I imagine that every practicing scientist has experienced, in one form or another, the tendency of many non-scientists to expect all research to be directly beneficial to human health and well-being. I used to respond facetiously to these kinds of expectations when expressed by friends or family members, with something along the lines of “My work has absolutely no practical applications to human welfare whatsoever”.
Of course, this is not true. Genome size is becoming very relevant to fields of inquiry that are likely to have major significance for medicine. Notably, genome size data provide an important indication of the cost and difficulty of sequencing a given genome, and thus represent a prime criterion in the choice of sequencing targets. As an example, I performed a genome siz
e estimate for Biomphalaria glabrata, a planorbid snail that serves as an intermediate host for the trematode flatworm Schistosoma mansoni which causes the debilitating disease known as schistosomiasis. The genome of B. glabrata is one of the smallest so far reported for a gastropod, and is now being sequenced (along with S. mansoni).
More recently, Jenner and Wills (2007) made explicit mention of genome size as an important factor in deciding on the next set of models for evo-devo studies. Discoveries regarding the fundamental genetic underpinnings of development have obvious implications for medical science and here, too, genome size is becoming increasingly seen as important. As they put it,
Whole-genome sequences are an increasingly important resource for many biological disciplines, including evo–devo15, 49, 50. However, financial and technical constraints mean that there is currently a preference for species with small genomes. This compounds the bias that is already introduced by the big six. First, putatively general conclusions about genome evolution might actually be specific to those smaller genomes that have been fully sequenced. For example, when focusing only on sequenced genomes, a close correspondence between genome size and gene number in eukaryotes is observed. The C-value paradox becomes apparent only when genome-size data from non-sequenced genomes is included51. Second, there are important genetic, morphological, physiological and ecological correlates of genome size in a range of animals and plants51, 52. Some correlates seem ubiquitous in animals and plants, such as those between genome size and cell size, body size and the inverse of developmental rate52. Others are group specific: genome size correlates mostly with metabolic rate in homeotherms, but with developmental type and ecology in amphibians53, and is positively correlated with egg size in copepods, plethodontid salamanders and fishes51, 52, 54. Studying these correlated traits in phylogenetically disparate taxa could illuminate the relationships between small genome size and rapid development, as well as the evolution of strongly cell-lineage-dependent development in taxa such as tunicates and nematodes, and the partial fragmentation of their Hox clusters55, 56.
References 51, 52, and 53 in that paragraph are papers of mine, so again I am forced to admit that my work may have some practical application after all.
My main focus is on genome size diversity in eukaryotes, which mostly means differences among species in the abundance of noncoding DNA. In bacteria, most of the genome is composed of protein-coding genes, so unlike in eukaryotes there is a very strong correlation between genome size and gene number. Genome size is generally small in parasites and endosymbionts and larger in free-living species (probably because population bottlenecks and relaxed selection on gene function result in gene loss by deletion bias in bacteria associated with hosts [Mira et al. 2001]).
But this observation is not the link between genome size and human health that I had in mind for this post. In this month’s issue of Antimicrobial Agents and Chemotherapy, Steven Projan argues that genome size is associated with the evolution of antibiotic resistance in bacteria. In Dr. Projan’s own words,
It is observed here that the ability of a given bacterium to evolve toward a multidrug resistance phenotype is a function of genome size. In Table 1, a number of examples are provided, but even an expanded analysis shows that this observation holds true. That is, the larger the genome the greater the propensity of a bacterium to display multidrug resistance phenotypes and the smaller the genome the less likely it is that antibacterial resistance will emerge and disseminate within that species. What is proposed here is that, just as there is a continuum of genome sizes among bacteria, there is a continuum in the ability or propensity of a bacterium to 
become “multidrug resistant” and that continuum is reflected in the size of the genome. This is not to say that we do not observe resistance to certain agents even in organisms with the smallest genomes (macrolide resistance appears in virtually every pathogen at some level). There is probably a solid biological reason for this observation; organisms with larger genomes are more adaptable to environmental changes because they have more (genetic) information to draw upon. It appears that organisms with smaller genomes have become more “specialized,” residing in particular environmental niches (Treponema pallidum and the Chlamydiae are cases in point), and their lack of versatility in adapting to different environments is also manifest in an inability to develop mechanisms for coping with antibiotics. Indeed, we have learned that virtually each and every time a bacterium either acquires a novel resistance determinant or a mutant strain arises with decreased susceptibility to an antibacterial drug, the bacterium experiences a “fitness burden.” With time, compensatory mutations are selected in which the bacterium accumulates mutations that allow for something like wild-type growth in a strain that is now phenotypically resistant (e.g., topA mutations in gyrB mutant strains). Bacteria with larger genomes simply have a greater opportunity to develop these compensatory mutations. It must be emphasized that it does not matter whether we are discussing the acquisition of a novel resistance gene as opposed to a mutation that alters the target or results in up-regulation of an efflux pump. The accumulating evidence tells us that all require some form of adaptation. Another consequence of this phenomenon is that antibiotic cycling in health care settings is unlikely to result in a reversion of the local microflora to susceptibility as the compensatory mutations “lock in” the resistance phenotype.
