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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Identification of global regulators of T-helper cell lineage specification
|
|---|---|
| Published in |
Genome Medicine, November 2015
|
| DOI | 10.1186/s13073-015-0237-0 |
| Pubmed ID | |
| Authors |
Kartiek Kanduri, Subhash Tripathi, Antti Larjo, Henrik Mannerström, Ubaid Ullah, Riikka Lund, R. David Hawkins, Bing Ren, Harri Lähdesmäki, Riitta Lahesmaa |
| Abstract |
Activation and differentiation of T-helper (Th) cells into Th1 and Th2 types is a complex process orchestrated by distinct gene activation programs engaging a number of genes. This process is crucial for a robust immune response and an imbalance might lead to disease states such as autoimmune diseases or allergy. Therefore, identification of genes involved in this process is paramount to further understand the pathogenesis of, and design interventions for, immune-mediated diseases. We aimed at identifying protein-coding genes and long non-coding RNAs (lncRNAs) involved in early differentiation of T-helper cells by transcriptome analysis of cord blood-derived naïve precursor, primary and polarized cells. Here, we identified lineage-specific genes involved in early differentiation of Th1 and Th2 subsets by integrating transcriptional profiling data from multiple platforms. We have obtained a high confidence list of genes as well as a list of novel genes by employing more than one profiling platform. We show that the density of lineage-specific epigenetic marks is higher around lineage-specific genes than anywhere else in the genome. Based on next-generation sequencing data we identified lineage-specific lncRNAs involved in early Th1 and Th2 differentiation and predicted their expected functions through Gene Ontology analysis. We show that there is a positive trend in the expression of the closest lineage-specific lncRNA and gene pairs. We also found out that there is an enrichment of disease SNPs around a number of lncRNAs identified, suggesting that these lncRNAs might play a role in the etiology of autoimmune diseases. The results presented here show the involvement of several new actors in the early differentiation of T-helper cells and will be a valuable resource for better understanding of autoimmune processes. |
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% |
| France | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 79 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 24% |
| Student > Ph. D. Student | 12 | 15% |
| Student > Doctoral Student | 8 | 10% |
| Student > Master | 8 | 10% |
| Professor | 6 | 8% |
| Other | 15 | 19% |
| Unknown | 11 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 20 | 25% |
| Agricultural and Biological Sciences | 17 | 22% |
| Immunology and Microbiology | 12 | 15% |
| Medicine and Dentistry | 10 | 13% |
| Nursing and Health Professions | 2 | 3% |
| Other | 7 | 9% |
| Unknown | 11 | 14% |
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 23 November 2015.
All research outputs
#14,272,416
of 24,364,603 outputs
Outputs from Genome Medicine
#1,279
of 1,502 outputs
Outputs of similar age
#191,053
of 395,696 outputs
Outputs of similar age from Genome Medicine
#31
of 41 outputs
Altmetric has tracked 24,364,603 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 1,502 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.7. This one is in the 14th percentile – i.e., 14% 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 395,696 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 50% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.