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Abstract

It is impossible to 'clone' species for which no living cells exist. Genome editing may therefore provide the only means to bring extinct species - or, more accurately, extinct traits - back to life.

Introduction

Coincident with the release of the latest in the 'Jurassic Park' series of movies, George Church's lab at Harvard University's Wyss Institute reported their first successes in editing living elephant cells so that they contain gene sequences from the elephant's recently extinct relative, the woolly mammoth [ 1]. Using a CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 approach, Church's team replaced 14 loci in the elephant genome with the mammoth version of those sequences. Although they have not yet created a mammoth, their success blurred the already fuzzy line that separates science from science fiction, bolstering hopes (and fears) that de-extinction, the resurrection of extinct species, may soon be reality.

According to George Church, his team's goal is to create elephants that have mammoth-derived adaptations to cold climates. Their initial targets for genetic modification include genes that affect blood hemoglobin, ear size, subcutaneous fat, and hair. At present, they are focusing on transforming edited cells into tissues or stem cells to test for altered phenotypes. If the team succeeds in creating genetically engineered elephants, these animals could be introduced into the environments in which mammoths once lived, both expanding the range of habitats in which elephants can live and re-establishing ecological interactions that were lost when mammoths disappeared. This goal - to re-establish interactions between species that were lost as a consequence of extinction and hence to revitalize existing ecosystems - is the stated motivation for most existing de-extinction efforts, including those for passenger pigeons [ 2], aurochs [ 3], and American chestnut trees [ 4].

Existing technologies

The feasibly of de-extinction varies among organisms, and not all organisms face the same technical challenges in their resurrection [ 5]. For recently extinct species, it may be possible to use 'standard' cloning technology (such as the nuclear transfer followed by cellular reprogramming technique that most famously resulted in the birth of 'Dolly the Sheep' in 1996 [ 6]) and a closely related species as a surrogate maternal host. Cloning via nuclear transfer has been accomplished for a wide range of mammalian species, including several examples in which a species other than that of the developing embryo is used as a surrogate mother [ 7]. This inter-species nuclear transfer approach is being used to resurrect the bucardo, a subspecies of mountain goat that was endemic to the Pyrenees and went extinct in 2000 [ 8]. If extinction occurred before living tissues could be collected and preserved, however, cloning is not possible because DNA decay begins immediately after death. The first step to resurrecting long-extinct species is therefore to sequence and assemble a genome from the preserved remains of that extinct species. The past decade has seen enormous advances in technologies for ancient DNA isolation and genome assembly [ 9], and high-quality genomes are now available for several extinct species, including mammoths and passenger pigeons, while this work is in progress for many other species. Once genome sequences are known, genome-wide scans can be used to create lists of genetic differences between the extinct species and their closest living relatives (see [ 10], for example), which then become the initial targets for genome editing.

The successes of the Church lab and other groups demonstrate that genome editing using CRISPR/cas9 is feasible and efficient across a wide range of taxa [ 11]. The number of edits that would be required to turn, for example, an Asian elephant genome into a mammoth genome is not small; it is estimated that there are around 1.5 million nucleotide-level differences between these two species [ 10]. However, the number of edits can be minimized by replacing large pieces of the genome in a single edit or by focusing on changing only those genes that are phenotypically relevant. As links between genotype and phenotype remain largely unknown, in particular for non-model organisms, the capacity to engineer every change is likely to exist before we understand the function of every gene.

Next steps

What happens after an extinct genome is resurrected is less clear. For mammoths, Asian elephants may be a suitable maternal host, but cloning by nuclear transfer has not yet been achieved for elephants [ 12]. For other species, cloning is less likely to be successful. If the closest living species is evolutionarily distant or considerably different in size from the candidate species for de-extinction, incompatibilities between the developing embryo and the surrogate mother may mean that alternative technologies, for example artificial wombs (ectogenesis), will need to be developed. Some species, including birds, cannot be cloned by nuclear transfer [ 13] and other methods, such as germ-line engineering, will have to be used for these species. After birth, these organisms will be reared in captive environments, which will require knowledge of each species' welfare needs. Captive breeding may also have lasting consequences for behavior and physiology, which may affect the organism's survival after release into the wild. As genome-engineering technologies advance to the stage where the first phase of de-extinction - birth - is feasible, the second stage - release into the wild - will be enabled by ongoing work in conservation biology that aims to minimize the potentially negative consequences of captive breeding.

Organisms are, of course, more than just the sum of the nucleotides that make up their genome sequences. Embryos that are derived from engineered cells will be exposed to the developmental environment of a different species. Newborns will be raised in social groups that are necessarily different from those of their own species. They will be introduced to different habitats, will consume different diets, and will establish different microbiomes. All of these factors will influence phenotype, and these effects are likely to vary among species and environments. In summary, genome editing may someday create an organism whose genome sequence very closely matches that of an extinct species, but the organism that develops from those edited cells will not be the same as the organism that went extinct.

A new tool for biodiversity conservation

While extinction is forever, there is little doubt that genome engineering can and will be used to resurrect extinct traits. While this aspect of de-extinction is not as headline-grabbing as the idea of resurrected mammoths or massive flocks of passenger pigeons, it is potentially the most important. Human population growth and increasing per capita consumption are the main drivers of extinctions in the present day [ 14]. Climate change, much of it driven by anthropogenic factors, is reshaping the distribution of habitats too quickly to allow species to adapt to the changes. As populations decline, species are increasingly threatened by secondary drivers of extinction, including disease and inbreeding. Genome engineering enables the reintroduction of lost genetic diversity, or the introduction of traits that evolved in related species, into species that are struggling to survive. Thanks to advances in genome sequencing and assembly, the growing databases of population genomic data from non-model organisms, and the application of genome engineering tools to link these non-model genotypes to phenotypes [ 15], genome engineering could prove to be an important new tool for conserving biodiversity that is not yet extinct.

Abbreviations

CRISPR:

clustered regularly interspaced short palindromic repeats

References

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Acknowledgements

I thank George Church, Stewart Brand and Ryan Phelan for helpful discussion and comments on an earlier version of this manuscript, and the Gordon and Betty Moore Foundation for funding.

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Authors and Affiliations

Department of Ecology and Evolutionary Biology and UCSC Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA

Beth Shapiro

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Correspondence to Beth Shapiro.

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The author declares she has no competing interests.

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Shapiro, B. Mammoth 2.0: will genome engineering resurrect extinct species?. Genome Biol 16, 228 (2015). https://doi.org/10.1186/s13059-015-0800-4
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Keywords

Nuclear Transfer

Genome Editing

Genome Engineering

Captive Breeding

Extinct Species

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Mammoth 2.0: will genome engineering resurrect extinct species? - Genome Biology

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Genome Biology 16, Article number: 228 (2015) Cite this article 19k Accesses 34 Citations 83 Altmetric Metrics It is impossible to 'clone' species for which no living cells exist. Genome editing may therefore provide the only means to bring extinct species - or, more accurately, extinct traits - back to life.

