A burning set of questions in my old shop when I was there, and I have every reason to think is still aflame, is why does Velcade work in some tumors but not others and how could you predict which tumors it will work in. Does the sensitivity of myelomas & certain lymphomas generally (and a seemingly random scatter of solid tumor examples) to proteasome inhibition follow a pattern? And is this pattern a reflection of the inner workings of these cells or more how the drug is distributed throughout the body?
An even broader burning question is whether any other proteasome inhibitor would behave differently at either level. Would a more potent inhibitor of the proteasome have a different spectrum of tumors which it hit?
Now, while Velcade (bortezomib, fka PS) is the only proteasome inhibitor on the market, it will probably not always be that. Indeed, since Velcade has proven the therapeutic utility of proteasome inhibition, other companies and academics have been exploring proteasome inhibitors. The most advanced that I am aware of is a natural product being developed by Nereus Pharmaceuticals, which I will freely confess to not really following.
The featured (and therefore free!) article in July's Cancer Cell describes a new proteasome inhibitor, another natural product. Argyrin A was identified in a screen for compounds which stabilize p27Kip1, an important negative regulator of the cell cycle. Kip1 is one of the a host of proteins reported to be an important protein stabilized by proteasome inhibition (one of duties back on Landsdowne Street was to catalog the literature on such candidates). While there are probably many ways to stabilize p27Kip1, what they reported on is this novel proteasome inhibitor.
By straightforward proteasome assays Argyrin A shows a very similar profile to Velcade. That is, the proteasome has multiple protease activities which can be chemically distinguished, and the pattern of inhibition by the two compounds is very similar. However, by a number of approaches they make the case that there are significant biological differences in the response to Velcade & Argyrin A.
Now there is a whole lot of data in this paper & I won't go into detail on most of it. But I will point out something a bit curious -- very curious. They performed transcriptional profiling (using Affymetrix chips) on samples treated with Velcade, Argyrin A, and siRNA vs an ensemble of proteasome subunits, each at different timepoints. In their analysis they saw lots of genes perturbed by Velcade but a very small set perturbed by Argyrin A and the siRNA. Specifically, they claim 10,500(!) "genes" (probably probesets) for Velcade vs 500 for Argyrin A. That's a huge fraction of the array moving!
Now, I'll confess things are a bit murky. Back at MLNM I would have had the right tools at my disposal & could quickly verify things; now I have to rely on my visual cortex & decaying memory. But when I browse through their lists of genes for Argyrin A in the supplementary data, I don't see a bunch of genes which are a distinct part of the proteasome inhibition signature. At MLNM, huge numbers of proteasome inhibition experiments were done & profiled on arrays, using a number of structurally unrelated proteasome inhibitors in many different cell lines. Not only does a consistent signal emerge, but when an independent group published a signature for proteasome inhibition in Drosophila there was a lot of overlap in their signature & our signature once you mapped the orthologs.
What's the explanation? Well, it could be that I'm not recognizing what is there due to poor memory, though I'm pretty sure. One thing that is worrisome is that the Argyrin A group's data is based on a single profile per drug x timepoint; there are no biological replicates. That's not uncommon due to the expense and challenge of microarray studies, but good experiments are easy. Nor was there any follow-up by another technology (e.g. RT-PCR) to show the effects across biological replicates or other cell lines. Given that these are in tissue culture cells, which can behave screwy if you stare at them the wrong way, that's very unfortunate. Even small differences in the culturing of the cells -- such as edge effects on plates or humidity differences, can lead to huge artifacts.
Another possible explanation is that the Bortezomib cells were watched too late; the first Velcade timepoint is at 14 hours. After 14 hours, the cells are decidedly unhealthy and heading for death. The right times to sample were always a point of contention, but one suggestion that there is an issue is the lack of correlation between the different timepoints for Velcade vs the strong correlation for the other treatments (Figure 7). That works (in my head at least) in reverse too -- it's downright odd that their other treatments are so auto-correlated between 14 and 48 hours with Argyrin A -- if cells are not yet dead at 14 hours but committed to die, one would expect there to be some sort of movement away from the original profile.
