Team:Hamburg/Implementation


Unicorn

Implementation

Transcriptional Sychronisation


Transcriptional synchronisation is a principle designed to precisely and naturally regulate the transcription of virtually any possible output - be it mRNAs for proteins (e.g. transcription factors, enzymes, structural proteins) or non-coding RNAs such as tRNAs, trans-acting ribozymes, siRNAs or miRNAs.

Thus, it can be used for a multitude of genetic applications:

  • An endogenous pathogen resistance gene can be synchronised with a new synthetic output that combats a targeted disease.
  • Endogenous genes can be synchronised with a signal molecule for further processing e.g. by molecular logic gates. (see “cancer” below)
  • The output might be used for a direct positive/negative feedback loop of the synchronised gene.
  • Multiple synchronized outputs for just one endogenous gene are possible - allowing simple amplification of transcription or operon-like expression even in eukaryotes
  • For analysing the function of genes - by synchronising an RNAi e.g. with a meiotic gene, this RNAi is only expressed during meiosis and could target genes, which function during meiosis is unknown.

While the use of our invention is focused on crop resistances against fungi, the same mechanism could also synchronise other genes to produce resistances against bacteria, viruses or abiotic stress factors.

Resistance against plant pathogens


Transcriptional synchronisation can also be used to create synthetic pathogen resistance in crop plants. For this, any gene, whose transcription rate is influenced by the pathogen, can be transcriptionally synchronised with a resistance output against the mentioned pathogen. For example, the FAD7 gene could be transcriptionally synchronised with a RNAi against fungal infections: If a plant cell is wounded, FAD7 expression is upregulated (Nishiuchi et al., 1999). Fungi need to penetrate the plant cell for infection, thus upregulating the expression of FAD7 (Kirsch et al., 1997). If FAD7 is now transcriptionally synchronised with e.g. an RNAi against the respective fungi, the expression of the RNAi is triggered upon infection.

Using transcriptional synchronisation against plant pathogens has at least two distinctive advantages compared to using either constitutive or additional endogenous promoters coupled with a resistance mechanism: First, a constitutive promoter for a resistance is always active – even if the pathogen is not present. This means that the plant cell is wasting its resources to produce a resistance that is not needed most of the time. This again will influence the growth and yield of crops in a negative way, depending on how many resistances are used in the plant. Also, the resistance could have unwanted off-target effects against the plant itself or other organisms [3]. Second, if the resistance is controlled by using an endogenous promoter of the plant (e.g. the FAD7 promoter), the aforementioned problem is not reliably solved: The promoter is not solely controlling the transcription rate of a gene, but the genetic context is also important, including enhancer and silencer sequences, intronic effects, etc. Thus, it would be necessary to test every resistance, which is only regulated by an endogenous promoter, if its expression is regulated as intended. Depending on the number of needed resistances, this can become time- and cost-intensive. Transcriptional synchronisation solves both problems, by using the existing transcriptional regulation pathways of the cell to express the synthetic resistance only if needed.

These advantages enable another possibility: Instead of directly synchronising only one resistance output with an endogenous gene, the endogenous gene can be synchronised with a transcription activating output, which in turn activates the transcription of multiple resistance genes. Thus, the synchronization would hardly influence the transcription time for the synchronised endogenous gene, while still being able to activate a plethora of resistance genes. Further, multiple endogenous genes can be synchronised with one (or more) synthetic resistance genes so that one pathogen triggers one (or more) resistance gene(s) by upregulating the expression of all the synchronised endogenous genes. Using multiple inputs (synchronised endogenous genes) and multiple outputs (synthetic resistances) would make it near impossible for any pathogen to adapt to the synthetic resistances by evolutionary means. All this, while having a relatively low impact on plant metabolism. Also, the number of genetic modifications can be reduced significantly compared to introducing the same number of resistances by using other methods, making transcriptional synchronisation very cost-efficient.