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Genes, Cells and Brain Areas of Intelligence

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What is the neurobiological basis of human intelligence? The brains of some people seem to be more efficient than those of others. Understanding the biological foundations of these differences is of great interest to basic and applied neuroscience. Somehow, the secret must lie in the cells in our br…

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Abstract

Antidepressants are often only moderately successful in decreasing the severity of depressive symptoms. In part, antidepressant treatment response in patients with depression is genetically determined. However, although a large number of studies have been conducted aiming to identify genetic variants associated with antidepressant drug response in depression, only a few variants have been repeatedly identified. Within the present review, we will discuss the methodological challenges and limitations of the studies that have been conducted on this topic to date (e.g., 'treated-only design', statistical power) and we will discuss how specifically drug-gene interaction models can be used to be better able to identify genetic variants associated with antidepressant drug response in depression.

Keywords: : antidepressive agents, drug-gene interaction models, pharmacogenomics

Background

The lifetime prevalence of major depressive disorder (MDD) is approximately 12-18% in American and European populations [ 1-4], with a higher disease burden in female (notably two-times as high as in men) and older populations [ 1, 5]. MDD is usually treated with psychotherapy or with antidepressant drug treatment, the latter being frequently divided in three main drug classes that affect different neurotransmitter pathways by inhibiting or enhancing signal transduction across neurons: classical tricyclic antidepressants (TCAs); selective serotonin reuptake inhibitors (SSRIs); and other antidepressants (e.g., venlafaxine and mirtazapine). For example, SSRIs affect the serotonergic signaling pathway by inhibiting serotonin reuptake from the synaptic cleft of serotonergic presynaptic neurons. This inhibition of serotonin reuptake results in a higher bioavailability of serotonin to postsynaptic neurotransmitter signaling pathways. Alternatively, TCAs affect several neurotransmitter signaling pathways, including dopaminergic, noradrenergic and serotonergic signaling pathways. As TCAs work through multiple neurotransmitter pathways, adverse drug reactions are more common with TCAs than with SSRIs. Because of the lower risk of adverse drug reactions during the use of SSRIs than during the use of TCAs, SSRIs are also more frequently used for the treatment of depressive indications [ 6]. Out of these two main antidepressant drug classes, most recent studies have been focused on the SSRIs.

Despite the fact that multiple antidepressant drugs have been launched on the market during the past decades, failure of antidepressant treatment, defined as insufficient decrease in depressive symptoms within a certain period of time, is common [ 7], and may be partly genetically determined [ 8-10]. Genes affecting antidepressant drug response have all been identified in candidate gene studies, in which a gene was tested in relation with antidepressant drug response in depression on the basis of a predefined hypothesis [ 11, 12]. Within these efforts, multiple genes have been identified that potentially affect antidepressant treatment response in depression [ 11, 12]. However, heterogeneity of the findings between studies was large [ 12], and only few genes were well replicated in more recent studies or were only associated with antidepressant drug response in depression in subsequent studies comprising patients with specific subtypes of depression (e.g., melancholic or psychotic depression [ 13, 14]). For this reason, only few genes have been repeatedly associated with antidepressant drug response in depression in meta-analyses [ 12].

There are two biological processes governing the success of antidepressant drug treatment: pharmacokinetics and pharmacodynamics. For this reason, genetic studies on antidepressant drug response in depression have primarily evaluated genes encoding proteins that affect the pharmacokinetics of an antidepressant drug (e.g., drug metabolism) or genes encoding proteins that affect pharmacodynamics (e.g., treatment target). With respect to genes that affect antidepressant pharmacokinetics, two independent genes have to date been repeatedly identified in relation to antidepressant drug response in depression, CYP2D6 and ABCB1 [ 11, 12]. In addition to antidepressant drug response in depression, these two genes have also been associated with the risk of adverse drug reactions during antidepressant drug treatment (e.g., [ 15-19]). However, even though genetic variation in CYP2D6 was one of the robust genetic variants associated with antidepressant drug response in the meta-analyses [ 11, 12], studies conducted in both the STAR*D and GENDEP trials did not confirm these results [ 20, 21], although GENDEP showed that genetic variation in CYP2D6 was associated with serum concentrations of both escitalopram and nortriptyline [ 20].

In addition, also two independent genes that are related to antidepressant pharmacodynamics have been identified in relation to antidepressant drug response, SLC6A4 and HTR2A [ 11, 12, 22]. Both genes encode for proteins that affect the synaptic cleft of serotonergic neurons, and SLC6A4 encodes the serotonin reuptake transporter, the main pharmacological target of SSRIs [ 11, 12, 22]. Nevertheless, more genes have been identified in studies in relation to antidepressant drug response in depression, including for example FKBP5 and BDNF, but evidence is weaker (e.g., heterogeneity between studies is larger or studies were conducted with limited sample size) and requires additional studies to fully elucidate their role in antidepressant drug response in depression. Although a small number of genes have been repeatedly associated with antidepressant drug response in depression, the clinical benefit of knowing the status of these genes is considered to be limited at this time [ 12]. Furthermore, genome-wide association studies (GWAS), that aimed to identify genetic variants in relation to antidepressant drug response across the genome without having a predefined hypothesis [ 23-27], have not detected genetic loci associated with antidepressant drug response that reached the level of statistical significance, not even the above-mentioned genes that have been previously reported in relation to antidepressant drug response in depression.

Within the present review, our main aim is to provide an overview of the methodological limitations and challenges associated with genetic studies of antidepressant response in depression. Next, we will discuss how specifically drug-gene interaction models in studies can contribute to identify genetic variants affecting antidepressant drug response in depression.

Challenges to identify genetic loci associated with antidepressant response in depression

One of the major challenges to the identification of genetic loci associated with antidepressant response is low statistical power, especially in the GWAS that have been published to date [ 23-27]. For example, a recently published meta-analysis had a discovery sample of 865 MDD patients in which a GWAS was conducted, and the meta-analysis that also included the replication samples had a total sample size of 2394 MDD patients [ 27]. With such relatively small sample sizes, the identification of significant effects at the accepted GWAS significance threshold of p < 5 × 10⁻⁸ would require very large effect sizes on decrease in depressive symptoms over time. For this reason, larger collaborating efforts, as were also required to identify genetic loci in other phenotypes (e.g., [ 28, 29]), are likely required to have well-powered studies to identify genetic loci with modest effects on the decrease in depressive symptoms [ 30]. Besides increasing the sample size, researchers have also applied other solutions to overcome the issues of the sample size, which includes studies of intermediate phenotypes. However, also this strategy has some important limitations that need to be considered. For example, a GWAS on plasma drug concentrations of citalopram and escitalopram, which was performed in 435 MDD patients, identified two genome-wide significant independent loci that included the CYP2C9 and CYP2D6 genes [ 31]. These genes are encoding for proteins that affect drug metabolism. However, studies as these are not able to identify genes affecting antidepressant pharmacodynamics [ 31]. Furthermore, although genetic variation in CYP2D6 is also associated with citalopram and nortriptyline in the GENDEP trial, no significant association was observed with drug response [ 20]. The clinical implication with respect to antidepressant drug response needs therefore to be elucidated.