One other curiosity. They do report looking for the Unfolded Protein Response (UPR) and report seeing it in the Velcade treated cells but not Argyrin A treated ones. The UPR is the cell's response to misfolded proteins -- and since disposal of misfolded proteins is a role of the proteasome, it has never surprised anyone that the UPR is induced by proteasome inhibitors. Can you really have a proteasome inhibitor that doesn't induce the UPR? If this is truly the case, it is very striking and deserves its own study.
Is the paper wrong? Obviously I can't say, but I really wonder about it. I also wonder if the referees brought up the same questions. Hopefully we'll see some more papers in the future which explore this compound in a wider range of cell lines and with more biological replicates
Nickeleit et al
Argyrin a reveals a critical role for the tumor suppressor protein p27(kip1) in mediating antitumor activities in response to proteasome inhibition.
Cancer Cell. 2008 Jul 8;14(1):23-35.
Monday, July 21, 2008
Wednesday, July 16, 2008
Forging into the gap
Gaps are important. There is a major brand by that name. Controversy over a perceived "missle gap" was a major issue in the Nixon-Kennedy election of 1960. Budget gaps cause governments to trim services. About a half an hour's drive west of where I grew up is the town of Gap, and a bunch of generations ago my ancestors probably passed through the Cumberland Gap.
Gaps occupy a special place in computational biology, specifically in the alignment of sequences and structures. As sequences evolve, they can acquire new residues (insertions) or lose residues (deletions), and so if we wish to align a pair of sequences we must put a gap in. Pairwise algorithms such as Needleman-Wunsch-Sellers and Smith-Waterman insert the optimal gaps -- given certain assumptions which include, but are not limited to, the match, mismatch, gap insertion and gap deletion penalties. Some pairwise alignment problems have been addressed by even more complicated gapping schemes. For example, if I am aligning a cDNA to a genomic sequence I may wish to have separate consideration of introns (a special case of gaps), gaps that would insert or remove multiples of three (codons) or gaps which don't all in either of those categories.
Multiple sequence alignment gets even harder. There are no exact algorithms to compute a guaranteed best alignment, so all methods have some degree of heuristics to them. Many algorithms are progressive, first aligning two sequences and then aligning another to that alignment and then another and so on, or perhaps aligning pairs of sequences and then aligning the aligned pairs and so on. Placement of gaps becomes especially tricky, as their placement in early alignments greatly influences the placement in later alignments, which could well be a bad thing.
Protein alignments in particular have the problem of trying to serve three masters, who are often but not always in agreement. An alignment can be a hypothesis of which parts of a protein serve the same role, a hypothesis as to which amino acids occupy similar positions in space, or a hypothesis as to which amino acids derive from codons with a shared ancestry. Particularly in the strongly conserved core of proteins these three are likely to be in agreement, but in the hinterlands of structural loops in proteins or disordered regions it's not so clear. There is also a bit of aesthetics that comes in; alignments just look neater and simpler when there are fewer gaps. Perhaps not quite Occam's Razor in action, but simplicity is appealing.
The June 20th issue of Science (yep, Science & Nature have been piling up) has a paper that addresses this issue and builds an algorithm unapologetically aligned to just the one goal: find the most plausible evolutionary history. They point out that while insertions and deletions are treated symmetrically by pairwise programs, they are quite asymmetric for progressive multiple alignment. The alignment gets to pay once for deleting something, but insertions (like overdue credit cards) incur a penalty with each successive alignment. It seems unlikely that nature works the same way, so this is undesirable.
One solution to this has been to have site-specific insertion penalties. Loytnoja & Goldman point out that this compensation often doesn't work and causes insertions to be aligned which are not homologous, in the sense that they each arose from a different event (indeed, these insertions should not be aligned with anything from an evolutionary point-of-view, though structurally or functionally an alignment is reasonable).
As an alternative, their method flags insertions made in early alignments so that they are treated specially in later alignments. The flagging scheme even allows insertions at the same position to be treated as independent -- they neither help nor penalize the alignment and are reported as separate entities.
Using synthetic data they tested their program against a number of other popular multiple aligners and found (surprise!) it did a better job of created the correct alignment. They also simulated what getting additional, intermediate data does for the alignments -- and scarily for the older alignment programs gap placement got worse (less reflective of the actual insertion/deletion history of the synthetic data).
The article closes with an interesting question: has our view of sequence evolution been shaped by incorrect algorithms? Is the dominant driver of sequence change in protein loops point mutants or small insertions/deletions.
