Genetic Engineering & Biotechnology News

DEC 2017

Genetic Engineering & Biotechnology News (GEN) is the world's most widely read biotech publication. It provides the R&D community with critical information on the tools, technologies, and trends that drive the biotech industry.

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Page 17 of 69

16 | DECEMBER 2017 | | Genetic Engineering & Biotechnology News Brian Dranka, Ph.D., and Luke Dimasi The immune system is continually surveilling the body—looking for pathogens, xenobiot- ics, and other non-self signals. The various cell types which constitute the immune sys- tem are, thus, incredibly dynamic and ca- pable of upregulating the processes that go into handling these insults on a time scale of minutes to hours. Many cell types in this sys- tem are capable of activation to secrete cyto- kines, rapidly proliferate, or otherwise com- municate to surrounding cells that there is a pathogen to consider. Upon clearance of the pathogen, the cell population must then con- tract in a controlled manner. Furthermore, in some cell populations (e.g., T cells) a subset of the cells are retained as long-lived memory cells to protect and prime the system for fu- ture insults. To enable this dynamic function, immune cells integrate internal cellular sig- nals, cues from the tissue microenvironment, and cellular metabolic activity. The dynamic nature of immune-cell func- tion poses a unique challenge to the analysis of these cell types. Many of the typical meth- ods that are used to quantify immune-cell function are in fact not "functional" assays at all. Typically, researchers utilize snapshot or point-in-time methods to interrogate whether the cells are activated. Alternatively, many scientists screen for marker expression to characterize which cell subpopulations are present. For example, expression of the CD69 surface antigen is frequently used as a marker of T-cell activation. Notably, this approach is most relevant for defining or quantifying a subpopulation of cells used in an experiment. Surface markers used to identify activated cells often require many hours to become present on the cell surface. While these antigens are often used to inform experiments, they don't provide the tempo- ral discrimination to see activation kinetics or allow researchers to modulate immune functionality in real time. Recent advances in the field of immu- nology identify cell metabolism as a critical regulator of immune-cell function. Indeed, changes in cellular metabolism are not only permissive for altered cell function but are, in fact, sufficient to cause these changes. Metabolic reprogramming occurs in the or- der of minutes to allow changes in cell func- tion, providing a novel and unique marker for functional analysis. Agilent Seahorse XF technology is at the forefront of the field of immunometabolism, enabling researchers to measure cellular metabolic activity acutely and continuously, as cells are stimulated in a real-time manner. Label-free sensor technol- ogy coupled with integrated drug/compound injection ports and real-time interrogation provide a powerful platform to quantify and modulate the dynamic function of immune cells in a manner that is complementary to traditional point-in-time analysis methods. To illustrate the principles described above, we present three vignettes of immune-cell acti- vation, each of which highlight a unique aspect or need for researchers attempting to uncover the points of regulation in their cell models. Access to the Earliest Events in T-Cell Activation Recent data from the lab of Christoph Hess, M.D., at the University of Basel, Swit- zerland, embraces the idea that T-cell activa- tion is correlated with rapid shifts in cellular metabolism. 1 This shift in metabolism is es- pecially prominent for the glycolytic path- way, which provides the necessary cellular building blocks and energy required for high sodium)-induced colitis. Two markers of in- flammation and barrier function were sup- pressed using SYN363." In anticipation of future clinical studies, the Synlogic scientists stably integrated the engineered cassette for the circuit into the E. coli Nissle genome. This cassette was shown to produce butyrate at levels comparable to those produced by the plasmid version of the circuit. To evaluate the strain further, Synlog- ic is working with AbbVie. The companies have been collaborating to investigate treat- ments for inflammatory bowel disease. Machines of Iterative Grace Synthetic biology may realize the uto- pian vision described in the Richard Brauti- gan poem, All Watched Over by Machines of Loving Grace, which suggested, back in 1967, that mammals and computers could live together in "mutually programming har- mony." Brautigan's vision has always seemed far-fetched, not to mention naïve, to technol- ogy skeptics. But now that synthetic biology is on the