An additional option for an intermediate phenotype could be, theoretically, the prescribed antidepressant dosage, as has also been done to identify loci involved in treatment resistance to coumarins [ 32-34]. However, coumarin treatment is precisely titrated by reference to the international normalized ratio (INR) while any antidepressant dose titration would rely on clinical response (unless blood levels are assessed on a regular basis). In the case of antidepressant drugs, participants at the upper end of the dosage phenotype distribution will include patients who were prescribed a higher prescribed antidepressant dosage because of a more severe type of depression as well as patients who were prescribed a higher antidepressant dosage because of nonresponse.

Of equal importance is the choice of the design to be able to identify genetic variants associated with antidepressant drug response. The study design most commonly used to date in genomic studies of antidepressant drug response is the 'treated-only' design, which is restricted to MDD patients who initiate antidepressant treatment and who are then followed over time to characterize treatment resistance. As was shown before in simulation models [ 35], genetic variants related with response in a treated-only design could theoretically reflect the main effect of the SNP on the indication of use. For example, it has been previously shown that antidepressant drugs have only a significant benefit over placebo treatment in patients with severe depressive symptoms [ 36]. When a study consists of a heterogeneous population of patients with mild and severe depressive symptoms, a larger drop in depressive symptoms during follow-up could theoretically be restricted to individuals with more severe depressive symptoms. In this scenario, genes associated with more severe depressive symptoms could thus be associated with heterogeneous antidepressant drug response. Although the effect of baseline severity of the depressive symptoms can be decreased by taking the percentage decrease in depressive symptoms, which is done in a limited number of the studies, this effect estimate can still be in part dependent on the severity of the depressive symptoms at baseline. Genes that have been previously published in relation with depressive symptoms or MDD as well as with antidepressant drug response could be examples of genes that only show an association with antidepressant drug response because of their association with a subset of severe depressive symptoms. Indeed, a systemic review and meta-analysis reported that the S variant of the HTTLPR polymorphism in the SLC6A4 gene, a variant that has been repeatedly associated with antidepressant drug response [ 12], was associated with an 11% higher risk of MDD [ 37]. In theory, the observed association between SLC6A4 and antidepressant drug response also could be influenced by the association between SLC6A4 and the risk of MDD, and the true effect of SLC6A4 on the therapeutic response of antidepressants could be in theory be smaller. Yang et al. observed that genetic variation in the APC gene was associated with both MDD as well as with the therapeutic response to antidepressants [ 38]. In addition to these examples, more genes have been shown to associate both to MDD as well as to the decrease in depressive symptoms after initiation of treatment (e.g., [ 18]). As no reference group was taken into account in the pharmacogenetic analysis, it is currently unknown whether or not these variants are truly associated with the therapeutic response of antidepressants in depression [ 38] or simply reflect genetic main effects.

An additional challenge to successfully identify genetic variants associated with antidepressant drug response is the reduction in depressive symptoms that is unrelated to the use of antidepressant drugs themselves [ 39], which is fairly common following antidepressant drug treatment in MDD patients. When people with MDD are enrolled at the height of their symptoms, subsequent improvement may be spontaneous, the response to the nonpharmacological part of treatment, or the result of 'regression to the mean' [ 40]. This issue is visualized in Figure 1 , in which a hypothetical placebo-controlled randomized trial on antidepressant response in depression is displayed. Here, both the group treated with antidepressants and the placebo-treated group showed significant reduction in depressive symptoms. However, one should take into account that only the area displayed in gray is attributable to the use of the antidepressant. When a treated-only design (with only depressive patients that are treated) is used to identify loci associated with antidepressant drug response in depression, the decrease in depressive symptoms will be in part be the result of the antidepressant drug treatment, but is also in part unrelated to the antidepressant drug treatment. In this case, both the depressive indication of antidepressants as well as the natural disease course of the depression can influence the results from a pharmacogenomic study on antidepressant drug response in depression. Again, taking into account an untreated reference population may be able to disentangle the reduction of depressive symptoms caused by the drug with the reduction of depressive symptoms caused by nonpharmacological reasons (e.g., if patients expect improvement because of receiving treatment) or by the natural disease course.

Taken together, studies using a treated-only design to identify genetic variants associated with antidepressant drug response in depression will not only identify loci that are associated with the therapeutic response of the antidepressant drug treatment, but also identify loci associated with the severity of the depressive indication and with the natural disease course of the depression. As we describe below, choosing a study design that includes a reference population with similar indication that is using placebo treatment will be able to disentangle the true antidepressant response related to the drug from the 'response' that is unrelated to the use of antidepressants.

Drug-gene interaction models in pharmacogenetic studies

In epidemiology, interaction terms are used to demonstrate whether the association between a determinant and outcome is modified by another factor. This way, patients at excessively high risk to develop a certain disease can be identified. For pharmacogenomics, this means that the association between the drug and outcome is modified by certain genetic factors, and that patients with excessively high risk to develop adverse drug reactions or treatment resistance can be identified. However, these so-called 'drug-gene interaction studies' to identify pharmacogenetic loci that are associated with response or adverse drug reactions are not often performed. Using collaborative efforts, the 'Cohorts for Heart and Aging Research in Genomic Epidemiology' consortium [ 41] aimed to identify genetic loci that interact with drug use on multiple outcomes of drug use. However, neither genome-wide drug-gene interaction studies of antihypertensive agents and cardiovascular disease outcomes [ 42] nor of predefined drug classes and QT interval [ 43] identified genetic loci at genome-wide significant thresholds. Although these results could mean that there are no genetic loci that substantially modify the association between the drug and study outcome (and thus have no or limited clinical importance), the statistical power of these studies could also be too limited. Notably, the statistical power of a genome-wide drug-gene interaction study is dependent on multiple factors [ 43]. First, the frequency of drug use in the study population should be sufficient, with less commonly used drugs necessitating larger study populations. Second, the effect size should be substantial at least for the interaction, otherwise larger sample sizes are required. And third, the effect size of the drug on the outcome of interest. In case of a fairly low effect size of the drug on the outcome (e.g., a rare adverse drug event or a relatively small intended drug effect), a larger study population is required. Because of the issue of a limited sample size, new statistical programs have been developed to further increase the statistical power by leveraging longitudinal drug and phenotype assessments often available in prospective cohort studies [ 44], which can considerably increase statistical power [ 43].

Despite indications of a better performance of studies that use drug-gene interaction analyses, studies analyzing the effect of a SNP on the change of the study outcome after initiation of drug treatment can still provide valuable results. For example, several loci were identified in a GWAS on cholesterol lowering in users of statin therapy [ 45]. Loci that were identified with the change in LDL cholesterol level after initiation of statin therapy include SORT1, SLCO1B1, APOE and LPA. Besides being related to change in LDL cholesterol level after initiation of statin therapy, three of these loci (notably SORT1, APOE and LPA) have also been identified in relation to LDL cholesterol level in the most recent GWAS [ 46]. Based on the simulation studies described earlier, these loci could, besides being related to the LDL cholesterol lowering response of statins, also be related to the main effect of the SNP on LDL cholesterol level [ 35], despite having adjusted for the baseline LDL cholesterol level [ 45]. Similar as discussed before, these genes do not necessarily reflect the biological mechanism how statins lower LDL cholesterol level despite showing an association with LDL cholesterol reduction in statin users. Different from antidepressant drug response in depression is that the response unrelated to the drug itself (e.g., placebo response or natural relief in depressive symptoms; Figure 1 ) is smaller in users of statins because LDL-cholesterol concentrations will not decrease spontaneously or as part of a disease course (e.g., [ 47]). This particular issue has been raised in a review by Hall et al. who showed that several genetic variants are associated with the placebo response [ 48]. In case of the pharmacogenetics of antidepressant drug response, several genes of particular interest to the field (e.g., TPH2, SLC6A4, HTR2A) are highlighted in this paper to be genes involved in placebo response. As we highlighted above that a relatively large placebo effect is a likely cause of the difficulties with antidepressant pharmacogenomics, it is not surprising that all genetic loci for LDL cholesterol reduction in statin users were replicated in a drug-gene interaction analysis [ 45].

