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The Wang Lab

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We are a band of engineers and biologists investigating the molecular principles involved in cellular decision making, aging, and cancer. Specifically, we study the tuning of biomolecular systems in human cells. We believe that living systems and the levels of their many molecular components must have been tuned and optimized during the course of evolution. Furthermore, diseases could result from the aberrant re-optimization of gene expression or molecular signaling pathways. To study biomolecular systems, we employ single-cell analysis, cellular and genetic engineering, and mathematical modeling. When methods and genetic tools are limiting, we invent them.

Tuning protein expression with upstream open reading frames
Early on we realized that if we were to quantitatively study gene expression, we would need to develop new methods to precisely control expression levels. Our goal was to systematically “dial-in” any expression level of a recombinant or ectopic gene. Most importantly, we aimed to achieve a range of low and intermediate expression levels—levels generally difficult to achieve with current state-of-the-art tools.

 
Figure 1. Tuning translation using engineered upstream open reading frames. (A) Precise expression levels of a recombinant protein (green ORF) can be specified by varying bases of uORF TIS sequences (NNN) and employing multiple upstream open reading frames (uORFs) in series (in parentheses with subscript n). 5’m, 5’-methylated mRNA cap; AAA, mRNA poly-A tail. Arrows indicates paths of ribosomes. (B) Control of green fluorescent protein (GFP) expression levels in different cell types (various symbols).
 

We have found that we can suppress translation by introducing short, two-amino acid open reading frames (ORFs) immediately upstream of the ORF encoding a recombinant protein (Fig. 1A). By varying the sequence of the translation initiation site (TIS) at the upstream ORFs (uORFs), we can control the degree of this suppression (Fig. 1B). Furthermore, control is achieved in a predictable manner—the greater the uORF translation, the greater the suppression. Mathematical modeling supported a mechanism where uORFs divert the flow of ribosomes away from the downstream protein-coding ORF. With a lower translation initiation rate at the uORF, more ribosomes “leak” past the uORF; consequently, more ribosomes reach and translate the downstream protein-coding ORF.

By engineering short RNA leader sequences equipped with uORF and TIS elements, we were able to control protein expression over a range as great as three orders of magnitude (Fig. 1B). Unlike our experience when using promoter mutants, our leader sequences have achieved reproducible expression control for every gene and cell type that we evaluated. Because of this success, we propose that our invention be the primary option for tuning constitutive expression in mammalian cells. Rather than using conventional expression systems, which often can produce hyper-physiological levels, researchers can engineer protein expression at relevant and physiological levels. In one of our first applications of the uORF technology, we tuned p21 over a range of physiologically relevant levels (Figs. 2A, B) and demonstrated control of cell-cycle activity (Fig. 2C).

Figure 2. Effect of p21 dosage on cell-cycle distribution. (A) Immunoblot of p21-/- cells expressing GFP-p21-ER through use of different translation initiation leader sequences. Endogenous p21 from wild-type (WT) cells with and without ionizing radiation (IR). (B) Ectopic expression levels in p21 -/- normalized to GAPDH measured by densitometry of immunoblot. Sequence denote the bases preceding uORFs and the ORF of GFP-p21-ER.  (C) Cell-cycle population distribution vs. ectopic expression levels for GFP-ER (top) and GFP-p21-ER (bottom) in p21 -/- cells. ER, estrogen-receptor domain which allowed induction of activity upon addition of 4-hydroxytamoxifen. Error bars represent standard deviation from three experimental replicates.

Because the control mechanism will not interfere with most other genetic programming methods (e.g., synthetic transcriptional activators, repressors, inducible promoters, and secondary RNA structures), genetic circuits can be first “wired” with these other components and then optimized by tuning translation levels. Furthermore, since the uORFs in our system encode peptides only two amino acids in length, they are unlikely to elicit an immune response if one day used in gene therapy applications. Finally, we anticipate that our sequence elements will perform successfully not only in any mammalian cell but any vertebrate system. In principle, our system can be adapted to control translation in other eukaryotes, including plant, yeast, and insects.

Optimization of Ras oncogene expression
We think of cancer as a disease where molecular expression and activity have been re-optimized so that cells now maximize replication and survival (Fig. 3).  Yes, this is only a partial picture, but (having said that) let’s continue. Previously, to quantitatively study the effect of oncogene expression, we developed technologies that allowed us to measure replication rates and cell-cycle activity by flow cytometry. While these were fruitful, we were missing the contribution from enhanced or diminished survival (in other words, reduced or increased death). We needed to measure a net proliferation rate—a replication rate minus a death rate.

 

Figure 3. Tumorigenesis as an intra-population competition
 

To study how the expression level of the Ras oncogene affects this net proliferation rate, we have devised an intra-population competition. Using our uORF technology, we engineered a heterogeneous population of cells expressing a broad range of Ras oncogene levels. Cells with different levels of the Ras oncogene were then allowed to “compete” for an increased share of the total population. Because the Ras was fused to a fluorescent protein, we could track the composition of the population over time (Fig. 4A) and calculate (Fig. 4B) the relationship between every oncogene level and net proliferation rate. After assessing this dose-response relationship, we observed that maximal proliferation occurred at an optimized level of Ras oncogene expression (Fig. 4C). Expression levels above the optimal level reduced cell survival and reduced the fitness of these cells.  Our work suggests that during the course of tumor evolution, mutations are not only activating and inactivating genes, but also tuning expression for maximal net proliferation.

 
Figure 4. Assessment of Ras oncogene levels through intra-population competition. (A) Engineered population expressing a range of BFP-Ras G12V levels. (B) Net proliferation rate, μ, calculated for one expression level (x) using population fractions measured over time (t).  Populations included cells varying in BFP-Ras G12V and a GFP reference. (C) Ras G12V oncogene level vs. net proliferation rate.

