Delayed Re-entry

The new year finds me working through the summary for the RECOMBsat/DREAM conference last month in Cambridge, MA (more than 10,000 words on over 20 separate subjects), so daily blogging will have to wait until the week of January 11.

Check back then--upcoming posts will include molecular transistors, the 25^th anniversary of one of my first scientific papers, issues raised by the "climategate" emails, a series on evolutionary biology inspired by the book, The Plausibility of Life, new stories I've written, and more.

In the meantime, check out this video (Click the "Technology" tab) explaining how modern "deep sequencing" technology (here from Illumina) can simultaneously determine the base sequences for something like a million short snippets of DNA simultaneously. In contrast to the "traditional" (decade-old) microarray method of matching to preselected targets, this method allows new sequences to be quickly found, for example in the human gut or other natural environments. In addition, by matching these sequences to previously mapped genomes, this technology has revolutionized the identification of short regulatory RNAs, alternative splicing of proteins, DNA changes in cancer, DNA binding sites for transcription factors, fractal DNA structure, and many other areas of biology.

And it's just beginning.

Fractal DNA

Packing meters of DNA into a nucleus with a diameter a million times smaller is quite a challenge. Wrapping the DNA around nucleosomes, and arranging these nucleosomes into 30nm fibers, both help, but these structures must themselves be packed densely. Beautiful new research, reported in Science in October, supports a 20-year old idea that some DNA is arranged in an exotic knot-free fractal structure that is particularly easy to unpack.

Alexander Grosberg, now at New York University, predicted (1M pdf) in 1988 that a polymer would initially collapse into a "crumpled globule," in which nearby segments of the chain would be closer to each other than they would be in the final, equilibrium globule. Creating the equilibrium structure requires "reptation," in which the polymer chain threads its way through its own loops, forming knots. This gets very slow for a long chain like DNA. Grosberg also applied (1M pdf) these ideas to DNA, and explored whether fractal patterns in the sequence could stabilize it. But experimental evidence was limited.

Now Erez Lieberman-Aiden and his coworkers at MIT and Harvard have devised a clever way to probe the large-scale folding structure of DNA, and found strong support for this picture.

The experiment is similar to chromatin immunoprecipitation techniques that look for DNA regions that are paired to target proteins by crosslinking and precipitating the pairs and then sequencing the DNA. In this case, however, the researchers crosslink nearby sections of the collapsed DNA to each other. To sequence both sections of DNA, they first splice the ends of the pairs to each other to form a loop, and then break them apart at a different position in the loop. The result is a set of sequence pairs that were physically adjacent in the cell; their positions along the DNA are found by matching them to the known genome.

The researchers found that the number of neighboring sequences decreases as a power law of their sequence separation, with an exponent very close to -1, for sequence distances in the range of 0.5 - 7 million bases. This is precisely the expected exponent for the crumpled--or fractal--globule. This structure is reminiscent of the space-filling Peano curve with its folds, folds of folds, and folds of folds of folds forming a hierarchy. In contrast, the equilibrium globule has an exponent of -3/2.

As a rule, I don't put a lot of stock in claims that a structure is fractal simply by seeing a power law, or a straight line on a double-logarithmic plot, unless the data cover at least a couple of orders of magnitude. After all, a true fractal is self-similar, meaning that the picture looks exactly the same at low resolution at high resolution, and in many cases there's no reason to think that fine structure resembles the coarse structure at all.

But when there's a good theoretical argument for similar behavior at different scales, I relax my standards of evidence a bit. For example, there's a good argument that rate the random walk of a diffusing molecule looks into neighboring volumes looks similar, whatever the size of the volume you consider--this is a known fractal. The standard polymer model is just a self-avoiding random walk, which adds the constraint that two parts of the chain can't occupy the same space. The DNA data are different in detail, but the mathematical motivation is similar.

At the conference I covered last week in Cambridge, MA, Lieberman-Aiden noted that the fractal structure has precisely the features you would want for a DNA library: it is compact, organized, and accessible. The densely packed structure keeps nearby sequence regions close in space, and parts of it can easily be unfolded to allow the transcription machinery to get access to it. Co-author Maxim Imakaev has verified all of these features with simulations of the collapsing DNA.

These experiments and simulations are fantastic, and the fractal globule structure makes a lot of sense. But this dense structure makes it all the more amazing what must happen when cells divide, making a complete copy of each segment of DNA (except the telomeres), and ensuring that the epigenetic markers on the DNA and histones of one copy are replicated on the other. It's still an awesome process.

Guilt by Association

Many of the molecular transformations in cells occur inside of complexes, each containing many protein molecules and often other molecules like RNA.

Determining which molecules are in each complex is a critical experimental challenge for unraveling their function.

Ideally, biologists would identify not just the components, but the way they intertwine at an atomic level, for example using x-ray crystallography. The Nobel-prize-winning analysis of the ribosome showed that this detailed structural information also illuminates how the pieces of the molecular machine interact to carry out its biochemical task.

