assertTrue( )

Parsing the DNA Crazy Quilt

2013-05-20T00:30:00.000-04:00

A measure of how little we know about the real-world workings of evolution is that science still can't explain why some organisms have huge imbalances in the chemical composition of their DNA. If you look at the genome of Clostridium botulinum (the botulism germ), 72% of the bases in its DNA are either 'A' or 'T': adenine or thymine. (The four possibilities are, of course, adenine, thymine, guanine, and cytosine.) Conversely, you can find many examples of organisms in which the DNA is mostly 'G' or 'C.' The question is why A, T, G, and C don't occur in roughly equal proportions (which is what you'd expect after millions of years of genetic averaging; you'd expect some sort of regression to the mean).

Just to give you an idea of what GC/AT imbalance really looks like, here's the gene for the enzyme adenine deaminase from Clostridium botulinum, with all the A and T values in red:

ATGTATAAAAATATACAAAGAGAAATCTATAAAAATACAAAAGGAGACGGGGATATGTTTAATAAATTTGATACAAAGCCTCTTTGGGAGGTAAGTAAA ACTTTATCAAGTGTAGCACAGGGGCTTGAACCGGCTGATATGGTTATTATAAATTCAAGGCTTATAAATGTCTGTACAAGAGAAGTCATAGAAAACACA GATGTAGCAATTAGCTGTGGAAGAATTGCTTTAGTAGGTGATGCAAAACATTGCATAGGGGAAAACACAGAGGTAATTGATGCAAAAGGACAATATATT GCACCAGGTTTTTTAGATGGTCATATTCATGTTGAATCATCAATGTTAAGTGTAAGCGAATATGCTCGTTCAGTAGTTCCACATGGTACTGTCGGAATA TATATGGATCCACATGAAATTTGTAATGTACTCGGATTAAATGGTGTACGTTATATGATTGAAGATGGCAAGGGTACTCCACTTAAAAATATGGTGACC ACACCATCCTGTGTACCAGCAGTTCCAGGTTTTGAAGATACAGGAGCGGCTGTAGGACCAGAAGATGTTAGAGAAACAATGAAGTGGGATGAAATAGTT GGATTAGGAGAAATGATGAACTTCCCAGGTATACTTTATTCTACAGATCATGCTCATGGAGTAGTAGGAGAAACTTTAAAAGCTAGTAAAACAGTAACA GGACATTATTCTTTACCTGAAACAGGAAAAGGATTAAATGGATATATTGCATCAGGTGTAAGATGTTGTCATGAATCCACAAGAGCGGAAGATGCTCTT GCTAAAATGCGCCTTGGAATGTATGCAATGTTTAGAGAAGGATCTGCATGGCATGACTTAAAGGAAGTAAGTAAAGCCATTACAGAAAATAAGGTAGAT AGTAGATTTGCTGTTTTAATATCTGATGATACTCACCCACACACATTGCTTAAGGATGGACATTTAGATCATATTATAAAACGTGCTATAGAAGAAGGG ATAGAGCCATTAACTGCAATTCAAATGGTAACAATAAATTGTGCACAATGTTTCCAAATGGATCATGAATTAGGTTCTATAACTCCAGGAAAATGTGCA GATATTGTATTTATAGAAGATTTAAAAGATGTAAAAATAACAAAGGTTATTATAGATGGAAATTTAGTTGCAAAGGGTGGACTATTAACTACTTCAATA GCTAAATATGATTATCCTGAAGATGCTATGAATTCAATGCATATTAAGAATAAAATAACACCAGATTCCTTTAATATTATGGCTCCTAATAAAGAAAAA ATAACTGCAAGGGTTATTGAAATTATACCTGAAAGAGTTGGTACATATGAGAGACATGTTGAACTTAATGTTAAAGATGATAAAGTTCAATGTGATCCA AGTAAAGATGTTTTAAAAGCAGTTGTATTTGAAAGACACCATGAAACAGGAACAGCAGGATATGGTTTTGTTAAAGGTTTTGGTATTAAGAGAGGAGCT ATGGCTGCAACAGTTGCCCATGATGCTCACAACTTATTAGTTATAGGAACAAATGATGAAGATATGGCATTAGCTGCTAATACATTAATAGAATGTGGT GGAGGAATGGTAGCCGTACAAGATGGTAAAGTATTAGGCTTAGTTCCATTACCAATAGCAGGACTTATGAGTAATAAGCCTTTAGAAGAAATGGCAGAA ATGGTAGAAAAACTAGATAGTGCATGGAAAGAAATAGGATGTGATATAGTTTCACCATTTATGACAATGGCACTTATTCCACTTGCCTGCCTACCAGAA TTAAGACTAACTAATAGAGGGTTAGTTGATTGTAATAAGTTTGAATTTGTATCATTATTTGTAGAAGAATAA

View gene at FastaView.

The organism Actinomyces oris (which occurs in the film that builds up on teeth) has an adenine deaminase gene that looks like this:

ATGGCCGATCAACCGTCCGCAGACCTGCTTATCAAGGACGCGCGCATCGTCCCTTTCCGGTCCCGTACCGAACTGGGTGCGCTGCGCCGAGGTGACCCT CACCCCGGCGCCTTGGCCGCGCCGCCGCCCCCGGGTGAGCCCGTGGATGTGCGTATCAAGGCGGGCCGGGTCGTCGAGGTGGGACAGGGGCTGAGTGCT CCCGGGACACGGGTCCTTGAGGCCGAGGGCTCCTTCCTCATTCCCGGCCTGTGGGACGCTCACGCCCACCTGGACATGGAGGCGGCGCGCTCGGCACGC ATCGACACGCTGGCCACCCGCAGCGCGGAGGAGGCCCTGGAGCTGGTGGCACGGGCGCTGCGGGATCATCCGGCCGGTTCGCCTCCGGCCACGATCCAG GGCTTCGGGCACCGCCTGTCCAACTGGCCCCGGGTGCCCACGGTGGCCGAGCTCGACGCCGTCACCGGGGAGGTTCCCACGCTGCTCATCTCCGGGGAC GTGCACTCCGGGTGGCTGAACTCGGCGGCGCTGCGTGTCTTCGGCCTGCCGGGGGCCAGCGCCCAGGACCCGGGAGCACCGATGAAGGAGGACCCGTGG TTCGCCCTACTCGACCGCCTCGATGAGGTCCCGGGGACACGCGAGCTGCGGGAGTCCGGCTACCGACAGGTCCTGGCCGACATGCTGTCCCGGGGCGTC ACCGGCGTGGTGGACATGAGCTGGTCGGAGGATCCCGATGACTGGCCGCGGCGCCTGCGGGCCATGGCGGACGAGGGCGTACTCCCCCAGGTGCTGCCC CGCATCCGCATCGGGGTCTACCGCGACAAGCTGGAACGGTGGATCGCCCGGGGCCTGCGCACCGGGACCGCGCTGGCAGGCTCACCCCGCCTGCCCGAC GGTTCCCCGGTGCTGGTGCAGGGGCCGCTCAAGGTGATCGCAGACGGCTCGATGGGCTCGGGCAGCGCACACATGTGCGAGCCCTATCCCGCCGAGCTG GGCCTGGAGCACGCCTGCGGCGTGGTCAACATCGACCGGGCCGAGCTCACCGACCTCATGGCCCACGCCTCCCGGCAGGGTTATGAGATGGCCATCCAC GCCATCGGGGACGCGGCGGTCGACGACGTCGCCGCGGCCTTCGCGCACTCGGGTGCCGCCGGGCG

For whatever reason (and that's the point: we have no idea why), Actinomyces has chosen an AT-poor dialect for its DNA, even though it has to make many of the same types of genes as Clostridium.

Some people don't see this as a major puzzle: One organism evolved its DNA to a super-AT-rich state, another one didn't. So what? It's all random drift.

I disagree. It's not drift. We know of two strong forces that should keep organisms like Actinomyces from developing high G+C content. First is "AT pressure." It's known that mutations naturally tend to go in the GC-->AT direction. (One study found that in Salmonella typhimurium, GC-->AT mutations outnumbered AT-->GC mutations 50 to 1.) In the absence of corrective measures, natural mutations would very quickly lead all organisms in the direction of DNA with a very low G+C content.

A second important force is that of lateral gene transfer, which we know is common in microorganisms; common enough, certainly, to "even out" GC/AT ratios over evolutionary timescales. Random uptake of foreign genes by cells should tend to make A, G, C, and T levels equal, over time. For organisms like Clostridium and Actinomyces (and many others), this clearly hasn't happened.

In an earlier post I mentioned one possible reason organisms drift away from the 50-50 GC/AT centerline. DNA replication is more efficient when the template is biased toward one extreme (GC) or the other (AT), assuming endogenous nucleotide levels can be regulated in a similarly biased fashion (which they presumably are, in these organisms).

One might speculate that GC/AT extremism also simplifies DNA maintenance and repair. Imagine that your DNA is 70% G+C. A super-simple DNA repair tactic for deaminated purines would be to just replace every defective purine with a guanine. Seven out of ten times, blind replacement of defective purines with guanine would be the correct repair, if you're Actionymyces. And one out of three times, mistakes wouldn't matter anyway, because high-GC codons tend to be fourfold degenerate. (In a fourfold degenerate codon, you can replace the third base with anything—A, G, C, or T—without changing the codon's meaning.) Blind guanine substitution would have a better than 80% success rate in a high-GC organism that needed to replace defective purines.

It turns out there are other reasons to live "away from centerline," if you're a bacterium. I'll talk about those in another post.

Information Theory in Three Minutes

2013-05-18T00:30:00.000-04:00

Claude Shannon, the father of information theory, used to play an interesting game at cocktail parties. He'd grab a book, open it to a random page, and cover up all but the first letter on the page, then ask someone to guess the next letter. If the person couldn't guess, he'd uncover the letter, then ask the person to guess the next letter. (Suppose the first two letters are 'th'. A reasonable guess for the next letter might be 'e'.) Shannon would continue in this manner, keeping score, until a good deal of text had been guessed. The further along one goes in this game, the easier it becomes (of course) to guess downstream letters, because the upstream letters provide valuable context.

What Shannon consistently found from experiments of this sort is that well over half of English letters are redundant, because they can be guessed in advance. In fact, Shannon found that when all forms of redundancy are taken into account, English is more than 75% redundant, with the average information content of a letter being approximately one bit per symbol. (Yes, one bit. See Shannon's "Prediction and Entropy of Printed English.")

Claude Shannon

Shannon became intrigued by questions involving the efficiency of information transfer. What is the nature of redundancy in an information stream? Are some encodings more redundant than others? How can you quantify the redundancy? Eventually, Shannon elaborated a mathematical theory around the encoding and decoding of messages. That theory has since become extremely important for understanding questions of encryption, compression, detection of faint signals in the presence of noise, recovery of damaged signals, and so on.

A central concept in Shannon's theory is that of entropy. "Shannon entropy" is very widely misunderstood and/or misinterpreted, so it's important to be clear on what it's not. It's not disorder: Entropy, in information theory, is not the same as entropy in thermodynamics, even though the mathematics are similar. Shannon liked to consider entropy a statistical parameter reflecting the amount of information (or resolved uncertainty) encoded, on average, by a symbol. We think of the English alphabet as having 26 symbols. Since 26 values can be encoded in log₂(26) == 4.7 bits, we say that the channel bandwidth for 26-letter English is 4.7 bits per symbol, but this is not the entropy. Shannon found that the entropy (the actual bits used per symbol) was closer to 1.0 than to 4.7. How can this be? The answer has to do with the fact that some symbols are used far more often than others; and also (as noted), some symbols are redundant by virtue of context.

Entropy gets to the actual (rather than ideal) information content of a message by taking into account actual frequencies of usage of symbols. If English text used all letters of the alphabet equally (and unpredictably), then the entropy of text would be exactly 4.7 bits per symbol. Each symbol would contribute 1/26th of -log₂(1/26) to the total. But because some letters are used more or less frequently than others, they contribute more or less than 1/26th of log₂(1/26), and that total can add up to less than 4.7.

It's easy to visualize this with a simple example involving coin-tossing. Suppose, for sake of example, that a series of coin tosses comprises a message. As a medium of communication, the coin toss is capable of expressing only two states: heads, or tails. This could be represented in binary form as 1 and 0. If half of all tosses are heads and half are tails, then the total entropy in the message is 0.5 * log₂(0.5) for heads plus 0.5 * log₂(0.5) for tails, or one bit per symbol (Note: If you actually do the math you'll come up with a negative-1. Hence, in entropy calculations, the result is usually multiplied by -1 so it can be expressed as a positive number.)

Consider now the situation of a two-headed coin. In this case, there is no "tails" term and the heads term is 1.0 * log₂(1.0), or zero. This means the tossing of a two-headed coin resolves no uncertainty and carries no information.

Continuing the example, consider the case of a weighted penny that falls heads-up two-thirds of the time. Intuitively, we know that this kind of coin toss can't possibly convey as much information as a "fair" coin toss. And indeed, if we calculate 2/3 * log₂(2/3) for heads plus 1/3 * log₂(1/3) for tails, we get an entropy value of 0.9183 bits per symbol, which means that each toss is (on average) 1.0 - 0.9183 == .0817 or 8.17% redundant. If one were to take a large number of coin tosses involving the weighted penny and convert those tosses into symbols ('h' for heads and 't' for tails, say), the resulting data stream would be compressible to 91.83% of its fully expanded size, and then it wouldn't compress any more beyond that, because that's the entropy limit.

Actually, that last statement needs to be qualified. We're assuming, throughout this example, that the result of any given coin toss does not depend on the outcome of the preceding toss. If that rule is violated, then the true entropy of the "message" could be much lower than 0.9183 bits per symbol. For example, suppose the result of 12 successive coin-tosses were: h-h-t-h-h-t-h-h-t-h-h-t. There's a recurring pattern, and the pattern makes the stream predictable. Predictability reduces entropy; remember Shannon's cocktail-party experiment. (You might ask yourself what a message with all possible redundancy removed would look like, and in what way or ways, if any, it would differ from apparent randomness.)

Technically speaking, when symbols represent independent choices (not depending on what came before), the entropy can be calculated as before, and it's called the order-zero entropy. But if any given symbol depends on the value of the immediately preceding symbol, we have to distinguish between order-zero and order-one entropy. There are also order-two, order-three, and higher-order entropies, representing contexts of contexts.

Suppose now I tell you that an organism's DNA can contain only two types of base-pairs: GC and AT. (You should be thinking "coin toss.") Suppose, further, I tell you that a particular organism's DNA is 70% GC. Disregarding higher-order entropy, does the DNA contain redundancy? If so, how much? Answer: 0.7 * log₂(0.7) for GC plus 0.3 * log₂(0.3) for AT equals 0.8813, meaning redundancy is about 12%. Could the actual redundancy be higher? Yes. It depends what kinds of recurring patterns exist in the actual sequence of A, G, C, and T values. There might be recurring motifs of many kinds. Each would send entropy lower.

Further Reading
Shannon's best-known paper, "A Mathematical Theory of Communication," Bell Systems Tech. Journal, October 1948
"A Symbolical Analysis of Relay and Switching Circuits," Shannon's unpublished master's thesis
Claude Shannon's contribution to computer chess
Shannon-Fano coding
Nyquist-Shannon Sampling Theorem

Back Pain: An Infectious Process?

2013-05-15T00:30:00.000-04:00

I wrote a piece for Big Think the other day about the recent finding that many cases of back pain are septic in nature: the pain comes from propionic acid (and other acids) released by anaerobic bacteria that have found their way into spinal-disc tissues. The principal offender is something called Propionibacterium acnes, a common mouth and skin germ that also often can be found in lung tissue.

Propionibacterium acnes can take on
an intracellular lifestyle.

Propionibacterium is, for most of us, a harmless stowaway. It is characterized as a "low virulence" organism, meaning it doesn't aggressively pathologize the host by default, the way (for example) a tuberculosis bacterium does. But for certain individuals, under certain conditions, Propionibacterium can be a major hazard. In addition to causing acne (and in severe cases, an accompanying arthritis), P. acnes is also seen in post-operative infections, prosthesis failure, breast-implant infection, corneal infection, sarcoidosis, bacteremia, and inflammation of lumbar nerves. Its involvement in sarcoidosis is controversial. What seems to be happening is that a special protein (a "trigger factor"), secreted by P. acnes, stimulates a cellular immune response in sensitive individuals. The macrophages that arrive to attack P. acnes become overwhelmed by the bacteria as they go into intracellular-parasite mode. Granulomas then form as P. acnes takes up residence in the aggregated macrophages. (More about P. acnes's role in sarcoidosis can be found in the March 2013 paper by Eishi in Respiratory Investigation.)

The immunological response triggered by P. acnes can be far-reaching. In the 1980s, back when P. acnes was called Corynebacterium parvum, researchers found that killed suspensions of the bacteria, injected into mice, caused 80% to 100% suppression of tumor growth. The dead bacteria stimulated the murine immune system to the point where mice could fight off cancer. Why this technique has not been used for human cancer treatment, I don't know. (It might be because it's too cheap and too easy. What do you think?)

In recent years, researchers have been finding P. acnes in the lumbar discs of back patients, typically at the rate of 40% to 50%. (About half of patients don't have the bacterium.) Simply finding the bacterium in discs doesn't prove a causal role for P. acnes in back pain, of course, but in a double-blind randomized controlled trial involving back patients who got either placebo or amoxicillin for 100 days, the amoxicillin-treated patients did better (both over the 100 days and a year later), which tends to suggest that P. acnes might well be playing a causal role in back pain.

People hurt their backs (to a greater or lesser degree) all the time without experiencing huge pain or lasting damage, but in a certain proportion of cases, disc herniation leads to Type 1 Modic Change (so-called bone edema) in nearby vertebrae, and at that point you're almost guaranteed to be in excruciating pain. But antibiotics might obviate the need for surgery, in at least some cases.

The nuclear material of intervertebral discs is an ideal place for P. acnes (an anaerobe) to grow, because it's warm, nutrient-rich, and (with no vascular content) oxygen-depleted. The question of how P. acnes finds its way into a disc in the first place is an interesting one (which I discuss in my post at Big Think). The short answer is, there's a ton of P. acnes in your mouth, especially if you happen to be (how shall we say?) not very attentive to oral hygiene, and bacteria can enter the bloodstream directly via the gums when you brush your teeth or have them professionally cleaned (or when a dentist picks and pokes at your teeth with one of those sharp pointy thingies). Almost any dental event, even vigorous brushing, can lead to a transient bactermia. Your spleen and white blood cells will clear bacterial cells from your blood very quickly, of course, and there are factors in your blood that are chemotoxic to most bacteria, but if a few P. acnes cells happen to stay in your blood long enough to find an inflammation zone in your body (where they can take up residency), you could be in trouble. By "inflammation zone," I mean an inflamed joint, a catheter or shunt, an implant of any kind, or any irritated tissue, really. Did you recently hurt your back? That counts.