Phylogeny-Aware Gap Placement Prevents Errors in Sequence Alignment and Evolutionary Analysis
Ari Löytynoja and Nick Goldman
http://www.sciencemag.org/cgi/content/abstract/320/5883/1632
p. 1632
Gaps occupy a special place in computational biology, specifically in the alignment of sequences and structures. As sequences evolve, they can acquire new residues (insertions) or lose residues (deletions), and so if we wish to align a pair of sequences we must put a gap in. Pairwise algorithms such as Needleman-Wunsch-Sellers and Smith-Waterman insert the optimal gaps -- given certain assumptions which include, but are not limited to, the match, mismatch, gap insertion and gap deletion penalties. Some pairwise alignment problems have been addressed by even more complicated gapping schemes. For example, if I am aligning a cDNA to a genomic sequence I may wish to have separate consideration of introns (a special case of gaps), gaps that would insert or remove multiples of three (codons) or gaps which don't all in either of those categories.
Multiple sequence alignment gets even harder. There are no exact algorithms to compute a guaranteed best alignment, so all methods have some degree of heuristics to them. Many algorithms are progressive, first aligning two sequences and then aligning another to that alignment and then another and so on, or perhaps aligning pairs of sequences and then aligning the aligned pairs and so on. Placement of gaps becomes especially tricky, as their placement in early alignments greatly influences the placement in later alignments, which could well be a bad thing.
Protein alignments in particular have the problem of trying to serve three masters, who are often but not always in agreement. An alignment can be a hypothesis of which parts of a protein serve the same role, a hypothesis as to which amino acids occupy similar positions in space, or a hypothesis as to which amino acids derive from codons with a shared ancestry. Particularly in the strongly conserved core of proteins these three are likely to be in agreement, but in the hinterlands of structural loops in proteins or disordered regions it's not so clear. There is also a bit of aesthetics that comes in; alignments just look neater and simpler when there are fewer gaps. Perhaps not quite Occam's Razor in action, but simplicity is appealing.
The June 20th issue of Science (yep, Science & Nature have been piling up) has a paper that addresses this issue and builds an algorithm unapologetically aligned to just the one goal: find the most plausible evolutionary history. They point out that while insertions and deletions are treated symmetrically by pairwise programs, they are quite asymmetric for progressive multiple alignment. The alignment gets to pay once for deleting something, but insertions (like overdue credit cards) incur a penalty with each successive alignment. It seems unlikely that nature works the same way, so this is undesirable.
One solution to this has been to have site-specific insertion penalties. Loytnoja & Goldman point out that this compensation often doesn't work and causes insertions to be aligned which are not homologous, in the sense that they each arose from a different event (indeed, these insertions should not be aligned with anything from an evolutionary point-of-view, though structurally or functionally an alignment is reasonable).
As an alternative, their method flags insertions made in early alignments so that they are treated specially in later alignments. The flagging scheme even allows insertions at the same position to be treated as independent -- they neither help nor penalize the alignment and are reported as separate entities.
Using synthetic data they tested their program against a number of other popular multiple aligners and found (surprise!) it did a better job of created the correct alignment. They also simulated what getting additional, intermediate data does for the alignments -- and scarily for the older alignment programs gap placement got worse (less reflective of the actual insertion/deletion history of the synthetic data).
The article closes with an interesting question: has our view of sequence evolution been shaped by incorrect algorithms? Is the dominant driver of sequence change in protein loops point mutants or small insertions/deletions.
Phylogeny-Aware Gap Placement Prevents Errors in Sequence Alignment and Evolutionary Analysis
Ari Löytynoja and Nick Goldman
http://www.sciencemag.org/cgi/content/abstract/320/5883/1632
p. 1632
Tuesday, July 15, 2008
If life begins at conception, when does life start & when does it end?
Yesterday's Globe carried an item that Colorado is considering adopting a measure which would define a legal human life as beginning at conception. Questions around reproductive ethics and law raise strong emotions, and I won't attempt to argue either one of them. However, law & ethics should be decided in the context of the correct scientific framework, and that is what I think is too often insufficiently explored.
Defining when life "begins" is often presented as a simple matter by those who are proponents of "life begins at conception" definition. However, to a biologist the definition of conception is not so simple. Conception involves a series of events -- at one end of these events are two haploid cells and at the other is a mitotic division of a diploid cell. In between a number of steps occur.
