Immunomodulatory Role of an Ayurvedic Formulation on Imbalanced Immunometabolics during Inflammatory Responses of Obesity and Prediabetic Disease

Tikoo, Kamiya; Misra, Shashank; Rao, Kanury V. S.; Tripathi, Parul; Sharma, Sachin

doi:10.1155/2013/795072

Cited by 8 publications

(22 citation statements)

References 35 publications

(44 reference statements)

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“…HFHSD fed mice were then further separated into subgroups that either received no additional treatment, or were treated with different doses of the Ayurvedic formulation KAL-1. Experimental results, based on the physiological parameters like body weight, blood glucose levels and cytokine profiles, confirmed the earlier findings [ 6 ] that KAL-1 prevented mice from both obesity and diabetes. Thus, by comparing between the different groups, our experimental system offered an opportunity to identify disease-specific perturbations in gene expression in different tissues.…”

Section: Introductionsupporting

confidence: 86%

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Molecular signatures for obesity and associated disorders identified through partial least square regression models

et al. 2014

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BackgroundObesity is now a worldwide epidemic disease and poses a major risk for diet related diseases like type 2 diabetes, cardiovascular disease, stroke and fatty liver among others. In the present study we employed the murine model of diet-induced obesity to determine the early, tissue-specific, gene expression signatures that characterized progression to obesity and type 2 diabetes.ResultsWe used the C57BL/6 J mouse which is known as a counterpart for diet-induced human diabetes and obesity model. Our initial experiments involved two groups of mice, one on normal diet (ND) and the other on high-fat and high-sucrose (HFHSD). The later were then further separated into subgroups that either received no additional treatment, or were treated with different doses of the Ayurvedic formulation KAL-1. At different time points (week3, week6, week9, week12, week15 and week18) eight different tissues were isolated from mice being fed on different diet compositions. These tissues were used to extract gene-expression data through microarray experiment. Simultaneously, we also measured different body parameters like body weight, blood Glucose level and cytokines profile (anti-inflammatory & pro-inflammatory) at each time point for all the groups.Using partial least square discriminant analysis (PLS-DA) method we identified gene-expression signatures that predict physiological parameters like blood glucose levels, body weight and the balance of pro- versus anti-inflammatory cytokines. The resulting models successfully predicted diet-induced changes in body weight and blood glucose levels, although the predictive power for cytokines profiles was relatively poor. In the former two instances, however, we could exploit the models to further extract the early gene-expression signatures that accurately predict the onset of diabetes and obesity. These extracted genes allowed definition of the regulatory network involved in progression of disease.ConclusionWe identified the early gene-expression signature for the onset of obesity and type 2 diabetes. Further analysis of this data suggests that some of these genes could be used as potential biomarkers for these two disease-states.

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Section: Introductionsupporting

confidence: 86%

“…al . [ 6 ] showed that KAL-1, a formulation derived from Ayurveda which a system of Indian Traditional Medicine, was extremely effective at suppressing the development of diet-induced obesity in the mouse model. Importantly, development of other related disorders of type 2 diabetes and systemic inflammation was also prevented.…”

Section: Introductionmentioning

confidence: 99%

Molecular signatures for obesity and associated disorders identified through partial least square regression models

et al. 2014

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“…Then, microarray experiment was performed on tissue samples and after suitable normalization of the signal intensities of each probe using Agilent Genespring GX software, three values of log fold change for the control sample and the treated sample were obtained for each probe and at each time for each tissue. Further details of the experiment are given in [17]. …”

Section: Methodsmentioning

confidence: 99%

“…The cancer dataset was taken from GDS3716. The liver microarray dataset used to show application of the algorithm was taken from [17]. This data is also available in from the NCBI repository under GEO accession number GSE63175.…”

Section: Acknowledgementsmentioning

confidence: 99%

A new method of finding groups of coexpressed genes and conditions of coexpression

2016

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BackgroundTo study a biological phenomenon such as finding mechanism of disease, common methodology is to generate the microarray data in different relevant conditions and find groups of genes co-expressed across conditions from such data. These groups might enable us to find biological processes involved in a disease condition. However, more detailed understanding can be made when information of a biological process associated with a particular condition is obtained from the data. Many algorithms are available which finds groups of co-expressed genes and associated conditions of co-expression that can help finding processes associated with particular condition. However, these algorithms depend on different input parameters for generating groups. For real datasets, it is difficult to use these algorithms due to unknown values of these parameters.ResultsWe present here an algorithm, clustered groups, which finds groups of co-expressed genes and conditions of co-expression with minimal input from user. We used random datasets to derive a cutoff on the basis of which we filtered the resultant groups and showed that this can improve the relevance of obtained groups. We showed that the proposed algorithm performs better than other known algorithms on both real and synthetic datasets. We have also shown its application on a temporal microarray dataset by extracting biclusters and biological information hidden in those biclusters.ConclusionsClustered groups is an algorithm which finds groups of co-expressed genes and conditions of co-expression using only a single parameter. We have shown that it works better than other existing algorithms. It can be used to find these groups in different data types such as microarray, proteomics, metabolomics etc.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1356-3) contains supplementary material, which is available to authorized users.

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“…Around the world millions of people are using traditional medicines and formulations based on Ayurveda as front line medicinal product for diabetes. 7 Ayurveda, which means "science of life", is a traditional Indian system of medicine that has been documented and practiced since 3000 B.C. 8 It is an alternative form of medicine, composed of elements of physiology, pathology, pharmacology, material medica and surgery.…”

Section: It Ismentioning

confidence: 99%

Effects of a standardized Ayurvedic formulation on diabetes control in newly diagnosed Type-2 diabetics; a randomized active controlled clinical study

Awasthi¹,

Nath²,

Usman³

et al. 2015

Complementary Therapies in Medicine

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Immunomodulatory Role of an Ayurvedic Formulation on Imbalanced Immunometabolics during Inflammatory Responses of Obesity and Prediabetic Disease

Cited by 8 publications

References 35 publications

Molecular signatures for obesity and associated disorders identified through partial least square regression models

Molecular signatures for obesity and associated disorders identified through partial least square regression models

A new method of finding groups of coexpressed genes and conditions of coexpression

Effects of a standardized Ayurvedic formulation on diabetes control in newly diagnosed Type-2 diabetics; a randomized active controlled clinical study

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