Michael Seringhaus & Mark Gerstein. American Scientist. Volume 96, Issue 6. Nov/Dec 2008.
Scientists strive to make sense of the natural world by defining its vital parts. As physicists anointed the atom, molecular biologists selected the gene as their basic unit. It was a smart choice: Virtually every observable property of any organism on Earth is derived from the action of one or more genes. Early on they were conceived as the physical embodiment of Gregor Mendel’s theory of heredity. By the mid-20th century, molecular science sharpened the picture. Genes became distinct spans of nucleotide sequence, each producing an RNA transcript translated into a protein with a tangible biological function.
Today, high-throughput genomics is generating data on thousands of gene products every month, improving our view once more. Biology’s basic unit, it is clear, is not nearly so uniform nor as discrete as once was thought. As a result, biologists must adapt their methods of classifying genes and their products. As Confucius once warned, defective language produces flawed meaning. But what is the best route toward improved precision? To try to answer that, we must understand how we reached where we stand today.
An Idea Blossoms
The word “gene” originally arose as a derivative of pangene, a term used to describe entities involved in pangenesis, Darwin’s hypothetical mechanism of heredity. The term derives from the Greek genesis (“birth”) or genos (“origin”). The term gene itself was first used by Wilhelm Johannsen in 1909, based on a concept Mendel had developed in 1866. In his famous breeding experiments with pea plants, Mendel showed that certain traits (such as height or flower color) do not appear blended in offspring. Instead, these traits are passed on as distinct, discrete entities. Furthermore, he demonstrated that variations in such traits are caused by variations in heritable factors. (In modern terminology, he showed that genotype dictates phenotype.) In the 1920s, Thomas Hunt Morgan demonstrated that genetic linkage, the tendency of certain traits to appear together, corresponds to the physical proximity of genes on chromosomes. The one-gene, one-protein view soon followed, as George Beadle and Edward Tatum demonstrated that mutations in genes could cause defects in specific steps of metabolic pathways. A series of experiments then established that DNA is the molecular vehicle for heredity, culminating in James Watson and Francis Crick’s famous 1953 solution of the three-dimensional structure of DNA.
The double-stranded double helix, with its complementary base-pairing, neatly explained how genetic material is copied in successive generations and how mutations can be introduced into daughter chromosomes by occasional replication errors. Crick’s continued work decrypting the genetic code laid the groundwork for the so-called central dogma of molecular biology: namely, that information travels from DNA through RNA to protein. In this scheme, a gene is a DNA region (or “locus”) that is expressed as messenger RNA (mRNA) and then translated into a polypeptide (usually a protein needed to build or operate a portion of a cell). This version of the general blueprint of life, with exceptions such as the RNA-based genomes of some viruses, is the overarching view that brought scientists to the doorstep of the genomic era.
This view has ramifications far beyond the nucleotide-sequence level. The central dogma also seeded what we’ll call the “extended dogma” of molecular biology. Within this conceptual framework, a transcribed mRNA (corresponding to a gene) gives rise to a single polypeptide chain that in turn folds to form a functional protein. This molecule is thought to perform a discrete and discernible cellular function such as catalyzing a specific chemical reaction. The gene itself is regulated by a promoter and transcription-factor binding sites assumed to be located on nearby DNA.
Genetic nomenclature developed to reflect the view that every gene has a discrete function. Each gene was given a name, and these names and their associated functions were arranged in a simple classification system. Such classification begins with broad functional categories (for instance, genes whose products catalyze a hydrolysis reaction or bind to other molecules) and moves to more specific functions (for example, the designation “amylase” describing the specific hydrolysis reaction involved in breaking down starch). Early attempts at functional classification of this sort, starting in the 1950s, include the International Commission on Enzymes Classification and the Munich In- formation Center for Protein Sequences. This unitary approach toward function still influences databases today when genes and their products are arranged by name and research articles are indexed to these names. To accomplish this, curators peruse manuscripts and synthesize from them a simple sum- mary statement of each gene’s function as described in the literature. This annotation is used to situate a given gene within the larger functional landscape.
This iterative one-gene, one-protein, one-function relationship paints a relatively straightforward picture of subcellular life. When describing the function of a given gene in a cell, biologists can conceive an individual protein as a single indivisible unit or node within the larger cellular network. In turn, when mapping genes across species using sequence similarity, they can assume a protein is either fully preserved in various organisms or entirely absent. Thus, related proteins in different organisms can easily be grouped together into consistent families, which can be given simple, unitary descriptions of their function. Thus, the extended dogma expands the central dogma to include regulation, function and conservation.
