Carlos Morra: Biomarkers to replace clinical endpoints for the generation of diagnostic hypotheses with the potential to guide the development of novel psychotherapeutic drugs.

 

Review of the modern gene editing techniques: CRISPR-Cas applications in iPSC-derived cultures

 

       The discovery of induced pluripotent stem cells (iPSC) by Yamanaka in 2006 is an important milestone for the field of neuroscience (Yamanaka 2008). The possibility of regressing any mature cell into pluripotent stem cells constituted a significant advance in human cell cultures that triggered the study of several diseases whose etiology was at least partial if not entirely unknown, such as schizophrenia, autism, epilepsy, Parkinson´s (PD) or Huntigton´s diseases (HD) (Wu, Chiu, Yeh and Kuo 2019; Hathy, Szabó, Varga et al. 2020; Csobonyeiova, Polak and Danisovic 2020; Hirose, Tanaka, Shibata et al. 2020; Larijani, Parhizkar Roudsari, Hadavandkhani et al. 2021). Moreover, culture patient´s or control´s cells introduced a newer instrument to identify disease-related genetic and molecular changes and pharmacological treatment targets in living human brain cells with the advantages of being accessible and susceptible to manipulation (Powell, O'Shea, Shannon et al. 2020; Larijani, Parhizkar Roudsari, Hadavandkhani et al. 2021).

       This essay will describe one of the most widely used genome editing techniques, CRISPER-Cas, its advantages and disadvantages compared with some of the other technologies. It will review its application to edit cell cultures generated from iPSC in neuroscience. Finally, it will present some of the future applications of this technology in the field of neurosciences.

       There are several genome editing techniques, such as meganucleases, zinc finger (ZF), transcription activator-like effector nucleases (TALENs), and lately clustered regularly interspaced short palindromic repeats (CRISPRs) (Doudna and Charpentier 2014). These techniques take advantage of the endogenous repair mechanisms; they break DNAs at specific genomic loci and trigger the activation of one of the endogenous cellular DNA repair pathways: non-homologous end joining (NHEJ) and homology-directed repair (HDR) (Doudna and Charpentier 2014).

       One the most widely used genome editing techniques is CRISPR-Cas; CRISPRs' sequences were first described in Escherichia Coli in 1987, but the role of CRISPRs and Cas proteins in the defense of bacterial DNAs from viruses or phages identified in 2005 using a mechanism comparable to the eukaryotic RNA interference systems (Makarova, Grishin, Shabalina et al. 2006; Mosterd, Rousseau and Moineau 2020). When infected by viruses, bacterial cells incorporate in their DNA short segments of the attacker's DNA as a natural defense mechanism for recognizing the sequence to guide nucleases to target the same virus or phage as an immune response to prevent reinfections (Chuang, Phipps, Lin et al. 2021). Hence, this technology has many applications in bacterial, agricultural, animal and human genetic research; genetic applications are diverse, such as knock-out and knock-in genes, genome purification and visualization, gene deletion, insertion and replacement in prokaryotic and eukaryotic organisms (Cho, Shin and Cho 2018; Chuang Phipps, Lin et al. 2021). Furthermore, they can be combined with fluorescent proteins such as the green fluorescent protein (GFP) or the red fluorescent protein (RFP) to mark DNA or RNA (Xu, Chen GJ, Luo et al. 2021).

       There are multiple subtypes of CRISPR-Cas systems which can be separated into two general classes according to the differences in the structure of their Cas genes:

       Class-I, group types I, III and IV where multiple Cas proteins integrate each system, and

       Class-II, group types II, V and VI which are composed by a single Cas multiple-target protein.

