Delivery of a protein transduction domain-mediated Prdx6 protein ameliorates oxidative stress-induced injury in human and mouse neuronal cells

Singh, Satwant; Chhunchha, Bhavana; Fatma, Nigar; Kubo, Eri; Singh, Sanjay; Singh, D. P.

doi:10.1152/ajpcell.00229.2015

Cited by 39 publications

(25 citation statements)

References 62 publications

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“…PRDX6 is a protective protein with GSH peroxidase. Studies showed that PRDX6 could scavenge reactive oxygen species (ROS) to regulate cellular physiology (Singh et al, ). Inhibition of the expression of PRDX6 could increase the ROS level (Zha et al, ).…”

Section: Resultsmentioning

confidence: 99%

Proteomic analysis of the molecular mechanism of curcumin/β‐cyclodextrin polymer inclusion complex inhibiting HepG2 cells growth

et al. 2019

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This research aimed to explore whether curcumin/β-cyclodextrin polymer (CUR/ CDP) inclusion complex caused inhibitory effect on HepG2 cells proliferation and its possible molecular mechanisms. We found that CUR/CDP inclusion complex exhibited inhibitory effects on HepG2 cells growth. To understand the underlying mechanism of how CUR/CDP inclusion complex inhibited HepG2 cells growth, we examined the proteome of HepG2 cells treated at 640 μg/ml CUR/CDP inclusion complex using proteomic approach. We found that 15 protein spots identified by MALDI-TOF/TOF MS were changed. Biological progress analysis demonstrated that protein related to cell cycle and apoptosis accounted for 33% of the detected proteins. Among these proteins, nucleophosmin (NPM1) and peroxiredoxin-6 (PRDX6) were involved in the ROS-P53 pathway. PRDX6 and NPM1 were down-regulated, thereby improving the expression level of phosphorylated p53 and ROS content, which regulated cell apoptosis. This findings provide a better understanding of CUR/CDP regulatory antitumor mechanisms. Practical applicationsCyclodextrins polymer was used to prepare the curcumin/cyclodextrins polymer inclusion complex to improve curcumin solubility and stability in our group. And we found that it showed novel anticancer activity. However, the molecular mechanisms is unclear. This research elucidates the underlying molecular mechanisms of the curcumin/cyclodextrins polymer inclusion complex-inhibited HepG2 cells growth. The inclusion complex has the potential to be a novel complex of curcumin to treat human cancers K E Y W O R D S apoptosis, curcumin/β-cyclodextrin polymer inclusion complex, HepG2 cells, proteomic analysis

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Section: Resultsmentioning

confidence: 99%

Proteomic analysis of the molecular mechanism of curcumin/β‐cyclodextrin polymer inclusion complex inhibiting HepG2 cells growth

et al. 2019

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“…Its use is children is specifically indicated for seizures related with Lennox-Gastaut syndrome (LGS) (Crumrine, 2011;Donegan, Dixon, Hemming, Tudur-Smith, & Marson, 2015;Hoy, 2016). Topic modeling showed a strong association of this agent with Lennox-Gastaut syndrome, a disorder which initiates seizures in children (Crumrine, 2011;Singh, 2016;Singh et al, 2016;VanStraten & Ng, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Although the mechanism of action of this drug is unknown, in vitro pharmacological studies suggest that lamotrigine inhibits voltage-sensitive sodium channels, thereby stabilizing neuronal membranes and consequently modulating presynaptic transmitter release of excitatory amino acids (e.g., glutamate and aspartate). Topic modeling revealed the association of this drug with the terms "dried blood spots", which suggests that research efforts have been focused on evaluating the safety profile of this drug, specifically in causing blood dyscrasias (Krasowski & McMillin, 2014;Milosheska, Grabnar, & Vovk, 2015;Baswan, Li, LaCount, & Kasting, 2016;Singh et al, 2016;.…”

Section: Discussionmentioning

confidence: 99%

The Application of Text Mining Algorithms In Summarizing Trends in Anti-Epileptic Drug Research

