of the data and hence progression of the disease.

Results

In the present study, we have considered a temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We have used this data along with an available Protein-Protein Interaction network to find a network of interactions between proteins which reproduces the next time point data from previous time point data. We show that the resulting network can be mined to identify critical nodes involved in the temporal progression of perturbations. We further show that published algorithms can be applied on such connected network to mine important proteins and show an overlap between outputs from published and our algorithms. The importance of set of proteins identified was supported by literature as well as was further validated by comparing them with the positive genes dataset from OMIM database which shows significant overlap.

Conclusions

The critical proteins identified from algorithms can be hypothesized to play important role in temporal progression of the data.