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Toll-Like Receptor Family Members and Their Ligands

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Attention for Chapter 1: Evolution of the TIR, tolls and TLRs: functional inferences from computational biology.
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Chapter title
Evolution of the TIR, tolls and TLRs: functional inferences from computational biology.
Chapter number 1
Book title
Toll-Like Receptor Family Members and Their Ligands
Published in
Current topics in microbiology and immunology, January 2002
DOI 10.1007/978-3-642-59430-4_1
Pubmed ID
Book ISBNs
978-3-64-263975-3, 978-3-64-259430-4
Authors

Beutler, B, Rehli, M, Beutler, B., Rehli, M., B. Beutler, M. Rehli

Abstract

The mammalian toll-like receptors (TLRs) are products of an evolutionary process that began prior to the separation of plants and animals. The most conserved protein motif within the TLRs is the TIR, which denotes Toll, the Interleukin-1 receptor, and plant disease Resistance genes. To trace the ancestry of the TLRs, it is desirable to draw upon the sequences of TIR domains from TLRs of diverse vertebrate species, including species with known dates of divergence (i.e., representatives of Mammalia and Aves) in order to establish a relationship between time and genetic divergence. It appears that a gene ancestral to modern TLRs 1 and 6 duplicated approximately 130 million years ago, only shortly before the speciation event that led to humans and mice. Though it is not represented in mice, TLR10 split from the TLR[1/6] precursor about 300 million years ago. The origins of other TLRs are more ancient, dating to the origins of vertebrate life, and some present-day vertebrate species appear to have many more TLRs than others. Moreover, the patterns of TLR expression are quite variable at the level of tissues, even among closely related species. A given TLR in species that are related by descent from a common ancestor may acquire different duties within each descendant line, so that some microbial inducers are avidly recognized in one species but not in others; likewise the intensity and the antomic location of an innate immune response may vary considerably. In this review, we discuss the computational methods used to analyze divergence of the TIR, and the conclusions that may be safely drawn.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 23%
Researcher 10 18%
Student > Bachelor 9 16%
Professor > Associate Professor 7 12%
Professor 3 5%
Other 4 7%
Unknown 11 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 30%
Biochemistry, Genetics and Molecular Biology 10 18%
Medicine and Dentistry 6 11%
Neuroscience 3 5%
Immunology and Microbiology 3 5%
Other 6 11%
Unknown 12 21%
Attention Score in Context

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 20 January 2020.
All research outputs
#7,468,612
of 22,833,393 outputs
Outputs from Current topics in microbiology and immunology
#199
of 678 outputs
Outputs of similar age
#29,601
of 122,939 outputs
Outputs of similar age from Current topics in microbiology and immunology
#2
of 11 outputs
Altmetric has tracked 22,833,393 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.
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We're also able to compare this research output to 11 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.