Together with the simulation analyses [ 35], it is likely that the chance of successfully identifying genetic loci with the therapeutic response is dependent on the choice of the design. For drugs such as antidepressants, that have a relatively small 'true therapeutic effect' (drug treatment effect - placebo treatment effect) [ 36], taking into account a reference group could therefore have important implications.

Application & considerations of drug-gene interaction models in pharmacogenetic randomized clinical trials on antidepressant response

To the best of our knowledge, no studies have yet studied the association between genetic variants and antidepressant drug response in depression using drug-gene interaction models in a population containing a group of MDD patients treated with an antidepressants as well as a reference group (e.g., MDD patients treated with placebo). Nevertheless, a limited number of studies compared their results with a placebo-treated population [ 49], used a model-based approach to define the nonspecific response [ 50], or evaluated whether SNPs are specifically associated with response in nortriptyline or escitalopram [ 51]. In the case of a formal drug-gene interaction analysis, both the group of patients treated with an antidepressant drug and the group of patients treated with placebo would be followed over time and the depressive symptoms would be assessed at multiple time points. This procedure is exactly the same as what has been used to determine whether an antidepressant drug is more effective than a placebo [ 36]. Within this setting a drug-gene interaction model will determine whether a genetic variant is associated with the decrease in depressive symptoms in the group treated with an antidepressant relative to the group treated with placebo. The regression formulas for the statistical analyses for the treated-only and place-controlled pharmacogenomics study are as follows:

Regression formula for treated-only designs

Change in depressive symptoms = α + β ₁ × SNP + βs for covariates

Regression formula for drug-gene interaction models in randomized clinical trials

Change in depressive symptoms = α + β ₁ × SNP + β ₂ × (drug/placebo group) + β ₃ × SNP × (drug/placebo group) + βs for covariates

In these formulas, α is the intercept of the regression line and the βs reflect the effect size of the independent variable on the change in depressive symptoms over time. In the treated-only design, β ₁ measures the effect size of the SNP on the drug effect (e.g., reduction in depressive symptoms) in users. For the regression formula that is required in drug-gene interaction models in placebo-controlled studies, β ₃ reflects the effect size of the effect allele on the change in depressive symptoms over time in users of antidepressants relative to the placebo group. In case of the treated-only design β ₁ is the beta estimate of interest whereas in the placebo-controlled pharmacogenomic study β ₃ is the beta estimate of interest.

Hypothetical examples of two situations of a drug-gene interaction analysis in a placebo-controlled pharmacogenomic study are presented in Figure 2 . According to the hypothetical situation depicted in the upper two graphs of the figure ( Figure 2 A), carriers of the variant allele (for simplicity reasons we combine heterozygous and homozygous variant allele carriers together in the examples) showed a larger reduction in the proportion of patients with clinical depressive symptoms over time than homozygous carriers of the major allele. However, the decrease in the proportion of patients with depressive symptoms over time is similar for users of antidepressant treatment (solid line) and for users of placebo treatment (dashed line). This indicates that, although associated with a faster relief in depressive symptoms, this allele is not modifying the antidepressant drug response. If we would have used a treated-only design, the conclusion for this specific hypothetical allele would be that it is associated with antidepressant drug response in depression. However, as the association of this genetic variant is similar in the placebo-treated group, this hypothetical allele is more likely to reflect associations with depressive indicators or natural disease course. In contrast, true drug-gene interactions are shown in Figure 2 B. In this example, the variant allele has no effect on reducing the severity of the depressive symptoms in patients with depression treated with placebo, as the proportion of patients with clinical depressive symptoms at day 180 is similar to that observed among homozygous patients carrying the major allele. However, in patients treated with an antidepressant (solid line) who carry the variant allele, we observe a larger reduction in the proportion of patients with clinical depressive symptoms compared with homozygous patients carrying the major allele or patients carrying the variant allele, but who treated with placebo (dashed line). Therefore, the variant allele in this hypothetical situation demonstrates an allele that modifies therapeutic response to antidepressants in depression.

Based on these hypothetical examples that are likely also influencing already published findings, the use of a reference population in studies that aim to identify genetic variants in antidepressant response will be able to disentangle the loci that are associated with the natural disease course or depressive indicators from the loci that truly modify antidepressant drug response in depression.

Drug-gene interaction analyses in population-based cohort studies

In addition to randomized controlled clinical trials, population-based cohort studies also can have valuable data available to identify genetic loci associated with response to antidepressants. For contributing in such studies, cohorts should have data available on drug use and have cross-sectional assessments of depressive symptoms collected concurrently [ 52]. Importantly, data on depressive symptoms should be collected for the total (unselected) study population. We defined genetic loci as being potential loci affecting antidepressant drug response in depression when genetic variants are associated with a different depressive symptoms score between users of antidepressants and nonusers of any antidepressant drug. The rationale of this theory is visualized in Figure 3 , where interaction between the SNP and drug is present only in Figure 3 C (the association between the genetic variant and depressive symptoms score is different between users and nonusers of antidepressants). In Figure 3 B, the SNP is associated with depressive symptoms, but the association does not differ between treated and untreated individuals. Therefore, this locus is likely not associated with the therapeutic response, but instead with the depressive indication. In Figure 3 A, there was interaction nor an association between the genetic variant and depressive symptoms. Likely, this variant has no role in depression nor with the therapeutic response to antidepressants in depression.

To test the hypothesis that drug-gene interaction models can also be used to identify SNPs related to therapeutic response to antidepressants, we conducted a drug-gene interaction analysis in the prospective population-based Rotterdam Study cohort [ 53]. The Rotterdam Study, comparable to other population-based cohort studies, was originally designed to study the incidence of, and risk factors for, age-related diseases, and had no inclusion criteria other than being older than 55 years of age. During center visits, which took place every 4-5 years, data on depressive symptoms (center of epidemiological studies depression score or CES-D score) were collected, and data on use of drugs were continuously monitored [ 54]. Taking into account longitudinal data during which information on depressive symptoms was collected [ 44], we identified two potential interesting loci on a genome-wide level that interacted with the use of SSRIs on the depressive symptoms score (notably FSHR and HMGB4). Although a role in response to SSRIs has to be confirmed in future studies, mRNA expression levels were altered after stimulation with an SSRI in lower model organisms [ 55, 56]. When we restricted our analysis to only genes that are involved in the serotonin signaling pathway [ 57, 58], we identified genetic variation in the PLCB1 gene, which encodes for a postsynaptic signal transduction protein, to significantly interact with SSRI use on cross-sectional assessments of depressive symptoms. Although not earlier described in the literature in relation to antidepressant drug response in depression, common genetic variation in PLCB1 has been included in a patent of a pharmacogenetic testing kit for antidepressant drug response [ 59]. Furthermore, we observed that common genetic variation in the HTR2A gene interacted with use of SSRIs on depressive symptoms, which can be seen as additional confirmation of the role of HTR2A in the therapeutic response of SSRIs in depression [ 12, 22].