The question is not mere semantics. Many observers have commented that a number of contraceptive measures, such as IUDs and the "morning after" pill would clearly be illegal under such a statute, as they work at least in part by preventing the implantation of a fertilized egg into the uterine wall. Anyone attempting to develop new female contraceptives might view the molecular events surrounding conception as opportunities for new pharmaceutical contraceptives. For example, a compound might prevent the sperm from homing with the egg, binding to the surface, entering the egg, discharging its chromosomes, locking out other sperm from binding, or prevent the pairing of the paternal chromosomes with maternal ones (there's probably more events; it's been a while since I read an overview). Which are no longer legal approaches under the Colorado proposal?
At the other end, if we define human life by a particular pairing of chromosomes and metabolic activity, then when does life end? Most current definitions are typically based on brain or heart activity -- neither of which is present in a fertilized zygote.
Again, the question is not academic. One question to resolve is when it is permissible to terminate a pregnancy which is clearly stillborn. Rarer, but even more of a challenge for such a definition, are events such as hydatiform moles and "absorbed twins".
In a hydatiform mole an conception results in abnormal development; the chromosome complement (karyotype) of these tissues is often grossly abnormal. Such tissues are often largely amorphous, but sometimes recognizable bits of tissue (such as hair or even teeth) can be found. Absorbed twins are the unusual, but real, phenomenon of one individual carrying a remnant of a twin within their body. Both of these conditions are rare (though according to Wikipedia in some parts of the world 1% of pregnancies are hydatiform moles!) but can be serious medical issues for the individual carrying the mole or absorbed twin.
Are any these questions easy to answer? No, of course not. But they need to be considered.
Defining when life "begins" is often presented as a simple matter by those who are proponents of "life begins at conception" definition. However, to a biologist the definition of conception is not so simple. Conception involves a series of events -- at one end of these events are two haploid cells and at the other is a mitotic division of a diploid cell. In between a number of steps occur.
The question is not mere semantics. Many observers have commented that a number of contraceptive measures, such as IUDs and the "morning after" pill would clearly be illegal under such a statute, as they work at least in part by preventing the implantation of a fertilized egg into the uterine wall. Anyone attempting to develop new female contraceptives might view the molecular events surrounding conception as opportunities for new pharmaceutical contraceptives. For example, a compound might prevent the sperm from homing with the egg, binding to the surface, entering the egg, discharging its chromosomes, locking out other sperm from binding, or prevent the pairing of the paternal chromosomes with maternal ones (there's probably more events; it's been a while since I read an overview). Which are no longer legal approaches under the Colorado proposal?
At the other end, if we define human life by a particular pairing of chromosomes and metabolic activity, then when does life end? Most current definitions are typically based on brain or heart activity -- neither of which is present in a fertilized zygote.
Again, the question is not academic. One question to resolve is when it is permissible to terminate a pregnancy which is clearly stillborn. Rarer, but even more of a challenge for such a definition, are events such as hydatiform moles and "absorbed twins".
In a hydatiform mole an conception results in abnormal development; the chromosome complement (karyotype) of these tissues is often grossly abnormal. Such tissues are often largely amorphous, but sometimes recognizable bits of tissue (such as hair or even teeth) can be found. Absorbed twins are the unusual, but real, phenomenon of one individual carrying a remnant of a twin within their body. Both of these conditions are rare (though according to Wikipedia in some parts of the world 1% of pregnancies are hydatiform moles!) but can be serious medical issues for the individual carrying the mole or absorbed twin.
Are any these questions easy to answer? No, of course not. But they need to be considered.
Wednesday, July 09, 2008
Do-it-yourself genomics: bad advice is bad advice
GenomeWeb's frequently entertaining Daily Scan notes that Wired magazine has a wiki which gives instructions on how to explore your own genome, including how to do your own genetic testing by home-PCRing your DNA and sending it to a contract lab for sequencing.
It isn't a very good idea, but that doesn't mean people won't try it. Doing a simple PCR really is pretty easy; I've done it in a hotel ballroom (proctoring a high school science fair sponsored by Invitrogen). Instructions for homebrew thermocyclers are surely out there; a number were published in the early days of PCR. But that doesn't mean getting good results is easy. Sticking to a purely technical level, are Wired's instructions very good?