       Currently, up to 33 distinct subtypes have been described (Makarova, Wolf, Iranzo et al. 2020; Mosterd, Rousseau and Moineau 2020; Fage, Lemire and Moineau 2021). CRISPR-Cas activity depends on two components: the Cas nuclease and the single-guide RNA (sgRNA) whose primary function is to distinguish it/self from viral DNA (Abudayyeh, Gootenberg, Konermann et al. 2016; Wang, La Russa and Qi 2016). In addition, the system requires targeting a specific sequence in the DNA called the protospacer adjacent motif (PAM) to recognize the area to cleave; this requirement is so restrictive that the nucleases ignore perfectly complementary sequences in the absence of a PAM (Zarei, Razban, Hosseini and Tabei 2019; Collias and Beisel 2021). For example, one of the most widely recognized systems CRISPR/Cas9 derived from escherichia coli or streptococcus pyogenes, targets a site located 3 to 4 bp upstream from the PAM (Zarei, Razban, Hosseini and Tabei 2019).

       One of the biggest challenges to the use of this technology is the accurate sequence recognition for reducing the possible off-target effects (Heidenreich and Zhang 2016); however, the use of machine and deep learning may constitute a significant improvement for predicting them. Also, several online sites are already offering the chance for predicting off-target recognition, such as CRISPR Design, CHOPCHOP, and E-crisp (Heidenreich and Zhang 2016; Wang, Zhang, Cheng and Luo 2020).

       Another difficulty lies in delivering the system to the cells, for this purpose several viral vectors have been utilized like non-replicable adenovirus, mRNA for Cas translation together with a guide RNA and Cas protein with guide RNA (Burger, Nash and Mandel 2005; Lino. Harper, Carney and Timlin 2018). Nevertheless, the CRISPR-Cas-system has many advantages over the former widely used technologies, such as the ease of customization, the specificity of the cleavage, the relative simplicity of the process and the broader practical applications associated with the mechanism of action and capability to simultaneously target multiple genomic loci of different kind of organisms (Batool, Malik and Andrabi 2021; Matos, Ho, Schrode and Brennand 2020). Furthermore, it can be used for RNA manipulation with the advantage of being transient and apparently safer than DNA for human applications (Guha, Wai and Hausner 2017; Eid, Alshareef and Mahfouz 2018).

       However, despite being the dominant technique, being less expensive and less time consuming, for certain applications TALEN is still considered a better choice. It has a more demanding targeting selection mechanism that makes it more predictable, with a lower incidence of off target effects (Kim and Kim, 2014; Wright, Li, Yang and Spalding 2014; Sommer, Peters, Baumgart and Beyer 2015; Eid and Mahfouz 2016; Quétier 2016). Nevertheless, CRISPR-Cas showed many advantages when compared to ZF, which is not sufficiently precise, can be expensive and the results are susceptible to alteration by DNA´s methylation (Hudson and Buck-Koehntop 2018).

       The combination of iPSC derived cultures with CRISPR technology has been already used for the design or validation of disease models in complex human diseases; these technologies introduced the possibility of studying the etiology and the molecular mechanisms of diseases induced by genetic variants and their relationship with environmental factors (Jaenisch, Zhang and Gage 2017; Matos, Ho, Schrode and Brennand 2020). Moreover, the possibility of silencing or expressing specific receptors associated genes may improve the knowledge about their role in heterogenic multifactorial diseases like schizophrenia (Heidenreich and Zhang 2016). One of the advantages of their combined use lies in purifying the culture by creating isogenic iPSC models of neurons and glial cells, reducing the interpersonal genetic variability and expressing only the variants associated with these disorders (Matos, Ho, Schrode and Brennand 2020). For example, studies carried out in Parkinson´s allowed validatation of the results of genes such as SNCA, which is associated with rare forms of mendelian transmitted PD (Soldner, Stelzer, Shivalila et al. 2016). Moreover, these combined technologies have been recently considered the gold standard for creating isogenic cell lines for studying phenotypes related to diseases (Soldner and Jaenisch 2017).

       Since the development of genome editing techniques, many concerns have been raised about animal and human in-vivo uses of genome editing techniques, such as the inheritable potential of undesired off-target genome modifications, the introduction of potentially harmful mutations to the ecosystem and the potential application of CRISPR-Cas technology to correct genetic defects in humans embryos (Batool, Malik and Andrabi 2021). However, using cultures derived from iPSCs may reduce the risks to human subjects and the ecosystem by studying these disorders in the patient's neurons and glial cells (Feng, Jensen, Greely et al. 2020). In addition, genetically modified cultures provide a more reliable means to study psychiatric disorders as cultures are more similar in brain structures and functions than rodents or other mammals (Feng, Jensen, Greely et al. 2020).

