The Red Queen and her king: cooperation at all levels of life.

The Red Queen in "Through the Looking Glass" is often used as a metaphor for the relentless, unremitting competitive struggle by which Darwin described life. That imagery fits comfortably in our culture, with its emphasis on competition and inequity, but less so for nature herself. Life is manifestly much more about cooperation, at all levels and through a variety of ubiquitous mechanisms, than it is about competition. Most organisms of most species are nowhere near the proverbial Malthusian edge of survival, such that selection will detect the tiniest difference in their performance and enhance its genetic basis. Cooperation through interaction of multiple entities is inherent in many fundamental aspects of life, and its importance is not widely enough appreciated. Here we discuss a set of principles by which this works. We illustrate the points with a computer simulation of a topic of interest to anthropology, the development of the head. In a sense, our culture has its metaphors reversed. The red royal family is a more accurate symbol for the true nature of life, human or otherwise.

Is life law-like?

Genes are generally assumed to be primary biological causes of biological phenotypes and their evolution. In just over a century, a research agenda that has built on Mendel's experiments and on Darwin's theory of natural selection as a law of nature has had unprecedented scientific success in isolating and characterizing many aspects of genetic causation. We revel in these successes, and yet the story is not quite so simple. The complex cooperative nature of genetic architecture and its evolution include teasingly tractable components, but much remains elusive. The proliferation of data generated in our "omics" age raises the question of whether we even have (or need) a unified theory or "law" of life, or even clear standards of inference by which to answer the question. If not, this not only has implications for the widely promulgated belief that we will soon be able to predict phenotypes like disease risk from genes, but also speaks to the limitations in the underlying science itself. Much of life seems to be characterized by ad hoc, ephemeral, contextual probabilism without proper underlying distributions. To the extent that this is true, causal effects are not asymptotically predictable, and new ways of understanding life may be required.

Biomineralization in humans: making the hard choices in life.

The skeleton, teeth, and otoconia are normally the only mineralized tissues or organs in the human body. We describe physiological biomineralization in collagenous matrices as well as a more derived noncollagenous matrix. The origin of the collagenous matrices used in mineralized skeletal tissues can be traced to a soft tissue in early Metazoa. In early vertebrates, a genetic system coding for ancient soft collagenous tissue was co-opted for biomineralization using redundant genes resulting from whole genome duplication. However, genes more specific to mineralized tissues arose subsequent to the genome duplication by genomically local tandem duplication. These new genes are the basis for a novel genetic system for various mineralized tissues in skeleton and teeth. In addition, any tissue can be abnormally mineralized, and many pathologies of mineralization in humans are known.

The cooperative genome: organisms as social contracts.

A predominant theme in much of evolutionary biology is that organisms are the product of relentless and precise natural selection among them, and that life is about the competition of all-against-all for success. However, developmental genetics has rapidly been revealing a very different picture of the nature of life. The organizing principles by which organisms are made are thoroughly based on complex hierarchies of molecular interactions that require multiple factors to be relentlessly cooperating with each other. Reconciling these two points of view involves changing the scale of observation, and a different understanding of evolution, in which cooperation and tolerance are more important than competition and intolerance.

What are genes "for" or where are traits "from"? What is the question?

For at least a century it has been known that multiple factors play a role in the development of complex traits, and yet the notion that there are genes "for" such traits, which traces back to Mendel, is still widespread. In this paper, we illustrate how the Mendelian model has tacitly encouraged the idea that we can explain complexity by reducing it to enumerable genes. By this approach many genes associated with simple as well as complex traits have been identified. But the genetic architecture of biological traits, or how they are made, remains largely unknown. In essence, this reflects the tension between reductionism as the current "modus operandi" of science, and the emerging knowledge of the nature of complex traits. Recent interest in systems biology as a unifying approach indicates a reawakened acceptance of the complexity of complex traits, though the temptation is to replace "gene for" thinking by comparably reductionistic "network for" concepts. Both approaches implicitly mix concepts of variants and invariants in genetics. Even the basic question is unclear: what does one need to know to "understand" the genetic basis of complex traits? New operational ideas about how to deal with biological complexity are needed.

