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Testing hypotheses on the rate of molecular evolution in relation to gene expression using microRNAs.

ABSTRACT: There exists an inverse relationship between the rate of molecular evolution and the level of gene expression. Among the many explanations, the "toxic-error" hypothesis is a most general one, which posits that processing errors may often be toxic to the cells. However, toxic errors that constrain the evolution of highly expressed genes are often difficult to measure. In this study, we test the toxic-error hypothesis by using microRNA (miRNA) genes because their processing errors can be directly measured by deep sequencing. A miRNA gene consists of a small mature product (?22 nt long) and a "backbone." Our analysis shows that (i) like the mature miRNA, the backbone is highly conserved; (ii) the rate of sequence evolution in the backbone is negatively correlated with expression; and (iii) although conserved between distantly related species, the error rate in miRNA processing is also negatively correlated with the expression level. The observations suggest that, as a miRNA gene becomes more highly (or more ubiquitously) expressed, its sequence evolves toward a structure that minimizes processing errors.

SUBMITTER: Shen Y

PROVIDER: S-EPMC3179107 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Testing hypotheses on the rate of molecular evolution in relation to gene expression using microRNAs.

Shen Yang Y Lv Yang Y Huang Lei L Liu Wensheng W Wen Ming M Tang Tian T Zhang Rui R Hungate Eric E Shi Suhua S Wu Chung-I CI

Proceedings of the National Academy of Sciences of the United States of America 20110912 38

There exists an inverse relationship between the rate of molecular evolution and the level of gene expression. Among the many explanations, the "toxic-error" hypothesis is a most general one, which posits that processing errors may often be toxic to the cells. However, toxic errors that constrain the evolution of highly expressed genes are often difficult to measure. In this study, we test the toxic-error hypothesis by using microRNA (miRNA) genes because their processing errors can be directly ...[more]

PMID: 21911382

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Project description:Although large scale informatics studies on introns can be useful in making broad inferences concerning patterns of intron gain and loss, more specific questions about intron evolution at a finer scale can be addressed using a gene family where structure and function are well known. Genome wide surveys of tetraspanins from a broad array of organisms with fully sequenced genomes are an excellent means to understand specifics of intron evolution. Our approach incorporated several new fully sequenced genomes that cover the major lineages of the animal kingdom as well as plants, protists and fungi. The analysis of exon/intron gene structure in such an evolutionary broad set of genomes allowed us to identify ancestral intron structure in tetraspanins throughout the eukaryotic tree of life.We performed a phylogenomic analysis of the intron/exon structure of the tetraspanin protein family. In addition, to the already characterized tetraspanin introns numbered 1 through 6 found in animals, three additional ancient, phase 0 introns we call 4a, 4b and 4c were found. These three novel introns in combination with the ancestral introns 1 to 6, define three basic tetraspanin gene structures which have been conserved throughout the animal kingdom. Our phylogenomic approach also allows the estimation of the time at which the introns of the 33 human tetraspanin paralogs appeared, which in many cases coincides with the concomitant acquisition of new introns. On the other hand, we observed that new introns (introns other than 1-6, 4a, b and c) were not randomly inserted into the tetraspanin gene structure. The region of tetraspanin genes corresponding to the small extracellular loop (SEL) accounts for only 10.5% of the total sequence length but had 46% of the new animal intron insertions.Our results indicate that tests of intron evolution are strengthened by the phylogenomic approach with specific gene families like tetraspanins. These tests add to our understanding of genomic innovation coupled to major evolutionary divergence events, functional constraints and the timing of the appearance of evolutionary novelty.

Project description:MotivationMethods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which do not typically provide a global test of microbiome effect. Without a unified approach, the findings of a global test may be hard to resolve with the findings at the individual OTU level. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR).ResultsWe introduce the linear decomposition model (LDM), that provides a single analysis path that includes global tests of any effect of the microbiome, tests of the effects of individual OTUs while accounting for multiple testing by controlling the FDR, and a connection to distance-based ordination. The LDM accommodates both continuous and discrete variables (e.g. clinical outcomes, environmental factors) as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based P-values that can control for sample correlation. The LDM can also be applied to transformed data, and an 'omnibus' test can easily combine results from analyses conducted on different transformation scales. We also provide a new implementation of PERMANOVA based on our approach. For global testing, our simulations indicate the LDM provided correct type I error and can have comparable power to existing distance-based methods. For testing individual OTUs, our simulations indicate the LDM controlled the FDR well. In contrast, DESeq2 often had inflated FDR; MetagenomeSeq generally had the lowest sensitivity. The flexibility of the LDM for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. We also show that our implementation of PERMANOVA can outperform existing implementations.Availability and implementationThe R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows.Contactyijuan.hu@emory.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Testing hypotheses on the rate of molecular evolution in relation to gene expression using microRNAs.

Publications

Testing hypotheses on the rate of molecular evolution in relation to gene expression using microRNAs.

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