       This technology brings an innovative way to study, in patient's cell cultures, pathways associated with neuropsychiatric disorders by inactivating (knock-out) or activating (knock-in) genes associated with their structures or functions and establishing their specific role in disease's physiopathology and treatment (Heidenreich and Zhang, 2016).  For example, the possibility to study genes associated with abnormal glutamatergic-signaling like GRIN2A or GRIA1 helped to understand the role of receptor hypofunction in the pathogenesis of schizophrenia and studying abnormal WNT signaling, altered migration capacity, irregularities in scaffolding proteins and susceptibility to oxidative stress,  provided additional insight about the role of neurogenesis and neurodegeneration in the disease's pathologic process (Srikanth, Lagomarsino, Muratore et al. 2018; Moslem, Olive and Falk 2019).

       Moreover, these technologies can help identify several neuropsychiatric diseases' causes and pathogenic mechanisms, becoming more accurate therapeutical targets (Powell, O'Shea, Shannon et al. 2020). Besides, early diagnoses and treatments might significantly reduce the burden of many neuropsychiatric diseases that constitute a significant percentage of worldwide health budgets (Insel 2014, 2016; Chong, Teoh, Wu et al. 2016).

       In the last decades, the quest for the molecular basis and biomarkers of disorders, such as schizophrenia, gained scientific interest when these promising new technologies brought new ways to challenge existing disease models and generate newer ones (Powell, O'Shea, Shannon et al. 2020). These disorders, whose colossal impact on worldwide healthcare budgets, spur the search for effective early interventions to reduce the long-term consequences of the disease to the patient, their families and society (Insel 2014). This interest is not merely academic, new targets for early interventions would produce a significant positive impact on the disease's burden (Insel 2016).

       If these technologies fail to produce significant change, future editing tools are likely to overcome their limitations, such as targeting mutations beyond their windows of activity, increasing precision and reducing undesired off-target effects, or improving the ability to target RNAs (Eid, Alshareef and Mahfouz 2018). Moreover, CRISPR-Cas technology is being developed beyond the limitations of the requirements to target PAMs and it is likely to increase its flexibility, which would expand future applications (Collias and Beisel 2021).

       In conclusion, the discovery of iPSC introduced a significant change in neurosciences. However, the application of genome editing techniques, such as CRISPR-Cas, has significantly expanded the range of potential uses, which would eventually impact several neuropsychiatric diseases diagnosis and treatment, reducing their burden and changing their prognosis.

 

References:

Abudayyeh OO, Gootenberg JS, Konermann S, Joung J, Slaymaker IM, Cox DB, Shmakov S, Makarova KS, Semenova E, Minakhin L, Severinov K, Regev A, Lander ES, Koonin EV, Zhang F. C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector. Science 2016;353(6299).  

Batool A, Malik F, Andrabi KI. Expansion of the CRISPR/Cas Genome-Sculpting Toolbox: Innovations, Applications and Challenges. Mol Diagn Ther 2021;25:41–57. 

Burger C, Nash K, Mandel RJ. Recombinant adeno-associated viral vectors in the nervous system. Human Gene Therapy 2005;16(7): 781–91.  

Cho S, Shin J, Cho BK. Applications of CRISPR/Cas System to Bacterial Metabolic Engineering. Int J Mol Sci 2018;19(4). 

Chong HY, Teoh SL, Wu DB, Kotirum S, Chiou CF, Chaiyakunapruk N. Global economic burden of schizophrenia: a systematic review. Neuropsychiatr Dis Treat 2016;12:357-73. 

Chuang Y-F, Phipps AJ, Lin F-L, Hecht V, Hewitt AW, Wang P-Y, Liu G-S. Approach for in vivo delivery of CRISPR/Cas system: a recent update and future prospect. Cell Mol Life Sci 2021;78:2683–2708. 

