Comparative modular analysis of gene expression in vertebrate organs

Deep homology in the age of next-generation sequencing

The principle of homology is central to conceptualizing the comparative aspects of morphological evolution. The distinctions between homologous or non-homologous structures have become blurred, however, as modern evolutionary developmental biology (evo-devo) has shown that novel features often result from modification of pre-existing developmental modules, rather than arising completely de novo. With this realization in mind, the term 'deep homology' was coined, in recognition of the remarkably conserved gene expression during the development of certain animal structures that would not be considered homologous by previous strict definitions. At its core, it can help to formulate an understanding of deeper layers of ontogenetic conservation for anatomical features that lack any clear phylogenetic continuity. Here, we review deep homology and related concepts in the context of a gene expression-based homology discussion. We then focus on how these conceptual frameworks have profited from the recent rise of high-throughput next-generation sequencing. These techniques have greatly expanded the range of organisms amenable to such studies. Moreover, they helped to elevate the traditional gene-by-gene comparison to a transcriptome-wide level. We will end with an outlook on the next challenges in the field and how technological advances might provide exciting new strategies to tackle these questions.This article is part of the themed issue 'Evo-devo in the genomics era, and the origins of morphological diversity'.

RNAseq analysis of heart tissue from mice treated with atenolol and isoproterenol reveals a reciprocal transcriptional response

Background

The transcriptional response to many widely used drugs and its modulation by genetic variability is poorly understood. Here we present an analysis of RNAseq profiles from heart tissue of 18 inbred mouse strains treated with the β-blocker atenolol (ATE) and the β-agonist isoproterenol (ISO).

Results

Differential expression analyses revealed a large set of genes responding to ISO (n = 1770 at FDR = 0.0001) and a comparatively small one responding to ATE (n = 23 at FDR = 0.0001). At a less stringent definition of differential expression, the transcriptional responses to these two antagonistic drugs are reciprocal for many genes, with an overall anti-correlation of r = −0.3. This trend is also observed at the level of most individual strains even though the power to detect differential expression is significantly reduced. The inversely expressed gene sets are enriched with genes annotated for heart-related functions. Modular analysis revealed gene sets that exhibit coherent transcription profiles across some strains and/or treatments. Correlations between these modules and a broad spectrum of cardiovascular traits are stronger than expected by chance. This provides evidence for the overall importance of transcriptional regulation for these organismal responses and explicits links between co-expressed genes and the traits they are associated with. Gene set enrichment analysis of differentially expressed groups of genes pointed to pathways related to heart development and functionality.

Conclusions

Our study provides new insights into the transcriptional response of the heart to perturbations of the β-adrenergic system, implicating several new genes that had not been associated to this system previously.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3059-6) contains supplementary material, which is available to authorized users.

What to compare and how: Comparative transcriptomics for Evo-Devo

Evolutionary developmental biology has grown historically from the capacity to relate patterns of evolution in anatomy to patterns of evolution of expression of specific genes, whether between very distantly related species, or very closely related species or populations. Scaling up such studies by taking advantage of modern transcriptomics brings promising improvements, allowing us to estimate the overall impact and molecular mechanisms of convergence, constraint or innovation in anatomy and development. But it also presents major challenges, including the computational definitions of anatomical homology and of organ function, the criteria for the comparison of developmental stages, the annotation of transcriptomics data to proper anatomical and developmental terms, and the statistical methods to compare transcriptomic data between species to highlight significant conservation or changes. In this article, we review these challenges, and the ongoing efforts to address them, which are emerging from bioinformatics work on ontologies, evolutionary statistics, and data curation, with a focus on their implementation in the context of the development of our database Bgee (http://bgee.org). J. Exp. Zool. (Mol. Dev. Evol.) 324B: 372–382, 2015. © 2015 The Authors. J. Exp. Zool. (Mol. Dev. Evol.) published by Wiley Periodicals, Inc.

Evolutionary dynamics of coding and non-coding transcriptomes

Gene expression changes may underlie much of phenotypic evolution. The development of high-throughput RNA sequencing protocols has opened the door to unprecedented large-scale and cross-species transcriptome comparisons by allowing accurate and sensitive assessments of transcript sequences and expression levels. Here, we review the initial wave of the new generation of comparative transcriptomic studies in mammals and vertebrate outgroup species in the context of earlier work. Together with various large-scale genomic and epigenomic data, these studies have unveiled commonalities and differences in the dynamics of gene expression evolution for various types of coding and non-coding genes across mammalian lineages, organs, developmental stages, chromosomes and sexes. They have also provided intriguing new clues to the regulatory basis and phenotypic implications of evolutionary gene expression changes.

Ligand specificities of Toll-like receptors in fish: indications from infection studies

Toll like receptors (TLRs) are present in many different fish families from several different orders, including cyprinid, salmonid, perciform, pleuronectiform and gadiform representatives, with at least some conserved properties among these species. However, low conservation of the leucine-rich repeat ectodomain hinders predictions of ligand specificities of fish TLRs based on sequence information only. We review the presence of a TLR genes, and changes in their gene expression profiles as result of infection, in the context of different fish orders and fish families. The application of RT-qPCR and availability of increasing numbers of fish genomes has led to numerous gene expression studies, including studies on TLR gene expression, providing the most complete dataset to date. Induced changes of gene expression may provide (in)direct evidence for the involvement of a particular TLR in the reaction to a pathogen. Especially when findings are consistent across different studies on the same fish species or consistent across different fish species, up-regulation of TLR gene expression could be a first indication of functional relevance. We discuss TLR1, TLR2, TLR4, TLR5 and TLR9 as presumed sensors of bacterial ligands and discuss as presumed sensors of viral ligands TLR3 and TLR22, TLR7 and TLR8. More functional studies are needed before conclusions on ligands specific to (groups of) fish TLRs can be drawn, certainly true for studies on non-mammalian TLRs. Future studies on the conservation of function of accessory molecules, in conjunction with TLR molecules, may bring new insight into the function of fish TLRs.

IQRray, a new method for Affymetrix microarray quality control, and the homologous organ conservation score, a new benchmark method for quality control metrics

MOTIVATION

Microarray results accumulated in public repositories are widely reused in meta-analytical studies and secondary databases. The quality of the data obtained with this technology varies from experiment to experiment, and an efficient method for quality assessment is necessary to ensure their reliability.

RESULTS

The lack of a good benchmark has hampered evaluation of existing methods for quality control. In this study, we propose a new independent quality metric that is based on evolutionary conservation of expression profiles. We show, using 11 large organ-specific datasets, that IQRray, a new quality metrics developed by us, exhibits the highest correlation with this reference metric, among 14 metrics tested. IQRray outperforms other methods in identification of poor quality arrays in datasets composed of arrays from many independent experiments. In contrast, the performance of methods designed for detecting outliers in a single experiment like Normalized Unscaled Standard Error and Relative Log Expression was low because of the inability of these methods to detect datasets containing only low-quality arrays and because the scores cannot be directly compared between experiments.

AVAILABILITY AND IMPLEMENTATION

The R implementation of IQRray is available at: ftp://lausanne.isb-sib.ch/pub/databases/Bgee/general/IQRray.R.

CONTACT

Marta.Rosikiewicz@unil.ch

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.