High-yield expression in Escherichia coli, biophysical characterization, and biological evaluation of plant toxin gelonin.

Gelonin is a plant toxin that exerts potent cytotoxic activity by inactivation of the 60S ribosomal subunit. The high-level expression of soluble gelonin still remains a great challenge and there was no detailed biophysical analysis of gelonin from Escherichia coli (E. coli) yet. In this study, the soluble and high-yield expression of recombinant gelonin (rGel) was achieved in E. coli BL21 (DE3) for the first time, with a yield of 6.03 mg/L medium. Circular dichroism (CD) analysis indicated that rGel consisted of 21.7% α-helix, 26.3% ß-sheet, 18.5% ß-turn, and 32.3% random coil, and it could maintain its secondary structure up to 60 °C. The antitumor activity of rGel was evaluated in two colon cancer cell lines-HCT116 and HCT-8, and it was clearly demonstrated that rGel exerted antiproliferative activity against these two cell lines by inhibiting cellular protein synthesis. These findings provide insights for researchers involved in the expression of similar biotoxins, and the biophysical characterizations of gelonin will favor its further therapeutic applications.

Dimension reduction with redundant gene elimination for tumor classification.

BACKGROUND: Analysis of gene expression data for tumor classification is an important application of bioinformatics methods. But it is hard to analyse gene expression data from DNA microarray experiments by commonly used classifiers, because there are only a few observations but with thousands of measured genes in the data set. Dimension reduction is often used to handle such a high dimensional problem, but it is obscured by the existence of amounts of redundant features in the microarray data set. RESULTS: Dimension reduction is performed by combing feature extraction with redundant gene elimination for tumor classification. A novel metric of redundancy based on DIScriminative Contribution (DISC) is proposed which estimates the feature similarity by explicitly building a linear classifier on each gene. Compared with the standard linear correlation metric, DISC takes the label information into account and directly estimates the redundancy of the discriminative ability of two given features. Based on the DISC metric, a novel algorithm named REDISC (Redundancy Elimination based on Discriminative Contribution) is proposed, which eliminates redundant genes before feature extraction and promotes performance of dimension reduction. Experimental results on two microarray data sets show that the REDISC algorithm is effective and reliable to improve generalization performance of dimension reduction and hence the used classifier. CONCLUSION: Dimension reduction by performing redundant gene elimination before feature extraction is better than that with only feature extraction for tumor classification, and redundant gene elimination in a supervised way is superior to the commonly used unsupervised method like linear correlation coefficients.

Robust Anticancer Efficacy of a Biologically Synthesized Tumor Acidity-Responsive and Autophagy-Inducing Functional Beclin 1.

As a potent autophagy inducer, Beclin 1 is essential for the initiation of autophagic cell death, and triggering extensive autophagy by targeted delivery of Beclin 1 to tumors has enormous potential to inhibit tumor growth. Yet, the therapeutic application of Beclin 1 is hampered by its inability to internalize into cells and nonselective biodistribution in vivo. To tackle this challenge, we employed a novel Beclin 1 delivery manner by constructing a functional protein (Trx-pHLIP-Beclin 1, TpB) composed of a thioredoxin (Trx) tag, a pH low insertion peptide (pHLIP), and an evolutionarily conserved motif of Beclin 1. This protein could effectively transport Beclin 1 to breast and ovarian cancer cell lines under weakly acidic conditions (pH 6.5), markedly inhibit tumor cell growth and proliferation, and induce obvious autophagy. Furthermore, the in vivo antitumor efficacy of the functional Beclin 1 against an SKOV3 xenograft tumor mouse model was tested via intravenous injection. TpB preferentially accumulated in tumors and exhibited a significantly higher tumor growth inhibition than the nontargeted Beclin 1 control, whereas no overt side effects were observed. Taken together, this study sheds light on the potential application of TpB as a highly efficient yet safe antitumor agent for cancer treatment.

Irrelevant gene elimination for partial least squares based dimension reduction by using feature probes.

It is hard to analyse gene expression data which has only a few observations but with thousands of measured genes. Partial Least Squares based Dimension Reduction (PLSDR) is superior for handling such high dimensional problems, but irrelevant features will introduce errors into the dimension reduction process. Here, feature selection is applied to filter the data and an algorithm named PLSDRg is described by integrating PLSDR with gene elimination, which is performed by the indication of t-statistic scores on standardised probes. Experimental results on six microarray data sets show that PLSDRg is effective and reliable to improve generalisation performance of classifiers.