Animal Rights

Genetically Modified Animals

| by Dr Ray Greek

In hopes of overcoming the poor track record that has been the norm for animals used to model human diseases, scientists have genetically modified many species of animals. The premise was that genetically altered animals would closer approximate their human counterparts. So far, this has been a bust. Genetically modified animals have not worked out well for predicting the function of human genes. Liu et al:

We identified numerous differentially expressed genes (DEGs) in the autoimmune strains compared to non-autoimmune strains. However, we found very little overlap in the gene expression profile between human autoimmune disease and murine models of autoimmune disease and between different murine autoimmune models. (1)

Popular Video

Miranda Lambert saw the sign a veteran was holding up at her concert, she immediately broke down in tears:

Popular Video

Miranda Lambert saw the sign a veteran was holding up at her concert, she immediately broke down in tears:

The function of a gene varies between strains of mice and as well as between species. Nijhout:

The importance of context is also illustrated by studies of the effects of “knockouts” of specific genes in mice, a method that completely eliminates the function of a gene’s product. For example, knockout of a retinoblastoma-related gene causes severe abnormalities and embryonic death in one strain of mice, but the same mutation in another strain has no effect. The mutant mice are viable and become fertile adults, as shown by Michael Rudnicki (2) and his colleagues at McMaster University. (3)

Why is this variation the case? The fact that life forms are examples of complex systems is important. Van Regenmortel:

However, there is probably a more fundamental reason for these failures: namely, that most of these approaches have been guided by unmitigated reductionism. As a result, the complexity of biological systems, whole organisms and patients tends to be underrated (4). Most human diseases result from the interaction of many gene products, and we rarely know all of the genes and gene products that are involved in a particular biological function. Nevertheless, to achieve an understanding of complex genetic networks, biologists tend to rely on experiments that involve single gene deletions. Knockout experiments in mice, in which a gene that is considered to be essential is inactivated or removed, are widely used to infer the role of individual genes. In many such experiments, the knockout is found to have no effect whatsoever, despite the fact that the gene encodes a protein that is believed to be essential. In other cases, the knockout has a completely unexpected effect (5). Furthermore, disruption of the same gene can have diverse effects in different strains of mice (6). Such findings question the wisdom of extrapolating data that are obtained in mice to other species. In fact, there is little reason to assume that experiments with genetically modified mice will necessarily provide insights into the complex gene interactions that occur in humans (Horrobin, 2003).

The disappointing results of knockout experiments are partly caused by gene redundancy and pleiotropy, and the fact that gene products are components of pathways and networks in which genes acting in parallel systems can compensate for missing ones (7). As many factors simultaneously influence the behaviour of a system, one part might function only in the presence of other components. The essential contribution of other genes in achieving a particular function will therefore be missed, which will further encourage the reductionist view that a single gene has adequate explanatory power (8). It remains true that human disease is best studied in human subjects (Horrobin, 2003). (9)

The above was reinforced this week with a publication by Dowell et al. They studied the genomes of the yeasts Saccharomyces cerevisiae strain 1278b and S288c. The variation in the two genomes is similar to the variation between two humans. They deleted some genes and compared results. They concluded:

Although 894 genes were essential in both S288c and 1278b, 44 genes were essential only in 1278b and 13 genes were essential only in S288c . . . Our genome-wide survey of conditionally essential genes demonstrates that in most cases a complex set of background-specific modifiers influence a mutation whose phenotype differs between individuals. These results raise the possibility that similar complex modifiers may largely explain the difficulty in identifying the genetic basis for individual phenotypes. The potential for genetic interactions to control individual phenotypes becomes even more important if different combinations of alleles can lead to the same physiological state. The ability to identify these conditional essential phenotypes in yeast provides a framework to unravel the fundamental principles of genetic networks resulting from natural variation, including those that underlie human disease. (Emphasis added.) (10)

Just because two individuals or two species share the same gene does not mean that gene does the same thing hence knocking genes in or out in mice will not predict human outcomes. This is not controversial. Van Zutphen:

The study of transgenic animal models is increasing our knowledge of gene function in physiologic and pathologic processes. However, the phenotypic effect of a transgene largely depends on the genetic background on which it is expressed, and is, therefore, still often unpredictable. This makes transgenesis often a rather inefficient procedure for creating animal models of human disorders. (Emphasis added.) (11)

PLoS Biology, in an editorial said this about mouse models of autoimmune diseases:

These results fall in line with mounting evidence that background genes are not silent partners in gene-targeted disease models, but can themselves facilitate expression of the disease. This finding underscores the notion that genes are not solitary, static entities; their expression often depends on context. With genetically complex diseases, having the requisite combination of susceptibility genes does not always lead to disease. (12)

Mepham et al.:

It is apparent from an analysis of some transgenic disease models that the actual benefits of using the models are rarely completely equivalent to the potential benefits . . . The currently available transgenic models for cystic fibrosis (CF) illustrate this point. None of the strains is ideal, with either the genotype and/or the phenotype of the mouse failing to accurately model the human condition . . . There are several limitations in relation to the usefulness of the current approaches to developing transgenic disease models, particularly since many diseases are multifactorial. Problems persist when extrapolating data obtained by using such transgenic animals to the disease condition in humans. (13)

The Scientist published an article heralding transgenic mice. Ironically it closed with this:

“There isn’t a single genetically manipulated mouse that has been used yet to produce a drug that cures a disease,” says [Kathleen] Murray of Charles River Laboratories. (14)

 (For more on why one complex system, such as a mouse or monkey, cannot predict drug or disease response for another, such as a human, see Animal Models in Light of Evolution.)


1. Z. Liu, K. Maas, T. M. Aune, Clin Immunol 112, 225 (Sep, 2004).

2. J. E. LeCouter, B. Kablar, P. F. Whyte, C. Ying, M. A. Rudnicki, Development 125, 4669 (December 1, 1998, 1998).

3. H. F. Nijhout, American Scientist 91, 416 (2003).

4. D. F. Horrobin, Nat Biotech 19, 1099 (2001).

5. M. Morange, The Biochemist 23, 37 (2001).

6. H. Pearson, Nature 415, 8 (Jan 3, 2002).

7. M. Morange, The misunderstood gene.  (Harvard University Press, Cambridge, 2001).

8. M. H. V. V. Regenmortel, Journal of Molecular Recognition 17, 145 (2004).

9. M. H. Van Regenmortel, EMBO Rep 5, 1016 (Nov, 2004).

10. R. D. Dowell et al., Science 328, 469 (April 23, 2010, 2010).

11. L. F. van Zutphen, Comp Med 51, 110 (Apr, 2001).

12. Editorial, PLoS Biology 2, e220 (August 01, 2004, 2004).

13. T. B. Mepham et al., Altern Lab Anim 26, 21 (Jan-Feb, 1998).

14. B. A. Maher, The Scientist 16, 22 (February 4, 2002).