These advantages compared to recent approaches regarding resistance regulation in plants, could make transcriptional synchronisation a valid alternative. It could be implemented for practically every crop plant that is susceptible to diseases that are influencing the transcription rate of the plant's genes (i.e. FAD7).

A glimpse into the future: Resistance against human cancer


Disclaimer: The approach for cancer is not thought to be used as a germline therapy due to ethical reasons. However, other gene therapy methods could be used, such as viral vectors for delivery.

Moving away from our plants, a fascinating future application against human cancer could be as following: In cancer cells, the transcription of tumour suppressor genes (TSGs) is downregulated by genetic/epigenetic means [4;5] or is even suspended through gene deletions [6; 7; 8]. All this, while the expression of proto-oncogenes is increased, thus, creating oncogenes [8].

The mechanism relies on sensing these genetic alterations by comparing the transcriptional activity of (proto-)oncogenes and TSGs. This is possible by adding a DNA sequence for STAR (Short Transcription Activating RNA) after a (proto-)oncogene DNA sequence. STAR is able to activate the transcription of a target gene by binding to an upstream transcription terminator (t500), which is located right after the genes’ promoter [9]. By default, the terminator t500 prevents the transcription of the gene behind it via a stem-loop [9]. Yet, if STAR binds to the t500 terminator, transcription is enabled [9]. For maximum efficiency of STAR, a 5’ hammerhead ribozyme [10] is added to the 5’ end of the STAR DNA sequence. The 3’ end of STAR has a 3’ self-cleaving ribozyme added, e.g. the hepatitis delta virus ribozyme (HDVRz) [10]. Flanking the STAR sequence with self-cleaving ribozymes results in a STAR transcript that is independently mobile from the oncogene. Furthermore, AntiSTAR DNA sequences are added in a similar way to (multiple) TSGs. The endogenous ORFs of the (proto-)oncogenes and TSGs have poly-T sequences added directly downstream to enable nuclear export and cytoplasmic translation.

STAR and AntiSTAR inhibit each other via antisense base pairing. The mechanism is further depending on an overall equal or higher transcriptional activity of TSGs as long as the cell is healthy. Thus, for every (proto-)oncogene that has the mechanism, there is one or more TSG chosen, which (always) have a (combined) much higher transcription activity than the (proto-)oncogene. This way, the default state is that the STAR-activity is quenched by the much more abundant Anti-STAR.

This sensing mechanism is a first control and safety mechanism. By choosing TSGs that are often deleted or silenced in cancer cells, e.g. the gene for MHC 1 [11], transcription of a therapeutic output is still possible. As soon as the transcription of these genes is significantly downregulated and AntiSTAR is produced in a lower quantity than STAR, the transcription of the therapeutic output can be induced by STAR.

A second safety mechanism could be constituted by the therapeutic output itself: A proton channel could be produced and located to the cell membrane, enabling passive proton transport across the membrane. Thus, in an acidic tumour microenvironment, the channel will import protons into the cell. This will acidify cancer cells, while also reducing the extracellular proton concentration. This should result in cancer cell termination due to the now acidic cellular milieu, futile ATP consumption for proton export [12], glucose deprivation [13] and a reduced capability for tumour invasion [14]. Also, it should improve an immune answer due to the less acidic tumour microenvironment [15]. The proton channel could be the otopetrin Otop1 or Otop3 [16] or a modified viral M2 proton channel [17].

Also, a proton unloading enzyme inhibitor instead of the proton channel could be chosen: The cancerous cell couldn’t unload excess protons after the pathological gene alterations. V-ATPase unloads excess protons and prevents intracellular acidification [12]. Thus, inhibiting V-ATPase function should acidify and kill cancer cells, while other proton unloading enzymes (e.g. monocarboxylate transporters, Na+/H+ exchanger [18] and carbonic anhydrases [19] might have to be targeted too.

