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

Schizandrin A Protects Human Retinal Pigment Epithelial Cell Line ARPE-19 against HG-Induced Cell Injury by Regulation of miR-145

Diabetic retinopathy (DR) is a serious complication of diabetes, which is the main cause of blindness among adults. Traditional Chinese medicines (TCMs) have been proven to delay the development of DR. Nonetheless, the effect of Schizandrin A (SchA) on DR remains uninvestigated. The present study aimed to probe the protective effect of SchA on high-glucose (HG)-induced injury in ARPE-19 cells. We observed that SchA accelerated cell proliferation, prohibited apoptosis, and restrained pro-inflammatory cytokines (monocyte chemoattractant protein-1 [MCP-1], interleukin-6 [IL-6], and tumor necrosis factor alpha [TNF-α]) and reactive oxygen species (ROS) level in HG-stimulated cells. Additionally, miR-145 expression was upregulated in HG and SchA co-treated cells, and miR-145 inhibition reversed the protective effect of SchA on HG-managed ARPE-19 cells. Interestingly, downregulated myeloid differentiation factor 88 (MyD88) was found in HG and SchA co-treated cells, and upregulation of MyD88 was observed in miR-145 inhibitor-transfected cells. Additionally, SchA hindered nuclear factor κB (NF-κB) and p38 mitogen-activated protein kinase (p38MAPK) signaling pathways in HG-treated ARPE-19 cells. The findings validated that SchA could protect ARPE-19 cells from HG-induced cell injury by regulation of miR-145.

Extracting proteins involved in disease progression using temporally connected networks

BACKGROUND

Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time in an individual and through the perturbations of genes. Systematic experiments tracking disease progression at gene level are usually conducted giving a temporal microarray data. There is a need for developing methods to analyze such complex data and extract important proteins which could be involved in temporal progression of the data and hence progression of the disease.

RESULTS

In the present study, we have considered a temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We have used this data along with an available Protein-Protein Interaction network to find a network of interactions between proteins which reproduces the next time point data from previous time point data. We show that the resulting network can be mined to identify critical nodes involved in the temporal progression of perturbations. We further show that published algorithms can be applied on such connected network to mine important proteins and show an overlap between outputs from published and our algorithms. The importance of set of proteins identified was supported by literature as well as was further validated by comparing them with the positive genes dataset from OMIM database which shows significant overlap.

CONCLUSIONS

The critical proteins identified from algorithms can be hypothesized to play important role in temporal progression of the data.

Tracking disease progression by searching paths in a temporal network of biological processes

Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time through the perturbations of biological processes. These perturbed biological processes usually work in an interdependent way. Systematic experiments tracking disease progression at gene level are usually conducted through a temporal microarray data. There is a need for developing methods to analyze such highly complex data to capture disease progression at the molecular level. In the present study, we have considered temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We first constructed a network between biological processes through common genes. We analyzed the data to obtain perturbed biological processes at each time point. Finally, we used the biological process network to find links between these perturbed biological processes. This enabled us to identify paths linking initial perturbed processes with final perturbed processes which capture disease progression. Using different datasets and statistical tests, we established that these paths are highly precise to the dataset from which these are obtained. We also established that the connecting genes present in these paths might contain some biological information and thus can be used for further mechanistic studies. The methods developed in our study are also applicable to a broad array of temporal data.

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

Background

To 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.

Results

We 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.

Conclusions

Clustered 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 material

The online version of this article (doi:10.1186/s12859-016-1356-3) contains supplementary material, which is available to authorized users.

A Comprehensive Inter-Tissue Crosstalk Analysis Underlying Progression and Control of Obesity and Diabetes

Obesity is a metabolic state associated with excess of positive energy balance. While adipose tissues are considered the major contributor for complications associated with obesity, they influence a variety of tissues and inflict significant metabolic and inflammatory alterations. Unfortunately, the communication network between different cell-types responsible for such systemic alterations has been largely unexplored. Here we study the inter-tissue crosstalk during progression and cure of obesity using multi-tissue gene expression data generated through microarray analysis. We used gene expression data sets from 10 different tissues from mice fed on high-fat-high-sugar diet (HFHSD) at various stages of disease development and applied a novel analysis algorithm to deduce the tissue crosstalk. We unravel a comprehensive network of inter-tissue crosstalk that emerges during progression of obesity leading to inflammation and insulin resistance. Many of the crosstalk involved interactions between well-known modulators of obesity and associated pathology like inflammation. We then used similar datasets from mice that in addition to HFHSD were also administered with a herbal concoction known to circumvent the effects of HFHSD in the diet induced model of obesity in mice. We propose, the analysis presented here could be applied to understand systemic details of several chronic diseases.

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

Background

Obesity 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.

Results

We 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.

Conclusion

We 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.