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Fulltext Fluorinated curcumin derivative (Shiga-Y6) modulates the level of thioredoxin-interacting protein (TXNIP) in a mouse model of diabetes

Aldoghachi AF, Yanagisawa D, Pahrudin Arrozi A, Abu Bakar ZH, Taguchi H, Ishigaki S, et al.

Biochem Biophys Res Commun, 2024 Jan 29;694:149392.
PMID: 38142581 DOI: 10.1016/j.bbrc.2023.149392

Thioredoxin interacting protein (TXNIP) has emerged as a significant regulator of β-cell mass and loss, rendering it an attractive target for treating diabetes. We previously showed that Shiga-Y6, a fluorinated curcumin derivative, inhibited TXNIP mRNA and protein expression in vitro, raising the question of whether the same effect could be translated in vivo. Herein, we examined the effect of Shiga-Y6 on TNXIP levels and explored its therapeutic potential in a mouse model of diabetes, Akita mice. We intraperitoneally injected Shiga-Y6 (SY6; 30 mg/kg of body weight) or vehicle into 8-week-old Akita mice for 28 consecutive days. On day 29, the mice were euthanized, following which the serum levels of glucose, insulin, and glucagon were measured using ELISA, the expression of TXNIP in pancreatic tissue lysates was determined using western blotting, and the level of β-cell apoptosis was assessed using the TUNEL assay. TXNIP levels in the pancreatic tissue of Akita mice were significantly elevated compared with wild-type (WT) mice. Shiga-Y6 administration for 28 days significantly lowered those levels compared with Akita mice that received vehicle to a level comparable to WT mice. In immunohistochemical analysis, both α- to β-cell ratio and the number of apoptotic β-cells were significantly reduced in SY6-treated Akita mice, compared with vehicle-treated Akita mice. Findings from the present study suggest a potential of Shiga-Y6 as an antidiabetic agent through lowering TXNIP protein levels and ameliorating pancreatic β-cells apoptosis.

Matched MeSH terms: Thioredoxins/genetics
NETASA: neural network based prediction of solvent accessibility

Ahmad S, Gromiha MM

Bioinformatics, 2002 Jun;18(6):819-24.
PMID: 12075017

MOTIVATION: Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction.
RESULTS: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed.
AVAILABILITY: Online predictions are freely available at: http://www.netasa.org. Linux ix86 binaries of the program written for this work may be obtained by email from the corresponding author.

Matched MeSH terms: Thioredoxins/genetics
Fulltext Proteomic analysis of whole human saliva detects enhanced expression of interleukin-1 receptor antagonist, thioredoxin and lipocalin-1 in cigarette smokers compared to non-smokers

Jessie K, Pang WW, Haji Z, Rahim A, Hashim OH

Int J Mol Sci, 2010 Nov 09;11(11):4488-505.
PMID: 21151451 DOI: 10.3390/ijms11114488

A gel-based proteomics approach was used to screen for proteins of differential abundance between the saliva of smokers and those who had never smoked. Subjecting precipitated proteins from whole human saliva of healthy non-smokers to two-dimensional electrophoresis (2-DE) generated typical profiles comprising more than 50 proteins. While 35 of the proteins were previously established by other researchers, an additional 22 proteins were detected in the 2-DE saliva protein profiles generated in the present study. When the 2-DE profiles were compared to those obtained from subjects considered to be heavy cigarette smokers, three saliva proteins, including interleukin-1 receptor antagonist, thioredoxin and lipocalin-1, showed significant enhanced expression. The distribution patterns of lipocalin-1 isoforms were also different between cigarette smokers and non-smokers. The three saliva proteins have good potential to be used as biomarkers for the adverse effects of smoking and the risk for inflammatory and chronic diseases that are associated with it.

Matched MeSH terms: Thioredoxins/genetics

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