I'd say no. I suppose I should even register to edit the wiki, but at the moment I'll limit myself to pointing out some of the technical issues that are ignored or glossed over (the material I quote below may well change, since it is a wiki).
The first obvious area is primer design. Wired's instructions are pretty simple
Alas, this will frequently be a recipe for disaster. As for my own qualifications for making that claim I will state that (a) I regularly design PCR amplicons in my professional life and (b) I have a much greater appreciation for my ignorance about how PCR can go awry than the average biologist. Leading the list of pitfalls is designing a primer with too low a Tm -- if those 20 nucleotides are mostly A & T, it won't work well. Second would be if the two primers will anneal to each other; you'll get lots of primer-dimer and little else. Equally bad would be a primer that can prime off itself. Third would be if the primers aren't specific to your targeted region of the genome. Prime off a conserved Alu piece and you are in real trouble.
The really silly part about this advice is that there are free primer design programs all over the internet, and some of the sites will perform nearly all of the checks mentioned above.
The rules for placement are much trickier than suggested. If you are going to sequence (and you might be sequencing heterozygous DNA; see below), then you really need the primers to be at least 50 nucleotides away from what you care about -- there is a front of unincorporated dye which often drops the quality any closer than this.
Even more of a concern is the sequence data itself. Wired makes it sound easy
If you are sequencing uncloned PCR products, then you are sequencing a population. If you are heterozygous for a single nucleotide, that means that nucleotide will read out as a mix -- two overlapping peaks of perhaps half height. A deletion or insertion ("indel") will make the trace "double peaked" from that spot on.
Those are the best case scenarios. If you had poor quality amplification (due to badly designed primers or just a miserable to amplify region), all those truncated PCR products will be in the sequencing mix as well -- further degrading your signal. If your SNP is in a region expanded due to copy number variation, then life is even harder.
Which gets to another point: Wired seems to be ignorant of copy number variants. Their testing recipe certainly won't work there.
The idea of untrained, emotionally involved individuals trying to interpret good genetic data is scary enough (Wired's example of celiac disease, as pointed out over at DNA and You, is a particularly problematic one); scarier is to overlay lots of ambiguity and error due to sloppy amateur technique. Hopefully, few will have the energy & funds to try it.
It isn't a very good idea, but that doesn't mean people won't try it. Doing a simple PCR really is pretty easy; I've done it in a hotel ballroom (proctoring a high school science fair sponsored by Invitrogen). Instructions for homebrew thermocyclers are surely out there; a number were published in the early days of PCR. But that doesn't mean getting good results is easy. Sticking to a purely technical level, are Wired's instructions very good?
I'd say no. I suppose I should even register to edit the wiki, but at the moment I'll limit myself to pointing out some of the technical issues that are ignored or glossed over (the material I quote below may well change, since it is a wiki).
The first obvious area is primer design. Wired's instructions are pretty simple
Designing them may be the hardest step. Look up the DNA sequence flanking your genetic marker of interest in a database like dbSNP. Pick a segment that is about 20 bases long and slightly ahead of the marker. That is your forward primer. Pick another 20ish base sequence that is behind the region of DNA that you want to study. Use a web app of your choice to find its reverse complement.
Alas, this will frequently be a recipe for disaster. As for my own qualifications for making that claim I will state that (a) I regularly design PCR amplicons in my professional life and (b) I have a much greater appreciation for my ignorance about how PCR can go awry than the average biologist. Leading the list of pitfalls is designing a primer with too low a Tm -- if those 20 nucleotides are mostly A & T, it won't work well. Second would be if the two primers will anneal to each other; you'll get lots of primer-dimer and little else. Equally bad would be a primer that can prime off itself. Third would be if the primers aren't specific to your targeted region of the genome. Prime off a conserved Alu piece and you are in real trouble.
The really silly part about this advice is that there are free primer design programs all over the internet, and some of the sites will perform nearly all of the checks mentioned above.
The rules for placement are much trickier than suggested. If you are going to sequence (and you might be sequencing heterozygous DNA; see below), then you really need the primers to be at least 50 nucleotides away from what you care about -- there is a front of unincorporated dye which often drops the quality any closer than this.
Even more of a concern is the sequence data itself. Wired makes it sound easy
Once that's done, you can buy sequencing equipment and do it yourself, or send the sample off to any one of many sequencing companies and they will do it for about five dollars.