Collias D, Beisel CL. CRISPR technologies and the search for the PAM-free nuclease. Nat Commun 2021;12:555. 

Csobonyeiova M, Polak S, Danisovic L. Recent Overview of the Use of iPSCs Huntington's Disease Modeling and Therapy. Int. J. Mol. Sci. 2020;21(6):2239. 

Doudna JA, Charpentier, E. The new frontier of genome engineering with CRISPR-Cas9. Science 2014:346(6213):1258096. 

Eid A, Alshareef S, Mahfouz MM. CRISPR base editors: genome editing without double-stranded breaks. Biochem J 2018;475(11):1955–64. 

Eid A, Mahfouz MM. Genome editing: the road of CRISPR/Cas9 from bench to clinic. Exp Mol Med 2016;48:e265.  

Fage C, Lemire N, Moineau S. Delivery of CRISPR-Cas systems using phage-based vectors. Current Opinion in Biotechnology 2021; 68:174-80. 

Feng G, Jensen FE, Greely HT, Okano H, Treue S, Roberts AC, Fox JG, Caddick S, Poo MM, Newsome WT, Morrison JH. Opportunities and limitations of genetically modified nonhuman primate models for neuroscience research. PNAS 2020;117(39):24022-31. 

Guha TK, Wai A, Hausner G. Programmable Genome Editing Tools and their Regulation for Efficient Genome Engineering. Computational and Structural Biotechnology Journal 2017;15:146-60. 

Hathy E, Szabó E, Varga N, Erdei Z, Tordai C, Czehlár B, Baradits M, Jezsó B, Koller J, Nagy L, Molnár MJ, Homolya L, Nemoda Z, Apáti Á, Réthelyi JM. Investigation of de novo mutations in a schizophrenia case-parent trio by induced pluripotent stem cell-based in vitro disease modeling: convergence of schizophrenia- and autism-related cellular phenotypes. Stem Cell Research & Therapy 2020;11:504.  

Heidenreich M, Zhang F. Applications of CRISPR-Cas systems in neuroscience. Nature reviews. Nature Reviews Neuroscience 2016;17:36–44. 

Hirose S, Tanaka Y, Shibata M, Kimura Y, Ishikawa M, Higurashi N, Yamamoto T, Ichise E, Chiyonobu T, Ishii A. Application of induced pluripotent stem cells in epilepsy. Molecular and Cellular Neuroscience 2020;108:103535. 

Hudson NO, Buck-Koehntop BA. Zinc Finger Readers of Methylated DNA. Molecules 2018;23(10):2555. 

Insel TR. The NIMH Research Domain Criteria (RDoC) Project: precision medicine for psychiatry. The American Journal of Psychiatry 2014;171(4):395–7.  

Insel TR. RAISE-ing Our Expectations for First-Episode Psychosis. The American Journal of Psychiatry 2016;173(4):311–12.  

Jaenisch R, Zhang F, Gage F, editors. Genome Editing in Neurosciences. Springer; 2017.  

Kim H, Kim JS. A guide to genome engineering with programmable nucleases. Nat Rev Genet 2014;15:321–34.  

Larijani B, Parhizkar Roudsari P, Hadavandkhani M, Alavi-Moghadam S, Rezaei-Tavirani M, Goodarzi P, Sayahpour FA, Mohamadi-Jahani F, Arjmand B. Stem cell-based models and therapies: a key approach into schizophrenia treatment. Cell Tissue Bank 2021;22:207–23.  

Lino CA, Harper JC, Carney JP, Timlin JA. Delivering CRISPR: a review of the challenges and approaches. Drug Delivery 2018;25(1): 1234–57.  

Makarova KS, Grishin NV, Shabalina SA, Wolf YI, Koonin EV. A putative RNA-interference-based immune system in prokaryotes: computational analysis of the predicted enzymatic machinery, functional analogies with eukaryotic RNAi, and hypothetical mechanisms of action. Biol Direct 2006;1:7. 