He continues by noting, “I and several of those I have discussed this observation with were perplexed that it had not previously been articulated. Although to be fair, others have suggested it is a trivial, if not nonsensical, observation and worthy only of cocktail party conversation… in fact, I believe that this is an important guide as to where and which organisms we actually need novel antibacterial agents for.” Projan blames an overemphasis on individual organisms with small genomes for the overlooking of this potentially important pattern. In other words, it is the sort of thing that can only be applied to human health research if one takes a broad view of genomic diversity.
As much fun as it is to study genome size for purely academic reasons, it seems it actually may be good for us too.
Ryan Gregory has serious doubts about the usefulness of the term as he explains in his excellent article A word about “junk DNA”.
Just to clarify, I think the term could be useful — indeed, it was useful when Ohno coined it. The problem is that it is seldom used in an appropriate way. If the meaning were specified explicitly to be “regions strongly suspected of being non-functional with evidence to back it up” (which, incidentally, is not the original definition according to Ohno (1972) or Comings (1972)), and if people used it only in this way, then I would not have a problem with this. But given the difficulty that people seem to have in accepting that some DNA may truly not have a function at the organism level, I don’t know if we could ever get it to be used with such precision.
…a new term, Junctional DNA, to describe DNA that probably has a function but that function isn’t known… think we don’t need to go there. It’s sufficient to remind people that lots of DNA outside of genes has a function and these functions have been known for decades.
That neologism was suggested in response to Minkel’s appeal for a term that would “make the distinction between functional and nonfunctional noncoding DNA clear to a popular audience”. My main suggestion was to call DNA by what it is known to be, if at all possible, by function (“regulatory DNA”, “structural DNA”) or by type (“pseudogene”, “transposable element”, “intron”). Your definition of “junk DNA” is also more precise than most usages, meaning that you specify that the term only be applied to sequences for which there is evidence (not just assumption) of non-function. That leaves us with something in between for journalists to talk about with a catchy buzzword. “Junctional DNA” lets them specify that we’re not talking about “junk DNA” or “functional DNA” — i.e., there is some evidence for function (e.g., being conserved) but no evidence of what that function is. The main utility would be to stop the very frustrating leap that gets made from “this 1% of the genome may have a function, so the whole thing must have this function” kind of reporting. Now they could say “another 1% has moved into the category of ‘junctional DNA'”. I think that would be considerably less misleading than current wording.
Note that I’m avoiding the term “noncoding” DNA here. This is because to me the term “coding DNA” only refers to the coding region of a gene that encodes a protein … there are many genes for RNAs that are not properly called coding regions so they would fall into the noncoding DNA category … introns in eukaryotic genomes would be “noncoding DNA” as far as I’m concerned. I think that Ryan Gregory and others use the term “noncoding DNA” to refer to all DNA that’s not part of a gene instead of all DNA that’s not part of the coding region of a protein encoding gene. I’m not certain of this.
By definition, non-coding DNA is, and always has been, everything other than exons. The reason this is relevant is that early work in genome biology assumed that there should be a 1 to 1 correspondence between DNA content and protein-coding gene number. This is work that occurred for at least two decades before the discovery of introns, pseudogenes, and other non-coding DNA. Now we have more descriptive names for the categories of DNA that are not the genes, all the genes, and nothing but the genes. I actually don’t know of anyone else who would have a problem calling introns, pseudogenes, and regulatory regions “non-coding DNA”. Certainly, Ohno, Crick, and many others have historically put introns in the same non-protein-coding grouping as pseudogenes. It’s just a category — you also have more specific subcategories to apply to each of the types of non-coding DNA. Perhaps your objection relates to an undue emphasis on the distinction between exons and everything else — well, that’s the history of the past half century of this field, so it should be no surprise that the terminology reflects this.
