Analyzing Gene Expression from Whole Tissue vs. Different Cell Types Reveals the Central Role of Neurons in Predicting Severity of Alzheimer’s Disease

Alterations in gene expression resulting from Alzheimer’s disease have received considerable attention in recent years. Although expression has been investigated separately in whole brain tissue, in astrocytes and in neurons, a rigorous comparative study quantifying the relative utility of these sources in predicting the progression of Alzheimer’s disease has been lacking. Here we analyze gene expression from neurons, astrocytes and whole tissues across different brain regions, and compare their ability to predict Alzheimer’s disease progression by building pertaining classification models based on gene expression sets annotated to different biological processes. Remarkably, we find that predictions based on neuronal gene expression are significantly more accurate than those based on astrocyte or whole tissue expression. The findings explicate the central role of neurons, particularly as compared to glial cells, in the pathogenesis of Alzheimer’s disease, and emphasize the importance of measuring gene expression in the most relevant (pathogenically ‘proximal’) single cell types.     

RNA inhibition highlights cyclin d1 as a potential therapeutic target for mantle cell lymphoma

Mantle cell lymphoma is characterized by a genetic translocation results in aberrant overexpression of the CCND1 gene, which encodes cyclin D1. This protein functions as a regulator of the cell cycle progression, hence is considered to play an important role in the pathogenesis of the disease. In this study, we used RNA interference strategies to examine whether cyclin D1 might serve as a therapeutic target for mantle cell lymphoma. Knocking down cyclin D1 resulted in significant growth retardation, cell cycle arrest, and most importantly, induction of apoptosis. These results mark cyclin D1 as a target for mantle cell lymphoma and emphasize the therapeutic potential hidden in its silencing.     

An explorative study on potent Gram-negative specific LpxC inhibitors: CoMFA,CoMSIA, HQSAR and molecular docking

Pathogenic Gram-negative bacteria are responsible for nearly half of the serious human infections. Hologram quantitative structure–activity relationships (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) were implemented on a group of 32 of potent Gram-negative LpxC inhibitors. The most effective HQSAR model was obtained using atoms, bonds, donor, and acceptor as fragment distinction. The cross-validated correlation coefficient (q²), non-cross-validated correlation coefficient (r²), and predictive correlation coefficient (r²_Pred) for test set of HQSAR model were 0.937, 0.993, and 0.892, respectively. The generated models were found to be statistically significant as the CoMFA model had (r²?=?0.967, q²?=?0.804, r²_Pred?=?0.827); the CoMSIA model had (r²?=?0.963, q²?=?0.752, r²_Pred?=?0.857). Molecular docking was employed to validate the results of the HQSAR, CoMFA, and CoMSIA models. Based on the obtained information, six new LpxC inhibitors have been designed.     

Effect of mannose-binding lectin from peanut and pea on the stem borer Chilo partellus

A mannose-binding lectin found in vegetative tissues of peanut, Arachis hypogaea, was compared with mannose-binding lectin from pea, Pisum sativum, for toxic effects on larvae of the stem borer Chilo partellus (Swinhoe). After 10 days, the mortality of larvae fed on artificial diet containing 0.5% (m/m) peanut lectin was 46.2%. The mortality of larvae fed on 1.0% peanut lectin was similar (48.1%) but insects were significantly smaller than those of the 0.5% treatment. Larvae of both lectin treatments stopped feeding within three days. Larval size and mortality was not significantly reduced by 0.1% peanut lectin and 1% heat-treated lectin did not show toxic effects. The mannose-binding lectin from pea was not toxic to C. partellus at concentrations up to 1%. Peanut lectin bound to the apical membranes of columnar epithelial cells in the mid-gut of C. partellus. This suggests that peanut lectin has an antinutritive action and that it may protect vegetative tissues of peanut against insect pests.     

Alu Exonization Events Reveal Features Required for Precise Recognition of Exons by the Splicing Machinery

Despite decades of research, the question of how the mRNA splicing machinery precisely identifies short exonic islands within the vast intronic oceans remains to a large extent obscure. In this study, we analyzed Alu exonization events, aiming to understand the requirements for correct selection of exons. Comparison of exonizing Alus to their non-exonizing counterparts is informative because Alus in these two groups have retained high sequence similarity but are perceived differently by the splicing machinery. We identified and characterized numerous features used by the splicing machinery to discriminate between Alu exons and their non-exonizing counterparts. Of these, the most novel is secondary structure: Alu exons in general and their 5′ splice sites (5′ss) in particular are characterized by decreased stability of local secondary structures with respect to their non-exonizing counterparts. We detected numerous further differences between Alu exons and their non-exonizing counterparts, among others in terms of exon–intron architecture and strength of splicing signals, enhancers, and silencers. Support vector machine analysis revealed that these features allow a high level of discrimination (AUC=0.91) between exonizing and non-exonizing Alus. Moreover, the computationally derived probabilities of exonization significantly correlated with the biological inclusion level of the Alu exons, and the model could also be extended to general datasets of constitutive and alternative exons. This indicates that the features detected and explored in this study provide the basis not only for precise exon selection but also for the fine-tuned regulation thereof, manifested in cases of alternative splicing.