Selective pressure from pathogens is considered a key selective force driving the evolution of components of the immune system. Since single components of the immune system may interact with many pathogens, and single pathogens may be recognized by multiple components of the immune system, gaining a better understanding of the mechanisms of parasite-driven selection requires the study of multiple genes and pathogens. Toll-like receptors (TLRs) are a large gene family that code for antigen-presenting components of the innate immune response. In the present paper we characterize polymorphism and signatures of selection in seven TLRs in free-living bank voles Myodes glareolus. We report the first evidence of balancing selection in several TLR genes, supported by positive values of Fu and Li's D* in TLR2 and TLR5, and positive values of Tajima's D in LRR regions within TLR1 and TLR2. We further found significant associations between amino-acid alleles of TLR1 and TLR5 and susceptibility to infection with the blood pathogen Bartonella. Interestingly, selection patterns in TLRs presenting virus-derived motifs (TLR7 and TLR9) differed considerably from those interacting with bacterial PAMPs. In contrast to the highly variable TLRs presenting bacterial motifs, TLR7 and TLR9 had low polymorphism and displayed signatures of directional selection. These findings suggest different functional responses across the TLR gene family and highlight the complexity of parasite-driven selection.

/pdf/signatures-of-balancing-selection-in-toll-like-receptor-tlrs-3l2g2qs8h4.pdf

Signatures of balancing selection in toll-like receptor (TLRs) genes - novel insights from a free-living rodent.

The structure of RNA, which is considered to be a second layer of information alongside the genetic code, provides fundamental insights into the cellular function of both coding and non-coding RNAs. Several high-throughput technologies have been developed to profile transcriptome-wide RNA structures, i.e., the structurome. However, it is challenging to interpret the profiling data because the observed data represent an average over different RNA conformations and isoforms with different abundance. To address this challenge, we developed an RNA structurome quantification method (RSQ) to statistically model the distribution of reads over both isoforms and RNA conformations, and thus provide accurate quantification of the isoform-specific structurome. The quantified RNA structurome enables the comparison of isoform-specific conformations between different conditions, the exploration of RNA conformation variation affected by single nucleotide polymorphism (SNP) , and the measurement of RNA accessibility for binding of either small RNAs in RNAi-based assays or RNA binding protein in transcriptional regulation. The model used in our method sheds new light on the potential impact of the RNA structurome on gene regulation.

/pdf/rsq-a-statistical-method-for-quantification-of-isoform-4d36jlvmnp.pdf

RSQ: a statistical method for quantification of isoform-specific structurome using transcriptome-wide structural profiling data [preprint]

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RSQ: a statistical method for quantification of isoform-specific structurome using transcriptome-wide structural profiling data [preprint]