If you are sequencing uncloned PCR products, then you are sequencing a population. If you are heterozygous for a single nucleotide, that means that nucleotide will read out as a mix -- two overlapping peaks of perhaps half height. A deletion or insertion ("indel") will make the trace "double peaked" from that spot on.
Those are the best case scenarios. If you had poor quality amplification (due to badly designed primers or just a miserable to amplify region), all those truncated PCR products will be in the sequencing mix as well -- further degrading your signal. If your SNP is in a region expanded due to copy number variation, then life is even harder.
Which gets to another point: Wired seems to be ignorant of copy number variants. Their testing recipe certainly won't work there.
The idea of untrained, emotionally involved individuals trying to interpret good genetic data is scary enough (Wired's example of celiac disease, as pointed out over at DNA and You, is a particularly problematic one); scarier is to overlay lots of ambiguity and error due to sloppy amateur technique. Hopefully, few will have the energy & funds to try it.
Monday, July 07, 2008
History Forget: How not to explain the impact of Prozac
Having escaped the usual abode for the weekend, there were a pile of the accumulated newspapers to digest on the train this morning. The Sunday Globe Ideas section caught my eye with an item by Jonah Lehrer titled "Head Fake: How Prozac sent the science of depression in the wrong direction". It's not an awful article -- once you get past that subtitle. But, it isn't a great article either.
The article puts forth the thesis that Prozac led to a chemical theory of depression, which recent literature has seriously upended. Alas, that greatly distorts the history.
Prozac was not the first successful drug nor the real antecedent to a chemical theory of depression. Early antidepressives such as the tricyclics and monoamine oxidase inhibitors opened the path to thinking that depression was due to imbalances in specific neurotransmitters. Prozac itself, as a Selective Serotonin Reuptake Inhibitor (SSRI), was an outgrowth of that work -- given the previous success with psychoactive drugs which seemed to affect many neurotransmitters and evidence that specific neurotransmitters might be more important for specific psychological diseases, it was natural to try to zoom in on one neurotransmitter. Prozac then is not a paradigm shifter (ala Kuhn) but was an extension of the existing paradigm. The success of SSRIs, partly due to a significantly attenuated side effect profile and partly due to a lot of popular press and partly due to marketing, merely pushed an existing theory up the ranks, particularly in the popular zeitgeist.
Lehrer does do a nice job of summarizing some recent work suggesting how antidepressants may really work, which is that they may help neurons heal (a new paradigm of depression as a neurodegenerative disease). In a recent conversation a clinician acquaintance noted to me some of the same key points (I'll confess to having not read the literature myself), so there's nothing wrong here. He also notes that it was the investigation of inconsistencies of observation with the predictions of the chemical imbalance theory, such as the frequently observed time lag between beginning antidepressant therapy and seeing results, which led to the new theory.
But getting back to that irksome subtitle, did Prozac steer "the science of depression in the wrong direction" or simply on a winding path? Yes, the chemical imbalance theory looks like it may be down for the count. However, it was that very same theory, via its shortcomings, that led to the new theory. This is how science works -- it's often indirect & messy. That's an important message that's lost (or nearly so) in the piece. SSRIs were perhaps a blunt tool, but they are the tool which has unlocked a new understanding of the topic.
Could we have gotten to the current understanding of depression without SSRIs and other chemical antidepressants? That's an exercise in alternative history best left to experts in the field, if anyone. Perhaps we might have, but perhaps not -- or would have via an even more tortuous path. It is important to get out the story of how pharmaceutical antidepressants do and do not work, but it is equally important to get out the story of how science really works.
The article puts forth the thesis that Prozac led to a chemical theory of depression, which recent literature has seriously upended. Alas, that greatly distorts the history.
Prozac was not the first successful drug nor the real antecedent to a chemical theory of depression. Early antidepressives such as the tricyclics and monoamine oxidase inhibitors opened the path to thinking that depression was due to imbalances in specific neurotransmitters. Prozac itself, as a Selective Serotonin Reuptake Inhibitor (SSRI), was an outgrowth of that work -- given the previous success with psychoactive drugs which seemed to affect many neurotransmitters and evidence that specific neurotransmitters might be more important for specific psychological diseases, it was natural to try to zoom in on one neurotransmitter. Prozac then is not a paradigm shifter (ala Kuhn) but was an extension of the existing paradigm. The success of SSRIs, partly due to a significantly attenuated side effect profile and partly due to a lot of popular press and partly due to marketing, merely pushed an existing theory up the ranks, particularly in the popular zeitgeist.
