Background

Alzheimer's disease (AD) is the most common cause of dementia characterized by progressive cognitive impairment in the elderly people. The most dramatic abnormalities are those of the cholinergic system. Acetylcholinesterase (AChE) plays a key role in the regulation of the cholinergic system, and hence, inhibition of AChE has emerged as one of the most promising strategies for the treatment of AD.

Methods

In this study, we suggest a workflow for the identification and prioritization of potential compounds targeted against AChE. In order to elucidate the essential structural features for AChE, three-dimensional pharmacophore models were constructed using Discovery Studio 2.5.5 (DS 2.5.5) program based on a set of known AChE inhibitors.

Results

The best five-features pharmacophore model, which includes one hydrogen bond donor and four hydrophobic features, was generated from a training set of 62 compounds that yielded a correlation coefficient of R = 0.851 and a high prediction of fit values for a set of 26 test molecules with a correlation of R2 = 0.830. Our pharmacophore model also has a high Güner-Henry score and enrichment factor. Virtual screening performed on the NCI database obtained new inhibitors which have the potential to inhibit AChE and to protect neurons from Aβ toxicity. The hit compounds were subsequently subjected to molecular docking and evaluated by consensus scoring function, which resulted in 9 compounds with high pharmacophore fit values and predicted biological activity scores. These compounds showed interactions with important residues at the active site.

Conclusions

The information gained from this study may assist in the discovery of potential AChE inhibitors that are highly selective for its dual binding sites.

Cancer is a multifaceted disease that results from dysregulated normal cellular signaling networks caused by genetic, genomic and epigenetic alterations at cell or tissue levels. Uncovering the underlying protein signaling network changes, including cell cycle gene networks in cancer, aids in understanding the molecular mechanism of carcinogenesis and identifies the characteristic signaling network signatures unique for different cancers and specific cancer subtypes. The identified signatures can be used for cancer diagnosis, prognosis, and personalized treatment. During the past several decades, the available technology to study signaling networks has significantly evolved to include such platforms as genomic microarray (expression array, SNP array, CGH array, etc.) and proteomic analysis, which globally assesses genetic, epigenetic, and proteomic alterations in cancer. In this review, we compared Pathway Array analysis with other proteomic approaches in analyzing protein network involved in cancer and its utility serving as cancer biomarkers in diagnosis, prognosis and therapeutic target identification. With the advent of bioinformatics, constructing high complexity signaling networks is possible. As the use of signaling network-based cancer diagnosis, prognosis and treatment is anticipated in the near future, medical and scientific communities should be prepared to apply these techniques to further enhance personalized medicine.

Since current microbiology methods are not suitable to detect Clostridium perfringens in formalin-fixed, paraffin-embedded tissue samples, we developed a PCR assay to detect toxin-encoding genes and the 16S rRNA gene of C. perfringens. We successfully detected and genotyped C. perfringens in tissue sections from two autopsy cases.

Escherichia coli O157:H7 and other Shiga toxin-producing E. coli (STEC) strains are important human pathogens that are mainly transmitted through the food chain. These pathogens have a low infectious dose and may cause life-threatening illnesses. However, detection of this microorganism in contaminated food or a patient's stool specimens presents a diagnostic challenge because of the low copy number in the sample. Often, a more sensitive nucleic acid amplification method, such as PCR, is required for rapid detection of this microorganism. Ramification amplification (RAM) is a recently introduced isothermal DNA amplification technique that utilizes a circular probe for target detection and achieves exponential amplification through the mechanism of primer extension, strand displacement, and ramification. In this study, we synthesized a circular probe specific for the Shiga toxin 2 gene (stx2). Our results showed that as few as 10 copies of stx2 could be detected, indicating that the RAM assay was as sensitive as conventional PCR. We further tested 33 isolates of E coli O157:H7, STEC, Shigella dysenteriae, and nonpathogenic E. coli by RAM assay. Results showed that all 27 STEC isolates containing the stx2 gene were identified by RAM assay, while S. dysenteriae and nonpathogenic E. coli isolates were undetected. The RAM results were 100% in concordance with those of PCR. Because of its simplicity and isothermal amplification, the RAM assay could be a useful method for detecting STEC in food and human specimens.