 

 


Ongoing projects
We have several projects in the pipeline that we are quite excited about. We are studying the role of p21 in regulating population heterogeneity in cell-cycle arrest, potentially providing a mechanism for the increased cellular heterogeneity that accompanies human aging.  We are investigating the role of p53 in maintaining quality control during the development of skeletal muscle from stem cells.  We are also using high-throughput DNA sequencing methods to decode the sequence-programmed response to cellular stressors, including oncogenic and genotoxic stress. Stay tuned.

 

Immunostaining of p16 (green), an aging marker and cell-cycle inhibitor, in cells expressing p53-mKate (red)
 

Publications (PubMed)

Overton K.W., Spencer S.L., Noderer W.L., Meyer T, and Wang C.L. (2014) Basal p21 controls population heterogeneity in cycling and quiescent cell-cycle states. Proc Natl Acad Sci U S A. pii: 201409797. pdf

Noderer, W.N., Flockhart, R.J., Bhaduri, A., Diaz de Arce, A.J., Zhang, J., Khavari, P.A., and Wang, C.L. (2014). Quantitative analysis of mammalian translation initiation sites by FACS-seq. Molecular Systems Biology. doi: 10.15252/msb.20145136. pdf

Yang, Z.J., Broz, D.K., Noderer, W.L., Ferreira, J.P., Overton, K.W., Spencer, S.L., Meyer, T., Tapscott, S.J., Attardi, L.D., and Wang, C.L. (2014) p53 suppresses muscle differentiation at the myogenin step. Cell Death Diff.

Ferreira, J.P., Noderer, W.L., Diaz de Arce, A.J., and Wang, C.L. (2014). Engineering ribosomal leaky scanning and upstream open reading frames for precise control of protein translation. Bioengineered 5, 186-92

Ferreira, J.P. and Wang, C.L. (2013). Optimization of oncogene expression through intra-population competition. Biotech J 8, 1476-1484. pdf

Ferreira, J.P., Overton, K.W., and Wang, C.L. (2013). Tuning gene expression with synthetic upstream open reading frames. Proc Natl Acad Sci U S A 110, 11284-11289. pdf

Spencer, S.L., Cappell, S.D., Tsai, F.C., Overton, K.W., Wang, C.L., and Meyer, T. (2013). The proliferation-quiescence decision is controlled by a bifurcation in CDK2 activity at mitotic exit. Cell 155, 369-383.

Peacock, R.W., Lawhorn, I.E., Ferreira, J.P. and Wang, C.L. (2012). Flow cytometry of v-Abl transformed pre-B cells heterogeneous in ectopic expression levels reveals Ras dose-response. J Immunol Methods 384, 177-183.

Peacock, R.W., Sullivan, K.A. and Wang, C.L. (2012). Tetracycline-regulated expression implemented through transcriptional activation combined with proximal and distal repression. ACS Synthetic Biology 1, 156-162.

Ferreira, J.P., Lawhorn, I.E., Peacock, R.W., and Wang, C.L. (2012). Quantitative assessment of Ras over-expression via shotgun deployment of vectors utilizing synthetic promoters. Integr Biol 4, 108-114.

Baik, J.Y., Wang, C.L., Yang, B., Linhardt, R.J. and Sharfstein, S.T. (2012).Toward a bioengineered heparin: Challenges and strategies for metabolic engineering of mammalian cells. Bioengineered 3, 227-231.

Ferreira, J.P., Peacock, R.W.., Lawhorn, I.E., and Wang, C.L. (2011). Modulating ectopic gene expression levels by using retroviral vectors equipped with synthetic promoters. Syst Synth Biol 5, 131-138.

Peacock, R.W. and Wang, C.L. (2011). A genetic reporter system to gauge cell proliferation rate. Biotechnol Bioeng 108, 2003-2010. pdf

Lin, D.W., Bettinger, C.J., Ferreira, J.P., Wang, C.L. and Bao, Z. (2011). A cell-compatible conductive film from a carbon nanotube network adsorbed on poly-L-lysine. ACS Nano 5, 10026-32.

Vroom, J.A. and Wang, C.L. (2008). Modular construction of plasmids through ligation-free assembly of vector components with oligonucleotide linkers. Biotechniques 44, 924-926.

Wang, C.L., Wang, B.B., Bartha, G., Li, L., Channa, N., Klinger, M., Killeen, N., and Wabl, M. (2006). Activation of an oncogenic microRNA cistron by provirus integration. Proc Natl Acad Sci U S A 103, 18680-18684.

Wang, C.L., Yang, D.C., and Wabl, M. (2006). Slow, stochastic transgene repression with properties of a timer. Genome Biol 7, R47.

Wang, C.L. and Wabl, M. (2005). Hypermutation rate normalized by chronological time. J Immunol 174, 5650-5654.

Wang, C.L., Yang, D.C., and Wabl, M. (2004). Directed molecular evolution by somatic hypermutation. Protein Eng Des Sel 17, 659-664.

Wang, C.L., Harper, R.A., and Wabl, M. (2004). Genome-wide somatic hypermutation. Proc Natl Acad Sci U S A 101, 7352-7356.

Wang, C.L., Hodgson, J.G., Malek, T., Pedersen, F.S., and Wabl, M. (2004). A murine leukemia virus with Cre-LoxP excisible coding sequences allowing superinfection, transgene delivery, and generation of host genomic deletions. Retrovirology 1, 5.

Lab Members
Alex Diaz de Arce
Bill Noderer
Cliff Wang (CV)
Ingrid Lawhorn
Jane Yang
Josh Ferreira
Stephanie Doong
Wes Overton

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