However, although being a promising model for future studies on the genetic basis of antidepressant drug response in depression, there are some important limitations that need to be taken into account. For example, it should be noted that antidepressants were prescribed for indication other than depression. In case of The Netherlands, SSRIs are most often prescribed for depressive indications [ 6], but participants with an SSRI prescribed for another indication might negatively affect the study results. Furthermore, this approach does also not take into account factors associated with a GP's decision whether or not to initiate antidepressant drug treatment in case of a diagnosed MDD. As long as the genetic variant is unrelated to the indication 'depression' as well as with factors related to the choice of treatment, this will result in increased heterogeneity and variation in the outcome, meaning that the observed effect sizes are likely underestimated. In any case, larger sample sizes are required. Furthermore, as already highlighted earlier, it should be taken into account that for drug-gene interaction analyses in cohort studies using cross-sectional assessments of depressive symptoms requires large sample sizes, especially to capture genetic variants with a relatively low effect size and relatively low allele frequency. Nevertheless, future studies that are done with larger sample sizes will likely provide additional insights in the underlying biology of antidepressant treatment resistance in depression. However, significant interactions from these type of studies should be replicated in placebo-controlled trials, as in theory the scenario depicted in Figure 3 C can also be the other way around when the SNP is associated with depressive symptoms in untreated individuals, but not in treated individuals. Visualization, as done in our study in case of the HTR2A findings [ 53], is one way to understand whether the observed interaction is driven by the treated or untreated individuals.

Conclusion & future perspective

With the increased possibilities to study genetic determinants in relation to certain study outcomes, researchers all over the world recognized the possibilities to increase the accuracy to predict disease outcomes and investigated the possibilities of precision medicine. Despite many initiatives, only a handful of genetic loci have been repeatedly associated with antidepressant drug response in depression [ 12]. These loci, however, could theoretically also be associated with aspects of decrease in depressive symptoms that are unrelated to the use of an antidepressant drug, including the depressive indication as well as the natural disease course of the depression. Furthermore, studies aiming to identify genetic loci associated with antidepressant drug response in depression are often performed with a small number of depressive patients, including the GWAS which have been done to identify new genetic variants associated with antidepressant drug response. For example, the largest GWAS to date on lipid lowering during statin therapy required more than 18,000 users of statin therapy to identify four independent genetic variants associated with LDL cholesterol lowering after initiation of statin treatment [ 45]. Still, these identified loci explained only approximately 5% of the total variation in LDL cholesterol response in statin users. Although successful in identifying genetic markers in relation to cholesterol-lowering response in statin users, there is still a long way to accurately predict before initiation of statin treatment whether a patient will develop sufficient lowering in cholesterol level or not. A meta-analysis of the GENDEP, MARS and STAR*D trials on the association between genetic variants and antidepressant drug response in depression only comprised 2256 treated MDD patients, and no loci were identified that reached the level of statistical significance [ 26]. Therefore, also with respect to other pharmacogenomic studies on other phenotypes, this indicates that the sample size is one major limitation [ 60]. Drug-gene interaction studies on antidepressant drug response in depression should also be aware of this, as such studies normally require larger sample sizes.

In summary, the search for genetic markers associated with antidepressant drug response in depression is difficult and only moderately successful to date. Although some genetic loci associated with antidepressant treatment have been replicated by others, generally the heterogeneity is large between studies, possible because of the lack of a reference population in the design of the study. Drug-gene interaction models, which additionally take into account the association between the genetic variant and the reduction in depression symptoms that is unrelated to the use of the antidepressant, will probably increase the ability to identify new loci and to confirm some of the already identified loci. This will also mean that loci previously associated with antidepressant drug response in depression in a treated-only design could be left unconfirmed in these newer drug-gene interaction studies. We expect that drug-gene interaction models will reduce the heterogeneity between studies and will increase the list of loci affecting the therapeutic response of antidepressants. When ethical issues prevent the use of a placebo-reference population especially in studies to be conducted in severe cases of MDD, studies can selected different reference population in their study design, including a reference population on psychotherapy or on different antidepressant drug treatment. Nevertheless, studies should be performed in larger (collaborative) settings to increase the statistical power whatever the composition of the reference population is. Likely, the use of drug-gene interaction models will increase our understanding of the genetic basis of antidepressant response.

Although many studies have been conducted aiming to identify genetic variants in relation to antidepressant drug response in depression, only few genes have been repeatedly and robustly identified, notably CYP2D6, ABCB1, SLC6A4 and HTR2A.

Important limitations of studies that have been conducted include the used sample size (and thus statistical power), but also the study design itself: the so-called 'treated-only design' in which only treated depressive patients are followed in time.

The reduction in depressive symptoms that is observed in a study with a treated-only design can have different origins, including the indication of use, the natural disease course of the depressive indication (independent of drug use) and the true response of the antidepressant drug treatment.

The inclusion of a reference group, such as a placebo-treated population, could disentangle the mix of origins why depressive patients show a decrease in depressive symptoms over time.

The drug-gene interaction model in a placebo-controlled trial investigates what genetic variants associate with antidepressant drug response in antidepressant-treated depressive patients beyond the association that is observed in placebo-treated depressive patients.

Genetic variants associated with 'antidepressant response' in both antidepressant-treated depressive patients and placebo-treated depressive patients are unlikely variants involved in antidepressant drug response in depression.

Drug-gene interaction models in prospective cohort studies, in which genetic variants are differently associated with depressive symptoms in antidepressant-treated participants and untreated participants, might be an alternative strategy to identify genetic variants of interest for antidepressant drug response in depression.

Future studies might benefit to investigate drug-gene interactions instead of main effects to identify genes for antidepressant drug response in depression.

Footnotes

Disclaimer

The funding agencies had no role in the preparation, review or approval of the manuscript.

Financial & competing interests disclosure

CL Avery and BH Stricker acknowledges funding from R01HL103612 (NIH). CL Avery also acknowledges funding from 15GPSPG239 (American Heart Association). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript

References

Papers of special note have been highlighted as: * of interest; ** of considerable interest

16. Bijl MJ, Visser LE, Hofman A, et al. Influence of the [[ CYP2D6*4 polymorphism on dose, switching and discontinuation of antidepressants. PMC free article] [ PubMed] Google Scholar]

33. Teichert M, Eijgelsheim M, Uitterlinden AG, et al. Dependency of phen-procoumon dosage on polymorphisms in the [ VKORC1, CYP2C9 and CYP4F2 genes. [ PubMed] Google Scholar]

34. Giri AK, Khan NM, Grover S, et al. Genetic epidemiology of pharmacogenetic variations in [ CYP2C9, CYP4F2 and VKORC1 genes associated with warfarin dosage in the Indian population. [ PubMed] Google Scholar]

38. Yang Z, Ma X, Wang Y, et al. Association of [ APC and REEP5 gene polymorphisms with major depression disorder and treatment response to antidepressants in a Han Chinese population. [ PubMed] Google Scholar]

40. * Describes the concept of regression to the mean when studying the pharmacological response of a drug.