Also, AntiSTAR genes should be as close as possible to STAR (for direct quenching of STAR) and to the t500 terminator (close-target quenching). Thus, quenching STAR activity is less a matter of chance. So, it’s more related to differences in quantity between STAR and AntiSTAR, while stochastic problems due to diffusion area are reduced.

Additionally, the regulation via the RNA-based STAR/AntiSTAR-system should be quickly reversible due to fast RNA degradation [20]. The system described above is depicted in figure 1 below. Note, that the figure shows a simplified version of the mechanism for transcriptional synchronisation, which does not contain poly-T tails.

Figure 1: Molecular circuit against cancer development. A) Shown is the function of the mechanism in a healthy cell: Multiple TSGs (here two), which are coupled with AntiSTAR sequences, quench the transcription-activating capability of STAR. No therapeutic output (proton channel) is produced. B) In a cancerous cell, proto-oncogenes become oncogenes and experience an increased transcription. Contrary, TSG transcription is reduced. Thus, in a cancerous cell, the inhibition of STAR by AntiSTAR is suspended. C) Without AntiSTAR, STAR is able to induce the transcription of the proton channel. Note: The therapeutic output could also be a V-ATPase inhibitor or something else. Also, both STAR and AntiSTAR are flanked by a 5’ and 3’ self-cleaving ribozyme (SCRz). The 5’ SCRz here is a hammerhead ribozyme (HHRz), while the 3’ SCRz could be a hepatitis delta virus (HDV) ribozyme. The mechanism is shown simplified (e.g. without a synthetic poly-A tail).

References


[1] Nishiuchi T, Kodama H, Yanagisawa S, Iba K (1999). Wound-induced expression of the FAD7 gene is mediated by different regulatory domains of its promoter in leaves/stems and roots. Plant Physiol.;121(4):1239-1246. doi:10.1104/pp.121.4.1239

[2] Kirsch C, Takamiya-Wik M, Reinold S, Hahlbrock K, Somssich IE (1997). Rapid, transient, and highly localized induction of plastidial omega-3 fatty acid desaturase mRNA at fungal infection sites in Petroselinum crispum. Proc Natl Acad Sci U S A.; 94(5):2079-2084. doi:10.1073/pnas.94.5.2079

[3] Latham, JR; Wilson, AK (2015): Off-target Effects of Plant Transgenic RNAi: Three Mechanisms Lead to Distinct Toxicological and Environmental Hazards. The Bioscience Resource Project, Ithaca, NY 14850, USA. https://bioscienceresource.org/wp-content/uploads/2015/04/RNAi-Biosafety-DraftPaper-2015-LathamWilson.pdf

[4] Wang LH, Wu CF, Rajasekaran N, Shin YK (2018): Loss of Tumor Suppressor Gene Function in Human Cancer: An Overview. Cell Physiol Biochem 2018;51:2647–2693. https://doi.org/10.1159/000495956

[5] Liu B, Song J, Luan J, Sun X, Bai J, Wang H, Li A, Zhang L, Feng X, Du Z (2016). Promoter methylation status of tumor suppressor genes and inhibition of expression of DNA methyltransferase 1 in non-small cell lung cancer. Exp Biol Med (Maywood). 2016 Aug;241(14):1531-9. doi: 10.1177/1535370216645211. Epub 2016 Apr 26. PMID: 27190263; PMCID: PMC4994907.

[6] Dong JT (2001): Chromosomal deletions and tumor suppressor genes in prostate cancer. Cancer Metastasis Rev. 2001;20(3-4):173-93.

[7] Cai Y, Sablina AA (2016). Cancer-associated chromosomal deletions: Size makes a difference. Cell Cycle. 2016 Nov;15(21):2850-2851. doi: 10.1080/15548627.2016.1204869. Epub 2016 Jul 26. PMID: 27458787; PMCID: PMC5105910.

