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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism
|
|---|---|
| Published in |
BMC Immunology, August 2015
|
| DOI | 10.1186/s12865-015-0107-y |
| Pubmed ID | |
| Authors |
Juilee Thakar, Boris M. Hartmann, Nada Marjanovic, Stuart C. Sealfon, Steven H. Kleinstein |
| Abstract |
Comparative analysis of genome-wide expression profiles are increasingly being used to study virus-specific host interactions. In order to gain mechanistic insights, gene expression profiles can be combined with information on DNA-binding sites of transcription factors to detect transcription factor activity (by analysis of target gene sets) during viral infections. Here, we apply this approach to study mechanisms of immune antagonism elicited by Influenza A virus (New Caledonia/20/1999) by comparing the transcriptional response with the non-pathogenic Newcastle disease virus (NDV), which lacks human immune antagonism. Existing gene set approaches do not quantify activity in a way that can be statistically compared between responses. We thus developed a new method for Bayesian Estimation of Transcription factor Activity (BETA) that allows for such quantification and comparative analysis across multiple responses. BETA predicted decreased ISGF3 activity during influenza A infection of human dendritic cells (reflected in lower expression of Interferon Stimulated Genes, ISGs). This prediction was confirmed through a combination of mathematical modeling and experiments at different multiplicities of infection to show that ISGs were specifically blocked in infected cells. Suppression of the transcription factor SATB1 was also predicted as a novel effect of influenza-mediated immune antagonism, and validated experimentally. Comparative analysis of genome-wide transcriptional profiles can reveal new effects of viral immune antagonism. We have developed a computational framework (BETA) that enables quantitative comparative analysis of transcription factor activities. This method will aid future studies to identify mechanistic differences in the host-pathogen interactions. Application of BETA to genome-wide transcriptional profiling data from human DCs identified SATB1 as a novel effect of influenza antagonism. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Taiwan | 1 | 4% |
| Unknown | 26 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 22% |
| Student > Ph. D. Student | 5 | 19% |
| Student > Bachelor | 4 | 15% |
| Professor > Associate Professor | 3 | 11% |
| Student > Master | 3 | 11% |
| Other | 1 | 4% |
| Unknown | 5 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Immunology and Microbiology | 8 | 30% |
| Agricultural and Biological Sciences | 3 | 11% |
| Medicine and Dentistry | 3 | 11% |
| Biochemistry, Genetics and Molecular Biology | 2 | 7% |
| Computer Science | 2 | 7% |
| Other | 3 | 11% |
| Unknown | 6 | 22% |
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 27 January 2016.
All research outputs
#7,767,027
of 23,605,418 outputs
Outputs from BMC Immunology
#142
of 586 outputs
Outputs of similar age
#90,553
of 265,456 outputs
Outputs of similar age from BMC Immunology
#3
of 13 outputs
Altmetric has tracked 23,605,418 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 586 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 64% of its peers.
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 265,456 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.