Because even tooth brushing poses a significant risk of bacteremia, you may want to consider investing in a stock of mouthwash and using it before every brushing, to cut the live-bacteria count down and thus reduce your risk of lumbar disc infection, endocarditis, sarcoidosis, acne, and other bacteremic sequelae involving P. acnes. If you think I'm being alarmist, fine; you're entitled to your opinion. For me, it's mouthwash five times a day.

DNA G+C Content and Survival Value

2013-05-11T12:15:00.000-04:00

One of biology's big open questions is why organisms differ so much with regard to the relative amounts of GC and AT in their DNA. You'd think that if there are only two kinds of DNA base pairs (see diagram) they'd be more-or-less equally abundant. Not so. There are organisms with DNA that's mostly GC (and/or CG) pairs; there are organisms with very-AT-rich DNA; and within the chromosomes of higher organisms you find large GC-rich regions (isochores) in the midst of great swaths of AT-rich DNA.

DNA contains adenine and thymine in equal amounts, and
guanine and cytosine in equal amounts, but it does not
usually contain GC pairs and AT pairs in equal amounts. And
it doesn't seem as if there is an "optimum" GC:AT ratio. The
GC:AT ratio varies by species. Within a species, it's constant.

There are two really odd facts at work here:

1. The GC content of DNA varies by species, and it varies a lot.

2. Evolution doesn't seem to trend toward an "optimum CG:AT ratio" of any kind.

If there were such thing as an optimum GC:AT ratio for DNA, surely microorganisms would've figured it out by now. Instead, we find huge diversity: There are bacteria on every point in the GC% spectrum, running from 16% GC for the DNA of Candidatus Carsonella ruddii (a symbiont of the jumping plant louse) to 75% for Anaeromyxobacter dehalogenans 2CP-C (a soil bacterium). At each end of the spectrum you find aerobes and anaerobes; extremophiles and blandophiles; pathogens and non-pathogens. About the only generalization you can make is that the smaller an organism's genome is, the more likely it is to be rich in A+T (low GC%).

Genome size correlates loosely with GC content. The very smallest
bacteria tend to have AT-rich (low GC%) DNA.

The huge diversity in GC:AT ratios among bacteria is impressive. But does it simply represent a random walk all over the possibility-space of DNA? Or do the various points on the spectrum constitute special niches with important advantages? What advantage could there be for having high-GC% DNA? Or high-AT% DNA?

Some subtle clues tell us that this is not just random deviation from the mean. First, suppose we agree for sake of argument that lateral gene transfer (LGT) is common in the microbial world (a point of view I happen to agree with). Over the course of millions of years, with pieces of DNA of all kinds (high GC%, low GC%) flying back and forth, LGT should force a regression to the mean: It should make genomes tend toward a 50-50 GC:AT ratio. That clearly hasn't happened.

And then there's ordinary mutational pressures. It's beginning to be fairly well accepted (see Hershberg and Petrov, "Evidence That Mutation is Universally Biased Toward AT in Bacteria," PLoS Genetics, 2010, 6:9, e1001115, full version here) that natural mutation is strongly biased in the direction of AT by virtue of the fact that deamination of cytosine and methylcytosine (which occurs spontaneously at high frequency) leads to replacement of 'C' with 'T', hence GC pairs becoming AT pairs. The strong natural mutational bias toward AT says that all DNA should creep in the direction of low GC% and end up well below 50% GC. But again, this is not what we see. We see that high-GC organisms like Anaeromyxobacter (and many others) maintain their DNA's unusually high (75%) GC content across millions of generations. Even middle-of-the-road organisms like E. coli (with 50% GC content) don't slowly slip in the direction of high-AT/low-GC.

Clearly, something funny is going on. For a super-high-GC organism like Anaeromyxobacter to maintain its DNA's super-high GC content against the constant tug of mutations in the AT direction, it must be putting significant energy into maintaining that high GC percentage. But why? Why pay extra to maintain a high GC%? And how does the cost get paid?

I think I've come up with a possible answer. It has to do with DNA replication cost, where "cost" is figured in terms of time needed to synthesize a new copy of the DNA (for cell division). Anything that favors low replication cost (high replication speed) should favor survival; that's my main assumption.

My other assumption is that DNA polymerases (the enzymes involved in replication) are not clairvoyant. They can't know, until the need arises, which of the four deoxyribonucleotide triphosphates (dATP, dTTP, dGTP, dCTP) will be needed at a given moment, to elongate the new strand of DNA. When the need arises for (let's say) an 'A', the 'A' (in the form of dATP) has to come from an existing endogenous pool of dNTPs containing all four bases (dATP, dTTP, dGTP, dCTP) in whatever concentrations they're in. The enzyme has to wait until a dATP (if that's what's needed) randomly happens to lock into the active site. Odds are only one in four (assuming equal concentrations of dNTPs) of a dATP coming along at exactly the right moment. Odds are 3 out of 4 that some incorrect dNTP (either dGTP, dTTP, or dCTP) will try, and fail, to fit the active site first, before dATP comes along.

But imagine that your DNA is 75% G+C. And suppose you've regulated your intracellular metabolism to maintain dGTP and dCTP in a 3:1 ratio over dATP and dTTP. The odds of a good random "first hit" go up.

To simulate the various possibilities, I wrote software (in JavaScript) that simulates DNA replication, where the template DNA molecule is 1000 base-pairs in length and the dNTP pool size is 10000 bases. The software allows you to set the organism's genome GC% to whatever you want, and also set the dNTP pool's relative GC percentage to whatever you want. The template DNA is just a random string of A, T, G, and C bases (1000 total), reflecting their relative abundances as set in the GC% parameter. The pool of dNTPs is set up to be a randomized array (again reflecting abundances set in a GC% parameter).

The way the software works is this. Read a base off the template. Fetch a base randomly from the base pool. If the base happens to be the one (out of four) that's called for, score '1' for the timing parameter, and continue to read another base off the template. If the base was not the one that's called for, put it back in the pool array in a random location, then randomly fetch another base from the pool; and increment the timing parameter. (For each fetch, the timing parameter goes up by 1.) Keep fetching (and throwing back bases) until the proper base comes up, incrementing the time parameter as appropriate. (The time parameter keeps track of the number of fetch attempts.) When the correct base turns up, the pool shrinks by one base. In other words, replication consumes the pool, but as I said earlier, the pool contains ten times as many bases (to start) as the DNA template. So the pool ends up 10% smaller at the end of replication.

Each point on this graph represents the average of 100 Monte Carlo runs, each run representing complete replication of a 1000-bp DNA template, drawing from a pool of 10,000 bases. The blue points are runs that used a DNA template containing 25% G+C content. The red points are runs that used DNA with 75% G+C. The X-axis represents different base-pool compositions. See text for details. Click for larger image.

I ran Monte Carlo simulations for DNA templates having GC contents of 75%, 50%, and 25%, using base pools set up to have anywhere from 15% GC to 85% (in 2.5% increments). The results for the 75% GC and 25% GC templates (representing high- and low-GC organisms) are shown in the above graph. Each point on the graph represents the average of 100 complete replication runs. The Y-axis shows the average number of fetches per DNA base (so, a low value means fast replication; a high value means slower DNA replication). The X-axis shows the percentage of GC in the base-pool, in recognition of the fact that relative dNTP abundances in an organism may vary, in accordance with environmental constraints as well as with organism-specific homeostatic setpoints.

Maximal replication speed (the low point of each curve) happens at a base-pool GC percentage that is displaced in the direction of the DNA's own GC%. So, for the 25%-GC organism (blue data points), max replication efficiency comes when the base-pool is about 33% GC. For the 75% GC organism (red points) the sweet spot is at a base-pool GC concentration of 65%. (Why this is not exactly symmetrical with the other curve, I don't know; but bear in mind, these are Monte Carlo runs. Some variation is to be expected.)

The interesting thing to note is that max replication efficiency, for each organism, comes at 3.73 fetches per base-pair (Y-axis). Cache that thought. It'll be important in a minute.

The real jaw-dropper is what happens when you plot a curve for template DNA with 50% GC content. In the graph below, I've shown the 50%-GC runs as black points. (The red and blue points are exactly as before.)

This is the same graph as before, but with replication data for a 50%-GC genome (black points). Again, each data point represents the average of 100 Monte Carlo runs. Notice that the black curve bottoms out at a higher level (4.0) than the red or blue curves (3.73). This means replication is less efficient for the 50%-GC genome.

Notice that the best replication efficiency comes in the middle of the graph (no big surprise), but check the Y-value: 4.00. The very fastest DNA replication, when the DNA template is 50% GC, requires 4 fetches per base, compared to best-case base-fetching efficiency of 3.73 for the 25%-GC and 75%-GC DNAs.What does this mean? It means DNA replication, in a best-case scenario, is 4.25% more efficient for the skewed-GC organisms. (The difference between 3.73 and 4.00 is 4.25%.)

This goes a long way toward explaining why GC extremism is stable in organisms that pursue it. There is replication efficiency to be had in keeping your DNA biased toward high or low GC. (It doesn't seem to matter which.)

Consider the dynamics of an ATP drawdown. The energy economy of a cell revolves around ATP, which is both an energy molecule and a source for the adenine that goes into DNA and RNA. One would expect normal endogenous concentrations of ATP to be high relative to other NTPs. For a low-GC% organism, that's also a near-ideal situation for DNA replication, because high AT in the base pool puts you near the max-replication-speed part of the curve (see blue points). A sudden drawdown in ATP (when the cell is in crisis) shifts replication speed to the right-hand part of the blue curve, slowing replication significantly. This is what you want if you're an intracellular symbiont (or a mitochondrion, incidentally). You want to stop dividing when the host cell is unable to divide because of an energy crisis.

Consider the high-GC organism (red dots), on the other hand. If ATP levels are high during normal metabolism, replication is not as efficient as it could be, but so what? It just means you're willing to tolerate less-efficient replication in good times. But as ATP draws down (perhaps because nutrients are becoming scarce), DNA replication actually becomes more efficient. This is what you want if you're a free-living organism in the wild. You want to be able to continue replicating your DNA even as ATP becomes scarce. And indeed that's what happens (according to the red data points): As the base pool becomes more GC-rich, replication efficiency increases. The best efficiency comes when base-pool A+T is down around 35%.

I think these simulations are meaningful and I think they help explain the DNA-composition extremism seen among microorganisms. If you're a professional scientist and you find these results tantalizing, and you'd like to co-author a paper for PLoS Genetics (or another journal), please get in touch. (My Google mail is kas-dot-e-dot-thomas.) I'd like to coauthor with someone who is good with statistics, who can contribute more ideas to this line of investigation. I think these results are worth sharing with the scientific community at large.

Hydrogen Peroxide Powers Evolution

2013-05-06T00:30:00.001-04:00

I'm about to offer a conjecture that is a bit preposterous-sounding but could well hold true. I actually think it does.

I propose that evolution, at the level of bacteria (though probably not at higher levels), is driven by hydrogen peroxide.

This theory rests on three assumptions: One is that the creation of new bacterial species happens almost entirely via lateral gene transfer, not heritable point-mutations. Secondly, bacteria (marine and terrestrial) are regularly exposed to challenges by hydrogen peroxide in the environment. Thirdly, those challenges drive lateral gene transfer.

Evidence for the first assumption is embarrassingly abundant. If you're not up to speed on the subject, I suggest you read the excellent paper, "Lateral Gene Transfer," by Olga Zhaxybayeva and W. Ford Doolittle in Current Biology, April 2011, 21:7, pp. R242-246 (unlocked copy here). It's now common to find that any given bacterial species can trace a good percentage of its protein base to "ancestors" that are too far removed horizontally to be ancestors in the conventional sense.

Consider E. coli. There are hundreds of strains of E. coli, with genes ranging in number from 4,100 to about 5,300 per strain. The problem is, the various strains of E. coli have only about 900 genes in common (and that's far too few genes to render a fully functional E. coli). The E. coli pan-genome actually takes in more than 15,000 gene families, total. Certainly, you can draw a family tree of E. coli based on 16S ribosomal polymorphisms, but that doesn't explain where the 15,000 pan-genome genes came from. The "family tree" metaphor quickly breaks down if you start drawing trees based on proteins. You get many conflicting trees—all of them correct.

Trees like this are fiction where bacteria are concerned.
The tree of life is more like a net of life or web
of life than a directed acyclic graph.

Where are all of the genes coming from? Other species, of course. They arrive by way of mechanisms like transformation, transduction, and conjugation. all of which allow direct entry of foreign DNA into a bacterial cell. At one time it was thought that conjugation could only occur between bacteria of the same species, but it is now known that cross-species conjugation also occurs (as, for example, between E. coli and Streptomyces or Mycobacterium).

Transduction, which is where viruses package up an infected host's genes in virus capsules that are then taken up by another cell, occurs naturally in bacterial populations in response to environmental factors like ultraviolet light and hydrogen peroxide. Exposure of a virus-carrying (lysogenic) cell to UV light or peroxide can induce runaway production of virus, and in fact this mechanism is used by Streptococcus to kill competitive Staphylococcus cells, in a clever bit of chemical warfare. It's been known for years that hydrogen peroxide can cause many types of bacteria to shed DNA. Now we know why: Hydrogen peroxide is a signalling molecule. It signals (among other things) lysogenic bacteria to go into a lytic cycle. It also signals cells to mount what's known as the SOS response, which is a global response to oxidative challenge. Years ago, Bruce Ames and his colleagues showed that exposing Salmonella to very dilute (60 micromolar) hydrogen peroxide caused the cells to differentially express 30 "SOS" proteins, including heat-shock proteins and low-fidelity DNA-repair systems. We know that hydrogen peroxide as dilute as 0.1 micromolar can induce phage (virus) production in up to 11% of marine bacteria. This is significant, because rainwater contains hydrogen peroxide in concentrations of 2 to 40 micromolar, and ocean water has been known to reach millimolar levels of H2O2 after a rain storm.

If you're wondering why rain contains hydrogen peroxide, the peroxide gets there in two ways. One is UV-frequency photochemistry (where water is cleaved to H and OH, then reforms as H2 and H2O2); the other is via ionization reactions caused by lightning. (Lightning is energetic enough to bring airborne oxygen and water to a plasma state. The resulting ionization and rearrangement of free atoms yields a certain amount of hydrogen peroxide.) The presence of H2O2 in rainwater has been confirmed many times, and in fact there's a well-preserved "fossil record" of it in polar icepacks, going back centuries. (Polar snowpacks contain from 10 to 900 ppb of H2O2; it varies seasonally, the max coming in summer.)

Bottom line, every rain event (over land, over sea) constitutes a hydrogen peroxide challenge for microbes. Which induces viral transduction (and a release of whole-cell DNA through lysis, some of which will be inevitably be used in transformation). It also induces low-fidelity DNA repair (which is guaranteed to help evolution along). Every rain event, in other words, is a chance for evolution to do its thing. For bacteria, that means gene-sharing within and across species lines.

Darwin's theory of a tree-like ancestor basis
for all living things is dead wrong, at
least for bacteria.

W. Ford Doolittle (who wrote a classic book chapter about lateral gene transfer called "If the Tree of Life Fell, Would We Recognize the Sound?") estimates that if a horizontal gene transfer occurs once every ten billion vertical replications, "it would be enough to ensure that no gene in any modern genome has an unbroken history of vertical descent back to some hypothetical last universal common ancestor." (See this article.)

It's obvious (to me, at least) that every rain event carries with it the potential to cause far more gene transfers than are necessary (according to Doolittle) to make vertical inheritance fade into insignificance as an evolutionary bringer of change. The hydrogen peroxide in rain has been driving lateral gene transfer in bacteria for eons. In fact, it is arguably the dominant driver of evolution in bacteria.

Sorry, Mr. Darwin. Point mutations handed down to sons and daughters just isn't cutting it.

More Science on the Desktop

2013-05-05T00:30:00.000-04:00

Not to keep harping on the amazing power of desktop omics tools, but I thought I'd share a tip for those of you into genome-mining. The tip in a nutshell is that if you gang-load a bunch of FASTA sequences (DNA sequence data) into the FeatView form at http://genomevolution.org, then click the rather inconspicuous button labeled "Phylogeny.fr" at the bottom left of the FeatView page, you'll be taken automatically to http://www.phylogeny.fr, where you'll get a realtime-generated phylogenetic tree based on the sequence data you provided in FeatView, with no effort on your part (it's truly a one-click operation). Copy and paste DNA sequences into FeatView, click one button, and 30 seconds later a tree shows up on your screen, looking (perhaps) something like this:

The reason I made this tree is that I wasn't satisfied with my knowledge of the relatedness of certain weird microorganisms I've recently run into. Namely:

Ralstonia (which I mentioned yesterday), WEIRD BECAUSE: It turns hydrogen gas and CO2 into plastic.
Bordetella, a bronchial infection agent; WEIRD BECAUSE: It turns out to be very similar, genetically, to Ralstonia
Burkholderia, a soil organism (and human and animal pathogen), WEIRD BECAUSE: It has an unexpectedly large amount of genetic similarity to Ralstonia and Polynucleobacter
Polynucleobacter, a ditch-water bacterium, WEIRD BECAUSE: It can live as an intracellular parasite of freshwater ciliates or it can live independently in soil (making it potentially a great study organism for determining the genetic bases of intracellular symbiosis)
Thiomicrospira, a very tiny CO2- and sulfur-loving organism, WEIRD BECAUSE: It can only be found near deep-sea thermal vents (see my previous writeup)
Polaromonas, a relatively newly discovered and still poorly understood bacterium, WEIRD BECAUSE: It is abundant in glacier ice on multiple continents. Plus it has an amazing (and totally unexpected) amount of genetic overlap with our good friend Bordetella, the whooping-cough bug.

If you're not familiar with how bacterial classification works, let's just say it's a mess. There's a long historical tradition of classifying microorganisms based on a hodgepodge of ad hoc methods involving everything from physical appearance under the microscope (especially after staining with crystal violet), to the habitat of the organism, to its ability to metabolize various substances, its ability to make spores, adaptation to oxygen or lack of oxygen, serological characteristics, etc. It's always been an error-prone system, resulting in many misclassifications and later corrections, owing to its inconsistency and basic irrationality, to put it bluntly. With the advent of molecular genetic techniques, it's now possible to create accurate phylogenies based on little more than DNA sequence differences, usually involving the 16S ribosomal RNA (more here).

Freshwater ciliates (like this Euplotes) are
home for Polynucleobacter endosymbionts.