To the modern genomics scientist, the classical image of a gene and the extended dogma associated with it are quaint. High-throughput experiments that simultaneously probe the activity of millions of bases in the genome deliver a far less tidy view. First, the process of creating an RNA transcript from a DNA region is more complex than once was imagined. Genes make up only a small fraction of the human genome. But RNA expression studies on human DNA suggest that a substantial amount of the genome outside the boundaries of known or predicted genes is transcribed. Among the evidence are results published last year from the pilot phase of the Encyclopedia of DNA Elements (ENCODE) project. This massive, international collaboration intends to identify all functional elements in the human genome. The pilot studies on a representative 1 percent of the genome (roughly 30 million base pairs) suggest that nongenic transcription is very widespread. Precisely how wide is not yet known.
Moreover, the function of this nongene, transcribed material is unclear. So is how best to classify and name it. Since genetic nomenclature is keyed to discrete genes, short transcribed regions located outside of identified genes are troublesome. They sometimes end up listed in sequence databanks sporting identifiers similar to those of genes, which can be confusing. To further complicate things, experiments on non-gene transcription show that some of this activity occurs in pseudogenes, regions of the genome long considered fossils of past genes. In a transcriptional sense, dead genes appear to come to life, with some clues even suggesting they may help regulate other genes.
The phenomenon of alternative splicing must be considered. In eukaryotes, genes typically are composed of short exons, coding regions of DNA that are separated by long DNA stretches called introns. Scientists have long understood that introns are transcribed to RNA that is discarded (or “spliced out”) before proteins are produced. However, it now appears that for a given gene-containing locus this splicing can be done in multiple ways. For instance, individual exons can be left out of the final product. Sometimes, only portions of the sequence in an exon are preserved. When a sequence from outside the conventional bounds of a gene is spliced in as well, the number of variants climbs further. What once was thought to be a system to reliably remove introns can itself yield many variants of a single gene. This variation too appears to be considerably more prevalent than once was thought.
Our understanding of gene regulation is also changing. The traditional view” of the gene assumed that the protein-coding portion of a gene and its regulatory sequences existed in tight proximity on a chromosome—in some definitions the regulatory areas were considered part of the gene. The classical picture of gene regulation has long been taught via the lacoperon, a simple bacterial example of repressors, operators and promoters clustered near one another. This model describes a direct, proximal relationship between transcription factors and genes, with regulatory sequences of a particular gene directly upstream. But this simple schematic does not apply very well to mammalian systems and other higher eukaryotes. In that setting, genes can be regulated very far upstream by enhancers over 50,000 base pairs away, even beyond adjacent genes. The looping and folding of DNA can bring distant spans into close spatial proximity. Moreover, gene activity can be influenced by chemical alterations called epigenetic modifications. These can come in the form of modifications to the DNA itself (such as the attachment of methyl groups) or modifications to histones, support structures in chromosomal DNA. Depending on such modifications, a gene may be functionally active or silent in different circumstances with no change to its sequence. This further challenges the notion that a DNA sequence in a single region is sufficient to describe a gene.
The transcriptional and regulatory peculiarities described above never meshed well with the traditional notion of the gene, but they were thought to be fairly rare. Again, the recent ENCODE results suggest that deviations from the traditional model could be the norm.
In the quest to accurately describe biological systems, defining basic units is only part of the job. Scientists ultimately want to understand biological function. Function in the genetic sense initially was inferred from the phenotypic effects of genes. A person might have green or blue eyes and a gene related to this characteristic could then be assigned the “eye color” function. Phenotypic function of this sort is most directly shown by deleting or disrupting, or “knocking out,” a particular gene. Disrupting a gene in this way might cause an organism to develop cancer, to change color or to die early. Disabling the yeast mitochondrial gene FZOl, for instance, causes mutant strains to display slow growth and a petite phenotype.
But a phenotypic effect doesn’t capture function on the molecular level. To really elucidate the importance of a gene, it’s vital to understand the detailed biochemistry of its products. For instance, the yeast gene FZOÏ mentioned above displays GTPase enzyme activity, a molecular-level action not immediately apparent from its ultimate phenotypic effect. Fzol protein, it’s now clear, helps fuse mitochondrial membranes in yeast, protecting the cellular power plants. The biochemical effect explains the phenotypic effect.