53. ** First antidepressant-gene interaction study in a cohort study.

59. Athanasiou M, Holick K, Reed C, Salisbury B, Serretti A, Zou W. Genetic markers associated with reponse to antidepressants (US 20090233942 A1) 2009. Patent.

Articles from Pharmacogenomics are provided here courtesy of Future Science Group

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Identifying genetic loci affecting antidepressant drug response in depression using drug-gene interaction models

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Antidepressants are often only moderately successful in decreasing the severity of depressive symptoms. In part, antidepressant treatment response in patients with depression is genetically determined. However, although a large number of studies have been conducted aiming to identify genetic variants associated with antidepressant drug response in depression, only a few variants have been repeatedly identified.

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Major mental disorders traditionally thought to be distinct share certain genetic glitches, according to a new study. The finding may point to better ways to diagnose and treat these conditions.

Scientists have long recognized that many psychiatric disorders tend to run in families, suggesting potential genetic roots. Such disorders include autism, attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depression and schizophrenia. Symptoms can overlap and so distinguishing among these 5 major psychiatric syndromes can be difficult. Their shared symptoms suggest they may also share similarities at the biological level. In fact, recent studies have turned up limited evidence of shared genetic risk factors, such as for schizophrenia and bipolar disorder, autism and schizophrenia, and depression and bipolar disorder.

To take a broader look, an international research consortium conducted an analysis that incorporated data from genome-wide association studies (GWAS) of the 5 major disorders. This type of study involves scanning through thousands of genetic markers in search of tiny variations that appear more often in people who have a particular condition than in those who don't. The research received primary funding from NIH's National Institute of Mental Health (NIMH), along with other NIH components.

As reported online in the Lancet on February 28, 2013, the scientists screened for evidence of illness-associated genetic variation among over 33,000 patients. All had been diagnosed with at least 1 of the 5 disorders. A comparison group included about 28,000 people who had no major psychiatric diagnosis.

The analysis revealed variations significantly associated with all 5 disorders. These included variations in 2 genes that code for the cellular machinery that helps regulate the flow of calcium into neurons. Variation in one of these, called CACNA1C, had previously been linked to bipolar disorder, schizophrenia and major depression. CACNA1C is known to affect brain circuitry involved in emotion, thinking, attention and memory - functions that can be disrupted in mental illnesses. Variation in another calcium channel gene, called CACNB2, was also linked to the 5 disorders.

In addition, the researchers discovered illness-linked variation for all 5 disorders in certain regions of chromosomes 3 and 10. Each of these sites spans several genes, and causal factors haven't yet been pinpointed. The suspect region along chromosome 3 had the strongest links to the disorders. This region also harbors certain variations previously linked to bipolar disorder and schizophrenia.

"Although statistically significant, each of these genetic associations individually can account for only a small amount of risk for mental illness," says study co-author Dr. Jordan Smoller of Massachusetts General Hospital. Because of this, the variations couldn't yet be used to predict or diagnose specific conditions. But these results may help researchers move closer to making more accurate diagnoses. They may also help lead to a better understanding of the factors that cause these major mental disorders.

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Common Genetic Factors Found in 5 Mental Disorders

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Major mental disorders traditionally thought to be distinct share certain genetic glitches, according to a new study. The finding may point to better ways to diagnose and treat these conditions. Scientists have long recognized that many psychiatric disorders tend to run in families, suggesting potential genetic roots.

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Writing a Scientific Article: Synesthesia

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Writing a Scientific Article: Synesthesia

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What Does it Mean to be Genetically Jewish?

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What Does it Mean to be Genetically Jewish?

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What Does it Mean to be Genetically Jewish_

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Marijuana is a widely used drug yet it remains unpredictable. Seasoned users can usually manage the effect that marijuana has on them, while also staying calm during an unpredictable high. For newcomers, however, it's different; novice users usually can't predict how the drug will affect them, whether it'll lead to a paranoid high or giggle fest.

Science says your genes might play a part in how your body reacts to cannabis.

Cannabis functions by binding itself to the cannabinoid receptors in our bodies, which are located in our cells, containing our individual DNA. Mutations in CB1 or CB2 receptors can make you more vulnerable to different illnesses, such as Chron's disease or anorexia. These changes could also impact how your cells bind to different molecules including the ones in cannabis.

In a 2019 study, published in the journal Nature Neuroscience, researchers found that a variable in the gene CHRNA2 could increase the risk of becoming addicted to cannabis. Cannabis addiction is something that's not all that understood, with many people doubting its existence. Symptoms of marijuana withdrawal include depression, irritability, a higher heart rate and more.

While this gene doesn't indicate whether or not someone is a marijuana addict, it does increase the odds of these kinds of responses to heavy use of the drug.

All of this means that when sharing a bong or a joint with friends, a few of them can have slightly different reactions depending on several factors including their genome, personal experience with the drug and the strain they're ingesting.

Genes are extremely complex. Although we're born with some genetic mutations, other mutations can occur due to the things we're exposed to throughout our lives, such as the foods we eat, the germs we interact with, our levels of stress, and more.

There's a lot we don't understand about cannabis and its impact on our genes, but there's a lot of possibilities once you start playing around with these variables, hopefully resulting in more medicinal and recreational effects of the drug.

The Fresh Toast is a daily lifestyle platform with a side of cannabis. For more information, visit www.thefreshtoast.com.

Distributed by TRIBUNE CONTENT AGENCY, LLC.

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Your genes have a surprising impact on how marijuana affects you

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Marijuana is a widely used drug yet it remains unpredictable. Seasoned users can usually manage the effect that marijuana has on them, while also staying calm during an unpredictable high. For newcomers, however, it's different; novice users usually can't predict how the drug will affect them, whether it'll lead to a paranoid high or giggle fest.

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CRISPR-Cas9 is a genome editing tool that is creating a buzz in the science world. It is faster, cheaper and more accurate than previous techniques of editing DNA and has a wide range of potential applications.

What is CRISPR-Cas9?

CRISPR-Cas9 is a unique technology that enables geneticists and medical researchers to edit parts of the genome by removing, adding or altering sections of the DNA sequence.

It is currently the simplest, most versatile and precise method of genetic manipulation and is therefore causing a buzz in the science world.

How does it work?

The CRISPR-Cas9 system consists of two key molecules that introduce a change (mutation) into the DNA. These are:

an enzyme called Cas9. This acts as a pair of 'molecular scissors' that can cut the two strands of DNA at a specific location in the genome so that bits of DNA can then be added or removed.

a piece of RNA called guide RNA (gRNA). This consists of a small piece of pre-designed RNA sequence (about 20 bases long) located within a longer RNA scaffold. The scaffold part binds to DNA and the pre-designed sequence 'guides' Cas9 to the right part of the genome. This makes sure that the Cas9 enzyme cuts at the right point in the genome.