But growing and analyzing crystals takes years of effort. In many cases researchers are happy just to know which molecules are in which complexes. As a first step, biologists have developed several clever techniques to survey thousands of proteins to see which pairs interact, and to confirm whether those interactions really happen in cells.

Identifying additional protein members of complexes requires chemical analysis like chromatography and increasingly powerful mass spectrometry techniques. In contrast, to explore how DNA and RNA act in complexes, researchers can take advantage of the sequence information available for humans and most lab organisms.

To find out which DNA regions bind with a particular protein transcription factor, for example, biologists use Chromatin ImmunoPrecipitation, or ChIP. Bound proteins from a batch of cells are chemically locked to the DNA with a cross-linker like formaldehyde.

This technique then requires an antibody that binds only to the protein (and its bound DNA), and which is sooner or later tethered to a particle. After breaking apart the DNA, the particle precipitates to the bottom of the solution carrying ("pulling down") its bound molecules, which are then separated and analyzed.

A related technique identifies proteins bound to an antibody-targeted partner. Ideally, the methods identify components that were already bound just before the cells are broken up to begin the experiment, rather than all possible binding sites, so they flag only biologically relevant pairings.

For DNA, the state of the art until recently was "ChIP-chip," which uses microarrays to try to match the pulled down DNA to one of perhaps a million complementary test fragments on an analysis "chip." The advent of high-throughput sequencing has allowed "ChIP-seq," in which the sequence of the bound DNA is directly measured and compared by software to the known genome to look for a match. This was the method used recently to find enhancer sequences by their association with a known enhancer-complex protein.

A similar method can identify the RNA targets of RNA-binding proteins, as discussed by Scott Tenenbaum of the University at Albany at a 2007 meeting that I covered for the New York Academy of Sciences (available through the "Going for the Code" link on my website's NYAS page).

Once the fragments are identified, researchers can try to dissect the elements of the sequence that make them prone to binding by a particular protein. When successful, this procedure allows them to identify other targets for interaction with proteins using only computer analysis of sequence information. These bioinformatics techniques are a critical time saver, because the experiments show that each protein can bind to many different molecules in the cell.

Experiments like these are revealing many of the intricate details of cellular regulation, but also how much more there is to learn.

E pluribus unum

A few diseases can be traced to specific genetic variants. The nerve degeneration of Huntington's Disease, for example, arises exclusively from alterations of either copy of a gene on chromosome 4. This gene specifies a protein that is now called huntingtin. Such diseases are referred to as Mendelian, since they follow the simple rules of inheritance that Gregor Mendel observed in his pea plants.

For most diseases, though, it has proved difficult to find individual genes that explain much of the risk. Instead, the growing evidence from large-scale studies is that many variants contribute, each contributing only weakly. Even then, the genes alone do not condemn a person to the disease, which may also depend on microbes or non-living elements of the environment or on lifestyle. These "complex" diseases include all of the biggies, like heart disease and stroke, cancers, and many mental illnesses.

In some ways, the failure of the "one-gene/one-disorder" hypothesis shouldn't be too surprising. After all, a gene that reliably causes a fatal disease should have been largely weeded out by natural selection. Huntington's disease avoids this fate because it usually appears late in life, often after people have already had children (including, fortunately for us, Arlo Guthrie). Sickle-cell anemia persists because people with a single variant gene are resistant to malaria, although two copies cause the disease.

Nonetheless, lots of other diseases have an import genetic component, which can be determined by comparing the disease rate for close relatives. For example, if pairs of "identical" twins are more likely to both get a disease than are fraternal twins, the difference presumably arises because they share their entire genome, rather than only half.

For simple Mendelian diseases, researchers have extended this approach to locate where the disease gene resides in the chromosomes. This "linkage" analysis looks at which close relatives inherited a disease, and what known chromosome features they also inherited. This technique was applied in the 1980s to locate the Huntington's gene by testing dozens of residents of a Venezuelan village that had unusually many cases.

But human populations aren't particularly well suited for linkage studies. People don't have a lot of children, and they resist attempts at controlled breeding. As a result, it's hard to see weak genetic effects.

To get more subjects, researchers use association studies, which compare the genetics of unrelated individuals. Historically, you really had to know where to look to make associations studies work. But in the past few years researchers have done dozens of "genome-wide association studies," or GWAS, that look without prejudice across the entire human genome.

These studies are tricky. For one thing, since they monitor perhaps a million genetic markers at once, the chances are good that a marker will correlate with the disease by dumb (bad) luck. In individual experiments, researchers traditionally ignore a result if the probability of it arising by chance isn't less than 5% (P<0.05). For testing a million markers, they might need to ignore a result unless the effect is so strong that the probability that it arose by chance is less than perhaps 5x10^-8. To get such a convincing effect requires a lot of human subjects, generally hundreds or thousands. Even so, GWAS results often fail to recur when someone else tries the experiment.