As big an advance as ribosome-based phylogeny is, it's pretty far from ideal (IMHO), mainly because it ignores phenotypes. In fact it's pretty far removed from anything at all having to do with an organism's ecology, metabolism, mode of living, etc. What are we really measuring when we measure relatedness according to a 16S ribosomal yardstick? Just the rate of random mutation accumulation in a pretty uninteresting cell artifact. I'd rather have a yardstick that's tied to phenotypic reality than to a slow-to-change, "highly conserved" piece of cold dead scaffolding.

So to create my own "family tree" of two dozen or so microbes, I said to hell with 16S ribosomes and decided to use, as my yardstick, genetic variation in the GroEL gene, which codes for the 60-kiloDalton heat-shock protein. I chose this protein (or rather, the gene for it) as my phylo-yardstick for a number of reasons. First, the DNA sequence is sizable, at about 1643 nucleotides (making it somewhat bigger than the 16S rDNA). It's important to have a large yardstick gene when looking for faint genetic signals. Secondly, this protein is essentially universal in prokaryotes. It's ubiquitous but not necessarily highly conserved, in the same sense that rRNA is highly conserved. ("Highly conserved" is not what you want. Think about it. Taken to the extreme, a "highly conserved" sequence is invariant. It never changes. And is therefore useless for phylogenetics.) Thirdly, the GroEL heat-shock protein has multiple intracellular touchpoints: It's known to interact with GroES, ALDH2, and dihydrofolate reductase, and it's involved in signal tranduction (it's induced not just by heat but by hydrogen peroxide). Not to overlook the obvious, but it is also a touchpoint protein for any enzyme that can be repaired by the 60kDa heat shock protein. That's probably dozens if not hundreds of enzymes. Why is that important? Think about it: A protein that is sensitive to the 3D conformational requirements of other proteins has to evolve in response to the needs of all the proteins it services. A thermophile (Thermomicrospira) is going to need a different heat-shock repair system than a psychrophile (Polaromonas). A salt-lover needs a different one than a freshwater-lover. GroEL has to reflect, in its own structure, the many shifting requirements of the host proteome. These considerations make GroEL a highly appropriate basis gene for phylogenetic analysis.

And frankly, I think the GroEL-based phylo-tree phylogeny.fr spit out for me (see illustration further above) speaks for itself. It's a remarkably informative (and accurate) tree. GroEL evolutionary differences not only accurately grouped endosymbionts together, soil organisms together, aquatic organisms, etc., it also correctly grouped the "enteric-alike" Erwinia with E. coli and Shigella, and it cannily put Polaromonas with soil organisms (rather than aquatics), which I think is correct, based on recent Polaromonas isolates being found in soil rather than snow. Likewise, it's good to see Bdellovibrio (a freshwater bug) clustered with Polynucleobacter (which is symbiotic with a ciliate protozoan), with Thiomicrospira (the saltwater hydro-vent organism) a very nearby out-node.

If you get an infection while in a hospital, pray
it's not Clostridium difficile, which is often deadly.

A harder call to make is Clostridium difficile, which is present in 1% to 5% of non-ill people's intestines. Is it an enteric (a la E. coli)? Definitely not. The Clostridia (botulism, tetanus, etc.) are spore-forming soil bacteria. Their placement in the tree not far from the soil-dwelling spore-former, Bacillus thuringensis, is thus eminently correct. Bacillus is a proximal out-node relative to Clostridium, which is understandable in that Bacillus is aerobic whereas Clostridia are strict anaerobes.

Buchnera (an aphid symbiont) comes at an odd location, much further away from the insect-dwelling Wolbachia than I would have predicted, but then again Buchnera's host feeds on cold sap where Wolbachia's hosts typically feed on warm blood. All the organisms around Wolbachia in the tree are hemophiles.

Our good friend Bordetella (of pertussis fame) is placed firmly in the soil group. I think that's real and significant. When you start to look at Bordetella's high DNA sequence similarity with Ralstonia and Burkholderia, it would be surprising, actually, if it fell anywhere else in the tree.

Honestly, when I took Bacterial Ecology 201 in college, many years ago, it was under duress and I hated the experience. But now, decades later, I'm starting to like it. With tools like those available for free at http://genomevolution.org and http://www.phylogeny.fr, what's not to like?

A Tale of Two Microbes

2013-05-04T01:39:00.000-04:00

One area where Big Data has started to pay big dividends is in genome research, and you can begin to taste the payoff yourself, right now, if you want to come along as I show you how to mine genetic data from public databases in the service of a little desktop microbial genetics. You'll be amazed at what you can do.

No one knows why, but when Ralstonia eutropha
eats too much, it produces plastic granules
instead of, say, starch or fat. Go figure.

For today's experiment, we're going to compare the genomes of two bacteria, one of which you know very well, the other of which you don't, unless you've got way too much time on your hands. The germ you already know is Bordetella, the whooping cough bug. The bug you haven't heard of is Ralstonia eutropha, a soil organism that has the amazing ability to subsist only on hydrogen gas, nitrate, and carbon dioxide. In return, it produces wicked-crazy quantities of plastic (yes, plastic—it stores carbon as polyhydroxybutyrate), and because it's potentially useful to industry, Ralstonia's DNA, like Bordetella's, has been fully sequenced.

If you go right now to http://genomevolution.org/r/8o1x, you'll see that I've set up a little experiment for you. You shouldn't have to press the pink "Generate SynMap" button on that page. It should run automatically (but if you don't see an image like the one below, hit the button).

Every dot in this dot-plot represents a match between
a gene in Bordetella bronchiseptica and a gene in
Ralstonia eutropha. See text for discussion.

What has happened is that the SynMap server has been instructed to go find the complete DNA sequence of Ralstonia eutropha Strain H16 as well as the complete DNA sequence for Bordetella bronchiseptica Strain RB50, and run a comparison of one against the other. It so happens Bordetella has a single chromosome with 5,339,179 base pairs, whereas our hydrogen-loving, plastic-storing friend Ralstonia has 3 chromosomes totalling 7,416,678 base pairs. (It has one main chromosome, and two small auxiliary chromosomes called plasmids.)

Every point on the above graph represents a match between a gene in Bordetella and a gene in Ralstonia. The X-axis represents locations on the Bordetella genome (starting from one end and going to the other). The Y-axis plots locations on the Ralstonia genome. All we're doing is mapping one genome to another and tallying the significant matches.

This is a massive number of matches (well over 10,000), just to let you know. Usually, when you compare organisms, you don't see this many dots. I chose Bordetella and Ralstonia because I knew there'd be a lot of hits, based on my own prior experiments. And by the way, I don't think most microbiologists are aware (yet) that Bordetella and Ralstonia are extremely closely related. This is new information I'm sharing with you.

It's one thing to get a bunch of points on a dot-plot, but how do we really know these two organisms are related? This is where synteny comes in. Synteny is the degree to which two chromosomes share blocks of order. The key intuition is that merely sharing genes isn't enough; what counts is whether matching genes are in the same arrangements. If genome A has genes X, Y, and Z, in that order, and genome B also has genes X, Y, and Z (in the same order), we say that A and B share a syntenous triplet. The genomes have a degree of synteny.

The SynMap tool is very powerful because it lets you find syntenous regions in DNA, and it's tunable. If you go to the Analysis Options tab on the SynMap page, you'll see that you can set two parameters called Maximum Distance Between Two Matches, and Minimum Number of Aligned Pairs. The URL that I sent you to (for our experiment) has values of 50 and 2, respectively, already dialed in. That means the graph is plotting every occurrence of 2 gene-pair matches that occurred between genes no more than 50 genes apart. That's a pretty liberal setting. If two organisms are related, you can expect to see a lot of matches.

But what I propose you try (if you want) is setting "Maximum Distance Between Two Matches" to 500 and "Minimum Number of Aligned Pairs" to 250. (Then click the Generate SynMap button to refresh the graph.) This is a much more stringent requirement: It tells SynMap to try to find 250 matched genes within any given 500-gene region, do it for all regions of both genomes, and plot the results, if any. A 250-gene chunk is a pretty large syntenous region for a creature that has only 10,000-or-so genes to begin with.

The result of our hunt for super-large 250-gene syntenous regions is shown in the first graph below. The red dots represent the regions. They run from the top of the Y-axis to the lower right corner. Remember that the axes map directly to positions on the genome. What the diagonal line says is that there's a near-linear mapping of syntenous regions from one genome to the other.

The second graph below shows what happens when we re-tune our DNA-matching parameters to find blocks of 200 ordered genes within each 500-gene domain. We're looking for shorter runs of genes (200 instead of 250), which should be more plentiful. And they are. This time our graph looks like an 'X'. Why? Bacterial chromosomes do a lot of rearranging, and one of the most common events is a symmetric inversion around the origin of replication (and/or the terminus of replication). If you get enough of these inversions of various sizes, you end up with pieces of DNA that used to be near the start of the chromosome ending up near the end, and vice versa. (Repeat for all intermediate locations as well.) If you want to know more about how and why this ends up making an X-pattern on a dot-plot, be sure and read the classic paper by Eisen et al. called "Evidence for symmetric chromosomal inversions around the replication origin in bacteria," Genome Biology 2000, 1(6):research0011.1–0011.9 (unlocked PDF here).

Genomes compared with synteny-block size 250.

Synteny block size 200.

Block size 175.

Block size 120, max domain size 180 genes.

Block size 90, max domain 130.

Block size 2, max domain size 50.

The third and fourth graphs in this series show what happens when we tune our match for smaller block sizes. In the third graph, we've set "Maximum Distance Between Two Matches" to 500 and "Minimum Number of Aligned Pairs" to 175, which produces what looks like two really poorly drawn X's superimposed on each other. As we get more permissive with our synteny matches, we start to see the results of more inversion events. It makes sense that shorter synteny blocks will be swept up in more successful inversions, because an inversion that cuts across a large synteny block is probably fatal in many cases. (Some large groups of genes need to be kept together, for proper gene regulation. If an inversion event cuts through a critical regulon at the wrong spot, the cell might not go on to reproduce.)

As we keep tuning the "Minimum Number of Aligned Pairs" downward, the graphs become more cluttered as we see the results of many thousands of inversion events in the history of the chromosomes.

The fourth graph uses values of 180 and 120 for Max Distance and Minimum Number of Aligned Pairs, then in graph five we have values of 130 and 90. And finally, in the last graph, we have 50 and 2. The final graph is mostly noise. But buried in the noise are many faint signals that can be seen by twiddling the knobs on the synteny settings.

I hope this bit of desktop genomics has convinced you that desktop genomics has reached an exciting stage indeed. (I've only scratched the surface, here, of what the tools at http://genomevolution.org can do.) I also hope I've convinced any microbial geneticists who might be reading this that Bordetella and Ralstonia are very closely related indeed. (Which should come as news. I don't think it's been reported.) You wouldn't think a hydrogen-loving soil organism would have much in common with a throat-dwelling pathogen, but as I like to say: DNA doesn't lie!

Deep-Sea Vents: The Mosquito Connection

2013-05-01T11:34:00.002-04:00

Quick: What species of life on earth is the most abundant? (Which species has more living members than any other species?) Hint: If an alien probe lands in a random location on earth, chances are better than 70% that the probe will encounter this organism.

If you're thinking in terms of the ocean, you're on the right track. What may surprise you is the connection between the world's-most-populous-organism (to be revealed shortly) and the mosquitoes that've been dive-bombing your neck all week. Equally amazing is the link between the mosquitoes in your back yard and hydrothermal vents in the ocean floor.

The hundreds of bright little particles at the
narrow end of this wasp egg are Wolbachia cells.

I wasn't thinking about marine biology or deep-sea hydrothermal vents when I went online at http://genomevolution.org the other day to do a little nosing around into the genome of Wolbachia pipientis, the ultra-tiny bacterial parasite carried by nearly every mosquito on earth. (Caution: Don't attempt the following DNA-analysis tricks on your own unless you want to become thoroughly addicted to desktop omics. I'm a microbiologist by training. I can do these stunts safely.) "Parasite" is actually the wrong word. Our tiny friend Wolbachia doesn't just parasitize the mosquito; it's an integral part of the mosquito. Wolbachia can't live outside its insect host—and guess what? The host frequently can't live without Wolbachia. The two provide essential services for each other, an arrangement known as mutualism.

I would argue that Wolbachia is more than a mutualistic symbiont: It's a proto-organelle, something very close to what Lynn Margulis had in mind as the ancestor of today's mitochondrion.

Wolbachia can't live on its own in the outside world (as far as anybody knows): it needs to live inside a host (generally an arthropod, although filarial worms also carry Wolbachia). Inside its host it occupies a very special niche: It lives in the nursery cells of the insect's ovary—the cells that will go on to become egg cells.

This is no ordinary symbiosis. I mentioned in an earlier post that Wolbachia carries with it genes for reverse-transcriptases, resolvases, recombinases, transposases, translocases, DNA polymerases, RNA polymerases, and phage integrases—a complete suite of retroviral machinery, designed for export of foreign DNA into host DNA. And indeed, researchers have found that Wolbachia DNA is quite often embedded in the host's own nuclear DNA. (One group, looking at four insect hosts and four nematode hosts, found anywhere from 500 base-pairs to over a million base pairs of Wolbachia DNA residing in the nucleus. Another group found 45 Wolbachia genes incorporated in a fruit-fly host's nuclear DNA.) The situation with Wolbachia thus parallels the situation with mitochondria, where we know that 97% of the gene products that go to make up a mitochondrion are actually encoded in nuclear DNA, not mitochondrial DNA.

When you encounter an organism as baffling as Wolbachia, oftentimes you want to know what its relatives are—what it's most closely related to. When a new or poorly understood organism has a close relative that's already well-studied, sometimes you learn a lot in a hurry. That's particularly true of pathogens (not that Wolbachia is a pathogen per se). Pathogens have virulence strategies of various kinds. Maybe Wolbachia has symbiosis strategies that it learned from a relative?

The problem with a lot of the super-tiny microbes (which Wolbachia definitely is, with only a quarter as much DNA as E. coli) is that their relatedness is not always well understood. Organisms are assigned a taxonomic slot, then the assignment changes a few years later, after they're better-studied. (So for example, Cowdria ruminantium was eventually renamed Ehrlichia ruminantium, and a bunch of former Ehrlichias are now Neorickettsias, except the ones that attack red blood cells, which are now Anaplasmas.) Taxonomy at this end of the evolutionary tree is definitely a work in progress.

Deep-sea thermal vents like this one
are home to organisms like Thiomicrospira
that can grow on sulfide, CO2, and basic salts.

Fortunately, it's easy nowadays (what with so many organisms' DNA sequences available online) to go on the web and compare genomes directly, using a tool like SynMap, which is what I started doing with Wolbachia. I started going down the list of mini-microorganisms and began running DNA similarity tests of Wolbachia against Ehrlichia, Neorickettsia, Anaplasma, Chlamydia, and "the usual suspects" at the ultra-small-chromosome end of the tree of life.

What I found surprised me. A bizarre little bacterium called Thiomicrospira kept showing up in my BLAST searches as having many genes in common with Wolbachia (based on sequence matches in large numbers of genes). None of the taxonomy charts showed the two to be related. But DNA doesn't lie. I kept coming up with matches across hundreds of genes. (Bear in mind, Wolbachia has only about 1300 genes to begin with, which is very small, even for a bacterium.)

What's bizarre about Thiomicrospira is that it's one of those fairly newly discovered microbes that lives on sulfur, heat, and CO2 at the bottom of the ocean, in total darkness, in the vicinity of thermal vents. Thiomicrospira is the kind of life form NASA takes a great interest in, because it could be a prototype for exactly the type of survive-in-the-dark CO2-using organism that might live under the ice crust of Europa (Saturn's moon). In theory, there could be geothermal vents on the floor of the large ocean of liquid water that NASA is pretty sure exists under Europa's ice. If there's life down there, it could very well look like Thiomicrospira.

But why should Thiomicrospira have so many genes in common with a mosquito symbiont? Thiomicrospira organism lives at the bottom of the ocean; Wolbachia lives inside arthropod eggs. One obtains its carbon in the form of CO2; the other produces CO2 as a waste product. One is adapted to live in warm salt water; the other lives in cold-blooded insects. In theory, these two germs couldn't be further apart. And yet, oddly enough, they not only have hundreds of genes in common, the genes are well-matched from a DNA sequence-similarity standpoint. Thiomicrospira's DNA even incorporates a prophage module, and some of its phage genes show a high percentage base-pair similarity with the phage genes of Wolbachia. (See screen shot below.)

Remarkably, Thiomicrospira and Wolbachia share certain phage genes in common, as shown here. The genes have a DNA sequence identity of about 60%.

After doing a little more detective work, I found an organism that might very well form a "missing link" between the mosquito symbiont and the thermal-vent dweller. This organism kept showing up in my analyses as having a high degree of DNA similarity with both Thiomicrospira and Wolbachia. The organism in question is Pelagibacter ubique (now known as Candidatus pelagibacter, although some might question this taxonomic assignment since all other Candidatus members are obligate intracellular symbionts), and it's an astonishing organism in two ways: First, it's the smallest non-parasitic (free-living) bacterium known to science, with only 1.3 million base-pairs in its DNA (making it slightly smaller than Wolbachia and its tiny cousins). Secondly, it's the most numerous living thing on earth. It's present in large amounts in every one of earth's oceans.

Pelagibacter was placed in the Candidatus clade in 2007 due to its small genome and cell size and certain ribosomal markers. It has a very mitochondria-like genetic profile, and in fact some people think Pelagibacter is the ancestor of today's mitochondrion, a theory that's all the more satisfying when you consider that Pelagibacter is both ancient and tied to the sea.

My analysis using SynMap found that Pelagibacter and its thermal-vent-dwelling cousin Thiomicrospira share about 660 genes (out of 1480 or so for Pelagibacter), whereas Wolbachia and Pelagibacter share around 581, and Thiomicrospira and Wolbachia share around 1000. These are so-called non-syntenous point matches between genes; instances where the same gene occurs in both organisms, with a high percentage of base-pair matching. Synteny is a concept that takes gene-matching one step further and says that clusters of similar genes are what count. Synteny at the level of higher plants and animals is one thing, but at the level of a mini-microbe it tends to lack meaning, because the genes of bugs like Wolbachia are notoriously mobile: They find new positions on the chromosome over time (probably because of the large number of transposases, nucleases, and integrases in the genome). Even so, I decided to carry out a bit of syntenic analysis to see what I could find out.

For purposes of my analysis I defined a "syntenon" as three or more co-proximal genes that match three or more genes on the other organism's genome. But to be part of a syntenon, all three genes in a triplet have to occur within a 30-gene span (and match 3 genes in a 30-gene span on the other organism's DNA) plus the genes have to be in the same order in both organisms.