Makarova KS, Wolf YI, Iranzo J, Shmakov SA, Alkhnbashi OS, Brouns S, Charpentier E, Cheng D, Haft DH, Horvath P, Moineau S, Mojica F, Scott D, Shah SA, Siksnys V, Terns MP, Venclovas Č, White MF, Yakunin AF, Yan W, Koonin EV. Evolutionary classification of CRISPR-Cas systems: a burst of class 2 and derived variants. Nat Rev Microbiol 2020;18:67–83.  

Matos MR, Ho SM, Schrode N, Brennand KJ. Integration of CRISPR-engineering and hiPSC-based models of psychiatric genomics. Mol Cell Neurosci 2020;107:103532. 

Moslem M, Olive J, Falk A. Stem cell models of schizophrenia, what have we learned and what is the potential? Schizophrenia Research 2019;210:3-12. 

Mosterd C, Rousseau GM, Moineau S. A short overview of the CRISPR-Cas adaptation stage. Canadian Journal of Microbiology 2020;67(1):1–12.  

Powell SK, O'Shea CP, Shannon SR, Akbarian S, Brennand KJ. Investigation of Schizophrenia with Human Induced Pluripotent Stem Cells. In: DiCicco-Bloom E, Millonig J, editors. Neurodevelopmental Disorders. Advances in Neurobiology. Springer; 2020.  

Quétier F. The CRISPR-Cas9 technology: Closer to the ultimate toolkit for targeted genome editing. Plant Science 2016;242:65-76. 

Salick MR, Lubeck E, Riesselman A, Kaykas A. The future of cerebral organoids in drug discovery. Seminars in Cell & Developmental Biology 2021;111:67-73.

Soldner F, Jaenisch R. In Vitro Modeling of Complex Neurological Diseases. In: Jaenisch R, Zhang F, Gage F, editors. Genome Editing in Neurosciences. Springer; 2017.  

Soldner F, Stelzer Y, Shivalila CS, Abraham BJ, Latourelle JC, Barrasa MI, Goldmann J, Myers RH, Young RA, Jaenisch R. Parkinson-associated risk variant in distal enhancer of α-synuclein modulates target gene expression. Nature 2016;533(7601): 95–99.  

Sommer D, Peters AE, Baumgart AK, Beyer M. TALEN-mediated genome engineering to generate targeted mice. Chromosome Res 2015;23:43–55.  

Srikanth P, Lagomarsino VN, Muratore CR, Ryu SC, He A, Taylor WM, Zhou C, Arellano M, Young-Pearse TL. Shared effects of DISC1 disruption and elevated WNT signaling in human cerebral organoids. Transl Psychiatry 2018;8:77.  

Wang H, La Russa M, Qi LS. CRISPR/Cas9 in Genome Editing and Beyond. Annual Review of Biochemistry 2016;85:227–64.  

Wang J, Zhang X, Cheng L, Luo Y. An overview and metanalysis of machine and deep learning-based CRISPR gRNA design tools. RNA Biology 2020;17(1):13–22.

Wright DA, Li T, Yang B, Spalding MH. TALEN-mediated genome editing: prospects and perspectives. Biochemical Journal 2014;462(1):15–24.  

Wu YY, Chiu FL, Yeh CS, Kuo HC. Opportunities and challenges for the use of induced pluripotent stem cells in modelling neurodegenerative disease. Open Biology 2019;9(1):180177.  

Xu CF, Chen GJ, Luo YL, Zhang Y, Zhao G, Lu ZD, Czarna A, Gu Z, Wang J. Rational designs of in vivo CRISPR-Cas delivery systems. Advanced Drug Delivery Reviews 2021;168:3–29.  

Yamanaka S. Induction of pluripotent stem cells from mouse fibroblasts by four transcription factors. Cell Proliferation 2008;41(1):51–6.

Zarei A, Razban V, Hosseini SE, Tabei SMB. Creating cell and animal models of human disease by genome editing using CRISPR/Cas9. The Journal of Gene Medicine 2019;21(4): e3082.

 

October 7, 2021