Also key to understanding function are the processes or pathways a gene product engages within a given cell. For instance, a gene may be involved in secretion or amino acid biosynthesis and thus could be classified functionally in this manner. Identifying where a protein is found within various cell compartments offers additional functional insight. A protein may be found only in the nucleus or in a cell membrane. Fzol protein, as would be expected, localizes to the mitochondrial membrane in yeast.
Deciding which qualities of a gene and its products to record, report and classify is not trivial. All of the aforementioned approaches to functional classification assume a simple hierarchical classification scheme (for example, gene X is a member of group Y, which is part of superclass Z). Because of the complexity of functional classification, scientists now are pursuing several additions and enhancements to hierarchies to integrate information from multiple levels of function.
One popular approach that arose with widespread genome sequencing and the avalanche of data it produces is the Gene Ontology, best known as GO. This system is more complicated than a simple hierarchy since it employs a directed acyclic graph (DAG) structure. Both DAGs and simple hierarchies classify from the general to the specific, but because a DAG can have multiple parents for any given node, the latter is more flexible. In a simple hierarchy a gene has only one functional parent or classification; in the DAG approach it can have any number. For instance, a gene product might belong to a subset of proteins involved in cell-cycle control while at the same time belonging to a group of transcription factors. Individual entities thus are more fully described.
The DAG approach does have some shortcomings. Expansion of the classification depends on the degree of knowledge available about a particular cellular process. Not all aspects of subcellular life are equally well studied, a fact that can reflect the interests of biologists or funding agencies more than actual biological complexity. So some areas of a DAG can be much richer structurally than others—for the wrong reasons.
Another approach to making the most of floods of new genomic data, particularly from large-scale experiments that sample many genes at once, is to assign uniform attributes to each gene. For instance, biologists can measure the expression level of the same gene in a variety of cellular conditions using DNA microarrays. Or they can compare how stringently its protein products bind to a battery of metabolites with a protein array.
Scientists can also attempt to describe gene function completely in terms of molecular networks. This approach focuses less on what a particular gene does and more on which other genes it is connected to, in much the same way social network research does with people. As the old saying goes, what you do might matter less than who you know.
Capturing the functions of proteins can be challenging. A single gene frequently does not yield a protein with a single function, despite the one-to-one-to-one implications of the extended dogma. Individual proteins often are comprised of domains, each a different segment of polypeptide sequence with a distinct folded structure that might serve a discrete cellular need. A protein that catalyzes a certain reaction through one domain may have an additional domain responsible for DNA binding. Conversely, an assemblage of proteins produced by two or three genes can be necessary to carry out a single identifiable function in a cell.
In addition, any given domain might belong to a distinct protein family occurring in many different species. But only certain domains, or even subdomains, may be preserved across species. Trying to describe such “subgenic” conservation with traditional gene names or database identifier tags can quickly become confusing. The names don’t always clarify whether a whole protein is conserved, or just some part of it.
Given this, some people have argued that protein domains offer a more workable basic unit for molecular biology than entire genes. Focusing on domains does facilitate certain functional descriptions, but this approach is far from perfect. For instance, two mRNA splice variants may share the same exon and presumably the same domain structure- but they may still produce different products, depending on the presence or absence of a short, several nucleotide leader sequence.
What’s in a Name?
The lack of reliable central nomenclature standards is becoming a more urgent concern in biology. Prior to the genomics age, gene nomenclature was a relatively small-scale endeavor that produced, on the whole, carefully chosen and sometimes whimsical, even sassy, names. Without gobs of raw sequence data and super-powerful computers, homology mapping of similar genes between species was modest. Research communities working on a given model organism, say a fruit fly, a yeast or a cress weed, developed informal naming standards among themselves.
Scientists lucky enough to identify a novel gene were free to name it without consulting any central body or rules beyond those community standards. These standards generally came in the form of species-specific naming conventions. Scientists working with the yeast Saccharomyces cerevisiae frequently combined three letters and a number, for instance FZOl, to name genes. Some specialties were more flamboyant. Consider the gene sonic hedgehog, named for a spiky video-game character, and the gene yippee, capturing a scientist’s apparent delight at discovery. Both genes were detected in the fruit fly Dwsophila melanogaster.
Many cases exist where, with so many genomes now sequenced, similar genes are named one thing in one organism and something else in another. One example is lov-1 in round worms and PKDl in people, genes of interest in part because the latter is implicated in human polycystic kidney disease. Names that seemed meaningful in one context can be confusing in another. For instance, the pair of gene names superman and kryptonite is significant for a research community concerned with one model organism, the cress Arabidopsis thaliana, where a suppressing action of kryptonite upon superman is observed. But such rrtonikers would make no sense in another organism where only one of the pair is present.