[8] Cooper GM (2000). The Cell: A Molecular Approach. 2nd edition. Sunderland (MA): Sinauer Associates. Tumor Suppressor Genes. Available from: https://www.ncbi.nlm.nih.gov/books/NBK9894/

[9] Chappell J, Takahashi MK, Lucks JB (2015): Creating small transcription activating RNAs. Nature Chemical Biology volume11, pages214–220. doi: 10.1038/nCHeMBIO.1737

[10] Gao Y, Zhao Y (2014). Self-processing of ribozyme-flanked RNAs into guide RNAs in vitro and in vivo for CRISPR-mediated genome editing. Journal of Integrative Plant Biology, 56(4), 343–349. doi:10.1111/jipb.12152

[11] Garrido F, Aptsiauri N, Doorduijn EM, Lora AMG, van Hall T (2016): The urgent need to recover MHC class I in cancers for effective immunotherapy. Curr Opin Immunol. 2016 Apr; 39: 44–51. doi: 10.1016/j.coi.2015.12.007

[12] Whitton B, Okamoto H, Packham G, Crabb SJ (2018). Vacuolar ATPase as a potential therapeutic target and mediator of treatment resistance in cancer. Cancer Med. 2018 Aug;7(8):3800-3811. doi: 10.1002/cam4.1594. Epub 2018 Jun 21. PMID: 29926527; PMCID: PMC6089187.

[13] Xun H, Ming C, Hao Wu (2017): Central role of lactate and proton in cancer cell resistance to glucose deprivation and its clinical translation. Signal Transduction and Targeted Therapy (2017) 2, e16047; doi:10.1038/sigtrans.2016.47; published online 10 March 2017.

[14] Estrella V, Chen T, Lloyd M, Wojtkowiak J, Cornnell HH, Ibrahim-Hashim A, Bailey K, Balagurunathan Y, Rothberg JM, Sloane BF, Johnson J, Gatenby RA, Gillies RJ (2013). Acidity generated by the tumor microenvironment drives local invasion. Cancer Res. 2013 Mar 1;73(5):1524-35. doi: 10.1158/0008-5472.CAN-12-2796. Epub 2013 Jan 3. PMID: 23288510; PMCID: PMC3594450.

[15] Huber V, Camisaschi C, Berzi A, Ferro S, Lugini L, Triulzi T, Tuccitto A, Tagliabue E, Castelli C, Rivoltini L (2017): Cancer acidity: An ultimate frontier of tumor immune escape and a novel target of immunomodulation. Seminars in Cancer Biology, Volume 43, April 2017, Pages 74-89. https://doi.org/10.1016/j.semcancer.2017.03.001.

[16] Saotome K, Teng B, Tsui CCA, Lee WH, Tu YH, Kaplan JP, Sansom MSP, Liman ER, Ward AB (2019): Structures of the otopetrin proton channels Otop1 and Otop3. Nature Structural & Molecular Biology, VOL 26, 518–525. https://doi.org/10.1038/s41594-019-0235-9

[17] Cady SD, Luo W, Hu F, Hong M (2009). Structure and function of the influenza A M2 proton channel. Biochemistry. 2009 Aug 11;48(31):7356-64. doi: 10.1021/bi9008837. PMID: 19601584; PMCID: PMC2879269.

[18] Aoi W, Marunaka Y (2014). Importance of pH homeostasis in metabolic health and diseases: crucial role of membrane proton transport. Biomed Res Int.; 2014:598986. doi: 10.1155/2014/598986. Epub 2014 Sep 11. PMID: 25302301; PMCID: PMC4180894.

[19] Spugnini EP, Sonveaux P, Stock C, Perez-Sayans M, De Milito A, Avnet S, …, Fais S (2015). Proton channels and exchangers in cancer. Biochimica et Biophysica Acta (BBA) - Biomembranes, 1848(10), 2715–2726. doi:10.1016/j.bbamem.2014.10.015

[20] Chappell J, Westbrook A, Verosloff, M et al. (2017): Computational design of small transcription activating RNAs for versatile and dynamic gene regulation. Nat Commun 8, 1051. doi:10.1038/s41467-017-01082-6