Yeast de résistance
I kickstarted my now defunct academic lab 5 ½ years ago with a simple yeast genetics experiment that I now want to repeat and scale up as an independent scientist in preparation for rare disease drug discovery. The inspiration for this experiment dates back to my grad school days, when I observed that yeast cells overdose on psychoactive drugs. At the time, many an estimable scientist told me that psychoactive overdose in yeast was non-specific, but underneath the jargon what they were really saying was that it was uninteresting.
But I couldn’t understand how that could be when yeast cells were overdosing on antidepressants like Zoloft, a selective serotonin reuptake inhibitor (SSRI) whose seemingly sole drug target — the serotonin transporter protein — isn’t even present in yeast. Single-celled yeast don’t produce serotonin let alone have a brain, so what’s at the root of Zoloft overdose? Conflating non-specific and uninteresting doesn’t make sense from a mechanistic standpoint, because there are many ways to kill a cell, but it’s a matter opinion as to whether these mechanisms are deemed worthy of study.
So in the Fall of 2007, I let genetics be the arbiter: I and Meredith Rainey McClure, my first technician at Princeton, selected for yeast mutants that are resistant to Zoloft overdose. If the molecular basis of overdose were non-specific, resistance-conferring mutations would occur in genes that are involved in drug removal or detoxification, pathways that would also confer resistance to drug overdose in general, without regard for chemical structure. On the other hand, if the mechanism of overdose were specific, resistance-conferring mutations would occur in genes that did not modify cellular responses to just any drug.
As shown in this table from my Princeton lab’s 2010 Genetics paper, we observed resistance-conferring mutations that fall into two classes:
The first class includes CUP5, TFP1 and VMA9, all components of the V-ATPase complex (below, left). Mutations in the V-ATPase affect drug response by a mechanism that was first elucidated decades ago by Christian de Duve, who discovered the waste management facility of the eukaryotic cell called the lysosome (vacuole in yeast), and after which the drug accumulation known as lysosomotropism is named. I blogged last summer about the second class, SWA2 and CHC1, the latter encoding for a protein called clathrin. Clathrin molecules assemble in to the buckyball structures (below, right) that encapsulate lipid vesicles, including the vesicles that store neurotransmitters like serotonin in the synapse:
As we argued in the Genetics paper, the pattern of Zoloft overdose-resistant mutations was consistent with specific mechanisms that modify the accumulation of drugs called cationic amphipaths, or in slightly friendlier terms, hydrophobic weak bases. So what about overdoses caused by other psychoactive cationic amphipaths? In unpublished work that we’ll be submitting soon, it turns out that the genetic basis of psychoactive drug-overdose resistance is complex in yeast, such that distinct pharmacological classes like antipsychotics and antihistamines exhibit more than one resistance mechanism.
In 2007-2008, the state of the art in mutation detection (“genotyping”) were whole-genome yeast tiling arrays from Affymetrix. (Remember them?) Those arrays cost $150 a pop, and that was with an institutional discount to Princeton researchers, who originally helped design and validate the technology, and as far as I can tell were the only people ordering these arrays. However since 2008, mutation detection has advanced to the point where one can sequence an entire 12 million base pair yeast genome 30-times over for $15 or less, so I myself could in theory do in my second bedroom in a few weeks what it once took a full-time technician six months at more than 10-times the cost. But I’m not sure my wife is too keen on the idea of an experimental Man Cave just yet, so I’m on the hunt for kosher lab space.
If I had gotten a job as an assistant professor, I would have done exactly these experiments right out of the gate. Here’s what I wrote in the research statement that was part of my application package:
“I propose to generalize yeast-based genetic resistance selections that made possible Project 1 to other antidepressants, as well as to three other pharmacological classes bedeviled by molecular promiscuity: antipsychotics (both typical and atypical), antihistamines (both sedating and non-sedating), and anesthetics. Using whole-genome sequencing technologies, robotic automation and miniaturization, it will be possible to perform many single, or even combination, de novo drug-resistance selections in parallel. Follow up investigation of specific conserved cellular pathways revealed by this approach will be guided by the template established by my experience with sertraline, including the use of radiolabeled analogs to validate novel targets or target pathways.”
As I blogged earlier this year, no dice. At least one of the departments I applied had the professional courtesy to actually explain albeit in a terse, unofficial email of rejection why I didn’t make the cut: “one of the issues was the relevance of understanding how neuro drugs like antidepressants work by studying their action in non-mammalian organisms.” I obviously couldn’t disagree more strongly with this short-sighted view, which shows just how far pharmacology has drifted away from the tenets of cell biology and genetics. Eroom’s Law and the wholesale abandonment of CNS drug pipelines by Big Pharma is proof that we don’t have enough basic understanding of how existing drugs work. So how on Earth are we supposed to create new first-in-class drugs for common or rare diseases without the basic understanding that comes from studying simple genetic model organisms like yeast?
So here’s my plan. Before, I was interested in studying individual drug-resistant mutants, but as I’ll explain I now want to take a statistical approach by pooling all resistant colonies from a drug-laden agar plate, like this one:
Each of these colonies sprouts from a single spontaneous mutant. In fact, some of these colonies are clones, so the same mutation may be represented on the plate in multiple copies. And as the above Genetics table shows, en toto there are multiple genes with resistance-conferring mutations. So is there a way in one fell swoop to identify all mutations? I plan to scrape all mutants off a plate like the one above into a gene pool, literally; then extract genomic DNA and outsource sequencing using a site like Science Exchange. These pooled whole-genome datasets would yield the entire distribution of resistance-conferring mutations for each tested drug, with a given mutation present at a frequency in the pool that is proportional to the number and size of colonies harboring it.
I’d start with Zoloft as the baseline, since I have a concrete expectation of the genes that are likely to pop up. For example, I expect that V-ATPase mutants will be common across many resistance selections. The downside of this distributional approach is that I lose information like whether a mutation is recessive or dominant, or even whether resistant colonies harbor one or more than one resistance-conferring mutations. And I wouldn’t be able to study mutants individually. But using yeast strain construction techniques, I could after the fact regenerate specific mutants to test in secondary assays of cellular physiology.
While I figure out where to do these experiments, which in theory could take a few weeks if all goes without a hitch, I’d be interested in finding a computational collaborator who would help me with variant calling from pools of drug-resistant mutants. It would also be helpful to do some simulations before any physical experiments are done to get a sense of sensitivity. In other words, if there are 100 mutant colonies on a plate ranging in size, would this approach be able to detect the rarest mutant? What is the minimum genome coverage required to identify all mutants on a plate? How many independent drug-laden plate replicates would be needed to distinguish in a statistically significant way between close structural analogs, for example between a secondary and tertiary amine tricyclic?
Think of this post as an experiment in open review of a pre-experiment hypothesis. Keen to hear your thoughts!