Bench Press

In order to deal with the global outbreak of swine flu effectively, tracking the number of swine flu cases is imperative. Having as much accurate data as possible regarding the epidemic is essential for evaluating what moves the global community ought to start taking to make it through this outbreak. Thus, using quick and accurate tools to evaluate the countless samples  being collected around the world is an absolute necessity. Luckily scientists at the University of Colorado and InDevR, a small biotech in Colorado, may have exactly what the world needs in a microarray chip dubbed the FluChip.

In 2005 Dr. Kathy Rowlen, CEO of InDevR, led a team at the University of Colorado working with the Centers for Disease Control and Prevention (CDC) in developing the FluChip in order to allow labs across the world to quickly distinguish samples between 72 different influenza strains. Her group’s work produced a viable testing platform that produced results in less than 12 hours with impressive accuracy.

Now Dr. Kathy Rowlen and InDevR have licensed the FluChip technology from the University of Colorado. InDevR has arranged to begin testing samples of the swine flu on a M-gene variant of the FluChip while also working on improving the initial design by incorporating new technologies, hopefully making a new assay basic enough that any lab with PCR capabilities will be able to utilize it. Here’s to hoping the FluChip will help us get a better picture of the current state of the swine flu epidemic.

InDevR Press Release:

InDevR, a small biotech company in Boulder, CO, announced today that they have licensed the FluChip technology from the University of Colorado.  The FluChip was invented by a joint team of scientists at the University of Colorado and the Centers for Disease Control and Prevention in an NIH sponsored effort led by Professor Kathy Rowlen.  Rowlen, now the CEO of InDevR, said that InDevR has arranged to test genetic material from the recent swine H1N1 virus on the MChip as well as other versions of the FluChip which are under development.  According to Rowlen “Based on work we conducted a couple of years ago, it appears that the M-gene version of the FluChip will be able to distinguish human H1N1 viruses from the new swine H1N1 virus.  If that proves to be the case, the FluChip will be a much needed and powerful new tool for surveillance since all of the current influenza diagnostics on the market are unable to subtype this virus.” The most popular diagnostic tests for influenza include rapid immunoassays, which are only able to identify the type (A or B) of influenza virus, and reverse-transcriptase polymerase chain reaction assays, which were designed for human-adapted influenza viruses and are not able to identify the swine H1N1 subtype.  State Public Health Laboratories must now send any influenza A viruses that cannot be subtyped using existing diagnostics to the CDC for analysis by genome sequencing or viral isolation.  The CDC must select viruses to analyze since it is not possible to run every sample collected from a large number of Public Health Labs.

The M-gene based FluChip has been demonstrated to delineate human-adapted viruses from non-human viruses, such as the H1N1 virus that caused the 1918 “Spanish Flu”.  “Since the FluChip assay can be conducted within a single day it could be employed in State Public Health Laboratories to greatly enhance influenza surveillance and our ability to track the virus,” Rowlen said.  InDevR will combine the FluChip technology with an innovative detection technology (NESATM), which InDevR also licensed from the University of Colorado and further developed with NIH sponsorship, to make the FluChip assay inexpensive and easy to use in any lab that has basic PCR capabilities.  “Kathy and her team have been engaged with this and similar diagnostic technology for many years,” said Mary Tapolsky, Senior Licensing Manager at the University of Colorado Technology Transfer Office. “CU TTO is excited about this experienced and motivated group developing and commercializing this promising technology.

A lot of scientific circles are talking about how best to spur collaboration, and that’s spawned a number of movements, such as “open access” and “open science” — both inspired by the “open source” movement in programming — that fight to end the fencing of science into proprietary, commercial enclaves that require fees to access. Clearly, in terms of fostering the trade of knowledge, an open, free highway is better than a highway with a large toll.

Although much of this movement towards open science has focused on journals and their large subscription fees, there’s another area of open science that’s drawn my attention: Gene Ontology (GO) annotations, which are a set of standardized annotations to classify genes according to their biological, such as “amino acid metabolism.” These annotations are, as of now, curated by experts. What I’ve noticed in particular is that GO has thrived in one community, and withered in another, and I’m curious as to why.

The yeast community is famous amongst all the molecular biology communities as being open and collaborative, to the extent that almost all gene names have been systematized, annotations for genes are very extensive and well-structured, a strain is available for the deletion of every gene, many genes are available fused to a fluorescent marker for easy microscopy, and so on. Just go to the Saccharomyces Genome Database, and there’s a wealth of all this sort of information at your fingertips, centralized, standardized, interconnected, and easy to use. In particular, the Gene Ontology annotations are considered superb and accurate, allowing for easy computational interpretation of large-scale experiments involving hundreds and thousands of genes and their interactions. Yeast genomicists use GO all the time, and contribute to its development very often.

In contrast, the human Gene Ontology annotations are considered sparse and relatively uninformative, and generally they aren’t quite as useful for interpreting things like gene expression microarrays. Instead, one of the most successful and popular sets of biological function annotations is called Ingenuity, which is a commercial software package, well developed by the large amount of money poured into it by pharmaceutical companies and other health science research and development.

Why did the two communities end up going in two directions, one towards a more collaborative, “open science”-friendly annotation system, and the other towards a proprietary, commercial annotation platform? Undoubtedly, part of the reason is the structure of financial incentives; human biology has unique opportunities for direct commercialization via drug or health research, and so people would naturally focus their efforts on things that can win them fortune. But the first yeast biology research done by Louis Pasteur was probably related to budding (pun intended) commercial R&D on reproducible bread/wine/beer recipes, so what prevented the yeast community from, say, balkanizing yeast research because of incentives from the beer brewing and bread-making industries?

Perhaps it is because the yeast community arrived at common standards and nomenclature for information sharing long before it got very large. After all, yeast doesn’t nearly have the same problem of having multiple names for the same genes that humans do (just look at the gene RANKL, which is also known as OPGL, ODF, CD254, TNFSF11, TRANCE, and hRANKL2). They also don’t have nearly as much of a problem with the explosion of gene database IDs (humans have, as a small sample: RefSeq, HGNC, Ensembl, EMBL/GenBank, Entrez, MIM, Unigene, UniProt/SwissProt, and UCSC). Perhaps having a common, universal standards-making institution is the answer, to make sure all the railroad tracks are the same width, to use an analogy.

Or perhaps its the size of the community. There are many, many more labs studying human biology than yeast biology, not only because of the financial incentives, but also because of the huge size of the human genome (1000 times bigger than the yeast genome). Maybe it’s just easier to coordinate fewer people into one community.

I think as the scientific community moves forward, especially in embracing new collaborative methods on the internet, we should closely examine what’s worked so far and what hasn’t, so that we don’t end up fording through endless patents, fees, and proprietary, non-interoperable data structures to get what we need.