A planet-spanning waterworld is thought to exist under
Europa's icy outer crust. If thermal vents exist at the
bottom, any life that exists may look a lot like Thiomicrospira.

Using SynMap, I found that whereas Wolbachia and Pelagibacter share around 157 syntenic genes, and Thiomicrospira and Wolbachia share around 132, Thiomicrospira and Pelagibacter share 250 (which makes sense in that both are ocean-dwellers). For comparison-and-control purposes, I did a triplet match of Thiomicrospira against another chemoautotroph (an organism that gets energy from inorganic chemicals, and carbon from CO2), namely Methanothermobacter marburgensis. There were only 53 syntenic triplets in common between the two chemoautotrophs. (Between Wolbachia and Methanothermobacter, on the other hand, there were only 3 triplet-matches.) Doing a match between two Wolbachia species (a mosquito-dwelling variety and a fruit-fly-dwelling cousin) produced 522 gene matches in syntenic triplets.

It seems reasonable to me, based not just on the previous sorts of analysis but also direct inspection of the genomes (in terms of their respective protein products), that Thiomicrospira evolved from Pelagibacter. Pelagibacter is the most abundant life form in the ocean, and perhaps the oldest. Pelagibacter is also very mitochondria-like, and so is Thiomicrospira, which has rhodanese-like proteins, the full cytochrome system, redox enzymes, citric-acid-cycle enzymes, plus certain characteristic membrane and sensor proteins, flippases, etc. (For what it's worth, Thiomicrospira has the highest signal-transduction profile I've ever seen at http://mistdb.com, again making it very mitochondrial-feeling.)

I'm tempted to say, similarly, that Thiomicrospira and Wolbachia are related. They have phage proteins in common. They both have genes for patatin proteins. They share multiple drug resistance genes. (That's not so strange. Antibiotics occur naturally in the environment.) They share genes for Flp-type pilins. Plus many more coincidences, big and small.

At first blush, a deep-sea thermal vent seems pretty far removed, environmentally, from the egg cell of a mosquito. How to reconcile the difference? Actually, I see similarities. Thiomicrospira thrives at temperatures of 28 to 32 degrees Celsius (which is also true of mosquitoes, although they prefer the 28-degree end of the scale). And blood (the preferred food source for mosquitoes) is comparable in pH and salinity to seawater. Also, mosquitoes have an aquatic lifecycle: they require brackish water in which to lay eggs. Mosquitoes and salt marshes go back millions of years.

It's even possible that Wolbachia might live in deep-sea-vent-dwelling host organisms. In fact, I predict they will be found there. Why? Because in addition to inhabiting flying insects, spiders, mites, and ticks (and filarial worms), Wolbachia have also been found in a very high percentage of crustaceans. We know that crustaceans are often found living near deep-sea thermal vents; and many crustaceans show the characteristic feminization of genetic males that's so often the tipoff to a massive Wolbachia presence in insect populations.

Insects and crustaceans represent two of the oldest, most successful, and most widely distributed life forms of the animal kingdom. Would it really be so surprising if the bacteria that colonize these life forms are closely related to the most common marine bacteria on the planet? I don't think so. Stranger things have happened.

Science on the Desktop

2013-04-26T00:30:00.000-04:00

For decades, I've been hoping I'd live long enough to see a day when serious science could be done on the desktop by dedicated amateurs. Amateur astronomers know what I'm talking about. You can't do much particle physics on the desktop, and there are no affordable desktop electron microscopes (yet), but if comparative genomics is your thing? Get ready to rock and roll, my friend.

Over the weekend I discovered http://genomevolution.org and promptly went nuts. Let me take you on a tour of what's possible.

First I should explain that my background is in microbiology, and I've always had a soft spot in my heart (not literally) for organisms with ultra-tiny genomes: things like Chlamydia trachomatis, the sexually transmitted parasite. It's technically a bacterium, but you can't grow it in a dish. It requires a host cell in which to live.

It turns out there are many of these itty-bitty obligate endosymbionts (at least a dozen major families are known), and because of their small size and obligate intracellular lifestyle, they have a lot in common with mitochondria. Which is to say, like mitochondria, they're about a micron in size, they divide on their own, they have circular DNA, and they provide services to the host in exchange for living quarters.

When you look at one of these little creatures under the microscope (whether it's Chlamydia or Ehrlichia or Anaplasma or what have you), you see pretty much the same thing. (See photo.) Namely, a tiny bacterium living in cytoplasm, mimicking a mitochondrion.

When Lynn Margulis wrote her classic 1967 paper suggesting that mitochondria were once tiny bacterial endosymbionts, it seemed laughable at the time, and her ideas were widely criticized (in fact her paper was "rejected by about fifteen journals," she once recalled). Now it's taught in school, of course. But we have a long way to go before we understand how mitochondria work. And we really, really need to know how they work, because for one thing, mitochondria seem to be deeply involved in orchestrating apoptosis (programmed cell death) and various kinds of signal transduction, and until we understand how all that works, we're going to be hindered in understanding cancer.

When I discovered the tools at http://genomevolution.org, one of the first things I did, on a what-the-hell basis, was compare the genomes of two small endosymbionts, Wolbachia pipientis and Neorickettsia sennetsu. The former lives in insects; the latter, in flatworms that infect fish, bats, birds, horses, and probably lots else. Note that for a horse to get Potomac horse fever, first the Neorickettsia has to infect a tiny flatworm; then the flatworm has to be ingested by a dragonfly, caddisfly, or mayfly; then the horse has to eat (or maybe be bitten by, although only infection-by-ingestion has been demonstrated) the worm-infected fly. The parasite-of-a-parasite chain of events is not only fascinating in its own right, it suggests (to me) that parasites enable each other through shared strategies at the biochemical level, and I might as well spoil some suspense here by revealing that there's even yet another layer of parasitism (and biochemical enablement) going on in this picture, involving viruses. But we're getting ahead of ourselves.

I mentioned Wolbachia a second ago. Wolbachia is a fascinating little critter, because it's found in the reproductive tract of anywhere from 20% to 70% of all insects (plus an undetermined number of spiders, mites, crustaceans, and nematodes), but they don't cause disease, and in fact it appears many insects are unable to survive without them. Wolbachia are unusual in that the extracellular phase of their lifecycle (the part where they spread from one host to another) isn't known; no one has observed it. What's more (and this part is incredible), Wolbachia have adapted to a stem-cell niche: They live in the cells that give rise to insect egg cells. Thus, all newborn female progeny of an infected mother are infected, and all eggs pass on the Wolbachia. In this sense, the genetics of Wolbachia obey mitochondrial genetics (whereby the mother passes on the organelle and its genome).

I quickly found, via Sunday afternoon desktop genomics, that Wolbachia and Neorickettsia (and other endosymbionts: Anaplasma, Ehrlichia, etc.) have many genes in common—hundreds, in fact. And when I say "genes in common," I mean that the genes often show better-than-50% similarity in DNA base-pair matching.

It's important to put some context on this. These little organisms have DNA that encodes only 1,000 genes. (By comparison, E. coli has around 4,400 genes.) Endosymbionts lack genes for common metabolic pathways. They cannot biosynthesize amino acids, for example; instead they rely on the host to provide such nutrients ready-made. If 400 to 500 of an endosymbiont's 1,000 genes are shared across major endosymbiont families, that's a huge percentage. It suggests there's a set of core genes, numbering in the low hundreds, that encapsulate the basic "strategy" of endosymbiosis.

A little more context: Mitochondria have their own DNA and look a lot like endosymbionts. But here's the thing: Mitochondrial DNA is tiny (only about 15,000 base pairs, versus a million for an endosymbiont). It turns out, 97% of the "stuff" that makes up a mitochondrion is encoded in the nucleus of the host. If you include these nuclear genes, mitochondria actually rely on about 1,000 genes total, of which only 3% are in the organelle's DNA. Lynn Margulis would say that what happened is, the endosymbiont ancestor of today's mitochondrion originally had DNA of about a million base-pairs (1,000 genes), but some time after taking up residency in the host cell, the invader's DNA mostly migrated to the host nucleus.

Why did symbiont-to-host DNA migration stop at 97%? Why not 100%? If we look at that 3%, we find genes coding for tRNA and bacterial ribosomes (specialized protein-making machinery) plus genes for enormous, complex transmembrane enzyme systems: cytochrome c oxidase and NADH dehydrogenase. (The former is the endpoint of oxidative respiration; the latter the entry-point.) Obviously it must be advantageous for these genes to be proximal to the organelle.

But why even have an organelle (a physical compartment)? One might ask why it's necessary to have a mitochondrial parasite swimming around in the cytoplasm at all, when most of the genes are part of the host's DNA? The answer is, the stuff that goes on inside the confines of the mitochondrion needs to be contained, because it's violently toxic stuff involving superoxide radicals, redox reactions, "proton pumps," and Fenton chemistry (transition-metal peroxide reactions). A containment structure is definitely called for, to segregate this toxic chemistry from the rest of the cell.

We might ask how it is that the DNA of the protobacterial ancestor of today's mitochondria wound up in the host nucleus in the first place. Let's consider the possibilities. Protobacterial (symbiont) DNA may have transferred to the host all at once, or it might have migrated piecemeal, over time. Or both. Is it realistic that huge amounts of endosymbiont DNA could have migrated to the host nucleus all at once? Yes. It's been suggested that vacuolar phagocytosis drove invader DNA to the nucleus in a big gulp. Evidence? Wolbachia inhabits the vacuolar space.

But export of genes and gene products to the host might have occurred piecemeal as well. A little desktop exploration provides some clues. If you use GenomeView or any number of other online tools to explore the DNA of Wolbachia, several things pop out at you. First is that many Wolbachia genes are mitochondria-like: They encode for things like cytochrome c oxidase, cytochrome b, NADH dehydrogenase, succinyl-CoA synthetase, Fenton-chemistry enzymes, and a slew of oxidases and reductases (including a nitroreductase). Wolbachia is clearly engaged in providing what might be called redox-detox services for the host—the same value proposition that mitochondria offer. This makes sense, because if Wolbachia cells were a net drag on the respiratory potential of host-cell mitochondria (if they couldn't at least hold their own with respect to mitochondria), the host would die.

The second thing that jumps out at you when you look at the Wolbachia genome is the abundance of genes devoted to export processes: membrane proteins, permeases, type I, II, and IV secretion systems, ABC transporters, etc., plus at least 60 ankyrin-repeat-domain genes—all powerful evidence of specializations aimed at export of genes and gene products to the host. But the most stunning "smoking gun" of all is the presence, in Wolbachia DNA, of five reverse-transcriptase genes, plus genes for resolvases, recombinases, transposases, DNA polymerases, RNA polymerases, and phage integrases. In essence, there's a complete suite of retroviral machinery, designed for export of foreign DNA into host DNA.

An example of one of 113 phage-derived genes in Wolbachia (lower gene array). In this case, the gene matches a phage gene found in Candidatus hamiltonella (upper gene array). The two isoforms exhibit 59% DNA sequence similarity, despite widely differing GC ratios. See text for discussion.

But wait. There's more. The third thing that jumps straight in your face when you start looking at the Wolbachia genome is the presence of (are you ready?) no less than 113 genes for phage-related proteins, including major and minor capsid and HK97-style prohead proteins, plus tail proteins, baseplate, tail tube, tail tape-measure, and sheath proteins; late control gene D; phage DNA methylases; and so on. (For non-biologists: phage is the term for viruses that attack bacteria.)

In the above screenshot, I'm comparing Wolbachia DNA (lower strip) to DNA from another insect-infecting endosymbiont, Candidatus hamiltonella, which is known to contain an intact virus (phage) in its DNA. Many phage proteins in Wolbachia have corresponding matches in the Candidatus genome. In this case, we're looking at a gene (the gold-colored stretch pointed at by red arrows) that is 1440 nucleotides long, with a 59% sequence match across genomes. The match percentage is remarkably high given that the Candidatus version of this gene has a 51.7% GC content while the Wolbachia version has a 40.6% GC. Also, note that Wolbachia itself has an overall GC of 34.2%. The fact that Wolbachia's putative phage genes are significantly higher in GC content than Wolbachia's non-phage genes is good confirmation that the genes really are from phage.

It's 100% clear that viral DNA has made its way into the DNA of Wolbachia (either recently or long ago), and it's reasonable to hypothesize that Wolbachia has repurposed the retrovirus-like phage genes for packaging and exporting Wolbachia DNA to the host nucleus.

Okay, so maybe you have to be a biologist for any of this stuff to make your hairs stand on end. To me, it's a dream come true to be able to do this kind of detective work on a Sunday afternoon while sitting on the living-room couch, using nothing more than a decrepit five-year-old Dell laptop with a wireless connection. The notion that you can do comparative genomics and proteomics while watching an Ancient Aliens rerun on TV is (for me) totally cerebrum-blowing. It makes me wonder what's just around the corner.

When Vitamins Turn Deadly

2013-04-22T12:34:00.000-04:00

The other day I did something I swore I'd never do: I paid $31.50 to Elsevier for a copy of a scientific paper. I spent a good 30 minutes looking for a free version of the paper online first, of course, using every sneaky trick I know. Then I debated with myself for 30 minutes, saying things like "You are not seriously going to pay those crooks $31.50, are you?" And answering: "No, of course not." "But you have to." "I know." "So do it." "I can't. I'd rather set my face on fire." And on and on.

After an hour I realized, of course, that the time I'd wasted not buying the paper had been worth much more than the paper itself. So I bought the paper. Just this once.

The article in question is called "Stopping the Active Intervention: CARET," by Bowen et al., Controlled Clinical Trials 24 (2003) 39–50. As you might have guessed, I'm going to spend the rest of today's blog talking about it so you don't have to buy it yourself.

The paper I bought talks about the circumstances surrounding the halting of an infamous cancer-prevention study called CARET, otherwise known as the Carotene and Retinol Efficacy Trial. It was a large National Cancer Institute trial that didn't go well.

CARET was actually one of two large NCI vitamin studies that didn't go well in the 1980s. The other was the Alpha-Tocopherol and Beta-Carotene (ATBC) Lung Cancer Prevention Study. CARET was carried out in the U.S.; ATBC took place in Finland.

I'm not going to spend a lot of time talking about the studies here (for that, see my latest blog on this subject at Big Think). Suffice it to say that both studies began enrolling participants in 1985, the Finns at a rate of about 9,000 a year, the Americans at 2,000 a year. The ATBC study reached its enrollment goal of just under 30,000 people in three years. CARET went on enrolling for nine years, peaking at 18,314 people in September 1994.

It's customary in large trials to enroll people gradually—not just for practical reasons (it's infeasible to sign up 30,000 people at once) but also because if a treatment proves to be deadly, you want to be able to halt the study before huge numbers of people have been put at risk. Sadly, both ATBC and CARET put large groups at risk. Both should have been stopped prematurely. Only CARET was—and its halting came unnecessarily late.

The purpose of ATBC and CARET was to validate the usefulness of antioxidant supplements (Vitamins A and E, chiefly) as cancer-preventive agents. For ATBC, the study population consisted of male Finnish smokers aged 50 to 69. For CARET it was current and former smokers, male and female, aged 50 to 69, plus asbestos-exposed workers (all men) aged 45 to 74. (The asbestos workers made up 22% of the 18,314 study participants.) The experimental design in Finland was a 2x2 matrix design in which a quarter of the participants got 20 mg/day of beta-carotene, a quarter got 50 mg/day of alpha-tocopherol (Vitamin E), a quarter got both, and a quarter got placebo. Thus, treatment groups outnumbered placebo 3:1. In the U.S., CARET began with a 2x2 design but the design was changed early on so that the bulk of the study population got either placebo or a combo of 30 mg/day beta-carotene and 25,000 IU of retinol per day (thus a 1:1 ratio of treated to untreated populations).

According to the ATBC writeup that eventually appeared in The Journal of the National Cancer Institute, "An independent Data and Safety Monitoring Board convened twice annually to monitor trial progress and to study unblinded data that were relevant to intervention safety and efficacy." Which makes it all the harder to understand why the ATBC trial wasn't halted early when the beta-carotene groups separated from placebo—in the wrong direction. It was evident in Year 5 of the eight-year study that people in the treatment group were developing lung cancer at a heightened rate. In fact, that ended up being the key finding of the study: taking Vitamin E and beta-carotene leads to 18% more cancer.

Presumably, the Finns didn't halt their intervention early for the simple reason that the study's stopping criterion (whatever it happened to be; all large studies have them) wasn't met. Perhaps the divergence of treated and untreated group performance was deemed statistically non-significant. We'll never know for sure.

The ATBC results appeared in the April 14, 1996 issue of The New England Journal of Medicine. In "Stopping the Active Intervention: CARET" ($31.50 from Elsevier), we learn from the lead investigator and his coauthors:

On March 25, 1994, the NCI directed investigators of NCI-funded randomized trials involving beta-carotene to inform their data and safety monitoring boards of the ATBC trial findings, to review their data in light of these findings and to develop plans to inform their participants of the ATBC results and incorporate the findings in their consent forms.

This is an interesting statement in a couple of ways. First, it doesn't say exactly who at NCI "directed investigators" to review their data. The implication is that somebody higher up (than the lead investigator) gave an order. Whoever that person was, he or she knew the unblinded Finnish results ahead of publication. Secondly, it says safety boards were directed to "review their data." But in the CARET study (unlike ATBC), the safety board did not have access to unblinded data. How can you review data that's still blinded? And if you did so, what purpose would it serve? You'd look at the data and see that one group of people, under one set of codes, was doing worse than another group under another set of codes. You might very reasonably assume that the group doing worse was the placebo group. But you wouldn't know for sure.

This is where it gets interesting, because in "Stopping the Active Intervention: CARET" ($31.50 from Elsevier) we learn that

On March 29 and April 1 [1994], CARET’s Principal Investigator convened telephone conference calls with the SEMC during which the committee was unblinded to intervention group assignment.

Note the phrase "the committee was unblinded." In actuality, there was no unblinding over the phone. In a separate writeup in Data Monitoring in Clinical Trials: A Case Studies Approach (Springer, 2006), page 222, former CARET Safety Committee members Anthony B. Miller, Julie Buring, and O. Dale Williams explain that in August 1994—four months after the phone call—"we unanimously agreed that we should be unblinded as to the nature of the regimens given to the coded groups." The unblinding actually happened in August, after statisticians compiled their interim report for the Safety Committee—not March.

The account given by the lead investigators in "Stopping the Active Intervention: CARET" makes it sound as if urgent action was undertaken in March 1994 to begin a stand-down of the experiment. That's not what happened. Not by a long shot.

