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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes
|
|---|---|
| Published in |
PLoS Computational Biology, April 2010
|
| DOI | 10.1371/journal.pcbi.1000729 |
| Pubmed ID | |
| Authors |
Aleksej Zelezniak, Tune H. Pers, Simão Soares, Mary Elizabeth Patti, Kiran Raosaheb Patil |
| Abstract |
Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites--metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 221 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 2% |
| Spain | 3 | 1% |
| Netherlands | 2 | <1% |
| United States | 2 | <1% |
| Italy | 2 | <1% |
| Iran, Islamic Republic of | 2 | <1% |
| Switzerland | 1 | <1% |
| Singapore | 1 | <1% |
| France | 1 | <1% |
| Other | 5 | 2% |
| Unknown | 198 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 63 | 29% |
| Researcher | 53 | 24% |
| Student > Master | 24 | 11% |
| Professor > Associate Professor | 19 | 9% |
| Student > Bachelor | 19 | 9% |
| Other | 31 | 14% |
| Unknown | 12 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 107 | 48% |
| Biochemistry, Genetics and Molecular Biology | 36 | 16% |
| Medicine and Dentistry | 15 | 7% |
| Computer Science | 12 | 5% |
| Engineering | 8 | 4% |
| Other | 18 | 8% |
| Unknown | 25 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 November 2016.
All research outputs
#15,080,169
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#6,487
of 8,964 outputs
Outputs of similar age
#82,287
of 103,568 outputs
Outputs of similar age from PLoS Computational Biology
#44
of 55 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 103,568 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.