Gene duplication and the evolution of vertebrate skeletal mineralization.

The mineralized skeleton is a critical innovation that evolved early in vertebrate history. The tissues found in dermal skeletons of ancient vertebrates are similar to the dental tissues of modern vertebrates; both consist of a highly mineralized surface hard tissue, enamel or enameloid, more resilient body dentin, and basal bone. Many proteins regulating mineralization of these tissues are evolutionarily related and form the secretory calcium-binding phosphoprotein (SCPP) family. We hypothesize here the duplication histories of SCPP genes and their common ancestors, SPARC and SPARCL1. At around the same time that Paleozoic jawless vertebrates first evolved mineralized skeleton, SPARCL1 arose from SPARC by whole genome duplication. Then both before and after the split of ray-finned fish and lobe-finned fish, tandem gene duplication created two types of SCPP genes, each residing on the opposite side of SPARCL1. One type was subsequently used in surface tissue and the other in body tissue. In tetrapods, these two types of SCPP genes were separated by intrachromosomal rearrangement. While new SCPP genes arose by duplication, some old genes were eliminated from the genome. As a consequence, phenogenetic drift occurred: while mineralized skeleton is maintained by natural selection, the underlying genetic basis has changed.

Dissecting complex disease: the quest for the Philosopher's Stone?

Is the search for the causes of complex disease akin to the alchemist's vain quest for the Philosopher's Stone? Complex chronic diseases have tremendous public health impact in the industrialized world. Much effort has been expended on research into their causes, with the aim of predicting who will be affected or preventing effects before they arise, but progress has been halting at best. In this paper, we discuss possible reasons including the use of models and methods that fit point-source and Mendelian diseases but may not be as appropriate for complex diseases, reliance on causal criteria that may not be as relevant as they are for communicable diseases, and the biology of complex disease itself. Finally, we ask whether most complex diseases are even good candidates for the kind of prediction and prevention that we have come to expect based on experience with infectious and Mendelian disease.

The effects of scale: variation in the APOA1/C3/A4/A5 gene cluster.

While there is considerable appeal to the idea of selecting a few SNPs to represent all, or much, of the DNA sequence variability in a local chromosomal region, it is also important to quantify what detail is lost in adopting such an approach. To address this issue, we compared high- and low-resolution depictions of sequence diversity for the same genomic region, the APOA1/C3/A4/A5 gene cluster on chromosome 11. First, extensive re-sequencing identified all nucleotide and sequence haplotype variation of the linked apolipoprotein genes in 72 individuals from three populations: African-Americans from Jackson, Miss., Europeans from North Karelia, Finland, and European-Americans from Rochester, Minn. We identified 124 SNPs in 17.7 kb and significant differences in variation among genes. APOC3 gene diversity was particularly distinctive at high resolution, showing large allele frequency differences ( F(ST) values >0.250) between Jackson and the other two samples, and divergent population-specific haplotype lineages. Next, we selected haplotype-tagging SNPs (htSNPs) for each gene, at a density of approximately one SNP per kb, using an algorithm suggested by Stram et al. (2003). The 17 htSNPs identified were then used to reconstruct low-resolution haplotypes, from which inferences about the structure of variation were also drawn. This comparison showed that while the htSNPs successfully tagged common haplotype variation, they also left much underlying sequence diversity undetected and failed, in some cases, to co-classify groups of closely related haplotypes. The implications of these findings for other haplotype-based descriptions of human variation are discussed.

Evolution by phenotype: a biomedical perspective.

Genes are widely assumed to play a major role in the epidemiology of complex chronic diseases, yet attempts to characterize the genetic architecture of such traits have been frustrating. Understanding that evolution works by screening phenotypes rather than genotypes can help explain the source of this frustration. Complex traits are usually the result of long-term, often subtle, gene-environment interactions, such that individual life histories may be as important as population histories in predicting and explaining these traits. Recognizing that the problem is not due to technological limitations can help temper expectations and guide the design of future work in biomedical genetics, by allowing us to focus on better approaches where they exist and on those problems most likely to yield a genetic solution. We may even be forced to re-conceive complex biological causation.