Nonetheless, some genome-wide studies, like two for Alzheimer's I wrote about recently, have uncovered genes repeatedly associated with disease. In addition to variations of the DNA sequence, these studies often include structural variants such as copy-number variations, as well as "epigenetic" tags that change the expression of particular DNA regions. In spite of finding some likely genetic suspects, though, the total effect of all of the known variants is generally less than the known genetic component of these complex diseases. Researchers are actively debating the causes of this discrepancy; probably part of it comes because there are other contributions that are too weak to be seen in these studies.

Because complex diseases depend on the small contributions of many genetic variants, as well as the environment, buying your personal genome often won't tell you much definitive. But by studying these variants, and the way their effects interact in cells, researchers are learning a great deal about the nature of the diseases, including potential strategies for treating them.

The Firehose

When I arrived as an undergraduate at MIT, the orientation material likened an education there to drinking water from a firehouse. I get the same sense of frustrated exhilaration when I go to the amazingly useful web site of the National Center for Biotechnology Information. It's just plain humbling, how much there is to know.

The original raison d'être of NCBI, from its founding in 1988, was the maintenance of centralized databases of the staggering amount of genetic and other biological information, as well as tools for navigating through it. The center also funds extramural work to improve the acquisition, navigation and analysis of this and other data. Want to see the genetic markers in a particular section of human chromosome 13, together with nearby genes, hyperlinked to annotations of their function? It's all there for your perusal. And more. And more.

NCBI also hosts PubMed, a very useful hyperlinked database of journal articles, centered on biology and medicine, but also including other journals to some degree. It features really smart keyword searching, and includes links to online copies that are sometimes free. In fact, new rules also require, in principle, that a copy of any publication of research funded by the National Institutes of Health be deposited or freely linked at PubMed within a year of its publication, although I'm not sure they have the resources to enforce that requirement.

But one very cool resource, for those of us who don't have access to university libraries, is the bookshelf. I like Wikipedia a lot (although the memristor entry looks like it was written by HP), and I refer to it for background information in my stories, cautiously. But there's nothing like going straight to Alberts' Molecular Biology of the Cell, Stryer's Biochemistry, or dozens of other texts, for truly authoritative information (at least at the time it was written), systematically and comprehensively presented.

Take a sip!

Microarrays

The regulation of genes, which renders only some of them active in a particular cell, is critical for controlling life processes. But for many years it was tedious to measure which genes were active. That all changed, beginning about 15 years ago, with the microarray revolution, which lets researchers simultaneously monitor the activity of thousands or more genes.

The technique uses methods similar to those in microelectronics to create a huge, two-dimensional array of spots, each containing a short segment of DNA. Typically, this array is bathed with a sample containing a mixture of DNA or RNA molecules, which bind to any complementary sequences in the array. These sample molecules were duplicated from the original DNA or RNA to be tested, and in the process they were labeled with a fluorescent die. The pattern of glowing spots shows which sequences are present in the sample, and their brightness indicates how much is present.

The best-known use of these tools is to measure levels of messenger RNA (mRNA) that have been transcribed from known genes. Since mRNA is the template for making proteins, this level gives an indication of how active the gene is, and is sometimes referred to as "gene expression." However, this terminology is oversimplified, because many effects change the amounts and activity of the final protein, as well as the amount of mRNA in the cell.

The cleanest experiments compare the amounts under two conditions, for example in normal cells and after they are exposed to some perturbation, such as a drug. If the mRNA levels increase or decrease, the corresponding gene is said to be "upregulated" or "downregulated," respectively. Frequently the result for a single perturbation is presented as a column of red or green spots, each representing one gene. The overall pattern shows fingerprint of expression changes corresponding to various changes in the cell. For example, different types of stress tend to activate the same sets of genes and to de-activate others.

The technology is expensive, costing hundreds of dollars for a single commercial array, which can make it difficult for less well funded labs. On the other hand, researchers are expected to deposit their array data into publicly accessible databases that anyone can analyze. Naturally these databases are huge, so exploring them is a bioinformatics challenge that has spawned numerous software tools.

Array technology is most useful for identifying well known sequences, such as those derived from protein-coding genes. Indeed, arrays are available for the complete genomes of many model organisms such as E. coli, Drosophila, and mice, as well as humans. For exploring the large non-coding parts of the genome or the metagenomics of entire ecosystems, however, the need to pre-specify the sequences to be matched is a limitation.

For these and other reasons, researchers are increasingly turning to sequence-based technology, which simultaneously determines the base sequence of millions of short segments of DNA or RNA. Software then looks for matches between each sample and known genome sequences. This technology isn't cheap either, and the millions of sequences are an even larger bioinformatics challenge than the array data.