Evidence map›Paper›PMID 25789210›Full record

ArticleSpringerPlus2015

A novel gene network analysis in liver tissues of diabetic rats in response to resistant starch treatment.

Zhiwei Wang, Yinghui Zhang, Runge Shi, Zhongkai Zhou, Fang Wang, Padraig Strappe

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in SpringerPlus, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Zhiwei WangKey Laboratory of Food Nutrition and Safety, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457 China ; School of Food Engineering and Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457 China.
Yinghui ZhangKey Laboratory of Food Nutrition and Safety, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457 China ; School of Food Engineering and Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457 China.
Runge ShiKey Laboratory of Food Nutrition and Safety, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457 China ; School of Food Engineering and Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457 China.
Zhongkai ZhouKey Laboratory of Food Nutrition and Safety, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457 China ; School of Food Engineering and Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457 China.
Fang WangKey Laboratory of Food Nutrition and Safety, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457 China ; School of Food Engineering and Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457 China.
Padraig StrappeSchool of Biomedical Sciences, Charles Sturt University, WaggaWagga, NSW 2678 Australia.
Tianjin University of Science and Technology · CNCharles Sturt University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we investigated the genome-wide gene expression profiles in the liver tissue of diabetic rats before and after RS treatment. The microarray-based analysis revealed that a total of 173 genes were up-regulated and 197 genes were down-regulated in response to RS treatment. These genes were mainly related to glucose metabolism (e.g., hexokinase, pyruvate kinase and phosphotransferase etc.), and lipid metabolism (e.g., carnitine palmitoyl transfer 1, fatty acid transporter, beta hydroxyl butyric dehydrogenase etc.). Cluster analysis results showed that the up/down-regulated genes were highly responsive to RS treatment, and were considered to be directly or indirectly associated with reducing plasma glucose and body fat. To interpret the mechanism of RS regulation at the molecular level, a novel gene network was constructed based on 370 up/down-regulated genes coupled with 718 known diabetes-related genes. The topology of the network showed the characteristics of small-world and scale-free network, with some pathways demonstrating a high degree. Forkhead class A signaling pathway, with a degree of 8, was analyzed and was found to have an effect mainly on glucose and lipid metabolism processes. The results indicate that RS can suppress the development of type 2 diabetes in the STZ rat model through modulating the expression of multiple genes involved in glucose and lipid metabolism. The potential application of this novel gene network is also discussed.

Indexed as

Cluster analysisDiabetes mellitusGene networkMetabolismResistant starch

Identifiers

PMID25789210
PMCPMC4356702
OpenAlexW2145820263

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.