When the Safety Committee got its first look at unblinded data (in August 1994), there was no doubt that CARET's study population was experiencing the same elevated cancer rates seen by the Finns. The Safety Committee took a vote on whether to stop the CARET trial—and found itself deadlocked. Two members of the five-person committee were in favor of stopping CARET. The others thought it would be better to go ahead. The rationale for continuing in spite of elevated cancer in the treated group (here I quote from the account given in Data Monitoring in Clinical Trials) was:

The statistical significance of the difference had not crossed the O’Brien–Fleming boundary (i.e., this could still be a chance finding).
The effect was surprisingly rapid and must mean if real that preexisting (but undiagnosed) lung cancers had had their growth accelerated by the regimen.
We knew of no mechanism of the action of beta-carotene that could have induced such an effect.
There were other chemoprevention trials using beta carotene ongoing, to stop CARET now would have an undesirable adverse effect on these trials.
We owed it to science to be absolutely certain of the adverse effect before stopping the trial.

The two members who favored a stop cited these reasons:

This was the second trial to show an adverse effect of beta-carotene chemoprevention; it was extremely unlikely to be due to chance.
We owed it to the participants to prevent possible further harm to them. It was perhaps particularly unfortunate that the adverse effect appeared to be present in asbestos workers as well as current smokers.
The adverse effects appeared not to be restricted to lung cancer; there appeared to be an adverse effect on cardiovascular disease as well.

Incredibly, even though the Finns had just shown (in a much larger study population) a definite correlation between beta-carotene usage and lung cancer in high-risk individuals, and even though CARET's own data replicated those results perfectly, and even though CARET was administering a 50% higher dose of beta-carotene to its participants than the Finns had used (possibly putting people at much greater risk), a decision was made to continue CARET, on the absurd assumption that after more data were accumulated, the bad trend-line would prove to have been nothing more than a statistical fluke.

In September 1995, a year after the first interim analysis, the Safety Committee got a look at updated results. They were even worse than before.

Many memos and meetings and conference calls and cross-country flights by NCI personnel later, a decision was finally reached in mid-December 1995 to pull the plug on CARET. The decision was approved on December 18. Then everyone adjourned for Christmas holidays. Then on January 12, 1996, letters went out (yes, by snail mail) to all CARET participants, informing them of the decision to end the study (and the reasons for the decision).

CARET found increases in all-cause mortality, cardiovascular
mortality, and (not shown here) lung cancer mortality in high-risk
individuals who took beta-carotene and retinol. (N Engl J
Med 1996;334:1150-5.)

When CARET's results were published in May 1996 in The New England Journal of Medicine, the medical world was aghast to learn that administration of beta-carotene and retinol to high-risk individuals actually increased the rate of lung cancer by 28% compared to placebo. Cardiovascular mortality, likewise, went up 26%.

The Finnish ATBC study had found an increase in lung cancer of 18%, based on a beta-carotene consumption of 20 mg/day. In the CARET study, participants had taken a 50% higher dose of beta-carotene (30 mg/day). They got 50% more cancer.

Hindsight is an evil game, and it's easy to bash NCI for not pulling the plug on CARET earlier than it did without knowing all the particulars. But frankly, what's to know? The Finns published their results in April 1994. Anyone could have looked at those results, then looked at the CARET experimental protocol (and study population), and immediately seen the red flags. Even without knowing the unblinded results, it would have been prudent to halt (or at least begin winding down) the study in April 1994, three and a half years ahead of the planned stop date. Instead, a befuddled Safety Committee (and a bureacracy-entrenched National Cancer Institute) waited until January 12, 1996 to send letters by first-class mail to people who were unnecessarily dying. In the irritatingly self-congratulatory account of this travesty in "Stopping the Active Intervention: CARET," we're constantly reminded that the study was terminated 21 months early, as if it's something to be proud of. The fact is, the study was terminated a minimum of two years later than it should have been, due to negligent disregard of the Finnish results (which were known ahead of time to NCI higher-ups). The Finns, too, should have stopped early, as soon as it became apparent that the treatment groups had diverged (in the wrong direction) from the placebo group, which is to say, in Year 5 of the 8-year study.

To argue that it was okay to wait an extra two years to stop the CARET study because the formal stopping criteria had not been met is a phony argument. When CARET finally was stopped, the stopping criterion had still not been met.

As I said before, I've provided more detail on ATBC and CARET (and other deadly vitamin trials) in a separate post at Big Think. Please read that post before deciding whether to adjust your daily vitamin routine. In the end, you might want to consider scaling back your use of Vitamins E and A if you're at high risk of cancer (and maybe even if you're not at high risk). The evidence says these supplements are harmful for at least some people. I'll post more on the subject here over the next few days.

Please share this story with your social media contacts if you found it helpful. And please see the companion post at http://bigthink.com/devil-in-the-data/the-dark-side-of-antioxidants. Thanks!

Is Aging Caused by Oxidative Stress?

2013-04-20T00:30:00.000-04:00

Everybody wants to know what causes aging, and what can be done about it. As it turns out, we know a lot about aging. But we know very little about how to prevent it.

Of all the theories of aging that have been proposed, the most thoroughly researched, by far, is the Free Radical Theory of Aging, which is more properly now called the Oxidative Stress Theory of Aging. It's been 60 years since Denham Harman first proposed that senescence is driven by the buildup of oxidative damage to DNA and other biological macromolecules. Since then, extensive research has verified repeatedly that as tissues age, they do, in fact, accumulate a wide variety of types of oxidative damage.

It's because of the Oxidative Stress Theory of Aging that you see so many foods these days labeled "Rich in Antioxidants." Supposedly, antioxidants (like Vitamin E and beta carotene) confer resistance to heart disease and other ailments. In the U.S., food (and supplement) makers are forbidden by law from making specific therapeutic claims around antioxidants. But this restriction is actually a boon for marketers, because the nameless benefits that accrue to antioxidants, whatever they are, will be conjured in the buyer's mind with much greater force and power than anything a label or an ad could possibly say, the same way a person watching a horror movie will make a monster even scarier (in his or her imagination) if the monster isn't actually shown on screen.

In 2010, the Federal Trade Commission made Kellogg's stop using the "Immunity" claim (tied to antioxidants) on its cereal packaging. The packaging you see here was discontinued. Notice that the "25%" badge seems to imply that there is a "Daily Value" for antioxidants. There is no such thing.

The bogeyman, in this case, goes by the name "reactive oxygen species" (ROS), which covers a lot of ground, chemically. Originally, ROS meant free radicals—breakdown products of peroxides. Which is a perfect bogeyman, because free radicals are so fleeting their concentrations in living cells can't even be measured. (They're so chemically reactive that they last for only a nanosecond or so.) These days, ROS can also refer to superoxides, aldehydes (e.g., formaldehyde), and/or anything else that's reactive and contains oxygen. (Again, a conveniently vague category of "scary stuff.")

The way bogeyman research works in science is, when a new mediator of tissue damage is first identified (for example: nitric oxide, superoxide anion, prostaglandins, leukotrienes, interleukin-6, interleukin-8, tumor necrosis factor alpha) researchers rush to measure it in a host of human and/or animal diseases. Soon, the molecule in question is "implicated" in the pathogenesis of various diseases.

The problem is, finding that XYZ bogey-molecule is implicated in this or that disease is not the same thing as finding that it causes the disease. Free radicals have been implicated in over a hundred diseases (Gutteridge, JMC, "Free radicals in disease processes: a compilation of cause and consequence," Free Radic Res Commun 1993;19:141-58). They're the cause of none of them.

Until recently, it's been impossible to show a cause-effect relationship between oxidative stress and aging. We know the two are linked, but we don't know if it's in causal fashion.

Recent work with genetically modified mice may have finally provided some much-needed clarification on the role of oxidative stress in aging. I'm referring to work done by Viviana Pérez and her colleagues at the University of Texas Health Science Center in San Antonio, Texas, published in 2009. Pérez looked into the life-extending (or -reducing) effects of various mutations involving oxidative enzymes in mice, the idea being that if you knock out certain oxidative-damage-repair genes, mice should age prematurely (if they live at all), whereas if you amplify or upregulate certain damage-repair genes, mice should show fewer signs of aging (and maybe live longer).

The Pérez results take a while to explain, but it's worth looking at carefully, because it sheds much-needed light on the question of whether aging is actually caused by oxidative damage (or the converse).

When the Pérez team looked at genetically modified mice that lacked glutathione peroxidase 1 (Gpx1, a major scavenger of intracellular peroxides), they found, surprisingly, that the mice lived to normal age and showed no pathology. However, mice deficient in glutathione peroxidase 4 (genetic marker Gpx4) were embryonic-lethal. The latter finding tends to support Free Radical (Oxidative Stress) Theory.

An enzyme called methionine sulfoxide reductase-A (MsrA) repairs oxidized methionine residues in proteins and may also function as a general antioxidant. Pérez et al. found that mice null for MsrA lived a normal lifespan even though they showed some additional sensitivity to oxidative stress.

Thioredoxin 2 (Trx2) plays an important role in repairing oxidation of cysteine residues in proteins. It turns out Trx2-null mice are embryonic-lethal, but Trx2 +/- (heterozygous) mice, with just one copy of the gene instead of the normal two, had 16% longer maximum lifespan.

There are two major superoxide dismutases that break down superoxides in cells: CuZnSOD and MnSOD (genetic markers SOD1 and SOD2). Pérez et al. found that mice lacking the former suffer a 30% reduction in mean and maximum lifespan. Mice lacking the latter die within days of birth.

To recap so far: Mice null for SOD2 or Gpx4 are non-viable, while those null for SOD1 have 30% shorter lives. These results tend to support Free Radical Theory. But knockout mice lacking MsrA or Trx2 live normal lifespans, which contradicts Free Radical Theory.

How are we to interpret these results? One problem with knockout studies is that if a certain chemical reaction doesn't occur (because the responsible enzyme system is taken away—"knocked out" genetically), it's difficult to know whether resulting harm to the host is due to a buildup of unreacted precursor molecules, or (rather) the absence of crucial end-products. The end-products of the reaction might be vital to downstream metabolism. It might not simply be that the precursors to the reaction are toxic. After all, if hydrogen peroxide (the end-product of superoxide dismutases) is an important signalling molecule, as recent work seems to indicate, you would expect abnormalities in SOD1 or SOD2 to be harmful indeed—for reasons having nothing to do with aging.

Bottom line, the finding that mice lacking SOD1, SOD2, or Gpx4 are unhealthy is not sufficient to vindicate the Oxidative Stress Theory.

The ultimate test for Oxidative Stress Theory would be to see whether mice show fewer signs of aging (e.g., less DNA damage with age)—and actually live longer—when enzymes involved in combating oxidative stress are increased (over-expressed). The Pérez team tried exactly this approach.

As mentioned before, there are two major superoxide dismutases that break down superoxides in cells: CuZnSOD and MnSOD (genetic markers SOD1 and SOD2). When mice were made to over-express SOD1 (so that they had two to five times the normal activity of the CuZnSOD enzyme), the mice were indeed more resistant to oxidative stress as measured by standard tests involving tolerance of paraquat and diquat. But the mice lived no longer than ordinary mice.

The same was observed for mice that over-expressed SOD2.

When Pérez et al.created mice that over-expressed catalase (the enzyme that degrades hydrogen peroxide to water and oxygen), they found the mice were less prone to DNA damage—but lived no longer than normal.

In mice with upregulated glutathione 4, enhanced protection against various kinds of oxidative stress was demonstrated. But the mice lived no longer than normal wild-type animals.

The Pérez group also tried over-expressing more than one antioxidative gene at once. No combination produced any lifespan extension.

To recap: mice do not live longer when they over-express antioxidant enzymes (singly or in combinations), even though they show heightened protection against DNA damage, lipid damage, and other typical signatures of oxidative stress.

Pérez et al. concluded:

We believe the fact that the lifespan was not altered in the majority [of] the knockout/transgenic mice is strong evidence against oxidative stress/damage playing a major role in the molecular mechanism of aging in mice.

It's hard to disagree with that conclusion. Some of the genetic manipulations Pérez et al. tried were inspired by fruit-fly experiments that gave much more encouraging results. But mammals are not fruit flies. And it seems unlikely to me that the results reported by Pérez et al. are some kind of fluke, limited to mice. (It seems unlikely that entirely different results would be found in humans.) Altogether, Pérez et al. tried 18 different genetic manipulations. Not one extended the life of mice.

To me, it means we can put the oxidative-stress bogeyman to bed now, and go on to worry about other things. Whatever's keeping us from living to be 120, it's not oxidative stress.

For more on this subject, see my recent post at Big Think.

Hydrogen Peroxide and Scientific Dogma

2013-04-18T00:30:00.000-04:00

The catalase test is simple: Add a drop of hydrogen
peroxide to a sample of bacteria on a microscope slide and see
if it fizzes. Anaerobes (even aerotolerant ones, such as Streptococcus
pyogenes) won't fizz because they lack catalase. Aerobic bacteria
produce oxygen bubbles, as on the right.

I remember the first time I was introduced (as a young bacteriology student) to the catalase test. You scrape a colony of bacteria off the surface of an agar dish, rub it onto a microscope slide, then take an ordinary eyedropper filled with 3% hydrogen peroxide and drop a big fat drop of liquid onto the little slimy smudge of bacteria. If the smudge begins to fizz vigorously, like Alka Seltzer, the bacteria are catalase-positive. If no fizzing happens, they're catalase-negative.

The fizzing happens because of an enzyme called catalase that promotes the conversion of hydrogen peroxide to water and molecular oxygen:

2 H₂O₂ → 2 H₂O + O₂

For the last hundred years, every student of bacteriology has been taught that the reason aerobic bacteria (and indeed all aerobic life forms, up to and including humans) have catalase is that hydrogen peroxide is severely toxic and must be gotten rid of, lest it form highly reactive hydroxyl radicals:

H₂O₂ → OH + OH

The OH radicals, being extremely reactive chemically, will attack just about anything: DNA, RNA, proteins, lipids, mucopolysaccharides, what have you. Hydroxyl radicals are toxic. Catalase provides a way of neutralizing peroxides so that no radicals can form.

Anaerobic organisms, the story goes, lack catalase because they live in oxygen-poor environments where things like peroxides don't form. So-called strict anaerobes are actually killed by exposure to air. The reason they die when they come in contact with air (supposedly) is that in the presence of oxygen they experience an endogenous buildup of peroxides that (in the absence of catalase) go on to form toxic free radicals that, in turn, eventually cause damage to DNA, proteins, lipids, and other macromolecules.

That's the official dogma on peroxides, free radicals, and catalase.

The only trouble is, as with so much other dogma in this world, it's completely wrong.

And it would all be harmless prattle if it just applied to bacteria. But unfortunately, this bit of assumption-laden dogma about peroxides and free radicals leads to some rather fanciful notions about the role of "oxidative stress" in ordinary metabolism. The notion that peroxides and free radicals (so-called Reactive Oxygen Species) are harmful has led to the spending of billions of research dollars on"oxidative stress" and ways to stave off "oxidative damage" to DNA, proteins, etc. It has led to billions of dollars of misleading advertising around foods "rich in antioxidants." (And it has actually led to clinical trials of antioxidants like beta carotene and Vitamin E in which people died needlessly, something I'll discuss in more detail in a future post. For now, you might want to refer to this paper.)

Suppose that, rather than accepting the catalase-as-detoxifier/peroxides-as-evil theory at face value, we ask some fundamental questions. Such as:

What's the evidence that anaerobes exposed to air actually die of peroxide poisoning?

If peroxides are poisonous, why are there no anaerobes that have catalase? (If there's survival value for aerobes to have catalase, surely there's even more survival value for an anaerobe to have it?)

If catalase exists to protect aerobic cells from peroxide poisoning, then we should expect catalase-knockout mutations to be lethal, or at least gravely deleterious, yes?

Let's review some basic facts. First, hydrogen peroxide is not toxic at low concentrations. It is, in the words of one researcher, "poorly reactive: it does not oxidize most biological molecules including lipids, DNA, and proteins" (Halliwell et al., "Hydrogen peroxide: Ubiquitous in cell culture and in vivo?", IUBMB Life, 50: 251–257, 2000, PDF here). Concentrated hydrogen peroxide is toxic (it's a disinfectant), but at the dilute concentrations found in living cells, hydrogen peroxide isn't doing anything harmful to DNA, proteins, or lipids. The situation is analogous to that of hydrochloric acid. At high concentrations, HCl will eat through skin. Put a couple liters into an 80,000-liter swimming pool, though, and you can drink the stuff. So it is with peroxide.

Secondly, peroxides are ubiquitous in living systems (again see the Halliwell paper). In higher life forms H₂O₂is produced in vivo by monoamine oxidase, xanthine oxidases, various dismutases, and other enzymes, under homeostatic control. There's substantial evidence that hydrogen peroxide is a widely used signalling molecule (see references 21 to 26 in the Halliwell paper) and recent work has shown a role for hydrogen peroxide in reparative neovascularization. Recruitment of immune cells to wounds likewise appears to require hydrogen peroxide.

Far from being toxic, hydrogen peroxide is an important biomolecule, essential to ordinary metabolic processes. There is zero evidence that hydrogen peroxide does anything harmful in living tissues, at the concentrations normally found in vivo.

If hydrogen peroxide were toxic, we'd expect that a mutation that knocks out catalase would mean certain death for the host organism. After all, with no catalase to break down H₂O₂to oxygen and water, hydrogen peroxide would simply accumulate until reaching toxic levels. But it turns out, naturally occurring catalase-negative mutants of Staphylococcus aureas have been reported (laboratory-created catalase-negative mutant strains of E. coli and other organisms are known as well). Catalase-knockout mice have been created, and they develop normally. Humans lacking normal catalase were first identified in the early 1950s when a Japanese doctor found that pouring hydrogen peroxide onto a patient's infected gums caused no foaming. The catalase-negative condition in humans is known as acatalasemia or acatalasia. It results in no pathology except an increased tendency toward periodontal infection.

Catalase does serve an important function in aerobic organisms (having nothing to do with detoxification). With catalase, the oxygen in hydrogen peroxide can be scavenged for (re)use in respiration. This is important because every oxygen molecule is worth 38 ATP molecules in the aerobic breakdown of glucose. (ATP, adenosine triphosphate, is the high-energy molecule that powers the chemical machinery of cells. Without ATP, metabolism grinds to a halt.) By comparison, anaerobic breakdown of glucose (which is to say, fermentative breakdown) yields only 2 ATP molecules per sugar molecule. It's very much in the cell's interest to recycle the oxygen from hydrogen peroxide rather than let it go to waste. Catalase makes that reuse possible.

Anaerobic bacteria obtain energy solely from fermentation. They have no use for oxygen. Therefore they have no use for catalase. A catalase gene would simply be extra genetic baggage for an anaerobe. It would confer no survival value.

