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BLOG biotech : Jan 17, 2011
How Today's Discoveries Contribute to Tomorrow's Revolution
Over the course of a decade we've witnessed a change in the face of biotech and the way research is done. While the Information Age brought enormous advances in data acquisition, analysis, and sharing, the conversion of groundbreaking discoveries to revolutionary technologies continues to follow the same old pattern: a major breakthrough followed by a quiet revolution as those new ideas are put into practice.
Revolutionary discoveries may drive novel research, but it is new methodologies and new tools that support these new directions. The genetic engineering revolution rests on many game-changing ideas. The elucidation of nucleic acid structure (1953) and discovery and identification of transforming factors (1928, 1944) provided the basic model of DNA as genetic instructions. The methods for transforming bacteria in a lab setting didn't appear, however, until 1972, around the time that efficient DNA sequencing and recombination techniques made their way into the lab.
1982 saw the first commercial application of genetic engineering reach markets in the form of Genentech's Humulin, bacterially produced human insulin. At 51 amino acids (5.8 kDa), insulin is much smaller than today's standard fare of mAbs (146 kDa for muromonab-CD3, approved in 1986) and much simpler to make than multistep small molecule syntheses.
But today's breakthroughs stand on more than just the advancements that permitted cheaper, sterile insulin to better the lives of diabetics for decades. PCR worked its way into labs in the 1980s, and DNA sequencing technologies further improved with the appearance of automated sequencers and later pyrosequencers.
New techniques in the production of recombinant cell lines in plants, mammals, bacteria, and yeast supported new avenues in research. Basic scientific discoveries in the biology of genetic replication and human disease progression put new ideas on the table: Retroviruses became both tools and targets, diverse cancer biology implicated out-of-control signaling pathways, and the relationship between a cell and its environment was considered with increasing complexity as new physical and chemical interactions were identified.
Each Answer Brings Even More Questions
The value of scientific discoveries is easy to see in hindsight, but which of the ideas in vogue today will be tomorrow's revolutionaries? To consider a few examples: Will the discovery of RNAi, stem cells, and optogenetics bring about game-changing new applications? They've certainly attracted enough attention: a Nobel Prize, funding controversies and court cases, and Nature's 2010 Method of the Year, respectively.
These discoveries reflect our position in the midst of changing eras. The time for human genomics to be the driving force of innovation is ending. The human genome has been sequenced, and personal genomes will likely fall in price, becoming a more commonplace part of the portfolio of research and medical tools.
While this information remains useful in identifying genes involved in heritable diseases—the first disease etiology discovered through whole-genome sequencing was Charcot-Marie-Tooth, reported early in 2010—genomes proved to be only a fraction of the picture. The importance of other factors such as epigenetics and the chemical environment started to form the rest of the picture.
RNAi, stem cells, and optogenetics fit on a sort of spectrum. In each case, the phenotype of a cell is altered in real time not by changing its genome but by some sort of stimulus. In RNAi it is nucleic acid complements that prevent expression of proteins; with stem cells it is anything from methylation of histones and DNA to mechanical and chemical stimuli; for optogenetics a beam of light signals proteins to allow ion exchange, creating a neuronal impulse.
These developments mirror a move from genetics to a more holistic, environmental approach—first, by adjusting the nucleic acid pathways themselves; later, by modifying the proteins that interact with the genome and adjusting the cell's environment directly; finally, by avoiding the target cell altogether and instead tweaking its neighbors and watching the response.
Waiting Until the Next Revolution Rolls Around
“The most important discoveries will provide answers to questions that we do not yet know how to ask and will concern objects we have not yet imagined.”—<span style="font-style: normal;">John Bahcall, Ph.D., astrophysicist.</span>
These discoveries are all part of an epigenetic revolution; mile markers on the way to a fully integrated approach to cell physiology. They are revolutionary in their own right, but they will be superseded by the advances of the next great revolution. Still, it's likely that several breakthrough products will trace their strongest roots back to these advancements.
Stem cell research has already produced remarkable systems such as Geron's nerve regeneration therapy (in clinical trials now). But why stop at injecting oligodendrocytes into a spinal cord injury? Why not come up with some treatment that forces the patient's own cells to take up the task? Research has demonstrated both that differentiated cells such as fibroblasts can be treated in such a way that they regain some of their pluripotency. Other discoveries suggest that small populations of resident stem cells remain circulating in the body throughout an individual's lifetime.
So, while optogenetics might bring about an end to schizophrenia or elucidate depression, and RNAi could be the next cancer killer, the technological backbone of these discoveries is still far from mature. Difficulties in delivery and off-target effects threaten to keep RNAi restricted to eyedrops for macular degeneration and other surface approaches. These challenges also plague stem cell development.
As genomic medicine started reaching maturity around the time that epigenetics became cutting edge, it is not unreasonable to expect these new discoveries will produce their most useful products around the time that the focus on epigenetics gives way to the next model.
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