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Home | Pregnancy Timeline | News Alerts |News Archive Mar 26, 2015
Using OSKM or Oct3/4, Sox2, Klf4, and c-Myc genes [known as the "Yamanaka factor"]
will induce already specialized cells to revert back to an embryonic state. But the amount
of one factor — Klf4 (Ks) — greatly influences the success of cell reprogramming.
Image Credit: Stem Cell Reports |
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Induced pluripotent stem cell reprogramming
Anyone in cell biology recognizes the genes Oct3/4, Sox2, Klf4, and c-Myc, also known as "OSKM" or the "Yamanaka factor." Shinya Yamanaka isolated these factors and re-introduced them into ordinary adult skin cells which reprogrammed those cells back to an embryonic state. These first induced pluripotent stem (iPS) cells, won Yamanaka the Nobel Prize in Physiology or Medicine for 2012.
Originally, the four genes were delivered as single proteins made from mRNA or monocistronic (mRNA that encodes multiple chains of molecules containing two or more amino acids) vectors to induce cell reprogramming. In order to simplify, researchers began to deliver these as linked genes with each still producing four separate proteins. However, not all vectors were built the same, and it turned out that subtle variations influenced various attempts at skin cell reprogramming with many inconsistent results.
The length of Klf4 appears to be significant in whether a somatic cell is reprogrammed into a pluripotent state.
Klf4 was first identified in 1996 by two independent laboratories. Despite investigating the same gene, each of the two labs predicted different locations for the start of gene sequence, which translated as different protein lengths and nine amino acids. "Some labs used short Klf4S, some labs used long Klf4L. Some labs had even switched between the two lengths," explains Knut Woltjen, Ph.D., Associate Professor at the Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan.
Curious if these different amino acid lengths could explain the various "Yamanaka" reprogramming results from different labs, Woltjen and his team replicated the numerous varieties of reprogramming factors and found that deliberately introducing nucleic acids into cells (transfection) with vectors coding for more than one protein and carry the shorter Klf4 (Klf4S) resulted in more cells failing to complete iPS reprogramming.
In contrast, the majority of cells introduced with vectors carrying the longer Klf4L became true iPS cells. Further investigation found vectors with Klf4L showed much higher Klf4 protein expression, suggesting that the amounts of substances that are involved in reactions (stoichiometry) when reprogramming a cell are critical to efficient change.
According to Woltjen, "Stoichiometry is so important. No matter what system you use to establish it, the stoichiometry has a major impact on the quality of iPS cells." Other studies have noted stoichiometry effects, but Woltjen's team is the first to propose varrying a single factor's mRNA sequence in establishing stoichiometry.
Appending Klf4S with the missing nine amino acids resulted in its expression and reprogramming as a mirror of Klf4L.
Moreover, these differences in amounts of substances involved in inducing an iPS reaction were reflected in gene expression patterns. Although reprogramming with either Klf4S or Klf4L led to the activation of many hallmark reprogramming genes, the majority of gene regulation was clearly dissimilar. Studying the reprogramming process induced by eight different polycistronic vectors, the team observed that both reprogramming performance and gene expression split according to the form of Klf4.
This finding suggests that for popular vectors containing Klf4S, a simple modification of the Klf4 length could improve the number of successful reprogrammed cells. For researchers studying the reprogramming process itself, such vector differences should raise your caution about directly comparing reprogramming data between labs.
The research paper "KLF4 N-Terminal Variance Modulates Induced Reprogramming to Pluripotency" appeared March 12, 2015 in Stem Cell Reports.
Shin-Il Kim, Ph.D., first author of the study, stresses that just recognizing OSKM is not enough when reprogramming cells. The process must always take into account the expression of the four genes. "Initially, we had no idea how much of a difference the 9 amino acids would make. It goes to show how important it is to really know the materials you are working with."
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Abstract
Highlights
•Reprogramming vectors inconsistently employ one of two unappreciated Klf4 variants
•Polycistronic cassettes encoding Klf4 N-terminal variants drive distinct stoichiometry
•Reprogramming initiation and stabilization are sensitive to Klf4 protein levels
•Accordingly, gene expression elicited by public vectors forms two distinct clusters
Summary
As the quintessential reprogramming model, OCT3/4, SOX2, KLF4, and c-MYC re-wire somatic cells to achieve induced pluripotency. Yet, subtle differences in methodology confound comparative studies of reprogramming mechanisms. Employing transposons, we systematically assessed cellular and molecular hallmarks of mouse somatic cell reprogramming by various polycistronic cassettes. Reprogramming responses varied in the extent of initiation and stabilization of transgene-independent pluripotency. Notably, the cassettes employed one of two KLF4 variants, differing only by nine N-terminal amino acids, which generated dissimilar protein stoichiometry. Extending the shorter variant by nine N-terminal amino acids or augmenting stoichiometry by KLF4 supplementation rescued both protein levels and phenotypic disparities, implicating a threshold in determining reprogramming outcomes. Strikingly, global gene expression patterns elicited by published polycistronic cassettes diverged according to each KLF4 variant. Our data expose a Klf4 reference cDNA variation that alters polycistronic factor stoichiometry, predicts reprogramming hallmarks, and guides comparison of compatible public data sets.
Authors:Shin-Il Kim, Fabian Oceguera-Yanez, Ryoko Hirohata, Sara Linker, Keisuke Okita, Yasuhiro Yamada, Takuya Yamamoto, Shinya Yamanaka, and Knut Woltjen.
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