Read Gregory’s article for the short concise version of this dispute. What it means is that junk DNA threatens the worldviews of both Dembski and Dawkins!
Not quite. What you’re leaving out of this is the possibility of multiple levels of selection. In the original edition of The Selfish Gene (1976, p.76), Dawkins argued that “the simplest way to explain the surplus DNA is to suppose that it is a parasite, or at best a harmless but useless passenger, hitching a ride in the survival machines created by the other DNA”. Cavalier-Smith (1977) drew a similar conclusion (before he had read Dawkins), and Doolittle and Sapienza (1980) and Orgel and Crick (1980) [yes, that Crick] independently developed the concept of “selfish DNA” a few years later. This is an explicitly multi-level selection approach because it specifies that non-coding DNA can be present due to selection within the genome rather than exclusively on the organism (or gene, in Dawkins’s case) (see, e.g., Gregory 2004, 2005). (Incidentally, this idea of parasitic DNA dates back at least to 1945, when Gunnar Östergren characterized B chromosomes in this fashion). Of course, they tended to do what Ohno did and applied this one idea to all non-coding DNA, which is too ambitious. The modern view is more pluralistic (see, e.g., Pagel and Johnstone 1992 vs. Gregory 2003). Some non-coding DNA is just accumulated “junk” (in the definition of evidence-supported non-function that you espouse). Some (perhaps most) is “selfish” or “parasitic” and persists because there is selection within the genome as well as on organisms (in fact, an argument could be, and has been, made that “selfish DNA” would be a much more accurate term than “junk DNA” for most non-coding DNA). Some non-coding DNA is clearly functional at the organism level, including regulatory regions and chromosome structure components. Some of these latter functional non-coding DNA sequences are derived from elements that originally were of one of the first two types, most notably transposable elements that take on a regulatory function through co-option (or, in another manner of thinking, that undergo a shift in level of selection).
Junk DNA is not noncoding DNA and anyone who claims otherwise just doesn’t know what they’re talking about.
I’m afraid I don’t follow what you mean here. By your definition, “junk DNA” is any non-functional sequence of DNA, including pseudogenes (i.e., the original meaning). Those sequences do not encode proteins. Hence, your version of junk DNA is non-coding. I think this reflects the confusion that is imposed by the term “junk DNA”, which is why I generally think it is more obfuscating than enlightening.
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References
Cavalier-Smith, T. 1977. Visualising jumping genes. Nature 270: 10-12.
Comings, D.E. 1972. The structure and function of chromatin. Advances in Human Genetics 3: 237-431.
Dawkins, R. 1976. The Selfish Gene. Oxford University Press, Oxford.
Doolittle, W.F. and C. Sapienza. 1980. Selfish genes, the phenotype paradigm and genome evolution. Nature 284: 601-603.
Gregory, T.R. 2003. Variation across amphibian species in the size of the nuclear genome supports a pluralistic, hierarchical approach to the C-value enigma. Biological Journal of the Linnean Society 79: 329-339.
Gregory, T.R. 2004. Macroevolution, hierarchy theory, and the C-value enigma. Paleobiology 30: 179-202.
Gregory, T.R. 2005. Macroevolution and the genome. In The Evolution of the Genome (ed. T.R. Gregory), pp. 679-729. Elsevier, San Diego.
Ohno, S. 1972. So much “junk” DNA in our genome. In Evolution of Genetic Systems (ed. H.H. Smith), pp. 366-370. Gordon and Breach, New York.
Orgel, L.E. and F.H.C. Crick. 1980. Selfish DNA: the ultimate parasite. Nature 284: 604-607.
Östergren, G. 1945. Parasitic nature of extra fragment chromosomes. Botaniska Notiser 2: 157-163.
Pagel, M. and R.A. Johnstone. 1992. Variation across species in the size of the nuclear genome supports the junk-DNA explanantion for the C-value paradox. Proceedings of the Royal Society of London, Series B: Biological Sciences 249: 119-124.