It's astonishing to me that the catalase myth (the version of the myth that says catalase exists to detoxify hydrogen peroxide so as to keep the cell from dying of free-radical-induced damage) has survived for well over a hundred years without anyone questioning it. I see the myth repeated all over the Internet as if it's Gospel. Never do I see any substantiating research referenced in support of it. It's just propagated from one unquestioning drone to another.

Why? Why do myths like this take hold in science? Why do otherwise intelligent scientists cling to them and perpetuate them, regurgitating them in textbooks and handing them down to new generations of students?

I think the answer is, because it makes a good story, and as human beings we value a good story more than we value checking out the story to see if it's true (providing it's a suitably satisfying story).

Before there was science, stories were all the human race had as a way of trying to understand the universe. If someone told a good enough story, and the story provided a satisfying-enough explanation of something, the story endured. Some of humankind's most cherished stories have survived for thousands of years. They survive, in many cases, even if they're not verifiably true.

With science, the theory is the unit of storytelling. If a theory seems to fit the facts, it's accepted. If, on closer inspection, a story is found not to fit the facts, it will still be accepted by many people, if it's a satisfying enough story.

The ancient Greeks knew the earth was not flat. (According to Diogenes Laertius, "Pythagoras was the first who called the earth round; though Theophrastus attributes this to Parmenides, and Zeno to Hesiod.") Nevertheless it took centuries for flat-earth theory to fall into disrepute, and even to this day there are people who find flat-earth theory satisfying.

So it is with scientific theories. Good stories (and that's all scientific theories are: stories) have staying power. Even when they're wrong.

For more on the Oxidative Stress Theory of Aging (and why it's wrong), see my blog at Big Think.

Spontaneous Recovery from Depression

2013-04-09T07:58:00.003-04:00

Emil Kraepelin (1856-1926), who coined the term "manic depressive," found that in contrast to patients suffering from dementia praecox (schizophrenia), those suffering manic depression had a relatively good prognosis, with 60% to 70% of patients suffering only one attack and attacks lasting, on average, seven months.

Modern drug trials for antidepressants seldom take into account the fact that people with depression often get better on their own. The typical randomized controlled trial (RCT) has a placebo arm and a treatment arm, but no non-placebo/non-treatment arm (otherwise known as a wait-list arm). It's commonly assumed that people who get better on placebo, in drug trials, are experiencing the placebo effect when in reality a certain number of people just get better on their own even without placebo. Hence, the placebo effect is almost certainly overstated.

But do people really get better on their own? In the Netherlands, researchers looked at the progress of 250 patients who had reported an episode of major depression. Two thirds of the patients were female and for 43%, it was a recurrent episode. Some patients sought treatment at the primary-care level; others sought mental-health-system care; others sought no care. The researchers found that the overwhelming majority of patients recovered (defined as "no or minimal depressive symptoms in a 3-month period"), regardless of the level of treatment.

A 2002 study in the Netherlands found that people with depression tend
to get better regardless of level of care. See text for discussion.

The median duration of major-depressive episodes was 3.0 months for those who had no professional care, 4.5 months for those who sought primary care, and 6.0 months for those who entered the mental health care system. (It's not told what percentage of patients who sought care took meds, but for this discussion it doesn't matter. The point is, most people do get better, one way or another.) The differences in mean episode duration may reflect severity (no data were given for this). The people who recovered quickly on their own may have done so because they were less depressed. It stands to reason that those who sought help at the mental-health-system level were probably more depressed, hence took longer to recover.

In any case, the point is that today, as in Kraepelin's time, many depressed patients recover, with or without medical intervention, because that's the nature of the illness. It comes and it goes.

You can find the above study online at http://bjp.rcpsych.org/content/181/3/208.full. The full reference is Spijker, et al., "Duration of major depressive episodes in the general population: Results from the Netherlands Mental Health Survey and Incidence Study (NEMESIS)," The British Journal of Psychiatry (2002) 181: 208-213

When Psychotherapy Goes Wrong

2013-04-08T00:30:00.000-04:00

When talk therapy works, it can work wonders. When it doesn't work (which is a fair amount of the time), all bets are off, because anything can happen.

While psychiatric drugs are required to undergo efficacy testing before being released to the public, no efficacy requirements are placed on non-drug therapies. Talk therapy is just assumed to work for most people, and new therapies are routinely rolled out willy-nilly on live patients with no oversight by anyone and no guarantee of safety, much less efficacy.

That's not to say some therapies haven't been subjected to controlled testing. In recent years, Cognitive Behavioral Therapy (CBT), in particular, has been the subject of hundreds of published trials. There's reason to believe, however, that many of the CBT trials are biased and that (as with drug trials that don't come out the way the researchers wanted) unflattering trials simply go unpublished.

Cuijpers et al. in 2010 found substantial reason
to suspect publication bias in 175 talk-therapy
trials. Notice the asymmetry in this funnel plot.

In a 2010 paper in The British Journal of Psychiatry ("Efficacy of Cognitive Behavioral Therapy and Other Psychochological Treatments for Adult Depression," 196:173-178, non-paywall-protected version here) Cuijpers et al. looked at published trials that made 175 comparisons between particular talk therapies and a control group. The funnel plot for these 175 trials (see graphic) strongly suggests publication bias. The majority of the trials (92) involved CBT.

With drug trials, we usually think in terms of a med either helping or not helping. In reality, patients tend to follow one of three trajectories: the drug helps, it does nothing, or it actually makes the condition worse. (These trajectories exist for placebos as well.) Individual trajectories typically aren't reported in the literature (unless they culminate in adverse events, like suicide). Instead, they're lumped together into an overall score that shows yea-many-points average improvement on the Hamilton scale (or whatever), for the treatment arm as a whole.

It's difficult to say how often therapy goes off the rails. But a meta-analysis of 475 talk-therapy outcome studies (reported in Smith, Glass, and Miller, The benefits of psychotherapy, Baltimore: Johns Hopkins University Press, 1980) found that 9% of the time, effect sizes were negative, meaning patients got worse in therapy. Shapiro & Shapiro found much the same thing in "Meta-analysis of comparative therapy outcome studies: A replication and refinement," Psychological Bulletin, 1982, 92, 581–604.

In a 2006 paper, Charles M. Boisvert and David Faust ("Practicing Psychologists’ Knowledge of General Psychotherapy Research Findings: Implications for Science–Practice Relations," Faculty Publications. Paper 42, available here) tracked down the 25 most highly cited researchers in the Handbook of Psychotherapy and Behavior Change (4th ed.; Bergin & Garfield, 1994) and asked each one to agree or disagree with a number of assertions, including the statement: "Approximately 10% of clients get worse as a result of therapy." The average response to that statement was 5.67 on a scale of 1 to 7, where 1 meant "I'm extremely certain that the assertion is incorrect" and 7 meant "I'm extremely certain that the assertion is correct." (For comparison's sake, the statement "Therapy is helpful to the majority of clients" scored just 6.33.)

While 10% seems to be an accepted ballpark figure for iatrogenic talk-therapy outcomes, the true number could be as high as 30% (see this paper).

If we count incorrect diagnosis as a form of patient harm, two of the most harmful therapeutic tools in psychiatry are the Rorschach Test (or ink-blotch test) and the Thematic Apperception Test. Around 70% of normal individuals who take the Rorschach Test score as if they're seriously disturbed (Professor James M. Wood, Univ. of Texas, quoted here). In 2000, an extremely thorough meta-analysis found the Rorschach Test, the Thematic Apperception Test, and human figure drawing to have so many problems, not just with repeatability but with basic validity, that they shouldn't be used any more, basically.

In the much-cited "Psychological Treatments That Cause Harm," Perspectives on Psychological Science, March 2007 2(1):53-70 (PDF here), Emory University's Scott Lilienfeld identifies a number of different types of therapy that have been shown to have the potential to hurt patients more than they help them. Potentially harmful therapies identified by Lilienfeld include the following:

Type of Therapy	Adverse Outcome(s)	Source of Evidence
Critical incident stress debriefing	Heightened risk for post-traumatic stress symptoms	RCTs
"Scared Straight" interventions	Exacerbation of conduct problems	RCTs
"Facilitated Communication"	False accusations of child abuse against family members	Low base rate events in replicated case reports
Attachment therapies (e.g., rebirthing)	Death and serious injury to children	Low base rate events in replicated case reports
Recovered-memory techniques	Production of false memories of trauma	Low base rate events in replicated case reports
DID-oriented therapy	Induction of ‘‘alter’’ personalities	Low base rate events in replicated case reports
Grief counseling for bereavement	Increases in depressive symptoms	Meta-analysis
Expressive-experiential therapies	Exacerbation of painful emotions	RCTs
Boot-camp interventions for conduct disorder	Exacerbation of conduct problems	Meta-analysis
DARE programs	Increased intake of alcohol and other substances	RCTs

DID means Dissociative Identity Disorder; DARE refers to Drug Abuse and Resistance Education.

Lilienfeld is quick to point out that even a therapy that's not bringing about actual deterioration in a patient's condition can be harmful if it delays seeking out a more effective therapy. For example, it's well known that behavioral therapies tend to be more effective than nonbehavioral therapies for obsessive-compulsive disorder, generalized anxiety disorder, and phobias. Some patients have spent years trying to cure a phobia, only to find that by switching therapists (and using a more appropriate therapy) the phobia becomes manageable after one visit.

Lilienfeld and others have noted that negative outcomes tend to be far more frequent in treatment programs aimed at adolescents than those aimed at adults, especially in group-oriented programs, where social effects can overwhelm the therapy. Many examples of shockingly negative outcomes in youth programs can be found in the literature (start by reading this excellent paper by Rhule). Perhaps the most celebrated disaster (in the U.S., at least) is the Scared Straight program, which has repeatedly been shown to be counterproductive (actually increasing the odds of kids going to prison) yet has been rolled out to dozens of U.S. cities and continues to be the basis of popular TV shows. (See this meta-analysis.)

Additional Reading
See this unusual blog post that stirred an amazing 472 commenters to give their experiences with therapy gone awry. In the comment trail you'll find scores of fascinating outbound links to YouTube videos, books, forum discussions, and other resources.

If you read only one academic paper on this subject, be sure it's Scott Lilienfeld's seminal 2007 paper, "Psychological Treatments That Cause Harm."

If you've been abused in therapy: http://www.therapyabuse.org.

Bates, Yvonne, editor (2006) Shouldn’t I Be Feeling Better By Now? Client views of Therapy. Houndmills, Basingstoke, Hampshire, UK: Palgrave McMillan. A book that presents a variety of views from a variety of kinds of patients.

What Doctors Don't Know about the Drugs They Prescribe

2013-04-07T00:30:00.000-04:00

In this video, Dr. Ben Goldacre explains to a TED audience why publication bias has brought medical research to a crisis point that must be addressed immediately. If you enjoy the video, please share with others via social media and also visit alltrials.net to sign the AllTrials petition.

The Mother of All Depression Studies: STAR*D

2013-04-06T00:30:00.001-04:00

Given the amount of public funding ($35 million) that went into the six-year STAR*D study, it's utterly astounding that more people haven't heard of it. The Sequenced Treatment Alternatives to Relieve Depression study was easily the single largest study of its kind, ever, lasting six years and involving over 4,000 patients in more than 40 treatment centers.

Well over 100 scientific papers came out of the study (so it's not like you can assimilate the whole thing by reading any one article), and most are behind a paywall, which is outrageous when you consider that the entire study was funded by U.S. taxpayer dollars. Robert Whitaker, author of Mad in America and Anatomy of an Epidemic (superb books, by the way) has put one of the summary papers up on his web site. Be warned, however, that the STAR*D findings are subject to many interpretations. See this paper by H. Edmund Pigott for added perspective. Also see these STAR*D documents.

Some of the STAR*D researchers (e.g., M. Fava, S.R. Wisniewski, A.J. Rush, and M.H. Trivedi) went on to publish upwards of 15 papers a year (sometimes 20 a year) on the STAR*D results. How do you write that many papers? Short answer: with professional help. At least 35 STAR*D papers were, in fact, ghostwritten by freelance tech writer Jon Kilner, who lists the papers as "writing samples" on his web site.

The STAR*D study was unique in a number of ways. First, it was an uncontrolled study (which of course puts serious limitations on its validity). Secondly, it involved real-world patients in clinical practice, not paid volunteers. Thirdly, it tested eleven distinct "next step" treatment options, mostly involving drugs but also including talk-therapy arms. Fourthly, the primary outcome measure was remission (a fairly stringent measure of progress).

The idea of the study was to offer patients suffering from major depression intensive treatment with a well-tolerated first-line SSRI (namely Celexa) to see how many patients would go into remission with drug treatment alone; then offer those who didn't improve on Celexa a second line of treatment (involving various drug and talk-therapy options); then offer those who didn't respond to the second line of treatment a third type of treatment; and finally, offer a fourth type of treatment to anyone left.

Participants were given free medical care throughout the study. All drugs were provided at no cost by the respective manufacturers (including Viagra for those who wanted it) and patients were offered $25 as an incentive for completing follow-up assessments.

STAR*D study design (click to enlarge). While 4,041 patients were initially enrolled, only 3,671 participants actually entered the first level of the study, which involved taking citalopram (Celexa), an SSRI chosen for its supposed high efficacy and low level of side effects. Patients who failed to get better in Level 1 could progress to Level 2, then Level 3 and finally Level 4. Attrition was a huge problem, despite free drugs, free medical care, and free Viagra.

The overall study design (and the numbers of participants at each level) can be seen in the accompanying flow graph. Note that every participant got Celexa in the first level of the study. Any patient who wasn't happy with his or her current treatment option was allowed to progress to the next level of the study at any time, and the choice of treatment option was up to the patient. (All meds were open-label.) This proved to be disastrous for the cognitive therapy (CT) arms of the treatment, which saw only 101 people make it through to the end. (Not all of the 147 patients who started CT finished.) So few CT patients finished, and so many of them started taking other drugs, that no scientific papers about the talk-therapy part of the STAR*D study were ever published.

Participant attrition was a big problem for the study. Around half (48%) of the study population dropped out early, despite various inducements to stay.

What were the study's main findings? STAR*D researchers reported that for the "intent to treat" group(s), remission rates were 32.9% for Level 1 of the study, 30.6% for Level 2, 13.6% for Level 3, and 14.7% for Level 4. These numbers are likely inflated (read Pigott's analysis to learn why), but even assuming the numbers are right, it speaks to very disappointing effectiveness of conventional treatments for depression. This is especially true when you consider that studies on the natural course of depression show that half of all patients recover spontaneously in three months (see this study) to twelve months (this study) whether treated or not.

Another major finding of STAR*D was that no one treatment strategy (no one drug or combination of drugs) stood out as doing much better than any other. Changing from Celexa to Wellbutrin (bupropion), for example, saw 26% of patients either remit or get better on their assessment survey. Patients who were non-responsive to an SSRI (Celexa) and switched over to an SNRI (Effexor) got better 25% of the time. But 27% also got better when switched from the original SSRI (Celexa) to a different SSRI (Zoloft).

Another finding was that very few of the patients who scored a remission stayed in remission. The overwhelming majority of patients who got better eventually relapsed or dropped out. This fact was glossed over in all of the original STAR*D studies, then brought up by H. Edmund Pigott and others, who analyzed the data behind Figure 3 of this paper. It turns out that of 1,518 patients who remitted and stayed in the study, only 108 patients (7.1%) failed to relapse. This is a stunning indictment of the effectiveness of available treatment options for depression.

Space prohibits a thorough critique of the STAR*D trial here, but suffice it to say there were numerous problems not only with the study design itself (e.g., the study was uncontrolled; and patients were allowed to take anxiolytics, sleep aids, and other psychoactive drugs during their "treatment," confounding the results) as well as with the way the results were spin-doctored in the literature. If you're interested in the gory details, you can read an excellent critique here and also here. Plus there's an interesting paper behind Springer's paywall, mentioned in this Psychology Today blog, and there's a worthwhile Psychology Today post here.

Personally, I don't think you have to do much critiquing of the STAR*D results to see how dismal the findings were. After six years of work and $35 million spent, NIMH only confirmed what all of us already knew, which is that modern antidepressants aren't terribly effective and it doesn't much matter which one you use, because they all perform equally poorly.

But don't worry. Big Pharma will come up with something new and improved Real Soon Now.

BRAIN: A Penny for Your Thoughts

2013-04-05T00:30:00.000-04:00

President Obama's kickoff of the BRAIN initiative was a major news item the other day. Widely lauded as the kind of program that can keep America at the forefront of science, Brain Research through Advancing Innovative Neurotechnologies (or, if you prefer, Big Ridiculous Acronyms Inspired by Nonsense) was compared to the Apollo moon program, which "gave us CAT scans" (the President said) and to the Human Genome Project, which altered economic reality as we know it by creating $140 in return for every dollar invested.

The President hailed BRAIN as "a bold new research effort to revolutionize our understanding of the human mind and uncover new ways to treat, prevent, and cure brain disorders like Alzheimer’s, schizophrenia, autism, epilepsy, and traumatic brain injury."

And for all this great knowledge, we're going to pay just $100 million (first year).

Imagine: For just $100 million, we're going to find new ways to treat and cure schizophrenia, autism, epilepsy, traumatic brain injury, and (oh yeah, I almost forgot) Alzheimer's.

When I first heard the President mention the $100 million figure, I couldn't help thinking of Dr. Evil's demand for money in Austin Powers: International Man of Mystery:

Dr. Evil: Shit. Oh hell, let's just do what we always do. Hijack some nuclear weapons and hold the world hostage. Yeah? Good! Gentlemen, it has come to my attention that a breakaway Russian Republic called Kreplachistan will be transferring a nuclear warhead to the United Nations in a few days. Here's the plan. We get the warhead and we hold the world ransom for... ONE MILLION DOLLARS!

Number Two: Don't you think we should ask for *more* than a million dollars? A million dollars isn't exactly a lot of money these days. Virtucon alone makes over 9 billion dollars a year!

Dr. Evil: Really? That's a lot of money.

Did no one tell Mr. Obama, prior to the BRAIN press conference, that the National Institutes of Mental Health already spends $1.4 billion a year, of which $1.1 billion goes to research?

Does Obama not realize that the pharmaceutical industry spends $68 billion a year on research and development, much of it on brain research?

Did no one bother to tell Mr. President that between government and the pharma industry, there's been well over a trillion dollars spent on brain research in the last 20 years, and we're still not even close to understanding (much less curing) schizophrenia, autism, epilepsy, etc.?

Is our President not aware that the U.S. government spending another $100 million on brain research is like Bill Gates giving another fifty bucks to Ronald McDonald House?

Tomorrow, I want to talk, in depth, about an example of what real-world brain research (conducted with federal funding) costs. I'm going to talk about the STAR*D study, the most extensive (and expensive) single study ever conducted on the treatment of depression.