Lehrer does do a nice job of summarizing some recent work suggesting how antidepressants may really work, which is that they may help neurons heal (a new paradigm of depression as a neurodegenerative disease). In a recent conversation a clinician acquaintance noted to me some of the same key points (I'll confess to having not read the literature myself), so there's nothing wrong here. He also notes that it was the investigation of inconsistencies of observation with the predictions of the chemical imbalance theory, such as the frequently observed time lag between beginning antidepressant therapy and seeing results, which led to the new theory.
But getting back to that irksome subtitle, did Prozac steer "the science of depression in the wrong direction" or simply on a winding path? Yes, the chemical imbalance theory looks like it may be down for the count. However, it was that very same theory, via its shortcomings, that led to the new theory. This is how science works -- it's often indirect & messy. That's an important message that's lost (or nearly so) in the piece. SSRIs were perhaps a blunt tool, but they are the tool which has unlocked a new understanding of the topic.
Could we have gotten to the current understanding of depression without SSRIs and other chemical antidepressants? That's an exercise in alternative history best left to experts in the field, if anyone. Perhaps we might have, but perhaps not -- or would have via an even more tortuous path. It is important to get out the story of how pharmaceutical antidepressants do and do not work, but it is equally important to get out the story of how science really works.
Thursday, July 03, 2008
Myeloma unified?
Multiple myeloma is a complex disease. Perhaps one metaphor is that of the mythical Hydra -- each time a new molecular tool is thrown at it the number of vicious heads increases. For example, there are different chromosomal translocations which lead to myeloma. If you look at myeloma samples by transcriptional profiling, then one can find distinct expression signatures for each translocation -- and just as easily find ways to split those signatures into further subtypes. For example, some translocations activate one gene disrupted by the translocation whereas other instances of the same translocation will activate both deranged genes.
Another possible metaphor is the old fable of blind men examining an elephant -- each reports that the object is different, based on examining a different portion of the beast. In the case of myeloma, one examiner might focus on the subset with large portions of the genome amplified, others on specific deletions on chromosome 13, another on those cases where bone destruction is rampant. My own experience with palpitating the pachyderm looked at the response to a specific drug.
Now the Staudt lab has come out with a paper in Nature which proposes lumping everything back together again. Initially using a retroviral RNAi screen they identified the transcription factor IRF4 as a unifying theme of myeloma. IRF4 is activated in one characteristic translocation and plays an important role in B-cell development, so it's not a total shock. But linking it across multiple types is surprising.
The screen achieved 2-8 fold knockdown of IRF4 in 3 different myeloma cell lines, each possessing a different hallmark translocation (one of which was an IRF4 translocation). This was later extended to additional myeloma lines with similar lethality, but the knockdown of IRF4 in lymphoma lines had little effect, save one line possessing a translocation of IRF4.
One interesting surprise is that with the exception of the known IRF4 translocation bearing line, none of the lines have amplifications or other obvious derangements of IRF4. Only one showed point mutations upon resequencing. Hence, somehow IRF4 is being activated but not via a painfully obvious mechanism.
RNAi approaches can suffer from off-targets, genes not meant to be hit which cause the phenotype being studied rather than the believed target. The paper provides strong evidence that the effects really are driven by IRF4 knockdown -- not only were multiple shRNAs targeting IRF4 found to kill myeloma cells, but one of these targets the 3' untranslated region of IRF4 -- and the phenotype could be rescued by expressing IRF4 lacking the 3' UTR.
Transcriptional profiling of the knockdown lines in comparison with parental lines revealed a number of candidate IRF4 targets, and a large number of these were also identified by chromatin immunoprecipitation-chip (ChIP-chip) studies, confirming them as direct IRF4 targets. As noted, some direct targets may have been missed by ChIP-chip due to limitations with the arrays used. One other interesting aspect: the IRF4 target list in myeloma lines somewhat resembles a union of that in plasma cells (the normal cell myelomas are most kin to) with that of antigen-stimulated B-cells.
