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Comparative transcriptomics in human and mouse

Nature Reviews Genetics volume 18, pages 425–440 (2017)Cite this article

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Key Points

  • The mouse is the most widely used model organism to study human disease, but often mouse biology cannot be extrapolated to humans. A deep comparison of mouse and human physiology at the molecular level is essential for understanding under which circumstances the mouse can be a suitable model of human biology and for creating better mouse models. Advances in next-generation sequencing technologies fostered genome-wide annotation of functional DNA elements, enabling extensive comparison of the human and mouse genomes.

  • At the transcriptional level, human and mouse gene expression profiles are conserved overall, although the degree of conservation varies depending on the tissues and the genes that are compared. Therefore, the question of whether the human and mouse transcriptomes cluster preferentially by tissue or organ or by species does not have an answer overall, and it depends specifically on the genes being considered.

  • Conservation of expression is not a direct consequence of conservation in regulatory sequences, including promoters and enhancers. Although gene regulatory networks are preserved overall between human and mouse, transcription binding sites are often not conserved.

  • Inter-individual genetic variation can affect human gene expression, but such variation cannot be modelled in inbred strains of laboratory mice because their genetic variation is small compared to the human population. An expansion of the current studies on the relationship between genetic variation and gene expression in outbred mice might provide helpful insights to understand the same relationship in humans.

  • Emerging technologies — such single-cell genomics and single-cell spatial transcriptomics — and time series experiments will improve the annotation of human and mouse genomes, refine the current definitions of homologous cell types and homologous (molecular) phenotypes, and ultimately help scientists to identify which mouse models are the most appropriate to address a given biological question.

Abstract

Cross-species comparisons of genomes, transcriptomes and gene regulation are now feasible at unprecedented resolution and throughput, enabling the comparison of human and mouse biology at the molecular level. Insights have been gained into the degree of conservation between human and mouse at the level of not only gene expression but also epigenetics and inter-individual variation. However, a number of limitations exist, including incomplete transcriptome characterization and difficulties in identifying orthologous phenotypes and cell types, which are beginning to be addressed by emerging technologies. Ultimately, these comparisons will help to identify the conditions under which the mouse is a suitable model of human physiology and disease, and optimize the use of animal models.

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Figure 1: Homology of human and mouse genes and genomic elements.
Figure 2: Simplified clustering of human and mouse tissue samples and variance decomposition of gene expression.
Figure 3: Cellular composition of human and mouse pancreatic islets.
Figure 4: Multidimensional complexity of omics-layer integration across species.

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Acknowledgements

The authors acknowledge G. Schiavo and D. Cirillo for insightful discussion. The authors acknowledge support of the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013–2017' of the Centres de Recerca de Catalunya Programme/Generalitat de Catalunya and the Programa de Ayudas Formación de Personal investigador (FPI) del Ministerio de Economia y Competitividad (BES-2012-055848).

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Authors and Affiliations

  1. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003, Barcelona, Spain

    Alessandra Breschi & Roderic Guigó

  2. Universitat Pompeu Fabra (UPF), 08002, Barcelona, Spain

    Alessandra Breschi & Roderic Guigó

  3. Cold Spring Harbor Laboratory, Cold Spring Harbor, 11742, New York, USA

    Thomas R. Gingeras

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  1. Alessandra Breschi
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Glossary

Synteny

Preserved genomic order and orientation of genes or other elements between species.

Xenograft models of cancer

Created when cancerous tissue from a person's primary tumour is implanted directly into an immunodeficient mouse.

Chromatin domains

Functionally distinct chromosomal regions that confer structural organization to eukaryotic genomes and represent regulatory units for gene expression and chromosome behaviour.

Cap analysis of gene expression

(CAGE). In CAGE, short (∼ 20-nucleotide) sequence tags originating from the 5′ end of full-length mRNAs are sequenced to identify transcription events on a genome-wide scale.

GENCODE annotation

The GENCODE project produces high-quality reference gene annotation and experimental validation for human and mouse genomes.

Long non-coding RNAs

(lncRNAs). Non-protein coding transcripts that are longer than 200 nucleotides. This somewhat arbitrary limit distinguishes lncRNAs from small regulatory RNAs.

Orthologous

Homologous genes in different species that have evolved from a common ancestral gene by speciation.

MicroRNAs

(miRNAs). Derived from primary transcripts with features similar to mRNAs that are enzymatically processed to their mature length of 21–24 nucleotides by Drosha and Dicer enzymes.

Transfer RNAs

(tRNAs). Adaptor RNA molecules (76–90 nucleotides) that serve as the physical link between the mRNA and the amino acid sequence of proteins by carrying an amino acid to the ribosome, as directed by the codon in an mRNA.

Small nuclear RNAs and small nucleolar RNAs

(snRNAs and snoRNAs). Classes of short non-coding RNAs (100–200 nucleotides) that have important regulatory roles in nuclear ribonucleoprotein complexes.

Homologues

A pair of genes that descended from a common ancestral gene.

Hierarchical clustering

A statistical method in which objects (for example, gene expression profiles for different individuals or tissue samples) are grouped into a hierarchy, which is visualized in a dendrogram. Objects close to each other in the hierarchy, as measured by tracing the branch heights, are also close by some measure of distance — for example, between gene expression profiles. Individuals or samples with similar expression profiles will be close together in terms of branch lengths.

Euclidean distance

The Euclidean distance between points p and q is the length of the line segment connecting them in a multidimensional space. In gene expression analysis, p and q are usually vectors of expression values in two samples or conditions.

Dimensionality reduction techniques

These reduce multidimensional data to a minimal number of dimensions for visualization by identifying those dimensions that capture the most important information underlying the data structure.

Principal component analysis

(PCA). Orthogonal linear transformation that transforms the original data to a new coordinate system, such that the greatest variance of the projected data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on.

Multidimensional scaling

(MDS). A technique used to display the information contained in a distance matrix. It aims to place each object in N-dimensional space such that the between-object distances are preserved as well as possible.

t-Distributed stochastic neighbour embedding

(t-SNE). A nonlinear dimensionality reduction technique that is based on the probability distribution over pairs of high-dimensional objects that are embedded into a space of two or three dimensions. Similar objects are modelled by nearby points, and dissimilar objects are modelled by distant points.

Normalization

Methods used to adjust measurements so that they can be appropriately compared among samples. For example, in microarray analysis, methods such as quantile normalization manipulate common characteristics of the data.

DNA exaptation

The shift in the function of a DNA sequence during evolution.

Expression quantitative trait loci

(eQTLs). Genomic loci that contribute to variation in the expression levels of mRNAs.

Allele-specific expression

Expression variation between the two haplotypes of a diploid individual, as distinguished by heterozygous sites.

Ischaemic time

In the case of organ donors, the time elapsed between the death of a donor and the organ extraction.

Pseudogenes

Segments of DNA that originate from functional genes, but have lost at least some of the ability of the parent gene in terms of expression or coding potential.

Precision medicine

An emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person.

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Breschi, A., Gingeras, T. & Guigó, R. Comparative transcriptomics in human and mouse. Nat Rev Genet 18, 425–440 (2017). https://doi.org/10.1038/nrg.2017.19

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