The guide RNA is designed to find and bind to a specific sequence in the DNA. The guide RNA has RNA bases that are complementary to those of the target DNA sequence in the genome. This means that, at least in theory, the guide RNA will only bind to the target sequence and no other regions of the genome.

The Cas9 follows the guide RNA to the same location in the DNA sequence and makes a cut across both strands of the DNA.

At this stage the cell recognises that the DNA is damaged and tries to repair it.

Scientists can use the DNA repair machinery to introduce changes to one or more genes in the genome of a cell of interest.

Some bacteria have a similar, built-in, gene editing system to the CRISPR-Cas9 system that they use to respond to invading pathogens like viruses, much like an immune system.

Using CRISPR the bacteria snip out parts of the virus DNA and keep a bit of it behind to help them recognise and defend against the virus next time it attacks.

Scientists adapted this system so that it could be used in other cells from animals, including mice and humans.

What other techniques are there for altering genes?

Over the years scientists have learned about genetics and gene function by studying the effects of changes in DNA.

If you can create a change in a gene, either in a cell line or a whole organism, it is possible to then study the effect of that change to understand what the function of that gene is.

For a long time geneticists used chemicals or radiation to cause mutations. However, they had no way of controlling where in the genome the mutation would occur.

For several years scientists have been using 'gene targeting' to introduce changes in specific places in the genome, by removing or adding either whole genes or single bases.

Traditional gene targeting has been very valuable for studying genes and genetics, however it takes a long time to create a mutation and is fairly expensive.

Several 'gene editing' technologies have recently been developed to improve gene targeting methods, including CRISPR-Cas systems, transcription activator-like effector nucleases (TALENs) and zinc-finger nucleases (ZFNs).

The CRISPR-Cas9 system currently stands out as the fastest, cheapest and most reliable system for 'editing' genes.

What are the applications and implications?

CRISPR-Cas9 has a lot of potential as a tool for treating a range of medical conditions that have a genetic component, including cancer, hepatitis B or even high cholesterol.

Many of the proposed applications involve editing the genomes of somatic (non-reproductive) cells but there has been a lot of interest in and debate about the potential to edit germline (reproductive) cells.

Because any changes made in germline cells will be passed on from generation to generation it has important ethical implications.

Carrying out gene editing in germline cells is currently illegal in the UK and most other countries.

By contrast, the use of CRISPR-Cas9 and other gene editing technologies in somatic cells is uncontroversial. Indeed they have already been used to treat human disease on a small number of exceptional and/or life-threatening cases.

What's the future of CRISPR-Cas9?

It is likely to be many years before CRISPR-Cas9 is used routinely in humans.

Much research is still focusing on its use in animal models or isolated human cells, with the aim to eventually use the technology to routinely treat diseases in humans.

There is a lot of work focusing on eliminating 'off-target' effects, where the CRISPR-Cas9 system cuts at a different gene to the one that was intended to be edited.

Better targeting of CRISPR-Cas9

In most cases the guide RNA consists of a specific sequence of 20 bases. These are complementary to the target sequence in the gene to be edited. However, not all 20 bases need to match for the guide RNA to be able to bind.

The problem with this is that a sequence with, for example, 19 of the 20 complementary bases may exist somewhere completely different in the genome. This means there is potential for the guide RNA to bind there instead of or as well as at the target sequence.

The Cas9 enzyme will then cut at the wrong site and end up introducing a mutation in the wrong location. While this mutation may not matter at all to the individual, it could affect a crucial gene or another important part of the genome.

Scientists are keen to find a way to ensure that the CRISPR-Cas9 binds and cuts accurately. Two ways this may be achieved are through:

the design of better, more specific guide RNAs using our knowledge of the DNA sequence of the genome and the 'off-target' behaviour of different versions of the Cas9-gRNA complex.

the use of a Cas9 enzyme that will only cut a single strand of the target DNA rather than the double strand. This means that two Cas9 enzymes and two guide RNAs have to be in the same place for the cut to be made. This reduces the probability of the cut being made in the wrong place.

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CRISPR-Cas9 is a genome editing tool that is creating a buzz in the science world. It is faster, cheaper and more accurate than previous techniques of editing DNA and has a wide range of potential applications. CRISPR-Cas9 is a unique technology that enables geneticists and medical researchers to edit parts of the genome by removing, adding or altering sections of the DNA sequence.

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Have you heard? A revolution has seized the scientific community. Within only a few years, research labs worldwide have adopted a new technology that facilitates making specific changes in the DNA of humans, other animals, and plants. Compared to previous techniques for modifying DNA, this new approach is much faster and easier. This technology is referred to as "CRISPR," and it has changed not only the way basic research is conducted, but also the way we can now think about treating diseases [1,2].

What is CRISPR

CRISPR is an acronym for Clustered Regularly Interspaced Short Palindromic Repeat. This name refers to the unique organization of short, partially palindromic repeated DNA sequences found in the genomes of bacteria and other microorganisms. While seemingly innocuous, CRISPR sequences are a crucial component of the immune systems [3] of these simple life forms. The immune system is responsible for protecting an organism's health and well-being. Just like us, bacterial cells can be invaded by viruses, which are small, infectious agents. If a viral infection threatens a bacterial cell, the CRISPR immune system can thwart the attack by destroying the genome of the invading virus [4]. The genome of the virus includes genetic material that is necessary for the virus to continue replicating. Thus, by destroying the viral genome, the CRISPR immune system protects bacteria from ongoing viral infection.

How does it work?
Figure 1 ~ The steps of CRISPR-mediated immunity. CRISPRs are regions in the bacterial genome that help defend against invading viruses. These regions are composed of short DNA repeats (black diamonds) and spacers (colored boxes). When a previously unseen virus infects a bacterium, a new spacer derived from the virus is incorporated amongst existing spacers. The CRISPR sequence is transcribed and processed to generate short CRISPR RNA molecules. The CRISPR RNA associates with and guides bacterial molecular machinery to a matching target sequence in the invading virus. The molecular machinery cuts up and destroys the invading viral genome. Figure adapted from Molecular Cell 54, April 24, 2014 [5].
Interspersed between the short DNA repeats of bacterial CRISPRs are similarly short variable sequences called spacers (FIGURE 1). These spacers are derived from DNA of viruses that have previously attacked the host bacterium [3]. Hence, spacers serve as a 'genetic memory' of previous infections. If another infection by the same virus should occur, the CRISPR defense system will cut up any viral DNA sequence matching the spacer sequence and thus protect the bacterium from viral attack. If a previously unseen virus attacks, a new spacer is made and added to the chain of spacers and repeats.

The CRISPR immune system works to protect bacteria from repeated viral attack via three basic steps [5]:

Step 1) Adaptation - DNA from an invading virus is processed into short segments that are inserted into the CRISPR sequence as new spacers.