A particularly interesting direct IRF4 target identified in this study is the notorious oncogene MYC. A number of identified IRF4 targets are also known MYC targets, suggesting synergistic activation. They also found that both IRF4 and MYC bind upstream of IRF4 -- suggesting a complex web of positive feedback loops.
An interesting further bit of work targeted various identified IRF4 targets and showed these knockdowns to be lethal to myeloma cell lines. Hence it is suggested that IRF4 ablation in myeloma would lead to tumor cell death by many routes. Mice heterozygous for IRF4 deletion are viable, suggesting that IRF4 could be targeted safely.
The catch would be targeting IRF4 -- transcription factors are on nobody's list of favorite targets. The authors cite as points of optimism approaches targeting p53 & BCL6. However, the p53 targeting route is by inhibiting an enzyme which destabilizes p53, so an analogous approach to IRF4 would require first identifying key determinants of its stability. The BCL6 example they cite uses a peptide mimic, not something the medicinal chemists love much.
Other approaches to targeting IRF4 might focus on "druggable" (if any) genes in the IRF4 target lists, or perhaps something else. I'll try to put together a post next week on one of those candidate elses.
Now that Staudt's group has brought things together, it is tempting to contemplate slicing off some more Hydra heads. How do IRF4 target gene profiles differ across the chromosomal abberation subtypes of myleoma? Do IRF4 targets have any predictive value for determining the appropriate medication or show differential response to different medications?
Monday, June 30, 2008
Laying the groundwork for the one ton tomato
Somewhere in life I've heard a children's/novelty song about a one ton tomato; eventually (if I remember correctly) it ends up as a similar quantity of ketchup.
Nearly half-ton pumpkins show up pretty regularly at the big agricultural fairs every fall, but tomatoes aren't in that league. But, the difference between an ancestral tomato (small berries) and a multi-pound beefsteak is nothing to sneeze at. Domestication has made great strides.
A paper last month in Nature Genetics laid out part of this process. Interestingly, there are two different developmental processes that have been utilized to enlarge tomatoes. A tomato fruit is composed of multiple subunits, the carpels. One change has increased the number of cells per carpel by tinkering with the cell cycle -- a much more delicious change than what a similar process will yield in a person. The new work details the genetic change which increased the number of carpels.
Of course, of interest is how universal these mechanisms are. Most domestic fruits are greatly enlarged over their wild counterparts -- though perhaps raspberries show very little enlargement & blueberries it is a small multiple. On the other end are those monster curcurbits at the fair and their watermelon cousins.
But getting back to the title. Now the question is whether these mechanisms have reached their biological maximum or simply what a few mutations can do (there are also practical considerations, such as the stem strength required to support larger tomatoes). Or, can we use this new knowledge to bring up the laggards -- or figure out why there are no fist-sized raspberries or basketball-like blueberries? A strawberry the size of my dog? Of course, purely economic forces might lead to the fruits commanding the most money per unit weight -- perhaps pomegranates will have an order of magnitude more seeds! Healthy for you -- so long as you watch where you eat them.
Nearly half-ton pumpkins show up pretty regularly at the big agricultural fairs every fall, but tomatoes aren't in that league. But, the difference between an ancestral tomato (small berries) and a multi-pound beefsteak is nothing to sneeze at. Domestication has made great strides.
A paper last month in Nature Genetics laid out part of this process. Interestingly, there are two different developmental processes that have been utilized to enlarge tomatoes. A tomato fruit is composed of multiple subunits, the carpels. One change has increased the number of cells per carpel by tinkering with the cell cycle -- a much more delicious change than what a similar process will yield in a person. The new work details the genetic change which increased the number of carpels.
Of course, of interest is how universal these mechanisms are. Most domestic fruits are greatly enlarged over their wild counterparts -- though perhaps raspberries show very little enlargement & blueberries it is a small multiple. On the other end are those monster curcurbits at the fair and their watermelon cousins.
But getting back to the title. Now the question is whether these mechanisms have reached their biological maximum or simply what a few mutations can do (there are also practical considerations, such as the stem strength required to support larger tomatoes). Or, can we use this new knowledge to bring up the laggards -- or figure out why there are no fist-sized raspberries or basketball-like blueberries? A strawberry the size of my dog? Of course, purely economic forces might lead to the fruits commanding the most money per unit weight -- perhaps pomegranates will have an order of magnitude more seeds! Healthy for you -- so long as you watch where you eat them.
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