Singh¹,

Karkare²,

Baswan³

et al. 2018

IJSP

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Content summarization is an important area of research in traditional data mining. The volume of studies published on anti-epileptic drugs (AED) has increased exponentially over the last two decades, making it an important area for the application of text mining based summarization algorithms. In the current study, we use text analytics algorithms to mine and summarize 10,000 PubMed abstracts related to anti-epileptic drugs published within the last 10 years. A Text Frequency -Inverse Document Frequency based filtering was applied to identify drugs with highest frequency of mentions within these abstracts. The US Food and Drug database was scrapped and linked to the results to quantify the most frequently mentioned modes of action and elucidate the pharmaceutical entities marketing these drugs. A sentiment analysis model was created to score the abstracts for sentiment positivity or negativity. Finally, a modified Latent Dirichlet Allocation topic model was generated to extract key topics associated with the most frequently mentioned AEDs. We found the top five most common drugs that appeared from the analysis were Gabapentin, Levetiracetam, Topiramate, Lamotrigine and Acetazolamide. We further listed the key topics associated with these drugs and the overall positive or negative sentiment associated with them. Results of this study provide accurate and data intensive insights on the progress of anti-epileptic drug research.In this study, we analyze 10,000 PubMed abstracts related to AEDs with the end goal of content summarization and Due to the coupling between θ and φ in the integrand (above equation), the precise implication in mLDA is obstinate (Blei et al., 2003).The number of topics was selected according to the Rate of Perplexity Change (RPC) previously described by Zhao and colleagues (Zhao et al., 2015). This algorithm yielded two key topics on average which were further curated manually.

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“…The R packages "Cluster" (v 2.0.7.1) and "Dendextend' (1.8.0) was employed for this analysis. Significance testing for normal, non-normal and binary data was performed as described previously by the authors in other studies (Fleming et al, 2018;Hester et al, 2016;Karkare et al, 2014;Singh, 2015Singh, , 2016Singh et al, 2016;Singh, He, McNamara, & Danzer, 2013;Singh, Karkare, Baswan, & Singh, 2018;Singh, LaSarge, An, McAuliffe, & Danzer, 2015;Singh, Singh, Fatima, Kubo, & Singh, 2008;Singh & Karkare, 2017;Singh & Singh, 2017). De-identified data and the statistical analysis R code used was uploaded to an online repository.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised Machine Learning for Co/Multimorbidity Analysis

Singh¹,

Karkare²,

Baswan³

et al. 2018

IJSP

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Although co/multimorbidities are associated with a significant increase in mortality, lack of quantitative exploratory techniques often impedes an in-depth analysis of their association. In the current study, we explore the clustering of co/multimorbid patients in the Texas patient population. We employ unsupervised agglomerative hierarchical clustering to find clusters of co/multimorbid patients within this population. Our analysis revealed the presence of nine distinct, clinically relevant clusters of co/multimorbidities within the study population of interest. This technique provides a quantitative exploratory analysis of the co/multimorbidities present in a specific population.

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Delivery of a protein transduction domain-mediated Prdx6 protein ameliorates oxidative stress-induced injury in human and mouse neuronal cells

Abstract: Singh SP, Chhunchha B, Fatma N, Kubo E, Singh SP, Singh DP. Delivery of a protein transduction domain-mediated Prdx6 protein ameliorates oxidative stress-induced injury in human and mouse neuronal cells.

Cited by 39 publications

References 62 publications

Proteomic analysis of the molecular mechanism of curcumin/β‐cyclodextrin polymer inclusion complex inhibiting HepG2 cells growth

Proteomic analysis of the molecular mechanism of curcumin/β‐cyclodextrin polymer inclusion complex inhibiting HepG2 cells growth

The Application of Text Mining Algorithms In Summarizing Trends in Anti-Epileptic Drug Research

Unsupervised Machine Learning for Co/Multimorbidity Analysis

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