To tackle this problem, a number of organizations are attempting to standardize gene naming. The Life Science Identifiers project aims to make biological identifiers consistent and usable across different databases. A parallel goal motivates the Human Proteome Organisation, a global, collaborative group dedicated to furthering proteomics (the genome-level study of proteins). As a small part of its mission, the Gene Nomenclature Committee of the Human Genome Organization is weeding out some “jokey” names that in the wrong context could be disturbing. Last year they renamed three human versions of the fringe genes found in flies (which previously were named lunatic fringe, manic fringe and radical fringe) hoping to avoid worrying medical patients with such labels. These genes participate in the widely conserved notch gene-signaling pathway.
An Ancient Problem
The problem of devising standard nomenclature and classification categories is not unique to molecular biology. This problem lurks in many walks of life, whether in organizing stars in distant galaxies, books in a library or inventories on store shelves. The tremendous success of the World Wide Web has highlighted the importance of classifying information well on a large scale.
One of the most exciting proposals to improve standardization of online information is called the Semantic Web, a hallmark technology of the next generation of the World Wide Web. There, hyperlinks take on a meaning beyond simple connection and represent standardized relationships between a pair of entities. For instance, a given link from one high school student’s home page to another’s might represent her relationship to that person (for example, “friend of”). Links from a page about automotive parts might convey the car model each was designed for (representing a “part of” relationship). One can easily see how a web page marked up this way could be mined to extract meaningful information.
But how should all these relationships be standardized and organized? Computer scientists address this formally by developing precise specifications for a knowledge-classification system—an ontology. One of the challenges in creating an ontology is the tremendous amount of domain-specific knowledge that is required. Often no single person (or group) holds enough, so complete coverage of a domain can require a collective effort. Informally, collecting distributed intelligence from “many eyes” into a rough classification has proved successful on a number of prominent Web 2.0 sites. Rough classifications are established on Flickr for photographs, on Delicious for links and, perhaps most visibly, on Wikipedia for knowledge in general. The latter is an ever-evolving encyclopedia that is far larger than any one person, or reasonably sized group, could produce. And it can be updated more quickly than its traditional print counterparts. A parallel movement has begun in biology to develop a community tagging system for genes and proteins. The WikiProteins project, for instance, encourages distributed annotation of proteins.
Today effective gene classification can be thought of as a four-step process. First, a gene is identified and named as precisely as possible given the limited information available at the time of discovery. Second, based on functional experiments or sequence comparisons, brief descriptions of that gene are compiled. Third, from that data, standardized keywords can be created and used to categorize genes. Finally, those categories can be arranged into a hierarchy or another organizing template. The fourth and final step represents the state of the field at the moment. For instance, it is this sort of arrangement of functional terms that GO provides.
The main limitation to this approach will be its dependence on human curators. To understand where gene nomenclature could move next, consider another example from the Internet and the very different organizational approaches initially taken by the search engines Google and Yahoo. Yahoo originally was a manually curated directory, a DAGlike structure devised and revised by human hands. Web users submitted sites and curators slotted them into various categories. This approach made sense initially, when the number of new Web sites to add daily was small. But it was quickly challenged by the scalable, automated strategy of Google, which harvested keywords from each Web page and computed relationships between them based on their pattern of links. The very early Yahoo approach is similar in a sense to classifying genes with GO, dependent on meticulous study of each new specimen by careful human curators. But a large volume of new objects can completely paralyze even the most dedicated team. Precisely this problem is occurring in biology today.
It may be that an information-handling strategy that blossomed on the World Wide Web can help scientists solve this problem. What if molecular biologists abandoned manual classification of the massive amounts of genomics data pouring their way and gave up asking where a novel or confusing entity should fit on a chart? Instead, as results arrive, measures of similarity could be computed automatically, generating a Google-style collection of interlinked, interwoven information. With this approach, we would lose the comforting anchor of quaintly named genes and reassuring images of hierarchical classification. But we would gain, perhaps, a more useful and robust understanding of the myriad ways biological entities can be similar and can interact.
Of course, significant scientific labors await anyone pursuing this approach. Biologists will have to choose which standardized attributes are important enough to explore in any given highthrough put experiment. They must decide how best to cluster genes and their products to illuminate the truly important connections. Such challenges—opportunities really—are among the fruits genomics brings to biology.