Myth: Antidepressants Take Weeks to Work

2013-04-04T00:30:00.000-04:00

One of the more popular myths about antidepressants is that they take weeks to work. You'll find this "fact" stated axiomatically (which is to say without supporting citations) in many scientific papers, popular articles (web and print), and package inserts. "Everyone knows" SSRIs take weeks to work. It has to be true, because everybody says it is. Right?

Wrong. In 2006, JAMA Psychiatry published a paper by Matthew J. Taylor et al. called "Early Onset of Selective Serotonin Reuptake Inhibitor Antidepressant Action: Systematic Review and Meta-analysis" (full copy here) that set out to answer this very question (the question of whether it takes weeks for antidepressants to work). Taylor and his colleagues looked at 20 reports involving 28 separate trials of antidepressants. The drugs studied in the 28 trials included fluoxetine (Prozac), paroxetine (Paxil), citalopram (Celexa), escitalopram (Lexapro), fluvoxamine (Luvox),and sertraline (Zoloft). The result:

This analysis supports the hypothesis that SSRIs begin to have observable beneficial effects in depression during the first week of treatment. The early treatment effect was seen on the primary outcome of differences in depressive symptom rating scale scores [ . . .]

All of the 28 trials kept weekly assessments of patients using either the Hamilton Depression Rating Scale (HDRS) or the Montgomery-Asberg Depression Rating Scale (MADRS). All found improvement in Week 1, and in fact the greatest weekly improvement typically occurs in Week 1, with subsequent weekly scores improving less and less as time goes on.

A meta-analysis of 47 studies by Posternak and Zimmerman, published in the Journal of Clinical Psychiatry, 2005 Feb;66(2):148-58 found that by the end of Week 2, patients are experiencing 60% of whatever total effect they're ever going to see.

This set of treatment response curves (from a study investigating combined
use of olanzapine and fluoxetine, which is to say Zyprexa and Prozac) is
typical, in that the biggest single-week gain is measured in Week 1.

Interestingly, there's some evidence that you may be able to speed up the onset of effects even more simply by taking aspirin along with your SSRI. Mendlewicz et al., writing in International Clinical Psychopharmacology, July 2006 21(4):227-231, reported:

Participants were treated openly during 4 weeks with 160 mg/day ASA in addition to their current antidepressant treatment. The combination SSRI-ASA was associated with a response rate of 52.4%. Remission was achieved in 43% of the total sample and 82% of the responder sample. In the responder group, a significant improvement was observed within week 1 (mean Hamilton Depression Rating Scale-21 items at day 0=29.3±4.5, at day 7=14.0±4.1). [emphasis added]

ASA is acetylsalicylic acid -- plain aspirin.

For more on the counterfactual nature of the idea that antidepressants take weeks to act, see the paper by Alex J. Mitchell in The British Journal of Psychiatry (2006) 188: 105-106 (full copy here) and also the report by Parker et al. in Aust N Z J Psychiatry February 2000 vol. 34 no. 1, 65-70 (full copy here).

Does Antidepressant Dose Matter?

2013-04-02T00:30:00.000-04:00

One of the great myths about antidepressants is that higher doses are more effective than lower doses. That's not how these drugs work, though. Most studies show a flat dose-response "curve" with SSRIs. More isn't better.

This is an important point to understand, because antidepressants quite often don't work for a given patient on the first try, and then it's necessary to do some "adjusting," which means either increasing the dose, switching to another drug, or adding a second med. Fredman et al. found that among 432 attendees at a psychopharmacology course, asked what they would do for a non-responsive patient on SSRIs, most chose raising the dose as the best next-step option. But that's the wrong answer.

In "Dose-response relationship of recent antidepressants in the short-term treatment of depression," Dialogues Clin Neurosci. 2005 September; 7(3): 249–262, (full article here) Patricia Berney of Unité de Psychopharmacologie Clinique, Hôpitaux Universitaires de Genève, Chêne-Bourg, Switzerland, examines a large number of clinical trials to discover the extent to which dosage matters in antidepressant therapy. It's worth going through her discussion drug-by-drug, because if you're taking one of these drugs and your doctor suggests a higher dose, you may want to consider whether it's going to be worth the time and expense. Usually it's not.

Citalopram (Celexa)
"The short-term studies with citalopram did not show significant differences In terms of clinical efficacy across a dose range of 20 to 60 mg/day. Even a dose of 10 mg/day was effective compared with placebo. The results of the maintenance study by Montgomery et al. and the meta-analysis by the same authors support these findings. Therefore, for the majority of patients, there Is no advantage of increasing the dose of citalopram above 20 mg/day."

Escitalopram (Lexapro)
This drug is the S-stereoisomer of citalopram (Celexa), meaning that it's structurally no different from half of what's in citalopram. (Citalopram is a so-called racemic mixture of left- and right-handed versions of the same molecule.) According to Berney: "The only fixed-dose-response study with escitalopram indicates that 10 mg/day was equally as effective as 20 mg/day."

Fluoxetine (Prozac)
"The studies with fluoxetine did not show significant differences in terms of clinical efficacy across a dose range of 20 to 60 mg/day. Even a dose of 5 mg/day was effective compared with placebo. Therefore, for the majority of patients, there is no advantage of increasing the dose of fluoxetine above 20 mg/day. It might even be the case that the higher dose of 60 mg/day is less effective in major depressive disorder." The latter refers to a study by Wernicke: "Fluoxetine 20 and 40 mg/day were statistically superior to 60 mg/day." One study (Dunlop) involving 372 patients found: "Fluoxetine 20, 40, and 60 mg/day each produced an improvement that was no different from placebo on change in the HAMD total score in 355 ITT patients with LOCF at the end of 6 weeks." (ITT = Intent to Treat, LOCF = Last Observation Carried Forward.) The result with completer-case analysis, which is to say just considering the 214 patients who finished the study, was that changes in the HAMD total score "were similar to those of the ITT-LOCF analysis."

Fluvoxetine (Luvox)
"In this fixed-dose study on a large sample, only fluvoxamine 100 mg/day showed a significant therapeutic benefit over placebo at end-point analysis (on LOCF) on modified HAMD 13 items final score at the end of 6 weeks at fixed dose. Significant differences were not seen between fluvoxamine 25, 50, or 150 mg/day or placebo. On the HAMD 13-items responder analysis, the differences were significant for fluvoxamine 100 and 150 mg/day compared with placebo, but not between these two dosages on visual inspection of the figures in the publication on completer cases analysis."

Paroxetine (Paxil)
"In the publication by Dunner and Dunbar, there is a short description of a study involving 460 patients. The paroxetine 10 mg/day dose was no more effective than placebo, even on the HAMD depressed mood item. The authors reported also on a pooled analysis from a worldwide database, involving 1091 patients who remained on a fixed dose of paroxetine or placebo for at least 4 weeks, which showed no differences in terms of clinical efficacy across a dose range of 20 to 40 mg/day paroxetine. Therefore, for the majority of patients, there is no advantage in increasing the dose of paroxetine above 20 mg/day."

Note: It's also worth looking at a more recent study by Ruhé et al., "Evidence why paroxetine dose escalation is not effective in major depressive disorder: a randomized controlled trial with assessment of serotonin transporter occupancy," Neuropsychopharmacology 2009 Mar;34(4):999-1010, in which unipolar depressed patients on Paxil or placebo (double-blind) were scanned for SERT occupancy by single-photon emission-computed tomography (SPECT). The overall finding: "Paroxetine dose escalation in depressed patients has no clinical benefit over placebo dose escalation."

Sertraline (Zoloft)
"In the study by Fabre and Putman, sertraline 50 mg/day, but not 100 and 200 mg/day, was more effective than placebo at end-point analysis on change on the HAMD 17 items total score on ITT-LOCF at 6 weeks. There was no statistical analysis performed between the different doses, but inspection of the data in the publication suggests no differences."

Milnacipran (Savella)
This SNRI is approved for treating depression outside the U.S., but inside the U.S. it is approved only for fibromyalgia. Four studies were analyzed. They showed "flat dose-response relationship between 100 and 300 mg/day," with 50 mg/day being less effective than placebo.

Venlaxafine (Effexor)
This SNRI (inhibiting reuptake of both serotonin and norepinephrine) is a Top Ten antidepressant in the U.S. Patricia Berney's finding from reading the clinical trials:

In the venlafaxine studies, doses varied between 25 and 375 mg/day. A positive dose-response curve was only demonstrated with trend analysis. However, the difference between the higher dose range and placebo was not pronounced. Better efficacy could be obtained with a dose of venlafaxine above 75 mg/day in terms of remission rate. In a review concerning all aspects of antidepressant use, Preskorn mentioned an ascending then descending dose-response curve for venlafaxine in an evaluation comparing 7 dose levels between 25 and 375 mg/day with placebo, coming from fixed and flexible-dose studies. However, the major difference in terms of mean HAMD score change, ie, 2 points, was between a group of patients receiving 175 mg/day and another receiving 182 mg/day, hardly a different dose! This suggests a calculation artifact rather than a pharmacological dose-response curve.

For the majority of patients, a dose of venlafaxine 75 mg/day should be adequate.

Reboxetine (Edronax, Norebox, Prolift, Solvex, Davedax, Vestra)
A norepinephrine reuptake inhibitor, reboxetine is approved for depression outside the U.S. Said Berney: "Despite availability of several short clinical trials, we cannot comment on the dose-response relationship for reboxetine."

Duloxetine
"No positive dose-response relationship has been found for 40 to 120 mg/day."

The common thread here is obvious. Antidepressants, when they work at all, tend to work about as well whether you take a large dose or a small dose. The dose-response curve for most of these drugs is a straight horizontal line. That's the main takeaway not only from Berney's meta-analysis but a separate meta-analysis by Baker et al.,"Evidence that the SSRI dose response in treating major depression should be reassessed," Depression and Anxiety, (2003), 17(1):1-9. It's also the conclusion reached in yet another meta-analysis by Hansen et al., Med Decision Making January/February 2009, 29(1):91-103 (Said the Hansen group: "Dose was not a statistically significant predictor of categorical HAM-D response. Among comparative trials with nonequivalent doses, trends favored higher dose categories but generally were not statistically significant.")

The lack of a dose-response relationship above a certain minimum effect dose doesn't necessarily mean the drugs are doing nothing. It means that once you've achieved an in vivo concentration of the drug sufficient to saturate whatever transporter protein(s) or receptors the drug in question targets, there's nothing left for the drug to attach to. Thus any excess goes unused.

It's important to note, though, that these drugs do tend to show a strong dose-response relationship when it comes to side effects. (One 2010 meta-analysis found that across 9 different studies, "Higher doses of SSRIs were associated with significantly higher proportion of dropouts due to side-effects.")

Bottom line: Upping your dose, on any of the major antidepressants listed above, isn't a good strategy. It may worsen side effects, but it's unlikely to change your therapeutic outcome. If a particular drug isn't working for you, a far better strategy than upping the dose is to try a different drug and/or add talk therapy to the mix (if you're not already doing it).

Likewise: If you are seeing good therapeutic effect from a drug but side effects are causing problems, you should ask your doctor or nurse practitioner about dialing back the dosage to the minimum therapeutic dose, so as to keep side effects from swamping the therapeutic effect.

Searching for Aliens in our Genes

2013-04-01T00:30:00.000-04:00

I spent a lot of time thinking about whether it would be a good idea to write an April Fool's Day spoof blog for today, then this 100% legitimate scientific paper about evidence of alien intelligence in terrestrial DNA fell into my lap and I said to myself: Who the heck needs an April Fool's day joke-blog when you can just cite the amazing new scientific paper out of Kazakhstan called The "Wow! signal" of the terrestrial genetic code" by Vladimir I. shCherbak and Maxim A. Makukov?

But wait! Don't be too quick to dismiss it. The paper in question was accepted for publication by none other than the prestigious journal Icarus.

The paper's abstract says:

It has been repeatedly proposed to expand the scope for SETI, and one of the suggested alternatives to radio is the biological media. Genomic DNA is already used on Earth to store non-biological information. Though smaller in capacity, but stronger in noise immunity is the genetic code. The code is a flexible mapping between codons and amino acids, and this flexibility allows modifying the code artificially. But once fixed, the code might stay unchanged over cosmological timescales; in fact, it is the most durable construct known. Therefore it represents an exceptionally reliable storage for an intelligent signature, if that conforms to biological and thermodynamic requirements. As the actual scenario for the origin of terrestrial life is far from being settled, the proposal that it might have been seeded intentionally cannot be ruled out. A statistically strong intelligent-like “signal” in the genetic code is then a testable consequence of such scenario. Here we show that the terrestrial code displays a thorough precision-type orderliness matching the criteria to be considered an informational signal. Simple arrangements of the code reveal an ensemble of arithmetical and ideographical patterns of the same symbolic language. Accurate and systematic, these underlying patterns appear as a product of precision logic and nontrivial computing rather than of stochastic processes (the null hypothesis that they are due to chance coupled with presumable evolutionary pathways is rejected with P-value < 10^–13). The patterns are profound to the extent that the code mapping itself is uniquely deduced from their algebraic representation. The signal displays readily recognizable hallmarks of artificiality, among which are the symbol of zero, the privileged decimal syntax and semantical symmetries. Besides, extraction of the signal involves logically straightforward but abstract operations, making the patterns essentially irreducible to any natural origin. Plausible way of embedding the signal into the code and possible interpretation of its content are discussed. Overall, while the code is nearly optimized biologically, its limited capacity is used extremely efficiently to store non-biological information.

The authors -- Dr. Vladimir I. shCherbak, a mathematician at the al-Farabi Kazakh National University of Kazakhstan, and Maxim A. Makukov, an astrobiologist at Kazakhstan's's Fesenkov Astrophysical Institute -- list certain criteria they feel would have to be met by any "signal" placed in our DNA by aliens. For example, the signal should be in the form of a pattern (not randomness) and should be unlikely to exist by chance. Then they make the claim:

We show that the terrestrial code harbors an ensemble of precision-type patterns matching the requirements mentioned above. Simple systematization of the code reveals a strong informational signal comprising arithmetical and ideographical components. Remarkably, independent patterns of the signal are all expressed in a common symbolic language. We show that the signal is statistically significant, employs informational capacity of the code entirely, and is untraceable to natural origin.

Predictably, the intelligent-design crowd (always eagerly scouring latest Kazakhstani findings for evidence of intelligent design artifacts that can't be explained by natural phenomena) latched onto the paper and began touting it as evidence of God's hand at work.

But what have the Kazakhstani scholars really found? It seems they've discovered numerology. They fiddle with the atomic masses of amino acids, start partitioning codons in weird ways, then suddenly strange numbers like '111' and '333' and '666' start to appear and we begin seeing the (prime) number 37 crop up just a little too frequently to be explained by anything other than aliens (but always written as 037 for some reason), and before you know it, we're reading things like:

Meanwhile, there are 333 chain and 592 block nucleons and 333 + 592 = 925 nucleons of whole molecules in the IV-set. With 037 cancelled out, this leads to 3^2 + 4^2 = 5^2 – numerical representation of the Egyptian triangle, possibly as a symbol of two-dimensional space. Incidentally, codon series in the ideogram (Fig. 7a) are arranged in the plane rather than linearly in a genomic fashion.

Eventually we encounter the following helpful discussion:

It is often said that genomes store hereditary information in quaternary digital format. There are 24 possible numberings of DNA nucleotides with digits 0, 1, 2, 3. The ideogram seems to suggest the proper one: T ≡ 0, C ≡ 1, G ≡ 2, A ≡ 3. In this case the TCGA quadruplet (Fig. 9a), read in the distinguished direction, represents the natural sequence preceded by zero. Palindromic codons CCC and TCT (Fig. 10b) become a symbol of the quaternary digital symmetry 1114 and the radix of the corresponding system 0104 = 4, respectively. Translationally related AGC, or 3214, codons (Fig. 9b) possibly indicate positions in quaternary place-value notation, with higher orders coming first. The sum of digital triplets in the string TAG + TAA + AAA + ATG + ATG (Fig. 10c) equals to the number of nucleotides in the code 30004 = 192. Besides, T as zero is opposed to the other three “digits” in the decomposed code (Fig. 6). Finally, each complementary base pair in DNA sums to 3, so the double helix looks numerically as 333…4, and the central AAA codon in Fig. 10c becomes the symbol of duplex DNA located between genes. Should this particular numbering have relation to the genomic message, if any, is a matter of further research.

So let's recap. First, posit the existence of numerical patterns in the genetic code as evidence of non-earthly intelligence. Next, find said patterns, no matter how screwball they are. Then calculate the implausibility of said patterns occurring by chance. Voila! Intelligent design. (But is it by aliens, or by God? Further research may be necessary.)

I congratulate the authors, and the journal Icarus, for one of the most peculiar "scientific papers" seen this side of -- Kazakhstan.

For more, be sure to visit the authors' page at http://gencodesignal.org/.

Nitric Oxide, Antidepressants, and "Sexual Side Effects"

2013-03-31T00:30:00.000-04:00

Antidepressants, particularly those that affect serotonin metabolism, are famous for causing "sexual side effects." According to package inserts for the drugs, such side effects are relatively uncommon, but that's because patients in clinical trials don't readily volunteer information on sexual problems on their own; you have to ask them about it. When researchers have specifically asked patients about sexual side effects, the topic comes up 58% to 73% of the time, depending on the drug.

If you go looking in the scientific literature for an explanation of why "sexual side effects" happen at all, you'll mostly encounter rather vague, unsatisfying explanations. I did a little digging on my own and came up with what I think is a fairly edifying scientific explanation of what's going on. (From here, it gets a little technical. Bear with me if you can.)

"Sexual dysfunction" covers a lot of ground, and the gaps in our knowledge of the biochemistry underlying things like arousal and orgasm could better be called knowledge in our gaps. However, we do know that nitric oxide (NO) metabolism plays a huge role in stimulatory response. Clitoral erection as well as penile erection depend on production of NO and subsequent NO-induced accumulation of cyclic guanosine monophosphate, or cGMP (see this paper).

Nitric oxide is an interesting molecule. It's a gas at room temperature; soluble in oil as well as water; extremely corrosive; penetrates cell membranes with ease; and is probably the most widely distributed free radical in the human body. For many years, researchers knew of something called the endothelium-derived relaxing factor (EDRF), a substance produced by the inner cell lining of blood vessels, capable of signalling the surrounding smooth muscle to relax (resulting in vasodilation and increasing blood flow). It turns out EDRF and nitric oxide are one and the same. When NO is produced (from arginine and a bunch of cofactors, by the action of an enzyme called, shockingly enough, NO synthase), it triggers massive production of cGMP, which in turn activates protein kinases that (in turn) cause reuptake of calcium ion, which (through a bunch more steps) results in relaxation of smooth muscle.