scene, people and benevolent ma- chines may start working together via micro- bial intermediaries. Useful microbes don't have to be designed, built, tested, analyzed, and redesigned by humans. This cycle can be managed by ma- chines, guided by artificial intelligence or, rather, machine-learning systems. Such sys- tems are being developed by Zymergen. The company designs, engineers, and op- timizes microbes at every point in strain de- velopment for clients' industrial applications in agriculture, chemical and materials, and pharmaceuticals, among others. For one cus- tomer, Zymergen reduced time-to-market by more than three years; for another customer, the company more than doubled net product margin. The company can partner with clients in three ways: • Improve the economics of existing strains by identifying and optimizing on- and off-pathway targets. • Help customers bring products to market faster by enabling cost-effective commercial production by optimizing known biosynthetic pathways. • Develop novel molecules for materials not made through biology today by assembling biosynthetic pathways for new products or for products traditionally manufactured. "We recognize pathway and genome op- timization as a data and algorithms prob- lem; automation enables us to think of it as a search function," states Aaron Kimball, chief technology officer at Zymergen. "This search function involves two phases: explo- ration and exploitation." During exploration, it's not obvious whether a particular genetic change will be beneficial or deleterious, so we focus on making simple changes to find areas of inter- est. During exploitation, subsequent iterative changes are made to optimize a particular lo- cation in the genome. "Given finite lab capacity and client deadlines," notes Kimball, "we're constantly looking at data to inform how we trade-off capacity between the two [search phases]." There are also trade-offs between complex- ity versus number of changes. "Microbial genomes, particularly those that are industrially relevant, are poorly un- derstood today," admits Kimball. "The search space is too vast to identify all the ge- netic changes that could elicit phenotypic im- provement using traditional methods alone. "Mechanistic understanding of cellular metabolism remains very poor, so scientists don't know where in the genome to look for sources of improvement. And even if they did, the number of possibilities is too vast to test everything, even in an automated lab." Clients' needs determine the phenotypic improvements to target and the types of as- says to develop. Some clients want higher yield; others require more efficient feedstock use. "There is no one-size-fits-all approach," insists Kimball. Different assays are de- veloped for each cell, target product, and phenotype that provide enough predictive power to detect subtle improvements, and trade-offs are made between cost, speed, and precision. "The ability to perform tests in high throughput lets us run more hypotheses," Kimball continues. "High-throughput tests, however, may be less capable of picking up small signals than a slower or more expen- sive test." Factors such as epistasis are considered regardless of target phenotype. In epistasis, the (phenotypic) effect of one gene (locus) depends on that gene interacting with one or more genes at different loci. "With epistasis, we may find multiple individual genetic sources of improvement that when combined are actually deleteri- ous," states Kimball. "We have developed proprietary algorithms to predict the best- performing combinations of genetic changes to avoid these negative epistatic events in product optimization. This is driven by data rather than by a mechanistic understanding of the genome." Optimizing the Optimized Another important factor in product op- timization is the maturity level of a cell line. "A product in the early stages of develop- ment can take advantage of certain optimi- zation approaches that aren't as productive in a more mature cell that is already close to its theoretical maximum target," explains Kimball. For such a cell, the "lower-hanging fruit has been picked." Kimball thinks that taking a purely in sili- co approach is often a poor predictor of how biology behaves, and that the strategy of fo- cusing on molecular biology on the bench- top, with software taking a backseat—while having the benefit of empiricism—lacks high throughput. "Zymergen is the 'Goldilocks' of these approaches—fully technology and fully biology," asserts Kimball. "We use ma- chine learning to predict as much as possible to inform our search function, and still test it in a wet lab." Real-Time Discrimination of Immune-Cell Function and Fate Real-Time Discrimination of Immune Cell Function and Fate beyond Immune Cell Characterization Microbe Continued from page 15 OMICS Assay Tutorial

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