Step 2) Production of CRISPR RNA - CRISPR repeats and spacers in the bacterial DNA undergo transcription, the process of copying DNA into RNA (ribonucleic acid). Unlike the double-chain helix structure of DNA, the resulting RNA is a single-chain molecule. This RNA chain is cut into short pieces called CRISPR RNAs.

Step 3) Targeting - CRISPR RNAs guide bacterial molecular machinery to destroy the viral material. Because CRISPR RNA sequences are copied from the viral DNA sequences acquired during adaptation, they are exact matches to the viral genome and thus serve as excellent guides.

The specificity of CRISPR-based immunity in recognizing and destroying invading viruses is not just useful for bacteria. Creative applications of this primitive yet elegant defense system have emerged in disciplines as diverse as industry, basic research, and medicine.

What are some applications of the CRISPR system?
In Industry
The inherent functions of the CRISPR system are advantageous for industrial processes that utilize bacterial cultures. CRISPR-based immunity can be employed to make these cultures more resistant to viral attack, which would otherwise impede productivity. In fact, the original discovery of CRISPR immunity came from researchers at Danisco, a company in the food production industry [2,3]. Danisco scientists were studying a bacterium called Streptococcus thermophilus, which is used to make yogurts and cheeses. Certain viruses can infect this bacterium and damage the quality or quantity of the food. It was discovered that CRISPR sequences equipped S. thermophilus with immunity against such viral attack. Expanding beyond S. thermophilus to other useful bacteria, manufacturers can apply the same principles to improve culture sustainability and lifespan.

Beyond applications encompassing bacterial immune defenses, scientists have learned how to harness CRISPR technology in the lab [6] to make precise changes in the genes of organisms as diverse as fruit flies, fish, mice, plants and even human cells. Genes are defined by their specific sequences, which provide instructions on how to build and maintain an organism's cells. A change in the sequence of even one gene can significantly affect the biology of the cell and in turn may affect the health of an organism. CRISPR techniques allow scientists to modify specific genes while sparing all others, thus clarifying the association between a given gene and its consequence to the organism.

Rather than relying on bacteria to generate CRISPR RNAs, scientists first design and synthesize short RNA molecules that match a specific DNA sequence-for example, in a human cell. Then, like in the targeting step of the bacterial system, this 'guide RNA' shuttles molecular machinery to the intended DNA target. Once localized to the DNA region of interest, the molecular machinery can silence a gene or even change the sequence of a gene (Figure 2)! This type of gene editing can be likened to editing a sentence with a word processor to delete words or correct spelling mistakes. One important application of such technology is to facilitate making animal models with precise genetic changes to study the progress and treatment of human diseases.
Figure 2 ~ Gene silencing and editing with CRISPR. Guide RNA designed to match the DNA region of interest directs molecular machinery to cut both strands of the targeted DNA. During gene silencing, the cell attempts to repair the broken DNA, but often does so with errors that disrupt the gene-effectively silencing it. For gene editing, a repair template with a specified change in sequence is added to the cell and incorporated into the DNA during the repair process. The targeted DNA is now altered to carry this new sequence. In Medicine
With early successes in the lab, many are looking toward medical applications of CRISPR technology. One application is for the treatment of genetic diseases. The first evidence that CRISPR can be used to correct a mutant gene and reverse disease symptoms in a living animal was published earlier this year [7]. By replacing the mutant form of a gene with its correct sequence in adult mice, researchers demonstrated a cure for a rare liver disorder that could be achieved with a single treatment. In addition to treating heritable diseases, CRISPR can be used in the realm of infectious diseases, possibly providing a way to make more specific antibiotics that target only disease-causing bacterial strains while sparing beneficial bacteria [8]. A recent SITN Waves article discusses how this technique was also used to make white blood cells resistant to HIV infection [9].

The Future of CRISPR

Of course, any new technology takes some time to understand and perfect. It will be important to verify that a particular guide RNA is specific for its target gene, so that the CRISPR system does not mistakenly attack other genes. It will also be important to find a way to deliver CRISPR therapies into the body before they can become widely used in medicine. Although a lot remains to be discovered, there is no doubt that CRISPR has become a valuable tool in research. In fact, there is enough excitement in the field to warrant the launch of several Biotech start-ups that hope to use CRISPR-inspired technology to treat human diseases [8].
Ekaterina Pak is a Ph.D. student in the Biological and Biomedical Sciences program at Harvard Medical School.
References:

1. Palca, J. A CRISPR way to fix faulty genes. (26 June 2014) NPR < http://www.npr.org/blogs/health/2014/06/26/325213397/a-crispr-way-to-fix-faulty-genes> [29 June 2014]

2. Pennisi, E. The CRISPR Craze. (2013) Science, 341 (6148): 833-836.

3. Barrangou, R., Fremaux, C., Deveau, H., Richards, M., Boyaval, P., Moineau, S., Romero, D.A., and Horvath, P. (2007). CRISPR provides acquired resistance against viruses in prokaryotes. Science 315, 1709-1712.

4. Brouns, S.J., Jore, M.M., Lundgren, M., Westra, E.R., Slijkhuis, R.J., Snijders, A.P., Dickman, M.J., Makarova, K.S., Koonin, E.V., and van der Oost, J. (2008). Small CRISPR RNAs guide antiviral defense in prokaryotes. Science 321, 960-964.

5. Barrangou, R. and Marraffini, L. CRISPR-Cas Systems: Prokaryotes Upgrade to Adaptive Immunity (2014). Molecular Cell 54, 234-244.

6. Jinkek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity.(2012) 337(6096):816-21.

7. CRISPR reverses disease symptoms in living animals for first time. (31 March 2014). Genetic Engineering and Biotechnology News. < http://www.genengnews.com/gen-news-highlights/crispr-reverses-disease-symptoms-in-living-animals-for-first-time/81249682/> [27 July 2014]

8. Pollack, A. A powerful new way to edit DNA. (3 March 2014). NYTimes < http://www.nytimes.com/2014/03/04/health/a-powerful-new-way-to-edit-dna.html?_r=0> [16 July 2014]

9. Gene editing technique allows for HIV resistance? < http://sitn.hms.harvard.edu/flash/waves/2014/gene-editing-technique-allows-for-hiv-resistance/> [13 June 2014]

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CRISPR: A game-changing genetic engineering technique - Science in the News

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Have you heard? A revolution has seized the scientific community. Within only a few years, research labs worldwide have adopted a new technology that facilitates making specific changes in the DNA of humans, other animals, and plants. Compared to previous techniques for modifying DNA, this new approach is much faster and easier.

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Speaker notes: https://docs.google.com/document/d/1unGNxh-7saElXsxUcnvQWnOaJorvpcyj-6TgTf2pols/edit?usp=sharing

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CRISPR Technology Applications on Finding a Treatment for Pancreatic Adenocarcinoma: A Literature Review

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CRISPR Technology Applications on Finding a Treatment for Pancreatic Adenocarcinoma: A Literature Review

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Prenatal Genetic Screening Tests: More than a Diagnosis

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Prenatal Genetic Screening Tests: More than a Diagnosis

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Works cited: https://docs.google.com/document/d/14gk54q9zV9HdtVTb27oEAbDGOL9bJaHWIxj4y54XhsE/edit?usp=sharing