You wouldn't think relaxation of muscle would be the key to getting an erection going, but that's because you're thinking of the wrong "muscle." Here, we're concerned with blood-vessel smooth muscle. Relaxation of that kind of smooth muscle is crucial to allowing blood to find its way into the structures that ultimately cause a clitoris or penis to become engorged. Without an increase in NO production and all the downstream effects that lead to vasodilation, there's no arousal.

Nitric oxide is not just a vasodilator, though. It's also a gasotransmitter, which is to say a gas that has neurotransmitter properties.

The primary target of SSRIs in the body is the SERT (serotonin transporter) protein, which is the agent responsible for "reuptake" of serotonin. At first glance, it's not at all obvious how serotonin reuptake plays a role in nitric oxide formation. However, an extremely alert group of researchers at the Centre National de la Recherche Scientifique in Montpellier, France, realizing that SERT is known to associate with various proteins having a so-called PDZ domain, and also realizing that neuronal nitric oxide synthase (nNOS) has such a domain, decided to do an experiment. They fused a carboxyl-terminal SERT peptide to Sepharose beads and poured mouse-brain homogenate over the beads to see what stuck. The most abundant protein to stick? None other than nNOS.

The French team found that when nNOS and SERT were coexpressed in a cell line, they bound to each other in vivo. They also found that exposing serotonin to a cell line expressing SERT and nNOS caused the production of NO. Thus, if you blockade SERT (cutting off serotonin reuptake), you interfere with NO production at the source, because nNOS (the enzyme responsible for producing NO) and SERT (the serotonin transporter) are, in fact, joined together, in vivo. (See this PNAS paper for an overview.) Interfering with NO production is bad, of course. No nitric oxide, no erection.

To me, this provides a pretty believable model of how SSRIs interfere with erectile function.

Before leaving the subject of nitric oxide production, I should mention that a huge amount of work has shown that NO metabolism is profoundly impaired in patients with diabetes. The work of the Centre National de la Recherche Scientifique team (outlined above) suggests a mechanism by which SSRIs interfere with NO metabolism. Another connection between SSRIs and diabetes?

And finally: It's thought by some that Minoxidil owes its hair-regrowing effects to the fact that it's a nitric-oxide agonist (Mi-NO-xidil). This suggests that hair loss would be an expected "side effect" of SSRIs. And indeed there are scattered reports of such in the literature.

How Common Are "Sexual Side Effects"?

2013-03-30T00:30:00.001-04:00

It's easy to get the impression, when reading popular articles about antidepressants, that drugs like Prozac, Zoloft, Paxil, Celexa, Cymbalta, Luvox, etc. are primarily psychoactive drugs that specifically alter brain chemistry. Indeed, this is what the drug companies want you to think. Depressed? Take this pill: it's designed to work on your brain. Will it cause side effects? Maybe, but they're just side effects.

This is a mistaken view of pharmacology. Drugs don't produce side effects. They just produce effects. Also, serotonin is not a brain chemical. It's a total body chemical. Well over 90% of the serotonin in your body is in your intestines and sex organs. Only 5% occurs in the brain. So when you take an SSRI, the drug reaches your whole body. It doesn't just head for the brain and then, incidentally, produce "side effects."

People who take antidepressants of the selective serotonin reuptake inhibitor (SSRI) class quickly realize this truth, namely that SSRIs are whole-body drugs, because the first effects most people notice (and complain about in clinical trials) are digestive and sexual-dysfunction effects. In clinical testing, SSRIs seldom fail to separate from placebo on those. If you're lucky enough to be one of the 50% or so of patients who see beneficial psychological effects, good for you, but in the meantime, the physiological effects (which can range from mild nausea to drowsiness to erectile dysfunction, or if you're really unlucky, diabetes or gastrointestinal bleeding) will be every bit as real as any effects on your brain.

How common are "sexual side effects" from SSRIs? If you read the package inserts for the drugs, they all downplay sexual side effects. The inserts rarely tell of more than 10% of patients complaining of ED, reduced libido, or difficulty reaching orgasm. The real world tells a far different story. In one of the largest prospective studies of its kind, the Spanish Working Group for the Study of Psychotropic-Related Sexual Dysfunction found that "the incidence of sexual dysfunction with SSRIs and venlaxafine [Effexor] is high, ranging from 58% to 73%." (Possibly, the remaining 27% to 42% of patients were still too depressed to care about sex.) The patients in question were taking Prozac (n=279), Zoloft (n=159), Luvox (n=77), Paxil (n=208), Effexor (n=55), or Celexa (n=66).

In the Spanish study, Paxil was associated with "significantly higher rates of erectile dysfunction/decreased vaginal lubrication" compared to other antidepressants. Meanwhile, "males had a higher rate of dysfunction than females (62.4% vs. 56.9%), but females experienced more severe decreases in libido, delayed orgasm, and anorgasmia."

Some studies of sexual side effects have shown a dose-response relationship. What's interesting about this is that most SSRIs have a flat dose-response curve for psychological effects. In other words, the physiological (sexual) effects are dose-dependent, but the effects on mood generally are not. I'll devote more discussion to the latter in a later post. The takeaway for now is that if you're on an SSRI and you don't like the sexual side effects, ask your doctor to reduce your dosage to the minimum effective therapeutic dose (because taking more than that generally does no good anyway). A second takeaway is: If your doctor keeps upping your dose, it means he or she hasn't read the literature. The literature says that beyond a certain dose, more doesn't do anything.

Tomorrow: A look at why and how SSRIs mess up your sex life (latest biochemical findings).

SSRIs, Weight Gain, and Diabetes

2013-03-29T00:30:00.000-04:00

Yesterday I talked about the connection between antidepressant usage and diabetes. It may seem odd, at first, that antidepressants should double one's risk of diabetes, but given the pervasive involvement of serotonin in appetite and digestive processes (and the ability of antidepressants to make you fat; see discussion below), perhaps it shouldn't really come as such a shock.

To put things in perspective: Around 95% of the body's serotonin can be found in the gastrointestinal tract; only 5% is in your brain [reference]. Serotonin is the primary signalling molecule involved in motility, secretion, and vasodilation within the intestines, and just as in the brain, bioavailability of serotonin to target cells in the gut is dependent on the serotonin reuptake transporter (SERT). SERT, in turn, is the binding target of selective serotonin reuptake inhibitors. When you take an SSRI like Prozac, Zoloft, Paxil, etc., you're medicating your intestines (and your nearby reproductive system, which is highly innervated and dependent on serotonin for proper functioning); and also, your brain. So it should surprise no one that the main physiologic effects of SSRI administration are, in fact, on gut, sex organs, and brain. If you were thinking you were mainly medicating your brain, with your sex organs and gut experiencing "side effects," you've essentially got it backwards. Serotonin's main effect in your body is keeping your gastrointestinal system functioning. (It's worth noting that low-dose SSRIs have been used to treat irritable bowel syndrome, but it should also be noted that selective serotonin reuptake inhibitors increase the risk of upper gastrointestinal bleeding, particularly when used with NSAIDs.) For more on serotonin's action in the gut, see this paper and also this paper. I also recommend "Serotonin receptors and transporters — roles in normal and abnormal gastrointestinal motility," in Alimentary Pharmacology & Therapeutics ,Volume 20, Issue Supplement s7, pages 3–14, November 2004 (full article here).

Weight control is at least partially mediated through the 5-HT2c serotonin receptor in the hypothalamus. We know this is true because "knockout" mice with a targeted mutation of the 5-HT2c receptor gene engage in chronic hyperphagia (overeating), leading to obesity and hyperinsulemia [reference]. We also know this because obese humans who've been exposed to the potent 5-HT2c receptor agonist m-chlorophenylpiperazine (mCPP) experience weight loss and appetite changes. There's also very good evidence that polymorphisms in the promoter region of the 5-HT2c receptor gene play a direct role in obesity and diabetes in humans. These kinds of interactions led the authors of a report in Nature Medicine 4, 1152-1156 (1998) to state categorically that "a perturbation of brain serotonin systems can predispose to type 2 diabetes."

Finally, it's worth pointing out that some SSRIs (Prozac in particular) exhibit direct action on 5-HT2c receptors (and not just on SERT).

So bottom line, there's plenty of reason to believe that antidepressants can play a direct role in fostering diabetes.

And then there's the small matter of weight gain.

Most doctors tell their patients that weight gain is not a problem with SSRIs (that it's mainly a problem with older tricyclic drugs), but this is a myth. Drug trials of the kind that lead to FDA approval of antidepressants are far too short in duration to bring out long-term weight-change trends, which is why weight gain is hardly ever mentioned in package inserts for SSRIs. (In the rare instance when weight gain is mentioned, it's usually painted as restoration of appetite due to recovery from depression, which is self-serving nonsense, IMHO.)

In a study called "Real-World Data on SSRI Antidepressant Side Effects," published in Psychiatry (Edgmont). 2009 February; 6(2): 16–18, real-world patients taking citalopram (Celexa), escitalopram (Lexapro), fluoxetine (Prozac), paroxetine (Paxil), and sertraline (Zoloft) were asked about side effects. Among the 229 patients who noted specific side effects, the three most common side effects, by far, were sexual dysfunction, sleepiness, and weight gain (see graph below). All three occurred at about the same rate (56, 53, and 49 reports, respectively, out of 229 total).

Real-world SSRI users reported weight gain almost as often as sexual
side-effects. From http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2719451/

How much weight gain are we talking about? Very substantial weight gain. Long-term studies have reported mean weight gains of 15 lb (6.75 kg) for sertraline (Zoloft), 21 lb (9.45 kg) for fluoxetine (Prozac), and 24 lb (10.80 kg) for paroxetine (Paxil). Citalopram (Celexa) is often painted as being less likely to cause weight gain than other antidepressants, and yet in one trial, 8 of 18 patients reported an average weight gain of 15.7 lb (7.1 kg) after receiving citalopram for just 5 weeks. See this table for a rundown of weight-gain effects for various popular antidepressants

A Norwegian study (Raeder MB, Bjelland I, Vollset SE, Steen VM, "Obesity, dyslipidemia, and diabetes with selective serotonin reuptake inhibitors: the Horland Health Study," J Clin Psychiatry 2006; 671974-1982) found:

We observed an association between use of SSRIs as a group (N = 461) and abdominal obesity (OR = 1.40, 95% CI = 1.08 to 1.81) and hypercholesterolemia (OR = 1.36, 95% CI = 1.07 to 1.73) after adjusting for multiple possible confounders. There was also a trend toward an association between SSRI use and diabetes.

The Norwegian study involved patients taking paroxetine, citalopram, sertraline, fluoxetine, and/or fluvoxamine

The bottom line: Weight gain is a serious issue with SSRIs (not just older antidepressants), and the association of SSRIs with increased risk of diabetes is not a statistical fluke of some kind, but a very real outcome. Given what we know about serotonin's role in appetite, weight control, and gastrointestinal function, none of this should come as a surprise.

New Diabetes Risk Factor: Antidepressants

2013-03-28T00:30:00.000-04:00

If people knew that taking antidepressants for two years or more doubles their risk of diabetes, would they continue to take them? That's what I wondered when I saw the paper by Andersohn et al. in Am J Psychiatry 2009; 166:591–598 called "Long-Term Use of Antidepressants for Depressive Disorders and the Risk of Diabetes Mellitus." It's an eye-opening study, drawing on data from the U.K.'s patient database. The numbers are solid and the results hard to argue with. Even after controlling for body mass index (BMI), hypertension, hyperlipidemia, smoking, age, and other factors, the authors of the study found that long-term use of antidepressants (of any kind: tricyclic, MAOI, SSRI) was associated with an almost two-fold greater risk of diabetes.

This is a shocking result, because it indicates that antidepressants add significantly to the burden of disease. In the U.S., where 27 million people take antidepressants (60% of them for two years or longer), it could mean an extra million cases of diabetes.

From the 1988-1994 time period to the 2005-2008 period, antidepressant usage in the U.S. rose 400%, according to the Centers for Disease Control. This corresponds with an almost-quadrupling of diabetes cases in the same time frame.

Before you start thinking that maybe depression in and of itself is predisposing to weight gain and diabetes (which it is), go read the Andersohn paper. The authors already thought of such things and controlled for them in their study control populations. They found that even after controlling for the usual risk factors, recent long-term (24 months or more) antidepressant usage increased the risk of diabetes by 84%. (Consult the paper for a list of the 29 antidepressants included in the analysis and the individual risk ratios for each.)

The Andersohn study was motivated by an earlier finding that continuous antidepressant use over an average study duration of 3.2 years was associated with an 2.6-fold increased risk of diabetes (95% CI=1.37–4.94) in the placebo arm and 3.39-fold increase in risk (95% CI=1.61–7.13) in the lifestyle intervention arm of the study reported in Diabetes Care. 2008 Mar;31(3):420-6. The Andersohn study confirms the previous finding.

Independent confirmation of the foregoing results can be found in a 2010 cross-sectional study of patients in Finland. Mika Kivimäki et al., writing in Diabetes Care, December 2010 33:12, 2611-261, reported finding a two-fold increased risk of Type 2 diabetes in patients who had taken 200 or more "defined daily doses" (about six months' worth) of antidepressant medication. Stratification by antidepressant type found no significant difference for tricyclics versus SSRIs. Interestingly, diabetes risk was higher for patients who had taken 400 or more daily doses versus those who'd taken 200 to 400 daily doses, indicating a kind of dose-response relationship. The longer you're on meds, the more likely you'll get diabetes.

The graph depicted further above (from CDC's Diabetes Report Card 2012) shows pretty clearly that if there's one thing America doesn't need right now, it's more cases of diabetes. Diabetes is already out of control in the U.S. In many counties (all of those shown in dark red below), diabetes already afflicts more than 11% of the population.

We already know that high body mass index, out-of-band blood lipids, inactivity, and age are important risk factors for diabetes. But we now know a major new risk factor: antidepressants. As Richard R. Rubin writes in US Endocrinology, 2008;4(2):24-7:

Applying current estimates of the number of people in the US who have prediabetes (57 million with impaired glucose tolerance or impaired fasting glucose), and estimates of the prevalence of antidepressant use among adults in the US (at least 10%), it would seem that almost six million people in the US have pre-diabetes and are taking antidepressants. This is a fairly large number of people, and if future research confirms that antidepressants are an independent risk factor for type 2 diabetes, efforts to minimize the potentially negative effects of these agents on glycemic control should be pursued.

Heatmap Visualization of Antidepressants

2013-03-27T00:30:00.000-04:00

With so many antidepressants on the market now, it's hard to keep them straight. The categorizations that have been offered thus far are not uniformly helpful for understanding the drugs' in vivo targets. For example, a term like "tricyclic" refers to chemical structure of the drug molecule itself (and tells you nothing about what it does in the brain). Likewise, a more descriptive term like Selective Serotonin Reuptake Inhibitor is somewhat misleading in that many SSRIs actually have a fairly complex binding profile with regard to neurotransmitter receptors. For example, in addition to its action at the serotonin transporter (SERT), Prozac (fluoxetine) has potent 5-HT2C receptor antagonist effects along with being an agonist of the σ1-receptor. And then you have the fact that a drug like clomipramine is classified as a tricyclic, yet pharmacologically it shows a great deal of similarity with SSRIs. It can all get confusing in a hurry.

So the question is: How can we visualize what these drugs are doing, without reading long descriptions of their biological activities or trying to digest tables of receptor binding constants? Well, it turns out a team in the Netherlands has come up with an interesting way of visualizing antidepressant modes of action at a glance. It's shown in the graphic below.

Derijks et al. presented this nifty heatmap in "Visualizing Pharmacological Activities of Antidepressants: A Novel Approach," The Open Pharmacology Journal, 2008, 2, 54-62. They took the known binding constants for various antidepressants and calculated "receptor occupancies" for these drugs at the 5-HT (5-hydroxytryptamine; i.e. serotonin) reuptake transporter, 5-HT2c-receptor, histamine H1-receptor, norepinephrine reuptake transporter, alpha1-receptor, and muscarine M3-receptor, then subjected the results to principal component analysis to arrive at a hierarchical cluster scheme based on bioactivity homologies. What they found is that the 20 antidepressants they looked at grouped naturally into four main clusters. Within the clusters, drugs grouped together based (again) on similarity of activity at binding sites.

Basically, you can look at any given row in this picture as a kind of "barcode" (or fingerprint, if you like) for a particular drug based on its own particular mode(s) of action.

Buproprion (Wellbutrin) shows up as a solid yellow stripe, which might seem odd until you realize the Derijks group did not attempt to include dopamine-transporter occupancy in its model (nor did it look at nicotinic acetylcholine receptor binding, also important for bupropion). The group said they tried looking at dopamine transporter bindings but found it "did not change the overall classification in four clusters," so they left it out. For visualization purposes, it would have been nice if they'd left it in.

I think this kind of visual representation of drug activity is a very useful tool for differentiating antidepressants, and if psychiatrists (and nurse practitioners) knew about it they could use it to aid the decisionmaking process when it comes to trying a non-responsive patient on a different class of drug. If a drug from Cluster 1 doesn't work, it's only logical to try a drug from a different cluster rather than (say) a different drug from the same cluster.

In case you're having trouble with the generic drug names shown in the above figure, here's a table giving the translations between generic and trade names:

Generic Name	Trade Name (and Category)
amitriptyline	Elavil, Saroten (TCA)
bupropion	Wellbutrin, Zyban
citalopram	Celexa, Cipramil (SSRI)
clomipramine	Anafranil (TCA)
doxepin	Adapine, Sinequan (TCA)
duloxetine	Cymbalta, Ariclaim (SNRI)
escitalopram	Lexapro, Cipralex (SSRI)
fluoxetine	Prozac, Sarafem (SSRI)
fluvoxamine	Luvox (SSRI)
imipramine	Tofranil (TCA)
maprotiline	Deprilept, Ludiomil (TCA)
mianserin	Bolvidon, Norval (TeCA)
mirtazapine	Remeron, Avanza (TeCA)
nefazodone	Serzone, Nefadar
nortriptyline	Aventyl, Pamelor (TCA)
paroxetine	Paxil, Seroxat (SSRI)
reboxetine	Edronax, Prolift (NRI)
sertraline	Zoloft, Lustral (SSRI)
trazodone	Desyrel, Deprax (SARI)
venlafaxine	Effexor (SNRI)

NRI = Norepinephrine Reuptake Inhibitor
SARI = Serotonin Antagonist and Reuptake Inhibitor
SNRI = Serotonin-Norepinephrine Reuptake Inhibitor
SSRI = Selective Serotonin Reuptake Inhibitor
TCA = Tricyclic Antidepressant
TeCA = Tetracyclic Antidepressant